Consumer-driven innovation in food and personal care products (Woodhead Publishing Series in Food Science, Technology and Nutrition - Volume 195)

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Consumer-driven innovation in food and personal care products

© Woodhead Publishing Limited, 2010

Related titles: Consumer-led food product development (ISBN 978-1-84569-072-4) No matter how innovative or technologically advanced a new or reformulated food product may be, it will only be a success if it gains widespread consumer acceptance. Ensuring that food product development strategies are consumer-led, therefore, is of importance to the food industry. Edited by an expert in the field, this book reviews current state-of-art methods in this area. Understanding consumers of food products (ISBN 978-1-84569-009-0) It is very important for food businesses, scientists and policy makers to understand consumers of food products: in the case of businesses, to develop successful products; and in the case of policy makers, to gain and retain consumer confidence. Consumers’ requirements and desires are affected by issues such as culture, age and gender, and issues important to consumers such as diet and health or GM foods will not always be so significant. Therefore food businesses and policy makers need to understand consumers’ attitudes and influences upon them to respond effectively. Edited by two distinguished experts, this book is an essential guide for food businesses, food scientists and policy makers. Case studies in food product development (ISBN 978-1-84569-260-5) Product development is vital to the food industry but a successful outcome is often elusive. Many publications cover product development principles and techniques, but much can still be learnt from those engaging in product development for real in industry. From the study of actual product development projects, new ideas on the philosophies, systems, organisations and techniques of food product development can be generated. Edited by leading authorities on the subject, and with an international team of contributors, Case studies in food product development describes others’ involvement in developing new products and improving existing ones, and discusses what those participating in the same can gain from their experiences. Details of these books and a complete list of Woodhead titles can be obtained by: • visiting our web site at www.woodheadpublishing.com • contacting Customer Services (e-mail: [email protected]; fax: +44 (0) 1223 893694; tel.: +44 (0) 1223 891358 ext. 130; address: Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington, Cambridge CB21 6AH, UK)

© Woodhead Publishing Limited, 2010

Woodhead Publishing Series in Food Science, Technology and Nutrition: Number 195

Consumer-driven innovation in food and personal care products Edited by Sara R. Jaeger and Hal MacFie

Oxford

Cambridge

Philadelphia

New Delhi

© Woodhead Publishing Limited, 2010

Published by Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington, Cambridge CB21 6AH, UK www.woodheadpublishing.com Woodhead Publishing, 525 South 4th Street #241, Philadelphia, PA 19147, USA Woodhead Publishing India Private Limited, G-2, Vardaan House, 7/28 Ansari Road, Daryaganj, New Delhi – 110002, India www.woodheadpublishingindia.com First published 2010, Woodhead Publishing Limited © Woodhead Publishing Limited, 2010; Chapter 9 © MMR Research Worldwide, 2010 – All Rights Reserved The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publisher cannot assume responsibility for the validity of all materials. Neither the authors nor the publisher, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing Limited. The consent of Woodhead Publishing Limited does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing Limited for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. ISBN 978-1-84569-567-5 (print) ISBN 978-1-84569-997-0 (online) ISSN 2042-8049 Woodhead Publishing Series in Food Science, Technology and Nutrition (print) ISSN 2042-8057 Woodhead Publishing Series in Food Science, Technology and Nutrition (online) The publisher’s policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp which is processed using acid-free and elemental chlorine-free practices. Furthermore, the publisher ensures that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by Toppan Best-set Premedia Limited, Hong Kong Printed by TJI Digital, Padstow, Cornwall, UK

© Woodhead Publishing Limited, 2010

To my parents, Brit Roust and Niels-Jørgen Jæger, who are always there for me. Sara Jaeger To the memory of Jack Harries who got me interested in sensory science in the first place. Hal MacFie

© Woodhead Publishing Limited, 2010

Contents

Contributor contact details......................................................................... xv Woodhead Publishing Series in Food Science, Technology and Nutrition ............................................................................................... xxi Preface.......................................................................................................... xxix

Part I

1

New product development head-on: trends, processes and perspectives............................................................................

Consumer-oriented innovation in the food and personal care products sectors: understanding consumers and using their insights in the innovation process ........................................... K. G. Grunert, B. B. Jensen, A.-M. Sonne, K. Brunsø and J. Scholderer, Aarhus University, Denmark, D. V. Byrne and L. Holm, University of Copenhagen, Denmark, C. Clausen, A. Friis and G. Hyldig, Technical University of Denmark, Denmark, N. H. Kristensen, Aalborg University, Denmark and C. Lettl, Vienna University of Economics and Business, Austria 1.1 Introduction ............................................................................ 1.2 Understanding consumer preferences in food markets .... 1.3 Innovation management and market orientation .............. 1.4 Final perspective..................................................................... 1.5 References ...............................................................................

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Contents Changes in food retailing and their implications for new product development ......................................................................... J. Dawson, Universities of Edinburgh and Stirling, Scotland 2.1 Fundamental innovations in food retailing ........................ 2.2 Directions of change in food retailing................................. 2.3 Food retail growth model...................................................... 2.4 Key areas of innovation for food retailers ......................... 2.5 Conclusion ............................................................................... 2.6 Sources of further information and advice ......................... 2.7 References ............................................................................... Recent advances in commercial concept research for product development ......................................................................... S. Porretta, Experimental Station for the Food Preserving Industry, Italy, H. R. Moskowitz, Moskowitz Jacobs Inc., USA and J. Hartmann, Unilever Foods, The Netherlands 3.1 Prologue: corporate structures and the new role of research and development (R & D) as innovators in food and beverages............................................................ 3.2 Where do ideas reside? ......................................................... 3.3 Entry points for the big ideas and ideation in general ..... 3.4 Discovering opportunities and the use of deep knowledge ...................................................................... 3.5 The role of research and development (R & D) in food companies ....................................................................... 3.6 Different world-views: academia versus industry .............. 3.7 Concept writing is strategy exploration .............................. 3.8 Tapping the consumer mind ................................................. 3.9 Ideation tools to pull out good ideas .................................. 3.10 Concepts born of observing .................................................. 3.11 Concepts born of collaboration and the ‘wisdom of the many’ ................................................................................. 3.12 Concept writing – how to do it and how to do it well ...... 3.13 Concept screening .................................................................. 3.14 Qualitative screening ............................................................. 3.15 Screening promises and full concepts.................................. 3.16 Simulated market test at the concept level ........................ 3.17 Experimental design of concepts ......................................... 3.18 A short introduction to design: concepts about water ...... 3.19 Putting it all together: from the concept research to the design and sales messaging ............................................ 3.20 Creating the product and marketing it ............................... 3.21 Summing up ............................................................................ 3.22 Acknowledgement ................................................................. 3.23 References and further reading ...........................................

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Innovation strategies and trends in the global fast moving consumer goods sector: an interview with Mintel’s Jo Pye .......... J. Pye, Mintel International, Australia and S. R. Jaeger, The New Zealand Institute for Plant and Food Research Limited, New Zealand 4.1 Interview with Jo Pye ............................................................ 4.2 References and further reading ........................................... 4.3 Short biography for Jo Pye ................................................... Innovation in foods and personal care products: an interview with Gail Civille .................................................................................. G. V. Civille, Sensory Spectrum Inc., USA and S. R. Jaeger, The New Zealand Institute for Plant and Food Research, New Zealand 5.1 Interview with Gail Civille.................................................... 5.2 Sources of further information and advice ......................... 5.3 Short biography for Gail Civille .......................................... Innovation in sensory practice and education: an interview with Howard Schutz ........................................................................... H. G. Schutz, University of California at Davis, USA and S. R. Jaeger, The New Zealand Institute for Plant and Food Research, New Zealand 6.1 Interview with Howard Schutz ............................................. 6.2 References and further reading ........................................... 6.3 Short biography for Howard Schutz....................................

Part II

7

Hedonic scaling in new product development: past, present and future .............................................................

Hedonic measurement for product development: new methods for direct and indirect scaling ................................... A. V. Cardello, US Army Natick Soldier R, D & E Center, USA and S. R. Jaeger, The New Zealand Institute for Plant and Food Research, New Zealand 7.1 Introduction ............................................................................ 7.2 Historical developments in the scaling of hedonics .......... 7.3 Best-worst scaling: a modern approach to indirect scaling ........................................................................ 7.4 Labeled magnitude scales: a modern approach to direct scaling ........................................................................... 7.5 Comparisons among hedonic scaling methods .................. 7.6 Recommendations and conclusions ..................................... 7.7 References ...............................................................................

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The effects of context on liking: implications for hedonic measurements in new product development .................................. J. Delarue, AgroParisTech, France and I. Boutrolle, Danone Research, France 8.1 Introduction ............................................................................ 8.2 Current practice of hedonic tests: central location test (CLT) and home use test (HUT) ................................. 8.3 How context may affect preferences ................................... 8.4 When choosing central location tests (CLT) vs. home use tests (HUT): recommendations to manufacturers ...... 8.5 How to improve food testing to enhance integration of eating/drinking situation variables .................................. 8.6 Future trends .......................................................................... 8.7 References ............................................................................... Going beyond liking: measuring emotional and conceptual profiles to make better new products .............................................. D. Thomson, MMR Research Worldwide Inc., UK 9.1 Introduction ............................................................................ 9.2 Part 1: Understanding consumer choice processes ............ 9.3 Part 2: Measuring conceptualisations .................................. 9.4 Part 3: Conceptual profiling case studies ............................ 9.5 Conclusions ............................................................................. 9.6 Acknowledgements ................................................................ 9.7 Sources of further information and advice ......................... 9.8 References ...............................................................................

Part III

10

Consumer research methods in new product development ...............................................................................

Consumer understanding and reaction to health claims: insights and methodolgy .................................................................... M. Rogeaux, Danone Research, France 10.1 Introduction ............................................................................ 10.2 Functional foods ..................................................................... 10.3 The process of consumer understanding of the health benefit ................................................................... 10.4 How to evaluate consumer understanding with a consumer test? ..................................................................... 10.5 Introduction of a new method: consumer understanding test (CUT) ..................................................... 10.6 Future trends .......................................................................... 10.7 Sources of further information and advice ......................... 10.8 References ...............................................................................

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Pricing for new product development ............................................. L. Lockshin and S. Mueller, University of South Australia, Australia 11.1 Introduction ............................................................................ 11.2 Rules of thumb for pricing new flavours, styles, and brand extensions ............................................................. 11.3 Pricing for new to the world products or features ............ 11.4 Hedonic price analysis (HPA) .............................................. 11.5 Basic discrete choice experiments ....................................... 11.6 Summary .................................................................................. 11.7 Sources of further information and advice ......................... 11.8 References ............................................................................... Experimental auction markets for studying consumer preferences .......................................................................................... J. L. Lusk, Oklahoma State University, USA 12.1 Introduction ............................................................................ 12.2 Experimental auctions in action........................................... 12.3 Frontier research in experimental auction markets .......... 12.4 Sources of further information and advice ......................... 12.5 References ............................................................................... Doing consumer research in the field .............................................. C. R. Payne, New Mexico State University School of Business, USA and B. Wansink, Cornell University, USA 13.1 Introduction ............................................................................ 13.2 The nature of the field ........................................................... 13.3 Consumer field study considerations ................................... 13.4 Field mistakes ......................................................................... 13.5 Conclusion ............................................................................... 13.6 Sources of further information and advice ......................... 13.7 References and further reading ........................................... The importance of consumer involvement and implications for new product development ........................................................... I. Lesschaeve, Vineland Research and Innovation Centre, Canada and J. Bruwer, The University of Adelaide, Australia 14.1 Introduction ............................................................................ 14.2 Theoretical background of the involvement construct ..... 14.3 Measurement methods .......................................................... 14.4 Consumer involvement scales .............................................. 14.5 Moderating role of involvement on consumer purchase and consumption behaviours ............................... 14.6 Implications for consumer-driven innovation .................... 14.7 References ...............................................................................

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Part IV 15

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Statistics and new product development................................

Statistical design of experiments in the 21st century and implications for consumer product testing ..................................... B. T. Carr, Carr Consulting, USA 15.1 Introduction ............................................................................ 15.2 Advantages of statistical design of experiments (DOE) .... 15.3 Factorial experiments............................................................. 15.4 Screening experiments ........................................................... 15.5 Optimization experiments ..................................................... 15.6 Mixture experiments .............................................................. 15.7 Selecting experimental variables and their ranges ............ 15.8 Traditional designs and computer-aided optimal designs ....................................................................... 15.9 Implications of production testing with consumers........... 15.10 Further reading ....................................................................... 15.11 References ............................................................................... Data handling in cross-cultural studies: measurement invariance ............................................................................................. J. Scholderer, Aarhus University, Denmark 16.1 Introduction ............................................................................ 16.2 Assessing measurement invariance ..................................... 16.3 Numerical example of data handling in cross-cultural studies .............................................................. 16.4 Correcting for bias: three strategies..................................... 16.5 Conclusion ............................................................................... 16.6 References ............................................................................... 16.7 Appendix ................................................................................. Bayesian networks for food science: theoretical background and potential applications ................................................................. V. A. Phan and M. A. J. S. van Boekel, Wageningen University, The Netherlands and M. Dekker and U. Garczarek, Unilever Food and Health Research Institute, The Netherlands 17.1 Introduction ............................................................................ 17.2 Concepts of Bayesian networks ........................................... 17.3 Use of Bayesian networks..................................................... 17.4 Inference in simple models ................................................... 17.5 Inference in complex models................................................ 17.6 Learning Bayesian networks ................................................ 17.7 Discussions .............................................................................. 17.8 Sources of further information and advice ......................... 17.9 References ............................................................................... 17.10 Appendix .................................................................................

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Part V

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New product development in the future: new consumer trends, new science......................................................................

515

Corporate social responsibility – does it matter to consumers? .... S. C. Beckmann, Copenhagen Business School, Denmark 18.1 Introducing the topic ............................................................. 18.2 What constitutes corporate social responsibility (CSR)? ... 18.3 Mapping the field of consumers’ response to corporate social responsibility (CSR) ................................. 18.4 New product development and corporate social responsibility (CSR) .............................................................. 18.5 Future trends .......................................................................... 18.6 References and further reading ........................................... Anti-consumption: a cause for concern in the food and personal care products sectors?........................................................ M. S. W. Lee, The University of Auckland Business School, New Zealand 19.1 Introduction ............................................................................ 19.2 Anti-consumption and personal care products and innovation in food .................................................................. 19.3 Summary .................................................................................. 19.4 Future trends .......................................................................... 19.5 Sources of further information and advice ......................... 19.6 References and further reading ........................................... Genetic variation in taste and odour perception: an emerging science to guide new product development .................................... R. D. Newcomb, J. McRae, J. Ingram, K. Elborough and S. R. Jaeger, The New Zealand Institute for Plant and Food Research Limited, New Zealand 20.1 Introduction ............................................................................ 20.2 The genetics of human taste perception ............................. 20.3 Genetics of odour perception ............................................... 20.4 The impact of genetic variation on food preference and consumption .................................................................... 20.5 Industry opportunities and issues ........................................ 20.6 Summary .................................................................................. 20.7 Sources of further information and advice ......................... 20.8 References ............................................................................... Neuroimaging of sensory perception and hedonic reward ........... M. G. Veldhuizen, The John B. Pierce Laboratory and Yale University School of Medicine, USA 21.1 Introduction ............................................................................

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Neuroimaging techniques...................................................... Key neural substrates of pleasantness ................................. Product choice and neuroeconomics ................................... Pitfalls of neuroimaging of sensory perception and food reward ............................................................................. 21.6 Promises of neuroimaging for new product developers ... 21.7 Future trends .......................................................................... 21.8 Conclusion ............................................................................... 21.9 Sources of further information and advice ......................... 21.10 References ............................................................................... 22

Molecular gastronomy, chefs and food innovation: an interview with Michael Bom Frøst ............................................. M. Bom Frøst, University of Copenhagen, Denmark and S. R. Jaeger, The New Zealand Institute for Plant and Food Research Limited, New Zealand 22.1 Interview with Michael Bom Frøst ...................................... 22.2 Sources of further information and advice ......................... 22.3 Short biography for Michael Bom Frøst .............................

Index .............................................................................................................

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Contributor contact details

Editors

Chapter 1

Sara R. Jaeger The New Zealand Institute for Plant and Food Research Limited 120 Mt Albert Road Sandringham Auckland 1025 New Zealand

K. G. Grunert*, B. B. Jensen, A.-M. Sonne, Karen Brunsø and J. Scholderer MAPP Center for Research on Customer Relations in the Food Sector Aarhus University Haslegaardvej 10 DK-8210 Aarhus V Denmark

E-mail: sara.jaeger@plantandfood. co.nz

E-mail: [email protected] Hal MacFie 43 Manor Road Keynsham Bristol BS31 1RB UK E-mail: [email protected]

D. V. Byrne Department of Food Science University of Copenhagen Rolighedsvej 30 DK-1958 Fredriksberg C Denmark C. Clausen Department of Manufacturing, Engineering and Management Technical University of Denmark Produkstionstorvet Bygning 424 DK-2800 Kgs. Lyngby Denmark

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Contributor contact details

A. Friis DTU Nanotech Technical University of Denmark Ørsted Plads Bygning 345E DK-2800 Kgs. Lyngby Denmark L. Holm Department of Human Nutrition University of Copenhagen Rolighedsvej 30 DK-1958 Frederiksberg C Denmark G. Hyldig DTU Aqua Technical University of Denmark National Institute of Aquatic Resources Aquatic Process and Product Technology Søltofts Plads Bygning 221 DK-2800 Kgs. Lyngby Denmark N. H. Kristensen Aalborg University Department of Development and Planning Lautrupvang 2 DK-2750 Ballerup Denmark C. Lettl Institute for Entrepreneurship and Innovation Vienna University of Economics and Business Augasse 2-6 A-1090 Wien Austria

Chapter 2 J. Dawson Edinburgh University Business School 50 George Square Edinburgh EH8 9JY Scotland E-mail: [email protected]

Chapter 3 S. Porretta Experimental Station for the Food Preserving Industry Viale F. Tanara, 31A 43100 Parma Italy E-mail: [email protected] H. Moskowitz* Moskowitz Jacobs Inc. 1025 Westchester Avenue, 4th Floor White Plains, NY 10604 USA E-mail: [email protected] J. Hartmann Consumer and Market Insights Unilever Foods Hoevelaken The Netherlands E-mail: Johannes.hartmann@ unilever.com

© Woodhead Publishing Limited, 2010

Contributor contact details

xvii

Chapter 4

Chapter 6

Jo Pye Insights, Asia Pacific Mintel International Level 31, 50 Bridge Street Sydney NSW 2000 Australia

Howard G. Schutz UCD Extension University of California at Davis 1333 Research Park Drive Davis, CA 95618 USA

E-mail: [email protected]

E-mail: [email protected]

Sara R. Jaeger* The New Zealand Institute for Plant and Food Research Limited 120 Mt Albert Road Sandringham Auckland 1025 New Zealand

Sara R. Jaeger* The New Zealand Institute for Plant and Food Research Limited 120 Mt Albert Road Sandringham Auckland 1025 New Zealand

E-mail: sara.jaeger@plantandfood. co.nz

E-mail: sara.jaeger@plantandfood. co.nz

Chapter 5

Chapter 7

Gail V. Civille Sensory Spectrum Inc. 554 Central Avenue New Providence, NJ 07974 USA

Armand V. Cardello US Army Natick Soldier R, D & E Center Natick, MA 01760-5020 USA

E-mail: gvciville@sensoryspectrum. com

E-mail: Armand.Cardello@ us.army.mil

Sara R. Jaeger* The New Zealand Institute for Plant and Food Research Limited 120 Mt Albert Road Sandringham Auckland 1025 New Zealand

Sara R. Jaeger* The New Zealand Institute for Plant and Food Research Limited 120 Mt Albert Road Sandringham Auckland 1025 New Zealand

E-mail: sara.jaeger@plantandfood. co.nz

E-mail: sara.jaeger@plantandfood. co.nz

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Contributor contact details

Chapter 8

Chapter 11

Julien Delarue* AgroParisTech 1 avenue des olympiades 91744 Massy France

Larry Lockshin* and Simone Mueller Ehrenberg Bass Institute for Marketing Science University of South Australia GPO Box 2471 Adelaide 5000 Australia

E-mail: julien.delarue@ agroparistech.fr Isabelle Boutrolle Danone Research Route de la Vauves – RD 128 91767 Palaiseau France

E-mail: [email protected] [email protected]

Chapter 12 E-mail: isabelle.boutrolle@danone. com

Chapter 9 David Thomson MMR Research Worldwide Inc. Wallingford House 46 High Street Wallingford OX10 0DB UK

Jayson L. Lusk Department of Agricultural Economics Oklahoma State University 411 Ag Hall Stillwater, OK 74078 USA E-mail: [email protected]

Chapter 13 E-mail: D.Thomson@mmr-research. com

Chapter 10 Michel Rogeaux Consumer Science Danone Research Daniel Carasso Center RD 128 91767 Palaiseau Cedex France

Collin R. Payne* College of Business Marketing Department New Mexico State University MSC 5280, PO Box 30001 Las Cruces, NM 88003-8001 USA E-mail: [email protected]

E-mail: Michel.Rogeaux@danone. com

© Woodhead Publishing Limited, 2010

Contributor contact details Brian Wansink Cornell University Department of Applied Economics and Management 110 Warren Hall Ithaca, NY 14853-7801 USA E-mail: [email protected]

Chapter 15 B. T. Carr Carr Consulting 1215 Washington Avenue Suite 203 Wilmette, IL 60091 USA E-mail: tom.carr@carrconsulting. net

Chapter 14 Isabelle Lesschaeve* Vineland Research and Innovation Centre 4890 Victoria Avenue North PO Box 4000 Vineland Station L0R2E0 Ontario Canada E-mail: Isabelle.Lesschaeve@ vinelandresearch.com Johan Bruwer The University of Adelaide, Australia School of Agriculture, Food and Wine Wine Science and Business Group Waite Campus Glen Osmond, SA 5064 Australia E-mail: johan.bruwer@adelaide. edu.au

Chapter 16 J. Scholderer Department of Marketing and Statistics Aarhus School of Business Aarhus University Haslegaardsvej 10 DK-8210 Aarhus V Denmark E-mail: [email protected]

Chapter 17 V.-A. Phan* and M. A. J. S van Boekel Product Design and Quality Management Wageningen University Bomenweg 2 Buildingnummer 307 6703 HD Wageningen The Netherlands E-mail: [email protected] M. Dekker and U. Garczarek Unilever Food and Health Research Institute Olivier van Noortlaan 120 3133 AT Vlaardingen The Netherlands

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Chapter 18

Chapter 21

S. C. Beckmann Copenhagen Business School Department of Marketing Solbjerg Plads 3 DK-2000 Frederksberg

M. G. Veldhuizen The John B. Pierce Laboratory Department of Psychiatry Yale University School of Medicine 290 Congress Avenue New Haven, CT 06519 USA

E-mail: [email protected]; [email protected]

E-mail: [email protected] Chapter 19 M. S. W. Lee Department of Marketing The University of Auckland Business School 12 Grafton Road Private Bag 92019 Auckland 1142 New Zealand E-mail: [email protected]

Chapter 20 R. Newcomb*, J. MacRae, J. Ingram, K. Elborough and S. R. Jaeger The New Zealand Institute for Plant and Food Research Limited 120 Mt Albert Road Sandringham Auckland 1025 New Zealand

Chapter 22 M. B. Frøst Department of Food Science Faculty of Life Sciences University of Copenhagen Rolighedsej 30 CK-1958 Frederiksberg Denmark E-mail: [email protected] Sara R. Jaeger* The New Zealand Institute for Plant and Food Research Limited 120 Mt Albert Road Sandringham Auckland 1025 New Zealand E-mail: sara.jaeger@plantandfood. co.nz

E-mail: richard.newcomb@ plantandfood.co.nz

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Food chain integrity: a holistic approach to food traceability, authenticity, safety and bioterrorism prevention Edited by J. Hoorfar, K. Jordan, F. Butler and R. Prugger 213 Improving the safety and quality of eggs and egg products Volume 1 Edited by Y. Nys, M. Bain and F. Van Immerseel 214 Improving the safety and quality of eggs and egg products Volume 2 Edited by Y. Nys, M. Bain and F. Van Immerseel 215 Feed and fodder contamination: effects on livestock and food safety Edited by J. Fink-Gremmels 216 Hygiene in the design, construction and renovation of food processing factories Edited by H. L. M. Lelieveld and J. Holah 217 Technology of biscuits, crackers and cookies Fourth edition Edited by D. Manley 218 Nanotechnology in the food, beverage and nutraceutical industries Edited by Q. Huang 219 Rice quality K. R. Bhattacharya 220 Meat, poultry and seafood packaging Edited by J. P. Kerry 221 Reducing saturated fats in foods Edited by G. Talbot 222 Handbook of food proteins Edited by G. O. Phillips and P. A. Williams

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Preface

New product development (NPD) continues to be a key activity for companies to retain and grow market share. The global market places that these new products are intended for and the factors that shape them continue to evolve, and so do the consumer understanding and research methodologies that underpin NPD. Hence, up-to-date books that support NPD personnel in their innovation activities continue to be needed. Our aim for this book, which supplements a previous volume on Consumer-led food product development edited by Hal MacFie and published by Woodhead Publishing Limited in 2007, is that NPD personnel will be able to broaden their understanding of the many available approaches to understanding and measuring consumer behaviour and its antecedents. Given the sophistication of the competition that companies face, we believe that such understanding, coupled with technical excellence in product design, will be essential to survive the changing markets as consumers in India, China, South-east Asia and South America achieve parity with USA and Europe in terms of spending power. We have chosen to broaden the topic of this book beyond foods and beverages and include NPD for personal and home care products. There is little published research in this area, in part due to many publications restricting themselves to foods and beverages. Hopefully, the book will satisfy a need among NPD personnel in these industries, where in recent years NPD has become highly consumer orientated. A broad range of topics relating to NPD are covered and of particular note is the emphasis on business/marketing issues in NPD, as opposed to product formulation and sensory testing. This was an attempt at covering an area that was neglected in the previous volume, but also a reflection of

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the highly interdisciplinary nature of NPD and the multi-functional teams that make it happen in companies. The book has five parts: I. II. III. IV. V.

NPD head-on: trends, processes and perspectives Hedonic scaling in NPD: past, present and future Consumer research methods in NPD Statistics and NPD NPD in the future: new consumer trends, new science

We begin with six chapters, which in Part I directly address consumerdriven NPD. Grunert et al. (Chapter 1) set the scene by presenting an overview of how consumer insights can be used in the innovation process. Significant changes in the retailing sector motivated the contribution from Dawson (Chapter 2) who guides the reader through the factors that impact on this very important innovation stakeholder. Poretta et al. (Chapter 3) discuss commercial concept research for product development and is one of several chapters that touch on the different world-views of research that industry and academia have. An innovation strategy based on an approach that can be described as ‘it’s as simple as stealing with pride’ is presented by Jo Pye from Mintel in Chapter 4. Gail Civille and Howard Schutz make their contributions in Chapters 5 and 6 and address topics such as barriers companies face in succeeding in their NPD efforts and the importance of sensory product quality, relative to other product features in NPD failure/ success. Hedonic testing continues to be an important source of information in food and personal care NPD, and Part II is dedicated to this topic. New methods for hedonic scaling are the focus of Chapter 7, which in addition provides a comprehensive historical perspective of the development of the hedonic scales in use today. The growing understanding that there may be no such thing as true preferences but only context-dependent preferences motivated the contribution by Delarue and Boutrolle (Chapter 8) and these authors comprehensively summarise pros and cons of central location and home use testing in hedonic measurement. David Thomson (Chapter 9) challenges us to move beyond liking in the quest for success in NPD and discusses how the tools of emotional and conceptual profiling can aid innovation professionals in doing so. Consumer research methods for NPD are in focus in Part III, which presents six diverse approaches/perspectives. Michel Rogeaux introduces, in Chapter 10, a methodology used by Danone to evaluate consumer understanding of food and beverage health food claims. Lockshin and Mueller (Chapter 11) discuss three different methods for setting the retail price for newly developed products: heuristic, rule-of-thumb or competitive comparison method; hedonic price analysis; and discrete choice analysis. Dollar values also feature prominently in Chapter 12 by Lusk, which looks at experimental auction markets in which people bid to buy real products

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using real money. An outline of frontier research in experimental auction markets and applied perspectives such as profit calculations adds significant depth to this chapter. Consumer research is best done in real-life contexts, but many of us still rely on central location testing. With the contribution from Payne and Wansink (Chapter 13), who share their collective experience about how/how not to do field testing, we aim to make a small contribution in readdressing this imbalance. Lesschaeve and Bruwer (Chapter 14) discuss consumer involvement and new insights about consumers and consumer decision-making processes this construct can offer. The chapters in Part IV are about ‘statistics’ and while in parts technical, the general messages conveyed in this part of the book are important for almost anyone involved in NPD. Tom Carr has written an accessible text (Chapter 15) about statistical design of experiments as it pertains to both formulations and consumer testing and we have included this topic to highlight the importance of good experimental design and its value in aiding successful NPD. The rise of the global market place and the multinationals competing in these markets motivated Chapter 16 by Scholderer, which challenges us to think about the ways in which we compare data from different consumer markets and the extent to which we risk drawing the wrong conclusions because we ignore fundamental issues relating to measurement invariance. In Chapter 17, Phan et al. introduce Bayesian networks, which have gained widespread application in many other areas of science but are still to penetrate food and personal care innovation. There are five chapters in Part V, which looks towards the future of consumer-driven innovation in food and personal care products. Two value orientations that may come to dominate (segments) of consumers in the future and dictate their purchase/consumption behaviours are covered in Chapters 18 and 19. Beckmann’s contribution is on corporate social responsibility (CSR) and the chapter by Lee on anti-consumption offers a counterdominant perspective that will enable new product developers to understand consumers who may not desire innovative products and increased consumption but, rather, may prefer to reduce or resist consumption of (new) products. Chapters 20 and 21 foreshadow developments that are some years away from being implemented routinely in NPD, but which are set to have a big impact on not only our ability to explain behaviour, but also to predict it. Newcomb et al. find inspiration in the human genetic revolution and Veldhuizen’s contribution is on neuroimaging techniques, which visualise the brain at work. Molecular gastronomy, an emerging science that integrates technological advances in food science with gastronomes’ visions for new dishes that go beyond tasting fantastic, to surprising and challenging our senses and food experiences is introduced by Michael Bom Frøst in Chapter 22. Collaboration between chefs and food scientists as a route to new product development by industry is also covered in this chapter. We have encouraged all authors to express personal opinions and speculate about future developments in their topic areas. NPD is an imperfect

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science and there is more than one right way forward. We feel it is important to facilitate expression of diversity in opinion, where it exists. In working with our contributors it was clear that differences of opinion did exist in some instances, just as agreement on many topics was also evident. In a book that has innovation as its core theme, it seemed appropriate to include our own attempt at innovation. Several chapters have a novel format. They are not traditional book chapters, but instead present a written record of a conversation on an agreed topic between two people with a shared interest in consumer-driven innovation in food and personal care products. This has resulted in a communication style that is very accessible, as well as very personal. These interview chapters are easy to read, sometimes delightfully unpredictable as conversations took a tangential turn, and perhaps most of all they have allowed the interviewees an opportunity to speak more freely without being bound by the conventions of traditional scientific writing. The participants in these conversations expressed joy at this novel format. We hope you’ll find it inspiring and valuable too. Sara R. Jaeger Hal MacFie

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DEVELOPING THE NEXT GENERATION IN HEALTHY FOODS As our understanding of human health has advanced, society’s demand for food and products that improve health, wellness and lifestyle has grown. At Plant & Food Research, we realise this and are leading the way by developing new nutrient rich cultivars and food products with enhanced health promoting properties. Our sensory and consumer research is innovative; we’re exploring how our genetic make-up inﬂuences what we choose to eat. How we apply sensory evaluation and consumer science knowledge is integral to what we do. → Superfruits and Vital Vegetables → Gut health → Inﬂammation and immunity

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1 Consumer-oriented innovation in the food and personal care products sectors: understanding consumers and using their insights in the innovation process1 K. G. Grunert, B. B. Jensen, A.-M. Sonne, K. Brunsø and J. Scholderer, Aarhus University, Denmark, D. V. Byrne and L. Holm, University of Copenhagen, Denmark, C. Clausen, A. Friis and G. Hyldig, Technical University of Denmark, Denmark, N. H. Kristensen, Aalborg University, Denmark and C. Lettl, Vienna University of Economics and Business, Austria

Abstract: In this chapter, we clarify the concept of consumer-oriented innovation in the food and personal products sectors and define it as a process towards the development of a new product or service in which an integrated analysis and understanding of consumers’ wants, needs and preference formation play a key role. We then outline relevant streams of research that may promote the implementation of consumer-oriented innovation in these sectors. We first review research on understanding consumers, notably on quality perception, associated methods, and their application in innovation processes. We then review research on innovation management, emphasizing the use of consumer insight information in innovation processes. We conclude that a better integration of consumer research and research on innovation management would benefit the innovation process. Key words: user-driven innovation, market orientation, quality perception, consumer research methods.

1.1 Introduction Innovation is widely viewed as a major competitive parameter in the food and personal products industries. Recently, the political debate about 1

This chapter is the result of work in the Platform on User-Driven Innovation in the Food Sector sponsored by the Danish Centre for Advanced Food Studies (LMC). The support received from LMC is gratefully acknowledged.

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innovation has focused a great deal on user-oriented innovation. User-driven innovation is a phenomenon first observed and described in the 70s by von Hippel (1976; 1978), the innovation researcher. He documented a number of cases where customers did not wait for manufacturers to launch new products, but proceeded to modify or adapt existing products according to their own needs of their own accord (von Hippel, 1988). Well-documented examples can be found in a number of business-to-business markets, where customers, for example, adapt machinery and equipment to their own needs, for example medical surgery equipment (Lüthje, 2003), pipe hangers (Herstatt and von Hippel, 1992) and in the IT trade, where user communities modify software or in a few cases develop new products themselves (like the operating system Linux). Finally, a few examples exist on businessto-consumer markets. For example, some argue that the invention of mountain bikes primarily originates from cyclists who started modifying standard bikes for use outside the road system (Lüthje et al., 2005). In every case, a manufacturer subsequently adopted the idea, industrialized the production and thus commercialized the customers’ innovation. In the course of time the use of the term user-driven innovation has been extended considerably. Thus, today it is often used not only to cover situations where users initiate the innovation, but all forms of innovation where there has been a good measure of user involvement in the innovation process. This extended meaning of the concept thus also covers situations where the manufacturer initiates the innovation and subsequently involves users in the development process, and even situations where the manufacturer uses an agent to involve users in the innovation process (e.g., a trend spotter or a market research agency). A number of related concepts exist in the literature, including early customer integration (Gassmann and Wecht, 2005), participatory design (Mayhew, 1999), and user-centred development (Ketola and Ahonen, 2005). Users can be both direct customers and end-users. Product development takes place in a value chain where companies develop products for customers who make up the next step in the value chain, and in more or less consideration and understanding of the parts of the value chain that follow further downstream from the direct customers, up to the end-users (Grunert et al., 2005). The concept of user-oriented innovation includes both customers and end-users. In the present chapter, we will concentrate on the involvement of consumers in the innovation process. In order to distinguish the focus of this chapter from the original concept as defined by von Hippel, we will use the term consumer-oriented innovation in this chapter, and define it as a process towards the development of a new product or service in which an integrated analysis and understanding of the consumers’ wants, needs and preference formation play a key role. It is the purpose of this chapter to provide an overview of existing research and research methods that can be fruitfully applied in the area of consumer-oriented innovation in the food and personal products sectors.

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We will address two streams of research that are of relevance. First, research on how consumers form preferences for products and services can provide useful input for consumer-oriented innovation. Second, research on how consumer-oriented innovation processes can be managed in the innovating organization, including questions on how to integrate consumer information into the innovation process and on how to create cross-functional cooperation with those parts of the innovating organization dealing with consumer intelligence, production, and technological research and development, respectively.

1.2 Understanding consumer preferences in food markets Consumer markets are mass markets, and are therefore characterized by a lack of personal interaction between the innovator and the users – or, at least, most users. User-oriented innovation for consumer markets therefore usually involves characterizing the population of potential buyers by sampling techniques, and/or in-depth characterization of a small number of consumers where such insights are deemed to be especially valuable. Although consumer-oriented innovation is here defined as a process towards the development of a new product or service in which an analysis and understanding of the consumers’ needs and preference formation plays a key role, this does not imply that the aim is to develop products and services for which consumers are able to articulate the need themselves. It is a well-known phenomenon that consumers often are not able to articulate their need for really innovative products, because their thinking is framed by the products currently on the market. Also reactions of consumers when confronted with highly innovative new product concepts are not always reliable, as they may find it difficult to imagine the use of these innovative products in their daily lives. In order to deal with this problem, the innovation literature has used the term latent needs for needs that users are not aware of, but that they will become aware of once the product tapping those needs is on the market (e.g., Leonard and Rayport, 2000). Some methods for detecting such latent needs have been promoted; this we will come back to. However, we should also note that the concept of latent needs has no theoretical foundation in the buyer behaviour literature. The buyer behaviour literature has, however, carried out considerable research (mostly based on psychological theory) on how buyers form preferences for products that come on the market, and has developed methods that can be used to measure factors that may influence this preference formation process. Understanding the mechanisms by which consumers form preferences for products once they are on the market may therefore be a more promising attempt than trying to measure latent needs, because there is a body of research to draw upon.

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Preference formation is a complex process in which personal, situationspecific, and socio-economic factors play a role in addition to the attributes a new product possesses. While many different approaches to the analysis of preference formation have been proposed, a common theme is that preference formation is explained by some kind of subjective trade-off between what one has to offer in order to get the product – where price is the major component – and what one gets. What a potential buyer expects to get out of a new product is often analysed in terms of the perceived quality of the new product, relative to existing products. Food companies’ attempts to develop products that consumers will embrace may therefore be characterized as an attempt to create the right quality/ies for their customers. In the following, we will therefore give a brief introduction to the analysis of quality perception, drawing on the Total Food Quality Model, which is an integrative management-oriented framework for analysing consumer quality perception. While it has been developed to analyse quality perception in the food area, it can easily be extended to non-food cases.

1.2.1 Consumer quality perception Different research approaches have focused on questions related to consumer quality perception in order to improve the understanding and prediction of product choices and to provide inspiration to user-oriented new product development. Consumer quality expectations determine whether a new product will be purchased once, whereas fulfilment of the expectations determines whether the product will be purchased again. Thus, insight into how quality expectations are formed and into how consumers experience the quality after purchase is vital input in an innovation process. In the analysis of the quality perception process, a major distinction can be made between multidimensional and hierarchical approaches. Quality perception can be viewed as multidimensional, i.e. quality is perceived by combining a number of the product’s quality dimensions. In psychological approaches to subjective quality, this multidimensional nature is usually handled by invoking multi-attribute attitude models (Ajzen and Fishbein, 1980), where the overall evaluation of an object is explained in terms of its perceived characteristics, the evaluation of those characteristics, and an integration rule. Multi-attribute models have been used extensively in analysing quality perception, although the insight they provide is limited; most notably they do not explain why certain product attributes are regarded as desirable by consumers and others are not. These questions are dealt with in the hierarchical approaches to analysing subjective quality, where the means-end chain model is the most well-developed framework (Gutman, 1982; 1991). This model implies that consumers’ subjective product perception is established by associations between product attributes and more

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abstract cognitive categories such as values, which can motivate behaviour and create interest for product attributes. A product attribute is not relevant in and by itself, but only to the extent that the consumer expects the attribute to lead to one or more desirable or undesirable consequences. In turn, the relevance and desirability of these consequences are determined by the consumer’s own personal values. The consumer is motivated to choose a product if it results in desirable consequences, thereby contributing to the attainment of personal values (Grunert, 1995). Means-end chains are the links that a consumer establishes between product perceptions and abstract motives or values. They show how a product characteristic (e.g., ‘light’) is linked to consequences of consumption (e.g., ‘being slim’), which may lead to the attainment of important life values (e.g., ‘higher self-esteem’). The basic means-end model has later been extended into more complex models of subjective quality and value perception (e.g., Zeithaml, 1988). The means-end approach is the closest we get to a theoretical foundation of the latent needs concept in the consumer psychology literature. The Total Food Quality Model (Grunert, 2005a; 2005b; 2007; Grunert et al., 1996) integrates the multi-attribute and the hierarchical approaches to quality perception. In addition, it links quality perception to preference formation, explained as a trade-off between quality perception and perceived costs, and to an explanation of consumer satisfaction as the discrepancy between expected and experienced quality (Gardial et al., 1994). It thus deals both with the type of quality perception that will determine whether a new product is bought once, and the type of quality perception that will determine whether the product is bought again. The model is shown in Fig. 1.1. Before purchase, quality expectations are formed based on quality cues, where it is common to distinguish between intrinsic cues (physical characteristics of the product) and extrinsic cues (everything else, like brand name, advertising, packaging, store). Only those cues that consumers actually perceive will have an influence on expected quality and hence preference formation. According to the Total Food Quality Model, quality is not an aim in itself, but is desired because it helps satisfy purchase motives or values. The model therefore includes motive or value fulfilment, i.e. how products contribute to the achievement of desired consequences and values. The values sought by consumers will, in turn, have an impact on which quality dimensions are sought and how different cues are perceived and evaluated. Expected quality and expected fulfilment of the purchase motive constitute the positive consequences consumers expect from buying a product and are offset against the negative consequences in the form of various (mostly monetary) costs. The trade-off determines the intention to buy. In this connection, a high price may have a positive as well as a negative impact on the intention to buy. The negative impact follows from the role of price as a cost cue, whereas the positive impact follows from the role

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Before purchase

Technical product specifications

Cost cues

Extrinsic quality cues

Intrinsic quality cues

Perceived cost cues

Perceived extrinsic quality cues

Perceived intrinsic quality cues

After purchase

Shopping situation

Expected quality: •Taste •Health •Convenience •Process

Perceived costs

Intention to buy

Expected purchase motive fulfilment

Sensory characteristics Meal preparation Eating situation Experienced quality: •Taste •Health •Convenience •Process Experienced purchase motive fulfilment

Future purchases

Fig. 1.1 The Total Food Quality Model.

of price as an extrinsic quality cue. However, a general relation between price and perceived quality does not seem to exist (Zeithaml, 1988). After the purchase, the consumer will have a quality experience which often deviates from expected quality. The experienced quality is influenced by many factors: the product itself, in the case of food especially its sensory characteristics and meal preparation, situational factors, consumer mood, previous experience, etc. The expectation itself may also be an important variable in determining the experienced quality of the product (Schifferstein, 2001). The relationship between quality expectation and quality experience (e.g., before and after purchase) is commonly believed to determine product satisfaction, and consequently the probability of purchasing the product again. A number of implications for preference formation follow from the discussion above. Preferences are formed based on perceived available cues in the environment combined with existing knowledge. Quality cues are used to infer quality dimensions, which in turn indicate perceived suitability for purchase motives. Preference is stored in memory together with the object in question (the product), and may be retrieved whenever the object is activated from memory. Preferences can be more or less strong depending on personal relevance and experience, and they may change with new information and/or experience.

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Quality perception as starting point for consumer-oriented food innovation Søndergaard (2003) has, based on action research carried out in food companies (Søndergaard, 2005; Søndergaard and Harmsen, 2007), suggested a product development model that takes an understanding of consumer quality perception as its point of departure (Fig. 1.2). The basic message of the model is that the quality to be perceived by consumers is to be taken as a starting point, and that the concrete attributes to be built into the product, just as the concrete product attributes to be communicated to the prospective buyer, should be derived from this, and not the other way round. This contrasts with widespread practice in the food and other industries where the physical product is developed first, the positioning of the product in the mind of the consumer following later. According to Søndergaard, the starting point for successful consumer-oriented innovation should be a positioning of the product in terms of certain qualities that are desired by consumers because they tap into their life values, thus creating purchase motives. This positioning of the product has to be translated into a physical product in the product development process. The physical product will result in consumer exposure to intrinsic cues, which result in the perception of quality before/during the purchase and during preparation/consumption.

Value orientation of product

Value

Value orientation of communication

Which positive consequences for the consumer?

Creativity

Advertising message, creative strategy

How? Product development

Which attributes should the product have?

Fig. 1.2

How? Communication development

Consumer advantages

Which attributes/ consequences should be communicated?

Product development based on quality positioning.

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These have to be complemented by the right set of extrinsic cues so that the overall quality perception corresponds to the positioning.

1.2.3 Contexts and situations A full understanding of how consumers evaluate quality must also take contextual factors into account. Especially food consumption and quality preferences must be seen as integral parts of daily life, where food consumption is embedded in various kinds of practices that people engage in (Kjærnes and Holm, 2007). Food choice is one element in daily life, but it is influenced by important factors besides product attributes and personal values. When choosing a product, the prospective consumption situation is important for the range of product types and quality criteria consumers will consider. Situations in which food is consumed are part of varying practices that may be very different in nature. They may, for example, include a practice of family meals in order to accommodate for a harmonious family life, a practice of maintaining bodily well-being, strength and functionality while doing other things – work or leisure activities, or a practice of socialising with significant others, of taking a break and resting, of celebrating, etc. (Gronow, 2004; Kjærnes and Holm, 2007). And besides eating, food provisioning and preparation may or may not stand out as particular and significant parts of practices that the individual is involved in. Whether or not a consumer prefers ready made or semi-prepared products may vary according to resources available and practical arrangements connected to the specific situation. Social norms and conventions guide the use of particular food products for different kinds of events. The question of who, when, where and with whom is decisive for what is considered appropriate in any given situation, and such norms vary and change according to specific historical and cultural contexts. In Denmark, a business associate lunch may very well consist of sandwiches, whereas such a serving would be deemed highly improper in Sweden or Finland (Gronow and Jääskeläinen, 2001; Mäkelä et al., 2001). What teenagers find to be appropriate for a meal with their friends may differ from what those same teenagers prefer to find on the family dinner table (Iversen and Holm, 1999; Sylow, 2005). Linking food and situations involves deciding the appropriate ‘fit’ between the status of foods and the status of situations. Western cultures rank foods on a hierarchical scale, where animal products and especially meats rank higher than products of vegetable origin, which again rank higher than cereals (Twigg, 1984). And the status of eating situations may be ranked according to time spent on preparation, number and status of participants, expenditure of time and money, degree of ceremony, etc. According to norms in culinary culture, foods are appropriate in any given situation when its relative position on the food scale corresponds to the relative position of the situation in question (O’Doherty Jensen, 2002).

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Pizza may be appropriate for family meals in front of the telly on a Friday night, but not for formal celebrations among relatives. Research in quality preference and perception among consumers must take such contextual and situational factors into account. While the reasoning above was with regard to food products (where considerable research on such contextual factors has been done), it appears likely that personal products as well form part of daily practices and that an understanding of context factors is an important element in user-oriented innovation.

1.2.4

Methods for analysing consumer quality perception and preference formation The constraints of this chapter do not allow a thorough overview over methods for analysing consumer quality perception and preference formation. We will, however, briefly highlight what we believe are the most important techniques that are currently available (see van Kleef et al., 2005 for a review covering a broader range of techniques). Like in all of the social sciences, there is a basic distinction between qualitative and quantitative techniques. Qualitative methods are ideal in studies where the objective is to understand the meaning of a social phenomenon as experienced by the informants themselves. Such knowledge is obviously important in the context of user-oriented innovation. Qualitative methods are particularly useful when knowledge about a subject is sparse. Therefore, qualitative methods are ideal in the early fuzzy-front-end of the innovation process, where the objective is, for example, to gain a deeper understanding of user needs (manifest or latent) in order to be able to formulate a product concept, but knowledge about consumers’ experience, the beliefs and understandings may also be valuable in later phases of product development. There are two types of qualitative methods that are relevant when researching user needs: interviews and observation. A personal interview is a conversation between two people who exchange information and viewpoints on a theme of mutual interest. It deviates from ordinary conversation because it has a purpose and a structure and is defined and controlled by the researcher (Kvale, 1997). An in-depth interview makes it possible to go into depth with a subject and it can generate knowledge on, e.g., consumer preferences, attitudes, needs and habits that can be used in the idea generation as well as the concept development phases or test phase of product development. Qualitative interviews may be more or less structured. The laddering interview is one example of a structured interview technique that can be used to uncover the perceived links between quality cues, quality dimensions and purchase motives (see Grunert and Grunert, 1995; Reynolds and Gutman, 1988). Laddering is typically used as a tool to generate product and concept ideas, but laddering has also been suggested as a confirmatory tool in connection with product tests. Laddering has been successfully

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applied in the development of new food products (Grunert, 2005a; Søndergaard, 2005). Focus group interviews are a method for collecting data through group interaction on topics determined by the researcher. The researcher’s interest provides the focus, whereas group interaction produces the data (Morgan, 1997). Focus groups can be used to build an understanding of a product category from the consumer viewpoint, inspiring idea generation, but can also be used to evaluate new product concepts and even samples (see, e.g., Denzin and Lincoln, 1994; Griffin and Hauser, 1993). The observation method originates in ethnography, but is increasingly used as a tool that can provide input to the idea generation phase of product development. For instance, recently a major fish producer applied the observation method in Danish households in order to provide input to the development of new fish products, and in sports settings to help develop new and healthy products for children and adolescents (Sylow, 2005). Observation is often used to supplement interviews, as it can yield insights on issues that are not easily articulated. For example, consumers can be observed in the actual use or buying situation and issues that would otherwise not have surfaced through interviews may be detected. From the discussion above it can be concluded that qualitative methods play an important role when researching user needs. Qualitative methods are most useful when the objective is to gain a deeper understanding of user needs and are therefore mostly applied in the early phases of the product development process, such as idea generation, idea screening and conceptualization. Both interview and observation methods can be applied in research of consumers as well as of business-to-business users. Finally, it should be noted that qualitative methods can be used alone or they can be combined with quantitative methods that are discussed in the next section. Quantitative methods estimate relationships between concepts in a way that is generalizable to a sample of potential users of a product. In the following, three groups of methods that are particularly relevant in useroriented innovation are discussed. Most innovation in the food and personal products industries is incremental. Importance-performance analysis (IPA; see Martilla and James, 1977; Slack, 1994) is a tool that supports decision making on where to focus resources when attempting marginal improvement of products. The idea is quite simple: there is little point in wasting effort on those properties of a product that are unimportant to customers or already close to perfection. Instead, efforts should be concentrated on those properties that actually matter to end-users, and where the performance of the product is not yet quite on a par with customers’ expectations. The first step in IPA is to identify the product attributes that need to be improved. Once the attributes are identified, a survey is conducted with existing and potential customers in the target market. Each survey participant is asked to rate the performance of the product on each attribute (e.g.,

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Urgent

Importance Taste Freshness Healthiness Tenderness Juiciness Nutritiousness

Animal welfare

Leanness Domestic origin

Improve

Appropriate

Overkill

13

Performance

Price

Nearby production

Fig. 1.3 Importance-performance analysis for strategic prioritization of product improvement efforts (pork example).

on a scale from 1 to 10), and then state the importance of each attribute for his or her overall preference (e.g., also on a scale from 1 to 10). The importance and performance ratings are then averaged for each attribute and plotted against each other as in Fig. 1.3 (the data in the example are from a survey of Danish pork consumers, Scholderer and Bredahl, 2004). Category appraisal, or perceptual mapping, aims at visualizing how consumers perceive a certain product category on two or more dimensions. Products that consumers perceive as similar will be placed close to each other in the map, whereas products positioned far from each other are perceived as very different. The dimensions constituting the map can be viewed as the key factors driving consumer perception within the category (Carroll and Green, 1997; Green and Rao, 1972). In the context of useroriented innovation, perceptual mapping provides insight into these key dimensions, which can result in valuable inspiration for developing new products. There are several techniques that can be applied in order to derive perceptual maps (see, e.g., Hair Jr. et al., 1995) and which differ in the type of data to be collected (preference data, attribute ratings, or similarity ratings). Principal component analysis and multidimensional scaling are the most important techniques used to convert these data into perceptual maps. Fig. 1.4 shows an example of a perceptual map for cookies derived using principal component analysis (Juhl, 1994). The map shows how ten different cookies are perceived on two dimensions derived from attribute ratings. Three different clusters can be identified on the two dimensions (1) fat content and (2) flavour, and further analysis indicates that it is possible to develop butter cookies from a mixture of vegetable fat and butter without compromising the sensory experience.

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Consumer-driven innovation in food and personal care products 1.0 p1. p3.

0.5

p10. Flavour

p6.p4. 0.0

p2.

p9.

p5.

–0.5

–1.0 –1.5 p1: p2: p3: p4: p5: p6:

p8. –1.0

–0.5 0.0 Fat content

Butter, normal fat, vanilla taste Vegetable oil, normal fat, vanilla taste Mixture, normal fat, vanilla taste Butter, low fat, vanilla taste Vegetable oil, low fat, vanilla taste Mixture, low fat, vanilla taste

Fig. 1.4

0.5

p7. 1.0

p7: Vegetable oil, low fat, chocolate/coconut taste p8: Vegetable oil, low fat, chocolate taste p9: Butter, high fat, sugar taste p10: Butter, high fat, fruit taste

Perceptual map of cookies.

These methods are conservative in that they rely on data on existing products. A really innovative product may alter the perceptual map and even create new dimensions. But apart from radical innovations, category appraisal is well suited for gaining insight into consumer quality perception within a product category. Conjoint analysis (Green and Srinivasan, 1978; 1990) and discrete choice analysis (Maddala, 1983; McFadden, 1974) are a set of methods used to analyse how a set of predefined product attributes contributes to preference for a product. After having identified relevant attributes (e.g., on the basis of consumer interviews), the basic approach is to define realistic levels for each of these attributes (e.g., a basic level and two feasible improvement levels), combining them into a factorial design. The attribute combinations derived from the factorial design define a set of product profiles to be tested. A survey is then conducted where respondents are confronted with the product profiles in the form of a verbal description and/or picture and asked to indicate his or her preferences. The two methods differ in the type of preference data they require, namely either rating or ranking all product profiles in term of preference, or selecting the preferred product profile from a choice set. In both cases, the output of the statistical procedures describes the effect that a certain attribute level had on participants’

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preferences. Both methods can help the product developer assess what impact a given product attribute or improvement is likely to have on overall preference. Furthermore, optimum attribute level combinations can be identified (Carroll and Green, 1995; Green et al., 2001; Moore et al., 1999). Because of the fundamental importance of the sensory properties of foods, such as appearance, odour, taste, flavour, aftertaste, texture and trigeminal characteristics, sensory science has developed into a distinct methodological branch (e.g., see Lawless and Heyman, 1998; Martens, 1999; Meilgaard et al., 2007; Murray et al., 2001). The sensory properties of a food product that are experienced across the human sense modalities are decisive for whether a food product that has been bought for the first time will be repurchased or, indeed, a product which has been bought a number of times becomes a staple in people’s lives. The two fundamental questions that can be answered by sensory analyses are: 1) whether two or more products are perceived as significantly different in their sensory properties, and which attributes characterize the difference across the sensory modalities, and 2) which of two or more products are preferred for their sensory properties, and why. The first question is linked to descriptive sensory analysis, whereas the second one is linked to affective or hedonic liking/preference-based sensory analysis. Both can be linked via various data analytical techniques, such as Partial Least Squares Regression (PLSR), to allow conclusions on causality/predictability for preference (Martens and Martens, 2001). Thus, this allows the construction of a perceptual map of the product’s key sensory properties in relation to its inherent quality characteristics and the preferences associated with these. In many instances sensory analysis used to focus on a product’s physical (intrinsic) quality attributes, but more recently extrinsic quality cues have received more emphasis. Recently Dijksterhuis and Byrne (2005) have put forward that the time has come to move from – mere – description to explanation, i.e. to study why a certain attribute is perceived and preferred with a certain intensity, and why another may not be, and since the subject matter of sensory analysis is about perception, this means a rigorous linking of parts of sensory science with perception science. Sensory objective descriptive methods rely largely on panellists’ conscious action, but clearly much of food-related perception may not be conscious (Frandsen et al., 2003; Köster, 2003). When it comes to making choices about food, the underlying reasons for our likes and dislikes are not easily accessible to our reasoning (Dijksterhuis and Byrne, 2005). Memory and past experience are also critical to investigate in relation to perception and measurement of sensory stimulation in terms of preference. Memory for food-related stimulation (textures, tastes, flavours, odours) is likely to be different from other types of memory. In particular verbalization is shown to be able to modify the content of food-related memory (cf. Melcher and Schooler, 1996), and in addition food-related memory, at least odour memory, is to a very large extent implicit (cf. Degel, 2001). Mojet and Köster

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(2002) have illustrated the incidental manner in which memory for food texture properties seems to work. This can result in distorted memories for, for instance, the fattiness of previously encountered foods. The interpretation of sensory profiling studies can thus not teach us about consumer behaviour per se. It is an analytical measurement instrument and, because it uses humans, it is valid with respect to the perception of product properties, but not with respect to any food-related consumer behaviour. The latter is addressed by affective sensory tests, or perhaps even more validly by experiments in which actual observed behavioural acts in relation to sensory stimuli constitute the data collected (Dijksterhuis and Byrne, 2005). Descriptive sensory analysis per se is for the most part carried out with a trained panel, and in combination with affective sensory analysis carried out with potential users of the product; in this way sensory analysis can be considered central to promoting success in innovation with respect to the end-user. Objective sensory measurements combined with affective sensory analyses are particularly suitable for testing the effectiveness of product improvements/optimization and new product development (NPD) potential. Furthermore they allow a targeted adjustment of sensory properties with the purpose of obtaining a higher degree of consumer satisfaction (Byrne, 2006; Moskowitz et al., 2006; Muñoz, 2002). Sensory analysis can be based on samples/prototypes or existing products. In the latter case, affective sensory analyses can be used as input to derive a perceptual map, and thus to identify potential gaps in the market with respect to sensory attributes. Affective sensory tests can be based on pair-wise comparison, ranking, or rating data in more ‘controlled’ or field/supermarket type situations (Meilgaard et al., 2007). From a number of key perspectives sensory measurements are clearly integral to user-driven innovation adding much fundamental and applicable insight as to why consumers form preferences for certain foods and not others.

1.3

Innovation management and market orientation

1.3.1 Pros and cons of market orientation Generating information on potential buyers, their quality perception and preference formation, as discussed in the preceding section, is commonly regarded as a major element in having a market-oriented approach to product development. Market orientation is often defined as a three-step process of collecting, disseminating and responding to market information (Kohli and Jaworski, 1990). Generating and disseminating information on consumer and market needs and incorporating these into product development is a prerequisite for consumer-oriented innovation, because it is essential to gain an understanding of consumer needs and then to incorporate this knowledge into product development. Many studies have found market orientation to be positively related to company performance (Cano et al., 2004; Kirca et al., 2005), and one possible explanation for this positive © Woodhead Publishing Limited, 2010

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relationship is the impact that market-oriented activity has on product development. Several studies have concluded that market orientation is important for the successful outcome of innovation (Atuahene-Gima, 1996; Cooper and Kleinschmidt, 1987; Montoya-Weiss and Calantone, 1994), and has also been documented specifically for the food industry (Kristensen et al., 1998). However, it has also been argued that market orientation may lead to what is called the ‘incremental innovation trap’. Market orientation with its emphasis on expressed needs of current customers may lead companies towards incremental rather than radical innovation. Having the ear too close to the voice of mainstream customers may induce companies to ignore emerging technologies and emerging markets. To avoid getting caught in the incremental innovation trap, many scholars now agree that market orientation must be complemented by a learning orientation in order to gain sustainable competitive advantage and to develop radical innovations. Learning orientation is a means directly affecting a company’s ability to challenge old assumptions about the market and how the company should be organized to address it. With respect to the relationship between market orientation and organizational learning, three conceptual perspectives have been offered in the literature: (1)

(2)

(3)

Market orientation as a cultural antecedent for the learning organization (Slater and Narver, 1995). Market orientation must be embedded in an entrepreneurial culture and in organic structures, facilitative leadership and decentralized strategic planning in order to facilitate organizational learning leading to desired business outcomes such as customer satisfaction, new product success, growth and profitability. Market orientation and learning orientation as antecedents of a company’s innovation culture and innovative capability (Hurley and Hult, 1998). These authors distinguish two dimensions of organizational characteristics that enhance companies’ innovation capability: (a) structural and process characteristics such as organization size and resources, age, differentiation of the organization, low formalization, loose coupling, autonomy, lack of hierarchy, market intelligence, planning (b) cultural characteristics such as market focus, learning and development, power sharing, low status differential, participative decision making, support and collaboration, communication, conflict tolerance and risk taking. A synergistic relationship between market and learning orientation (Baker and Sinulka, 2002). It is argued that lower-order learning and market orientation lead to incremental innovation, while higher-order learning and market orientation lead to radical innovation. The authors thus propose a synergistic effect of both orientations and distinguish three types of sophistication of marketing firms. To develop a radical innovation capability three types of barriers must be reduced: belief barriers, information barriers, and behavioural barriers. © Woodhead Publishing Limited, 2010

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Another approach to shed light on the link between market orientation and a company’s radical innovation capability is to distinguish different types of market orientation, namely responsive and proactive market orientation. Responsive market orientation refers to the generation, dissemination, and use of market information pertaining to the current customers and product domain and focuses on expressed customer needs. In contrast, proactive market orientation is concerned with discovering and satisfying customers’ latent, unarticulated needs through observation of their behaviour in context to uncover new market opportunities; with working closely with lead users; with undertaking market experiments to discover future needs; and with cannibalizing sales of existing products. This short review has demonstrated an emerging discussion on how companies can prevent the ‘incremental innovation trap’ of being too consumer-oriented. Organizational learning, proactive market orientation by identifying unarticulated consumer needs and by cooperating with lead users have been proposed as promising avenues.

1.3.2

Cross-functional cooperation and representation of user knowledge New product development is an interdisciplinary activity requiring contributions from nearly all functions in a company, and especially between those doing actual development and those representing the consumer or even bringing them to the organization. By bringing together members from different functional departments and combining their different expertise and knowledge in one team, the aim is to reduce the uncertainty inherent in the development of new products, to increase speed of the development process and to heighten the quality of the end result. Griffin and Hauser (1996) have summarized empirical research relating to cooperation between marketing and R&D and concluded that all studies reviewed either support or are consistent with the hypothesis that cooperation enhances project success. Also research that has examined factors that impact the success of new products has found that the use of cross-functional teams in product development is a key success factor (Cooper, 1994; Cooper and Kleinschmidt, 1996). Despite the potential of cross-functional product development teams, the use of such teams is not without its problems. Obtaining successful collaboration can be a challenge. This is usually attributed to differences in orientations, goals, departmental cultures as well as languages that functional representatives bring to the team. Especially integration between marketing and R&D has been the focus of research indicating that disharmony between marketing and R&D is the rule rather than the exception (Moenaert and Souder, 1990). Also Dougherty (1992) found that functional diversity resulted in product development teams where members had very different views on the innovation process. When teams were unable to recognize and reconcile their different perspectives, they failed to be successful.

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Where the processing of knowledge within a certain knowledge domain may be guided by established methods, practices and rules of thumb, the exchange of knowledge across specialised domains is much more challenging (Bucciarelli, 1994; 2005). Carlile (2002) describes difficulties in transferring knowledge in new product development across the specialized functions of sales and marketing, product design, manufacturing engineering and production. Innovative solutions demand a transformation of knowledge across functions, where established understandings and knowledge practices within each individual domain are challenged. A growing strand of empirical research has pointed at the role of boundary objects in the successful transformation of knowledge across knowledge domains (Carlile, 2002; Star and Griesemer, 1999). The interaction between development and use may vary along the ‘biography’ or the ‘ life’ of a product. Synthesis-oriented approaches – like concurrent design or integrated product development – to product development (Hein and Andreasen, 1987) suggest an array of methods to be applied along a product’s life cycle from idea over conceptualization and product design to manufacturing, distribution, sales and scrapping, recycling, etc. In this perspective, it becomes clear that markets, prices and costing are interwoven with the creation of supplier-user networks in the development of product-service systems (McAloone and Andreasen, 2006). Also new approaches in conceptualization (Hansen and Andreasen, 2005) may favour user orientation by emphasizing analysis of user contexts and scenarios for user interaction with artefacts in line with integration of knowledge from engineering practices, technology and a range of other sources. Akrich (1995) has pointed at the users’ various perspectives and pictures that can be found in different departments (design, manufacturing, sales, marketing) and to different professional groups in a company. These departments may have different ‘sensors’, tools and ‘observatories’ in order to understand and work with users and markets. The implication is that different interpretations of user preferences compete and that the resulting configuration of users may act as an invisible hand instead of being the outcome of conscious choice. Based on these observations, user-oriented innovation should not just focus on users and their preferences, but also on the way users are ‘created’ in the design process.

1.4 Final perspective In this chapter, we have reviewed selected theories and methods from consumer research that can help to generate a better understanding of consumers in consumer-oriented innovation in the food and personal products sectors. We have, in addition, reviewed research on how such insight on consumers can be integrated into the innovation process, and the difficulties and pitfalls involved therein, most notably the threat of the incremental

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innovation trap and the problem of cross-functional cooperation. In this way we hope to have demonstrated that while there are lots of tools available that can help in understanding consumers, applying those tools does not by itself ensure more successful innovation processes. Many of the tools available in consumer research were developed for generating insights on consumer behaviour, but not necessarily for generating intelligence to be used in innovation processes. Consumer-oriented innovation is thus both a consumer research task and a R&D management task. It may be useful to pose the question how consumer research methods can be improved not only to achieve a better understanding of consumers, but also to provide a better basis for decision makers in innovation processes.

1.5 References Texts marked with an * are especially suitable for further reading ajzen, i. & fishbein, m. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall. akrich, m. (1995). User representations: Practices, methods and sociology. In A. Rip, T.J. Misa & J. Schot, Managing technology in society: The approach of constructive technology assessment (pp. 167–184). London: Pinter. atuahene-gima, k. (1996). Market orientation and innovation. Journal of Business Research, 35, 93–103. baker, w.e. & sinulka, j.m. (2002). Market orientation, learning orientation and product innovation: Delving into the organization’s black box. Journal of MarketFocused Management, 5, 5–23. bucciarelli, l.l. (1994). Designing engineers. Cambridge MA: MIT Press. bucciarelli, l.l. (2005). Design collaboration: Who’s in? Who’s out? In T. Binder & M. Hellström, Design spaces (pp. 64–71). Edita: IT Press. byrne, d.v. (2006). Integration of sensory and consumer drivers in quality control to optimise product production and development in the meat industry. In Proceedings of the International Congress of Meat Science and Technology: Harnessing and Exploiting Global Opportunities (pp. 551–552). Dublin, Ireland. cano, c.r., carrillat, f.a., & jaramillo, f. (2004). A meta-analysis of the relationship between market orientation and business performance: Evidence from five continents. International Journal of Research in Marketing, 21, 179–200. carlile, p.r. (2002). A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science, 13(4), 442–455. carroll, j.d. & green, p.e. (1995). Psychometric methods in marketing research: Part I, Conjoint analysis. Journal of Marketing Research, 32, 385–391. carroll, j.d. & green, p.e. (1997). Psychometric methods in marketing research: Part II, Multidimensional scaling. Journal of Marketing Research, 34, 193–204. cooper, r.g. (1994). Debunking the myths of new product development. Research Technology Management, July/August, 40–50. *cooper, r.g. & kleinschmidt, e.j. (1987). Success factors in product innovation. Industrial Marketing Management, 16, 215–233. cooper, r.g. & kleinschmidt, e.j. (1996). Winning business in product development. The critical success factors. Research Technology Management, 39, 18–29. degel, j. (2001). Implicit memory for odours. PhD Thesis. University of Utrecht, the Netherlands. denzin, n.k. & lincoln, y.s. (1994). Handbook of qualitative research. Thousand Oaks, CA: Sage Publications.

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gutman, j. (1982). A means-end chain model based on consumer categorization processes. Journal of Marketing, 46(2), 60–72. gutman, j. (1991). Exploring the nature of linkages between consequences and values. Journal of Business Research, 22, 143–149. hair jr., j.f., anderson, r.e., tatham, r.l., & black, w.c. (1995). Multivariate data analysis with readings. Englewood Cliffs, NJ: Prentice-Hall. hansen, c.t. & andreasen, m.m. (2005). On the content of a product idea. In Proceedings Engineering Design and the Global Economy, Barton, Australia. hein l. & andreasen, m.m. (1987). Integrated product development. London: IFS Publications Ltd. Springer Verlag. herstatt, c. & von hippel, e. (1992). From experience: Developing new product concepts via the lead user method: a case study in a low-tech field. Journal of Product Innovation Management, 9, 213–221. hurley, r.f. & hult, g.t. (1998). Innovation, market orientation, and organizational learning: An integration and empirical examination. Journal of Marketing, 62(3), 42–54. iversen, t. & holm, l. (1999). Måltider som familieskabelse og frisættelse [Meals and the making of family – and individualisation]. Tidsskriftet Antropologi, 39, 53–64. juhl, h.j. (1994). A sensory analysis on butter cookies – An application of Generalized Procrustes Analysis. MAPP working paper. Aarhus: Aarhus School of Business. ketola, p. & ahonen, m. (2005). Learning and innovating between users and developers within new technology development. In 12th International Product Development Conference, Copenhagen, EIASM. kirca, a., jayachandran, s., & bearden, w. (2005). Market orientation: A metaanalytic review and assessment of its antecedents and impact on performance. Journal of Marketing, 69(2), 24–41. kjærnes, u. & holm, l. (2007). Social factors and food choice: Consumption as practice. In L. Frewer & H.C.M. van Trijp, Understanding consumers of food products (pp. 511–533). Cambridge: Woodhead Publishing Ltd. *kohli, a.k. & jaworski, b.j. (1990). Market orientation: The construct, research propositions, and managerial implications. Journal of Marketing, 54(2), 1–18. köster, e.p. (2003). The psychology of food choice: some often encountered fallacies. Food Quality and Preference, 14, 359–373. kristensen, k., østergaard, p. & juhl, h.j. (1998). Success and failure of product development in the Danish food sector. Food Quality and Preference, 9, 333–342. kvale, s. (1997). Interview: En introduktion til det kvalitative forskningsinterview. København: Hans Reitzels Forlag. lawless, h.t. & heyman, h. (1998). Sensory evaluation of foods: principles and practices. New York: Chapman & Hall. leonard, d. & rayport, j.f. (2000). Spark innovation through empathic design. Harvard Business Review, 48(6), 102–113. lüthje, c. (2003). Characteristics of innovating users in a consumer goods field: An empirical study of sport-related product consumers. Technovation, 24, 683–695. lüthje, c., herstatt, c., & von hippel, e. (2005). User-innovators and local information: The case of mountain biking. Research Policy, 34, 951–965. maddala, g. (1983). Limited dependent and qualitative variables in econometrics. New York: Cambridge University Press. mäkelä, j., kjærnes, u., & ekström, m.p. (2001). What did they eat? In U. Kjærnes, Eating patterns. A day in the lives of Nordic people (pp. 65–90). Lysaker, Norway: SIFO – Statens institutt for forbruksforskning. martens, h. & martens, m. (2001). Validation X? Y??. In H. Martens & M. Martens, Multivariate analysis of quality. An introduction (pp. 177–205). London: Wiley.

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2 Changes in food retailing and their implications for new product development J. Dawson, Universities of Edinburgh and Stirling, Scotland

Abstract: Three phases of fundamental innovation in food retailing since the mid 20th century are described. Major dimensions of change presently underway are reviewed. The business model, with its core of innovation, underpinning the growth of large food retailers is explained. Five key aspects of the innovation at the core of the model are explored and examples provided of the innovations adopted by retailers. The implications of the changes in retailing for new product development are considered through the chapter. The innovation activity within retailers has become a major determinant of the acceptance of new products in the consumer market place. Key words: retail, innovation, retail formulae, retail brands, economies of scale.

Food retailing is a highly innovative sector within the total food chain. Recent innovation continues a long tradition. Innovation is present throughout the successful retail firm, including but not only in new product development activities. Considered broadly, the structure of food retailing in Europe has passed through three periods of fundamental innovation since the middle of the 20th century. These fundamental innovations have each changed the dominant business model of the sector. The structural innovations have impacted strongly on the everyday operations of retailers, not least in the changing ranges of products on sale and by implication on new product development activity. Cost and profit structures and institutional responsibilities for functions have changed with each innovation (Costa et al. 1997). Within each of the three phases, the underpinning fundamental innovation was subject to evolution through a constant sequence of operational innovations (Tushmann and O’Reilly 1996, 1997). The same patterns of innovation are evident in economies outside Europe although their timing is somewhat different, starting earlier within the USA but later in much of East Asia.

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2.1 Fundamental innovations in food retailing The three fundamental innovations have been (Dawson 2001, 2006): • the widespread adoption of customer self-service as the way of organising the interface between the firm and the customer • the adoption of marketing as the business philosophy of retailers • convergent information and communication technologies as the facilitator of vertical inter-firm relationships and horizontal intra-firm relationships. These innovations have affected all areas of retailing. Food retailers have been strongly influenced but the same trends are seen in non-foods, including health and beauty retailers, DIY retailers and clothing retailers. The innovation of customer self-service as the dominant mode of selling began in the USA but its widespread adoption across the world from the early 1950s transformed the way that food was presented for purchasing by customers. Packaging methods had to change. The nature of advertising changed. The relationship between customers and employees changed. The product knowledge required by employees changed. Customers were expected to be responsible for more costs within retailing and were expected to have, or to gain, the knowledge to make product choices without direct support from employees. The direct access to items that was given to consumers affected the nature of new product development and product introduction, with more opportunity for manufacturers to influence consumer choice in-store without the intervention of store staff. With the adoption of self-service, the allocation of shelf space took on a new and more important role in the overall process of food distribution, so new products had to compete more strongly for access to the limited customer-facing shelf space. The second fundamental innovation was the adoption of marketing by retailers. From the mid 1970s in Europe food retailers started to take an active role in marketing to consumers thus they began to wrestle the marketing initiatives and innovation away from food processors and manufacturers. Consumers graduated from wanting simply more product to wanting more choices of product, of items, of quality, of brands and of places to purchase. Retailers, through their direct contacts with consumers and through the collection of more detailed information about store sales and consumer actions, gained knowledge on purchasing behaviours and consumers’ perceived needs. Thus the retailers were able to start a process of providing the types of stores and the ranges of products that met consumer needs. Rather than being the passive agents of manufacturers and selling the items and brands decided as appropriate by manufacturers, the retailers became active intermediaries between consumers and suppliers and placed requirements on manufacturers to produce items as required by retailers. The effect of this was to encourage consumers themselves to become more active and demanding of retailers to provide improvements to shopping environments, more

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choice, lower prices, and improved quality. Product innovation, store innovation and process innovation became an additional aspect in inter-retailer competition alongside the fundamentals of price and promotion. Retailers became more and directly involved in new product development as they used the additional information they obtained from their contacts with consumers. Retailers began to develop their own in-house new product development teams, employing food scientists alongside retail marketing teams. The third fundamental innovation for retailers was the convergence and integration of information technologies (IT) and communication technologies (CT). This started to have a significant impact on the sector in Europe from the early 1990s. By integrating information and communication technologies retailers were able to manage substantially larger chains of stores across a wider geographical scope, thus gaining scale economies on many aspects of operation, including new product development and the returns obtained from new products. Additionally the integration of these technologies has given retailers increased control over supply chains allowing them to standardise supply chain systems to remove costs and increase effectiveness (Fernie and Sparks 2009). More detailed data collected at point of sale by IT were able to be integrated across the firm by CT so enabling greater customisation at the customer interface in the store. The combining of standardisation of supply chains and operating processes with greater customisation of product ranges, merchandising and customer services has improved the competitive positioning of firms. The associated larger, often international, markets opened to retailers has increased the demands for new products to meet the needs of a more diverse customer base. Each successive adoption of the three fundamental innovations provided the foundations for a restructuring of the retail sector from a sector characterised by small firms and a multi-layered food distribution system controlled by manufacturers to a concentrated retail sector dominated by a few large retailers that control an integrated international supply chain. The emergent structure of the retail sector is therefore notably different from that of retailing little more than a decade ago. This is the case whether we consider the mature Western style economies or emergent economies, of post communist and high growth emergent types. In the more mature economies the new structure has emerged steadily with the activities of indigenous firms generating change through competition. In the emergent economies there are stronger external influences from foreign firms and a more disruptive emergence of the new structures. The items that are sold by the retailer have been central to all three fundamental innovations. The items, the item ranges, the development of new items and the merchandising of the items have undergone radical changes with each innovation. The range of items expected by consumers to be present in a ‘supermarket’ has broadened steadily with additions of specialist foods, non-food grocery, health and beauty items and ‘convenience’ non foods such as stationery, pocket money toys, basic clothing. The growth of

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self-service required ranges of items to be displayed in ways that the consumer could see. This threw into sharp focus the gaps in ranges and the potential complementarities of items. New products were required to fill these gaps, with suppliers being the main agent for the creation of the new products. With the adoption of marketing so the nature of product management by retailers changed to encompass more complex and sophisticated pricing, promotions and branding targeted to specific consumer groups. New product development was undertaken in response to the perceived needs of specific groups becoming more customer driven (Pellegrini and Zanderighi 1991). Retailers thus became more heavily involved in new product development. In some cases retailers started to lead the NPD process. With the innovation of ICT, again product management requirements changed as a result of the new information generated from computer-simulated shelf layouts, improved supply chain networks typified by Efficient Consumer Response programmes (for example, Mitchell 1997, Kotzab 1999, Roland Berger Strategy Consultants 2003), direct communication with consumers through loyalty cards (Humby et al. 2003) and the opportunities deriving from better control over international sourcing of products. The NPD activities evolved as a response to ICT opportunities, with retailers being directly involved in NPD of their retailer brands and an increase in specialist new products whilst at the same time depending on NPD from suppliers for items in the non-food ranges. New product introductions, through retailer controlled NPD activity, now extend beyond staple and specialist food items to include a wide range of non-food items. The roles of products, their development as new items, and their management within retailers, have been central through all the sequence of innovation that has changed fundamentally retail structures and retail operations in the food chain.

2.2 Directions of change in food retailing The result of the innovations across the sector has been the establishment of a retail sector that is very different in 2009 from a decade previously. A number of features now characterise food retailing. 2.2.1 The increased scale of firms Between 2000 and 2007 Tesco increased its sales of food items by 96% and Wal-Mart by 46% within total sales growth respectively of 180% and 71% over the period. Similar growth has taken place with specialist health and beauty retailers, for example CVS/Caremark has become one of the largest retailers in USA and Schlecker in Europe increased its sales by 95% between 2000 and 2007. The major firms have increased their sales volumes dramatically in recent years. Table 2.1 shows the leading food retailers in the major world regions. North America, Europe and Asia each have their own leading firms with headquarters within the region. The position in

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© Woodhead Publishing Limited, 2010

Costco Walgreens Sears

CVS Safeway (USA)

73,228 53,388 52,000

50,887 50,492

50,376 47,695

Seven & I Aldi

29,697 18,703

34,664 32,510

46,923 42,328 39,536

228,576 53,388 50,492

æ Millions

Leclerc Ahold

Aldi Edeka

Carrefour Tesco Schwarz Group Metro group Auchan Rewe Group

Retailer

31,814 27,860

38,955 37,470

50,731 41,586 41,159

82,642 63,658 52,000

æ Millions

Europe

Metcash Lotte

LAWSON Family Mart

Aeon Seven & I Woolworths (Australia) Coles Group Uny Shinsegae

Retailer

10,042 9,826

10,229 10,157

21,745 12,211 11,381

42,516 37,256 29,281

æ Millions

Asia and Oceania

OXXO Commercial Mexicana SVH Makro Falabella

Cencosud Soriana D&S

Wal-Mart Carrefour Casino

Retailer

2,648 2,556

2,855 2,724

5,186 4,569 3,029

22,229 10,085 8,504

æ Millions

South America

Source: Adapted from Planet Retail. Sales figures include the non-grocery sales in these retailers. Sales figures have been converted to æ. Figures are influenced by relative exchange rate differences and so should be considered as indicative rather then precise.

SuperValu Rite Aid

Wal-Mart Kroger Target

285,874 103,109 74,077

Wal-Mart Carrefour Metro Group Tesco Kroger Schwarz Group Costco Target

Retailer

æ Millions

North America

Retailer

World

Table 2.1 The ten largest grocery sector retailers in selected markets – 2007

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Consumer-driven innovation in food and personal care products

South America is somewhat different with retailers from outside the region having leading positions. Within North America and Europe the major firms have increased their scale at a faster rate than general market growth and thus have become more dominant in the markets. In North America between 2000 and 2007 the overall growth of food retail sales within ‘modern grocery distribution’ was only 1.7% but the largest 10 firms increased their food sales by 25.6%.1 Within Asia the pattern is more complex with major growth in food sales of 30.2% across the region but the top 10 firms increasing sales by only 9.0%. In Asia, firms within a second tier have grown strongly particularly where national growth has been strong. Additionally several of the largest firms based in Japan had weak national growth. Further complicating the Asian picture is China where the national market growth was fourfold between 2000 and 2007 but the 10 largest firms only doubled their combined sales whilst second tier firms have grown more strongly. Nonetheless, the general pattern is for substantial increase in the scale of the larger firms with important consequences for relationships with suppliers and opportunities for the introduction of new products. 2.2.2 Widening scope of firms In addition to increasing their scale, firms are extending their scope of operations in several ways. Retailing is no longer confined to the national market of the firm but international retailing is commonplace. In early 2009 Carrefour were operating in 42 countries including French overseas territories, Tesco in 13 and Casino in 32. In addition to the international expansion, the food retailers are also extending their scope by operating several store formats. For example, Carrefour operates through hypermarkets, supermarkets, discount stores, convenience stores and the internet. Other major firms similarly operate a multi-format strategy. The broadening scope is also apparent in the product ranges within the stores of the major firms. Whilst food and associated household grocery items remain the dominant categories there is not only extension of ranges in these categories, for example in food, in frozen, chilled and hot items, but also the addition of extended ranges of health and beauty items, household hardware and clothing. 2.2.3 Increased market concentration The increases in scale and scope of the retail firms have resulted in a steady increase in market concentration over the last decade. Within Europe, by 2008, the ten largest retailers accounted for approximately 38% of the market with the largest 30 accounting for 65%. Table 2.2 shows the increase in concentration levels in Europe since 1999. Whilst market concentration limits the choice of firm for the consumer, because of the wider scope of firms, the choice of store and choice of product has widened. 1

These data are derived from the database of Planet Retail. See www.planetretail.net.

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Changes in food retailing and their implications Table 2.2 Europe

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

31

Concentration ratios in grocery sector in CR10

CR30

34.0 34.9 35.6 35.8 37.1 37.4 37.2 37.2 37.9 38.4

54.2 58.4 59.1 60.7 61.8 62.9 63.4 64.1 65.0 65.8

Based on data from Planet Retail and Company Annual Reports.

2.2.4 Retailer control of channel The emergence of larger and economically more powerful food retailers has generated a change in the power relationships within marketing channels. The power in the channel has moved from manufacturers to retailers such that most of the marketing decisions are now in the hands of retailers (Clarke et al. 2002, Dobson 2005). Retailers have become more powerful across many functions in the channel. Moves towards centralised distribution through distribution centres either operated by or contractually controlled by retailers, have given retailers greater power over the logistics processes. Tighter controls over product ranges in stores have resulted in retailers becoming more active in range building and consequently in new product development. With more accurate matching of product ranges to consumer demand so retailers, with their detailed sales information, become directly involved with product range extension, range building and new product development. In many product areas in Europe the control of the marketing channel is now firmly in the hands of food retailers (Dussart 1998). One of the many consequences of the increase in retailer control over the channel (Table 2.3) is an increase in new product development. 2.2.5 Changed relationship with consumer Alongside the structural changes in food retailing there has also been a change in the nature of the relationship between retailer and consumer. The choice of products offered to consumers has increased. The extended choice in foods has embraced a variety of ethnic foods, speciality items, special diet ranges, organics, and ‘functional’ foods. The increase in choice in foods has provided opportunities for new product development in the new ranges. With the wider choice available, so retailers have become more

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© Woodhead Publishing Limited, 2010

Increase in diversity of space, time and quantity gaps in the channel Increase in the range of variables used to define the relationship Increase in buying scale Access to/use of information on consumer purchasing activities More knowledge about purchasing processes of consumer Retailer branding of products Tighter control of shelf and store space Retailers seek to reduce transaction costs Increase in retailer branded products Objective to co-ordinate the supply chain with more smaller orders to reduce inventory More inertial buying Lower cost supply base in east Asia Facilitation by ICT International retailers find new suppliers More attempts by retailers to differentiate by product ranges Retailer and supplier both involved in NPD processes Use of lower cost supply sources by retailers Strong competition amongst retailers

More complex relationships between retailer and supplier

Strong downward pressure on prices in the relationship

More new product development

More international relationships

More longer-term relationships involving greater trust

Medium-sized firms [retailers and suppliers] under considerable pressure

Acceptance of new operating techniques, e.g. category management Use of ECR approaches Stabilises retailer ROCE at expense of supplier ROCE More repeat purchasing by retailer More complex financing of international buying International buying offices become more important Wider search activity by retailers More pressure on store and shelf space Use of rapid NPD processes by retailers Large number failed new products

Increased international sourcing More new product development Searches for new ways to reduce inventory levels More specialist logistics markets More specialist skills needed by participants More opportunities for negotiation Wider range of information required More promotion by retailer and less by supplier Shift in balance of negotiating power Relationship more reflective of strategic objectives of retailer Large retailers may dominate small suppliers Suppliers increase their investment in major brands

Increases in size of market Increased variety of demand from consumers

More volume and variety of products required by retailer

Retailers use their power in the channel

Consequences

Reasons for the change

Changes in the food distribution channel related to more control by the retailer

Change occurring

Table 2.3

Changes in food retailing and their implications

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involved in influencing the choices made by consumers. Consumer decisions on product choice made in store are subject to retailer-controlled influences in terms of store and shelf layout, merchandising methods, in-store promotional activity, and store level pricing. The extent of control by the retailer over the sales environment has increased as a result of both the fundamental sectoral innovations, outlined at the start of this chapter, and incremental store level innovations. The relationship between customer and retailer has also changed with the use by retailers of direct marketing methods involving ‘loyalty’ cards. The information collected from customer level sales data is used to communicate choice options to consumers and again places retailers in a position to have a stronger influence over consumer choice. Retailers therefore have increased their influence over the choices of food purchased by consumers so changing the nature of the relationship between retailer and consumer making it more directive from the retailer. Retailers have grown to have an increasing influence on the diets of consumers, not least through increased and often direct involvement in the NPD process.

2.2.6 Transferring technologies The combination of the growth of large retailers, their changed position in respect of suppliers (Gereffi 1994) and consumers and the emergence of transitional economies as viable markets have resulted in the transfer of supermarket and related technologies to developing countries (Reardon and Hopkins 2006). The opening of the central European markets after 1989 was followed by major investments by West European food retailers transferring retail technologies into these emerging markets. For example, by 2000, less than ten years after the market opened, all the major retailers in Poland were from Western Europe. A similar process is occurring in East Asia with the creation of a growing supermarket sector in Thailand, Malaysia, China, Indonesia and Vietnam with substantial foreign direct investment from Europe, USA and Japan. In Thailand, by 2008, 6 of the largest 8 food retailers were from outside Thailand and the 6 accounted for approximately 22% of the market. Tesco, in this market, had 110 large superstores or hypermarkets, 100 discount supermarkets and 412 small convenience stores. Thailand is typical of countries that are recipients of a major transfer of food retailing technologies that affect not only store operations and consumer choice but also change supply chain logistics and the relationships with primary agricultural producers.

2.3

Food retail growth model

In order to understand the nature of the change in food and related retailing it is useful to consider the business model that underpins the expansion of the large firms. The basic model is shown in Fig. 2.1.

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Consumer-driven innovation in food and personal care products Consumer literacy Increased sales

Better experiences for customers

Technology

Globalisation and tight cost control

Marketing INNOVATION

Managerial abilities and knowledge

Productivity growth on all types of asset Power and trust

More control of the channel

Fig. 2.1 The model of retail growth based on innovation.

The core of the model is competition by innovation. This implies that the firm competes with other firms through undertaking either new activity or existing activity in new ways. Competition therefore is through innovation not simply copying from other firms. The innovation results from the interplay of managerial knowledge and competencies (Teece 2009), on the one hand, and the development of technologies on the other. Many technologies are involved – information, communication, materials, food, construction, environmental, etc., technologies are all relevant. The innovation, if it is to be effective, results in an improvement in the productivity of an asset. A wide range of assets can be considered in this retail context. For example, there may be productivity gains in the use of space on the shelf resulting from innovation in packaging, labour productivity gains as a result of innovative scheduling or staff training, increased productivity of vehicles or inventory resulting from innovation in logistics processes, productivity gains in the use of finance as a result of innovative contract relationships with suppliers. It is possible to obtain a productivity gain in any of the assets with the gain resulting from some form of innovation. This continuous improvement in productivity from the application of the innovations provides a platform on which are built both more control by the retailer over channel relationships and increased customer satisfaction with the shopping experience. The increased control of channel relationships by retailers is a strong sectoral trend as indicated above and is a feature of distribution systems

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across many countries and extends beyond the food sector. Whilst the shift is most clearly seen and strongest with the larger retailers, it is evident for many different sized firms. Retailers by their productivity gains related to information and logistics assets particularly, have more knowledge of product demand and of efficient retail operations. Increased buying power is evident both in large integrated corporate retailers, such as Tesco, Wal-Mart and Carrefour, and also in the co-operative buying groups, such as Leclerc, Spar and CGC Japan, involving chains of smaller retailers. Power and trust relationships are central features of the mechanisms used by retailers to control, and increase, their channel power. Table 2.3 illustrates the changes taking place in the channel as retailers gain more control over the processes. The result is that increased efficiency within the channel results in an increase in ‘output’, usually considered as the sales of the retailer. The innovation based productivity improvements also provide the platform for a second route to increased sales, namely more effective relationships with consumers. Figure 2.1 suggests the link through marketing and particularly the development of the retailer’s formula. The retailer’s formula is the firm-specific combination of systems and processes that is used to interact with the consumer and undertake a transaction. The formula is firm-specific in distinction to the retail format which is generic. Thus a hypermarket is a format but a Tesco Extra Hypermarket is a formula, a convenience store is a format but a 7-11 convenience store is a formula, transactional web pages are a format but sainsburys.co.uk is a formula, a vending machine is a format but a Coffee Point vending machine is a formula, etc. The creation and development of a successful formula is the basis of the interaction between retailer and consumer. The formula is critical to the creation of the shopping experience for the consumer. With a more effective formula customer satisfaction increases and at the same time the customer gains better understanding of the way retailers operate; what can be termed consumer literacy (see Fig. 2.1) increases. With these increases in consumer satisfaction with the firm so sales for the firm increase. From productivity gain, the two routes, through enhanced channel control and customer satisfaction, result in increased sales and feed back to increased innovation. The cycle of growth is generated from continuous innovation across retail systems.

2.4

Key areas of innovation for retailers

Innovation takes places across a wide spectrum of retail activities but there are key areas where innovation has had important impacts on the development of retail competition. Five areas are particularly worth consideration:

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• Innovations in retail formats, formulae and items (products) • Brand innovation • Innovation in relation to expansion into new markets defined by consumer segments, retail sectors and countries • Innovation in the exploitation of scale and scope economies • Innovation to speed up of processes of change. In each of these areas of innovation there are implications for new product development. 2.4.1 Retail formats, formulae and items A major way that retailers compete by innovation is through their formats and formulae. The retailer’s formulae provide the routes that the retailer uses to sell items to customers. Most of the formats, for example convenience stores, hypermarkets, supermarkets, have been present for many years and most innovations are introduced through formula development within these formats. In recent years the only major new format has been the internet web page but many new formulae have been developed within existing formats. Innovation in formula development includes developing store types to target particular consumer groups and also adjusting existing formulae to respond to changes in market conditions and in consumer behaviour. A key consequence of formula development is a requirement for new product development to complement the innovations in design and operations. New formulae development is an important aspect of competitive strategy for large food retailers. In order to increase their market penetration it is important for the retailer to target more than one segment of shopping behaviour so a multi-format/multi-formulae approach is necessary. In the UK, Tesco illustrates this situation, operating formulae through several formats as shown in Table 2.4. Table 2.4 Tesco trading formulae in 2009 Format

Formula

Hypermarket Non-food superstore Large supermarket Supermarket Convenience store Convenience store Internet Internet Garden centre

Tesco Extra Tesco Homeplus Tesco Superstore Tesco Metro Tesco Express One Stop Tesco.com Tesco Direct Dobbies Total UK

No of Stores Feb 2009

Space ‘000 sq m

173 10 450 173 973 508

1,148 38 1,260 187 200 64

24 2,311

84 2,981

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Sales £ Million 14,047 19 21,925 1,828 1,806 155 1,541 260 96 41,676

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Tesco Express, a convenience store format, has been the most dynamic of the formats with initial organic development of the concept in 2000. There are approximately 2500 merchandise lines, many of which are Tesco branded items. Acquisitions in 2002 and 2004 have extended the chain in the UK to almost a thousand local stores operating alongside a smaller convenience store formula, One Stop, with 500 stores. The formula has been transferred, with local adaption, to other Tesco markets in Europe and Asia. Considerable innovation was required to create the formula and then to develop retail systems within a firm that was more knowledgeable about operating large stores. Innovation in new formula development is often required when new markets are entered. For example, Tesco operates within three main formats in Thailand: hypermarket, supermarket and convenience store. The hypermarket and convenience stores are adaptions of UK formulae, but the supermarket is a completely new formula, Talud Lotus, developed to operate with very low costs to enable low prices in the Thai market. By early 2009 100 of these were in operation. Similarly in USA a new supermarket formula was developed by Tesco, Fresh and Easy, with a focus on fresh foods and an innovation of only providing customer operated checkouts (see below). Comparable adaptions have been made in other formats with, for example a more compact hypermarket created for the Czech and Slovak markets and in China the hypermarket being integrated within shopping centres (Dawson et al. 2006) . Tesco is not unique in this approach through multiformat and multi-formulae. Most of the major food retailers now follow a similar strategy which depends heavily on innovation to create competitive differences within a segmented market. In addition to innovation in total formula development there are major process innovations taking place within formulae operations. Typical of these innovations is the move to Shelf Ready Packaging (IGD 2006a). This changes packaging such that items can be merchandised directly onto the shelf without extensive unpacking of cartons. In effect the packaging is part of the merchandising display. This innovation results from collaborative activity between retailer and supplier. The innovations in this area, however, have significant impact on productivity (Accenture 2006) with benefits including: • • • • • •

more efficient use of in-store labour improved on-shelf availability of items potential improvement of on shelf appearance reduced costs reduction of waste and improved re-cycling faster introduction of new products.

Innovation in this area has meant the linking of logistics processes to store replenishment and merchandising processes. Initially the focus was on ways to improve identification of products by shelf filling staff, to open the

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packaging and to display it. A second phase involved adding ease of transit from the back door to the shelf, including the off-loading from the vehicle, improved disposal of the used packaging off the shelf and better communication with customers. The third stage involved a focus on improving the delivery by the suppliers including handling and order sequencing. In the UK, ASDA have been active in innovation in this area, starting with 12 suppliers in 2005. By the end of 2007, all food products were being packaged this way and health and beauty items and other general merchandise were planned to be packaged this way by early 2009. The results have been reduced replenishment times and reduced waste with an improved presentation on shelf. New product development has to take account of innovations typified by shelf ready packaging in the creation of a product that can be easily merchandised directly to the shelf. Linked closely to the innovative activity in formula design and operation are specific innovations in product development, both at range level and item level. Retailers have been instrumental in innovations in new ranges of organic, fair-trade and ‘healthy’ products eating (Baourakis 2004, IGD 2006b). The level of innovation in organic products has probably peaked with the global economic crisis slowing development from mid 2008. Nonetheless prior to this, development was substantial. In the UK, market growth in the middle 2000s was at 30% per year with the major supermarkets accounting for much of this as they took market away from the specialist firms. By 2007 the major supermarkets accounted for over 75% of the UK market. Over the same period Wal-Mart in USA doubled the number of its organic products whilst its UK operation of ASDA increased the number of organic products from 300 to 1000. Within the discount sector there were also moves to increase the organic ranges in Lidl and Aldi in Germany. Rewe in Germany have integrated the innovations in formula design with those in organic range development and have created a small chain, seven in early 2009, of supermarkets that have only organic items in their 600–800 item product range (see www.vierlinden-biosupermaerkte.de).

2.4.2 Brand innovation Retailer innovation in branding has resulted in retailers becoming brands in their own right and also creating extensive ranges of retailer branded products which displace manufactured branded items. Branding the store formula has become an important aspect of retailer branding strategy. Many of the Tesco formulae shown in Table 2.4 are branded as Tesco locations. Creation of the individual nature of a Tesco, Sainsbury’s or Morrison branded superstore has become an important aspect of building customer loyalty. Store branding is linked closely to store loyalty and building the store brand has become important, particularly in the more highly concentrated markets where customers’ choice of firm becomes more limited.

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The branding of products by retailers has increased substantially since the mid 1980s with rapid rates of new product development within the retailer branded ranges. There are many reasons for this shift in brand power with an increase by retailers at the expense of processors and manufacturers (Burt 2000, Morton and Zettelmeyer 2004). With retailers gaining more information from the transaction data collected at point of sale so the retailers have a more accurate knowledge of customer demand, enabling them to develop product ranges tailored to their customer base. The retailer is in a position to develop the product specifications, contract production, monitor quality and brand the product, often from a supply sector with over capacity (Oubiña et al. 2006). The costs of such products are often less than the manufactured product of comparable quality, so allowing the retailer branded items to be sold at lower price without having a negative impact on retailer margins. The retailer also controls the shelf allocation in-store, promotion and merchandising and is able to highlight the retailer branded products (Nogales and Suarez 2005). Consumers have become better informed and after initial doubts over quality now recognise that retailer branded products are brands in the same way as manufacturer branded products. As retailers have developed their branding strategy so they have become more innovative in developing ranges (Planet Retail 2007, IGD 2006c). Most of the major retailers now have several brands with brands at three price-quality positions – discount, standard and premium – together with several brands for specialist categories. The three tier positions of selected major UK and French retailers are shown in Table 2.5. In addition to these retailer brands, the major retailers also have their own brands for more specialist products, for example organic ranges, fair-trade products, foods for babies, and for various non-food ranges, including health and beauty ranges. Some retailers, notably Aldi and Schwartz Group discount retailers, have an alternative branding strategy that uses ‘phantom’ brand names with different names being used for different ranges of products. The brand names

Table 2.5 The three tier branding strategy in selected major retailers

Tesco J Sainsbury ASDA Morrisons Carrefour Auchan Casino Leclerc

Economy

Standard

Premium

Value Basics Smart Price Best Buy No 1 Budget Booster Happy Euro Eco+

Tesco Sainsburys ASDA Morrisons Carrefour Auchan Casino Marque Rèpere

Finest Taste the Difference Extra Special The Best Sélection Mmmm! Délices Chemins de la Qualité

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Table 2.6

Retail brand shares in the sales of major retailers in 2006

Firm

% Retail brand sales

Total sales å million

94 61 48 46 42 38 37 35 35 32 32 28 27 25 25 25 25

45,149 46,253 69,150 26,728 51,161 290,503 26,530 97,499 17,882 41,576 30,383 54,739 38,993 54,960 43,117 34,999 62,441

Aldi Schwarz Group Tesco J Sainsbury Sears Walmart ITM (Intermarche) Carrefour Tengelmann Casino Leclerc Seven & I Rewe Kroger Auchan Safeway Ahold Source: Based on Planet Retail (2007).

remain totally controlled by the retailer concerned but there is no umbrella branding by market position as in the retailers shown in Table 2.5. The considerable expansion of branding activity by retailers has resulted in an increase in the number and variety of products in retailer brand ranges and also an increase in the market penetration of retail brands measured by firm and also by country. Table 2.6 shows estimates of sales shares in 2006 for the major retailers with over 25% of their sales in retail brands. Almost all products in Aldi are retailer branded products. Although with a lower percentage, the total value amount of retail brand sales is largest in Wal-Mart. At a national level in 2006, 53% of grocery sales (by volume) in Switzerland were accounted for by retailer brand products. The corresponding figure for UK was 43%, Germany 39%, Spain 35% and France 34%. Of the large European markets Italy lags the other large markets with an estimated 18%. Across the retail brand area there is substantial innovation not only with new product and range development but also with the marketing of retail brand products. The adoption of category management has had impacts on the balance between manufacturer and retailer brands and on the NPD process within the category. Retailers and manufacturers working within a category management framework are now a central feature of NPD within the major retailers. The NPD process under these new conditions depends on mutual trust between retailer and manufacturer. For a manufacturer brand, the retailer has to commit to not making a retailer brand copy of the new product and for a retail brand the manufacturer has to commit to retailer

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confidentiality and limited distribution. This mutual trust is a core prequisite for successful NPD but is not always easily established. With the agreement to work together in place, a launch plan is then required to establish schedules and responsibilities. Not only will the plan agree a timetable, it will also establish the criteria for success of a launch and how performance will be measured. There will need to be agreement on data sharing of value and volume figures and of product availability in stores and distribution centres. One objective of the launch plan will be to obtain rapid distribution through all stores. A rapid build of distribution is seen as an important aspect in the success of a new product (see Section 2.4.5 below). The rapid build is a combination of logistics activity and integrated marketing activity. The integration of brand building activity, e.g. advertising, and sales building, e.g. promotions, is likely to involve different functional managers in the manufacturer if the new product is a manufacture brand. A category management approach should enable the NPD process to operate successfully for retailer brand and manufacturer brand products. 2.4.3 Expansion into new markets Retail brand development has often supported retailer moves into new markets – categories, sectors and countries. The changes in the consumer socio-environment of new markets have been stimulants for new product development and new range development. Four key changes have been identified (IGD 2007) as important in stimulating innovation in category development. These four are: deeper and wider ethical concerns over food production methods, increased concerns over health, an ageing population, and the increasing demand for premium products. Within this framework it is possible to map the developments of new categories, for example fairtrade, functional foods, specialty ‘indulgent’ foods, environmentally friendly items, etc. Figure 2.2 illustrates this mapping. The result has been moves into these newly created markets. On page 32, the IGD report points out that: ‘These trends are driving new product development and in some cases, innovation of whole new categories. Additionally, trends are not operating in isolation and many developments are the result of pressures from one or more trends. For example, the ageing population has resulted in the emergence of a new consumer segment, and targeted products have begun to appear on the market as a result. The ageing population is also contributing to the drive towards well-being, as consumers look to the food that they eat to help provide quality of life for longer. As a result, a variety of functional foods have been developed that help to maintain healthy cholesterol levels and blood pressure.’

Healthcare is a category that has been developed into a substantial new market for many of the large retailers. This market includes both pharmacy items, with many major retailers incorporating a pharmacy into the store,

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PREMIUM

Environment

Fair-trade

Animal welfare

Local and regional products

Speciality foods Indulgence

Organics Premium brands

Functional foods

Product segmentation Low fat/sugar/salt

HEALTH

Diet and well-being

AGEING POPULATION

Source: based on IGD (2007)

Fig. 2.2

New category development linked to consumer socio-economic change.

and over the counter (OTC) items. The expansion into this category has meant that new forms of marketing have had to be considered. As in other areas, category management with joint action between retailers and manufacturers has become important in the OTC sector. An integrated marketing and merchandising approach is required as part of the NPD process as suggested in Table 2.7. This new market provides additional ways to segment the consumer market and also to link the whole store into a health and wellness policy. A second type of entry into new markets is seen with the growth of international operations by retailers. Food retailing in the larger firms is no longer limited to the home country. Whilst consumers are essentially local, the firms have become international in their operations. For example, Tesco outside the UK operates in 13 countries on three continents. It has substantial market shares in Thailand, Slovakia, Czech Republic and Ireland (Table 2.8) with substantial growth of operations over the last 10 years. Expansion at this level, seen also in other major firms, for example Carrefour, Aldi, Casino, Auchan, involves a high level of innovation in order to adapt to local consumer behaviour and trading conditions (Dawson 2007). Formulae and brands are transferred to the different countries of operation but in most cases require some form of adaption to local social and economic conditions although the retailer also transfers aspects of management into the host country (Fulponi 2006). There are implications for NPD and the wider distribution of brands in the growing pattern of internationalisation. For manufacturers with major brands the international expansion of

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Link to general retail and healthcare advertisements. Link to branding policy, e.g. three tier retailer brand policy. Use of general loyalty card linked promotions.

Coupons and information in store at shelf and checkout.

MARKETING

MERCHANDISING

Source: Adapted from IRI (2007).

Individual brand. Retailer brand and manufacturer brand.

Brand specific

Ailment-specific displays in-store. Merchandise support from manufacturer.

Store and shelf-based information and signage. Linked promotions through store.

Health and wellness link in general advertising. Highlight specific brands. Health information and advice kiosks. Use of loyalty card direct mail. Link to ailment specific marketing in society, e.g. TV, radio, etc., on specific ailments, e.g. diabetes.

Link to other products for the segment, e.g. food, household goods, etc. Link to branding policy for the segment. Use of targeted loyalty data. Shelf communication and signage. Link to store level merchandising plan for segment.

Brands with health and wellness benefits. Organics.

Health and wellness

Brands with ailment management benefits.

Ailment specific

Brands aimed at specific groups, e.g. seniors, pregnant women, babies.

Segment specific

Marketing and merchandising approaches in product development in OTC healthcare for retailers and manufacturers

SCOPE

Table 2.7

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Consumer-driven innovation in food and personal care products

Table 2.8

International store activity of Tesco 1999

Country

China Czech Republic Hungary Ireland Japan Malaysia Poland Slovakia South Korea Taiwan Thailand Turkey USA

2002

2005

2008

Sales £ Market Sales £ Market Sales £ Market Sales £ Market million share % million share % million share % million share % 179

4.0

385

4.8

182 941

4.4 10.5

735 1077

9.4 12.8

59 94 145

0.4 9.7 0.6

553 257 1218

2.5 11.5 2.2

352

5.1

70 860

0.4 7.7

483 561

0.2 5.4

800 1265

0.2 7.6

1294 1622 326 158 1039 495 2221

12.1 14.7 0.1 2.5 3.2 13.5 3.4

1774 2380 408 584 1964 894 3139

13.3 14.3 0.1 4.6 3.9 15.1 5.0

135 1211 235

0.7 9.2 0.9

2137 571 208

9.5 2.0 0.0

retailers provides increased market coverage and opportunities to expand in new markets. For smaller manufacturers involved in retailer brand products the opportunity exists in the transfer of key retail brands into new markets and a resulting increase in scale of operations. There are dangers, however, as retail brand items are not always transferable. For example, in Tesco Samsung in Korea, although there is a considerable presence of retail brand items, almost all are obtained from different suppliers to those used in the UK even when broadly similar items are involved. NPD in this context becomes a local market activity.

2.4.4 Exploitation of scale and scope economies Scale economies exist in many aspects of food retailing but they are most apparent in the buying operation. Sales growth is an important part of the business model (see Fig. 2.1) because increased sales not only creates stronger negotiation power with suppliers but also feeds back into increased innovation. The increased negotiating power occurs with manufacturer brands but even more strongly with retailer brand products. There are also returns to scale on transport costs in the buying activity, particularly if international trade is involved. Linked to the scale benefits of large-scale buying are benefits in working capital management. With larger purchasing volumes so the size of the negative working capital element in retailer finances increases. For many products, revenue is received from customers before invoices to suppliers have to be paid. With larger orders, a longer

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period can be negotiated with suppliers, so increasing the amount of ‘free’ short-term capital. In the UK in 2008 working capital as a percentage of sales was −3.3% for the sector as a whole (up from −2.7% in 2004) whilst for J Sainsbury in 2008 it was −8.3% and for Tesco −9.1%. In general there is a scale-related factor, with the larger firms having a higher percentage and a higher total of negative working capital. Larger-scale firms also are able to obtain scale-related benefits from their brand development processes. The greater the number of new products developed across retail brand ranges, the lower the average cost. The shared management and development costs of new retail brand products generates additional margin. Thus again the areas of innovation interact and provide mutual support to generate a productivity enhancement (see Fig. 2.1). Returns to scale are also linked to formula development. The scale economies arise through the development of a network of stores with additional stores joining the network adding only marginal costs to the operation of the network. These network scale factors are much more important than the scale of a store. Whilst there are some labour cost benefits in larger store operation the scale-related benefits to costs are limited at store level. The larger stores provide customer benefits rather than lower cost economies to scale. The exploitation of scope economies is apparent in the operation of multi-format/multi-formula strategies in which overhead costs are shared resulting in lower costs for any one formula than would be the case with a single formula strategy. In this way, some fixed costs, of the internet operations, Tesco.com and sainsburys.co.uk, of Tesco and J Sainsbury are shared by fixed store operations with internet customers being serviced from stores rather than a dedicated warehouse where extra costs would be incurred. The employees servicing the internet orders in store have flexible training such that they can be moved to supporting either the fixed store operation or the internet operation depending on demand. A further area where the economies of scope are used in increasingly sophisticated ways by the retailer is the design and development of product ranges such that complementarities exist for products within and between ranges. The criteria used by retail buyers are important in the creation of these ranges and innovations in the modelling of these ranges have increased profitability for the retailer. The modelling and adjustment of ranges are closely related to the development of new markets and the creation of new formulae of retailers. Adding new products to the ranges is an essential part of range reviews. The evaluation methods used by retailers to decide about new products vary considerably from firm to firm. Table 2.9 summarises the types of issue that are taken into consideration when a new product is offered for introduction into a range. The balance amongst these criteria may vary by firm, by buyer and by local situations. Introducing new products into an existing range can have implications across the specific range and also other ranges. The ways that retailers model these repercussions

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Consumer-driven innovation in food and personal care products

Table 2.9 Typical criteria used by retailer in the decision on whether to stock a new product

Major criteria

Broad issue

Detailed criteria

Consumer evaluation

Overall consumer value, retail price, product physical characteristics, product psychological characteristics, packaging, sensory characteristics Supplier’s price, gross margin, allowances and rebates, co-operative advertising, credit terms Reputation and reliability, services and functions, flexibility to store needs, product availability Overall profitability, sales potential Relationship of product in range, relationship to other brands (including retail brands), links to other ranges

Financial terms Supplier characteristics Profitability and sales Assortment issues

Lesser criteria

Supplier marketing Sales person presentation Tactical issues

Launch campaign, continuing marketing, information sharing Knowledge of presenter Ranges in competitors

has become more sophisticated as they seek to gain more productivity from the limited shelf space.

2.4.5 Faster operation of processes A variety of process innovations have been implemented by food retailers to speed up processes in many areas, for example in the supply chain and in store operations. In the logistics field innovations have included: • The use of RFID on pallets and on outer packaging in order to identify product location precisely and reduce checking times (Sparks 2009). • The use of cross docking methods in distribution centres such that less product is stored in the centre. • The use of multi-temperature vehicles for delivery to stores. • Collection of products from factory gate by retailer vehicles returning from store deliveries. • Development of shelf ready packaging (see above). These and other innovations have resulted in a faster flow of product through the supply chain and a resulting higher level of inventory rotation (Skjoett-Larsen et al. 2003). In Tesco in 2008 inventory rotation of food was over 30 times per year and is only slightly below this in J Sainsbury. An illustration of the innovation in store operations is the use of customer driven checkout systems in which customers check products and

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make payment without employee intervention. This has resulted from a combination of customer driven factors, of customer irritation with queues at the checkout and a desire to be more in control of the operation, and management driven factors of a need to reduce costs, with one employee becoming responsible for supervising 6 or 8 checkouts. Not only does this innovation illustrate seeking faster operation of the checkout process and the fast implementation of strategy but also it has implications for product development particularly in respect of packaging with the need for a higher proportion of pre-packed products in stores using customer self-checkouts and easier location of bar codes on packaging. For example, Wal-Mart decided in 2002 to install self-checkout alongside traditional operator controlled checkouts in USA. All new stores were equipped with at least 4 self-checkouts and 8 express self checkouts for smaller baskets and a rollout of the technology in existing stores began. By June 2004, 840 stores had the systems, a year later there were 1,150 and by mid 2007 all stores in USA had self-service checkouts installed and the programme had been extended to 80 stores in Canada. The faster implementation of strategy is also apparent in the speed of opening in retailer expansion programmes. Figure 2.3 shows the number of new hypermarkets opened per year by Carrefour since 1990. New openings fell back to about 50 per year during 2002–2006 but have since moved to well over double that number. Other large firms show a similar rapid rate of store expansion. This rapid rate of new store opening generates opportunities, controlled by the retailer, for the introduction of new products into ranges. 2.4.6 The interactions in innovation The examples of innovation in the five areas are illustrative of the nature of innovation in food retailing. The variety of innovative activity is substantial as it becomes applied to a very wide range of assets in the search for improved productivity. The examples above, drawn from five major areas of innovation, show: • In all cases there are implications for new product development. This may be direct implications for product composition and design or more indirect ones related to sourcing or display. Successful product ranges and new product development are dependent on innovation across a range of activities. In particular the seeking of faster ways of doing business results in demands for faster new product development processes, and the change in power balances in the chain results in increasing involvement, both direct and indirect, by consumers in new product development. • A very broad base of innovative activity exists within retailing. Innovation takes place in all the retailing functions across strategic and operational domains.

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0

20

40

60

80

100

120

140

160

0

9 19

19

91

19

92

93

19

98

19

99

19

00 20

01 20

20

02

03 20

04 20

Numbers of new hypermarkets opened by Carrefour worldwide.

97

19

Fig. 2.3

96

19

95

19

94 19

05 20

20

06

20

07

20

08

Changes in food retailing and their implications

49

• A consumer orientation is present in respect of the effects of innovative activity. The ultimate rationale for an innovation in retailing is to increase sales revenue. Therefore a consumer orientation has to be present, to some extent, in any investment in innovation. • Use of technology and its interaction with management processes generates innovation. The starting point for many of the innovations is some change in the use of an existing technology or the development of a new technology. On its own, however, the technology is not enough to sustain an innovation but managerial application within the firm operationalises the technology and makes the innovation of value. Although five areas of the application of innovation have been discussed individually, the reality is that these areas interact. Doing things faster applies to new market entry, to retail brand development and to increasing the scale of the firm. The development of new formulae interacts with market expansion, retail branding and exploiting scope economies. Innovation becomes integrated throughout the retailer and creates a culture of innovation leading to competitive advantage for the firm. A consequence of this is the internalisation, within the firm, of the processes of innovation. The focus in this chapter is on innovation by retailers but it is important to appreciate that these retailer innovations in NPD generate responses from manufacturers. Manufacturers have been active in innovating for new products, often as a response to the innovations seen in retailers, particularly in retailer brand innovations. Major manufacturer approaches are, drawing on the IGD (2006c) report: • Cross-pollination: using existing brand technologies to generate new innovative products with less investment, e.g. Gillette’s Mach 3, which shares technologies for Gillette, Braun and Duracell all controlled by Gillette. • Partnerships with other brands: using strength from other outside brands in a partnership, e.g. a new range of cosmetics, Inneov, was developed in a partnership between Nestlé and L’Oréal. • Limited editions: using customer excitement and collectability, e.g. HP Sauce created a limited edition for Harrods Department Store in a designer bottle. • Packaging development: using creative packaging that can influence use of the item, e.g. Yakult developed a pack containing seven small packs to encourage daily use. • Production methods: using a superior production method, e.g. Arla developed a filtration method on milk that has allowed brand development, Cravendale, within a commodity category. • External ideas: using internet and public appeals for new product ideas, e.g. Kraft had a public appeal and developed as a result a block of parmesan cheese encased in a disposable plastic grater. The interactions in innovation in NPD include therefore not only the interactions across the various arenas of innovation within the retail firm but

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also the interactions between retailer and manufacturers who also are involved in NPD.

2.5 Conclusion Retailers are central to the food supply system. Since the mid 20th century the sector has been the subject of three fundamental innovations that have resulted in periods of considerable change in strategy and operations. The most recent of these three is one associated with the convergence of information and communication technologies. But the cumulative effect of the three periods of fundamental innovation is a shift in the structures and processes of the retail sector. Firms in the retail sector have increased the scale and scope of their activity. The largest 100 retail firms now account for approximately 25% of world retail sales on a converted US dollar basis. Whilst the degree of the increase in market concentration varies by continent it is present in all continents. A result of this increased concentration has been an increase in the channel power of retailers at the expense of wholesalers, processors and primary producers. The large food retailers now control the channels of distribution in which they are involved. With the increase in scale and growth of channel power the large retailers have expanded beyond their domestic market and have transferred their retailing methods and technologies internationally. The transformation of the retail sector has been particularly apparent in Europe with major European retailers increasing their scale, gaining channel power and expanding within and beyond Europe. In order to grow at a faster rate than the overall market expansion the large retailers have developed a business model that depends on innovation as the vehicle to drive productivity across many types of asset. The improvement in productivity has the effect of providing the retailer not only with more control over the supply chain so reducing costs of supply and but also enabling marketing activity to generate higher levels of customer satisfaction. The result is a steady growth of sales at a rate greater than the overall market growth. Success in inter-firm competition for the food retailers therefore is based on innovation in different areas of the firm’s operations. The particular mix of innovations and applications determines the competitive advantage of the firm as shown by increased sales. The basis of the success of the large firms is therefore a continuous process of innovation. It is important for the retailer to manage the innovation processes internally in the firm as they represent the main source of competitive advantage. A major, but certainly not the only, area of innovation is in new product development, which increasingly is undertaken by the retailer, often as the ‘agent’ of the consumer. The importance of innovation as a means for competitive advantage results in the application of innovation and the processes being more

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confidential. Whilst many aspects of retailing are very open and transparent with competitors easily able to visit the store of any retailer, the processes behind the visible aspects of retailing become more closely guarded as competition by innovation becomes more important. Many processes formerly externalised, including NPD, increasingly have become internalised within the retailer. Innovation, including new product development, has become the basis for competition and the underpinning factor generating the major restructuring of food retailing in many markets.

2.6 Sources of further information and advice Fuller G W (2004), New food product development: From concept to marketplace, Boca Raton, CRC Press. Hughes A (1997), ‘The changing organization of npd for retailers private labels: A UK–US comparison’, Agribusiness, 13(2), 169–174. Mahajan V, Muller E and Wind Y (2000), New product diffusion models, New York, Springer. Randall G (2005), Supermarket wars: Global strategies for food retailers, Basingstoke, Palgrave Macmillan. Seth A (2001), The grocers: The rise and rise of the supermarket chains, London, Kogan Page.

2.7 References accenture (2006), Shelf ready packaging (Retail ready packaging): Addressing the challenge: a comprehensive guide for a collaborative approach, Brussels, ECR Europe. baourakis g (2004), Market trends for organic foods in the 21st century, Singapore, World Scientific Publishing. burt s (2000), ‘The strategic role of retail brands in British grocery retailing’, European Journal of Marketing, 34(8), 875–890. clarke r, davies s, dobson p and waterson m (2002), Buyer Power and Competition in Food Retailing, Cheltenham, Elgar. costa c, gerstenberger w, lachner j, nassau t, täger u and weitzel g (1997), Structures and trends in the distributive trades in the European Union, Munich, Ifo Institut. dawson j (2001), ‘Is there a new commerce in Europe?’, International Review of Retail, Distribution and Consumer Research, 11(3), 287–299. dawson j (2006), ‘Retail trends in Europe’, in Krafft M and Mantrala M K, Retailing in the 21st century, Berlin, Springer, 41–58. dawson, j (2007), ‘Scoping and contextualising retailer internationalisation’, Journal of Economic Geography, 7(4), 373–397. dawson j, larke r and choi s c (2006), ‘Tesco: transferring marketing success factors internationally’, in Dawson J, Larke R and Mukoyama M, Strategic Issues in International Retailing, Abingdon, Routledge, 170–195. dobson p (2005), ‘Exploiting buyer power: Lessons from the British grocery trade’, Antitrust Law Journal, 72, 529–562. dussart c (1998), ‘Category management: Strengths, limits and developments’, European Management Journal, 16(1), 50–62.

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fernie j and sparks l (2009), Logistics and retail management, London, Kogan Page. fulponi l (2006), ‘Private voluntary standards in the food system: The perspective of major food retailers in OECD countries’, Food Policy, 31, 1–13. gereffi g (1994), ‘The organization of buyer-driven global commodity chains: How US retailers shape overseas production networks’, in Gereffi G and Korzeniewicz M, Commodity chains and global capitalism, Westport, CT, Greenwood Press, 95–122. humby c, hunt t and phillips t (2003), Scoring points: How Tesco is winning customer loyalty, London, Kogan Page. igd (2006a), International shelf ready packaging, Letchmore Heath, Institute Grocery Distribution. igd (2006b), Ethical consumerism, Letchmore Heath, Institute Grocery Distribution. igd (2006c), European private label growth, Letchmore Heath, Institute Grocery Distribution. igd (2007), Shopper trends in product and store choice, Letchmore Heath, Institute Grocery Distribution. iri (2007), Retail heathcare marketing: New growth opportunities across the store. IRI Times and Trends, April, 1–19. kotzab h (1999), ‘Improving supply chain performance by efficient consumer response? A critical comparison of existing ECR approaches’, Journal of Business and Industrial Marketing, 14 (5/6), 364–377. mitchell a (1997), Efficient consumer response: A new paradigm for the European FMCG sector, London, FT Retail & Consumer Publishing, Pearson. morton f s and zettelmeyer f (2004), ‘The strategic positioning of store brands in retailer-manufacturer negotiations’, Review of Industrial Organization, 24, 161–194. nogales a f and suarez m g (2005), ‘Shelf space management of private labels: A case study in Spanish retailing’, Journal of Retailing and Consumer Services, 12, 205–216. oubiña j, rubio n and yagüe m j (2006), ‘Relationships of retail brand manufacturers with retailers’, International Review of Retail, Distribution and Consumer Research, 16, 257–275. pellegrini l and zanderighi l (1991), ‘New products: Manufacturers’ versus retailers’ decision criteria’, International Review of Retail, Distribution and Consumer Research, 1, 149–174. planet retail (2007), Private label trends worldwide, London, Planet Retail. reardon t and hopkins r (2006), ‘The supermarket revolution in developing countries: Policies to address emerging tensions among supermarkets, suppliers, and traditional retailers’, European Journal of Development Research, 18, 522–545. roland berger strategy consultants (2003), Optimal shelf availability – Increasing shopper satisfaction at the moment of truth, Brussels, ECR Europe. skjoett-larsen t, thernoe c and andresen c (2003), ‘Supply chain collaboration: Theoretical perspectives and empirical evidence’, International Journal of Physical Distribution and Logistics Management, 33, 531–549. sparks l (2009) ‘RFID: Transforming technology?’, in Fernie J and Sparks L, Logistics and Retail Management, London, Kogan Page, 233–252. teece d (2009), Dynamic capabilities and strategic management, Oxford, Oxford University Press. tushmann m l and o’reilly c a iii (1996) ‘Ambidextrous organizations: managing evolutionary and revolutionary change’, California Management Review, 38 (Summer), 8–30. tushmann m l and o’reilly c a iii (1997), Winning through innovation: A practical guide to leading organizational change and renewal, Boston, Harvard Business School Press.

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3 Recent advances in commercial concept research for product development S. Porretta, Experimental Station for the Food Preserving Industry, Italy, H. R. Moskowitz, Moskowitz Jacobs Inc., USA and J. Hartmann, Unilever Foods, The Netherlands

Abstract: Companies are in the business to make money, profits, and return dividends to their shareholders. When it comes to food and beverages, there are many other considerations, of a social nature and of a moral one. A company that fails to create and market foods that consumers accept or need will likely fail and either disappear or merge into some acquiring entity. To survive, a company has to innovate. Behavioural changes with respect to food and beverages, often the bedrock of innovation, always stand in direct competition with well-conditioned, existing behaviour. People have got to want to change, to want the next new product, to avidly embrace the ‘new’ for innovation to really take hold. Ambivalence between a company’s expectations and the behaviour change models are the most important psychological pre-conditions that make it difficult to succeed in the foods and beverage industry. Key words: conjoint analysis, segmentation, concept development, ideation, mind-set, messaging.

3.1

Prologue: corporate structures and the new role of research and development (R & D) as innovators in food and beverages

Companies are in business to make money, profits, and return dividends to their shareholders. Of course, when it comes to food and beverages, there are many other considerations, some of a social nature, others of a moral one. But in the end, a company that fails to create and market foods that consumers accept or need is likely to fail, and either disappear or merge into some acquiring entity after which they may often disappear anyway. It is a truism today that to survive a company has to innovate. Again and again, we read in business magazines that a company must ‘innovate or die’,

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or something to that effect. In the academic business journals, not those catering to popular readers but rather to serious students of business process, we see article after article about corporate structures that lead to the best innovation. Good examples of such journals range from the Harvard Business Review at one end to the Journal of Product Innovation Management at the other. Whether we have the so-called ‘Learning Company’ (Senge, 1990), or adopt ‘Open Innovation’(Chesbrough, 2004), the world of business is certainly not lacking in models for how to create the innovative company, the one that can prosper by promoting innovative products. Even business processes themselves are open to innovation, as is the actual structure of the company. That being said, we are dealing here with one particular industry, often considered a relatively slow mover. The food and beverages industry is, today, not particularly well regarded with respect to its attitude towards and track record on innovation. There is, of course, the ever-present mantra that ‘people will always have to eat’, but, in the main, food industries are better known for cost reductions and ingredient substitutions than they are for innovation. Occasionally, some new processing or packaging technology is perfected, leading to a spate of innovative products, especially when these innovations produce increased consumer acceptance (Bruhn, 2008). At the same time, we have to be aware that innovating in the sector of foods and beverages is a very ruthless business. In the beginning, lots of investments are made to find new product compositions that fit into the lives of prospects. Once the products hit the market, retailers and the internal finance community immediately examine whether or not the product, as well as the new venture itself, earns the expected share and desired volume. There is always the factor of ‘feedback’ to consider as well in the food business. In contrast to the personal care business, food and beverage life cycles are less steep and need, therefore, much more patience. Patience in corporations is an increasingly unusual, rare attribute, in short supply during hard times. The appreciation for category-specific business models is often over-shadowed by the fact that high-level professionals in this industry often can’t delay gratification. Behavioural changes with respect to food and beverage, often the bedrock of innovation, always stand in direct competition with wellconditioned, existing behaviour. Such homeostatic mechanisms, the gyroscope of the food business, make change difficult. People have got to want to change, to want the next new product, to avidly embrace the ‘new’ for innovation really to take hold. This ambivalence between a company’s expectations and the long-lasting behaviour change models can be seen as the most important psychological pre-condition that makes it so difficult these days to be successful in the foods and beverages industry. This leads us to the fundamental question for success factor when patience is not an option. The above-described circumstances shout for a professionalism that is able to smell big market gaps and the ability to translate

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the insight into a product that consumers will accept. In the first place, lacking patience, in a time of hyper-competition among manufacturers, there is a need to develop stream upon stream of workable ideas, simply to sift through and find an idea that works. In the second place, there is an everincreasing requirement to estimate market success, not only to convince retail partners (trade), but to convince the internal corporate forces, which have become ever more cynical about the success rates of new ventures.

3.2

Where do ideas reside?

So, where do the big ideas come from? Where do companies find the big hits that will guarantee survival? It would be naïve to believe that ideas come out of ‘the nothing’ into the business process. Perhaps this was the case a hundred years ago. Even then, however, the big inventions came about because leading edge entrepreneurs discovered a latent need in the society that they could match with an adequate product to answer that need. For example, after World War II, when people in Western Europe were suffering severe hunger, Unilever was able to develop its basic business that lasts until today. Think about margarine that was originally produced for its nutritional values, or Unox pea-soup for its ability to fill empty stomachs, giving physical energy during cold winters without being too expensive. These food categories are almost romanticized by their history. And, in turn, these categories possess a real sustainability. Their underlying value to people’s lives holds even today and only needs a nice advertising campaign to remind people about it. When we analyze the origins of these big ideas from the past and project the logic into our times we ought to ask ourselves what is needed for today’s food and beverage companies to create these behaviours and perceptions. In many cases, today’s companies make a strategic mistake. They jump immediately into an idea-focused or capability-focused process. Yes, they can make the ‘new’ something or other. On the other hand, does anyone really need the product? What is the deeper understanding of the human being which makes this product promising, gives it legs, endows it with sustainability? The ideation process is often based on assumed facts rather than built on an explicit scrutiny of internal and external factors that are needed to judge possible success of ideas. We can trace some of the issue to process, namely today’s slick, sophisticated, project-controlled process. Such superficial processes often occur in the absence of a real need, and in the absence of an opportunity. In contrast, the act of ideation should be seen as an event that answers a true opportunity, an event which occurs at a point in time, and finally an event that requires homework, namely lots of inputs to programme the thought-system of the company (Wenger, 2008).

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3.3 Entry points for the big ideas and ideation in general A significant entry-point for good ideation is the so-called ‘deep dive’, i.e. depth analysis in those areas where the company can claim expertise. That is, the company should have the expertise before dealing with the opportunity. The company should be good in a number of areas. Otherwise, the company may well be trying to ‘bolt on’ opportunities that are inappropriate to the core of the company. 1. Technology: where and how did the company developed competitive advantage? What kind of technological understanding marks the promising starting points for new ideas? 2. Understanding consumer audiences in a particular usage context: how much does a company understand about their audiences in the area where it plans to create new products? Does the company know the ‘what’ and ‘how’ of existing behaviours? Is the company mindful about the subtle applications of packaging and product features? For this area, companies currently spend millions to see the consumers holistically, which means: they want to understand the what, why, how, where and who of usage occasions. Without this holistic understanding of consumers in their cultural context, the generation of new ideas will be doomed to be coincidental. Knowing the context and getting it right can produce a Sony Walkman. 3. Mega-trends reviews: at one time or another, as a regular activity or as a special effort, every company commissions a piece of research or an internal activity which speculates how consumers will behave in the future. What are the big trends we observe, what will be hot tomorrow and which of these themes needs to be incorporated in the natural behaviour patterns of a company? Many of these studies create a kind of self-fulfilling prophecy on the market place. The reason is simple, but distressing. Big companies often use the same companies offering the same answers, so everyone moves with each other into a pre-described direction. The reader can sympathize with big companies in that sense. Yet, the big companies continue in lock step, lest one company lose the trend, and the competitors gain a momentary advantage, no matter how illusory. The hint of a trend produces both a sense of exhilaration and, at the same time, a sense of panic in companies. In the words of Conan Doyle’s immortal detective, Sherlock Holmes, ‘Come . . . the game’s afoot’. The game is always afoot with new opportunities. 4. Brands and brand-identity: new ideas need a home, a place they can reside. Under existing budget constraints companies are facing, building new brands with every new product idea is a luxury that almost no company can permit. Therefore, it is important to know how existing brands can evolve and then migrate into innovative product design territories. The starting point of these knowledge journeys begins with the

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understanding of the brand’s root strength and heritage. What is the identity of the brand? Can that story be magnified and leveraged into new product development? The answers to the above questions often mark ‘how’ a new idea needs to be explained and positioned to the audience. That effort, the story and its elaboration, create overall added value to a company and its brand portfolio. 5. Competition: companies do not operate in a vacuum. The company needs to know what is happening in its environment, what its competitors are doing, what are the new trends, and whether competitors are even aware of emerging opportunities. Companies always try to sense their competitive environment. Recent studies in certain categories of fast moving consumer goods companies suggest that being second these days dooms a company long term to spending a lot of money to survive in the market, if at all (Kandybin and Kihn, 2004; Baker, 2008). Being first with a new idea is critical. Being second is often not worth the effort, or only then when there is a serious price advantage that favours the copy-cat. 6. Innovative ideas are not necessarily only product-related: in 1998 Unilever used an existing product (Cup-a-Soup ®), but translated this product it into a new consumption context (snacking in offices). The translation step involved a new use for a product, rather than calling for a new product. However, the discovery of the new context can be seen as truly innovative as it defined (for the company) a new territory of capabilities and skills. Other business models may move out of the product itself, and move into related areas such as distribution, servicing systems, format changes of the same idea, etc. These lateral moves for a product actually represent strategic areas where the company can create new ideas and big opportunities. In Section 3.4.1, the authors describe this example, as it visualizes practical implications of the points made above.

3.4

Discovering opportunities and the use of deep knowledge

We can conclude that companies need a deep understanding of their audiences, technology and business models to know where they ought to look in order to uncover the genetic code of their new ideas. In this respect, having a demonstrated ability to intuit the right answer is not necessarily a long-term competitive advantage, even if people recount the Walkman story all over again. The real opportunity is a strategic understanding of the consumer, and the ability to translate that understanding to action. This combination of understanding and action is the fertile ground for ideas. When the company understands these areas as elements of a bigger underlying

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learning curve, they will be able to understand why certain ideas are really promising and new opportunities, whereas other opportunities are just restatements of old ideas that emerge in new guises due to rote ideation and inertia. Pro-forma development of ideas, the product of inertia and the need to ‘show progress and demonstrate the “new’’ ’ tends to fail, again and again. Truly profound understanding of the consumer is the basis of much real business development. Finally, despite the belief that all work can be outsourced, all ideas can be bought, the company itself is vital in the creation of new ideas. The scrutiny process that has been described cannot be done by the company leadership without involving all the creative departments of the company. These departments are not simply a group of selfproclaimed marketing experts. Rather, they comprise marketers who see the business opportunity in the consumer world, and the Research and Development group who sees the technological opportunity inherent in the product. In the next section we describe how Research and Development needs to find leverage in a successful innovation environment.

3.4.1 How platform innovation works If we look at where we have come so far, we see dynamic tensions, growth, development, modifications, maturation, and new growth. On the one side, we have a process that leads to a big product idea. Depending on the discipline in execution of this idea and the persistence a company shows over time, this idea will develop its own life cycle. Reality shows that life cycles of individual ideas are often short and fragile because the business environment requires healthy businesses on a frequent, continuing basis. New products have to perform quickly; if they don’t, their silent death is a question of months, sometimes even weeks. What about the bigger picture? What about game changing innovation, e.g. creating a new platform upon which further innovations can be developed? The ideas have the capacity to immediately mould a territory of new ideas one of two ways. The first way is by creating a very new market segmentation using the same technology but with different product experiences (e.g., iPOD – nano – chips, etc.). The second way is by creating a better product experience with completely new technology and an outlook for how this technology will evolve over time (e.g., Intel processors). Sometimes the innovation does both.

3.4.2 Case study: new concepts and new platforms for soup To illustrate what is needed to create one of these platform ideas and how to explore it over time, we now move to a real example that one of the authors was able to experience himself. In 1996, a group of very creative

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and dynamic marketers were trying to redefine the strategy for the Unilever soup and meat business in the Netherlands. The leadership of that time encouraged ‘thinking out of the box’ and allowed failures. In fact, this young team worked as if they were set free to roam on a playground for marketers. Despite the fact that this way of working caused the finance department a lot of headaches, it made people extremely sensitive to the world outside the company. The Unilever employees started to become very perceptive about their environment. And it was this closeness that produced the insights and the new platform. Situated close to the white collar business unit of the company was a large factory. From time to time, workers from the production lines visited colleagues in the business unit. During the cold winter months in 1996/97, a number of factory workers came over asking for permission to use products from the Cup-a-Soup ® line for their breaks. Cup-a-Soup ® is an instant dry soup product, which was not very successful at that time. Cupa-Soup ® had been launched in the early 1970s and was generally regarded to be an easy-to-make soup, fairly standard, probably old-fashioned product. Soup in the Netherlands was eaten as a meal (high carbohydrate soups like pea-soup) or as a starter. Cup-a-Soup ® belonged, from the Unilever company perspective, to the less important ‘starter’ products. In effect, the marketing mix for this product range was outdated and very stuffy. Nobody was really interested in it, and it was not surprising that Cup-a-Soup ® enjoyed negative growth. All the marketing ‘wisdom’ was overturned when the factory workers started to ask for the product for their break-moments. The responsible marketer had a huge ‘aha’ moment. A business changing idea occurred to him that, 11 years later, has a positive impact on the business. The marketing manager realized that the instant product format of Cup-a-Soup ® fits with snacking moments and, because it was a soup, it was a new, disruptive category competing against a field of very unhealthy (sugar containing) products. The marketing manager realized that it was possible to create a ‘space in the mind’ with this old fashioned concept, by translating it to a new need. Furthermore, it was clear that existing product format (six sachets in a carton box) was not the right delivery vehicle for these products for this new experience opportunity. The product had to be packaged for people having snacking moments, which happens mainly ‘on the go’, so he needed a product delivery system. And so was born the idea of a Cup-a-Soup ® machine, which provided the new platform. Many new innovations of this type thrive on stories, on the ‘legend’. In fact, creating a new platform requires the legend to give it credence, to make the concept concrete, and to allow for the next and ongoing generations. The marketer brought in product and packaging developers to design a high-tech Cup-a-Soup ® machine. He himself spent countless hours to calibrate the right amount of dispensing water and dry soup product. The first

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usage environment was the business unit environment, with the surprising and gratifying 300 usages per week on one machine on one floor in the company building. All does not go smoothly in this type of game-changing innovation. In the next stage of the Cup-a-Soup ® story the inventors had to face a serious draw-back which almost led to the end of the whole journey. The team took five of their newly developed high-tech machines to a soccer stadium. The logic was simple. There were about a quarter of a million people coming to the stadium every week for different events. The team thought this would create the right environment to showcase how fast the new idea could be leveraged. However, none of the fans were interested. Even when the product was offered free, there were no ‘takers’. The two-month experiment was a failure. The Dutch customers were not going along with the innovation, despite its apparent validity as far as satisfying a need. Or did the product really satisfy the appropriate need? Although the stadium event was a clear disaster, it showed the team a critical element of the platform idea. Cup-a-Soup ® snacking was not happening in a context of fun and celebration. It was happening in a context when people go through a mental transformation, like in an office or factory. This insight unravelled the genetic code for the marketing mix of Cup-aSoup ®. It started with the understanding that the product experience isn’t just created by the product usage, but what is more important to the preparation of the product. The transformation of a soup powder with hot water had a symbolical meaning for people in a snacking moment. The consumer using the product wanted to feel the same transformation for himself (or herself), when he/she was tired, nervous or too excited. The Cup-a-Soup ® moment was the specific point in time that helped to transcend those feelings and re-incarnated positive emotions. The soccer stadium was the incorrect context for these feelings. The rest of the story is told quickly. The team discovered that a ‘hightech’ machine wasn’t the appropriate product delivery system because it didn’t allow sufficient time and effort for the consumer to experience the full and adequate ‘Cup-a-Soup ® moment’. The new platform was a combination of product and experience. The team designed a specific mechanism that took the size of the company and the different required preparation steps into consideration. With every step, the team discovered how to deepen the offering, being more specific in product design (size, amount of water, variants, etc.). The efforts developed a road-map for the platform and included various new technological developments that would allow higher product quality for the Cup-a-Soup ® products. Over time, the specific knowledge became more and more granular and was internalized by the company. With the increasing scale it also became obvious who the real competitors were: not other Cup-a-Soup ® products but rather coffee, tea, sugar snacks, a walk to the restroom or a break visiting the internet. These insights further drove the

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advertising communication that was necessary to move the Cup-a-Soup ® experience to the new opportunity.

3.4.3

Key success factors which first enable and then define platform innovations 1. A company mentality that allows trial and error. This success factor obviously does not work in construction, but it does work in packaged goods. When you build a street or a tunnel it has to be right first time. In the fast moving consumer goods industry we see that new propositions are almost never right the first time as a whole. They require fine-tuning in the way communication of voice has to be handled, packaging features need to be optimized and streamlined towards shop requirements. The notion of ‘right first time’ assumes a static approach to innovation. However, we discover things along the way. We learn what can be possible once products are launched. It would be naïve to believe that failure is easy in these processes. It creates an enormous tension among everyone involved. Companies need to become conscious about panic attacks, and should analyze problems. Too often the panic attacks in response to momentary failure destroy future opportunities, when the reality is that only a minor correction is needed to get back on track. 2. The participating individuals must be able to perceive the ‘reality’ of the existing products, and at the same time have the mind-set of ‘how can we redefine what we are doing whilst stretching existing limits?’. The ability to observe and to analyze behaviour is often hard to find among many managers who work in innovation. If you take these managers to a mall and ask them to describe what they see, smell, feel and touch they might even get anxious about it, because it is mental territory that they would normally not touch. It is critical for companies that want to be successful to let their innovation managers reconnect with their senses and their ability to be empathic with their environment. Despite seeming to be straightforward, the reality is that this inability to see the ‘today’ as ‘tomorrow’ is perhaps the underlying reason why so many new things fail in the market. Managers often need only to construct and to describe, but not to feel the future. 3. A belief in experiences rather than products is becoming increasingly important. People don’t think in product attributes, they just see whether or not, and then specifically how a new product design fits with their life context. Successful invention should start with the analysis of how a product ‘creates’ a specific behaviour when the consumer uses the product. Knowing the nature of the particular behaviour will tell the inventor whether or not the specific behaviour that fits the product is appropriate for the particular occasion when the product is being used. Let’s think of the example of a formal meeting with one’s colleagues. During this moment the stress is great. One might like to entertain

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oneself, if only to release the pressure that builds up during these meetings. It is inappropriate in such situations to ‘tune out’, to music or to watch a movie. Yet, a socially appropriate device, such as a portable computer (e.g., one’s Blackberry®) is the ideal tool to provide the needed distraction. No one can say that the person is ‘tuning out’ rudely. That’s a perfect fit of product experience with a contextual need – a guarantee for a successful product 4. One should explore the context in which a product works, rather than fixating on the product features themselves. Audiences recognize how products fit and how they could give meaning to their lives. These associations are much more powerful to creating tension for a brand than the fixation on benefits only. 5. The bridge between the hard rational skill and the soft intuitive skill must be recognized and nurtured. Healthy organizations generally find their way through the complexities that are so inherent to innovation processes, whether they recognize the process and formalize it, or whether they do so by trial and error. The bridge often comes from the leader, and from intuition, rather than from formal processes. On the other hand, this is the ‘soft innovation’ skill that relies on the person, not on the process. It is not sufficient. Real innovation success needs hard rational skills, too. Big companies run through a number of development cycles during their existence. These cycles are distinguished by processes that change as a company matures. However, no matter what the stage of maturity in which the company finds itself, the ‘hard thinking’ will help deliver valid facts, benchmarks, and actionable directions.

3.5 The role of research and development (R & D) in food companies Research and Development (R & D) in many food companies has two tasks. One is to create new products. The other is to identify combinations of ingredients or processing in order to reduce costs. The latter is often easier, or at least gets the most attention. In recent years, with spiralling commodity costs, many companies spent a great deal of their R & D budgets trying to modify the products in their existing line in order to keep cost of goods within such limits as to ensure profitability. The second role, more relevant to this chapter, is the development of new products. R & D may either take a lead role or an associate role. In a lead role, R & D is responsible for identifying product opportunities, through whatever industrial analysis and development strategies it uses. In this lead role, it comes up with the product idea, develops concepts, creates protocepts (prototypes embodying the concept), tests for viability and commercialization, and finally turns it over to the marketing group (Brody and Lord, 2000; Fuller, 1994; Graf and Saguy, 1991).

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Another type, the associate role, is more common, and perhaps more comfortable. Here R & D comes up with the product, following specifications given to it by external groups in the company, such as management or marketing. Their role is not to develop the ideas or refine them, except as the ideas pertain to actual ingredients. Rather, R & D’s goal is to service the marketing and sales function, creating the ‘stuff’ that will be sold, and following the guidance of marketing, except perhaps in those areas that can be definitely construed as technical (e.g., ingredient and process optimization).

3.6 Different world-views: academia versus industry This chapter has two points of view. The first is the academic stance, which looks for knowledge, general rules, and which approaches science as something that is iconic, a ‘gold standard’. Science aims to understand the world, to discover how variables interact, to portray the workings of the world as a well-ordered machine, following rules. Academia, the daughter of science, is oriented to archival knowledge and understanding, with a long view spanning decades, and perhaps centuries. Methods in academia are ‘valid’ when they truly describe what occurs, and which lead to new propositions that can be validated through empirical testing. Thus the goal of an academic, scientific view of concept development is to understand how to make concepts, how concepts work and, in the end, systematize a database of information from which new concepts can be created (see Beckley and Moskowitz, 2002; Moskowitz et al., 2005a). The other point of view is business. Business cares about the science of concepts, but not so much because it is interested in a grand world-view that science provides as much as it is interested in a process which really ‘works’(Bhattacharya et al., 1998). Businesses focus on concepts as the preliminaries to a product, just as the artist focuses on the ‘cartoon’ as the preliminary to a painting (Cooper, 1993). To the business person, the interest lies in the product itself – was it successful, did it earn the requisite profits, did the company grow as a result and did the stock price rise? The concept, like the ‘cartoon’ is merely there, to be stored, perhaps to be thrown away. Whatever knowledge comes from the concept is knowledge about the particular product. Those who specialize in concept testing also don’t really follow a science of concept development as much as they incorporate concept scores into some type of model or set of norms. When you read this chapter, keep these two aspects in mind, for they represent the dual nature of concept research. It is probably no exaggeration to say that 95% of what we know about concepts comes from the world of business. Most marketers and marketing researchers, as well as product developers and sensory scientists, ‘know’ about concepts. However, what they know is the ‘business of concepts’, not the ‘science of concepts’. The

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major part of the research from Academia thinks that this is not ‘pure’ research, notwithstanding that experimental designs are statistically strong, and for this reason many papers submitted to technical journals are rejected and the approach is not shown widely enough as a result.

3.7 Concept writing is strategy exploration Before we start talking about the how to write concepts, we should evaluate the meaning of concept writing for a company. Factually, the written concept does two things: 1. It explains to the external audience how the new idea fits into their lives and why the audience really needs it. It puts the spotlight on a need or gap in the life of the audience. The new idea explains how it fills this gap and suggests a new and/or better fit than current solutions. The argument supports the desire to have (buy) it. 2. The process of concept writing clarifies to the internal audience (the people in the company) what makes the consumer outside tick. It delivers the answer to the very important question of how the company has to talk about the new idea. In the end, it is not to force ‘selling’ something because it is such a nice idea, but rather how to create a desire for buying it in the consumer’s mind. This process can potentially align internal departments by creating and understanding of how the desire for buying can be triggered. This trigger is commonly described as ‘insight’. Once the insight is understood and shared among the team members in the company, it creates an energy that aligns all upcoming work-streams and events (product/pack development, etc.). In fact, insight turns out to be the ‘special sauce’ that establishes true innovation teams. In the practice of concept writing the teams will always discover surprises about what needs to be explained about a new idea in order to make it work. The pity is that companies often undervalue the relevance of concept writing. In the worst case, concepts are written and tested and then forgotten during the next stages of mix-development. When such short corporate memories are the rule rather than the exception, a well-briefed and highly experienced outsider may be necessary to observe and then guide the herd of well-willing marketers and developers who actually lack the fundament of their work-streams. The content of their work concentrates on fixing issues in the process but they forget to see a bigger picture that potentially can align activities to create a congruent outcome. These teams develop step-solutions but not an innovative idea. They often struggle because they don’t have a strategy behind their efforts. They are best described by leveraging ‘operational hectic’ whilst there exists ‘strategic emptiness’. In corporations, the actual ‘writing’ of concepts has migrated down from senior management to junior management, and then to outside agencies,

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whether these be advertising agencies, marketing consultants, or off-thestaff, hired researchers. Where this migration might seem to be an efficient step because junior management and others outside the company are presumably closer to the consumer, there can be problems with this outsourcing. It is important to emphasize the fundamental, ‘true’ importance of concept writing. Concept writing sets the stage for development. Delegating it to people outside the company can cause significant misunderstanding once validating the concept with consumer research. The ‘magic’ of good concepts is extremely subtle and almost always subliminal for consumers even if the product benefit seems to be a very explicit one. Finally, concept writing has to be a democratic process, not merely a corporate ‘plum’ for those who want control, or a corporate ‘punishment’ for those who want to do strategy, but not get into the trenches and make the business grow. In order to get the most marketing success and corporate benefit out of the concept writing stage, it is very important to include all related departments in the effort of writing and evaluating concepts, independently of how concept development is ‘formally’ done in a company. That is, the rules of ‘how concepts are created’ don’t necessarily mean that these rules produce the best outcomes. The innovation teams need to own the process of concept development. This ownership means that the innovation team must internalize every piece that comes out of the process. In the ideal case, the innovation team actually uses the process to build its own identity. The process of creating a concept gives the project the energy, structure, binding ties that carry it through the good times, but also the difficult times that inevitably plague most innovation teams on one or another occasion. Finally, never underestimate the real benefit of the good concept, and the good process. A powerful concept insight generated from a defensible process, understood in the board room, instills conviction and support. The good concept attracts resources, and even allows the project, the team, the idea to survive bad results from evaluation testing.

3.8

Tapping the consumer mind

Whether new product ideas come from marketers or from product developers, most companies today realize the need to incorporate consumers in the process. Long gone are the days of the marketer or CEO with the so-called ‘golden tongue’, who ‘knew’ through some magical source what the product should be and how it had to be explained to the target audiences. Indeed, as companies have become increasingly competitive, the role of the consumer is becoming ever more important. It is rare that any serious product development goes on without consumer involvement, sometimes to the point where such consumer involvement may be simply ‘too much’, and hamstring the developer. Indeed, there are often cases in companies where

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the consumer research works so tightly with the product development and marketing processes that the timing of consumer tests becomes the key event which drives development, and not the other way around! We mentioned above the need to understand at a profound level the basic needs of a consumer, and how one’s products and technologies fit that need. Over the past decades this realization of the importance of consumers has increased. Thus, when we talk about the evolution of companies away from the ‘golden tongue’, and into consumer-driven development, we are actually talking about a major shift in the way companies do business. Company after company has gone from the judgment of the ‘one’ to the realization that ‘we are better than me’.

3.9 Ideation tools to pull out good ideas How does a company come up with good ideas? Today’s markets are awash with products, with the trade that requires a slotting fee simply to ‘rent space on the shelf’ and is merciless with failures, and with a consumer population that is jaded and that staggers from one trend or fad to another, in dizzying procession. Some years ago the notion that the collective mind of consumers could help became popular. Methods collectively called ‘brainstorming’ became very popular (de Bono, 1995). In these early methods a trained moderator would call together consumers or experts, unprepared or previously prepared by an exercise, and together come up with ideas. Locked together in quarters, often reasonably or even exquisitely pleasant, in conference meeting rooms or hotel quarters ‘off-site’, the group would meet for a few hours or sometimes longer, coming up with new ideas. Sometimes these ideas would be complete concepts, other times the ideas would be snippets that could be strung together later to form a myriad of different possibilities. The brainstorming was great fun, perhaps partly social, partly lubricated by copious amounts of food, often cookies, soda drinks, coffee, a buffet lunch of deli meats, and a group of happy-to-contribute participants. Free wheeling ideation, the exuberant brainstorming, was refined over the course of years, to produce other methods. Part of this evolution came about because of the nature of the practitioner. In new product development, and especially in ideation, the practitioner has to be a sensitive, creative individual, always searching for ideas, always seeking what is ‘next’, and ever primed to recognize that ‘next’ and adorn it and expand it when found so that the efforts are crowned as successes. Thus we have the evolution of other methods, such as metaphor elicitation, where the respondents find metaphors for products, and through these metaphors arrive at a new insight about what the product really does, or the true benefit of the product (Zaltman and Coulter, 1995).

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We find at least three classes of ideation today. These are ideas borne of observation (in context re-living versus ethnographic observation). Ideas borne of collaboration (we are smarter than me, the wisdom of crowds, collaborative filtering), and ideas borne of next generation ideation. There is a fourth class of ideation today, analysis of trends, but that is more like the preparation of fertile grounds rather than a method of coming up with ideas.

3.10 Concepts born of observing Observational: Here the researcher watches what happens in everyday life, records what a person does to select foods, prepare foods, and finally consume foods. When the researcher lives with the consumer, or at least spends a great deal of time passively observing; we call this method ethnography. Originally in the province of cultural anthropology, with the goal to ‘understand’ a culture by living within it, ethnography migrated to the world of market research and sensory analysis more than two decades ago. Texts have been written on ‘how to do ethnography correctly’ in the context of corporate learning about consumers (Mariampolski, 2006). What becomes very important in ethnography is the holistic understanding of what happens in the world of food and the consumer. Hitherto unmet needs are met through creative activities, many of which give rise to both understanding the consumer and understanding the type of food or beverage that might fit this situation. In context, but interactive: Here the researcher visits the home of the consumer, or brings the consumer into a test laboratory fitted out as the kitchen/dining area. The consumer is instructed to prepare the foods exactly the way he or she prepares them at home. The researcher watches what happens, or perhaps even engages the consumer in dialog to make the consumer re-live what is happening in his mind when the food is prepared. Through such in-depth interaction in the normal context of food preparation, the researcher forms hypotheses of what might be happening. More important for the food company is the overarching behaviour – what is really happening here, what types of needs exist in the consumer’s life, how do foods solve that need, and what are the opportunities for the company to take advantage of an existing need that is not being solved by an in-market product (Beckley and Ramsey, 2008)?

3.11 Concepts born of collaboration and the ‘wisdom of the many’ Brainstorming: We dealt above with the very traditional methods of brainstorming. Brainstorming led to the recognition that individuals can polish each other’s ideas, and that the sequential interaction of people can shape

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an idea. Thus, for example, one might start with the need for a sandwich that is eaten in the car. Through brainstorming, each person builds on the contribution of another person in the room, always trying to be positive, but always adding (Callahan et al., 2005) Electronic filtering and building. Most people are familiar with the ‘recommendation system’ popularized by Amazon.com, Inc. The system works very simply. It assumes that ‘birds of a feather flock together’ – i.e., people who buy the same book may have the same interests. Thus, if a person buys Book A and then Book B, Amazon’s algorithm assumes that another person who buys Book A might be interested in Book B, and recommends that book. This is an example of collaborative filtering. The people don’t have to know each other, but the assumption is that similar behaviours indicate similar interests. The notion of collaborative filtering has been advanced significantly in the Internet Age by methods which ‘seed ideas’, present these ideas to other people who participate in a test session by Internet (John-Steiner, 2000). These subsequent participants give their own ideas and rate the seeded ideas. Over time, ideas that are rated relevant by one or another criteria, and keep getting selected, stay in the ‘stream’ and keep appearing from time to time. Other ideas that, having been given by one respondent are determined as not particularly relevant by other respondents, eventually wither away and disappear. The basic idea is simple – keep voting on ideas, and prune away those ideas that just don’t succeed. The elegance is in the algorithms that are used. These approaches provide ideas for the food industry, because the questions used to elicit ideas and to judge ideas are based on common food-related behaviours such as packaging, consumption in a snack situation, etc. The approach works on two implicit beliefs. The first is that ‘we are smarter than me’ so-called Wisdom of Crowds, (Surowiecki, 2004). The second is that there are so-called ‘weak signals’ in the environment – i.e., ideas and opportunities that, if captured, provide the nucleus for a new business opportunity (Ansoff, 1975).

3.12 Concept writing – how to do it and how to do it well Ideation methods for creating concepts don’t actually create complete ideas. Rather, they create nuggets or germs of an idea. The real task is to fabricate a proposition about a product that presents the reason for the product, the description of the product, information about specific benefits of the product, usage occasions, price, etc. Unlike knowledge about the physical and sensory properties of food, knowledge and, more importantly, wisdom about concepts is not found in archival journals or textbooks, except for the simplest of courses in introductory books on market research and marketing. The reasons for this paucity of knowledge are straightforward, and relevant to this chapter:

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1. Concept-creation is often viewed as a science, not an art, especially by those whose job it is to be ‘creative’. For fifty years or more, since the early 1960s, the advertising agency has been the repository of creativity for many food companies, although recent trends suggest a return of such power to the client company (food manufacturer) and to the marketing specialists inside. The consequence of such divisions between ‘them’ (agency) and ‘us’ (client) is that concept development has grown up as an applied art, not a science, under the control of those who proclaim themselves the true understanders of what the consumers want. 2. There do not seem to be formal courses that teach concept development, except for those that involve advertising. Those who learn about advertising do so in formalized classes in some business schools. These courses about advertising also teach a bit about writing concepts, which we described above as the artist’s ‘cartoon’ of the ultimate advertisement, in the same way that painters create ‘cartoons’ prior to painting their murals. Some advertising agencies talk about creating concepts in the format of ‘problem-solution’ (e.g., BBDO, Inc., in the 1970s). The concept structure is fixed, so that the problem comes first, and then the solution comes second. There is a true paucity of books on concept development. The authors wrote two books on concept development (Moskowitz et al., 2005b; Porretta and Moskowitz, 2005). These books attempted to lay the intellectual foundations for learning about foods based on concepts and the experimental design of ideas. The books did not, however, try to teach the reader how to write concepts. Nor, in fact, could they.

3.13 Concept screening Since the primary use of concepts is to test consumer reactions to products, it should come as no surprise that most of the information about concepts resides in the corporate archives. The performance of concepts, rather than specific concepts, is of relevance here. The key issue for companies is whether or not the product idea has any ‘merit’. By merit we mean acceptance, profitability, uniqueness, ability to establish a beachhead in a competitive environment, or any of a host of other criteria. Concepts reflect the company’s idea of what a product could be. Depending upon how the consumer reacts to the concept, the news might indicate opportunities for the product, severe negatives that stunt consumer acceptance, a me-too idea that has little merit by itself, and so forth. Companies measure concept performance in a systematic way. Quantitative research for concepts is formalized and developed, often coming with ‘norms’, e.g. normative data about what performance scores really suggest about future performance. There are at least three quite different approaches

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to measuring concept performance. The first is qualitative discussions about concepts. The second is formalized screening, either of simple statements or full concepts. The third is modelling potential concept performance.

3.14 Qualitative screening Qualitative research for concepts typically comprises focus groups, wherein a group of prospective consumers sits with a moderator, and discusses their feelings about concepts (Kitzinger, 1995). The moderator begins the session with an introduction to the product area, presents the strategy behind the concept(s), and then presents the concepts to the participants. Individuals in the group are encouraged to voice their opinions, either undirected by the moderator, or directed in certain topics because those topics are of specific interest. The interplay among the participants reveals their reactions to the concept, what they feel the concept may be conveying, and so forth. Occasionally the moderator may even poll the respondents, so that they vote on the concept. All too often, with 6–8 respondents in a group, and perhaps with four or so groups, the moderator’s report will contain some statistical analyses of these ‘votes’. For the most part, those who commission the focus group project realize that the objective is to get a sense of how prospective consumers feel about the product idea, rather than measuring potential success or failure. One of the authors (SP), acting as reviewer for a leading sports apparel company of a focus group aimed to develop ideas for new kids’ gym shoes (1–3 years old), was present when the results of the focus underlined the fact that the shoe material should be soft and chewier because children love to put shoes in their mouths (has someone told them that shoes walk on dirty places?). Clearly, the group provided insight but not necessarily direction for marketing!

3.15 Screening promises and full concepts At the most primitive level of quantitative research we find so-called promise testing or benefit screening. Different companies have their own names for this type of research. Promise testing begins with a set of alternative statements about the product. Some of these statements can talk about what the product is, some talk about what the product does, what the product provides of a unique nature, how the product is made, how it is merchandized, stored, etc. The promises or benefits are single-minded ideas, rated by the respondent on a limited set of attributes, such as overall interest (usually the key evaluative criterion), uniqueness, fits a specific situation, etc. Interest and uniqueness are the most typical. Each respondent evaluates a number of different promises, in a randomized order to prevent bias.

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• Thick, ridged potato chips - stronger than a regular chip • When you think about it you have to have them … and after you start, you can’t stop eating them • From Kettle Chips

Fig. 3.1 Example of a food concept where the elements are ‘bulletized’, i.e. presented in short, easy-to-read phrases that are not linked together by connectives. (Source: Moskowitz Jacobs Inc.)

A chocolate chip cookie that tastes like homemade with a soft and chewy texture. It’s sweetened with natural fructose and contains no trans fats or preservatives so you can feel good about giving it to your kids.

Fig. 3.2 Example of a simple concept with a picture. The concept is presented as a single, easy-to-read paragraph. (Source: Moskowitz Jacobs Inc.)

The results are tabulated, and the report presented to product developers and to marketing. Beyond screening benefits lies the formalized screening of full concepts. These concepts can be simplistic, comprising ‘bullet-points’ such as the concept shown in Fig. 3.1, or more fully fleshed out concepts, such as those shown in Fig. 3.2 and Fig. 3.3. Consumers have no problem with either type of concept as a stimulus, although it is good practice to avoid concepts that are overly long, detailed and densely worded. In a conventional concept screen (also called ConScreen in the research industry), the researcher creates a number of different concepts, typically following the format of Fig. 3.1, Fig. 3.2 or Fig. 3.3. It is unusual for a ConScreen to mix formats in a single study for fear that the format itself might bias the results. There is no prescribed number of concepts for a ConScreen, nor is there a standard format. Some ConScreens are executed in a face-to-face interview in a mall, where the respondent is intercepted by an interviewer and invited to participate. Other ConScreens are executed by mail, where members of a panel receive a booklet of concepts and an answer form.

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Consumer-driven innovation in food and personal care products Mini Squares Bagelbread The 100 Calorie Way to Eat Smarter America’s, maker of America’s favorite English Muffins and Bagels, brings together the best of bagels and sandwich bread in a new convenient 100 Calorie size. New Mini Squares Bagelbread offers all the flavor of a bagel with the soft texture of bread in a new, calorie-controlled size. Now when making breakfast or a favorite sandwich, you don’t have to count calories. Simply reach for Mini Squares Bagelbread with only 100 Calories, and let us do the counting for you. With Mini Squares Bagelbread, you and your family will enjoy eating smarter without sacrificing great taste.

Fig. 3.3 A more complete concept. The concept is essentially a paragraph or more, and comprises a more or less complete description of the product, with a physical component and several benefits to the consumer.

More recently, with the popularization of the internet, many ConScreens have migrated to the internet, which makes the interview more automatic, better controlled, faster, and most importantly, cheaper (Dahan and Srinivasan, 2000). The standard output of the ConScreen is the performance of each test concept on the better of attributes, the most frequent being overall performance (e.g., purchase intent or interest) and uniqueness. These attributes may be supplemented by topic-specific attributes, such as expected good taste, expected nutrition, appropriate for adults versus for children (a bipolar scale), and the like. The specific content and wording of these attributes is a function of the product being tested, the goal of the ConScreen, and the predilections of the researcher doing the work. The array of attributes is presumed to provide a ‘concept signature’ that can be analyzed using standard statistical methods. The nature of the numbers reported deserves mention. Most scientists are accustomed to working with averages, whether the mean or the median as the measure of central tendency. The mean shows the central tendency of feeling. In the case of purchase intent rated on a 9-point scale, for example, the mean shows the average intensity of feeling. Two individuals, one rating the concept ‘1’ and the other rating the concept ‘9’ would thus generate an average of ‘5’. Consumer researchers look at the data differently from the way scientists do. Consumer researchers trace their intellectual history to sociology rather than to psychology or the biological/chemical/physical sciences. One major consequence of the intellectual heritage from sociology is the way consumer researchers think about the performance of concepts. To a consumer researcher, as to a sociologist, the mean is less interesting than the percent

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of people who provide a specific answer. Thus, when looking at the performance of a concept, the consumer researcher focuses on the percentage of respondents who accept that concept, rather than focusing on the intensity of feeling towards that concept. The difference is subtle but important. To the consumer researcher the crucial question is ‘will this concept be successful, and among how many people?’ Two people, one rating the concept ‘1’ and the other rating the concept ‘9’ indicates that one will accept the concept, the other will reject it. The fact that the two concepts average to 5 is, for all intents, irrelevant. The mean fails to provide the necessary information about performance. The ‘bottom line’ of all ConScreens is really which concepts perform well, and which concepts perform poorly. Most companies that perform ConScreens for clients have available to them banks of normative data on other concepts tested previously, allowing the client to compare the performance of the new concepts to the performance of older concepts. These banks of ‘norms’ reassure the client, although they may not be sufficiently up-to-date to give accurate reference values in a rapidly changing environment. Nonetheless, they are important to the user. Beyond performance, however, those who commission these ConScreens want to know ‘why’ a concept performs well, or poorly. The necessary information to answer the ‘why question’ is usually not available within the concept itself because a conventional ConScreen tests many different types of concepts for a variety of products. There is no internal structure to the concept to help the researcher learn what drives the performance of the concept. In such situations, the researcher uses the diagnostic questions. The respondent rates the concept on other attributes of a more descriptive, factual, specific nature. Examples of such diagnostic attributes are: • communicates nutrition • communicates good value • communicates good taste. Table 3.1 shows an example of data that a researcher might obtain for three concepts, from a ConScreen. Data from company after company Table 3.1 Simulated results from a ConScreen with three concepts. All attributes are rated on a 9-point scale, anchored at both ends

Mean – Purchase % Top 3 box Mean – Expected good taste Mean – Expected nutritious Mean – Expected for child Mean – Uniqueness % Top 3 box uniqueness

Concept #1

Concept #2

Concept #3

6.7 72.0 6.1 5.8 4.8 6.1 64.0

5.3 61.0 6.2 6.4 7.2 5.8 66.0

4.8 40.0 4.6 5.4 5.3 7.2 78.0

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testing products will generate results that, except for the numbers, look surprisingly like Table 3.1.

3.16 Simulated market test at the concept level During the past thirty years packaged goods manufacturers brought in a great deal of research talent from different universities, both as research directors and as research suppliers. These professionals had been schooled from the 1960s in both straightforward research methods and quantitative modelling. The modelling in both business schools and in departments such as psychology, sociology and geography, focused on what we might call complex behaviours of various types by people in normal situations. A pervading interest of these modellers was, and remains, predicting of market success of a product (e.g., BASES, 2008). No doubt the interest was supported to a great degree by availability of data on sales from companies such as A.C. Nielsen and Information Resources Inc. (Nielsen, 2008; IRI, 2008). In the end, the manufacturer companies wanted the ability to predict sales volume or market share, given information about the different marketing factors, including product acceptance, positioning (advertising expenditure), promotions, etc. It would be some few years until the supplier companies realized that they could estimate potential volume through concept testing, or at least get a relative measure of product performance in the market. It was this type of thinking that led to the third concept screening mechanism – namely ways of testing multiple concepts with the appropriate measures and data bases so that the outcome was both performance of the concept as a concept, and some rough indication of the performance of each concept as a product in the market. Of course, the estimates provided by the method were by their very nature approximate and rough, since there was no physical product to use. Nonetheless, the methods were considered sufficiently robust to be used in concept screening.

3.17 Experimental design of concepts Experimental design refers to a class of statistically based approaches in which the researcher combines different variables, normally in a fractionated pattern to save time and money, measures the response to these combinations, and through statistical modelling, e.g. regression, identifies the part-worth contribution of each variable to that response. In a phrase, experimental design analyzes the mixtures of variables. The reaction to this systematic approach was nothing short of excitement and unexpectedly new applications. Originally developed by statisticians to investigate the effects of treatments on crop yields and chemical processes, experimental design

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proved sufficiently valuable in a host of areas to promote its widespread use in basic and applied science. Marketing was one of these new areas. In the early 1970s, Paul Green and his associates at the Wharton School began their pioneering research on experimental design of messages applied to the creation of ideas (Green and Srinivasan, 1980). The approach, called conjoint measurement, was essentially the statistical design of different ideas in test concepts. The goal was to discover what was important to consumers in different products and services. The experimental design, or conjoint measurement, seemed perfectly appropriate because many of these services and products comprise mixtures of different features, benefits, prices, and the like. Although Green and his Wharton and Bell Laboratories colleagues are usually credited with the development of conjoint analysis in the field of marketing and consumer science, similar work was going on in the world of experimental psychology, published by the psychologist Norman Anderson, and called functional measurement (Anderson, 1970). For our purposes here in explicating current day commercial methods, especially advanced ones, experimental design represents one of the pinnacles. A history of applications is beyond the scope of this chapter, but a complete volume for non-statisticians has been written on the application of experimental design to food concepts (Moskowitz et al., 2005b). We will abstract some of the basic ideas and give a quick overview of the thinking and the application by means of case histories. These case histories allow us to illustrate a number of key points, and drive home those points through actual data.

3.18

A short introduction to design: concepts about water

We explicate the approach using experimental design through a discussion of two studies. These two studies used the same set of 36 elements. One study positioned concepts to be a sports drink. The second study positioned the concepts to be drinking water. These studies were designed to identify ‘weak signals’, i.e. ideas that could become strong products, but were not yet well recognized (Ansoff, 1975; Flores et al., 2003). Experimental design begins with the raw material, the elements that will be systematically combined through statistical rules. We see the elements in Table 3.2. The water project comprises four silos, or general ‘themes’. In turn, each silo comprises nine elements. If you look at these elements, you will see that they are simple. They are presented as stand alone ideas, which can be inserted or removed from a concept. That is, the elements are ready to act independently of each other. As noted above, the elements are the same for the two studies. Both studies worked with an appreciable number of respondents, 226 for sports drink and 209 for water.

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Table 3.2 The four silos, 36 elements, and their utility values from the two studies. The elements are sorted by utility value within each silo by ‘positioned as sports drink’ (Source: Moskowitz Jacobs Inc.) Positioned as sports drink

Positioned as drinking water

226 44

209 49

5

8

1

0

−1

−1

−1

−4

−7 −10

−15 −11

−19

−18

−22

−29

−34

−41

4

3

4

1

3

3

3

1

1 0 −2 −4

2 −2 2 0

−5

−5

4

4

4

3

3

6

Base size Additive constant Silo A – Flavour Fortified water . . . no colour, or flavour added, just the crisp taste and smell of pure water Berry crazy flavours . . . choose from raspberry, strawberry, kiwi-strawberry Apple, banana, peach or cherry flavours . . . just the right touch of fruit The refreshing taste of lemon-lime, just quenches your thirst Watermelon or melon . . . a taste of summer in every sip The refreshing taste of citrus or grapefruit to add a little tang Enjoy the taste of winter and the holiday season . . . cinnamon, vanilla or almond flavour Enjoy the light taste of peppermint or wintergreen flavours . . . oh, so refreshing A touch of vegetable flavor like carrots, asparagus, tomato Silo B – Healthful additions Enhanced with all the vitamins and minerals your body needs Added minerals like calcium and magnesium . . . for healthy bones With all the essential vitamins, such as Vitamin C, B6 and B12 Enhanced with energy boosting vitamins and minerals or other functional ingredients for your health Added vitamin C to boost your immune system A choice of high or low level electrolytes Enhanced with natural flavours Enhanced with new soluble fibre you won’t know is there . . . an effortless way to meet your daily fibre intake Healthy drops/tablets added to the bottled water – just choose the right packet for you . . . containing minerals and vitamins to supplement your daily diet Silo C – Purity, reliability Reliable labelling – Added ingredients are clearly labelled on the front . . . so you can see what you’re getting or not getting More natural flavours without added sugar or other sweeteners Pure and clear . . . contains no colour or heavy metals like lead and mercury

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Table 3.2 Continued Positioned as sports drink

Positioned as drinking water

Pure satisfaction . . . no smell or aftertaste All the flavour . . . none of the calories Crisp and clear with all the flavour and health benefits Fortified natural spring water Clear, blue or green labelling . . . reminds you how good water is for you Non-carbonated purified drinking water Silo D – Ease of use, packaging A light weight bottle so you can take it with you anywhere

3 3 3 2 2

6 4 1 3 1

1

3

4

2

Available in a variety of bottle sizes, shapes and closures

3

2

Specially designed bottle with an easy to hold grip for people on the go With a sports top that allows you to drink without removing the cap . . . minimizes spills Specially designed bottle with a small base that will fit standard car cup holders A bottle with a leak proof spout Slide-action caps for bikers, athletes and other active people Specially designed bottles for kids such as animal shaped bottles Add the flavour you like . . . drops/tablets attached on the bottle with different flavours such as strawberry (red), apple (green) etc.

3

1

3

0

2

3

2 0

1 −1

−2

−2

−4

−3

Running one of the designed studies has become very easy using today’s modern Internet-tools, such as IdeaMap ® (Moskowitz et al., 2001). The user simply creates the elements, classifies them into the proper silos, types the elements into the program, and the program does the rest. That ‘rest’ includes pulling down a specific experimental design, unique for each respondent, populating each concept in the design with the proper elements, presenting the combination to the consumer respondent, acquiring the data, and then analyzing the results using simple statistics, ordinary least-squares regression, and later clustering to discover segments (Box et al., 1978; Gofman, 2006). The structured approach to evaluating experimentally designed concepts continues with the study executed on the internet. For some situations the actual evaluations may take place in a supervised location, where an interviewer can ‘work with’ the respondent. The concepts are presented to the respondents on a computer monitor linked to the internet. During the past

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decade the internet has gone from an interesting new development to an omnipresent reality. In turn, a majority of the concept work has migrated from more expensive person-to-person and mail surveys to the far less expensive, far more efficient internet execution. Developers and marketers in most packaged goods companies, of which food and beverages are good examples, have become increasingly comfortable with these internet-based studies. We see an example of the orientation page for one of these studies in Fig. 3.4. The orientation shows the respondent what the study is about, and then introduces the respondent to the scale to be used. This type of orientation is important because it makes the respondent feel comfortable. The evaluation is made using a 9-point scale, selected because the 9 points correspond to the numbers on the keyboard, and because the scale is easy to use and unambiguous. The actual test concepts appear similar to the concept shown in Fig. 3.4. The number of elements in any particular concept varies, being a function of the experimental design, which specifies which particular elements will appear in each concept. We see four elements in Fig. 3.5, one element from each category. Another concept might comprise a different three elements, with one silo absent.

Fig. 3.4 Orientation page for water study. Here the water product is ‘positioned’ as a sports drink. (Source: Moskowitz Jacobs Inc.)

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Fig. 3.5 Example of a test stimulus used in the study of experimentally designed concepts. (Source: Moskowitz Jacobs Inc.)

From the practical point of view respondents find it easy to respond to concepts presented in this fashion. Although the purist might wish to present a fully structured paragraph (see Fig. 3.3), respondents have a harder time with paragraphs than with so-called bulleted concepts (see Fig. 3.1). The ease experienced when reading these sparsely worded concepts, with respondents ‘filling in the missing text’ may even increase over time as readers who work with computers continue to skim and graze text, rather than reading deeply. In any event, exit interviews with respondents have never uncovered problems with understanding the text presented in bulleted fashion. Although the respondents evaluated more or less full concepts, the important thing to discover is the marginal or part-worth contribution of each element, as well as to determine how positioning the beverage changes the impact of the individual elements. Does, for example, positioning the beverage as appropriate for exercise affect how the individual elements perform? The assumption here is each of the elements contributes some degree of interest. Ordinary least-squares regression reveals the relation between the presence/absence of the elements and either the rating itself, or some transform of the rating, such as interested vs. not interested. For these studies on the components of concepts, the standard businessoriented as well as science-oriented research once again follows the convention of sociology and marketing research. Originally, every concept had been rated on the anchored 1–9 scale (see Fig. 3.5). We transform these 9-point ratings into a binary rating. A rating of 1–6 was arbitrarily defined as the respondent not being interested in the particular concept being rated,

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so that rating was transformed to the value ‘0’. A rating of 7–9 was, in turn, arbitrarily defined as the respondent being interested in the particular concept, so that the rating was transformed to the value ‘100’. Once this transform is done, we relate the presence/absence of the 36 elements to the binary value 0 or 100. This analysis can be done for each person because each person evaluated a unique set of 60 combinations (Moskowitz and Gofman, 2004). The parameters then can be averaged across individual respondents to generate a consensus. Or, the analysis can be run on the full set of raw data without regard to the individual-level models. Both approaches yield similar estimates for the impact of the different elements. Let us now interpret the data, and in doing so, understand how the researcher works with the ratings from the experimentally designed concepts. 1. The analysis uses ordinary least squares regression. The independent variables are the 36 elements in Table 3.3, which take on the value 0 if the element is absent from a concept or the value 1 if the element is present in the concept. 2. The regression analysis is run on the individual respondent data. That is, each respondent generates an equation from regression analysis. The regression analysis is called dummy-variable modelling because the independent variables, i.e. the 36 elements, take on either the value 1 if present in a test concept, or 0 if absent. A variable of this type which takes on one of two values is called a dummy variable. 3. The regression model returns with a simple, 37-term equation, whose 36 coefficients show the contribution of each of the elements, and whose additive constant shows the expected rating for the hypothetical situation of no elements present in the concept. This simple regression model is expressed by: Binary rating = k0 + k1(Element A1) . . . k36(Element D9) 4. The additive constant, k0, can be formally defined as the conditional probability that a concept will be rated as ‘acceptable’ (i.e., 7–9) in the absence of any elements. As noted above, this is an estimated parameter. The additive constant is a good baseline to show the predisposition to rate the concept as interesting in the absence of any specific communications. Thus, for the study where the water was positioned as a sports drink, the additive constant is 44, meaning that without any elements, 44% of the respondents would rate the concept 7–9, i.e., accept, without the presence of elements. Slightly more respondents, 49%, would rate the concept 7–9 when the concept is positioned as regular water for drinking. 5. Each of the elements has its own average utility value, shown in the body of Table 3.3. The positive utility value is the incremental percent of the respondents who would rate the concept 7–9, above and beyond

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Table 3.3 Elements and their utility values for a home water purifier (Source: Moskowitz Jacobs Inc.)

Base size Additive constant Removes germs, including 99.8% of bacteria Removes metals and chemicals, including lead and 99.9% of chlorine Turns tap water into bottled water Know your children will have a constant supply of fresh drinking water Gives peace of mind by supplying clean, healthy water anywhere, anytime Turns water from any source into pure drinking water Used by the Red Cross in disaster areas Be confident that you’ll always have a clean water supply when travelling A 20 oz, bottle costs $14.99, which is good for 850 refills A 20 oz. bottle costs $9.99, which is good for 320 refills Exceeds the minimum EPA standards for drinking water

Total sample

Not responsive

Quality seeker

(476) 36

(138) 50

(193) 28

6 5

3 4

13 11

4 4

−1 1

12 10

4

2

10

3

−6

12

3 3

−3 −3

11 10

3

−2

10

2

−6

11

2

−6

10

the additive constant, if the element appears in the concept. Thus, the element ‘fortified water . . . no colour, or flavour added, just the crisp taste and smell of pure water’ has an additive constant + 5 when the concept positioned as a sports beverage. This means an additional 5% of the respondents above the base line of 44% would rate the concept as 7–9 if the element were present in the concept. A one-element concept would have a sum of 44 + 5 or 49% rating that concept 7–9. 6. Sometimes the element has a negative utility, meaning that a reduced number of respondents would rate the concept 7–9. Thus, the element ‘Add the flavour you like . . . drops/tablets attached on the bottle with different flavours such as strawberry (red), apple (green), has a utility value of -4 when the concept is positioned as water drunk for sports activities. This means that when the element is inserted into the concept 4% fewer respondents rate the concept 7–9. Therefore, a one-element concept with this particular element would have 44 – 4 or 40% rating the concept 7–9. 7. Norms for the elements are the following, based on thousands of these studies: a. 15+ = superb performance, keep in the concept b. 10–15 = excellent performance, will definitely drive interest c. 5–10 = good performance, but probably not breakthrough

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8.

9.

10.

11.

Consumer-driven innovation in food and personal care products d. 0–5 = element is probably irrelevant e. Negative = element detracts from acceptance, and should be avoided With these norms in mind, one can then look at the utility values of the different elements to understand which elements perform well, which perform poorly. Furthermore, one can develop working hypotheses about what types of elements perform well or poorly. For example, unusual flavours perform poorly. Measuring the utility of the same elements studied in two different projects with the end use positioned differently allows the researcher to understand how positioning drives end use. In these studies we don’t find any dramatic effect of end use. In other studies, however, there may be demonstrable effects. The nature of the effect remains an empirical topic to be investigated. Our data in this study came from the total panel. Researchers often divide the data by subgroups of interest. We don’t show the data for subgroups. It occasionally turns out that certain subgroups respond more strongly to some elements than do other subgroups. The increasing popularity of segmentation works in the case of concepts as well. Segmentation divides the respondent population by the pattern of their individual utilities. The segments often show radically different patterns of elements that perform well versus poorly. These segments cannot be typically described by their gender, age, product usage, income, or even by their self-proclaimed attitudes about the product category (e.g., Wells, 1975). Rather, these are emergent segments. They are consistent – i.e., they will perform the same way again if given the same set of concepts, so we are dealing here with a new way of dividing consumers by mind-sets. Discovering those mind-sets and the elements that work for each mind-set allows the product developer and marketer to create new, very powerful concepts, and from those concepts create products that appeal to the segments (see Luckow et al., 2005; Moskowitz et al., 2005a).

3.19 Putting it all together: from the concept research to the design and sales messaging The easiest way to present the practical outcome of the experimental design approach is through an application. The application illustrates the way one thinks about the data. Our application of designed concepts deals with a technology that produces clean water for drinkers. The case history is particularly relevant for food and beverages in light of the vastly increased consumption of bottled water over the past decade, the introduction of many brands, and the ongoing evolution of the product category to include beverages with slight degrees of flavouring and sweetener. © Woodhead Publishing Limited, 2010

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Water bottle - scripting and tonality You’re going to appeal to about 40% of the people. This is the group on the right, labelled the ‘Quality seeker’. They want to know that this system is going to give them spring-quality, bottled-water quality from their tap. They don’t want bacteria and other bad things around. Here is the information that should provide some of the features and how to communicate with consumers Now … the Personal Water Machine You’ve always wanted the purest drinking water. Like the rest of us … you want bottled water because it’s pure, clean The personal water machine … removes germs, including 99% of bacteria So good … so effective … it’s used by the Red Cross. Tums water from any source into pure drinking water The personal water machine - you and your children will have a constant supply of fresh water

Fig. 3.6 Instructions for creating a concept and a set of selling messages for the water purified product, whose utility values are shown in Table 3.3. (Source: Moskowitz Jacobs Inc.)

The particular issue facing the manufacturer was how to create a product that would produce the same quality as commercially available bottled waters, but do this at home. Thus, the problem facing the manufacturer was to create a product concept that embodied certain technologies and benefits, phrased in consumer language. The company did not yet know which specific technologies to incorporate into this new water-producing product. Do consumers simply want pure water, do they want bottled water, do they want technical pure water, etc? These were questions that would be answered by the experimental design. We have already gone through the experimental design approach. The results in Fig. 3.6 show the utility values for the elements, from a total of 476 respondents who participated in the study. Each respondent evaluated a unique set of combinations. This time we show the different elements rank ordered by utility value for the total panel. We also divided the respondents into mind-set segments. Individuals in the same mind-set segment show a similar pattern of elements that they find interesting. As in many of these studies, we are able to extract three or more segments. Two of the segments are meaningful, and we show their utility values. The third segment showed relatively flat data with no strong elements among the set, and therefore the third segment does not appear. It is clear that the two segments differ radically.

3.20

Creating the product and marketing it

Without a call to action, the aforementioned processes described in this chapter remain academic applications of a research method. For business, application based on science is paramount. Product developers and marketers who work with concepts emphasize the need to apply the data. © Woodhead Publishing Limited, 2010

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In that spirit, consider the ‘call to action’ and next steps from the concept development phase for the water bottle, as shown in Fig. 3.6. It is clear that the information in Fig. 3.6 comes from a combination of the data and the creative abilities of the company beyond the simple research.

3.21 Summing up We have focused this chapter on recent development for concept creation, and, by extension, platform creation. As we stated at the start of this chapter, concept research lies at the heart of innovation. Without a concept for a product, the innovation exercise loses its direction. The concept gives the product a blueprint to follow. Perhaps the concept is not a technical blueprint, but nonetheless general direction about what the product should have and should not have. Although typically done by almost every company that makes products, concept research suffers from a lack of archival literature and solid science. Companies do strive to do the proper concept research when creating ideas, and, of course, try to fit in the insights that they gain. Yet, in the bigger picture, there is a long way to go. Today most of the research knowledge about writing concepts, about measuring the response to concepts, and about turning the concepts into action, resides in the domain of business, virtually inaccessible to readers. Hopefully, this chapter, and indeed this entire book, will bring some of that capability and effort out from the closed domain of business to the open domain of science and students.

3.22 Acknowledgment The authors are grateful to Ms. Linda Lieberman, editorial assistant to Dr Howard R. Moskowitz at Moskowitz Jacobs Inc., for her extensive help in preparing this manuscript.

3.23 References and further reading anderson nh (1970), ‘Functional measurement and psychophysical judgment’, Psychological Review, 77, 153–170. ansoff hi (1975), ‘Managing strategic surprise by response to weak signals’, California Management Review, 8, 21–33. baker e (2008), ‘Survival-of-the-Fittest Innovation’, Strategy + Business. Available from: http://www.strategybusiness.com/leadingideas/li00087 [Accessed 12/09/08]. bases (2008), Available from: http://www.bases.com/ [Accessed 12/09/08]. beckley jh and moskowitz hr (2002),’Databasing the consumer mind: The Crave It!, Drink It!, Buy It!, Protect It! & the Healthy You! Databases’. Paper presented at the Institute of Food Technologists, Annual Meeting, June 2002, Anaheim, CA. beckley jh and ramsey c (2008), ‘Observing the Consumer in Context’, unpublished paper.

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bhattacharya s, krishnan v and mahajan v (1998), ‘Managing new product definition in highly dynamic environments’, Management Science, 44 (11), 50–64. box gep, hunter j and hunter s (1978), Statistics For Experimenters, New York, John Wiley. brody al and lord jb (2000), Developing New Food Products for a Changing Marketplace, Lancaster, PA, Technomic Publishing Company. bruhn cm (2008), ‘Consumer acceptance of food innovation’, Food Related Innovation, Technology, Genetics, and Consumer Impact, 10 (1), 91–95. callahan r, ishmael, g and namiranian l (2005), ‘The Case For In-The-Box Innovation’, Paris, ESOMAR, Innovate! chesbrough h (2004), ‘Managing open innovation’, Research-Technology Management, 47, 23–26. cooper rg (1993) Winning at new products: accelerating the process from idea to launch (2nd edn), Boston, Addison Wesley Publishing Co. dahan e and srinivasan v (2000), ‘The predictive power of Internet-based product concept testing using visual depiction and animation’, Product Innovation Management, 17 (2), 99–109. de bono e (1995), ‘Serious Creativity’, The Journal for Quality and Participation, 11–13. flores l, moskowitz hr and maier as (2003), ‘From “weak signals” to successful product development: using advanced research technology for consumer driven innovation’, Cannes, ESOMAR, Technovate. fuller gw (1994), New Food Product Development: From Concept to Marketplace, Boca Raton, CRC Press. gofman a (2006), ‘Emergent Scenarios, Synergies, And Suppressions Uncovered Within Conjoint Analysis’, Journal of Sensory Studies, 21, 373–414 graf e and saguy s (1991), Food Product Development: From Concept to the Marketplace, New York,Van Nostrand Reinhold. green pe and srinivasan v (1980), ‘A general approach to product design optimization via conjoint measurement’, Journal of Marketing, 45, 17–37. information resources inc (2008). Available from: http://usa.infores.com/ [Accessed 12/09/08]. john-steiner v (2000), Creative Collaboration, Oxford, UK, Oxford University Press. kandybin a and kihn m (Summer, 2004), ‘The Innovator’s Prescription: Raising Your Return on Investment’, Strategy + Business. Available from: http://www. strategy+business.com [Accessed 12/09/08]. kitzinger j, ‘Qualitative research: Introducing focus groups’, British Medical Journal, (29 July 1995), 299–302, 311. luckow t, moskowitz hr, beckley jh and hirsch j (2005), ‘The four segments of yogurt consumers: Preferences and mindsets’, Journal of Food Products Marketing, 11 (1),1–22. mariampolski h (2006) Ethnography for marketers: A guide to total immersion, Thousand Oaks, CA, Sage Books. moskowitz hr and gofman a (2004), ‘System and method for performing conjoint analysis’. Provisional patent application, 60/538,787, filed 23 January, 2004. moskowitz hr, gofman a, itty b, katz r, manchaiah m and ma z (2001), ‘Rapid, inexpensive, actionable concept generation and optimization – the use and promise of self-authoring conjoint analysis for the foodservice industry’, Food Service Technology, 1, 149–168. moskowitz hr, german jb and saguy is (2005a), ‘Unveiling health attitudes and creating good-for-you foods: The genomics metaphor, consumer innovative web based technologies’, CRC Critical Reviews in Food Science and Nutrition, 45, 165–191.

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moskowitz hr, poretta s and silcher m (2005b), Concept Research In Food Product Design & Development, Ames, IA, Blackwell Publishing Professional. nielsen ac (2008), The 2008 Nielsen Grocery Report, www.acnielsen.com. porretta s and moskowitz hr (2005), ‘Elementi e concetti nello sviluppo di alimenti’, Chiriotti editori. senge p (1990), The Fifth Discipline: The Art and Practice of the Learning Organization, New York, Doubleday Currency. surowiecki j (2004), The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, New York, Little Brown, Inc. Systat (2004), SYSTAT for Windows, Version 11. Chicago, SYSTAT Software Inc. wells wd (1975), ‘Psychographics. A critical review’, Journal of Marketing Research 12, 196–213. wenger w (2008), Windtunnel!!! A New Creative Problem-Solving Procedure. Available from:http://www.winwenger.com/part55.htm [Accessed 12/09/08]. zaltman g and coulter r (1995), ‘Seeing the Voice of the Customer: MetaphorBased Advertising Research’, Journal of Advertising Research, 35 (4), 35–51.

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4 Innovation strategies and trends in the global fast moving consumer goods sector: an interview with Mintel’s Jo Pye J. Pye, Mintel International, Australia and S. R. Jaeger, The New Zealand Institute for Plant and Food Research Limited, New Zealand

Abstract: Jo Pye, Director of Insights for Mintel in Asia Pacific speaks about fast moving consumer goods (FMCG) innovation in the foods, beverages and personal care sectors. Topics covered include the scale of global innovation, innovation myths and the four pillars of global innovation: Health and Wellness, Premiumisation, Convenience and Environmental, which are illustrated with specific examples. Jo speaks about what innovation means and discusses how innovation does not have to be complex to be successful. She introduces “it’s as easy as stealing with pride” as a legitimate form of innovation and illustrates the four platforms that underpin this approach: packaging, market position, concept and ingredient. The chapter is accompanied by charts and pictures to illustrate the trends and products Jo speaks about. Key words: innovation through ‘stealing with pride,’ category cross-over, packaging, concept, market position, health and wellness, premiumisation, convenience, sustainability, natural, value.

4.1

Interview with Jo Pye

Interviewer1 In the FMCG sector innovation is a strategy that many companies pursue for growth. Globally, what is the scale of FMCG innovation? Jo The easiest way to look at the scale of FMCG innovation is via the number of new products, both food and non-food being launched globally. In 2007 1

Interview with Sara Jaeger, December 2009.

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Table 4.1 Global new production introductions and the percentage change for the time period 2007–2009. Source: Mintel, GNPD Classification Food Non-food TOTAL

2007

2008

2009

% Change 07–09

136,102 106,572 242,674

131,826 100,468 232,294

142,037 111,424 253,461

4.4 4.6 4.4

there were approximately 240 000 new products introduced, and of these approx. 135 000 were foods and approx. 105 non-foods (Table 4.1). The number of introductions dropped a bit in 2008, to about 230 000, and then increased again in 2009 to a total of approx. 250 000. 2008 was quite an anomaly where innovation slowed down due to the impact of the global financial crisis and businesses on the whole being nervous about product change and investment. All categories were affected, in particular the high price point products such as functional foods. The USA was particularly affected by the drop in innovation activity in 2008, a result of it being a lead market for new product innovation, which can be attributed to the size of the market and the diversity of manufacturers and brands that exist. I should clarify that the number of products launched are any products that enter the market with some kind of change; the numbers do not refer to a “brand new” to the market product. Typical product changes include packaging, a flavour change or the introduction of new formats, for example a multipack. So a key point is that in many cases the innovations are not entirely new as such, just new to that particular product or brand. Actually many product introductions are often based on simple changes and in many cases those changes are aimed at keeping the category alive, exciting and interesting for the consumer and of course profitable for the producer and the retailers. Interviewer Where does this detailed information about global product innovation activity come from? Jo It comes from a particular insight tool that is owned by Mintel called GNPD – Global New Products Database. This particular database is used by global manufacturers of consumer packaged goods to keep abreast of exactly what is being launched globally and the associated trend activity around that. The way that Mintel produces and operates this particular product database is through the employment of a global shopper network. Essentially these shoppers keep track of what new products are being launched in the FMCG arena. The shopper buys the product, and sends it to a central office in the

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UK where the information on each product is recorded and uploaded to the database. The GNPD covers 49 countries across five regions, tracks 39 product categories and 227 sub-categories. It is a very broad approach that seeks to cover as many markets as possible, although some markets are quite difficult to track, particularly in Asia where infrastructure for retail distribution is very fragmented and not as sophisticated as westernised markets. Of course with such a comprehensive coverage of product information including everything from packaging to ingredient listings, Mintel is able to perform analysis to determine trends and key insights. Interviewer The terms ‘product development’ and ‘innovation’ are often used interchangeably. For you, is innovation the same as product development? Jo To answer this we need to define innovation and product development. Product development can be viewed as a process implemented by organisations which allows them to improve existing products or to create new products, while innovation is also about applying a process or a way of thinking which involves change. One only has to Google the word “innovation” to see there are numerous definitions for this term. I like some of the simpler ones such as Merriam-Webster online, which defines innovation as, “The introduction of something new”. The thing that people get hung up on in understanding innovation is the “new” part! With regard to FMCG products the concept of “new” can simply be a packaging change which improves the product and makes it new and more functional for the consumer. When it comes down to it, innovation is very simply about doing something better, and in a commercial world it is about doing something better with a commercial return. Given that both the terms product development and innovation refer to change, modification and introduction then yes, we could say they both can be used interchangeably. But let me say that I do worry that innovation has become such an overused word that many involved in its implementation have actually lost sight of what it means to innovate. From a NPD perspective I think there is much to be gained by simplifying the notion of what innovation actually is. Interviewer Can you expand on that last statement – that there is much to be gained by simplifying the notion of what innovation is. Jo The basic point is that innovation can be simple; it doesn’t have to be complex. But it often becomes complex, and I think that has something to do with the way companies structure and think about innovation. Many businesses today have innovation managers, innovation teams, and

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innovation budgets. Businesses talk about being more innovative but aren’t thinking carefully about what it actually means – which as I defined can be ‘doing things simply’ or ‘making improvements with commercial success’. It is wonderful idealism to want to produce new to the market products with never before seen ingredients, packaging, concepts, benefits, etc.; however, that doesn’t necessarily guarantee commercial success. If we look specifically at food innovation the key stakeholders in the process are often food scientists and technologists and they tend to be motivated by applying new technology to products to achieve change, and that sometimes can impede simple innovation. Rather than think about a simple change that will meet a consumer need, the focus is the new technology. My concern is that such a perspective can overshadow simple yet impacting changes that can improve a product. I read a fantastic text a number of years ago – Creativity, invention and innovation. The author was Alan Williams (1999) and he spoke about myths of innovation and one of them was that innovation depends entirely on new technology. I think that’s a very, very valid point because innovation doesn’t have to depend entirely on new technology, it can be small changes and it’s not to say that by being small, it’s easy, but it does say that complexity, and new technology is not always the right answer either. One of the other myths that this author referred to was that innovation involves making big changes. But, in reality, to have commercial success with new product development businesses can’t always afford big changes that are time consuming and require substantial investment because the market is too dynamic and competitive. There has to be a lot of fluidity and a lot of flexibility around getting products on the market quickly and a complex process underpinning innovation will create a barrier to achieving this. Interviewer Is there a simple approach to making successful innovation less complex? Jo Yes, I believe there is. It’s an approach that can be referred to as a “stealing with pride” strategy. Essentially stealing with pride is about looking at other products, their concepts, packaging and marketing positions and looking to see what can be applied to one’s own products and brands. It is about taking yourself outside of your own category and exploring what learning can be made from other categories. It is about looking at what is working elsewhere and applying it to your business. The basic idea behind “it’s as simple as stealing with pride” is moving away from thinking about innovation as something that has to be unique and entirely new. It is challenging to always think that way, particularly in food NPD because there are certain limitations, for example legislation, and claim substantiation can be a major constraint to product development. For some new product developers and NPD teams I think a stealing with pride approach can be difficult to adopt because they almost feel like they’re not being innovative and developing

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anything new because they’re stealing the idea from someone else. Don’t get me wrong, I think aspiration for uniqueness is needed, but if the ideology that an innovation has to be totally new and that ‘stealing’ is not innovative, then that ideology can impede the NPD process. It can slow down your innovation thinking and make it more complex, and then the idealism around innovation becomes detrimental to the business. Many people will debate that stealing with pride is not innovative but that debate is purely dependent upon how innovation is defined, perceived and adopted. If we go back to the definition of innovation as doing something better with commercial success, then stealing with pride is a legitimate strategy. Interviewer What intelligence is required to implement ‘stealing with pride’ as an approach to innovation? Jo The GNPD, as I mentioned earlier, is one tool that would help a business adopt a “stealing with pride” approach and many businesses globally invest in GNPD as a key innovation tool. But a small business with very few resources can adopt this strategy too. The easiest place to start is by walking through a supermarket, buying products, examining them through taste or functionality, and getting inspiration from them. From the graphics on the packaging, the way in which messages have been communicated, to the way certain ingredients have been used, offers a form of stimulation to create an idea which then can be adopted. This approach is invaluable given a new product development process begins with ideation. Of course the supermarket is not the only way to explore products outside of your category given the wealth of information available on the internet today. I think that’s the beauty in this approach, it can be for every type of organisation, from large multinationals to small sole traders, and it does not have to involve a huge investment. It’s a philosophy; a way of thinking about innovation, and by approaching the innovation process with a frame of mind that it is okay to steal with pride and that it is vital to look outside the category, this philosophy introduces a level of simplicity. I want to emphasise this point. It is okay to steal ideas and take them across into one’s own category because at the end of the day the brand is still unique to a particular business and a particular product. Sometimes there’s so much effort and so much focus on trying to come up with “new” ideas for the product itself that brand equity can be pushed to the side, and I think that from an NPD perspective, brand equity is crucial in the innovation process along with other key attributes such as the formulation, the performance and the packaging. Interviewer Can you give an example that illustrates how a “stealing with pride” strategy is typically implemented?

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Jo Imagine that in a business a decision has been made to follow a certain strategy for an innovation initiative, it could be, for example, an indulgent and premium positioning around which the business wants to launch a new brand. If the category this business is currently in is not very premium, one way to get inspiration is to look at a category that does have a premium offering and in which it is executed well. So let’s use the example of a fruit juice manufacturer who has a basic value offering but wants to expand the range to include a premium product. The first category for inspiration which springs to mind is alcohol, especially given the scope of premium offerings which this category has. The NPD team could approach their exploration by visiting alcohol stores, reading trade magazines, researching products from different alcohol beverage manufacturers online or using web-based tools such as GNPD. By pooling together what this category is offering with packaging, the question can then be asked: “what elements can I take from these ideas and apply to the premium juice brief?” So, as I said, it is a simple approach, but I think that there can be constraints with certain manufacturers and certain NPD teams because they stay focused on what is happening in their own category. It’s important to look broadly for inspiration. Interviewer What are some good examples of “stealing with pride” innovation? Jo There are four key platforms upon which the stealing with pride strategy can be executed. They include packaging, market position, concept and ingredient. I can give you an example of each, so let’s begin with packaging. For innovation in packaging, one example which immediately jumps to mind is the SunRice Pour and Store product (Fig. 4.1). Rice is a commodity product which is subjected to pricing fluctuations and it is difficult for a rice manufacturer to increase the cost of a base product without giving consumers something beneficial in return. Therefore the challenge for a commodity producer, in this case SunRice, is to add value so that a consumer is prepared to pay more for the product. SunRice made an inspirational packaging change, from the standard pillow pack which rice is commonly sold in, to rice in a plastic bottle that was easy to pour from and could be easily stored, thus the “pour-and-store” concept. So, on its own merits this packaging change was innovative. Using this as an example to illustrate the “stealing with pride” strategy, we saw Sugar Australia stealing the pour-and-store idea for their own product range. Sugar Australia and SunRice share similar business challenges given their agribusiness roots and therefore their innovation is driven by offering consumers a strong value proposition, something consumers are willing to pay more for. Being a consumer myself I thought pour-and-store sugar was brilliant! Having been a rice convert and a keen cook I thought: now I can store my sugar alongside my rice and it

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Fig. 4.1 Stealing with pride as demonstrated by the Pour and Store concept, initially launched by SunRice and then followed by Sugar Australia. Source: Mintel, GNPD.

is so easy to use. An NPD team may dismiss this change for Sugar Australia as being innovative. They might think that there was nothing new about the packaging given the concept had already been introduced. But it was new for sugar, and it offered a benefit to consumers. So, to me this is an excellent example of simple innovation where: 1) a particular category adopted a simple change to begin with and 2) a similar product simply stole the packaging idea and ran with it, thereby meeting a consumer need.

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Interviewer Moving to the second main type of stealing with pride innovations, which relate to market positioning, what example would you highlight? Jo For innovation based on market position, the beverage category is inspirational and water would be one of the most applicable products to best represent a stealing with pride strategy. The first major innovation, which in itself was very simple, but hugely successful, was bottled water. More recently, water is no longer just a beverage of choice for hydration, but has hybridised with various other categories and we now see water products that are positioned to offer vitamin fortification, beauty benefits, caffeineboosting benefits, and even water to assist with the ironing! (Fig. 4.2). If we look at Aqua Bimini this is a product that’s taken a beauty position. The manufacturer most likely looked at the beauty category and recognised that there were many claims around anti-aging, and then decided to develop anti-aging water, making it possible by formulating with a proprietary blend of ingredients to make that particular claim. Another example is ACTIVATE Vitamin water which is a brand that offers vitamin fortification A

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Fig. 4.2 Concept innovation. Anti-aging claims are no longer for the beauty counter. Vitamin fortification can now be delivered via a beverage as opposed to taking a supplement.

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in a very easy and convenient format – an offering which is particularly appealing to Generation Y given they are so accustomised to a wide range of beverage choices and are not a consumer group which buys into vitamin and mineral supplement products. A final point to make about the water category and its ability to be innovative by means of new positions is that is has a neutral base to begin with, which gives product developers greater flexibility than other products or categories. Interviewer The third area you mentioned as forming a basis for stealing with pride innovations is concept. Please talk me through a successful example. Jo The essence of concept-based innovation is looking at the concept for one product and asking whether that concept can be used elsewhere. The nonfood category has been inspirational here and anti-aging is once more a good example. Anti-aging claims have taken off in the last few years. There were probably less than 100 in 2006, and in 2009 the number was just shy of 6000. A specific example is the OlayTM brand of moisturiser cream which a few years ago launched a concept around the seven signs of anti-aging. More recently, this concept, which up until then had been associated with skin care, was picked up by the Tide® brand, who took the concept of antiaging to laundry care, migrating the idea of looking after our skin and preventing the signs of aging, to looking after our clothes and preventing their aging (Fig. 4.3). Another example to demonstrate innovation via a B

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Fig. 4.3 Applying the anti-aging concept to laundry care. The OlayTM total effects anti-aging moisturiser and Tide® Total care for 7 signs of laundry care. Source: Mintel, GNPD.

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Fig. 4.4 Concept innovation. On-the-go began in the snacking category but transferred across into non-food products such as laundry care. In this particular case Tide® brand offers an on-the-go stain remover. Source: Mintel, GNPD.

particular concept is the “on-the-go” concept, which began in the snacking category, a natural development given the nature of snacking which is typically something that consumers do while on the run. More recently this concept has grown in popularity and expanded into different categories (Fig. 4.4) including breakfast, confectionery, and laundry care and even into cosmetics which are already portable and ready to go! The growth in the number of product introductions with an “on-the-go” claim exceeded 5000 in 2009, up from around 2000 introductions the year before. The first introductions emerged around 2003, but were only a few hundred yearly. Interviewer Innovation based around new ingredients and flavours was the fourth area you mentioned as underpinning the “stealing with pride” approach. This is a key area for sensory scientists, and I’m curious about examples you will highlight here. Jo As with the other platforms, the basic principle for ingredient exploration is around taking inspiration from ingredients and flavours used in one

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Chocolate is no longer only for the confectionary aisle with a cross-over into a depilatory application and body soap. Source: Mintel, GNPD.

category and adopting them in another category. A major trend is food ingredients and food products crossing into non-food. It seems that many consumers view non-food products made with food ingredients as being more natural and more trustwothy. They are easier to understand than the more complex chemical ingredients more commonly used. Chocolate has crossed into the skin care domain (Fig. 4.5), for example to soap and hair removal, and part of the position of using chocolate in those particular products is that chocolate is gentle on the skin. So it provides a functional benefit, but it is also indulgent and has a strong feel-good factor associated with it due to the pleasurable consumption of actually eating chocolate. Ingredients also cross over in the other direction, from non-food to food. Just recently I saw an example of a risotto that contained lavender. This was interesting, since lavender is predominantly a position used in the nonfood skin and household area in products such as hand soap, washing detergents and body creams. As a dominant botanical ingredient it is commonly used for aromatherapy and its associated relaxing benefits. By including into a risotto one can only think that it was intended to offer a form of relaxing aroma while consuming the risotto – very interesting. A final example around new flavours and ingredients is the movement of super

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fruits into skin care. As mentioned earlier legislation can impede new product launches with claims but in the case of skin care we saw the use of super fruits, well known for their high antioxidant properties incorporated into skin care and hair care with simplicity. This transfer could occur as it relied on the consumer knowledge that super fruits had an antioxidant benefit, which is a key claim in this area. Interviewer I would like to move from talking about innovation strategy to talking about global trends in global innovation. Can you describe these, using successful examples? Jo Yes I can, but I first want to make a point about terminology, because I distinguish between “pillars of innovation” and “trends in innovation”. To me a trend is something that is relatively short term and may come and go, whereas a pillar of innovation is established and continues to evolve and take shape. There are four key pillars of innovation and one of the most significant is Health and Wellness. From my experience in dealing with NPD teams, everyone wants an insight on what is happening with this pillar. Trend activity within the Health and Wellness pillar is based on two distinct streams: basic and targeted. The basic stream of innovation focuses on the “better for me offerings”, for example products such as low/no reduced fat and low/no reduced sugar, but it also includes products positioned as “natural”, which can be perceived as being better for you. The second stream of targeted Health and Wellness, is the momentum around functional foods driven by consumers who want to address certain health conditions either in a proactive or reactive manner. This targeted Health and Wellness stream is about consumers saying, for example, “I want to boost my immune system to prevent getting a bad flu or a bad cold” or “I want to improve my digestive system because that makes me healthy” or someone knowing that they have a heart condition and being motivated to bring their cholesterol down. It is hard to talk about health and wellness innovation without mentioning Japan, who have led and inspired Westernised markets in the functional food area, particularly around the embracing of preventative health. Japan has been the home of digestive health and immune health areas for some time, areas which are just becoming entrenched in the Westernised world. With regard to a successful Health and Wellness product you can’t look past the Innocent Juice, launched in the UK. The simplicity of this product meant consumers were basically buying fruit in a bottle, a simple yet impacting innovation. Interviewer I would expect “premium” or “indulgence” to be one of the global pillars of FMCG innovation. Is it?

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Jo Yes, absolutely, and let’s look at this as being our second pillar of innovation. This pillar of innovation can be thought of as an interesting story to tell, one which has a little more depth over a specific functionality when applied to a product. From an innovation perspective this continuing and evolving “story” can stem from any stage of a product’s life cycle, for example it can originate from the point of formulation, detailing the source, the authenticity or provenance. Conversely, the innovation may be derived from further along the product’s life stage, i.e. from its point of purchase or consumption, engaging the consumer via its exclusivity, pack design or an experience associated with the product. This pillar of innovation has received a lot of attention recently given the current economic climate. People within the industry are asking how premiumisation will fare in light of a struggling consumer. To some surprise we occasionally see categories performing well; take the confectionery market, for example. The reason behind this is that people’s desires and aspirations remain, no matter what state the economy is in. They still strive to indulge but simply find new ways to do so if the purse strings become too tight. Premium brands wanting to succeed in this environment need to be that new route to indulgence and offer further justification through an even more interesting story if they want to persuade a consumer who is increasingly asking “do I really need this?” Interviewer Is convenience still a strong driver of FMCG innovation? Jo Indeed, and like premiumisation this is a pillar of innovation that continues to evolve based on changing consumer lifestyles and demographic segmentation. Consumers continue to be busy and the need for convenience will continue to be a key pillar, and what our job is, is to figure out how we can best meet consumers’ needs for portability, convenience and ease of use. Interviewer The pillars you have spoken about so far are the common top-3 drivers of innovation. Are there other pillars of global innovation? Jo Yes, and one which is very much picking up speed is that of sustainability and the environment. Consumers are much more aware than they were just a few years ago, but when it comes down to the crucial point of purchase and a decision has to be made between two very similar products, this is usually the point at which an environmentally sensitive message may persuade the consumer into picking it up over an environmentally insensitive competitor. For the masses, a green positioning is currently viewed as a

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“nice to have” rather than being a key influencer, but as sustainability messages are striding into new categories, this level of importance to the consumer may change. Where green products seem to be flourishing is in their ability to have a positive impact on other areas of consumers’ lives. Quite often attempts to be more pleasant to the planet, e.g. less packaging, smaller pack size and lower weight often have the knock-on effect of being produced at a snip of the cost and in a more convenient format for the end user. This positioning of “better for people and better for the planet” or “saving the planet and saving you money” currently seems to be satisfying two very important and very current consumer needs. A quick example is Kellogg’s who have launched what they call a space saving box that is designed to help consumers fit the product into their pantry. That’s the primary function but the second benefit is that the actual amount of packaging has been reduced and recycled material has been used so in this case it really is an offering which presents an innovation that is better for the consumer and better for the environment. Interviewer I think the distinction between pillars of innovation and trends in innovation is useful, and having just spoken of the four pillars of innovation, can you describe some current trends in innovation? Jo Given the strong need for consumers to seek balance, stability, trust and most importantly value we expect to see a period of time where innovation will offer something familiar paired with something new to better satisfy consumer needs. The key pillars of innovation: health and wellness, convenience, premiumisation, sustainability and additionally specific targeting via age and gender will continue to be a main focus. However, we expect these areas to get a fresh new approach – almost a period of tweaking the old to make new again. In terms of innovation the opportunity is ripe for getting back to basics and generating value especially as consumers implement changed behaviour developed during the difficulties of the global financial crisis. Innovation will drive products that address consumer questions such as “do I really need this?”, “is this of value?”, “where has this come from?”, “am I being wasteful?”, “can I trade up or trade down?”. More importantly innovation will ensure products take on much more of a transparent approach. Tired and fatigued from having to deal with too much information, consumers will seek products which address the need for simplicity, both in their visual appearance and in the messages they communicate. This will become particularly evident in the area of health and wellness where it is no longer just about weight loss but instead encompasses all offerings, particularly naturalness and goodness. These positions are resonating with consumers as a “better for me” offering. In summary, current innovation trends will be all about creating a fresh appeal to familiar areas. The

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turbulent environment consumers have come from has allowed them to create newly developed coping strategies making them savvier than ever before and making the need for innovation to be as simple as ever. Interviewer Is there such a thing as innovation cycles? For example, products which emerge again after a few years away from the market? Jo Sometimes a new technology becomes available and a product is launched, but it may be a little bit before its time because the consumer needs just aren’t there yet – maybe the result of an eager scientist wanting to jump onto the new technology without any real consumer need or demand! One example which comes to mind is a self-tanning product. In 2004 I noticed on GNPD a self-tanning product which enabled the user to regulate the degree of tanning, but there seemed to be little follow-on activity and then it seemed to disappear. In recent years consumers have become much more mindful of damage from the sun and on the back of that tanning products have become very popular. As a result there has been a significant amount of NPD around in this space. Recently Piz Buin launched a customised self-tanning product, very similar to the concept I had seen earlier (Fig. 4.6). The Piz Buin product was considered new despite a product with the same application being launched some 5 years earlier. So it is possible that certain technology can be ahead of consumer need and that can result in something already explored returning at a later stage. Interviewer Something you just said – the bit about consumers not being ready for a product – made me think to ask you about barriers to innovation. Are there systematic factors in the global environment that impede NPD? Jo One of the key things that can really foster or impede innovation is legislation. Globally, legislation surrounding innovation is quite different from one market to another. For example, the legislation in Australia/New Zealand is quite different to, say, Japan. In Australia it is, for example, very difficult to claim the benefit of a proven pro-biotic added to yoghurt, but in Japan legislation allows the claim to be much more direct and the benefit stated openly. So, the legislative framework can have a great impact on product development and the decision to adopt a certain approach will be questioned if it is not possible to make an overt claim on its functionality. Japan can almost be viewed as a crystal ball for where product innovation is heading because their ability to do things is so far greater than other markets. If we look back over the last few decades Japan was always a market where innovations were almost a little bit too weird and whacky,

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Shades of Darkness was launched in 2004 while Piz Buin arrived 5 years later. Source: Mintel, GNPD.

and I say that with the most respect, but something that was kind of almost a bit of fantasy. We thought we would never be able to do that, we thought that we would never be able to adopt that kind of NPD process or that kind of product innovation, and sometimes I think that where Japan is today is perhaps where the rest of the Westernised world will be in 20 years time. If we look at functional foods, for example, the Westernised markets are just starting to fully embrace probiotic and prebotics for gut health – whereas in Japan it has been established for many years. So, for inspiration, Japan is a good market to look at. Interviewer What about the growth in retailer power? Has that had any impact on the level of global innovation activity or how the innovation process occurs? Jo The growth in retail power has had a phenomenal impact on the innovation process and the way manufacturers approach their NPD thinking. With a market like Australia I have seen manufacturers approach innovation not for their own business strategy but in fear of how they will fare in the retail

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environment with private label. In working with FMCG companies in my Mintel role I am constantly asked the question “how do we innovate against private labels?” In some ways this is alarming because the dominance of private label has caused people to focus on what “new” things can be launched that a private label can’t do – once again that thinking of it having to be totally new! But don’t forget that the retailers are savvy enough and have the power to simply “steal with pride” and contract manufacture. They also have the dominance to remove poor-performing products or products where they want their own brand to be dominant. This certainly makes manufacturers nervous to make any capital investment for fear that the life cycle of their product will be short lived given they feel they are at the mercy of a retailer, so in some ways innovation is impeded due to a fear factor. Outside of a market like Australia retailer power has been truly inspirational. Marks & Spencer’s, in the UK, for example, seem to be able to emanate most key pillars of innovation with simplicity and impact. They are of course an excellent example of how retailer power is not just about offering a consumer a cheaper or a budget product but an actual branded product. Looking at retailer power and private label products is in fact a great lesson in brand creation as retailers globally are just not launching great products but they are doing it with great brand equity. So when I get asked the question “how do we compete against private labels?” my answer is to always ensure there is a focus on being innovative with your brand as it is the key point of difference between two comparable products. Just think of Coke: would a loyal coke drinker switch to a private label just because it was a little bit cheaper? I imagine not! Interviewer In these interviews I try to connect with who people are, in addition to the role they perform. You clearly have a passion for your job. What is it that you particularly love about it? And how did you come to work as a Director of Insights at Mintel? Jo I have a food and nutrition applied science degree, so my life began in a laboratory working on the formulation and technical side of the NPD process. However, I’ve always been intrigued about what makes consumers tick, wanting to know how consumers are adapting to what’s happening in the greater environment, and how this translates to their needs for consumer products. So with a strong drive for greater consumer understanding I undertook an MBA and concentrated on developing my marketing knowledge and skills. I guess I ended up with an educational background and job experience that covered everything from technical development and technical sales, through to product management and marketing management. I began with Mintel in an account director role for Asia Pacific where I was actively involved with clients who were implementing NPD processes and

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looking for inspiration and trends and ideas to help funnel their pipeline of new products; actually I am still quite involved. It was in this role that I really started to see how business approached innovation and how everyone was looking for a great insight to steer them in the right direction. I’m just very, very passionate about NPD, and making NPD a fun and simple process and something that results in great products being launched which meet the needs of consumers and the needs of manufacturers; it’s exciting and stimulating and I am constantly amazed at some of the simple yet fantastic products that are launched, especially those products that make competitors sit back and say “why didn’t we think of that!”. It’s fun logging on to a product like Mintel’s GNPD and following a product being launched, seeing the simplicity in it and the way it’s been executed and this helps me stay passionate about the continued evolution of consumer needs and helping manufacturers respond with great products. Interviewer What a passion! This is a great note to finish on. Thank you.

4.2 References and further reading 1. scott burkin, 2007, The Myths of Innovation, O’Reilly Media Inc, Canada. 2. clayton christensen, 2001, Harvard Business Review on Innovation, Harvard Business School Publishing Corporation. 3. tony davila, marc j. epstein, robert shelton, 2005, Making Innovation Work – How to Manage It, Measure It, and Profit from IT, Wharton School Publishing, New Jersey. 4. michael j gelb, 2000, “How to Think Like Leonardo da Vinci: Seven Steps to Genius Every Day”, Dell Publishing, New York. 5. Harvard Business School, Managing Creativity and Innovation, 2003, Business School Publishing Corporation. 6. frans johansson, 2006, The Medici Effect: Breakthrough Insights at the Intersection of Idea, Concepts and Cultures, Harvard Business School Publishing, USA. 7. Project Leaders, what is holding us back from being more innovative. Projectleaders International, wwwproject-leaders.net. 8. twyla tharp, 2006, The Creative Habit: Learn It and Use It for Life, Simon & Schuster, New York. 9. paul trott, 2008, Innovation Management and New Product Development, Pearson Education Limited. 10. alan williams, 1999, Creativity, invention and innovation: a guide to building your business future, Allen & Unwin, St Leonards, NSW.

4.3 Short biography for Jo Pye Jo Pye is the Insights Director for Mintel in Asia Pacific and also heads the Regional Custom Solutions Innovation team. She has over 15 years of

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experience, from local and global organisations, in senior marketing and technical roles in the ingredients & FMCG industry. Jo regularly presents product insight sessions to the leading FMCG organisations in the Asia Pacific region and her unique practical perspective on the consumer market place and the future of product development help her turn insights into tangible opportunities for NPD.

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5 Innovation in foods and personal care products: an interview with Gail Civille G. V. Civille, Sensory Spectrum Inc., USA and S. R. Jaeger, The New Zealand Institute for Plant and Food Research, New Zealand

Abstract: Gail Civille, founder and President of Sensory Spectrum, speaks about the innovation process as it applies to foods and personal care products. She talks about the necessity of upper management support and confidence in moving beyond ideation and translating ideas on a piece of paper to actual prototypes and products that can be tested. For an earlier step in the innovation process, where unmet consumer needs, whether articulated or unarticulated are uncovered, Gail speaks of the importance of knowing and understanding the consumer and finding ways of communicating that insight to product developers who have to convert the creative brief into actual prototypes and products. Sensory professionals have, in Gail’s opinion, a great opportunity to become leaders of innovative research in their organisation, and she explains why. Gail also speaks of her use of literature from other fields to keep her creativity alive and she shares the story of her successful company. Key words: importance of uncovering unmet needs; barriers to NPD; compelling research to guide business decisions; moving beyond ideation; role of sensory science in product innovation; central location testing.

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Interviewer1 Welcome to the interview and thank you very much for agreeing to take part. You’ve been a huge influence in the world of sensory science for quite a while and I’m absolutely delighted that you’ve agreed to speak on the topic of consumer-driven innovation in foods and personal care products. What does consumer-driven innovation entail? 1

Interview with Hal MacFie in October 2009 and Sara Jaeger in January 2010.

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Gail The essence of consumer-driven innovation is to discover what the consumer really needs instead of what they articulate that they want. For me the key factor is that someone, often the sensory professional, really understands the consumer and makes a concerted effort to understand their needs, whether they’re articulated or unarticulated, and often they are unarticulated, and then works with the team to develop a product that is in response to unmet needs or that delights the consumer. Bob Cooper, who’s responsible for developing the Stage Gate method calls this step – understanding the consumer’s needs – Stage Gate zero. It’s before the first gate. That’s where you start to innovate and I think that there are too many product developers and too many sensory people who are not willing to get involved at this early stage. My preference is to start asking questions of the consumer as early as possible, and I do not necessarily mean that literally. Sometimes we do not need to talk to the consumer. Instead we may choose to do observational research and watch consumers in their homes or in other locations. This approach is grounded in the anthropological and ethnographic traditions and by observing a consumer and the way they behave with a product, I can uncover how they respond to it. Interviewer Do observational approaches to uncovering unarticulated and unmet consumer needs work equally well for innovation in foods, personal and home care products? Gail The actual observing of product usage and the revelations that it brings are probably more clear and dramatic in personal care and home care products. In food innovation this first stage of uncovering unmet needs is more likely to focus on helping consumers with language to describe products and experiences related to consumption and use. Well-designed creative consumer groups, and I won’t say focus groups because they have a bad reputation, can teach you a lot about how the consumer describes products and when they cannot describe a product, when they are inarticulate about a product, then you can help them in these groups to clarify the language. Historically we have made all kinds of assumptions about what the consumer means when they use certain words to describe food and sometimes these assumptions are wrong. Helping the consumer clarify their language better is very important in this early part of the innovation process, because when you later on go out to run a study then you are not wasting your time asking the wrong questions, which invariably lead to bad data. Good business decisions rarely come on the back of bad data.

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Interviewer Are there times where the unmet consumer needs that you have uncovered have been radically different from what the company thought their consumers wanted? Gail That can happen. I remember one time where we were brought in to help develop a brand new product in a personal care category. The company was convinced that what the consumer wanted from this product was a specific attribute. We talked to consumers and came back and said no, what they really wanted were two completely different properties. We did rapid prototyping with small groups of consumers and a design engineer and quickly came up with a product. The research was strong, but the company had trouble accepting something new that wasn’t according to its historical myth. They couldn’t move, and a year later their biggest competitor came out with that exact product. Our client launched immediately with their version of the product; they were second in the market, not first, and they could have had a year leap on the competition. Interviewer Do you see the sensory person as a small or large cog in the chain of new product development? Gail I think there are many opportunities for sensory people to become leaders of innovation within their organisations, but it requires willingness to look strategically at what we’re doing and not just be someone who is simply a test executor. This gets to the idea of thinking of ourselves as having the ability to influence the insights and information that come from the data and not just dump data at the product developers. For me sensory science is a profession that helps R&D understand how to make decisions about their products. For example, what is safe to release and what is risky to release? I see my job as standing right next to the VPs and directors of R&D to help them understand completely how the products perform, both from a descriptive standpoint and from a consumer standpoint. I really want to help them make good decisions. Interviewer Can I quickly pick-up on the point about turning research into insights? At a previous Pangborn conference, Dwight Riskey, who was VP at Frito Lay, made the point that we should be doing research which is so compelling that it must be acted on. The phrase ‘compelling research’ has stayed with me . . .

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Gail Yes, it is a great and enlightening phrase. It’s a good way to think about what the purpose of research in NPD is. I absolutely agree that it is our job to not just give people data but to convert it to information and then convert it even further into insights, so that sound business decisions can be made. And if I can add another comment here, it would be that to do compelling research you really have to think about your methods. Is it the textbook stuff that’s called for or is it something a bit different, something that’s modified to this client’s specific needs? I like to talk to the client about the possible ways that we can collect information and what are we able to do given the budget. Interviewer What are the barriers that prevent new product development research having a real impact on business decisions? Gail A big problem, both in the US and in Europe, is that people are locked into ways that they’ve always done research to support innovation. For example, many believe that you have to have a BASES® test in order to make a decision to launch a product. BASES® is a marketing test (http://en-us.nielsen. com/tab/product_families/nielsen_bases) to ascertain and predict volumetric performance of the product in the first months after launch. Many companies are locked into BASES®. It’s the way they’ve made decisions for years and they cannot change. Inability to change goes all the way through the product development cycle. Everyone – marketing, marketing research, R&D and sensory – can be locked into processes that they’ve always used. Often researchers hide behind these processes so they cannot be accused of not doing the right thing, even though what they do may be ineffective. It’s been really surprising to me to discover that often people in R&D are more willing to change than people in marketing and marketing research, who just want to stay with their classic tests and the formulaic way of doing things. It was a real eye opener to realise that R&D people, who have been perceived as being stodgy and staid are the people who often are better at talking to the consumer. At Sensory Spectrum we actually train product developers to talk to their consumers. It’s been great success – helping PD talk to consumers and listen to them so that they can respond appropriately with new product development and new ideation. But it’s when somebody says: no R&D can’t do that, it’s not an R&D job to talk to customers, that you are more likely to face failure, because now you stop doing what needs to be done to move the whole product development along. Interviewer If we think of innovation as a series of steps in a process, then NPD failure can be attributed to one or more of these steps as not having been done well enough. What steps typically cause problems for businesses?

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Gail Moving beyond ideation can be a real challenge. I meet people who tell me that they had an innovation session that morning and came up with 300 ideas, to which I answer that they don’t have ideas and only several pieces of dirty paper. Unless you can make your ideas operational and bring them to fruition, you have nothing. Sitting down to ideate only means something when you move beyond writing lists and actually take the ideas to the next step, which is to choose ideas to be developed and turn the idea into a real product. I purposefully use singular here – idea not ideas, and I do that to make the point that you have to make selections among the ideas. Perhaps not from 300 to 1, but perhaps retain only a handful. For most people that’s very hard to do, often because they are afraid to make mistakes. But you have to select which ideas to work on and then begin to make them real by figuring out what you can do at the bench and in the pilot plant. You have to turn ideas into physical products, or you have nothing. You have to get to the point where you’re creating a thing that you’re then going to put in front of people and test whether or not that thing is acceptable. If you don’t move beyond ideation, if you are not willing to “go and do”, then I think you’re failing to do true innovation. Perhaps a final point is that it does not help when upper management think that everything after ideation is an instant process. It’s not; and to innovate successfully you need a commitment from upper management that they are willing to spend money to put the ideas through the process and get the right final products or prototypes. Interviewer What about the step of making ideas operational? How easy is it when you are standing at the bench trying to make the actual products that match the creational brief? How big a stumbling block in the innovation process is this step? Gail Yes, that can be a difficult step too, but for a different reason. The first step in the move to product from idea, is to improve the idea on paper. There are myriad processes to highlight the positives and eliminate the negatives for any idea. Then the work at the bench begins. One of the problems I’ve noticed is that product developers often have a difficult time communicating with ingredient suppliers about what precisely they are looking for in a fragrance or flavour or texture. If you are unable to communicate about the sensory properties that need to be built into the product, you slow down the whole innovation process and that has a negative impact on success. The sensory scientist can play a role here, encouraging the product developer to communicate the needed sensory sensations. It is even better if the product developer is able to connect back to the language of the consumer and really understand what consumer needs can be delivered.

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Interviewer Earlier on you spoke briefly on how you think that there is a huge opportunity for sensory people to become leaders in innovative research within their companies. Can you expand on that? Gail A thing that sets sensory people apart is our understanding of the product, and this, I believe, can help us to make a really big contribution to the innovation process. I would never agree to work on a project unless I had used the product, or unless I’d eaten the product. I need to get familiar with the product – it’s imperative! Then when consumers make certain responses, whether that’s in the form of quantitative or qualitative data, I have a better sense of what they are responding to. In this respect I think sensory scientists have a tremendous advantage position, because they are able to talk to product development about what is going on with their product, and they can do that because they know the product really well. So many times I’ve experienced that I know a company’s products better than some of the people in the company, and this is particularly true of marketing people. They often don’t know their own products. They never buy them from the shelves, never look at them, never talk to people about them and therefore they cannot respond appropriately to a consumer because they don’t know the consumer experience. Interviewer I’ve heard other senior sensory professionals make the point about knowing your products really well and I think it is a simple but powerful message to people that work in NPD: don’t forget to taste/use and become acquainted with your product. It should be obvious, but it never hurts to repeat good points . . . Gail I’m reminded of an episode from my past. When I, way back in the 60s worked at General Foods with Elaine Skinner and Alina Szczesniak, it was not uncommon to hear Alina admonish the people who worked in her laboratory about the importance of knowing the product and how it is consumed. This was from the person who invented food rheology! She would remind us that you can’t measure texture on an instrument, you can only mimic it and therefore you have to understand the product attributes before you ever get started on instrumental measures. For me that was so revealing, so enlightening and that learning has never ever left me: that you really have to understand the product in order to help people design their research properly and to more accurately get at what consumers want and need. Interviewer Your business is based on supporting organisations with NPD. What research do you draw on to support your company’s growth?

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Gail I make a point of not restricting myself to a few fields of science, and I strive to keep an open mind. Once you adopt that mindset and start to expand your thinking, then you can take ideas from ethnography, you can take ideas from anthropology, you can take ideas from creative problem-solving, etc. When working with businesses on NPD, we can create an entirely new method or process if that’s what’s needed to get the job done. To get inspiration for these new methods going to Pangborn is helpful, but it’s not just sensory science that you have to know about. We work with creative problem-solving institutes like CPSI and we have learned a lot from people who do design of graphics about how to present data so we can learn to better communicate what we know so that people can act on it better. We need to be innovative in everything we do: in the way that we present the data and the way that we talk about the data, and in the way that we make recommendations to you by using language that is understandable to you as a product developer, because it is the product developer whose job is on the line. My job is to help you be less at risk by giving you better information and that requires that I expand my horizons. I love how at Pangborn we sometimes have people who work on human genetics or early childhood imprinting. In many respects that has nothing to do with everyday sensory science, but it absolutely informs the way you think about how to sell and communicate to mothers and parents about baby food or baby behaviour. Some of the research on children and the imprinting at home and in early childhood, right back to in breast feeding, is fascinating. If I can quickly go down a tangent here, I think that we need to uncover ways to get to people’s amygdala early in order to condition them – and that’s a terrible word . . . – to eat better. I believe that there are ways that we can help people to eat better by giving them stimuli from before birth, and right after birth that will help them eat better and not do the salt/sweet/fat buy-in. Interviewer I just have to stop you and say that it’s very motivating to hear you speak. I’m not quite sure how you are able to run a business and retain all your enthusiasm, but I can hear that you do that. Gail Thank you. Yes, I do have a great deal of passion and enthusiasm. The reason I have not retired is because I’m still having too much fun and as long as I’m having fun I’m going to continue to do this. It’s a passion for me and I think I still have some things to contribute. That’s actually a great feeling! Interviewer If I ask you how important you think sensory quality is in determining a new product’s success, I’m sure you are going to tell me that it’s everything.

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So a better question may be: what do you say to those people who point to the powerful roles of price, brand, and packaging in determining success? Gail All of those things will get you your first purchase but if the product fails to deliver on its sensory properties, if the flavour or the fragrance is off, then that product will not sustain repeat purchase, no matter how much money is spent on marketing and advertising. Of course there are exceptions, for example when products are driven by performance benefits. People will put up with an awful lot of bad sensory properties if the product actually is successful on some level in delivering technical performance. A cleaning product may stink like anything but if it really cleans my bathtub, then I’m willing to put up with the smell. Products that deliver pain relief could be another example where people may be willing to put up with a bad smell or an unpleasant texture, say when rubbing in an ointment. Interviewer Time-limited line extensions that are only around for a single season, say orange flavoured KitKat®, are important in keeping brands alive and retain market share. Is sensory everything for this type of innovation too? Gail Spin-offs like that are not what I would call innovative ideas in any way, but that aside, sensory is still very, very important. I would maintain that if the product is not delivering long term against the concept or is not aligned with the other products in the brand, then eventually the entire product category is going to fail. If you consistently under deliver by not giving people the sensory attributes that you promised them, then eventually I think you’re going to have a very hard time getting people to believe anything about the brand itself. Interviewer Are there any systematic differences between foods/beverages and personal/home care products that need to be taken into consideration in the NPD process? Gail As a general rule, no. But there is another dimension that impacts significantly on consumer expectations, which can lead to failure if not satisfied. Apples on a tree are all different and consumers will forgive that variability, but they will not forgive it in, say, Kraft cheese singles that are packaged in little plastic squares. The more processed a food is, or the more processed a personal care product is, and the more that the consumer has gotten used to that level of consistency, the more that they will hold you to that criterion. With fruits and vegetables, or fish or a loofah sponge, which is a

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natural thing, people have a much, much broader range of acceptance than they do with highly processed and packaged foods and personal care products. Another thing to bear in mind is that people are a little fussier about some indications of aging or shelf life when the product is one that we put in our body or on our body. For example, I know there is an expiration date on my floor cleaner, but I’m not going to care as much about that as I am when it is an anti-aging cream. So I think it’s less about the food and personal/home care distinction and more about what are consumers’ range of expectation of the product category and how they use the product. Interviewer How do you know when your innovation is good enough to be launched? How do you make that decision? Gail Two factors are at play here, company culture and how risky the product category is. Some product categories are riskier than others and some companies are more risk averse than others. Cola-Cola, for example, are not going to just take a stab and do a release. They are very careful and they never do anything that doesn’t involve very extensive testing. They are risk averse, and you can understand why if you think about it from the point of market share and brand equity – what are they dragging with them if they fail? But not all companies keep testing until there is 99.9% surety in the data that the innovation will be a success. They stop before that and make an informed decision about whether what they have is good enough. It is the kind of decision that says: “given the budget that we have, this is all we can know about the product and based on our previous experience of this product category we think the risk will be X and we are willing to take a flyer with it. Let’s launch and see what happens!” Interviewer Acceptance testing with consumers is one of the fundamental sensory techniques used in new product development. CLTs – central location tests – used to be the standard of doing that. Is that still the case? Gail CLTs are still used a lot, but one change compared to a number of years ago is that we have become much more aware of the language we use, and that may require doing some qualitative research first. You shouldn’t construct the CLT only based on your product understanding, you need to get some consumer language and use that understanding in order to construct the CLT. An important function of the CLT is in screening, because when I’m working with a client, I’m not sending eight prototypes into a home use

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test. I’m going to screen those down and then maybe go with three products in the home use test. But let me say, although some would frown, that I think that it’s perfectly okay for people who understand the product including the product developers to stand around the bench and taste products and screen products. If you are good enough tasters you don’t always have to rely on formal sensory numbers. A slight side-step perhaps, but I’m of the opinion that if you have to be always pure, pure, pure, then you’re not going to be able to respond to clients that have very short time frames around their information needs, and I think it is better to provide some direction rather than none. I prefer to ask: what can I do for you with that little amount of product or this little amount of time? Can I give the client some help to move the project forward? Interviewer One criticism of the CLT, and Ep Köster has been an advocate of this point, is that it is not realistic. Gail He is right, of course. It is not realistic. Typically in a CLT, products are tested outside normal use context. A CLT it is not a full use test. I don’t think anybody thinks of it as such, but the question to ask is whether a CLT can give you the information you need. A few years ago, in a workshop I ran at a Pangborn conference, a lady from Kimberley Clark reported that you can test the softness of toilet paper in a CLT and get high correlation with what people experience in a two-week use test in the home. Much of the decision is made when consumers first grasp the product in their hands, which are much more sensitive. So I believe that it is possible, if you do it right, to short circuit doing a home use test and get some information and the question to ask yourself is: do I really need to go the real world every time, because it is going to cost me a fortune? I want to go back to the point about context, because that’s important. What I have seen again and again is that people are not thinking when they design their tests. They are trying to get a number, say 7.5, for acceptability, and they fail to understand that the number is derived from a particular context. People like Ep Köster and Herb Meiselman have argued this for years, but some sensory people and product developers don’t care how the number came about. They’re just happy they got a good number. They fail to realize that when you put it in the context of the brand, or in the context of the home, or how the product is used at home, then that number can change, and so you ultimately have to put in some kind of context. Interviewer We’re approaching the end of the interview. But first, I’d like to ask you a few more personal questions. What makes you innovative, because you clearly are?

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Gail If I’m innovative, it’s because I am open to the ideas of other people and because I’m willing to listen to the different ways that people approach research so that I can learn from them. To me innovation is all about being open to someone else’s ideas, not just to create things yourself. To me the birth of innovation is that we’re willing to listen to another way to do it. If we could get people to be more open to other disciplines, to other ways of doing things, to doing things in ways that we haven’t done before, then we sow the seeds for innovative thinking and action. And on that note, let me say that I think it is brilliant that you have asked me for suggested reading, because I think people need to start reading something different, something outside of their normal technical sources. I like to read things about innovative ideas and the more people do that, the more I think they’ll be open to how other people think. And as I started out by saying, that, I think, is fundamental to being innovative. Interviewer I feel that an interview with you would not be complete without asking about how you got to where you are today. You run a large and very successful consultancy business. How did you get started? Gail I graduated in the 60s with a degree in chemistry and realised that I did not want to be a PhD chemist. Instead I started to look for applied work and I went to General Foods for an interview and they tried to convince me I should be a product developer and I actually had the temerity to leave the interview [it’s one of the best decisions that I’ve ever made]. I left the interview but a senior person there saw me on the tour with the HR person and said who was that young woman and would she like to be in sensory? So I was called back to an interview with Elaine Skinner and the people on her team and I spent the entire day trying to convince them I wasn’t a science geek. I looked very scientific, because I had a degree in chemistry, I had a minor in math and physics. I got the job and went to work for Elaine and was very much influenced by her philosophy and the philosophy at General Foods, which was that sensory works in support of product development and you must learn to communicate with marketing and product development in order to be successful. It is never about the data. It is about the communication of the data and so I spent the late 60s working with Alina Szczesniak, who was in the laboratory right next door. I often say that I went to the General Foods Graduate School of Food Science and Sensory. After I got married and became pregnant, I gave up the job which was over an hour commute. When my daughter was a year old I got the opportunity to start teaching a course for the Centre for Professional Advancement. I really wanted to get back to work, but I didn’t want to commute to General Foods. Through the teaching I began to take on a few clients and that way

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I began to grow my business. But the foundation was the learning that I got at General Foods – it was remarkable in terms of both the strategy and the methodology. They were very advanced. We were doing correlations of texture instrumental, texture sensory descriptive and consumer responses with an ideal profile in 1967. We were also training quality control coffee panels. Interviewer The combination of motherhood and growing a business does not sound easy . . . Gail The way I like to describe it is this: I had a three-quarter time business and I was a three-quarter time mother, and people who are mathematicians add that up and say: that doesn’t work. Well, it does work when you have a child on your lap while you’re writing a proposal and cooking dinner. It helped to be a multi-tasker. By the late 70s I pretty much had a full-scale business and that’s when I started developing the Spectrum Method based on the flavour profile method, which is what I had learned at General Foods. I decided to play off of the idea that the profile method was seen to be weak because it couldn’t be treated statistically and set out to develop a method where the data could be treated statistically. That was one part. Another impetus for the Spectrum Method came from my philosophy about product development. You cannot go to somebody at Nabisco or General Foods with a different scale for ginger snaps and a different scale for Oreos and a different scale for macaroni and cheese, a different scale for ice-cream. That’s confusing and that’s why the universal scale was developed so that the product developer could understand that five was a five was a five. Interviewer Finally, I would like to ask you to say a few words about Morten Meilgaard, one of the giants of sensory science who sadly passed away in 2009. Gail Yes, Morten passed away last April much to the sadness and disappointment of the sensory community in general and particularly to Tom Carr and me. Morten was a sensory giant. If you go online and look for the beer wheel, which in its inception was a collaborative work by a group of beer chemists, it became known as The Meilgaard Beer Wheel. He was a really great researcher, a real stickler for details and a great person to go and dig and find background information. He was a great diplomat in terms of the way he managed things. He was a very, very hard worker and one of his sons told me how he remembered him sitting in the living room with all the papers spread out editing pages of our sensory evaluation techniques book. It was our great honour to be associated with him and he brought a very

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different perspective from me, and from Tom, which I think was partly because he was a European. What Morten also understood, because of his work at Stroh’s Breweries, was that all that we do needs to be communicated in a form that people can use to make decisions to end up with better products. Interviewer I crossed paths with Morten in Denmark, both our home country, in the mid 90s. Some of my early work was on sulphur compounds in beer and I’d used a neural network to predict sensory from instrumental data. Morten was interested in my methods and results and his enthusiasm and passion about the topic made a big impression on me and I remember thinking that it would be great to feel like that when I got to his age. I know now that that I will, and it was great to meet a sensory giant like him so early in my career. Morten, rest in peace.

5.2 Sources of further information and advice Zaltman, Gerald, and Lindsay Zaltman. Marketing Metaphoria: What Deep Metaphors Reveal About the Minds of Consumers. Harvard Business School Press, 2008. Zaltman, Gerald. How Customers Think: Essential Insights into the Mind of the Markets. Boston: Harvard Business School Press, 2003. Tom Kelly, The Art of Innovation; The Ten Faces of Innovation. Christina H. Brodie and Gary Burchil, Voices into Choices: Acting on the Voice of the Customer 1 (Hardcover – Jul 1, 1997). Michael Michalko, Cracking Creativity: The Secrets of Creative Genius. Arthur Van Gundy. Training Your Creative Mind, Rev., 2nd Ed. Buffalo, NY: Bearly Limited, 1991. Idea Power: Techniques and Resources to Unleash the Creativity in Your Organization. New York: AMACOM (Division of the American Management Association), 1992; Creative Problem Solving for Managers. College Park, MD: University of Maryland, 1997.

5.3 Short biography for Gail Civille Gail Vance Civille, President of Sensory Spectrum, Inc., has pioneered advanced sensory evaluation approaches for industry, academia and the government. For over 40 years her application of strategic business initiatives to R&D and marketing projects has impacted sensory science globally. Her fundamental development of flavour, texture, fragrance, Skinfeel and Fabricfeel Spectrum Descriptive Analysis methodology, references and

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protocols is the foundation for sound analytical tools in sensory science. As an expert in the evaluation of sensory properties, Ms. Civille has worked with thousands of food and personal care products using sophisticated consumer and descriptive techniques. Ms. Civille has published articles on general sensory methods, as well as sophisticated application of sensory strategy, and is co-author of Sensory Evaluation Techniques, Sensory Evaluation in Quality Control and co-editor of Aroma and Flavor Lexicon for Sensory Evaluation. Ms. Civille was awarded in 2002 the David R. Peryam Award given to outstanding professionals in the applied sensory science field by the ASTM Committee E18. In 2006 she was awarded the ASTM Award of Merit. As part of the organising and founding committee Ms. Civille was instrumental in the establishment of The Society of Sensory Professionals and serves as its current Chair.

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6 Innovation in sensory practice and education: an interview with Howard Schutz H. G. Schutz, University of California at Davis, USA and S. R. Jaeger, The New Zealand Institute for Plant and Food Research, New Zealand

Abstract: Howard Schutz speaks about his career and contributions in the field of sensory and consumer science, including the pioneering work in defining and measuring situational appropriateness of food items, and development of labelled magnitude scales for liking, satiety, comfort and satisfaction. The conversation is embedded in the context of ‘the role of sensory in innovation’, and drawing on a career that began almost 60 years ago, Howard shares some thoughts on the skills that are required to be successful in an innovation role and emerging research approaches that have the potential to contribute to the advancement of science and successful innovation. More thorough training of sensory professionals is the foundation for bringing the field forward and Howard speaks about his vision for sensory education in the future. Key words: situational appropriateness, item-by-use, labelled magnitude scale, LAM, training in sensory science.

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Interview with Howard Schutz1

Interviewer1 I feel privileged to have this opportunity to speak with you. Not only are you an internationally acclaimed scientist and arguably the person with the longest tenure in our field, you’re also widely regarded as an innovative person. Let me start by asking you: What’s the first thing that comes to mind when I say “The role of sensory in innovation?” 1

Interview with Sara Jaeger in October/November 2009.

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Howard Mostly, I think of it in terms of what is the role of sensory in the innovation process and ordinarily it’s a role which begins when product ideas are first researched and here sensory plays an important role. As a sensory scientist I’ve always been humbled by the fact that the sensory component of innovation, which I believe is essential, does not replace those aspects of success in the marketplace which have to do with brand, advertising, packaging, etc., all the marketing variables which can, in many cases enhance or “do in” a product in the marketplace. So I don’t think that sensory input automatically guarantees success in NPD, but I think it makes its contribution in research guidance early in the process of product development. Interviewer One thing in your answer intrigues me. Are you saying, between the lines, that as sensory scientists we think our role in NPD is more important than it really is? Howard I think some sensory scientists might feel that. I think as the area has grown – and it’s grown considerably over the years – sensory and consumer testing have become more involved in the innovation process particularly in the way sensory people interact with other components in the company that include marketing, marketing research and production, and many times advertising agencies. So I think many sensory scientists are becoming more sophisticated with regard to the role they play. I think in many ways they could play a more important role. I think there have been changes in the field such that many of the teams no longer call themselves sensory evaluation and instead they use terms like consumer insight and are becoming involved in what many feel are the front edge of research – concept development. Personally, I think that’s quite good. Interviewer Drawing from your lengthy career, are there some examples that you can share about sensory playing an important role in innovation? Howard I cannot share much of the consulting work I’ve done since it may be proprietary in nature, but in my time at Hunt-Wesson Foods I was involved in what I considered to be the development of innovative products in which sensory played a very critical role. For example, we took a very simple product, an oil, and by modifying it with some additive flavours we created a butter-flavoured oil, which was a very simple change, but through the application of sensory procedures it was developed so that it could be used on popcorn and in baking products. We were able to demonstrate

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it was an acceptable product and that it was worth investing in the subsequent market research. This innovation is still in the market place. Another example which is a simple innovation as well was a variation of tomato sauce. The tomato sauces from competitors were very much the same, so we decided to put in some diced tomatoes that would create a chunky tomato sauce. It worked out quite well and it is still in the market place. Another example started out as a failure. We tried to develop a spaghetti sauce and we just could not get one that did very well, so we took the spaghetti sauce and repositioned it as a Sloppy Joe sauce that we called Manwich. It turned out to be very popular and is still in the marketplace. Interviewer A common feature of the examples you mentioned from your time at HuntWesson Foods is simplicity. You said that only simple changes were made to existing products. Do you think that the simplicity of the changes were critical to their success? And more broadly, are we making NPD more complex than it needs to be, perhaps to the detriment of commercial success? Howard I don’t think simplicity of an innovation is a guarantee for success, but neither is complexity. I think there is a need for simple innovations such as line extensions where, for example, a different flavour of an item is introduced. There is also a need for pioneering new products, and that’s typically where a new technology is involved. Freeze dried processing, although not a recent innovation is a good example, because it was a pioneering change that introduced a whole range of new products that weren’t possible before. So I don’t think only paying attention to very minor changes in products is enough, I think a company has to have a portfolio of different types of innovations, from simple to complex. Interviewer In your Hunt-Wesson examples, you also emphasised that the products are still in the marketplace. We constantly hear that there are a lot of failures or products that make it into the marketplace for only a limited period of time. How important is sensory in determining whether a product is a failure or a success? Howard There have been many products which were considered acceptable by the standards that are used in sensory that subsequently failed. Equally, there are products that were failed in sensory, but not in the market. We don’t see those examples much but they’re there. We know that most ideas do

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not develop into successful products and that’s where sensory plays an important role in the innovation process – minimising the amount of expenditure that one has to make in order to increase the likelihood that a product will not fail in the marketplace, at least not because of its sensory attributes. Interviewer From your perspective, what is the most significant barrier that hinders successful new product development? Howard It’s very difficult in a sensory lab to create all of the situational conditions that exist in the consumption of products. We have the problem of the limited amount of product that people consume in typical sensory tests, which means that we don’t get a natural way of use. In many cases we don’t have the other meal items that are ordinarily served with that particular item. At least in the early stages of the innovation process we often lack packaging and brand and they may be powerful aspects of success with consumers, so it could be that some products are not evaluated in the most appropriate way, which may result in the premature abandonment of a product. Perhaps if the innovation had been more thoroughly investigated it could have been considered more successful. Interviewer In your opinion, what other barriers hinder success in NPD? Howard The extent to which sensory professionals, regardless of what their department name is, fail to work closely with market research information and market research’s insights to build a knowledge base that would create a more meaningful product evaluation – I see that as a barrier to successful NPD. The same can be said for links to the production department. So I think that sometimes the barrier is the lack of good communication. Another barrier which is a very interesting one is this: many people in industry who come from an academic background, and especially if they’re a PhD, are not used to failure. They don’t realise that in an industry orientation, for many reasons that have nothing to do with how good a job they’re doing, work on a particular product can stop due to overriding marketing reasons. To be able to deal with this you have to have confidence in the system and ability to move on. Interviewer I would like to have a personal flavour to this conversation. For me you’re a living icon in sensory science and one question I’ve always wanted to ask you is: “How did you become a sensory scientist?”

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Howard By accident. In 1951, as a graduate student at Illinois Institute of Technology I was in an Experimental Psychology programme and my G.I. Bill2 ran out and I needed to get some extra money so I started looking around for a job and went to the Illinois State Employment bureau and they said they had an apprentice job once, but that it was now gone. It was with the Quartermaster Food and Container Institute and they were looking for a psychologist. I said, I think there’s somebody in my evening class that works there, and there was, and his name was Dave Peryam and he said the job was not gone, it just hadn’t been filled. They were bringing in somebody from Pittsburg by the name of Frank Pilgrim, and anyway, Dave said why don’t I come in and interview and I did and all of a sudden I became somebody working in the area of sensory and eventually consumer work, but it was just serendipitous. Interviewer The illustrious names of your co-workers are known to all in this field. Were you working alongside them at the time that the 9-point scale was developed? Howard Yes I was. It is interesting that in recent work I’ve been doing with Armand Cardello on the 9-point hedonic scale, we have discovered that the common attribution of the scale to the Peryam and Pilgrim paper from 1957, and in turn to the paper by Jones, Peryam and Thurstone (1955), is not the source of the 9-point hedonic scale. It appears that it was a rather casual development that ended up as a universally utilised scale. I had the opportunity to do the first validity study of the 9-point hedonic scale at an army base where it was possible to measure preferences with the 9-point scale and relate them to actual food that was chosen and consumed. It turned out that the 9-point scale would account for approximately 50% of the variation in people’s consumption and choice of food. Interviewer ‘By accident’ and ‘just serendipitous’ were words that caught my attention when you spoke about your path to becoming a sensory scientist. I have a sense that perhaps your story isn’t that different to what a lot of us experience in terms of how we end up coming into this field. Would you agree? 2

The G.I. Bill (officially titled Servicemen’s Readjustment Act of 1944, P.L. 78–346, 58 Stat. 284 m) was an omnibus bill that provided college or vocational education for returning World War II veterans (commonly referred to as GIs) as well as one year of unemployment compensation. It also provided many different types of loans for returning veterans to buy homes and start businesses. Since the original act, the term has come to include other veteran benefit programs created to assist veterans of subsequent wars as well as peacetime service (source: Wikipedia).

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Howard Yes, and to some extent this has to do with the limited educational offerings in sensory and consumer science. Interviewer Returning to the topic of sensory and innovation, what do you consider to be your own best sensory innovations? Howard Probably, when I developed the concept of adding appropriateness to the evaluation of products. Before that time, although there were some limited times in which a consumption situation might be involved, there was no really comprehensive way of looking at contextual influences. I think that it became very clear in the work that I did and when other people started using the appropriateness concept that the fact that a product was right in a sensory and hedonic sense would not guarantee that it would be one that would be used very often or considered appropriate for the particular situation that a marketer or developer might have in mind. For example, many items which are well liked may not be considered appropriate for a situation such as for a picnic. So I think that I added a dimension that was quite critical and I feel very good about it. One way or another the idea of situational appropriateness survived and I think is embedded now in the field. Interviewer Do you think the concept of situational appropriateness has also become embedded in innovation activities? Howard Yes. It is not so much that people have adopted exactly the format that I used when I introduced the concept, but I think situational appropriateness, and more broadly, context, has become an integral part of the innovation process. The idea that a product’s success in the marketplace from a sensory standpoint is more than just liking has become taken for granted and I think we have all come to fully realise the need to look beyond the simple liking of products in order to be able to do a better job of predicting their usage. For people setting out on the path to learning about situational appropriateness it may be useful to read some of the early papers which demonstrate a variety of ways in which one can get an estimate of a product’s appropriateness in a variety of situations and uses. It’s not that these methods have to be followed slavishly, but they would help you getting familiar with a type of approach that make innovations more likely to be successful. Can I add that in some of that early work, the situations we looked at, besides specific locations, time of day, etc., also included uses such as “when you want to be happy”, and now these more emotional states have become a topic of interest again.

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Interviewer What about some of your other innovations in sensory science? I know there are many! Howard To the best of my knowledge I was the first to utilise multiple regression to relate sensory attributes to liking. Today there are many statistical procedures for this purpose. Another related method that I believe I developed first was the use of preference grouping in predicting liking from sensory attributes as a superior way of guiding product development. As you can tell, I’ve always been interested in methodology and working at the Natick Research and Development Centre with Dr Armand Cardello in the summers for the last 16 years, I’ve been concentrating on developing improved scaling methods and one of those was the labelled affective magnitude scale – the LAM scale. This is a category ratio scale which we believe is more sensitive to differences among well-liked products and which has ratio characteristics. The scale is well documented by now, thanks also to some of the work you have been doing together with Armand. Subsequent to the LAM scale, I’ve been involved in developing a comfort scale, a satiety scale and recently one in the area of satisfaction, and we’re just analysing one in the area of trust. I don’t know how many people have adopted each of these scales, but I know that the comfort scale has been utilised for a number of products in which comfort is a factor. In fact, under management by the Natick US Army Research and Development Lab there is a major project in developing a uniform which would be chemically resistant where comfort is an important aspect. Interviewer What is it that makes you so innovative? Howard It’s a good question. I think that I’m a very curious person and I’m also a person that likes to do different things. I don’t like routine, so innovation and developing new ways that will allow us to better understand products and services is very attractive to me. Interviewer Do you think you have these skills and behaviours in common with other people that are very innovative? Howard That’s a tricky question. The younger counterparts to the likes of Herb Meiselman, Howard Moskowitz, Armand Cardello and those that were at the Quartermaster Institute, Frank Pilgrim and Dave Peryam, do not exist today. One thing they all have in common is that they were psychologists.

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I believe that somehow psychologists are more innovative in coming up with ways of dealing with issues in product development. Coming up from a more pure food technology background, I feel is less likely to lead to innovative ways of thinking. Interviewer In the field today, where do you see innovative thinking coming from? Howard It can of course come from anybody and anywhere, and I think it’s important in any particular organisational structure for the managers to encourage the type of thinking which is off-beat and allows employees to try new things. In terms of new methods, I think it is interesting that many of these have come from vendors, from those who sell services to organisations. I’m glad that they’re active and have promoted new methods and created a kind of educational workshop, but each one tends to beat their own drum usually to the unnecessary disadvantage of all the other vendors. I think that what I’ve tried to do to is to fill that gap and build a more common educational background by having a certificate programme in sensory science and consumer testing. Interviewer Yes, and I think there’s many people, including myself, that want to thank you for that, because there is not a lot of structured professional training available and I think it’s great that there are people in the field who are committed to providing opportunities for others to learn. I want to move on, and one of the things that’s fantastic about speaking with you, is that you’ve been in the field for a very long time. What, in your mind, are the greatest changes that have occurred in the past decades? Howard I would say that a critical change is the expanded role of individuals in the sensory and consumer testing area within organisations. It’s larger in terms of numbers of employees, and it covers more areas, including front-end research and activities post-launch. The other thing that I think has been a major change, and I feel that I’ve contributed a bit to that, is that we’ve gone beyond the idea of just being able to describe a product, find differences between products or measure how much different products are linked. We are able to build connections between the physical and sensory attributes of products and what people like, so that we can determine the relative importance of sensory attributes. I think this has been a major area of contribution and many, many people have developed their own particular statistical methods for doing this, but the basic principle is the same and documenting this relationship can be very helpful to product developers because they then know what attributes to concentrate on and what

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attributes they don’t have to be as concerned about. I think this has been quite important. Interviewer In relation to innovation, what are the major differences between working in a sensory role in academia and in industry? Howard There are quite important differences, although one would like to believe that the degree of scientific integrity and efforts would be identical. They are obviously not identical. If you are working for academia your primary interest is in publication because it contributes to your reputation, and there is not necessarily a focus on commercially successfully products. If you work for a vendor, then your role is to sell. That’s an important part of your job, to understand your methods, be able to write a proposal, do the job quickly and to finish it up and write a report that’s viewed by the sponsor as a very successful one. If you’re working within an organisation that develops products then obviously the responsibility is utilising sensory methods to obtain the appropriate information over a set period of time. Drawing on my experience from over 250 students from all over the world who have taken our certificate programme, many start with very little training and they’re generally using methods based on what’s been told them in a workshop or what they have inherited in their particular organisation. It really becomes a challenge for them to develop their skills to a point where they can make appropriate innovative contributions to their organisations. Interviewer Looking towards the future, what do you think it holds for sensory science? Howard My sense is that within commercial organisations the sensory and consumer testing group is going to become more integrated with market research activities and in some organizations they may not be distinguishable. I think that’s inevitable in the future and it doesn’t mean the loss of sensory importance, it means that the sensory people will become involved in all the stages of the product development process, all the way from the concept stage to post marketing activities. Interviewer Looking towards the future again, what emerging methods do you think will have a big impact? Howard The area of emotions research is certainly an area to watch. At the Florence Pangborn conference there were a number of workshops, papers and

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posters in this area. Personally I’m not sure whether I can clearly differentiate emotion from liking and from whether it’s a concept of what is appropriate to say in a particular situation based on one’s experience. I have no doubt that since we feel emotions and because they are important to us, there’ll be a lot of work in that area and one can hope that some of it adds to our ability to make appropriate decisions, because quite frankly that is the whole role of sensory is, to reduce the degree of uncertainty so one can make decisions on the allocation of resources. If we can reduce uncertainly then we’re playing an important role. And where emotions research fit into that perspective is that if the goal is to develop a product which leads an individual when they consume it to feel happy, then having a product association with that particular emotion will be of some value in guiding the development process. Interviewer What about sensory education in the future? Howard There’s one thing that I would like to see happen, and that’s for psychology departments at the undergraduate level to create a track for people that involves what might be considered a sensory science or consumer testing option where they could take courses which involve sensory and perception, as well as some courses that involves statistics and business. I believe this type of education would be very useful to industry. One of my biggest hopes for the future is that more people get more thoroughly trained before they get into the application of sensory. But that’s only a hope because the educational system in sensory is mostly buried in food science and technology departments who themselves are facing difficulty in adding new personnel, sensory faculty included. Another aspect that you might say is a bias, but is actually reality, is that there are many more women in the field than men. Not that they don’t do a good job, it’s just that it would be nice if it opened up a little bit. How to attract men to the field is difficult because the area is not one that they are exposed to in many of the disciplines that they are concentrated in. Interviewer Activity in the non-foods area has been growing over a number of years. What is your view of the combination of sensory science and non-foods? Howard I do not see the food and non-food areas as fundamentally different in terms of the application of sensory and consumer science. Many of the students in the certificate programme come from personal care products, pet food, automobile, and at the Pangborn conference there are many non-foods applications. I think the Europeans may be more active in the

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non-food area, or perhaps the Americans are a little more proprietary about sharing their knowledge, but there is no doubt in my mind that this is a growing area of sensory evaluation. More non-foods publications will, I think, help the area gain more acceptance, and I think in the future there will either be more acceptance of the non-foods area in the standard sensory journals or perhaps a journal dedicated to this area. Interviewer To finish, let me ask you a slightly odd question. If you were to have coffee tomorrow with a fellow scientist, dead or alive – who would it be? Howard L.L. Thurstone, the eminent scientist from whom Thurstonian scaling takes its name. He made significant theoretical contributions in scaling, putting forward theories around perceptual variability to be measured using d’ scores, and his approaches have become quite important at least in some areas. What I would like to talk to him about is the development of the 9-point hedonic scale. I would be interested in his opinion of the scale and how well it works. That would be a great conversation to have over coffee. Interviewer I think it is entirely appropriate that we should come back to the 9-point hedonic scale which is so central to our science and frequently used in evaluating new sensory innovations, and on that note I’d like to end this conversation. Thank you.

6.2 References and further reading cardello a v, schutz h g & given z a (2008), Development and testing of a labeled magnitude scale for consumer product satisfaction, Third European conference on Sensory and Consumer Research, Hamburg, Germany. Abstract P88. cardello a v, schutz h g & lesher l l (2005), Development and testing of a labeled magnitude scale of satiety, Appetite, 44 (1), 1–13. cardello a v, schutz h g, snow c & lesher l l (2000), Predictors of food acceptance, consumption and satisfaction in specific eating situations. Food Quality and Preference, 11, 201–216. cardello a v & schutz h g (2004), Research note: numerical scale-point locations for constructing the LAM (labeled affective magnitude) scale, Journal of Sensory Studies, 19, 341–346. cardello a v & schutz h g (1996), Food appropriateness measures as an adjunct to consumer preference/acceptability evaluation. Food Quality and Preference, 7 (3–4), 239–249. jones l v, peryam d r & thurstone l l (1955), Development of a scale for measuring soldiers’ food preferences, Food Research, 20, 512–520. peryam d r & pilgrim f j (1957), Hedonic scale method of measuring food preferences, Food Technology, 11, 9–14.

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schutz h g (1983), Multiple regression approach to optimization. Food Technology, 37 (11), 46–49. schutz h g, damrell j d & locke b h (1972), Predicting hedonic ratings of raw carrot texture by sensory analysis. Journal of Sensory Studies, 3, 227–232. schutz h g, moore s m & rucker m h (1977), Predicting food purchase and use by multivariate attitudunal analysis. Food Technology, 29, 50–64. schutz h g (1988), Beyond preference: Appropriateness as a measure of contextual acceptance. In, Thomson, D.M.H (ed.) Food Acceptability, pp. 115–134. London: Elsevier. schutz h g & cardello a v (2001), A labeled affective magnitude (LAM) scale for assessing food liking/disliking, Journal of Sensory Studies, 16, 117–159. schutz h g & pilgrim f j (1957), Sweetness of various compounds and its measurement, Food Research, 22, 206–213.

6.3 Short biography for Howard Schutz Dr Schutz is presently special assistant to the Dean of UC Davis Extension where he is the Academic Director of an online certificate programme in sensory science and consumer testing. He is a Professor Emeritus of Consumer Science at UC Davis. He previously was an Associate Director of Research at Hunt-Wesson Foods, a senior scientist at Battelle Memorial Institute, and a research scientist at the Quartermaster Food and Container Institute.

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7 Hedonic measurement for product development: new methods for direct and indirect scaling A. V. Cardello, US Army Natick Soldier R, D & E Center, USA and S. R. Jaeger, The New Zealand Institute for Plant and Food Research, New Zealand

Abstract: Hedonic measures, e.g. judgments of liking/disliking, pleasure/displeasure, preference, etc., obtained in response to products are used to make critical decisions throughout the R&D cycle. Although the history of hedonic testing is long, new methods of measurement continue to evolve in the literature. This continual development is driven by the importance of the hedonic construct to sensory scientists working in industry and to scholars interested in the quantification of human sensory, attitudinal and emotional responses. In this chapter we examine two recent developments in hedonic scaling: the method of best-worst scaling and the method of labeled affective magnitude scaling. These two techniques represent examples of indirect and direct scaling, respectively. In order to fully understand both the theoretical and practical differences between these methods, the chapter begins with an historical account of indirect and direct scaling methods in psychophysics. Next, the methods and techniques of best-worst and labeled affective magnitude scaling are introduced in detail, focusing on practical aspects. The chapter concludes with a comparison among methods of hedonic scaling and we offer recommendations regarding the most effective and efficient assessment of hedonic differences among test stimuli, from a methods perspective. Key words: scaling methodology, labeled affective magnitude scale (LAM), best-worst scaling (BWS).

7.1 Introduction Hedonic or affective responses are emotional reactions (feelings) that frequently accompany the perception of stimulus objects. In consumer behavior they manifest themselves as feelings of liking or disliking in response to a product or its sensory or ideational attributes. As such, the study and measurement of hedonics has great importance to the food and consumer

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product industries. Hedonic measures, e.g. judgments of liking/disliking, pleasure/displeasure, preference, etc., obtained in response to products are used to make critical decisions throughout the R&D cycle, including decisions about product concepts, alternative product formulations, the readiness of an item for introduction to the marketplace and predictions of its potential success. Although the history of hedonic testing is long, new methods of measurement continue to evolve in the literature due to the importance of this construct to sensory scientists working in industry and to scholars interested in the quantification of human sensory, attitudinal and emotional responses. The purpose of this chapter is to examine two recent developments in hedonic scaling: the method of best-worst scaling and the method of labeled affective magnitude scaling. The former method, as introduced by Finn and Louvierre (1992), represents a form of psychophysical judgment known as indirect scaling, i.e. the quantitative index of the intensity of an hedonic experience is derived indirectly from discrete choice judgments made by the subject. The latter, as exemplified by the Labeled Affective Magnitude (LAM) scale (Schutz and Cardello, 2001; Cardello and Schutz, 2004) represents a form of psychophysical judgment known as direct scaling, i.e. the quantitative index of the intensity of the hedonic experience is estimated directly by the observer. In order to fully understand both the theoretical and practical differences between these methods, we will begin with an historical account of indirect and direct scaling methods in psychophysics, focusing on the application of these methods to hedonics. This will be followed by an exposition of the methods and techniques of best-worst and labeled affective magnitude scaling. Although there exist a number of other techniques to index the acceptance and preference for products, e.g. preference ranking methods, barter scaling, category scaling, etc., we do not cover these topics here, except as they relate to the evolution of the labeled magnitude and best-worst scaling techniques. Rather, our intent is to provide the reader with a focused exposition of these two newer methods. In the later sections of the chapter, we will explore the empirical relationships among labeled magnitude scaling and best-worst scaling, as well as comparisons of both to other scalar techniques, e.g. the 9-pt hedonic scale (Peryam and Pilgrim, 1957) and visual analogue scales. Lastly, we will provide recommendations regarding the optimal situations in which to use these methods in order to achieve the most effective and efficient assessment of hedonic differences among test stimuli.

7.2 Historical developments in the scaling of hedonics 7.2.1 Indirect and choice-based scaling Early origins Gustav Fechner, the father of psychophysics, developed many of the methods of psychophysics that are still used today. In his seminal treatise, © Woodhead Publishing Limited, 2010

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Elemente der Psychophysik (1860), Fechner outlined a variety of techniques by which sensory thresholds and supra-threshold stimulus magnitudes could be measured. Fechner was greatly influenced by the Zeitgeist of mid-19th century Germany, which maintained that sensations could not be measured directly. As a consequence, Fechner developed methods to assess sensory processes that were indirect in nature, i.e. the observer was never asked to directly judge his/her sensory experiences. Instead, Fechner designed methods that would enable inferences to be made about these processes from choice responses made by the subject between or among two or more stimuli. For example, for the assessment of absolute thresholds, Fechner’s method of limits required the subject to make repeated yes-no choice judgments regarding the presence or absence of each stimulus in a series of ascending or descending stimulus intensities that bounded the presumed threshold. The stimulus intensity that was detected 50% of the time was taken as the threshold. For difference thresholds, Fechner’s method of constant stimuli required the subject to make same-different choice judgments between a constant (control) stimulus and a series of stimuli that differed only slightly from it. The difference threshold was taken to be the difference in intensity from the control that could be detected between 50% and 75% of the time. In what was perhaps his most controversial approach to the measurement of sensation, Fechner extended his choice-based approach to the measurement of sensation magnitude (1860). His “psychophysical law” of sensation magnitude was derived by determining difference thresholds (using same-different choice methodology) across the entire stimulus continuum. Then, by making the assumption that all of these just noticeable differences (JNDs) were psychologically equivalent, Fechner used the total number of JNDs from absolute threshold to the stimulus intensity of interest as his index of the perceived magnitude of that stimulus. Since none of the judgments made by the observer were direct estimates of the intensity of the target stimulus, his method of summing JNDs became a classic example of indirect methods of sensory scaling. After publication of Elemente, Fechner turned his attention to the study of hedonics and, not unexpectedly, applied the same choice-based approaches to quantify affective responses to stimuli. His initial work in this area focused on aesthetics, where he examined the pleasantness of the shapes of rectangles (Fechner, 1871, 1876). In perhaps the earliest rendition of what would later be called best-worst scaling, Fechner had subjects choose from among a series of rectangles the one that was the most pleasing and/or the one that was the least pleasing. In order to index the aesthetic appeal of the different stimuli to the observer, Fechner calculated the relative frequencies of choices among the stimuli, and used these relative proportions as a quantitative index of the aesthetic appeal of each stimulus. Again, the underlying sensation or psychological property (i.e., aesthetic appeal) was inferred indirectly from the responses of subjects who compared two or more stimuli and chose one as most or least appealing – © Woodhead Publishing Limited, 2010

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the observer never giving a direct estimate of aesthetic appeal for any of the stimuli. Thurstone’s choice-based methods Although Fechner’s comparative, choice-based methods gained popularity in psychology and were applied to the study of hedonics by various investigators interested in visual aesthetics and color preferences, e.g. Cohn (1894) and Garth (1922), a rapid growth in the use of choice-based, indirect methods came in 1927 with Leon Thurstone’s formulation of the Law of Comparative Judgment (Thurstone, 1927a). Thurstone defined all psychophysical problems as the “association between a stimulus series and the discriminable processes with which the organism differentiates the stimuli” (Thurstone, 1927b). Like Fechner, Thurstone did not believe that sensations could be measured directly, and like Fechner’s research on hedonic preferences, Thurstone’s techniques were based on choice behavior among a set of stimuli and the calculation of the relative frequency of choices, e.g. “greater than” or “less than,” “better” or “worse,” etc., for each stimulus. These frequency distributions or “discriminal dispersions,” as Thurstone referred to them, were presumed to be normally distributed. Using the statistical theory of Gaussian distributions, Thurstone was able to relate the distributions of discriminal dispersions between stimuli to the underlying differences among stimuli on the psychological dimension by making the assumption that equally often noticed differences were psychologically equivalent (an assumption reminiscent of Fechner’s earlier assumption about the equality of JNDs). This indirect approach to indexing sensory differences among stimuli, first formulated by Fechner and elaborated upon by Thurstone, is essentially the same approach that would be used still later in the development of the Theory of Signal Detection by Green and Swets (1966) and which was subsequently popularized through its application to sensory difference testing methods by O’Mahony and Ennis (O’Mahony, 1979; O’Mahony and Odbert, 1985; Ennis and Mullen, 1986; Ennis, 1990, 1993; O’Mahony et al., 1994; Ennis and O’Mahony, 1995). Soon after Thurstone published his Law of Comparative Judgment, he and others began to apply choice-based techniques to the measurement of social attitudes, values, and hedonics, including studies of the offensiveness of crimes (Thurstone, 1927c), nationality preferences (Thurstone, 1928a), the excellence of handwriting (Hevner, 1930), and even the perceived “wetness” or “dryness” of alternative forms of alcoholic prohibition (Thurstone, 1928b). Thurstone’s approach was well received by the community of researchers working on measuring attitudes, values, emotions, and aesthetics, because the psychological dimensions associated with these phenomena did not have readily identifiable, underlying physical or objective continua. Thurstone’s statistically based, indirect methods enabled the quantification of these dimensions without the need to identify such objective stimulus continua.

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Applications to food and food-related stimuli At about the time that Thurstone was developing his Law of Comparative Judgment, another psychologist, Beebe-Center, began a focused program on the study on hedonics. In 1925 he conducted a choice-based preference test on the liking/disliking of food-related odorants. In keeping with the indirect approach to measurement that was popular at the time, he applied a two-sample paired comparison method in order to establish the hedonic rank orders for 14 different spice odors (cited in Beebe-Center, 1929). Some years later, a food scientist faced with assessing the tenderness of meat products adapted methods developed in nutritional studies of food choice in animals to construct a “paired eating method,” in which tasters would masticate pairs of samples and select the one that was more or less tender (Cover, 1936, 1940). Shortly after, similar methods were applied to the study of food preferences by Platt (1937a,b). These early paired comparison approaches to the study of the sensory and hedonic properties of food would soon lead to the development of numerous forms of difference tests for use in discriminating perceptual difference among foods, including the duo-trio, the dual standard and the triangle test, e.g. Helm and Trolle (1946) and Scofield (1948) (cited in Peryam and Swartz, 1950) and Boggs and Hanson (1949). A turning point in the use of indirect methods In the late 1940s Thurstone joined the Advisory Board for the Quartermaster Food and Container Institute of the Armed Forces (QMFCI) in Chicago where the staff of the Food Acceptance Branch and the Food Research Branch had begun the study and development of new methods for measuring soldiers’ preferences for food. In this role and later in his capacity as a contractor at QMFS, Thurstone gained exposure to sensory food testing. After observing and working on the problem of scaling food hedonics and seeing the practical difficulties involved in the preparation, presentation, and evaluation of multiple food samples, Thurstone came to the conclusion that using paired comparison research for food was impractical. He stated that “such a procedure is almost out of the question in dealing with taste” (Thurstone, 1950). Ironically, he went on to say that “the best procedure is probably to have a fairly large number of subjects and to ask each subject to sample each specimen only once . . . he (the subject) may be asked to allocate the specimens to a set of, say, 10 steps. These might be numbered from 1 to 10, and he might be asked to let number 1 represent the most disagreeable, while number 10 represents the most agreeable taste. These ten steps would really represent intervals on a subjective scale of taste preference . . . (alternatively, the subject) states his degree of preference in terms of one of a number of short descriptive phrases which are assigned to the successive intervals. There is no assumption that these successive intervals of the scale are in any sense equal. . . . For convenience, these descriptive phrases may be denoted by numbers or letters” (Thurstone,

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1950, pp. 86–87). With these words, Thurstone gave tacit support to an alternative form of hedonic scaling – one that involves the direct rating of food liking and disliking. Ironically, it would be one such direct method developed at QMFCI that would soon take precedence for the scaling of food liking – a method that would stand in contrast to the choice-based methods that Thurstone had championed throughout his career.

7.2.2 Direct scaling methods Early origins Direct methods for scaling sensation go back thousands of years. It has commonly been cited (e.g., Gescheider, 1976) that the Greek astronomer, Hipparchus, developed a scale of stellar magnitude by which the brightness of stars could be judged directly by observers using a 6-pt category scale. Although Galton (1883) is often given credit for developing the first rating scales in psychology (Garrett, 1930), other authors (Ellson and Ellson, 1953) have attributed the first rating scale in psychology to Robert Owen, who in 1825 developed a method for rating the capabilities (strength, courage, imagination, etc.) of children using a 100-pt rating scale, while still others (McReynolds and Ludwig, 1984) have attributed it to Christian Thomasius, who in 1692 developed a 12-pt rating scale to rate individuals on such psychological variables as sensuousness and social ambition. More pertinent to our current discussion, it was not until the early 1900s that food scientists working in commodity areas, e.g. dairy and bread, introduced techniques in which the sensory attributes of foods could be directly ranked or rated by subjects using scales that varied in length and could be summed to achieve a total “score” for the product. In the case of hedonics, the first rating scales appear in the work of Beebe-Center (1929, 1932) who had subjects assign words to describe stimuli that had a presumed rank order of affective meaning, e.g. “pleasing, neutral, or displeasing.” At about the same time, Young (1930) used a rating scale that ranged from +5 “very great pleasure” to −5 “very great displeasure” to scale the affective response to odorants. However, these rudimentary scales were never widely adopted by food researchers. By the late 1940s psychologists were applying direct rating scales to index the pleasantness of a wide variety of stimuli. However, it was in August of 1947 that a direct method for scaling the hedonics of food began to evolve at the Quartermaster Food and Container Institute. It was during that year that a rating scale using the words “like” and “dislike” appeared in a food preference study conducted by the Institute. This study, conducted under the direction of Cpt. Joseph Jones and Dr Bernice Westerlund at Ft. Sheridan in Illinois employed a 7-pt balanced scale and used the word labels “dislike it very much,” “dislike it,”, dislike it slightly,” “indifferent,” “like it slightly,” “like it,” and “like it very much” (Quartermaster Food and Container Institute, 1949). Shortly thereafter, Erling Eng, a graduate student at

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Northwestern University conducted his Masters Thesis research at QMFCI under the direction of Franklin Dove, the Head of the Food Acceptance Research Branch. Eng’s research compared 5-, 7-, and 9-pt category scales of liking/disliking, as well as a linear graphic scale of liking/disliking and a 10-pt category scale of quality in a laboratory study of grapefruit juices (Eng, 1948). Among the terms used by Eng in his 9-pt hedonic scale are several word labels subsequently used in the published versions of the “9-pt hedonic scale.” The terms that Eng used were “neither like nor dislike,” “like (and dislike) slightly,” “like (and dislike) moderately,” “like (and dislike) highly,” and “like (and dislike) very highly.” But it was in August of 1949 that the first study of soldiers’ liking/disliking for foods was conducted using the exact phrases that have since become universally known as the “9-pt hedonic scale.” This pilot study, published by Polemis and Jones (1950) was conducted at Ft. Riley, Kansas and used the word phrases “neither like nor dislike,” “like (and dislike) slightly,” “like (and dislike) moderately,” “like (and dislike) very much,” and “like (and dislike) extremely.” Following the 1949 pilot study, the 9-pt hedonic scale was used in a series of food preference surveys in 1950 and 1951 (Polemis, 1950a,b; Polemis, 1951a,b; Polemis, 1952) to identify highly liked and disliked foods. In addition, these studies examined certain characteristics of the scale, e.g. reliability and directionality. Following these studies, the scale was presented externally at a research conference sponsored by the American Meat Institute (Tuxbury and Peryam, 1952) and subsequently reported in the commonly cited publications by Peryam and Girardot (1952) and Peryam and Pilgrim (1957). The 9-pt hedonic scale The 9-pt hedonic scale is a form of category scale, or what psychophysicists refer to as a partition scale, in which the like/dislike dimension is partitioned into a number of discrete categories. Unlike Fechnerian and Thurstonian scales, the 9-pt hedonic scale is a direct scale of affective magnitude, because the observer directly assesses his/her hedonic experience in response to a stimulus and then assigns the magnitude of that experience to one of a series of nine labeled categories that represent different semantic magnitudes along the affective dimension (refer to the left-hand side of Fig. 7.3 for the 9-pt hedonic scale and its category labels). Although the 9-pt hedonic scale has become the international workhorse of sensory and consumer research on hedonics, it is not without problems. Jones et al. (1955) in research sponsored by QMFCI presented evidence that the neutral category, “neither like nor dislike,” decreases the efficiency of the scale by encouraging complacency of judgments, potentially allowing consumers to dump marginal stimuli into this safe category (Gridgeman, 1961). Olsen (1999) has discussed this “ambivalence” problem and other shortcomings associated with the use of a neutral category in both hedonic

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and other bipolar attitude scales. Similarly, Jones et al. (1955) and Jones and Thurstone (1955) showed that the intervals on the 9-pt hedonic scale labels were not equal. Interestingly, while this research used an interval-based procedure to scale the semantic meaning of the word labels used in the 9-pt hedonic scale and numerous other hedonic word phrases, the resulting data were never used to revise the 9-pt hedonic scale to choose phrases that would make it an equal interval scale. The non-equivalence of the intervals of the 9-pt hedonic scale was subsequently confirmed by Moskowitz and Sidel (1971) and Moskowitz (1977a, 1980) using direct ratio methods to scale the semantic meaning of the labels. The inequality of the scale intervals reduces the mathematical level of the 9-pt hedonic scale to simple ordered metric. Lastly, as with all category scales, there is a “central tendency” (Hollingworth, 1910) or “regression” (Johnson, 1952) effect that results in underuse of the end categories (Stevens and Galanter, 1957). Subjects avoid these end categories, because once a stimulus is assigned to them, subsequent stimuli that are perceived as more well liked/disliked cannot be placed in a more extreme category. This avoidance effectively reduces the 9-pt scale to a 7-pt scale and limits its ability to differentiate among very well liked or very disliked samples. In spite of these shortcomings, the simplicity of the scale and its ease of use, compared to laborious paired preference methods, led to its widespread and international adoption by scholars and practitioners alike. S.S. Stevens and direct ratio scale methods As a direct measure of food affect, the 9-pt hedonic scale was (and is still) widely acclaimed. However, at about the same time that the 9-pt hedonic scale was being popularized, the mathematician Stanley Smith Stevens, published several theoretical and empirical papers on the properties of psychophysical scales that would provide fodder for the development of new methods for scaling both sensory and affective attributes (Stevens, 1956, 1957; Stevens and Galanter, 1957). Just as Thurstone had revolutionized the indirect approach to scaling sensory and psychological attributes, Stevens would revolutionize the approach to direct scaling by criticizing the indirect methods that Fechner and Thurstone had championed, which he said were based on measures of a subject’s “confusion,” because the basic unit of choice-based methods is the frequency of responding “correctly” or “incorrectly” to a stimulus. Further, he noted that these methods then “unitize the error” by assuming that equally sized dispersions of data were psychologically equivalent. “Normally a nuisance to science, dispersion among people’s judgments becomes grist for the mill when Fechner makes dispersion into a “difference limen” and calls it the unit of his scale; and it keeps the mill wheel whirling when Thurstone enters dispersion into his “equation of comparative judgment and computes scale values for the stimuli.” S.S. Stevens, 1961, p. 81.

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Stevens approach was to seek direct measures of sensory and psychological dimensions and he predicated his search on fundamental tenets of measurement theory. Namely, that there is a hierarchy of four different levels of mathematical measurement and that the way in which numbers are assigned to objects dictates the scale level of the data and the mathematical transformations that leave the scale form invariant. These scale levels include nominal, in which numbers serve merely as descriptive labels to differentiate objects; ordinal, in which the orders or ranks are maintained; interval, wherein the differences between scale points are equal; and ratio, in which there is a true zero point and the ratios among numbers on the scale have mathematical meaning (Stevens, 1951). Stevens went on from here to develop methods that would meet the strictest criteria of these – ratio scales. Although Stevens and co-workers developed a number of different ratio scaling techniques, magnitude estimation (Stevens, 1956), in which individuals freely assigned numbers to stimuli in a ratio manner, is the best known of these techniques. Stevens also criticized all forms of category scales, because even though they were direct measures of sensation, they did not meet the ratio criterion nor were they linear with ratio scales on most continua (Stevens and Galanter, 1957; Marks, 1968; Stevens, 1961). For most continua, including pleasantness (Engen and McBurney, 1964) and liking (Moskowitz and Sidel, 1971), category scales were found to be concave downward relative to ratio scales, indicting a compression of responses at the high end of the scale. Immediately after their development, the ratio methods that Stevens developed began to be applied to both affective judgments (e.g., Indow, 1961) and the more complex psychological dimensions that Thurstone had earlier addressed (Ekman and Kunnapas, 1962; Koh, 1965; Stevens, 1966). Soon direct ratio methods, like magnitude estimation, were being applied to food-related sensory judgments, e.g. odor pleasantness (Engen and McBurney, 1964), and the first use of magnitude estimation for the measurement of food acceptance was undertaken by McDaniel and Sawyer (1981) on whisky sour formulations. Later, in several papers and books, Howard Moskowitz, a student of S.S. Stevens, championed the use of magnitude estimation in sensory evaluation of foods (Moskowitz, 1974, 1975, 1977b). Impetus for more recent developments in hedonic scaling Although magnitude estimation and the other ratio procedures that Stevens developed had mathematical advantages over the 9-pt hedonic scale, they too were not without controversy. Early on, Birnbaum and Veit (1974) provided evidence that magnitude estimation may not be truly linear with subjectively perceived magnitudes. Furthermore, stimulus and instructional context effects have been shown to influence magnitude estimates, raising the issue of whether the method provides invariant measures of sensation or true ratio scales (Poulton, 1968; Birnbaum, 1982a,b; Mellers, 1983). However, the main disadvantages of magnitude estimation that have

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interfered with its adoption by the food industry are practical ones – consumers sometimes have difficulty learning magnitude estimation procedures (Giovanni and Pangborn, 1983; Shand et al., 1985) and, after collecting the data, magnitude estimates require further mathematical manipulation before the data can be statistically analyzed. Thus, at the end of the 20th century, although a large number of methods had been developed for scaling hedonics over the previous century and a half, the 9-pt hedonic scale was still used almost exclusively in the international consumer products industry to assess the liking/disliking and other affective properties of food and non-food products. With the advent of the 21st century, new methods began to evolve for both the direct and indirect scaling of hedonics. In the sections that follow, we detail two of those methods – best-worst scaling (a form of indirect, choice-based scaling) and labeled magnitude scaling (a form of direct scaling of hedonic magnitude).

7.3

Best-worst scaling: a modern approach to indirect scaling

7.3.1 Introductory example The purpose of this section is to provide a detailed introduction to the methodology of best-worst scaling. Briefly, best-worst scaling was formally introduced by Finn and Louviere (1992) and requires the consumer to choose both the best (most liked) and worst (least liked) among a set of test stimuli. As such, it is a choice-based or Thurstonian approach to scaling. Many applications followed the seminal paper by Finn and Louviere (1992), including those by Owen et al. (2001); Cohen (2003); Cohen and Neira (2003); Chrzan (2005); Goodman et al. (2007a,b); Auger et al. (2007); Flynn et al. (2007); Lee et al. (2007, 2008); Louviere and Islam (2008); and Cohen et al. (2009). As a methodology, best-worst scaling is most easily introduced through an example. For simplicity, consider a study involving four samples (A, B, C and D). An experimental design is used to define the presentation of subsets of samples, and in the example shown in Fig. 7.1, a balanced incomplete block design is used to generate four sets of three samples. For each set, participants are asked to indicate the one sample they like most and the one sample they like least. By way of a simple two-step process these responses can be converted into individual-level scales for each sample: 1) counting the number of times each sample is chosen as ‘most liked’ and the number of times it is chosen as ‘least liked’, and 2) on a sample-by-sample basis subtracting these two numbers. For the responses shown in Fig. 7.1 doing so results in the following ‘best-minus-worst’ (B − W) scores: Sample A = 3, Sample B = −1, Sample C = −2 and Sample D = 0. In this example, where each of the four samples appeared a total of three times, the individual-level scales for each sample can range from +3 to −3. For example, a value of −1 could be obtained if a respondent selected a sample as most liked once and twice selected the same sample as least liked (e.g., Sample

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Hedonic measurement for product development Least liked sample

Set 1

145

Most liked sample

Sample A Sample D Sample C Least liked sample

Set 2

Most liked sample

Sample B Sample C Sample A Least liked sample

Set 3

Most liked sample

Sample C Sample B Sample D Least liked sample

Set 4

Most liked sample

Sample D Sample A Sample B Note. The summary scores are shown as participant 1 in Fig. 7.1.

Fig. 7.1

Example of best-worst scaling using four samples.

Table 7.1 Example of data from six participants evaluating four samples using best-worst scaling B−W (Sample A)

B−W (Sample B)

B−W (Sample C)

B−W (Sample D)

1 2 3 4 5 6

3 0 −3 −1 −3 −3

−1 0 −1 −3 3 2

−2 3 2 3 1 −1

0 −3 2 1 −1 2

Aggregate

−7

0

6

1

Participant

Note. The raw data for participant 1 is shown in Fig. 7.1.

B). A value of zero means that a sample is chosen as ‘best’ equally often as it is chosen as ‘worst’, or alternatively that it is never selected as ‘best’ or ‘worst’. With a score of +3, the data in Fig. 7.1 means that Sample A was most preferred. Conversely, Sample C, with a score of −2 was least preferred. Table 7.1 features a scaling of the four samples by six participants,

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with the raw data for participant 1 being shown in Fig. 7.1. The individuallevel scales are easily comparable across participants and when aggregated across all participants, the net frequency of B − W scores gives an overall score for a sample. In Table 7.1, across the six participants, Sample A is least preferred and Sample C is most preferred.

7.3.2 Application of best-worst scaling to hedonics Case study: introduction and methodology To illustrate the application of best-worst scaling we present a case study in which a scaling was obtained of the importance of nine meal choice factors that were relevant to participants’ choice of a meal consumed the previous evening. The nine factors were: ‘meal looks, smells and tastes nice’, ‘total cost of meal’, ‘meal is convenient’, ‘naturalness of foods in meal’, ‘freshness of foods in meal’, ‘familiarity with foods in meal’, ‘mood/emotions at time of meal’, ‘political, ethical and/or religious reasons’ and ‘health reasons (illness prevention and/or control)’. Participants were told that the task concerned their choice of last night’s meal and that the researchers were interested in learning about the factors motivating their choice of that particular meal. Participants were informed that they would be presented with 12 groupings of three different factors. For each set of three factors they were asked to select the most and least important factor influencing their choice of the previous evening’s meal. The experimental design presented the nine factors in sets of three, and across the 12 sets or groupings each possible combination of two factors was featured once (Cochran and Cox, 1957). Two survey versions were used, in which the presentation order of factor groupings differed. Participants (n = 46) were employees at the US Army Natick R, D & E Center (NSRDEC). Nearly two-thirds were males living with a partner and/ or dependent children. Close to 100% were university educated. Ages ranged from ~20 to ~90 years old (mean = 42.2, std = 12.8), and the sample was diverse with respect to household income (less than $20,000 yearly to more than $140,000). None of the participants had previously taken part in a best-worst task. Case study: analysis of best-worst data Providing the first presentation of the theoretical properties of probabilistic models of best-worst choice, Marley and Louviere (2005) state: “. . . the maximum-difference model is of the well-known Luce (1959), equivalently Multinomial Logit (McFadden, 1974) form with ratios of scale values, and has difference scores for best versus worst choices that are sufficient statistics for the parameters of the model” (p. 478). In other words, there are two different alternative approaches to the analysis of the data. One is to fit a logistic regression model to the observed pairs of most and least liking choice frequency data. Typically multinomial

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regression (MNL) is used and the resulting logistic regression coefficients scale the best-worst items in relation to a reference item, which provides an arbitrary basis for comparison (i.e., origin of the scale). To ease interpretation of the MNL coefficients, it is possible to convert these to ratio-scaled probabilities that sum to 100 using Equation 7.1, where Ui is the weight (utility) of the ith item (Sawtooth Software, 2005). However, it is also possible to calculate best-minus-worst scores for each sample. This practise is acceptable due to Marley and Louviere (2005) who formally showed that the difference scores (i.e., best-minus-worst scores) are sufficient statistics for the scale values derived through analysis of the elicited choice data by MNL. That said, before proceeding to use the bestminus-worst scores in subsequent analyses, we consider it good practice to verify for each data set that the MNL parameter estimates are proportional to the best-minus-worst scores. This is easily done with a scatter plot and simple linear regression. As outlined in the introductory example, best-minus-worst scores are obtained by counting the number of times a sample is considered “best” relative to the number of times it is considered “worst,” resulting in individual-level scales for each sample that are easily comparable across all participants. However, it is important to realise that the B − W scores depend on the frequency that each item appears in the best-worst choice sets, and that the scores at the aggregate level also depend on the number of participants. To enable comparison across studies, which may vary in the number of participants and/or number of items considered, Goodman et al. (2006) recommend calculating standardized B − W scores (Equation 7.2). In Equation 7.2, Zj is the standardized B − W score for item j (Zj is also referred to as the standard level of importance for item j); j is the attribute number (j = 1, . . . , n); Bwscorej is the total B − W frequency for attribute j over all participants (i = 1, . . . , m); MaxBW is the highest frequency of B − W scores over all attributes; and MinBW is the lowest frequency of B − W scores over all attributes. P = 100 Zj =

eU i ∑ eUi

BWscore j MaxBW − MinBW

7.1

7.2

Case study: results obtained with best-worst scaling The main findings from the case study are shown in Table 7.2, which contains different summaries of the meal choice factor importances. Attention is first directed to the B − W scores. The mean values provide a simple summary and it fits expectations that ‘meal looks, smells and tastes nice’ was the most important factor among those studied. Similarly, it was unsurprising that the factor ‘political, ethical and/or religious reasons’ be the least

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2.29 −1.00 2.49 −0.76 0.68 0.54 0.15 −3.54 −0.85

1.54 2.10 2.10 1.96 1.59 1.73 1.97 1.43 2.32

B−W score SD

B − W score standardized 0.38 −0.17 0.41 −0.13 0.11 0.09 0.02 −0.59 −0.14

B − W score kurtosis −0.65 −1.12 0.33 −0.34 −1.04 −0.08 −0.53 15.80 −0.76

1.47 0.06 1.61 0.05 0.67 0.61 0.44 −1.89 0.00

MNL coefficient

Note. Analyses are based on the 41 participants who provided complete best-worst data. The probabilities in the last column sum to 100.

Meal looks, smells and tastes nice Total cost of meal Meal is convenient Naturalness of foods in meal Freshness of foods in meal Familiarity with foods in meal Mood/emotions at time of meal Political, ethical and/or religious reasons Health reasons (illness prevention and/or control)

B−W score mean

Meal choice factor importances measured by best-worst (B − W) scaling

Meal choice factor

Table 7.2

24.4 5.3 27.9 5.9 11.0 10.4 8.9 0.9 5.6

Factor probability

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important. There was significant heterogeneity in the importance attached to the different factors, as shown by the standard deviation and kurtosis values. Inspection of the raw data aids in interpretation of these summary statistics. For example, the large values for “political, ethical and/or religious reasons” was revealed to be caused by the majority of participants rating this factor as “least important” 75% or 100% of the time. The only exception was two participants who rated it as most, rather than least, important. There are many likely contributors to this heterogeneity but it is beyond the scope of this chapter to examine these in detail. Table 7.2 also presents the results as standardised B − W scores. These are useful for comparing with other studies, for example a replication of the current study at a different point in time or with a similar study including more/less factors. Where such comparisons are of interest, standardized scores should be used. They are easy to calculate using Equation 7.2, which, when applied to “freshness of food in meal” gives: Z = 0.113 = [41 × 0.683]/[41 × (2.488 − (−3.537))]. As an aside, note that standardization does not alter the relative importance attached to meal choice factors. While analysis with B − W scores is simpler than MNL modeling, one advantage of using logistic regression is that it is possible to convert the MNL estimates to ratio scaled probabilities that sum to 100. As illustrated in Table 7.2 this can be a useful approach to translating MNL estimates into more easily understood and comparable values. Since the rescaled scores are ratio in nature, we may say, for example, that the most important meal choice factor, “meal is convenient” with a score of 27.9 is more than 30 times as important as the least important factor, “political, ethical and/ or religious reasons.” This pattern of factor importances, where sensory properties are a key driver of food choice, fits expectations. It is also not unexpected that convenience is a very important factor. The fact that it is 5 times more important than cost can probably be explained by the scaling task relating to one specific meal – last night’s dinner – and not to food choices in general. For this case study, the final result we present is Fig. 7.2, which compares the factor importance estimates derived as MNL regression coefficients and as best-minus-worst scores. As expected, the MNL estimates are proportional to the B − W scores, and this justifies the use of B − W scores in subsequent analyses. We note as an aside that proportional relationships have also been established in applications of best-worst scaling to acceptability testing (Jaeger et al., 2008; Jaeger and Cardello, 2009). Our recommendation is that this comparison be done for each new study, especially if the data are only analyzed as B − W scores. Beyond the approaches to data analysis shown here, others specifically designed for best-worst data exist (e.g., Louviere et al., 2008). Recently, Mueller et al. (2010) and Mueller and Rungie (2009) also applied latent class clustering to best-worst acceptability data for red wine.

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MNL coefficients

1.5

0.5

–0.5

–1.5

y = 0.5524x + 0.3222 R2 = 0.9807

–2.5 –4

–3

–2

–1

0 B – W scores

1

2

3

4

Note. Because 5 participants provided erroneous best-worst data, analyses are based on data from 41 participants only (89%). Because in the survey each factor was presented four times, the range of the scale of average B – W scores is –4 to +4. The MNL model was significant (–2 Log L = 1296.83; likelihood ratio Chi-Square = 462.67; df = 8; P < 0.0001).

Fig. 7.2

7.4

Relationship between multinomial logit regression (MNL) estimates and best-worst (B − W) scores.

Labeled magnitude scales: a modern approach to direct scaling

7.4.1 Early research leading to labeled magnitude scales As discussed in the Introduction, although magnitude estimation had certain mathematical advantages over the 9-pt hedonic scale, practical difficulties encountered in its use prevented its adoption in the food and consumer products industries. However, spurred on by the possibility of obtaining ratio-like data and of eliminating problems inherent in category scaling, investigators soon began the development of simpler, direct methods for scaling sensory magnitude. In the first of these efforts, Borg (1982) developed a category-ratio scale for the measurement of perceived exertion. His original scale comprised a set of 13 verbal labels that ranged from “nothing at all” to “maximal.” Specific labels and associated numbers (0, 0.5, 1–10, except for “maximal” which had no number) were determined from magnitude estimation studies of the semantic meaning of the word labels. In subsequent applications (e.g., Marks et al. 1983; Borg et al. 1985), the scale was modified by placing the verbal labels along a linear graphic rating scale, so that continuous judgments could be made. Such a scale had been suggested earlier by Borg (1972) himself. Some years later, Marks et al. (1992) adapted the Borg category-ratio scale for application to taste intensity. However, it was Green and colleagues (Green et al., 1993, 1996) © Woodhead Publishing Limited, 2010

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who developed the first labeled magnitude scale (LMS) of oral sensation, using procedures similar to those used by Borg (1982). The Green scale had a high end anchor corresponding to the “strongest sensation imaginable,” a quasi-logarithmic spacing of intermediate word anchors determined by magnitude estimation scaling of their semantic meaning, and a response mode that required placing a slash mark on a vertical line with the data consisting of the distance scored from the bottom end. A critical characteristic of both the Borg and Green scales was the presence of an end-point anchor, such as “maximal” or “strongest imaginable.” These phrases were used as a fixed end-point of sensation magnitude that served to place judgments of different subjects on a common sensory “ruler” (Borg, 1961, 1962; Marks et al. 1983). Data obtained from labeled magnitude scales, such as Borg’s and Green’s are presumed to have ratio-like properties because the obtained data have been shown to be similar to magnitude estimation (Green et al., 1993; Schutz and Cardello, 2001; Lim et al., 2009), the ratio procedure upon which these scale methods are based. More recently, Bartoshuk and colleagues (Bartoshuk, 2000; Bartoshuk et al., 1999, 2003, 2004) modified the LMS by choosing an end-point anchor that was more extreme than the one Green had used (“greatest imaginable sensation”). Bartoshuk chose to use an end-point anchor that went outside the bounds of the sensory attribute being measured to include all sensory experiences, i.e. “strongest imaginable sensation of any kind.” She adopted this more extreme anchor to enable comparisons among subject groups who may have differences in their sensitivity and responsiveness to specific sensory attributes, e.g. differences among individuals in sensitivity to propylthiouracil (PROP). Bartoshuk’s modified LMS, now commonly referred to as the general LMS (gLMS) has been proven effective in uncovering differences among groups varying in PROP sensitivity to other sensations (Bartoshuk et al., 2003, 2004, 2006). Bartoshuk and colleagues (Duffy and Bartoshuk, 1996) also began the measurement of hedonics using scales that had been developed for taste intensity by Marks et al. (1988) and by direct application of the LMS of Green (Bartoshuk et al., 1999; Duffy et al., 1999; Peterson et al., 1999) and the gLMS (Bartoshuk et al., 2005, 2006). Since the LMS and gLMS are uni-directional intensity scales, when used for hedonics, subjects must first indicate whether the stimulus is liked or disliked and then make their judgment of the like/dislike intensity on the scale.

7.4.2 The development of the labeled affective magnitude (LAM) scale While Bartoshuk and others had applied labeled magnitude scales of intensity to the study of hedonics, this approach begs the question as to whether the growth of sensory magnitude is the same as the growth of hedonic magnitude. From a more practical perspective, this question can be phrased as “do the magnitudes of quantity expressed by a set of adverbial descriptors (e.g., very, moderately, extremely) remain the same, regardless of the adjectives that they modify (e.g., strong, pleasant, intelligent, tall, etc.)?” In © Woodhead Publishing Limited, 2010

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early research by Cliff (1959), it was found that the semantic meaning of such adverb-adjective combinations was a multiplicative function of the two words, but Cliff offered the possibility that the adverbs might “have different multiplying values in different dimensions.” Guest et al. (2007) has pointed out that the LMS has often been used outside of its intended perceptual domain and that the adverbial descriptors of the intensity LMS, e.g. “strong” may not be appropriate for other perceptual dimensions, e.g. “pleasantness,” and could create artificial-sounding labels, e.g. “strong pleasantness.” Similarly, these intensity scales developed for taste and oral sensation may have interior label spacing that is quite different than what might be found in other dimensions, e.g. pleasantness or roughness (Guest et al., 2007). Aware of these issues, Schutz and Cardello (2001) and Cardello and Schutz (2004) developed the first labeled magnitude scale that was specifically designed for the study of hedonics, i.e. like/dislike in response to foods. Using a similar approach to Green et al. (1993), they scaled the semantic meaning of 39 positive and negative phrases that were culled from the hedonic literature (e.g., Young, 1930; Jones and Thurstone, 1955; Jones et al., 1955) and that were commonly used to describe different levels of liking/ disliking for foods (Table 7.3). Among these 39 phrases were all of the labels used in the 9-pt hedonic scale and the phrases “greatest imaginable liking” and “greatest imaginable disliking.” In addition to these phrases, neutral phrases, e.g. “neither like nor dislike,” “barely like” and “fair” were included. Members of an employee consumer panel then used modulus-free magnitude estimation to assess the semantic meaning of all of the phrases (see Schutz and Cardello, 2001 for procedures). Table 7.3, which is taken from Schutz and Cardello (2001), shows the geometric mean ratings for the 19 negative phrases (left) and 20 positive phrases (right). In keeping with previous findings on the non-equivalence of intervals on the 9-pt hedonic scale (Moskowitz, 1977a, 1980), this table demonstrates that the phrases used in the 9-pt hedonic scale (asterisked) are not perceptually equivalent. For example, while the interval between the phrases “like slightly” and “like moderately” is 160 units, the interval between the phrases “like very much” and “like extremely” is only 116 units. In terms of ratios, “like moderately” is 3.2 times more positive than “like slightly,” while “like extremely” is only 1.3 times more positive than “like very much.” Similar examples of non-equivalence of intervals can be seen for the negative phrases. Using the data in Table 7.3, Schutz and Cardello (2001) constructed a series of balanced and unbalanced labeled magnitude scales of liking/disliking, pleasantness/unpleasantness, and “mixed label” scales. In subsequent testing of these scales they found high session to session reliability (r = .97–.99) for all scales, but a slightly better sensitivity to stimulus differences for the balanced, like-dislike scale, which utilized all of the labels of the 9-pt hedonic scale plus “greatest imaginable liking/disliking.” As a consequence, this scale, now known as the Labeled Affective Magnitude (LAM) scale, was adopted for use. The scale is shown on the right side of Fig. 7.3.

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Table 7.3 The word label phrases used in the development of the LAM scale, along with the obtained geometric mean magnitude estimates and measures of dispersion. The labels of the 9-pt hedonic scale are asterisked (from Schutz and Cardello, 2001) “NEGATIVE PHRASES AND GEOMETRIC MEAN RATINGS”

“POSITIVE PHRASES AND GEOMETRIC MEAN RATINGS”

PHRASE

PHRASE

GM

SE

SE/GM

GM

SE

SE/GM

Greatest Possible Dislike Greatest Imaginable Dislike Despise Detest Dislike Extremely* Dislike Intensely Extremely Unpleasant Terrible

−624.91

59.67

0.10

Greatest Imaginable Like Greatest Possible Like Best of All Like Intensely Like Extremely* Excellent

640.91

66.22

0.10

−624.75

58.48

0.09

633.75

67.14

0.11

−515.01 −487.38 −471.75

34.07 30.19 24.95

0.07 0.06 0.05

512.67 485.39 475.71

27.62 29.47 32.15

0.05 0.06 0.07

−462.04

28.92

0.06

454.81

25.17

0.06

−422.38

22.82

0.05

405.03

17.76

0.04

359.64

17.65

0.05

0.06

Extremely Pleasant Like Very Much* Like Very Well

−438.05

28.94

0.07

Strongly Dislike Dislike Very Much* Very Unpleasant Dislike Moderately* Poor

−358.05

22.47

332.81

16.07

0.05

−346.71

22.84

0.07

Very Good

320.73

18.86

0.06

−287.72

28.35

0.10

Very Pleasant

295.08

18.54

0.06

−199.17

19.30

0.10

Enjoy

236.81

15.45

0.07

−190.09

19.44

0.10

232.21

14.18

0.06

Moderately Unpleasant Don’t Like Dislike Mildly Dislike

−175.08

14.05

0.08

Like Moderately* Good

210.10

16.15

0.08

−166.49 −146.78 −68.40

20.03 18.26 18.30

0.12 0.12 0.27

169.09 147.19 145.97

12.95 19.50 21.54

0.08 0.13 0.15

Dislike Slightly* Slightly Unpleasant

−66.40

6.70

0.10

108.80

11.42

0.10

−63.32

8.59

0.14

Pleasant Like Like Fairly Well Mildly Pleasant Slightly Pleasant Like Slightly*

80.93

10.52

0.13

72.01

7.81

0.11

* phrases used in the 9-pt hedonic scale (Peryam & Pilgrim, 1957).

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Scales for Rating Product Liking Experience Nine-point Hedonic Scalea

Labeled Affective Magnitude Scaleb,c Greatest Imaginable Liking

Like Extremely Like Very Much

Like Extremely

Like Moderately Like Slightly

Like Very Much

Neither Like Nor Dislike Like Moderately

Dislike Slightly Dislike Moderately Dislike Very Much

Like Slightly

Dislike Extremely

Neither Like Nor Dislike Dislike Slightly Dislike Moderately

Dislike Very Much Dislike Extremely

Greatest Imaginable Disliking a

Peryam and Pilgrim (1957)

b

Schutz and Cardello Cardello and Schutz

c

Fig. 7.3 The 9-pt hedonic scale (left) and the LAM scale (right) (from Cardello and Wise, 2007).

7.4.3 Application and testing of the LAM scale In subsequent experiments conducted on the LAM scale (Schutz and Cardello, 2001), it was found that the scale was as easy to use by consumers as the 9-pt hedonic scale, that the nature of the obtained data was independent of whether the users were familiar with the scale, that the data were independent of whether numbers were used on the scale or not, and that the scale was unaffected by whether it was oriented vertically or horizontally. In a direct comparison with the 9-pt hedonic scale and the method of magnitude estimation, the labeled affective magnitude scale was found to

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have a distribution of data that was more normal than either of the other two scales (Schutz and Cardello, 2001), thereby better meeting the assumption for parametric statistical analysis of the data. This greater normality was also found by Lim et al. (2009) in their work on developing an alternative labeled magnitude scale for hedonics. In addition, the LAM scale produced a higher proportion of significant differences among pairs of means for well liked foods as compared to the hedonic scale, and the ratios among means for the LAM scale were more highly correlated with the ratios generated by magnitude estimation than was the 9-pt hedonic scale. The greater sensitivity of the scale is partly attributable to the fact that the LAM scale enables consumers to give ratings above “like extremely,” which is the top of the 9-pt hedonic scale, thereby eliminating any artificial “ceiling” on responses. Early work on the development of the LMS also showed the importance of eliminating the ceiling effect imposed by category scales of intensity (Green et al., 1993, 1996; Bartoshuk et al., 2005). Schutz and Cardello (2001) found that approximately 16% of LAM responses fell above “like extremely,” with more recent studies (Cardello et al., 2008a; Lawless et al., 2010a,b) showing that anywhere from 10 to 30% of responses fall above “like extremely.” Lastly, Schutz and Cardello (2001) showed the LAM scale to produce fewer “neutral” responses than either of the other two scales, thereby minimizing the “complacency” problem. The LAM scale has been applied successfully in a number of studies to quantify the hedonic response to food preferences (Pasquet et al., 2002), off-flavors in peanuts (Greene et al., 2006), juice flavor (Forde and Delahunty, 2004; Cardello et al., 2008a), milk aftertaste (Porubcan and Vickers, 2005), model soft drinks (Zhao and Tepper, 2007), whole grain breads (Bakke and Vickers, 2007), breakfast bars (Hein et al., 2008), probiotic juices (Luckow et al., 2006), functional beverages (Pohjanheimo and Sandell, 2009), fruit gum drops (Urban et al., 2009), the sweetness of teas (Chung and Vickers, 2007a,b) and other foods (Keskitalo et al., 2007a,b; Lawless et al., 2010b; El Dine and Olabi, 2009), toothpaste (Simchen et al., 2007: Urban et al., 2009) and oral sensations (Guest et al., 2007). In some studies where the LAM scale or its variations have been compared to the 9-pt hedonic scale, greater sensitivity to product differences with the use of the LAM scale has been confirmed (Leathwood et al., 2005: Greene et al., 2006; Simchen et al., 2007; El Dine and Olabi, 2009, Lawless et al., 2010a [experiment 2]), either through the finding of a larger F-value for the main effect of samples, an index suggested by Lawless and Malone (1986a,b), or through frequency counts of the total number of significant post-hoc comparsions. The observation of better stimulus sensitivity is consistent with other literature comparing labeled magnitude scales to category scales or even some visual analogue scales (Merrill et al., 2004; Cardello et al., 2005, 2008b). However, some studies have failed to find such differences (Hein et al., 2008; Lawless et al., 2010a [experiment 3], 2009b), suggesting that better sensitivity may be observed only when the elimination of the ceiling

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effect produces a large enough increase in the overall spread of scores to improve statistical differentiation among samples.

7.4.4 Recent developments in labeled magnitude scaling End anchor label effects In the Introduction it was pointed out that a critical characteristic of labeled magnitude scales, like the LAM, is the presence of an extreme end-point anchor, such as “maximal,” as used by Borg (1982) or “greatest imaginable sensation,” as used by Green et al. (1993, 1996). In the case of the LAM scale, which is bi-directional, these phrases are “greatest imaginable like” and “greatest imaginable dislike.” The purpose of these extreme anchors is to bring the ratings of all subjects onto a common ruler – the assumption being that these extreme end-anchors are constant among different individuals (Borg, 1961, 1962; Marks et al., 1983). As also noted, Bartoshuk has argued strenuously that such end-anchors should be still more extreme by spanning the breadth of all human sensation, e.g. “greatest imaginable sensation.” While it is agreed that this approach would better serve to bring together individuals who have different attribute sensitivities and who, thus, live in different sensory worlds, it is possible that in practical applications of food product testing, contextual effects produced by such extreme anchors may compress the data and, in turn, reduce the ability to detect differences among stimuli. In fact, Horne et al. (2002) demonstrated this type of compression in a comparison of the LMS and gLMS. In the case of hedonics, Cardello and Schutz (2007) examined the influence on sensitivity to product differences of altering end-anchors to encompass varying perceptual ranges. This was accomplished by changing the referent stimulus category to which the endanchors referred. End-anchor labels were compared that read either “greatest imaginable like/dislike” (no referent), “greatest imaginable like/dislike for this type of food (or beverage)” or “greatest imaginable like/dislike for any food (or beverage).” ANOVAs conducted on data from studies of both foods and beverages showed significant effects of the end anchors with no interaction by products. In both cases, foods or beverages rated with the endanchors “greatest imaginable like/dislike for any food (beverage)” had more compressed like/dislike ratings (lower for liked foods, higher for disliked foods) than either of the less expansive end-anchor sets. Thus, the anchor referent “for any food/beverage” resulted in smaller observed differences among products. Although not significantly different from one another, product differences observed with use of the end-point anchors of “greatest imaginable like/dislike for foods (beverages) like this” were greater than for the no referent condition. These data were interpreted to mean that endpoint anchors that refer to a more extreme perceptual range reduce the likelihood of identifying differences in product liking/disliking, as compared to less expansive end-anchor referents. When no referent is offered, ratings take on an intermediate value. As importantly, Cardello and Schutz (2007)

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showed that this same effect occurred when using the 9-pt category scale, i.e. when “like/dislike extremely for any food” was used instead of either “like/ dislike extremely” (no referent) or “like/dislike extremely for foods like this.” In a recent report (Lim et al., 2009), a labeled magnitude scale for hedonics was developed using end-anchors similar to those used by Bartoshuk et al. (1999), i.e. “most liked/disliked sensation imaginable.” Although sensitivity to product differences was not found to be better than the 9-pt hedonic scale, the authors did observe better normality in the labeled magnitude scale data and a resistance to ceiling effects. More recently, Cardello et al. (2008a) examined the effect of the extreme end-anchor recommended by Bartoshuk et al. (2006), i.e. “greatest imaginable like/dislike for any experience.” Using fruit juices as stimuli, they compared this extreme end-anchor to the one used in the LAM scale, i.e. “greatest imaginable like/dislike.” Since changing the end-point anchors of a scale without re-establishing the perceptual spacing of the interior labels violates the ratios of semantic meaning among the phrases, Cardello et al. (2008a) reconstructed a new labeled affective magnitude scale by rescaling all of the interior and endpoint anchors using magnitude estimation. These new re-scaled values produced a labeled magnitude scale whose interior labels were compressed relative to the original LAM scale spacing. They then compared the original LAM scale, this “new” LAM scale with compressed label spacing but the same end-anchors (greatest imaginable like/dislike), and a scale with endanchors “greatest imaginable like/dislike for any experience.” Figure 7.4

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Fig. 7.4 Mean ratings (+ 1 st. error) for each of five juices (1–5) when scaled using either the original LAM scale (OL), the “compressed” LAM scale that was rescaled within the context of “greatest imagninable like/dislike for any experience” (RE), or a scale that used the actual anchors “greatest imaginable like/dislike for any experience (AE). Letters (A, B, C) represent significant differences among juices using a Tukey test. Juices not sharing a common letter are significantly different (after Cardello et al., 2008a).

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Fig. 7.5 A plot of mean responses to 20 different verbal descriptions of varied sensory experiences using either the original LAM scale, the “compressed” LAM scale, or a scale using the end anchors “greatest imaginable like/dislike for any experience” (from Lawless et al., 2010a).

shows the results of this study. As can be seen, the range of product ratings decreased when the end-anchor label was “experiences of any kind,” and an intermediate level of compression occurred with the scale that had compressed interior labels. Of some further interest in this study is the fact that the ratios among the common labels on the scales did not differ, suggesting that the original ratios of the LAM scale are preserved when rescaled in a different context. In a second study of the compression phenomenon, the same three scales were used by consumers to rate their liking/disliking of 20 written descriptions of taste, odor, visual, tactile and auditory stimuli (Lawless et al., 2010a). Figure 7.5 shows these data. Again, there was a dramatic compression effect when the most extreme end anchors were used and there was the least compression with the original LAM scale. Based on the results of these studies, the authors concluded that there is no advantage to the use of extreme end-anchor terms referring to “experiences of any kind” and that the use of such anchors may compress the range of scale ratings used by consumers.

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7.4.5 Continuous versus categorical responding Another issue that has received some attention regarding the labeled affective magnitude scale, is whether or not consumers treat the scale as is intended, i.e. responses are made in a continuous manner along the scale, or if some respondents treat the labels as categories and respond in a categorical manner. In the development of this scale, Schutz and Cardello (2001) examined this phenomenon, but for only one category, i.e. “neither like nor dislike.” By analyzing the proportion of hash marks that fell between 45 and 55 on the scale (50 = neither like nor dislike) and comparing it to the proportion of “neither like nor dislike” circled on the 9-pt hedonic scale, they observed a lower proportion of neutral category responses with the LAM scale. This finding has since been confirmed by Simchen et al. (2007). In addition, a recent reanalysis of responses made on the LAM scale when used to evaluate both food names and actual foods in one of the studies reported in Schutz and Cardello (2001) showed that the probability of hash marks being placed within specific statistical boundaries around the labels of the scale was not significantly different from the probability that one would expect by chance, regardless of the orientation (vertical vs. horizontal) of the scale. In spite of these findings, it has been reported that categorical responding may occur under certain circumstances (Lawless et al., 2010b). Such an occurrence is a possibility with all labeled magnitude scales. In fact, Green et al. (1993) pointed out that subjects use semantic scales, like the LMS, gLMS and LAM, by first examining the labels on the scale and then interpolating between the labels to make their judgments. To the extent that a subject is “lazy” in their use of the scale, they may simply stop after examining the scale labels and not bother to interpolate between adjoining labels. Indeed, the instructional set given to subjects who use such scales should be clear. In our laboratory at NSRDEC, we provide instructional sets that state that the subject should: 1) place their hash mark “anywhere on the line,” 2) that they should not circle or otherwise mark the labels, and 3) we provide consumers with an actual example that shows the entire scale and a hash mark that has been placed in-between scale labels. In a recent study examining categorical behavior (Lawless et al., 2010c), it was observed that such responding also could be reduced by enlarging the physical length of the LAM scale. This approach, combined with proper instructions should minimize the potential for consumers to respond in a categorical manner. Since categorical responding is possible when consumers are not given proper instructions, what are the implications if one finds such responding? First of all, such responding should not influence the potential of the LAM scale to better discriminate among products falling at the extremes. This is because the LAM scale still offers the opportunity for responding above or below “like/dislike extremely.” Secondly, the ratio-like properties of the scale are built into the placement of the labels on the LAM scale, i.e. without the labels, the scale has no ratio-like properties; it is the placement

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of the labels themselves in accordance to the ratios in their semantic meaning that provides ratio-like properties to the scale. As such, the user of the scale does not need to make his/her judgments in a ratio manner (as, for example, in magnitude estimation). In point of fact, if contrary to instructions, a respondent was to circle “like slightly” for one stimulus and “like extremely” for another stimulus, we would still be able to approximate the ratios of liking between the two stimuli, because we know the ratios of semantic meaning between those two labels. What categorical responding does, is increase the error of estimation of those ratios. In practice, it is still better to know that one product is liked approximately twice as much as another than it is to simply know that one is “liked extremely” and one is “liked slightly”.

7.5 Comparisons among hedonic scaling methods Insofar as the methods of best-worst scaling and LAM scaling are relatively new, the number of studies that have compared these methods to older or more traditional methods is small, but growing. In this section, we review the available literature in which such comparisons have been made and summarize what appear to be the major advantages and/or disadvantages of these methods for scaling hedonics.

7.5.1

Comparison of best-worst scaling to other methods, advantages and disadvantages Best-worst vs. other hedonic scaling methods One of the findings that motivated the exploration of best-worst scaling for hedonic measurement was earlier comparisons with category rating, which had found that best-worst scaling lead to greater discriminatory ability among the set of items under consideration. In the methodological study reported in Cohen (2003) and Cohen and Orme (2004) comparing bestworst scaling, paired comparisons and monadic rating demonstrated the superior discrimination among items achieved with the former. The case study concerned IT managers’ assessment of the importance of 20 items for choice of IT hardware and one of the ways in which the authors assessed discriminatory power was to perform t-tests of mean item difference. A reference item was selected and this item’s importance score was compared to all other item importance scores. The differences between averaged t-values (3.3 for importance scores elicited using monadic rating, 6.3 for paired comparisons and 7.7 for best-worst scaling) was evidence of the discriminatory superiority of best-worst scaling. Jaeger and co-workers initiated the evaluation of best-worst scaling in hedonic testing and a fundamental question was whether best-worst scaling when applied to taste testing would demonstrate superior performance

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similar to that reported by Cohen and co-workers. Across three studies best-worst scaling was compared to other hedonic methods. Jaeger et al. (2008) compared best-worst scaling and an unstructured line scale in a study with beef patties; Jaeger and Cardello (2009) compared best-worst to LAM scaling in a study with fruit juices; and Hein et al. (2008) compared bestworst to an unstructured line scale, the 9-pt hedonic scale and the LAM scale in a study with cereal bars. The studies revealed similarity in the elicited preference structures and in some comparisons there was evidence of slightly higher discriminatory ability of best-worst scaling compared to direct scaling. However, a recent study by Mueller et al. (2010) suggested that these results may not apply universally. In a study on red wine, these authors compared best-worst scaling to the 9-pt hedonic line scale and found that different preference structures were elicited They concluded that best-worst scaling is not suitable for hedonic measurement of red wine, stating that the factors of alcohol, tannin and memory fatigue make it less practical for red wine acceptability testing than hedonic rating. In situations where best-worst scaling is used to elicit survey-based hedonic responses, we are aware of only a single study that compares the methodology to other hedonic scales. Jaeger and Cardello (2009) used a survey to elicit food preferences and found somewhat more between-item discrimination. However, in that study the task of completing the best-worst survey was also found to be more confusing and difficult compared to LAM scaling. Advantages/disadvantages of best-worst scaling The research cited above demonstrates that best-worst methodology has potential for hedonic taste testing, and we recommend that this new approach should be viewed as an addition to the methods available for hedonic scaling. Best-worst scaling is unlikely to be the preferred option for every application, but there are situations where it offers advantages over other hedonic scaling approaches commonly used by sensory and consumer science professionals today. Survey-based hedonic research is one such area and Jaeger and Cardello (2009) have applied it to food preferences and discussed pros/cons. The lack of a response scale also makes best-worst scaling attractive for use in cross-national research. There is no need to worry about differences in scale use and application of advanced statistics to correct for these. Cohen et al. (2009) compares wine purchase intentions among Australian and Israeli consumers and discusses how best-worst data yield meaningful cross-cultural comparisons that are easy to conduct. His study was part of a larger 12-country comparison and papers by, for example, Goodman et al. (2007a,b) and Goodman (2009) are clear demonstrations of the effectiveness of best-worst scaling in such research applications. Auger et al. (2007) is another example of best-worst scaling being applied in cross-cultural research, in this case to measure consumer ethical beliefs.

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One characteristic of best-worst scaling that some may consider a limitation is that it does not permit evaluation of the absolute level of liking. Whereas the LAM and 9-pt hedonic scales, for example, use numerical values anchored to absolute word phrases, the magnitude of best-worst data are relative and specific to each study. This makes comparison across studies less intuitive, but as illustrated in Section 7.3, it is possible. The recent development of best-worst scaling means that some methodological questions still require attention. For taste testing applications, the question of order effects is among them. There is no research on best-worst scaling and order effects, but we consider it prudent to mitigate these as is routinely done in sensory research. This may require taking account of presentation order within best-worst sets, as well as across sets. On a practical note, we have experienced some difficulty in obtaining complete best-worst data. When participants complete a best-worst table, two responses must be provided: the least liked/preferred item and the most liked/preferred item. Careful checking of paper ballots is required, because missing data mean that the data cannot easily be analysed as B − W scores. Choice modeling is still possible, but less accessible in terms of exploring preference heterogeneity. If possible, we recommend using electronic data collection that checks for missing data before participants proceed to the next best-worst set. Commercial software providers exist; see, for example, www.sawtooth.com, offering MaxDiff Designer and MaxDiff/Web. As an aside we note that research has emerged (e.g., Sethuraman et al., 2005) supporting the use of internet/web-enabled technology, relative to the paper-and-pencil method, for preference surveys and conjoint analysis data collection. Future applications of best-worst scaling in sensory and consumer science Best-worst scaling has gained considerable popularity in marketing research and is routinely implemented by some commercial providers of sensory and consumer science research. These applications are typically not in hedonic scaling as considered in this chapter, but in scaling of a variety of other responses that do not rely on tasting. Written brief stimuli or visual stimuli are ideal as they can be scanned quickly and easily compared and the best and worst alternatives identified. In particular, the approach is being used to capture purchase decisions and how such different factors are traded against each other, e.g. Jaeger et al. (2009), and more recently in the scaling of product elicited emotions (Thomson et al., 2009).

7.5.2

Comparison of labeled affective magnitude scaling to other methods, advantages and disadvantages Labeled affective magnitude scaling vs. other hedonic scaling methods Since the LAM scale was developed to overcome many of the disadvantages of the 9-pt hedonic scale, most of the comparisons of the LAM scale have

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been to this popular scale. However, as the popularity of the LAM scale has grown, it has begun to be compared to a number of other types of hedonic scaling. In the original research by Schutz and Cardello (2001), the LAM scale was compared to both the 9-pt hedonic scale and to magnitude estimation in several studies. Foremost among the findings were that, although the LAM scale, the 9-pt hedonic scale and magnitude estimation all resulted in the same rank ordering of stimuli, the LAM scale was able to detect more differences among stimuli, especially at the extreme of the scale (very wellliked foods). This finding was confirmed in subsequent studies by Greene et al. (2006), Simchen et al. (2007) and El Dine and Olabi (2009) when comparing the LAM scale to the 9-pt hedonic scale, and similar results were found by Leathwood et al. (2005) using a scale that was very similar to the LAM scale. This advantage is not unexpected because it has been shown on numerous occasions that category scales are concave downward relative to ratio scales, reflecting a compression of ratings at the extremes of the scale. The LAM scale overcomes this compression by enabling ratings above “like extremely,” thereby allowing the stimuli that have reached the “ceiling” of the 9-pt hedonic scale to be spread above “like extremely.” The logical consequence of this effect is a wider distribution of scores at the extreme and a better likelihood of detecting significant differences among these stimuli. Although, this improved sensitivity has been shown in several cases using different stimuli and different testing conditions, it is not always found. Studies by Hein et al. (2008) and Lawless et al. (2010b) did not show such an advantage. Whether this is due to the particular choice of stimuli in those studies, the range of the stimuli used, panellist instructions, or some other variable is difficult to say. It would seem justified, however, to say that, perhaps due to the elimination or mitigation of ceiling effects or due to some measurement characteristic of the LAM scale, better sensitivity to product differences may be seen, depending on the particular stimulus set, the instructions to consumers, or other procedural factors. Other comparative findings in the studies reported by Schutz and Cardello (2001) include the fact that: 1)

2) 3)

4) 5)

the ratios among stimulus judgments for the LAM scale are similar to the ratios generated by magnitude estimation scaling and that both are different from the “ratios” obtained with the 9-pt scale that the LAM scale minimizes the number of neutral “neither like nor dislike” responses (a finding also reported by Simchen et al. (2007)) that the LAM and 9-pt hedonic scale both show normality in responses versus magnitude estimation (although El Dine and Olabi (2009) found the distribution of LAM scores as being more normal) that the LAM scale, 9-pt hedonic scale, and magnitude estimation all had equivalent reliability (r > .97), and the LAM and 9-pt hedonic scale had equivalent ease of use by consumers, but magnitude estimation was rated as more difficult.

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Guest et al. (2007) compared the LAM scale to a labeled magnitude scale of pleasantness/unpleasantness that they created and found similarity in scale construction, with the exception that the label positions of the LAM scale were shifted slightly toward the lower end of the scale as compared with their pleasantness scale. Lim et al. (2009) in their comparison of an alternative labeled magnitude scale of liking to the 9-pt hedonic scale found similar sensitivity to stimulus differences, but found their labeled magnitude scale to be resistant to ceiling effects and more normally distributed than the 9-pt hedonic scale. Hein et al. (2008) compared the LAM, the 9-pt hedonic scale, best-worst scaling and unstructured line scales of hedonics. They found best-worst scaling to provide better sensitivity to product differences than either the LAM or 9-pt hedonic scale, although all scales produced very similar preference structures and none were found to be easier/more difficult to use by consumers. In a more recent comparison of the LAM scale to best-worst scaling, Jaeger and Cardello (2009) found better sensitivity of the LAM scale to differences among tasted products, but better sensitivity of best-worst scaling to differences among food names. However, again the preference structures did not differ between the scales nor were there any important differences in ease of use (both methods were considered relatively easy to use). The one practical difference that emerged was that LAM scaling was less time-consuming and less complex to execute in the study of tasted foods, because it uses a single stimulus procedure, rather than a multiple comparison procedure. This fact is reminiscent of Thurstone’s comments regarding the difficulties involved in using paired comparison methods to assess hedonic responses in taste tests (page 140). The overall conclusion from Jaeger and Cardello (2009) was that the choice of best-worst scaling or LAM scaling for hedonics needs to be made “within the context of the nature and complexity of the study to be conducted, the nature of the respondents, and the nature of the test samples.” These words remain good advice when selecting among any of the available methods for hedonic scaling. Advantages/disadvantages of labeled affective magnitude scaling There are several potential advantages of hedonic labeled magnitude scales over other forms of hedonic scaling. The first is that it is a direct measure of liking/disliking. Thus, there is no need to make assumptions about the equality of dispersions, and there is no need to infer the psychological scale from other measures. The user directly judges the magnitude of his/her liking/disliking by placing a slash mark on the scale. For many researchers who hold the philosophical view that direct measurement is a more valid form of psychological measurement (see Moskowitz (2005), Cardello (2005) and related comments), this is an advantage over all indirect methods. For those who are more practical minded, direct measures also afford greater simplicity in execution. That is, rather than requiring the presentation of a lengthy series of paired or multiple stimuli about which subjects must make comparative judgments, direct measures, like the LAM scale, merely require

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single stimulus presentation, thereby minimizing the time and effort required to collect data. In this regard, the LAM scale has equal ease of use to other direct rating scales. Secondly, as compared to most other direct scales of liking or pleasantness, like the 9-pt hedonic scale, labeled magnitude scales afford the user the ability to judge products as being liked/disliked greater than “extremely.” This has the potential to expand the distribution of product ratings and to better discriminate among products that are all very well-liked (a common occurrence in industrial applications) or that are all very disliked (a much rarer occurrence). The reasons for differences in whether or not such sensitivity differences are found likely relate to the specific products being tested and the range or distribution of liking/disliking of the samples. There is no evidence in any study that has been conducted to date that the LAM or other labeled magnitude scales result in a loss of sensitivity relative to category scales. Thirdly, the LAM scale affords the researcher ratio-like data, i.e. data that is similar to magnitude estimation. In fact a number of studies have demonstrated that data obtained using the LAM and other labeled magnitude scales have a high correlation with data obtained from magnitude estimation and a much lower correlation relative to data obtained by category methods (Greene et al., 2006; Schutz and Cardello, 2001; Lim et al., 2009). The ratio-like nature of LAM ratings enables important statements to be made about the relative magnitude of liking between two or more products, enables valid use of parametric statistics, and enables direct comparisons and trade-offs with other variables measured at a ratio-level, e.g. instrumental data, price, etc. Lastly, the LAM scale and other labeled magnitude scales, reduce the percentage of ratings that fall near “neither like nor dislike” (Schutz and Cardello, 2001; Simchen et al., 2007), thereby minimizing the number of “safe” judgments that consumers give. The primary disadvantage of LAM scaling is that in order to construct the scale on paper ballots or on a computer-based data capture system, care needs to be taken in the exact placement of the verbal labels on the associated line scale. The practical issues involved in this have been discussed in Cardello and Schutz (2004). Also, in terms of data analysis, the data from paper ballots must be transcribed from the scale by measuring the distance of each slash mark from the zero point using a ruler. Although this can be time-consuming, a digitizer can be used to speed transfer and entry of the data into an electronic format.

7.6 Recommendations and conclusions Food and beverage NPD is a multifaceted process in which hedonic measurement is one of many steps. The importance of this construct to sensory

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scientists working in industry and to scholars interested in the quantification of human sensory, attitudinal and emotional responses motivated this chapter, and we set out to examine two recent developments in hedonic scaling. We have comprehensively reviewed the origins of labeled magnitude scaling and best-worst scaling and documented their development and application. In doing so we particularly intend for readers to gain awareness of the advantages/disadvantages of these two scaling techniques, relative to other commonly used scales. Armed with this knowledge practitioners and scholars are in a position to make more informed decisions about choice of hedonic scaling methods and more fully match scaling technique to study objectives. In this spirit, we conclude this chapter on hedonic measurement with some recommendations regarding the optimal situations in which to use best-worst and labeled affective magnitude scaling. The LAM scale and other forms of labeled magnitude scales for hedonics are recommended for those cases in which the test involves multiple samples that are all extremely well liked. In traditional consumer tests in industry this is often the case, because companies are often comparing slight variations on a product, all of which have relatively high acceptance. As noted previously, the advantage of the LAM scale in this situation is that it allows ratings above “like extremely,” thereby reducing any ceiling effects and opening up the possibility of finding product differences at the extreme. The LAM scale can be used to evaluate a variety of different stimulus types and can be used with a variety of consumer segments. However, one must use caution that the consumer understands how to use the scale in an appropriate manner and that they do not resort to simply checking the labels, otherwise the advantages of the scale may be compromised. Best-worst scaling for food-related hedonic measurement is less intensively researched than labeled magnitude scaling. However, based on current knowledge we conclude: for applications in taste testing, best-worst scaling is more cumbersome and this may be the factor that will most negatively impact on its wider adoption by sensory scholars and professionals. We also note that at least one group of authors have recommended against its use, notably in the case of red wine, which is a sensorially demanding and fatiguing product category. For hedonic scaling involving survey-based research, best-worst scaling is relatively easy to implement and appears to have significant potential. We recommend implementing automated data collection and giving consideration to mitigating the potential effects of presentation order effects. Because an experimental design allocating items to best-worst sets is needed, implementation will always be a bit more cumbersome than direct scaling. However, it seems that greater betweenitem discrimination can be achieved. Theoretically, there are also advantages for cross-cultural applications, but in the realm of hedonic scaling, published comparisons to methods of direct rating are still to emerge. On a final and broader note, we recommend that choice of hedonic scaling method be made with consideration of the specifics of the study for

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which the method is being used, taking into account factors such as the nature of the test samples, the characteristics of respondents, resource issues, etc.

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8 The effects of context on liking: implications for hedonic measurements in new product development J. Delarue, AgroParisTech, France and I. Boutrolle, Danone Research, France

Abstract: In the industry, consumer hedonic tests are frequently conducted before launch and after the products are fully developed, but sometimes they are also conducted in the course of the development process (e.g., for selecting the best option, the best ingredient . . .) or even before any development is done, notably for analyzing the existing products on the market place and getting insights into drivers of preference. For the benefit of accuracy, these hedonic tests are usually implemented in controlled settings, most often in a central location (e.g., hall tests, lab tests). However, some studies show that the results obtained from such tests may differ quite substantially from the results obtained in more naturalistic conditions (e.g., tests at home, in restaurants . . .). It is widely assumed that the latter type of tests better predict real consumer behaviors and hence product successes or failures. In this chapter, we review the differences observed between these two types of tests and we provide some background to help understanding how contextual variables may affect the hedonic response. This will give the reader the necessary information in order to decide which test is to be preferably implemented depending on the goals, the product type and the logistical and cost constraints. Eventually we present some attempts to improve the hedonic measurements in controlled settings either by manipulating contextual variables or by eliciting context and priming participants’ autobiographic memory using scenarios. Key words: context, validity, liking, preferences, HUT, CLT, situational tests.

8.1 Introduction Hedonic testing is used extensively by the “fast moving consumer goods” industry to guide new product development. Food developers, for instance, frequently check the liking for their recipes by asking consumers to state

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their liking or their preferences. Reasons behind such hedonic testing are diverse and go from the assessment of the effect of a product change (new ingredients, new process, new packaging . . .) to the more general understanding of how product characteristics drive liking or dislikes. In all cases, testing the product with consumers gives confidence when strategic decisions are to be made during the product development process. To answer this need for consumer feedback, survey institutes propose the implementation of hedonic blind tests conducted with a large number of interviewed consumers (one hundred participants usually being considered to be a minimum). Ultimately, consumer tests should allow evaluating the potential success of a product to be launched on the market or alternatively to anticipate a possible failure following a change in the product. Unfortunately, it turns out that the outcome of preference tests may actually be poor predictors of success (Köster and Mojet, 2007). The hedonic tests are set up to compare the overall sensory performance of various recipes. For comparison purposes both sensory scientists and product developers tend to favor standardized condition testing. Thus in practice, the products are usually tested alone in a controlled environment. Yet, some companies prefer home testing with the view that it conveys more natural perceptions and is conducive to better prediction of actual consumer behaviors. The question of whether the testing conditions affect consumer perception of products thus arises. While context is often overlooked when testing foods, it is in most cases a necessary part of the evaluation when testing cosmetic products because they are usually used or worn during a whole day. Developers are thus interested in the evaluation of these products’ in-use properties, some of which relate to efficiency and some of which relate to sensory perception (e.g., persistence, comfort, fragrance base notes, moisturizing effect, etc). The same would apply to cloths and fabrics. Hence, testing personal products in-use often implies in-home testing. Besides, the cosmetic industry has understood for a long time that consumers differ in their use of the products and that consumer testing methodologies can only be standardized up to a certain point and will frequently depend on inter-individual differences of a morphological or physiological nature, not to mention differing gestures. In other branches of industry, context may be even more important in the judgment construction process. Although it is not in the scope of this chapter, it is noteworthy that hedonic tests of cars while driving are definitely in strong interaction with the road context and thus raise specific issues (Astruc et al., 2007). This chapter will thus mainly focus on context effects in the perspective of food development but the considerations on hedonic test designs may also very well apply to the testing of other kind of products. From a theoretical point of view, consumption situations and eating/drinking modalities would be expected to exert a strong influence on how food products are perceived and liked. Many variables differ depending on the eating

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situation. Some studies have shown, for example, that food liking differs depending on the meal settings (hospital, restaurant, army cafeteria, etc.). For food companies and scientists who study food choices, study design is thus important, irrespective of whether the study is conducted at home or in the laboratory. This chapter first presents the current practice of hedonic testing of food and personal care products. We will then give some theoretical notions about context effects on liking and attitudinal responses. This will give the reader clues for a thorough understanding of how contextual variables affect the result of hedonic tests as detailed in the following literature review. The consumer test practitioner will then find the necessary information in order to decide which test should be preferably implemented in the perspective of a product development, depending on the goals, the product type and the logistical and cost constraints. Eventually we present some attempts to improve the hedonic measurements in controlled settings either by manipulating contextual variables or by eliciting context and priming participants’ autobiographic memory using scenarios.

8.2 Current practice of hedonic tests: central location test (CLT) and home use test (HUT) This chapter will only address declarative hedonic measurements principally based on a liking score and the manner in which they are routinely obtained to assist developers in the issues of developing new products, improving products, reducing costs, or product positioning with respect to the competition. Several methodological alternatives are available for getting insights into consumer perception and for the acquisition of liking data. Among the methodological alternatives is the choice of the test site: a sensory evaluation laboratory, a room in a public facility or the subject’s home. Many other characteristics of the protocol generally necessitate choices on the part of the investigator: the order of product presentation, the quantity of product presented, the number of products presented, the timing of the session, etc. The foregoing examples of the choices the investigator has to make when setting up a consumer test protocol suggests that protocols are highly flexible. However, in practice, the assessment site usually conditions the other characteristics of the protocol. Thus, currently, study institutes and universities distinguish between two main types of test: the central location test (CLT) in a facility or laboratory and the home use test (HUT). The two types of test will be addressed in order to differentiate them on the basis of numerous methodological aspects other than the evaluation site. 8.2.1 Tests under controlled conditions The methodologies set up for central location tests are relatively similar to those set up in the laboratory. They are both tests under controlled test

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conditions, unlike home tests. Compliance with instructions, manner of examining samples and ways of responding are easy to monitor and to control in this way. The rooms in which central location tests are conducted under controlled conditions are generally equipped with several tables independent of each other (or individual booths in the case of a laboratory) in order to prevent subject distraction by the other subjects being interviewed at the same time. The tasting session generally involves several samples to be evaluated. The portions presented are frequently smaller than usual portions. It is, however, frequent that investigators require a minimum quantity of product to be consumed (e.g., “drink at least half the glass before giving your opinion”). Once tasting has been completed, the interviewer asks the subject to formulate an overall assessment of the product. One may also ask the subject a number of questions related to the perception and liking of a few specific sensory characteristics or elicit the subject’s opinion on items concerning the product. The subject is then required to rinse his/her mouth before tasting the next product(s) proposed and for which the subject answers the same questions. The number of products presented during such tests may be relatively high but it is nonetheless necessary to restrict the number of samples tasted in a given session because of physiological fatigue (satiation, sensory adaptation or irritation) and psychological fatigue. The sequential monadic presentation mode necessitates a sample presentation order that is balanced over the panel interviewed (MacFie et al., 1989). However, some studies may be conducted with a pure monadic presentation involving tasting of a single product by subject. This has an impact on the cost of the study. In contrast to laboratory tests requiring subject pre-recruitment, centralized location tests are set up in rooms located in facilities frequented by potential purchasers (high streets, malls). The subjects are generally intercepted and selected on the spot throughout the day by the investigators. Once a consumer has been selected as belonging to the target, he/she is invited to take part in a testing session that does usually not exceed 20 minutes. A longer tasting session would require pre-recruitment and compensating the subjects for taking part in the test. The subjects selected on the spot have less time to spare than pre-recruited subjects. This restricts the number of samples that can be presented and the number of questions relating to the sample. Study institutes generally propose simple face-toface interviews rather than a self-administered questionnaire enabling the subject to focus on his/her interview.

8.2.2 Tests under natural conditions The objective of this type of tests is to evaluate products under normal use or consumption conditions. The most widely used design is to leave the subjects testing the products at home. The subjects are generally prerecruited from databases to be representative of the target population of

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interest and are therefore frequently compensated. In general, the products, masked and coded, are distributed one after the other with a variable time interval between each drop-off (from a few days to a few weeks). This sequential monadic product presentation is time consuming and thus a limited number of different samples are usually tested in home tests (at most 2 or 3). Certain home studies also employ a pure monadic product presentation in which each subject only evaluates a single product. The product is usually distributed in a large quantity and accompanied by a leaflet containing tasting instructions of variable precision. The consumer is to use the product over a predetermined duration as he/she wishes and often at the frequency the subject chooses. The subject’s opinion on the product is then acquired during a second visit of the interviewer or through a pre-distributed self-administered questionnaire. An increasing number of market research institutes now propose to collect consumers’ opinions online, which strongly reduces the cost of the test and also allows different ways of interviewing consumers. It is noteworthy that the current use in the cosmetic industry is to set up home tests only. CLT are very seldom. One reason for this is the practical difficulty to implement pertinent use conditions in central facilities for testing personal care products such as shower cream or hair-removing creams for example. But most important, cosmetic manufacturers are interested in how consumers perceive their products in-use and for “longer-term” effects. For example, a foundation or a moisturizing cream is usually worn the whole day. It is thus usual to provide consumers with the sample to be tested and to let them use it at home for a few days. When some products like makeup require a specific gesture (i.e., the specific way the consumer uses a cosmetic product or a series of products as part of a cosmetic routine) that for comparison purposes needs to be standardized across the subjects, the participants are previously told how to use the product in a central location. Even in such cases, the samples are tested at home for appropriation. Also, it is very frequent in the cosmetic industry, that the person who evaluates the product and its cosmetic effects is not the person to whom it is applied. Manufacturers indeed ask professional aestheticians or hairdressers to evaluate the products when they apply them to consumers. Depending on the company, these professionals may test the products in a central facility or test them in their own beauty/hairdressing salon. In the specific case of fine fragrances however, fragrance companies are usually interested in consumers’ hedonic response both to the “first sniff” (usually evaluated in hall) and to in-use perception (usually evaluated at home) that include appraisal of bottom notes. This two-step evaluation yields different types of information. While the “first sniff” impression seems to relate rather directly to the sensory characteristics and to the overall intensity of the fragrance, the in-home evaluation allows consumers associating their sensory perceptions to images, personality and self-perception (Craignou and Bezault, 2009).

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8.2.3 Conflicting desiderata In the early stages of the development of consumer test methodology, Schaefer (1979) promoted the use of tests under standardized tasting conditions in the assessment of food product acceptability. Controlled consumer test methodologies are still widely recommended in most up-to-date sensory evaluation standards (AFNOR, 2009). These test methodologies are developed in order to minimize “noise” in the gathered data. In this view, “noise” refers to all the uncontrollable variables that are not related to the sensory food properties which could influence the food acceptability. Nevertheless, food acceptance and food choice may be considered in a totally different perspective. Many authors (Lawless and Heymann, 1999, Meiselman, 1992) point out the necessity to take real eating conditions into account in order to assess “real” food acceptability, i.e. to ensure good predictive validity of the gathered data. Even if the precision of the data is a valuable reason for the choice of controlled protocols, we cannot overlook the fact that the artificial conditions reduce the predictive validity of the data. Although more expensive, HUT with the more naturalistic tasting conditions enables hedonic measurements that are potentially more predictive of reality, but little controlled and thus more difficult to interpret with regard to development operations. The contrast between the precision of CLT and the supposed validity of HUT termed “conflicting desiderata” by Brinberg and McGrath (1985) is thus a problem with which manufacturers are continually confronted when selecting one methodology rather than the other. The central question for food industry players is to determine to what extent testing their products under controlled test conditions (centralized location or laboratory) modifies consumers’ perception and liking.

8.3

How context may affect preferences

Consumer tests based on sensory evaluation have, as their objective, the measurement of the perception and liking of a food solely focused on its intrinsic properties. However, a large number of studies have shown that almost all human behaviors are influenced by contextual variables and eating behavior is no exception.

8.3.1 Definition of context Context is a very broad concept (the term situation is also used). In the case of food consumption, the context may be defined as all the information (conscious and unconscious) not directly related to the food under study and exerting an influence on the perception of that food. Contextual variables may thus be defined as all the variables that influence the hedonic judgment collected other than the variables intrinsic to the test food, namely its sensory and nutritional properties. Meiselman (1996) identifies three

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Table 8.1 Examples of contextual factors cited in the literature for their influence on food acceptability Food-related factors

Individualrelated factors

Experimental-related factors

Past factors

Prior contacts with the tasted sample

Food historic: episodic memory, habits

Previous tasted sample

Simultaneous factors

Amount of food consumed at each consumption occasion Information concerning the product (price, origin, benefit information) Preparation of the product (temperature, seasoning)

Physiological state (hunger, thirst) Psychological state (mood, motivation, reward)

Eating situation: Time of the day Social environment (family, friends, colleagues . . .) Food environment (companion foods, meal components) Comfort of the location Choice of the tasted product Physical effort to obtain the food

Future factors

Anticipation of future physiological effects (satiety sensation, digestive discomfort)

Anticipation of future events

Next tasted sample

types of context variables depending on whether they are related to the food, to the individual or to the consumption situation. Rozin and Tuorila (1993) further divide the context into factors affecting attitude and choice at the time of eating (simultaneous factors) and factors that exert their influence either prior or subsequent to the eating experience (temporaneous factors). Table 8.1 provides an overview of the contextual factors often cited in the literature for their influence on food acceptance measures (hedonic scores, preferences, choice or intake) and structured according to both the Meiselman (1996) and the Rozin and Tuorila (1993) classification.

8.3.2

Taking into account the role of psychological constructs and attitudes on judgment when eating / theoretical background Attitude: definition and construction process While pleasure may be defined as an unconscious sensory sensation (Chiva, 1992, Pieron, 1974), the sensory message is subsequently interpreted by the subject and may be verbally expressed in a fully conscious manner as I like

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it or I don’t like it. Hedonic tests attempt to measure these consumers’ attitudes toward the sensory properties of the product that is being evaluated. Attitude may be defined as a cognitive component reflecting the subject’s position, from positive to negative, with respect to the object and orienting the subject toward particular actions. Attitudes are the result of a construction process that largely depends on both internal introspective processes and the external context in which the attitude is expressed. It will thus be understood that attitude is greatly subject to contextual effects. For instance, it is not rare to observe that people formulate different evaluations of the same object on different occasions, although the object has undergone no change between the two evaluation time points. What mechanisms enable context to influence judgment? It would be illusory to believe that, in their responses to surveys, the subjects interviewed retrieve a pre-existing response, one “tailor-made”, even though certain subjects may have stored in their memories a pre-existing evaluation of the problem. Attitudes and judgments are, on the contrary, products of the time when the question is asked, but also largely based on information stored in the long-term memory. Although attitudes that form in childhood may last throughout life, it may be considered that there is no definitive attitude (Tesser, 1978). On each consumption occasion, part of the stored information is activated and added to the new information to determine a behavior. Besides, individuals build their attitude periodically on the basis of information that is temporarily preeminent or accessible. Hence, the generation of responses to attitudinal questions is not necessarily based on a systematic and rigorous appraisal of all the appropriate information. On the contrary, given the short time the subject has to answer (or more generally to adapt his/her behavior), response generation is rather based on rapid sampling of the information. Products may hence be selected or rejected on the basis of a few salient characteristics. This tendency to base judgment on the most accessible information has been termed heuristic availability by Tversky and Kahneman (1973). As a matter of fact, it seems that it is precisely the information retrieval component of the overall judgment construction process that is mostly influenced by context (Tourangeau et al., 1989). In the field of attitudinal surveys, two types of contextual effect have been evidenced: assimilation and contrast. The two types of effect result from the level of interaction between contextual information and the characteristics of the product under evaluation. The assimilation effect results from diffusion of the contextual value judgment toward target judgment (i.e., product judgment) (Deliza and MacFie, 1996). This effect may be positive (e.g., presence at the time of consumption of positive information or an appreciated person) or negative (e.g., consumer’s negative mood). Numerous studies in the field of

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experimental psychology have observed that triggering a positive mood may induce more positive attitudes than induction of a neutral or negative mood (Bower, 1981, Deldin and Levin, 1986, Isen et al., 1978). Similarly, the influence of the subject’s psychological state on the evaluation of a food has been the subject of a few investigations. Siegel and Risvik (1987), for instance, observed that the hedonic scores assigned to a food are higher when the subjects had previously formulated an opinion on a positive subject than when the opinion was formulated on a more serious subject. The impact of remuneration is also noteworthy. Subject remuneration induces higher scores and decreases the discrimination between samples (Bell, 1993). The individual’s internal state at the time when he/ she tastes the test food is thus of primary importance with respect to the attitude formulated. Contrast effects are the result of the opposite scenario, i.e. when the contextual values and the target do not match. A contrast effect is observed when the judgment does not reflect any relationship or a strictly negative relationship between the value attributed to the target object and the values attributed to the contextual stimuli accompanying the target. The role of expectations in contrast effects is well known. Imagine that the context for food consumption is particularly favorable (an expensive menu in a famous restaurant, for example). The expectation constructed by the consumer before eating the meal is then probably very strong. If the food is finally rated less positively than what the consumer expected, the latter will tend to under-evaluate the food eaten in the restaurant with respect to the evaluation that he/she would have made in a less favorable environment. Thus, positive contextual information can result in a negative judgment of the target!

8.3.3

Differences between hedonic data obtained under standardized or naturalistic tasting conditions A number of researchers have addressed the influence of the testing environment overall on the hedonic perception of a food alone or a full meal. The change in the appreciation of a given food in different sites (different public places, home, restaurants, etc.) has been observed over several decades (Green and Butts, 1945, Maller et al., 1980, Miller et al., 1955, Meiselman et al., 1988, Meiselman et al., 2000, Edwards et al., 2003). However, even though these studies have the advantage of investigating behavior in natural conditions, they also have the disadvantage of only interviewing populations used to those environments. Results may thus be subject to sampling biases. This illustrates the complexity of the issue of generalizing a hedonic response obtained in a given environment to another environment. Besides, a number of studies compared the hedonic responses obtained in a naturalistic tasting environment (public places, home) and in a

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controlled test environment (laboratory, test room). From these studies, it is possible to examine the various consequences that setting up consumer tests under controlled conditions may have on the hedonic data collected and the strategic decisions based on those data. Three categories of changes have been observed: • a change in the overall level of the liking scores • a change in the degree of discrimination between the products compared • a change in the hedonic ranking of the various products compared. In the first case, a level effect may be statistically considered as a simple context effect, whereas in the latter two cases, span and order effects may be considered as product × context interactions and are much more delicate to deal with. Level effect Some studies have only reported a change in the score depending on the test conditions. Thus, for most of these studies, the scores obtained under naturalistic eating conditions are higher than the scores obtained under artificial conditions, irrespective of the product tested (De Graaf et al., 2005, Hersleth et al., 2003, King et al., 2004, 2007, Kozlowska et al., 2003, Meiselman et al., 2000, Pound et al., 2000, Shepherd and Griffiths, 1987, Boutrolle et al., 2007a). Even though the opposite phenomenon was observed in a few rare cases (Hellemann et al., 1993), it would appear that the under-scoring of test foods under controlled conditions is currently firmly established. Thus, numerous hypotheses may be formulated with a view to interpreting the change in score depending on eating environment. Among the hypotheses, one relates to the fact that natural evaluation conditions are, a priori, more comfortable than the evaluation conditions in a controlled test situation. In effect, the tasting rooms are frequently poorly decorated and not always very comfortable. In addition, the interviewees usually have little time to spare given that they are frequently not recruited beforehand but on an on-the-spot basis. Thus, the high scores assigned to foods under natural eating conditions may only illustrate an assimilation effect due to the participant’s well-being. However, few authors have specifically studied the impact of the comfort of the test site on hedonic measurements and those who have done so (Bonin et al., 2001, King et al., 2004) did not really observe any change in the hedonic scores following an improvement in the comfort of the test situation (pleasant welcome, topof-the-range materials, decorated tables). Very formal evaluation conditions may also induce the subject to perceive the tasting session as an examination and thus be much more demanding with regard to the test products than the subject would have been in a more natural consumption setting. Lastly, test conditions that are more or less conducive to consumption of the test product may also contribute to the difference in scoring. For

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example, an evaluation conducted at home enables the consumer to eat the product when he/she wants and under appropriate and optimal conditions. Thus, the simple fact of enabling natural conditions of product consumption could promote the participant’s pleasure in consuming the product and thus promote a favorable opinion on the test product. The phenomenon of under-scoring products under controlled conditions vs. the scores obtained under natural consumption conditions, is not, at first sight, particularly worrying given that hedonic sensory tests are set up to compare the performances of several recipes. Thus, if the deviations between the scores remain the same for the products, the statistical conclusions of the tests and, ultimately, the strategic decisions may not be affected even though the score levels are different. However, in the industry, certain developmental studies on new products or range extensions use action standards based, among other things, on the necessity of obtaining an assessment score greater than an imposed limit (frequently 7 on a scale from 1 to 10 in French companies). Obviously, the choice of a score cut-off as an indicator for rejection or acceptance of a product is open to criticism. Whatever the case may be, the scoring shift as a function of evaluation conditions in the context of use of that type of action standard is embarrassing. It would appear obvious that a score of 7 obtained in a controlled situation test does not represent the same degree of liking as a score of 7 obtained, for example, in a home test. The foregoing thus necessitates revising the action standards or, at the least, differentiating them as a function of test conditions. The variability in scores as a function of evaluation conditions also makes using databases generated using purely monadic methodology hazardous. Currently, manufacturers wish to set up consumer studies with purely monadic product presentation. That protocol design is particularly popular in that it removes the influence of other stimuli on the assessment of each sample. The hedonic judgments constructed in absolute terms by different groups of participants supposedly enable capitalization of consumer test data. Thus, the influence of consumption conditions on the scores observed also calls into question the pertinence of the capitalization of data with the objective of comparing hedonic scores across different studies. The authors therefore recommend the greatest prudence with regard to the comparison of product assessment scores formulated in different environments, even if the evaluations are in purely monadic mode. Thus, manufacturers need to set up a traceability system for evaluation conditions in which the scores were obtained in order to ensure that the scores obtained in different studies can be validly compared. Sensitivity effect In addition to the impact of test conditions on scores, other studies have clearly evidenced a change in the degree of discrimination between test products. This may have real consequences on the strategic decisions based

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CLT

10 9

HUT

NS

8

**

7 6 5 4 3 2 1

7.14

7.02

7.91

7.09

A

B

A

B

Fig. 8.1 Mean overall hedonic scores for two brands (A and B) of cheese crackers obtained with two panels of consumers either in a CLT (N1 = 240 women) or in a HUT (N2 = 240 women) (Boutrolle et al. 2007a).

on the tests. However, there does not appear to be any consensus with regard to the most discriminant method. While Miller et al. (1955) and Vickers et al. (1999) have reported that controlled test conditions generate more discriminant results than naturalistic conditions, Calvin and Sather (1959), Hersleth et al. (2005), McDaniel and Sawyer (1981) and Boutrolle et al. (2007a) have reported the opposite. King et al. (2004) observed both types of result in the same study, depending on test product category (while teas were better discriminated in a restaurant, salads were better discriminated in a central location). The inability to conclude that one methodology is more discriminant than another upsets the preconceived idea of enhanced discriminant potential for tests under controlled conditions related to sequential monadic product presentation in the course of a single session. Figure 8.1 illustrates the fact that in some cases, the HUT conditions may very well be more discriminant than CLT conditions, with the remarkable effect that a significant difference in liking is observed in the former and not in the latter. It would therefore appear illusory to select a test methodology oriented toward controlled conditions with the sole objective of enhancing the discrimination between the various samples compared. Besides, seeking an increased sensitivity may be considered to be contradictory to external validity. In effect, a test should not magnify the preference for one product if in a real life situation consumers equally like the products. Order effect Lastly, the results whose consequences are most important in terms of strategic decisions are those of the studies that have demonstrated a change

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in the hedonic ranking of samples depending on the test conditions used (De Graaf et al., 2005, McEwan, 1997, Posri et al., 2001). In the context of screening several samples of a given product pre-launch, an incorrect ranking would result in incorrect product selection. However, in the authors’ experience, the methodological comparisons conducted in the context of comparison of the assessment of two or three samples have never yielded such a result. Context effects depend on product type Focusing on the studies that have conducted several comparisons based on different food categories, it appears that the impact or absence of impact of evaluation conditions on the statistical conclusions of the tests are greatly related to the type of food tested. Table 8.2 shows the results of a series of experiments that illustrate this (Boutrolle et al., 2007a). As can be seen, for all product types there is a significant difference between the methods (i.e. level effect), the mean scores being systematically higher in HUT than in CLT. However, the sensitivity effect depends on product type as revealed by the product × method interaction which is significant for two out of four product categories. It may be supposed that foods associated with a very specific and/or personal mode of consumption and eaten at a specific time will probably be more sensitive to controlled tasting conditions. For instance, evaluation conditions seem to have a very strong influence on the assessment of foods normally eaten in meals and no influence on foods normally eaten as snacks (De Graaf et al., 2005, Boutrolle et al., 2007a). In addition, even in the context of foods usually eaten at mealtimes, King et al. (2004) observed that the influence of the evaluation conditions on the results seemed more important for accompanying foods (tea and salad) than for the main dish (pizza). The usual mode of eating the various categories of food may therefore be one of the principal reasons for the variability of the influence of evaluation conditions on test conclusions.

8.3.4

Contextual variables that may influence food choices and food liking in hedonic tests As indicated above, the integrative process of hedonic judgment construction largely depends on the contextual information in which it is formulated (Fig. 8.2). This section addresses the various studies that have investigated the influence of contextual variables involved in the construction of a hedonic judgment of a food and which may be the cause of differences in the results for tests conducted on the same products under standardized or more natural evaluation conditions. The quantity of food eaten during tasting The influence of the quantity proposed and eaten in hedonic tests has been observed in numerous laboratory tests. Several authors (Bellisle et al., 1988,

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CLT

HUT

Probiotic drink CLT

HUT

Cheese crackers CLT

HUT

Sparkling water

CLT

HUT

Fruit flavoured dairy drink(1)

(1) Test conducted according to a pure monadic evaluation procedure.

Consumer sample size

239 subjects 241 subjects 240 subjects 240 subjects 161 subjects 160 subjects 2 × 160 2 × 160 subjects(1) subjects(1) Mean score product A 6.96 7.24 7.14 7.91 6.74 7.54 7.70 8.07 Mean score product B 6.55 6.95 7.02 7.09 6.20 7.14 6.81 7.80 Mean score product C 6.28 7.37 Statistical inference A>B A>B A=B A>B A>B A>B A>B>C A=B>C (p = 0.004) (p = 0.029) (p = 0.448) (p < 0.001) (p = 0.007) (p = 0.033) (p < 0.001) (p < 0.001) ** ** ** ** Method (level effect) (p = 0.006) (p < 0.001) (p < 0.001) (p < 0.001) ns ** ns ** Product × method (p = 0.627) (p = 0.003) (p = 0.639) (p = 0.007)

Method

Table 8.2 Comparison of CLT and HUT results for four different products. Products were evaluated according to a monadic sequential procedure except for the fruit juice and milk beverages. Mean hedonic scores are analyzed using 3-way analysis of variance (score = product + method + product*method). (Boutrolle et al. 2007a)

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CONTEXT Amount consumed Number of exposures

SUBJECT

Time of the day

INTERNAL STATE

Food environment

Physiological: hunger/thirst

Social environment Possibility to choose

Psychological: mood, emotions

Hedonic response

FOOD HISTORIC

Product Product preparation Post-ingestive effects

Fig. 8.2

Schematic representation of contextual variables that may influence the hedonic response to a given food. Context variables (either external or related to the product) that may affect the subject’s perception. Internal context variables (related to the subject) also play a role. All these variables may directly or indirectly influence the hedonic response.

Lucas and Bellisle, 1987, Monneuse et al., 1991, Perez et al., 1994, Zandstra et al., 1999) have reported that high sugar or salt concentrations are more appreciated when the tasting imposes a small quantity of samples than when the samples are consumed ad libitum. However, other studies have yielded contradictory results (Popper et al., 1989, Daillant and Issanchou, 1991, Shepherd et al., 1991). In addition, Hellemann and Tuorila (1991), Lähteenmäki and Tuorila (1994, 1995), and Popper et al. (1989) have observed that the hedonic scores obtained after ad libitum consumption are higher than the hedonic scores obtained after consumption of smaller quantities. It may be assumed that the hedonic judgments constructed on consumption of the usual portion of a food are more pertinent than judgments based on brief exposure. Although the good practices for central location tests state that the portion of food presented and ingested is to be equivalent to the usual portion of the product (Köster, 1998), the principle is rarely applied. The practice is generally compromised by the fact that several samples of products are frequently tested during the same session and the participants cannot therefore eat realistic portions of all the samples. Home tests enable the consumption of a realistic quantity of the product at each consumption occasion. Naturally, the cost of sourcing

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products may also play a role, especially when a company wants to evaluate competitors’ products or when testing expensive products (e.g., luxury products such as champagne, most cosmetic products: skin creams, makeup . . .). Test food preparation and food environment This contextual variable, individual preparation of the food, is clearly involved in the formulation of the hedonic judgment and is unfortunately little taken into account in the setup of consumer test protocols under standardized conditions. The question of the value of acquiring a hedonic response on a food prepared in a standardized manner may be raised. The impact of food preparation on its assessment has been little investigated. Most of the studies show an improvement in food perception and liking when the latter are presented at a culturally appropriate temperature (Boulze et al., 1983, Cardello and Maller, 1982, Ryynanen et al., 2001, Zellner et al., 1988). The results obtained for soups or ice creams are not very surprising and the presentation of foods at an appropriate temperature (hot soup and cold ice cream) is currently well incorporated in central location test protocols. Matuszewska et al. (1997) compared three test procedures to evaluate margarine samples. Initially, the subjects were to spread each sample on slices of bread as they did at home. The second method consisted in tasting slices of bread already spread with 4 g of margarine. The third procedure consisted in a situation in which the margarine samples were tasted alone without bread. The results show that the various samples were better appreciated and discriminated between when consumed in accordance with an individual preparation protocol. Posri et al. (2001) also compared the appreciation of several samples of tea determined by three preparation conditions: an imposed preparation (all the samples were prepared with the same quantities of milk and sugar), an individual controlled preparation (the subjects chose their preferred quantities of milk and sugar and all the presentations were prepared accordingly) and a totally free preparation (the subjects were free to use the quantities they wished). Once again, the authors observed a strong influence of food/beverage preparation on liking. It will thus be observed that it is possible to obtain results close to those obtained with completely individualized consumption by proposing a controlled preparation but one adapted to the most widespread consumer practice. In addition to food preparation, the impact of the food environment surrounding test food eating is also frequently neglected in the setup of consumer tests. The impact of the assessment of the constituents of a meal on the overall assessment of the meal has been widely studied and observed (Hedderley and Meiselman, 1995, Turner and Collison, 1988, Popper et al., 1989, Tuorila et al., 1990). In addition, it has been observed that the appreciation of a food may vary depending on whether it is eaten alone or with other foods (Eindhoven and Peryam, 1959, King et al., 2004).

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Consumption time Taste test implementation at the appropriate times for product consumption is generally recommended. However, in contrast to other variables, which are well controlled in centralized tests, tasting time is rarely standardized. The logistic constraint is in fact very strong. For example, it is generally impossible for test institutes to rent a test room in a public place for just one or two hours. Thus, the central location test sessions are frequently run throughout the day at times when consumers are present. However, consumption time is an important contextual component. Birch et al. (1984) observed that the constituents of a breakfast are more greatly appreciated in the morning than in the afternoon, and the constituents of a dinner are more greatly appreciated in the afternoon than in the morning. However, Kramer et al. (1992) conducted a similar study without observing any influence of consumption time for the same food categories. It should also be noted that certain foods are clearly associated with a consumption time, while others such as snacks may more readily be eaten at any time of day. Cardello et al. (2000) thus demonstrated a time-of-day effect for pizzas but not for cereals. In addition to the fact that a subject naturally feels more like eating pizza between noon and 2 pm than at 10 am, the influence of the time of day is also manifested by the participants’ physiological state. The subject’s physiological state appears to be an important determinant for the pleasure reported on consumption. Cabanac et al. (1968), for example, showed that a sweet taste may be particularly appreciated by a fasting subject but not by a subject in a state of satiety. Similarly, Laeng et al. (1993) showed that subjects score sweet lemon beverages as less pleasant when tasted immediately after a meal. Hill (1974) and Bell (1993) in their respective studies also evidenced a significant effect of the subject’s state of hunger on their preferences and hedonic scores, resulting in inferior discrimination between various samples when the interviewees were hungry. Brunstrom and MacRae (1997) observed that the extent to which the mouth is dry influences the assessment of various beverages. The social environment The social environment has been shown to have a marked impact on the quantity of food eaten. One tends to eat more when eating takes place with other people than when eating alone. This effect is termed social facilitation (Berry et al., 1985, De Castro et al., 1990). However, the phenomenon has not been systematically verified (Feunekes et al., 1995, Pliner et al., 2003) and the opposite effect may even occur (Cardello et al., 2000). It is probable that the social facilitation effects are due to the time spent eating, which is longer when an individual eats with others than when he/she eats alone. However, Clendenen et al. (1994) have shown that the relationship between the protagonists at the time of eating is a factor that significantly contributes to the social facilitation effect. Thus, eating in a familial environment is

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reported to be more sensitive to the social facilitation effect than eating in a friendly environment or eating with strangers. Few studies have directly explored the link between the social environment and the degree of food perception and liking. King et al. (2004) have shown that the perception and liking of pizza significantly decreases when the pizza is eaten in a group setting, reflecting the opposite of the social facilitation phenomenon. In another study, King et al. (2007) did not observe any significant effect of a pleasant and social environment on the scores for various foods/beverages (lasagna, cannelloni, salad and iced tea). Although the eating behavior has undergone important changes, many people eat meals in company (in restaurants, in canteens, family meals at home). It is therefore legitimate to question the pertinence of tests in which contact between the various interviewees is prevented. However, imposing a social contact between people who do not know each other is not necessarily a pertinent solution. In fact, Pliner et al. (2003) have shown that social facilitation only has a positive effect in naturally formed groups. In addition, interviewees who may not have any other subjects of conversation may tend to share their opinions on the test foods in a non-natural manner. This opinion sharing may then influence the judgment of easily influenced people. Cardello and Sawyer (1992) showed that the appreciation of a food may be influenced by information on what other people think. Thus the setup of a central location consumer test in a social environment would necessitate an onerous pre-recruitment phase in order to only bring together people who know each other. The scope for choosing the food tasted Choice is present in most natural consumption situations. Given the abundance of food products available in developed countries, consumers often face choices among several types of foods or several versions of the same product. At the very least, consumers have the choice not to eat a food. Thus, it may be supposed that the absence of the choice component in tests under standardized conditions is one of the causes of their poor predictive validity. It is to be noted that with home tests, the subject can choose to consume the product when he/she wishes, but is nonetheless obliged to consume the test product and not a substitute. Pliner (1992) nonetheless draws attention to the fact that numerous natural situations involve components similar to the situation imposed in the tests. In particular, this is the case for family meals or meals with friends, in which, most of the time, the meal is put on the table without all the participants’ prior agreement. Realistic eating situations thus do not necessarily involve a total absence of constraints. Few studies have specifically addressed the relationship between the degree of freedom in food choice and the appreciation of that food. A few studies addressing monotony or boredom provide some information on the impact of the choice variable on the appreciation of regularly eaten foods.

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Zandstra et al. (2000) observed the time course of the perception and liking score for three sauces obtained after several home tastings with different degrees of choice. Repeated consumption of the same sauce for 10 weeks resulted in a decrease in the scores due to the monotony effect. In contrast, the perception and liking scores for sauces formulated by subjects who were free to choose the samples to be eaten over that period did not show the same decrease. Kramer et al. (2001) have also observed that allowing military personnel to choose their meals in the field results in an overall decrease in the monotony effect generated when the same foods are imposed in the laboratory. De Graaf et al. (2005) studied the impact of food choice on its appreciation. In the laboratory, the hedonic scores were lower for the imposed eating of various foods than when the foods eaten were pre-chosen from a list. Similarly, King et al. (2004) tested the impact of adding a choice of menu component in a test procedure conducted in a centralized location and social meal setting. The authors did not observe any influence of the choice variable on the overall assessment of the meal although the hedonic score for certain accompanying foods such as salad increased when those foods had been chosen. With a view to improving the predictive validity of hedonic tests, De Graaf et al. (2005) compared the performances of data generated by a conventional test with those generated by a test providing scope for choice to predict the hedonic scores obtained in natural consumption settings. The authors observed that the laboratory test with scope for choice enabled enhanced prediction from the data obtained in naturalistic settings compared to that generated by laboratory tests with no scope for choice. However, once again, incorporating the choice variable in hedonic test protocols would require a supplementary budget due to the participation of a large number of people in order for all the test samples to be chosen and evaluated the same number of times.

8.4 When choosing central location tests (CLT) vs. home use tests (HUT): recommendations to manufacturers From the foregoing, it will be more readily understood why a given product may be judged differently depending on whether it is evaluated at home or in a central location. This is particularly problematic when one considers that a primary objective of consumer tests is to predict future consumer behavior and hence the fate of the product on the market. As already pointed out both CLT and HUT have their limits. The question thus arises for industry players to decide when it is more appropriate to implement a CLT or a HUT, and what major consequences are to be expected. Naturally, budgetary and logistical constraints will be key determinants in this choice.

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8.4.1 External validity of CLTs into question The objective of standardizing and controlling test conditions is to control the influence of contextual variables in order to measure only the effect of the sensory characteristics of the product on the hedonic response. However, while the context may be considered as all the undesirable variables included in a study which may affect its results (Earthy et al., 1997), the standardization of food tasting conditions raises the problem of external validity of the responses obtained. The external validity (also referred to as the predictive validity) of a method, enables the investigator to generalize from the results obtained on the test population to the population of interest. In the more specific framework of hedonic measurement with consumers, the external validity is sought by means of its “ecological” component, which refers to the conditions under which the measurement was obtained. Thus ecologically valid experimental settings should resemble “real life” conditions. By setting up tests under standardized and hence artificial conditions, the investigators neglect the determinant contribution of contextual factors in the evaluation of food products. King et al. (2004) observed that excluding those variables from research may oversimplify the participants’ eating experience, thus providing incomplete and, in some cases, misleading results. It will thus be understood that the external validity of a measurement obtained under consumption conditions extremely remote from reality may be called into question (Drifford et al., 1995). Yet in many hedonic studies, there may be a trade-off between external validity and accuracy. Despite the external validity problem, the CLT has numerous advantages which underlie its success with manufacturers (in France, 70% of food product consumer tests are CLT). CLT are easy to set up in a relatively short time and with a reasonable budget. Moreover, the controlled evaluation conditions enable generation of more precise data that are easier to interpret than those generated by a HUT. Tuorila and Lähteenmäki (1992) consider that while laboratory situations are perhaps artificial, they constitute a context for studying dietary behavior which enables control over variables that are frequently mutually confounding in studies in natural settings.

8.4.2 Limitations of HUT Although the higher ecological validity of the hedonic test conditions brought about by home-use tests may be favored, natural settings also bear disadvantages (Meiselman, 1992, Mela et al., 1992, Pliner, 1992, Rolls and Shide, 1992). As Rolls and Shide (1992) point out, the problem with tests in natural settings is that most of the methods are not precise, do not enable experimental manipulation and are, in addition, very onerous. Since the participants to HUT are free to use the products without any kind of control, it may very well be that they do not even consume the product to be tested. We know for instance, from nutritional studies using biomarkers that there may be an important discrepancy between actual behaviors and

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self-reporting consumption data (Subar et al., 2003). Köster and Mojet (2007) identify some pitfall in the way HUTs are usually conducted. They observe that in most cases, participants get insufficient quantities of products to consume and that the time allotted to the test is probably not enough in order to correctly predict long-term product acceptance. Besides, they propose to get more information on the way products are consumed by the way of consumption diary, and unexpected home visits from the investigator. Also, they suggest that one week after the end of the experiment the investigator could ask the participants to give their opinion about the tested product. In addition, asking them if they are willing to participate in similar experiments in the future would also be a good indication of their overall degree of satisfaction regarding the product(s) they tested. 8.4.3 Budgetary and logistic considerations The budgetary and logistic limitations of HUT currently restrict their routine use by manufacturers and, even more so, by academic research. On the basis of estimates for tests conducted on consumer food products, the cost of a HUT in France is generally 50% higher than that of a CLT. The difference in budget mainly derives from the pre-recruitment of subjects, their remuneration and the investigators’ home visits. Moreover, the quantities of product to be supplied for the test are much greater given that each consumer must have sufficient product for few days of consumption. This also contributes to increasing the cost of a HUT relative to a CLT. With regard to logistics, numerous aspects of the HUT sometimes render implementation very difficult or even impossible. The principal difficulty of the HUT resides in anonymizing the test products. In CLT, the blind is generally achieved by masking the initial packaging (for individual portions) or presenting the sample in a neutral receptacle (plate, bowl, glass). In contrast, for HUT, product handling by the subjects frequently necessitates totally repackaging the products in a neutral packaging. Unfortunately, numerous manufactured food products cannot undergo repackaging without their sensory properties being impaired. These difficulties explain why blind HUT is little used for marketed products. The HUT is also associated with an effectiveness problem due to the fact that the HUT cannot be set up with a set of more than two or three products in succession, given the time interval necessary between each pair of products evaluated. The last major disadvantage of HUT is the weak reactivity of the method. Setup and implementation generally require at least one month (for two samples) before the initial results can be obtained, while a CLT may be set up so that the initial results are available in a week. 8.4.4 Implementing CLT or HUT Tables 8.3a and 8.3b show the reasons for preferring CLT and those for preferring HUT reported in the literature on consumer tests (Lawless and

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Table 8.3 HUT

(a) The reasons for preferring CLT; (b) The reasons for preferring

Advantages of CLT

Limitations of HUT

Low cost Results obtained rapidly Tests can be conducted in a mobile home to facilitate changing site and fast contact with different consumers

Expensive test Difficult and long to implement Consumer pre-recruitment necessary on the basis of databases. Consumers less “naive” than those recruited on-the-spot No environmental control: large variability in responses due to the variability in consumption conditions

Relative good control conditions (environment, product preparation and presentation, understanding of instructions, etc.): data precise and easy to interpret Compliance with the instructions

Few missing data Frequently easier for comparative approaches: several products can be tested during a session

Requires intense quality control to check compliance with the instructions (Was the product consumed by the right people?) High probability of missing data Evaluation of numerous products impossible

Advantages of HUT

Limitations of CLT

The product is prepared and consumed under normal use conditions Specific evaluation situations that require privacy are possible (in the shower, in the toilets) The assessment obtained is based on repeated consumption of a large quantity of product Numerous data on product use can be generated and used in the interpretation of the hedonic information No bias due to tasting several products in a short time interval: reliable implicit comparison with the subject’s usual product The product may be used over a longer period of time and postingestive effects (food) or in-use properties and after-effects (cosmetic, fragrances . . .) can be evaluated

The subject is outside of his/her usual consumption context: preparation, tasting time, social and dietary environment, etc. The opinions are collected after a brief contact with the product The information on uses and attitudes collected are only declarative and may not be what the consumer experiences at home Disturbance in the subject’s references which may lead to non-pertinent judgments The product is assessed for a brief period of time and properties such as comfort (clothes, cosmetics) may hardly be evaluated.

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Heymann, 1999, Meilgaard et al., 1999, Stone and Sidel, 1993). The tables show that the advantages of one method are generally the counterpoint of the limitations of the other. Thus, the standardized conditions for CLT make the method practical and inexpensive while generating precise and easily interpreted hedonic measurements even though those measurements may potentially be little predictive of reality. Choice of controlled or naturalistic testing conditions depending on the product to be tested Having evidenced the more or less pronounced influence of artificial conditions in controlled testing situations, depending on the category of food tested, it was considered of interest to review the characteristics of the eating mode liable to induce a difference between the results of a test under standardized and artificial conditions such as a CLT and a test under naturalistic conditions such as HUT. Comparative Table 8.4 is intended to help investigators evaluate the risks they incur when they set up a CLT instead of HUT for a specific food category. The table is thus intended as a warning to investigators, but also as an aid in deciding on the advisability of a CLT instead of an HUT. Thus, if the usual use of the test food meets several of

Table 8.4 Product consumption mode characteristics that may induce difference in CLT vs. HUT results Contextual variables Quantity consumed Mean duration of consumption Consumption time

Modalities more often associated to “snack-like” consumption

Modalities more often associated to “part of a meal” consumption

Standardized (individual packaging), smaller quantities Short

Adjustable, higher quantities

Product handling

Standardized: direct from the packaging (e.g., can of drink, snack bar)

Specific: morning, noon, night, etc. Specific: family meal, party, picnic, drinks, etc. Consumption with others Combination of foods and/or beverages Personalized: food temperature, seasoning, etc. Personalized: use of silverware or chopsticks for example

Consequences for CLT vs. HUT:

Low risk to obtain different results

High risk to obtain different results

Consumption settings Social environment Dietary environment Product preparation

Not specific: throughout the day Not specific

Long

Solitary consumption No other food or beverage consumed Not personalized

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the criteria in the left-hand column, the investigator can set up a CLT with less risk of obtaining different results from those generated by an HUT than if test product use were mainly listed in the right-hand column. It is important to note that the table is not exclusive. It is not because a single characteristic in the right-hand column applies to product consumption mode that a CLT is to be definitively excluded. In addition, given that there is no established knowledge about which of the contextual variables in Table 8.4 are most influential, the experimenter will have to rather consider these indications as a guide and as an incentive to consider all aspects of the product to be tested. Most of the time, appropriate methodological answers will ensue from good sense. Although there is no strict rule, in practice, the choice of a testing procedure will probably depend largely on the stage of the product development process at which the hedonic information is needed. Pre-launch hedonic tests are more likely to be HUT in order to minimize the risk taken when launching a new product on the market. Conversely, CLT will be favored when one has to select an option, a recipe, or an ingredient at an intermediate development stage when usually several products are to be tested. In this case, the decision making might be less critical and hedonic data may mostly back up the developer when proposing solutions to the marketing department for example, in which case a precise comparison of the product may be considered as more important than external validity.

8.5

How to improve food testing to enhance integration of eating/drinking situation variables

From the foregoing, it will be more readily understood why with the available hedonic test protocols, it seems difficult to simultaneously optimize both precision (advantage of lab studies) and external validity (advantage of real life studies) of hedonic data. Yet alternative methodological solutions can be investigated in order to overcome the conflicting desiderata. One could try to improve the validity of the data obtained in controlled test environments by integrating the usual conditions of consumption in the CLT tasting protocol. This could be done by implementing more naturalistic and hopefully more appropriate tasting conditions. Alternately, one could also consider a situational analysis approach and design the hedonic test accordingly. Another trade-off between precision and validity would be the implementation of tests in field environments (i.e., real-life settings) that enable some control of the way the products are tested. This last section will therefore address methodological approaches to enhancing both the pertinence and precision of the hedonic data generated by consumer sensory tests while considering the budgetary and logistic imperatives of industry.

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Two solutions may be considered with a view to integrating the usual conditions of consumption in the hedonic measurement: 1 2

Proposing a physical environment for consumption that is more appropriate for evaluation of the type of food considered. Evocation of a consumption context by the way of scenarios or vignettes.

8.5.1 More pertinent physical conditions for consumption Improvement of appropriateness of the physical tasting conditions in CLT or lab tests has been already attempted by some authors, in the view of setting up quasi-natural studies that capture the advantages of both the laboratory and the real world (Kanarek and Orthen-Gambill, 1986). Most of the studies investigated the manipulation of food or experimental related factors separately: • sample serving size: small vs. large sample size (Lähteenmäki and Tuorila, 1994, Popper et al., 1989) • temperature of the food: appropriate vs. inappropriate (Cardello and Maller, 1982) • preparation of the food: imposed vs. free (Matuszewska et al., 1997, Posri et al., 2001) • time of the day: appropriate vs. inappropriate (Birch et al., 1984, Kramer et al., 1992) • food environment: item tasted alone vs. food combination (King et al., 2004) • social environment: self vs. social (Cardello et al., 2000) • possibility to choose the tested product: no choice vs. choice (De Graaf et al., 2005, King et al., 2004) • comfort of the setting: non-cosy vs. cosy (Bonin et al., 2001, King et al., 2004). Few other studies investigated the appropriateness of the CLT tasting conditions in a global manner (Hersleth et al., 2003, King et al., 2007, Petit and Sieffermann, 2007). Along this line of thinking, we conducted two studies in which we chose to simulate two specific eating situations in a CLT setting: a full-meal, and a breakfast (Boutrolle and Delarue, 2009). In the first study, we compared the hedonic ratings of two brands of sparkling water collected either in a classical CLT or in a CLT as part of a meal context (meal-CLT) set up either at lunch or dinner time. The results clearly differed depending on the method used. The mean hedonic score obtained for both sparkling waters was higher for the meal-CLT than for the conventional CLT, which is consistent with the observations in other studies comparing controlled and natural settings as discussed above. During the meal-CLT, the time of the day and hence the physiological state of the participants were assumed to be appropriate for consumption of sparkling water, which was probably not the case during the conventional

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CLT. Furthermore, the meal environment may have been especially conducive to water drinking, which may have led to enhanced satisfaction (King et al., 2004). The positive effect on digestive function induced by the sparkling water was probably experienced during the meal-CLT, but could not have been during the conventional CLT. Naturally, the comfortable environment of the meal situation together with the free meals could also have enhanced the subjects’ overall well-being and hence scores (Bell, 1993). It is noteworthy that the evaluation of the sparkling water in the context of a full meal not only influenced the hedonic score levels, but also the conclusion inferred from the test. With the conventional CLT, one product was liked significantly more than the other (p < 0.001), while there was no significant difference in liking in the meal-CLT. The conventional CLT conditions, without a meal environment, may have magnified a difference in liking. Although it cannot be strictly verified, we hypothesized that because of the meal environment, there was a change in the predominance of the sensory drivers of liking. Sparkle and bubble size, for example, might not have been so important for consumers while eating, whereas saltiness and bitterness (which were rated as just about right for both samples) were more salient. Such information is crucial, given the fact that, in France, sparkling water is drunk with a meal in 50% of cases (Use and Attitude surveys carried out by Danone marketing research department). Thus, in order to enhance the prediction of liking for such products in real settings in which they are usually consumed as part of a meal, consumer tests under meal conditions are to be conducted. In a second study, we investigated the influence of a breakfast context (early in the morning tasting session, choice of a hot or cold beverage, various kinds of spreads at disposal) on the evaluation of two samples of crispy bread (product A vs. product B). The two types differed with respect to a number of attributes: shape (round for A and oval for B), texture (A less hard) and taste (A less sweet and more salty). First, we observed that the difference between products A and B in terms of liking was not significant for either protocol. The consumption conditions during the breakfastCLT clearly illustrated that eating conditions (100% beverage drinking and spread use) differed quite substantially from those in the conventional CLT (no beverage drinking or spread use). However, despite the numerous tasting condition differences, the overall liking scores were not greatly affected by the method. In this study, the tasting conditions did not influence the difference in liking for the two products. Also, contrary to what could be expected, the products were not scored lower in the standardized situation than in the breakfast situation. Similarly, the influence of the more naturalistic tasting condition of the restaurant-CLT set up by King et al. (2007) was not sufficient to modify the hedonic scores vs. the conventional CLT. This does not necessarily mean that our attempt to mimic a real eating situation failed. Actually, further analysis of the breakfast-CLT data shows that the “dunking behavior” influenced the difference between the overall

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likings for the two products. The data for the “dunkers” (38% of the subjects) showed a significantly greater liking for product B while the “nondunkers” liked the two products equally well. This was not observed in the conventional CLT for the self reported “dunkers”. The partial hedonic scores that were collected in this study enabled further insights. In the conventional CLT, the taste of product B was significantly more liked than that of product A. In contrast, during the breakfast session, the tastes of the two samples were liked equally well. Thus, spreading and potential dunking may mask differences in taste and influence liking differences. The pertinence of focusing on the taste of neutral basic products may be questionable in that, under natural conditions, very few people eat crispy bread without spreads. The responses on shape are also informative. During the conventional CLT, participants did not report a difference of liking for the two shapes (round or oval), while, under the breakfast conditions, the round shape was significantly less liked than the oval shape. Participants may have interpreted the same question in two different ways: in the conventional CLT, the liking for the shape was probably evaluated with regard to visual appearance; in the breakfast situation, participants probably evaluated the convenience of the shapes (e.g., for spreading and dunking). The latter information is important for product developers seeking to improve specific convenience aspects. In conclusion, the analysis of the diagnostic data revealed that the preparation of the products (spreads) and their use (dunking behavior) had a strong influence on the hedonic perception of product taste and shape. The breakfast-CLT demonstrated the impact of dunking behavior on overall liking data. Given that in France 50% of people dunk bread in hot beverages at breakfast, those consumers’ opinions should be taken into account when breakfast bread products are tested under realistic conditions.

8.5.2

Hedonic response measured in various evoked consumption situations Various attempts to incorporate more realistic tasting conditions in CLT designs have succeeded in integrating specific contextual factors in consumer test designs. However, this reductionist approach (singling out contextual factors) presupposes determination of the most natural consumption setting for the test food product. Besides, food products are rarely consumed in only one kind of eating situation, which makes the prediction of “real life” appraisal from single-situation CLTs more difficult. Furthermore, as Köster (2003) has pointed out, perceptual situations are not exclusively defined by objective criteria; they are also defined by the subjects’ conscious and subconscious intentions. Everyday life is a world of meanings rather than one of objective facts. Even though a situational CLT would be a more meaningful approach to measuring hedonic responses than a conventional CLT, many of the contextual variables that can be manipulated are

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objective properties of the context. In real life, they may have different meanings for different people. Thus, Köster (2003) suggests the use of a situation-oriented (“deductive”) approach based on evoked rather than physical situations in order not to disturb the subjects’ natural perceptions of the situation. Situational CLT designs hence do not necessarily enable the subject to become involved and really “project” him/herself into the situation in question. One way of improving situation appropriation by the subject is to make use of his/her autobiographic memory. The subject is thus considered a historical being endowed with an autobiographic memory composed of events that have been personally experienced, located and dated, and are specific to each individual (Drifford et al., 1995). All those episodes are processed by the subject, encoded and transformed into a memory and are thus naturally activated when the subject is in a context associated with indices similar to those present in the remembered situation (Tulving, 1984). The aseptic conditions of CLT tasting prevent activation of the autobiographic memory and it is therefore necessary to help the subject recover the memory of consumption events. The approach has already been used by Bonin et al. (2001) to study the influence of the priming of a past positive or negative consumption event with respect to the choice of foods. Köster (2003) proposes that imaginary situations are evoked and primed with the help of auditory or written scenarios. The scenarios are short stories that describe a particular situation meant to evoke a sense of presence in a real situation. The interviewee is asked to respond to the situation. While that approach has long been used in attitudinal surveys, particularly in the social sciences (Finch, 1987, Nossanchuck, 1972, West, 1982) and marketing (Bitner, 1990, Folkes, 1984, Surprenant and Solomon, 1987), it is only just beginning to be used in the field of food preference measurement (Jaeger and Meiselman, 2004, Hansen, 2005). Below, we detail two recent studies designed to evoke context under controlled settings by eliciting the autobiographical memory in two different ways. Context evocation with a written scenario (Hein et al., 2010) This study, conducted by Hein et al. (2010), was designed to evoke a refreshing context by means of subjects’ autobiographical memory, using a written scenario. The authors attempted to evoke a refreshing context for the evaluation of apple juice. Four samples of apple juice with modified flavors were submitted in a monadic sequential way to a panel of 70 consumers who were invited to attend a 60-minute evaluation session in a sensory laboratory setting. But before sample evaluation instructions were given, consumers were presented with the following written scenario: “Think about an occasion when you want something refreshing to drink. Clearly imagine you are experiencing this occasion. Now, write down a detailed description of the occasion you are imagining. Please take your time and provide a description that is as

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complete as possible.” They were then instructed to keep in mind the occasion they had described throughout evaluation and were allowed to re-read their description at any time. For each presented sample the participants indicated their overall liking. They also filled a questionnaire regarding how they felt during the evaluation. Notably, they were asked to indicate to what extent they felt the liking evaluation they had given was accurate and if it was easy for them to rate their liking. Their responses were compared to those of another group of about 70 consumers who evaluated the samples under the same conditions but with no context evocation (control condition).As revealed by a two-way analysis of variance, a greater between-sample discrimination was observed in the evoked context condition (F3, 207 = 2.19, p = 0.091) than in the control condition (F3, 213 = 0.92, p = 0.830). Perhaps more important the consumers who evaluated the products under the evoked context condition more likely reported that they felt their liking evaluation was accurate. In addition to this, consumers under the context condition felt it was easier to rate their liking/dislike of the apple juices than did consumers in the control setting. Interestingly, when asked what was the study about, 24% of the consumers in the control condition responded that specific sensory attributes (i.e., sweeteners, additives, flavor strength, etc.) were being investigated, whereas none of the consumers in the context condition had such a response pattern. This probably indicates that a diversive exploration behavior (Berlyne, 1960) was preferentially stimulated in the evoked context condition. This result is very important since it shows that even though consumers might be distracted from the underlying purpose of hedonic testing the product, their responses are more discriminant and they find it easier to rate their liking. These results indicate that using the written scenario elicitation method may be effective at making participants imagine an occasion when they desired a refreshing beverage. However, as pointed out by the authors, thorough pilot testing of the scenario instructions has to be done. Although in the future this kind of experiment would deserve thorough analysis of individual response patterns, further studies in this direction should enable proposing improved CLT testing protocols with a relatively simple setting. Context simulation with audio scenarios (Boutrolle et al., 2007b) The objective of this study was to measure the fittingness of food products to situations, as had already been proposed by Schutz et al. (1977) in the “item by use appropriateness” method. This method consisted in presenting the subjects with a list of foods and list of possible uses (time of day, site, occasion, physiological state, person, etc.) and having the subjects score the appropriateness of each food product for the set of uses proposed. Few studies have tested the utility of that type of measurement in real food testing (Cardello and Schutz, 1996, Cardello et al., 2000, Lähteenmäki and Tuorila, 1995, Lähteenmäki and Tuorila, 1997).

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In this study, it was decided to evoke the various situations with a comprehensive audio scenario rather than with just a few words (e.g., “when I’m eating in front of the television”). The efficacy of the approach was tested on 240 women by collecting consumption intentions in various situations evoked through audio scenarios with a view to comparing the organoleptic performances of two types of salty cheese-flavored crackers. The authors’ aim was to verify whether the sensory differences conditioned a different consumption intent in six consumption situations: week-day drinks before the meal, weekend drinks before the meal, picnic, snacking while relaxing, snacking while working, and snacking during transport. The various scenarios were compiled in the first person singular in a relatively simple style and gave information on a certain number of contextual variables such as the physical environment, but also on the subject’s interior condition. For example, the scenario used to illustrate the weekend drinks before the meal situation was as follows: “It’s the long awaited time for drinks before dinner at the weekend. I can at last spend some time with my loved ones. As usual, the conversation is lively and drinks last longer than expected. After a few drinks, it’s probably time to have something to eat but nobody seems to want to sit down to the meal. Despite the big meal that’s on the way, I take a dish of crackers and offer them around without forgetting to help myself.” Each subject thus listened to six scenarios one after the other and formulated his/ her intent to eat the test crackers in the situation (from 1 “no, certainly not” to 10 “yes, absolutely”) together with the frequency of that situation in everyday life (from 1 “never” to 10 “very regularly”). Figure 8.3 shows the mean intent scores for each cracker, by situation, and the results of Student’s t test for each situation. The intent to consume recipe B was higher than that for recipe A in almost all situations, but the difference was only significant for the weekend drinks before the meal scenario. The situational hedonic information was compared with that obtained on the basis of an overall assessment question using a scale from 10 9 8 7 6 5 4 3 2 1

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Weekend drinks Week-day drinks Picnic Snacking relaxing Snacking working Snacking transport

Group 1 (N = 77)

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Fig. 8.4 Mean situation frequency profiles for the four groups of subjects identified.

1 to 10 in a conventional CLT conducted with 240 other women. The results evidenced that the overall liking scores for the two products were not significantly different. However, the two measurements do not address the same concept. It is then appropriate to consider the most pertinent hedonic information to be generated by consumer tests. The intent to consume by situation data show very high standard deviations reflecting the diversity of the participants with regard to the intent to eat crackers in the various situations. This affords the possibility of investigating the data by subject group on the basis of the responses formulated with the situation occurrence frequencies in the participants’ everyday lives. Four different cracker consumption profiles were thus determined. Figure 8.4 shows the mean situation frequency profiles for the four groups of subjects identified. Group 1 consists of subjects who prefer, for the food type in question, consumption situations associated with a social event (drinks before the meal on weekends and picnics) or a solitary but relaxing event (drinks before the meal during the week or relaxed snacking). The situations in which consumption is more utilitarian or functional (work, transport) are less associated with the food category. Group 2, which was smaller, consisted in the consumers who do not eat crackers in an environment with marked social interaction (weekend drinks before the meal and picnics) but only when alone (drinks before the meal during the week and snacking). Group 3 consists of the subjects who mainly eat the food type during drinks before the meal at the weekend. In contrast, Group 4 consists of participants who eat crackers regularly in all of the proposed situations. Figure 8.5 illustrates the analysis of the intent-to-consume data for the two recipes in the light of the consumer typologies and shows that the intent-to-consume profiles differ depending on the use type. It will be observed that the data by typology generate results that are more discriminant than those obtained for the panel overall, except in the case of Group 3 for which the two recipes are overall associated with the same intent-to-consume scores, irrespective of situation. In addition, a particularly interesting weak product

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Fig. 8.5 Mean intent to eat scores for the two cracker products by situation in the light of the consumer typologies. t-test results: NS: not significant; *: p-value < 5%.

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× situation interaction was observed for Group 4, illustrating the fact that a consumer may not look for the same sensory properties in a given food category when making different uses of the food. The variation in test product consumption intents, depending on the situation, observed in the study points to the value of taking into account food product use in consumer tests. In conclusion, the authors stress the fact that product use is to be taken into account not only in study design but also in the interpretation of the data generated. An understanding of the typology based on food eating habits seems essential in order to elucidate consumer preferences.

8.5.3 Bringing together field and lab hedonic studies Many published studies have investigated food preferences and consumers’ eating behavior in public locations such as restaurants, food service, hospitals, etc. Yet such field studies are extremely seldom in the industry. Sensory scientists and developers in the industry will certainly have to reconsider testing in such environments given today’s predominance of food consumption patterns other than traditional in-home meals. Besides, situationalCLTs and naturalistic settings may also extend to the study of nutrition and health-related issues. For example, Wansink and Park (2001) have observed the influence of the container size and of social interaction on the quantity of popcorn eaten by moviegoers in a movie theatre. Talbot et al. (2009) also observed that improving the tasting context in sensory booths by the way of simple means (providing magazines, hot beverages . . .) increased the quantity of biscuits eaten when provided ad libitum. Their data even suggest that sensory specific satiety may be context dependent. As we already pointed out, a limitation of field studies is the poor representativeness of the consumer sample. Most often, field studies only recruit people who already frequent the location in which the study is being implemented, which may limit the possibilities to gather a representative sample of the targeted consumer population (Edwards et al., 2003). Although it is not common, a simple solution is to pre-recruit participants as it is frequently done in hall tests. Another option would be to run these studies in different geographic locations. In this respect, investigators may draw inspiration from the mobile sensory lab (i.e., a fully equipped sensory bus) implemented in Europe by the Puratos food ingredient company and that allows reaching consumers that are otherwise very difficult to recruit in traditional CLTs. Several recent projects aimed at bringing together field and lab consumer studies by the way of both controlled and naturalistic installations such as the “Restaurant of the future” at the Wageningen University or the modular research restaurant of the Institut Paul Bocuse (Ecully, France). In addition to improving contextual evaluation conditions, these testing facilities allow behavioral measurements (food choice, amount consumed,

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leftovers . . .). They also allow monitoring individual consumption patterns and their evolution over a given period of time. This may be a major advantage of these experiments since most of the time, regular hedonic tests are based on a single exposure to the test food and thus do not take the dynamics of preferences into account. This may be decisive since the evaluation process based on the first impression only is very different from that based on a longer period or on repeated exposures (Köster and Mojet, 2007). Also testing over a few days or a few weeks certainly helps avoid some experimental biases linked to the fact that people know they are being observed (Hawthorne effect, social desirability bias . . .). Other attempts have been made in this direction by taking advantage of automated vending machines in public locations (Vickers et al., 1999, Hoyer et al., 2003). In this case, each consumer is identified by the way of a chip-card and his/her choices may also be individually recorded over a given period of time. In the same line of thinking, Köster (1981) has suggested the design of preference tests based on a “time and frequency analysis”. The principle is based on behavioural observation and consists of organising parties at which the food and drinks to be tested are available at an ad libitum basis. In the perspective of our work on the improvement of hedonic test methodology, we experimented this approach in order to compare the liking for two brands of salted cheese crackers (national brand vs. a private label copy). An evening party was thus organized with about 120 participants. The locality of the party was open between 8:30 p.m. and 10:30 p.m. At the time they arrived, each of the participants received a coded sticker and had to rate how hungry they were, using a five-point scale (from “I am not hungry at all” to “I am extremely hungry”). They were instructed to eat and drink any item they wanted but not to fetch items for other participants. The atmosphere at the party was very friendly with music and filtered light. Furthermore, alcohol (beer and planter’s punch) and non-alcohol beverages (soda, iced tea and fruit juices) were at their disposal. Participants knew each other and therefore social interaction was high. Concerning the food environment, it was decided to allow a real choice by proposing two types of salted snacks in addition to the tested cheese crackers: chips and another kind of salted crackers. In the design, this choice possibility is quite important because it means that, for once, the participants who ate the tested crackers were not forced to. Each of the snacks was presented in small coded containers with about 5g of product. Containers were presented on two tables arranged at two different locations of the room to allow the participants an easy access to the food. Both tables were divided into two areas: one where the national brand cracker was proposed and the other where the private label was proposed. Both areas proposed the tested crackers among the other snacks. Each time a participant got any tested item (national brand or private label sample), observers noted the participant’s code, the item he/she got and the corresponding time.

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The total amount of crackers consumed during the session was calculated on the basis of the number of small containers gotten by the participants. As a result of the use of a free-choice protocol, 35 of the 120 participants did not eat any of the two tested items, thus leading to a sub-sample of 85 subjects who actually consumed the crackers for this test. Overall, these 85 participants ate a significantly higher proportion (Binomial test, p = 0.008) of the national brand (142 containers = 57.9%) over the private label (103 containers = 42.1%). The behavioural data hence reveal a preference for the national brand over the private label that the declarative data of a previously run traditional CLT did not suggest (no significant difference between the mean hedonic scores). In the party study the two crackers were presented simultaneously allowing the participants to go freely from one product to the other. This design allows the collection of dynamic hedonic information by exploring the development of the choices over the session. We carried out such an analysis with the data of the 60 participants who ate at least two containers of cheese crackers. Figure 8.6 illustrates the proportion of national brand and private label choices according to individual sequence of choice (choice position). As can be seen in Fig. 8.6, in the first choice, exactly as many national brand samples as private label samples are chosen. The second choices show that there are more participants who chose the national brand than participants who chose the private label. The third, fourth and fifth choices confirm the formation of a clear preference for the national brand over the private label. Actually, the second choices are more informative than the first ones, because they may have resulted from the first tasting experience. The fact that the national brand was significantly more often chosen than the private National brand 100% 90% 80% 70% 60%

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label in the second choices seems to be a good indicator of the difference in overall hedonic impression induced by the two brands. It indicates that more participants who first consumed the private label decided to switch toward the national brand than vice versa. This suggests that switches were probably induced by a deception with the first chosen product rather than by mere curiosity behavior invoked by the test conditions. We could thus conclude that the private label recipe is more deceptive than the national brand recipe. This “social CLT” revealed a significant difference of amount consumed between the two types of tested crackers whereas the traditional CLT hedonic ratings showed no significant difference of liking. It is worth mentioning that a great number of contextual factors differ between the two protocols. First, samples were presented in a monadic sequential way during the traditional CLT, whereas they were simultaneously presented during the party. It may be expected that products were better discriminated in the simultaneous presentation procedure (Delarue et al., 2001). Furthermore, the different protocol designs implied that participants ate a larger quantity of products during the party than during a traditional CLT. Thus, the output of the traditional CLT ratings probably only reflects the first hedonic impression of the samples, whereas the behavioral data of the social CLT probably better reveals the long-term acceptability of the products. Finally, participants to the party had the choice to consume the crackers or not and they had to make an effort to access them (even though it was a minimal effort). These conditions may have increased the natural impact of hedonic expectations about the effects to be experienced when eating the samples. Naturally, it is difficult to know to what extent the difference in hedonic output between the social CLT and the traditional CLT was due to the tasting conditions or to the use of the two different hedonic measurements. Furthermore, in this case, it is difficult to dissociate the various contextual factors that might explain this difference in results. Contrary to our other studies implying eating situations (meal and breakfast) where the food combination was certainly the most influential contextual factor, the social event is in fact a combination of several aspects, which has wider implications than just allowing a contact between participants. In fact, many different contextual factors interact during a social event (e.g., comfort, long period, friendly and relaxed atmosphere). Feunekes et al. (1995) and Pliner et al. (2003) showed no direct effect of the number of other participants on intake. They suggested that social facilitation effects on eating might be influenced more by the duration of the meal than by the presence of other people. Clendenen et al. (1994) found that the relationship between meal companions is an important factor contributing to social facilitation. Eating with family or friends has more of an impact than eating with strangers. In the same way, Pliner et al. (2003) showed that a social facilitation effect occurs especially when the groups are created naturally. As Stroebele and De Castro (2004) pointed out, the set of contextual factors is probably

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closely interwoven in a social event and may be called “ambience” factors. It seems evident that the re-creation of a social atmosphere requires the manipulation of many more “ambience” factors than just allowing socialisation between participants. In our experience of eating context manipulation, setting up a social eating ambience was the most complicated eating situation to re-create, notably because it requires a long period of consumption in a friendly and relaxed atmosphere. Note that implementing contextualized product assessment may also be useful in non-food testing. For instance, one could imagine implementing hedonic tests in gyms or swimming pools for testing personal care products, or sport clothes. For instance, Bleibaum and Willis (2007) have assessed runners’ perception of shirt performance during use and after extended use. One of the most challenging work on contextualized hedonic measurements is the attempt of Astruc et al. (2007) to collect consumers’ hedonic perception of cars while driving. As the object being evaluated is in strong dynamic interaction with context, it is essential for comparison purposes, that a common route and an appropriate driving procedure are defined. In order to frame a route that reflects the consumer contexts of use, the authors first built a representative virtual route on the basis of 64 consumers’ free choices of roads for car assessment under real driving conditions. The major difficulty is then to transfer a virtual route to a real context of driving. This methodological development nevertheless helped the experimenters understanding the different manners consumers have to assess cars.

8.6 Future trends A major difficulty in the choice of a hedonic testing methodology lies in the fact that there is no reference method. Behavioral observations may be considered as a reference (Köster, 1981, Leon et al., 1999), but they are expensive and not always possible to implement. Although the standard economic theory states that consumers’ preferences can be revealed by their purchasing habits, there is a growing body of research in experimental economics that indicates that preferences are often constructed – not merely revealed – in the elicitation process (Kahneman and Tversky, 2000). Hence, however decisive it may be, the accuracy of a hedonic test cannot be readily estimated since there might not be such thing as true preferences but rather context-dependent preferences (Tufano, 2009). The various studies reviewed in this chapter show that there is no perfect method for measuring liking or preferences. Also it is observed that the advantages of HUT are generally the counterpoint of the limitations of CLT, and vice-versa. The main criticism raised against CLT is that the tasting conditions are very different from natural eating situations and do not allow the respondents to be emotionally involved like in actual food consumption,

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while making them often consider aspects they would never take into account when consuming normally. It is very likely that the design of the protocol affects the subjects’ internal state and thus the form of their involvement (analytical rather than intuitive) in the evaluation task. The lack of commitment and the fact that participants’ attitude may be either casual or analytical is a possible explanation to the non significant difference in liking sometimes observed with CLT while significant differences are observed at home. Taking the context into account is thus a necessary step toward improved hedonic measurements. Nevertheless, it is also possible to focus more specifically on consumers’ involvement during the test. In this line of thinking, some researchers have proposed to make use of economic experiment protocols in the design of sensory hedonic tests (Lange et al., 2002, Jaeger et al., 2004, Boutrolle, 2007, Combris et al., 2009). These methods are designed so that the participants face non hypothetical choices (i.e., with actual purchase consequences). But even when such protocols are implemented, it seems necessary to pay attention to the context in which the experiments are conducted (Shogren et al., 1999, Jaeger and Rose, 2008). Another option is to attempt to trigger more affective responses. This may be achieved by the use of an upsetting cover story prior to the test. To this end, we recently proposed the use of a test of perceived authenticity (Boutrolle et al., 2009). In addition to eliciting more discriminant preferences than in regular CLT, the authenticity test procedure allows driving participants into eating several samples repeatedly without explicitly forcing them. This may lead to a better external validity than that of hedonic data collected from first impression only. This can be related to Berlyne’s categorization of hedonic task response behaviors into either specific or diversive exploration (Berlyne, 1960). Specific exploration is not concerned with pleasure but with resolving puzzling stimulation and reducing uncertainty. Thus, instead of real preference or liking, when they test first impression, market researchers obtain responses that are mostly based on curiosity and on the desire to learn more about the products. The diversive exploration focuses on the pleasurable aspects of the product and thus for the more durable appreciation of it. This is only possible after specific exploration is completed and the uncertainties are resolved. Certainly future methodological developments will have to further explore this dimension, as suggested by Köster and Mojet (2007).

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kramer fm, rock k & engell d (1992), “Effect of time of day and appropriateness on food intake and hedonic ratings at morning and midday”. Appetite, 18, 1–13. kramer fm, lesher ll & meiselman hl (2001), “Monotony and choice: repeated serving of the same item to soldiers under field conditions”. Appetite, 36, 239–240. laeng b, berridge-kent c & butter cm (1993), “Pleasantness of a sweet taste during hunger and satiety: effects of gender and ‘sweet tooth’ ”. Appetite, 21, 247–254. lähteenmäki l & tuorila h (1994), “Liking for ice cream measured with three procedures: side-by-side, after consumption and single samples”. Journal of Sensory Studies, 9, 455–465. lähteenmäki l & tuorila h (1995), “Consistency of liking and appropriateness ratings and their relation to consumption in a product test of ice cream”. Appetite, 25, 189–198. lähteenmäki l & tuorila h (1997), “Item-by-use appropriateness of drinks varying in sweetener and fat content”. Food Quality and Preference, 8, 85–90. lange c, martin c, chabanet c, combris p & issanchou s (2002), “Impact of the information provided to consumers on their willingness to pay for Champagne: Comparison with hedonic scores”. Food Quality and Preference, 13, 597–608. lawless h & heymann h (1999), Sensory evaluation of food. Principles and practices. Gaithersburg, MD, Aspen Publishers, Inc. leon f, couronne t, marcuz mc & koster ep (1999), “Measuring food liking in children: a comparison of non verbal methods”. Food Quality and Preference, 10, 93–100. lucas f & bellisle f (1987), “The measurement of food preferences in humans: Do taste-and-spit tests predict consumption?” Physiology & Behavior, 39, 739–743. macfie hj, bratchell n, greenhoff k & vallis lv (1989), “Designs to balance the effect of order of presentation and first-order carry-over effects in hall tests”. Journal of Sensory Studies, 4, 129–148. maller o, dubose cn & cardello av (1980), “Consumer opinions of hospital food and foodservice”. Journal of the American Dietetic Association, 76, 236–242. matuszewska i, barylko-pikielna n, szczecinska a & radzanowska j (1997), “Comparison of three procedures for consumer assessment of fat spreads: short report”. Polish Journal of Food and Nutrition Sciences, 6, 139–142. mcdaniel mr & sawyer fm (1981), “Preference testing of whiskey sour formulations: magnitude estimation versus the 9-point hedonic”. Journal of Food Science, 46, 182–185. mcewan ja (1997), “A comparative study of three product acceptability trials”. Food Quality and Preference, 8, 183–190. meilgaard m, civille gv & carr bt (1999), “Sensory Evaluation Techniques”. in Meilgaard M, Civille GV & Carr BT (eds.) Sensory Evaluation Techniques. 3rd edn. Boca Raton, FL, CRC Press, pp 37–41. meiselman hl (1992), “Methodology and theory in human eating research”. Appetite, 19, 49–55. meiselman hl (1996), “The contextual basis for food acceptance, food choice and food intake”. in Meiselman HL & Macfie H (eds.) Food choice, acceptance and consumption. Glasgow, Blackie Academic and Professional, pp 139–263. meiselman hl, hirsch es & popper rd (1988), “Sensory, hedonic and situational factors in food acceptance consumption”. in Thomson DMH (ed.) Food acceptability. London, Elsevier, pp 77–87. meiselman hl, johnson jl, reeve w & crouch je (2000), “Demonstrations of the influence of the eating environment on food acceptance”. Appetite, 35, 231–237. mela dj, rogers pj, shepherd r & macfie h (1992), “Real people, real foods, real eating situations: real problems and real advantages”. Appetite, 19, 69–73.

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subar af, kipnis v, troiano rp, midthune d, schoeller da, bingham s, sharbaugh co, trabulsi j, runswick s, ballard-barbash r, sunshine j & schatzkin a (2003), “Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: The OPEN study”. American Journal of Epidemiology, 158, 1–13. surprenant cf & solomon mr (1987), “Predictability and personalization in the service encounter”. Journal of Marketing, 51, 89–96. talbot l, delarue j & sieffermann j-m (2009), “Taking context into account for the study of satiation and liking for biscuits”. The 8th Pangborn sensory science symposium, Florence, It, July, 26–30. tesser a (1978), “Self-generated attitude change”. in Berkowitz L (ed.) Advances in experimental social psychology. San Diego, CA, Academic Press, pp 289–338. tourangeau r, rasinski ka, bradburn nm & dandrade r (1989), “Belief accessibility and context effects in attitude measurement”. Journal of Experimental Social Psychology, 25, 401–421. tufano f (2009), “Are ‘true’ preferences revealed in repeated markets? An experimental demonstration of context-dependant valuations”. Experimental Economics, online, doi:10.1007/s10683-009-9226-8. tulving e (1984), “Precis of elements of episodic memory”. Behavioral and Brain Sciences, 7, 223–268. tuorila hm & lähteenmäki l (1992), “When is eating ‘real’? Response to Meiselman”. Appetite, 19, 80–83. tuorila hm, lehtovaraa a & matuszewska i (1990), “Sandwiches and milk with varying fat and sodium contents: what is the best combination?” Food Quality and Preference, 2, 223–231. turner m & collison r (1988), “Consumer acceptance of meals and meal components”. Food Quality and Preference, 1, 21–24. tversky a & kahneman d (1973), “Availability: a heuristic for judging frequency and probabiliies”. Cognitive Psychology, 5, 207–232. vickers z, mullan l & holton e (1999), “Impact of differences in taste test ratings on the consumption of milk in both a laboratory and a foodservice setting”. Journal of Sensory Studies, 14, 249–262. wansink b & park s-b (2001), “At the movies: how external cues and perceived taste impact consumption volume”. Food Quality and Preference, 12, 69–74. west p (1982), Reproducing naturally occuring stories: vignettes in survey research. Working Paper, Aberdeen, MRC Medical Sociology Unit. zandstra eh, de graaf c, van trijp hcm & van staveren wa (1999), “Laboratory hedonic ratings as predictors of consumption”. Food Quality and Preference, 10, 411–418. zandstra eh, de graaf c & van trijp hcm (2000), “Effects of variety and repeated in-home consumption on product acceptance”. Appetite, 35, 113–119. zellner da, stewart wf, rozin p & brown jm (1988), “Effect of temperature and expectations on liking for beverages”. Physiology & Behavior, 44, 61–68.

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9 Going beyond liking: measuring emotional and conceptual profiles to make better new products D. Thomson, MMR Research Worldwide Inc., UK

Abstract: The formula for success in new product development is straightforward in theory: a sufficiently large number of people must want the new product, they must have fairly ready access to it and they must choose it repeatedly in preference to whatever alternatives are available to them. Only then will the new product deliver the expected return on investment. In practice, it’s relatively easy to make new products that people like, but it’s much more difficult to create products that people want. The distinction to be made between liking and wanting is very important! This chapter looks beyond liking to consider the role that emotion and functionality play in creating products that people want. Key words: wanting, immediate liking, choice, emotion, functionality, conceptualisation, conceptual profiling, best/worst (maximum difference) scaling.

9.1 Introduction It’s common knowledge that most new products fail. Casualty rates amongst newly launched brands are even higher. How can this possibly be so? Consumer packaged goods (CPG) companies spend a fortune researching new products and brands, with a view to maximising the chance of success and screening out probable failures. Assuming that nobody of sound mind would launch a new product in the knowledge that it will fail, it figures that where there is failure there once was hope. Misplaced hope as it so often turns out! The consequent waste of money, time, effort, human endeavour and worse still these days, the waste of physical resources, is mindboggling, especially when considered on a global scale. There are lots of reasons why new CPG products fail, including poor execution of what is essentially a good idea. However, there’s no escaping the charge that research is giving the ‘green light’ to new products that

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should not be launched. One of the main reasons for this is that the criteria and action standards used to judge new product ideas and new products, especially measures of liking, acceptability and purchase intent, are inadequate. Not wrong – inadequate! The appropriateness and adequacy of liking or acceptability being the primary criteria for judging future marketplace success was called into question by Schutz (1988) over two decades ago; a call subsequently repeated recently by Köster and Mojet (2007). Clearly, new product developers and researchers need to look beyond liking, acceptability and purchase intent as a means of judging new products. The question is to what? Part 1 of this chapter takes a brief theoretical look at the factors that may influence choice decisions within the human mind. In doing so, it identifies the importance of conceptualisation (the process of attaching meaning to what we experience) and makes the case for augmenting our understanding of product performance by supplementing measures of liking with the emotional and functional (conceptual) profiles of branding, packaging and product. Part 2 reviews some of the processes currently used to measure emotion (or emotional conceptualisations). Part 3 introduces and describes the use of emotional/conceptual lexicons combined with best/worst (maximum difference) scaling as a means of obtaining conceptual profiles for branding, packaging and products. Case studies involving car marques (brand conceptualisations) and dark chocolate (unbranded and branded conceptualisations) are presented in detail. It is hoped that some of the new ideas and methodologies described in this chapter will help brand and product developers, in all areas of consumer goods and services, to create new products that are both liked and wanted. Liking and wanting are not the same! It’s entirely possible to create a new product that is liked but not wanted. Conversely, consumers don’t necessarily need to like a product to want it (think of vitamins and supplements or car insurance). Hopefully, this chapter will give some insight into what’s beyond liking and how this can be captured, measured and introduced into new products.

9.2

Part 1: Understanding consumer choice processes

9.2.1 Perception versus conceptualisation In spite of recent advances in brain physiology and psychology, scientists still have an incomplete understanding of how physiological activity in the brain finally becomes psychological activity in the mind. This is one of life’s great mysteries and definitely something to be marvelled at! What is known for sure is that we interact with the physical world primarily via our senses: sight, hearing, touch, taste, smell, temperature (thermoception), pain (nociception), balance (equilibrioception) and body

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position (proprioception). All of our senses function by transforming energy in one form or other (sonic waves, light, kinetic energy, etc.) into electrical impulses. This electrical activity is transmitted via the nervous system to various parts of the brain, where it induces localised physiological activity. What happens next is also shrouded in mystery but, somehow or other, physiological activity becomes psychological activity. This is the ‘magical’ transition point between the outer physical world and the inner psychological world. Modern brain imaging techniques such as fMRI (functional magnetic resonance imaging) have allowed neuroscientists to confirm that mental activity is invariably accompanied by physiological activity in the brain, and vice versa, but even these most sophisticated tools shed no light on exactly how one becomes the other. All we know is that it does! What follows thereafter can be usefully divided into two activities: perception and conceptualisation. Perception is the process of assigning definition to what is being experienced (what it is). Conceptualisation is the process of assigning meaning to what is perceived. Purists might argue that both are part of the same process (perception). Perhaps so, but failure to distinguish between the processes of assigning definition and meaning is, in my opinion, the root cause of many of our woes. The distinction between perception and conceptualisation can be illustrated very simply using sugar and sweetness as an example. Sucrose (common sugar) comprises molecules of glucose and fructose in equal proportions. These are physical entities. Sugar is absorbed into the body through various channels but it’s the interaction between sugar molecules and the gustatory (taste) receptor cells, and the processes that this interaction sets in train that finally causes us to perceive sweetness. But that’s definitely not the end of the matter. During the course of our lifetime we assign meaning to sweetness, either through our own personal experiences or through knowledge gleaned from others. For example, most people learn that sweet foods contain relatively large amounts of sugar, making them relatively energy dense. As a consequence, sugar is conceptualised as being fattening and causing dental caries and even diabetes. In other words, too much sugar is ‘bad’ for you. The notions of being fattening, of causing dental caries or diabetes and generally being ‘bad’ for you are conceptualisations that we’ve created and come to associate with sweetness. Sweetness isn’t all bad though! Many of the foods we enjoyed when young are sweet. We enjoyed them then and we may still enjoy them now, because humans are born with an instinctive, positive disposition towards sweetness. This is due, we might presume, to the quirk of fate that made sugar a safe, rich and very accessible source of energy, as well as being sweet. Consuming sugar in appropriate quantities energises us, makes us feel good and otherwise allows us to do things that are intrinsically pleasurable. Experiencing pleasure is part of the reward mechanism that encourages us to

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repeat whatever it is that we’ve just done, presumably with the ultimate goal of replicating our genes through procreation. Since sweetness has obviously become associated with reward, most cultures in the world use sweetness for treating, gifting and comforting, for expressing feelings of love, gratitude, happiness, contentment and also as an inducement. Indeed, an entire product category (‘sweets’) has evolved for exactly this purpose. When someone perceives sweetness, it will automatically trigger an array of conceptualisations. Some of what we conceptualise could be newly created at that particular moment in time. If so, this will be added to our stored ‘knowledge’ of sweetness. However, because sweetness is so familiar to us, most of what is conceptualised is recalled from the established knowledge that’s stored in the memory. Part of what we conceptualise will be apparent to us, and part not, but all of what we conceptualise has the potential to influence how we feel and how we behave. These could be ‘negative’ conceptualisations associated with issues of diet and health, ‘positive’ conceptualisations associated with comfort, love, happiness or reward, or a mixture of both. Whether it’s the positive or the negative conceptualisations of sweetness that will prevail on a particular occasion, perhaps causing us to accept or reject that huge, tantalising and delicious looking slice of chocolate gateau, obviously depends on the person and the prevailing circumstances, but the size of the ‘sweets’ category and the ubiquity of sweet desserts speaks volumes. Conceptualisations of sweetness have become so deeply embedded in the psyche of the human race that they are even used metaphorically (figuratively) in language to describe objects far removed from sugar and the perception of sweetness; ‘What a sweet little house’, ‘She’s my sweetheart’, ‘Isn’t he a real sweetie?’. Of course, this type of metaphorical association extends far beyond sweetness. Vast tracts of language have evolved around the metaphorical meaning of the words we use to describe what we conceptualise. Towards the end of this chapter, we’ll take a more detailed look at the relationships that exist between what we perceive, what we conceptualise and the words we use to describe them, because this amounts to much, much more than mere description. Although what is conceptualised within the minds of humans is likely to be almost infinite in its variety and diversity, conceptualisations can be categorised simply into three broad categories: functional, emotional and abstract. Functional conceptualisations Referring back to sugar and sweetness, various notions such as sugar (as conveyed by the perception of sweetness) is fattening, energising, invigorating, causes tooth decay or causes diabetes, are all anticipated functional consequences (what sugar might do to you). For this reason, these are defined as functional conceptualisations. Needless to say, actual

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functionality and conceptualised functionality are not necessarily the same things, as the product might not deliver the functionality that it promises. If this was the case, and individuals were aware of a discrepancy between functional promise and functional delivery, then it might be imagined that individuals’ conceptualisations of product functionality would be modified accordingly. In many circumstances, though, the individual concerned may be unaware of the true functionality of the product because it isn’t apparent to them, or, if it is apparent, they don’t notice, or they choose to ignore it for one reason or another. If so, actual functionality and conceptual functionality may never be reconciled. Conversely, the individual may not stop and think about product functionality but may experience disappointment or dissatisfaction with what the product delivers without realising that this is due to a mismatch between conceptualised and actual functionality. This non-conscious reconciliation could influence future product choice. Either way, it’s how we conceptualise the world (knowingly or otherwise) rather than how the world really is, that matters to us. This is true for all product categories and, indeed, all objects, places, people, services and events. It follows therefore that conceptualised functionality rather than actual functionality will influence our choices and otherwise determine our behaviour. In other words, ‘Conceptualisation is reality’! Emotional conceptualisations When we interact with an object of any sort, or a person, we may experience feelings of love, happiness, affection, appreciation, being worthy of reward, annoyance, anger, hurt, neglect and so on. These are emotions. However, we may also recognise that the object or person might make us feel this way without actually experiencing that particular emotion there and then. For example, it’s possible to recognise that someone is a loving individual (emotional conceptualisation) without actually experiencing love (emotion). Again going back to sweetness for a moment, when we eat a product and it tastes sweet, the sweetness may have direct emotional consequences and/or it may trigger emotional conceptualisations. Of course, we don’t actually need to eat the product to be influenced by its sweetness. The mere sight or mention of a product that we know to be sweet can also trigger direct emotional consequences and emotional conceptualisations. The same is true for all objects, places, people, services and events; they can all trigger direct emotional consequences and emotional conceptualisations. This is somewhat analogous to functionality, as described above. In both cases there is the actual functional (or emotional) outcome. Knowingly or otherwise, these may influence our immediate reactions, shape the way that we conceptualise the object in the future and influence our future choice behaviour. Also, there are functional and emotional conceptualisations. Knowingly or otherwise, these too may influence our future choice behaviour.

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It’s very important to recognise the distinction between emotions (i.e. emotional experience) and emotional conceptualisation as this has huge implications in terms of the research methodologies that should and should not be used in the process of developing brands, products and packs. This issue is discussed further in Part 2. Abstract conceptualisations The notion that sugar (for example) is either ‘bad’ or ‘good’, depending on your point of view, is an abstract conceptualisation. Likewise, the notions that Volkswagen cars (for example) are trustworthy, Volvos are traditional, Audis are classy and Skodas are simple (see Section 9.4.1); these too are abstract conceptualisations. So how do abstract conceptualisations arise, how do they influence the way that we feel and how do they influence our behaviour? For example, what makes an Audi classy and a Skoda simple and how does this influence how we might feel about owning one or other? We conceptualise Audis as being classy (or some of us do) for many reasons: i) Brand communication and advertising (current and historical) and the fact that it is treated as an aspirational brand ii) Brand heritage and provenance (e.g., many Le Mans winners in recent years) iii) Range of models – including sports cars (Audi TT) and luxury saloons (A8) iv) Shape of the vehicle, the flow of the external lines and the appearance of the external features v) Internal features, gadgets and gismos vi) What other people have told us about Audis (reviews in the media) vii) Who else owns an Audi – and how we conceptualise these individuals viii) Price. Skoda and Audi are manufactured by the same company (Volkswagen Group) and share many common structural features and parts. Both brands are reliable, functionally excellent and look good, but Skodas are definitely not classy. Indeed, in spite of their physical and functional commonalities, Skoda and Audi are conceptually very different (see Section 9.4.1 for a detailed comparison). Most of these conceptual differences are abstract in nature, created via differences in brand provenance and heritage and differences in the way that the brands are presented (i.e. via i) to viii), above). Some of these abstract conceptualisations will impact on our emotions (e.g., feelings of being personally classy, being superior, being successful, etc.), so we could say that they have emotional consequences or connotations. Other abstract conceptualisations such as trustworthiness are based, at least in part, on the brand’s reputation for robustness and reliability which are, of

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course, functional conceptualisations. For this reason, we could say that trustworthiness is an abstract conceptualisation that has functional connotations. Other abstract conceptualisations such as powerful can have both emotional and functional connotations. The emotional and functional connotations associated with abstract conceptualisations go on to influence our emotions directly and otherwise determine and shape how we conceptualise an object either emotionally and/or functionally. This, in turn, determines our choices and our general behaviour. For this reason, abstract conceptualisations are akin to stepping stones or intermediaries in the process of causing emotional outcomes and creating emotional and/or functional conceptualisations. This being the case, it is possible to further simplify the process of conceptualisation to: • conceptualisations that have emotional connotations (emotionality) • conceptualisations that have functional connotations (functionality). In other words, all conceptualisations probably end up having either emotional and/or functional connotations. When people make product choices, their decision to choose a particular product is influenced by what they perceive (or what they anticipate they’ll perceive) and whether or not they like (or think they’ll like) what they perceive. Based on the foregoing, we should anticipate that choice decisions are also influenced by the nature and desirability of the corresponding conceptualisations. (There is no inference here that people would need to think about or should be aware of what they perceive or what they conceptualise, in order to be influenced by them. In all probability the choice process will be determined by a mixture of conscious and non-conscious events.) What are the implications for brand and product development? When creating new branding, new packaging or new products, developers must think long and hard about the conceptual meaning that should be designed into whatever it is they are developing. Although this is the norm with branding (after all, brands are almost entirely conceptual phenomena), the conceptual communication triggered by the corresponding unbranded packaging and the unbranded product is often ignored. It is especially important that the conceptual meaning associated with product, packaging and brand are aligned. Until recently, capturing what is conceptualised has been essentially the sole province of qualitative research. It’s this author’s view that this must be augmented with robust, quantitative research tools. (Some new options are described and demonstrated in Part 3.) Kansei design (engineering) is one procedure that has attempted to relate the physical parameters of products with what is conceptualised. Whilst sound in principle, published applications of the Kansei process are invariably marred in practice by rather inadequate conceptual elaboration and profiling techniques (Yang, 2009).

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In his book ‘Brand Sense’, Martin Lindstrom describes how the conceptualisations associated with particular smells and fragrances can be used to augment branding (Lindstrom, 2005). However, again, sound principle is marred by weak methodological elaboration! 9.2.2 Liking versus wanting We buy (or choose) products because, knowingly or otherwise, we want the functional and emotional benefits we believe they’ll deliver to us. What we want (i.e., our state of wanting) is shaped by the interaction of three factors: Underlying you – your psyche and your lifetime of personal experiences. Transient you – how you’re feeling mentally and physically at that particular moment. Situation and circumstance – where, when, why, what for, who with, who for. The ‘underlying you’ is essentially a constant whereas the ‘transient you’ and ‘situation and circumstance’ are in a state of dynamic flux. As a consequence, our ‘state of wanting’ is also in a state of flux, driven by the two transient elements described above. However, our state of wanting at any particular moment in time is unlikely to be totally random and unpredictable because the ‘underlying you’ is more or less constant and because the ‘transient you’ and ‘situation and circumstance’, although variable, will probably follow a restricted pattern. After all, we are creatures of habit and routine. The outcome is a limited repertoire of ‘circumstantially derived states of wanting’ that is fairly unique to you, your lifestyle and your circumstances. Over time you will have developed a repertoire of ‘product solutions’ that deliver the functional and emotional benefits that you want in particular situations and circumstances, often without realising it. On most occasions you will choose tried and trusted products that have a track record of seeming to satisfy your wants. Other times, you may seek new or better solutions. Either way, addressing our wants successfully brings a feeling of satisfaction and reward. This is the ultimate driver. However, the question now arises as to whether or not, in research, we can capture this capacity to satisfy wants using evaluative criteria such as liking, acceptability or purchase intent. I think not! There are three reasons for this: i) As explained above, wants are circumstantially dependent. Even the most carefully simulated research scenario isn’t ‘real life’, so we should not fool ourselves into thinking that the research environment can recreate within the minds of research participants, the subtleties of the ‘circumstantially derived states of wanting’ that prevail in real life. This obviously influences the basis on which evaluations are made. ii) The full emotional and functional impact/potential of a product might only become apparent after days, weeks or even months of usage. In

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research, interaction between product and person is typically measured in minutes (or days at most). iii) The evaluative processes used in research usually require participants to stop and think about liking, acceptability and/or purchase intent, and also about the associated measurement scales. The real life choice process may invoke other thought processes that will often be non-conscious. For these reasons, it is by no means certain that the overviews triggered by asking research participants how much they like a product, how acceptable it is to them or how likely they would be to buy it, are exactly the same as the overview taken when we make real-life product choices. As a consequence, some products that should have been rejected will continue through the new product development process to launch and inevitable failure. Conversely, some products with true potential will be stopped because they don’t meet the action standards for liking, acceptability or purchase intent. So, what do scales of liking, acceptability or purchase intent actually capture? With unbranded foods and beverages or personal care products, for example, liking and acceptability scales will probably capture the immediate enjoyment arising from the sensory characteristics of the product. Experience suggests that these scales perform this function rather well. Depending on the circumstances, they might also capture the extent to which research participants like (or find acceptable) what they ‘see’ in the product; i.e. the functional, emotional and abstract conceptualisations that may be apparent to them in that particular research scenario. However, liking these conceptualisations and wanting the implied functional and emotional benefits, are not the same things. This is the nub of the problem! Hence liking ratings probably do not access the same decision-making criteria as the choice process. Moreover, liking evaluations might only be based on a subset of the conceptualisations that determine choice, simply because the factors that influence choice may not be apparent to us. It’s the author’s view that this is perhaps where the disparity lies and may be why liking ratings do not predict choice behaviour. (Likewise for acceptability and purchase intent, both of which are closely correlated with liking.) Unfortunately, the action standards adopted in most product development processes are based on measures of liking (and/or acceptability and purchase intent). This means that the products that are taken forward to launch may be liked (often a great deal) but may not be wanted enough to motivate purchase. Hence the problem! Of course, this doesn’t mean that liking, acceptability or purchase intent ratings are of no value; far from it in fact. It simply means that they don’t tell enough of the story to facilitate reliable product selection in the new product development process. It also means that we need to look beyond liking in order to understand what a brand, pack or product actually delivers. In practice, this means that we should do the following:

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i) Continue to measure liking (or acceptability) as at present, but downgrade its importance as an action standard in the new product development decision-making process. ii) Rename ‘liking’ as ‘immediate liking’ to reflect the fact that it’s typically a fairly superficial overview based on a fairly superficial interaction with the product and probably not the same overview that influences real-life choice behaviour. I shall adopt this terminology henceforth. (This doesn’t mean that immediate liking is unimportant, we just need to be realistic about what it is and what is not being captured.) iii) Capture the emotionality and functionality of the product by conducting ‘conceptual profiling’ of functional, emotional and abstract conceptualisations. This process is explained in detail in Part 2 and illustrated via two case studies in Part 3. iv) Stop using the purchase intent/propensity-to-buy ratings as an action standard in product development (i.e., decisions based on X% ‘definitely would buy’ and Y% ‘probably would buy’). If it is to be used at all, its use should be limited to an otherwise unseen input to volumetric estimation. The purchase intent scale purports to measure behavioural intention (i.e. purchase) but the untransformed raw scores measure nothing of the sort and invariably correlate with liking. This scale is dangerously misleading, especially in the wrong hands and may be the root cause of much of the poor decision making in new product development. 9.2.3 The 3 × 3 ‘Matrix’ From the foregoing, we now know that the process of conceptualisation exerts a powerful influence on choice. We also recognise that although infinitely diverse, human conceptualisations finally reduce down to just two types; those with immediate or eventual emotional connotations and those with immediate or eventual functional connotations (emotionality and functionality for short). We might also anticipate that immediate liking exerts a huge influence on choice. As explained above, this probably relates to the immediate enjoyment experienced or anticipated when a product is used or consumed and this will undoubtedly influence (but not necessarily determine) choice. In other words, immediate liking contributes to but is not the final determinant of choice. This is evidenced by the fact that we buy and consume things that we don’t necessarily like. Conversely, we don’t buy everything we like. Other factors are obviously involved. To move forward, our first challenge is to find a research process that allows us to capture and measure the less accessible conceptualisations that exist within the human mind (more of which later). The second challenge is to persuade marketers and product developers to include emotional and

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Emotional

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Liking (immediate)

Branding

Packaging

Product

Fig. 9.1 ‘The matrix’.

functional conceptualisations more formally in product evaluation and otherwise in their decision-making processes. Most consumer packaged goods comprise three primary elements; the branding, the packaging and the product itself. When these are combined with emotionality, functionality and immediate liking, a simple 9-cell matrix is formed, as shown in Fig. 9.1. In practice, this means that branding, for example, exerts emotional and functional influences that impact on our feelings and our behaviour, whether we realise it or not. We can also form an immediate overview about how much we like the branding. This will be based partly on how much we like what we perceive which will obviously include the various design elements (e.g., colour, shape and sound). Immediate liking will also be based, to some extent, on the degree to which we are able to tune into and appreciate the emotional and functional aspects of the brand communication. However, as mentioned before, it is highly unlikely that immediate liking will be based on a comprehensive overview of all that is conceptualised emotionally and functionally because much of this is inaccessible via cognitive thought processes. From the foregoing, it should be anticipated that positive emotional and functional conceptualisations would impact positively on immediate liking. Conversely, experiencing negative conceptualisations may engender some degree of immediate disliking. It should also be anticipated that functional conceptualisations (e.g., ‘I believe that Brand X washes my clothes cleaner’) would impact on emotionality (e.g., ‘I know I’m clean and this makes me feel more self-assured and confident’), and vice versa. This indicates that emotionality, functionality and immediate liking are linked via a 3-way relationship and that they are unlikely to be completely independent of each other. From a data modelling point of view this means that some

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degree of association (e.g., statistical correlation) should be anticipated across the three entities. However, this does not mean that they are the same thing! It simply means that there is likely to be a partial cause and effect relationship. Moreover, the whole point of exploring emotionality and functionality separately, is to understand more about the emotional and functional conceptualisations engendered by branding, packaging and product and to integrate this into development and design processes. The key impression to create at this stage is of ‘influence’ streaming across from branding (in this case) to the individual concerned, via three channels (emotionality, functionality and immediate liking). Likewise for packaging and product! Product is particularly interesting in this regard. In the past, researchers and product development technologists have often limited their evaluation of product to immediate liking and to whether or not the sensory attributes are at the optimum level to deliver maximum enjoyment (often using relative-to-ideal or just-about-right scales). Until quite recently, few of us had imagined or appreciated that product sensory characteristics actually deliver so much more. We really should have done! After all, the influence of smell on emotion has probably been recognised since the beginning of humankind (‘the smell of love’ and ‘the smell of fear’). Likewise with feelings of reassurance, comfort and reward imbued by sweetness! As previously noted, sweetness also communicates functionality; some of it positive (to energise and invigorate) and some negative (dental carries, obesity and diabetes). Fragrance also influences functional conceptualisation. For example, lemon fragrance in a household cleaning agent communicates deep cleaning functionality, floral character communicates kindness to skin and the merest whiff of chlorophenol screams antiseptic action. In spite of this, it took Martin Lindstrom’s book, ‘Brand Sense’ (Lindstrom, 2005), to highlight the contribution made by product sensory characteristics in delivering the total brand experience. By way of example, Lindstrom explained how Singapore Airlines has infused the total travel experience with a carefully chosen fragrance (Stefan Floridian Waters). Personnel, the lounge, the aircraft cabin and the toiletries all bear the same subtle fragrance. In time, Lindstrom claims, this has become associated with other very positive emotional and functional aspects of the Singapore Airlines travel experience and so the fragrance has become part of the brand, in the same way as the logo, the uniforms and the aircraft livery. As might be expected, many food and beverage brands, either by design or by coincidence, have developed powerful linkages between the sensory characteristics of the product and the emotional and functional conceptualisations associated with the brand; e.g. Cadbury’s Dairy Milk Chocolate, Coca Cola, Red Bull, Guinness, Baileys Irish Cream, Kettle Chips and many, many more. However, this isn’t just the province of foods and other consumer packaged goods such as Ariel, Persil, Dettol, Savlon, etc., that have

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very distinctive smells; think about the sound of a Subaru or a Harley Davidson motorcycle, or the Nokia ring tone or the jingle when Microsoft Windows fires into life (Treasure, 2007). Each of these has a unique sensory signature that not only identifies the product but also helps to communicate the emotional and functional aspects of the brand message. Of course, it could be argued that all products have a sensory signature that is unique to some extent and that this could contribute either positively or negatively to immediate liking and/or purchase choice. However, what distinguishes these iconic brands, and their like, from the others, is the fact that each product has a distinctive sensory profile (before it got ripped off by copycats) and each brand has a distinctive emotional and/or functional message and the former triggers recall of the latter. The nature of packaging materials and the shape and size of a pack also communicates emotionality and functionality very forcefully. About 15 to 20 years ago, boxed breakfast cereal manufacturers went to extraordinary lengths to create an inner bag, made from modern plastic materials that had perceptual (sensory) characteristics similar to the traditional, translucent waxy bag. They were right to be so cautious. After all, the waxy bag was part of almost 100 years of tradition in the boxed breakfast cereals category and tradition (which is an abstract conceptualisation) has powerful emotional connotations. Also, the waxy paper, being non-plastic, communicated a simple wholesomeness which consumers linked to nutritional goodness (a functional connotation). For some strange reason, there is something quite shocking and terribly diminishing about seeing a breakfast cereal, exposed in all its ‘nakedness’, through the membrane of a clear plastic bag. In summary, it’s useful to envisage our interaction with consumer packaged goods as occurring along the nine channels of ‘the Matrix’ (Fig. 9.1). This being the case, one of the clear inferences is that the emotional impact and the functional promise made by the branded product totality will derive in part from its branding, in part from its packaging and in part from the product itself. Surely, it would be a good idea if the emotionality deriving from branding, packaging and product was congruent (consonant) rather than contradictory (dissonant)? Likewise with functionality! From a new product development perspective, there may be considerable advantage in setting out, right from the start, to create congruence (and hence consonance) in the emotional and functional messages communicated simultaneously by the branding, the packaging and the product, so that they are mutually reinforcing. Likewise, if the emotional and functional messages emanating from brand, pack and product are contradictory (and in the author’s experience, this is often the case), the resulting dissonance may weaken and distract greatly from the holistic product experience. In our increasingly competitive market places, creating new products that are handicapped by built-in dissonance is crazy. Creating consonance should be the norm. This needs to become as much a part of the new product development process as creating products that are liked.

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9.2.4 Summary of Part I In Part 1 of this chapter, I’ve attempted to highlight the importance of conceptualisation as an adjunct to immediate liking in influencing and determining choice. Allied to this, ‘the Matrix’ provides a simple template for disaggregating the complexities of the holistic choice process into nine measurable elements. Researchers have undoubtedly developed very reliable techniques for measuring acceptability and liking (or what I prefer to describe as ‘immediate liking’) and we should continue to use these with confidence. However, although ‘the matrix’ is nothing more than a representational model, it serves very well in making the point that immediate liking (or acceptability) only covers three out of nine possible channels. If we are to understand what determines choice and whether or not a new brand, pack or product is likely to succeed, we need to cast our net much wider so that we also capture and measure the emotional and functional conceptualisations represented by the other six cells of ‘the matrix’. To do this, new product development needs new but practical research tools. These are considered in Parts 2 and 3.

9.3

Part 2: Measuring conceptualisations

9.3.1 Capturing functionality Capturing and measuring functional conceptualisations is a relatively straightforward process, largely because it’s fairly easy for people to imagine the functional benefits that a brand or product might deliver to them. In other words, product users are usually somewhat aware of, and are able to describe, the functional benefits that influence them. (Not so with emotions!) The first step in the process of profiling functional conceptualisations is to create a fairly extensive, draft list of appropriate functional benefits and disadvantages for the product category in question. Typically, this would be distilled from previous research, manufacturers’ websites, on-pack information and exploratory qualitative research (either face-to-face or online) with target users. Lists of functional benefits typically comprise between 8 and 20 items, depending on the product category. Terms describing both positive and negative functionality should be included. The ratio depends on the category. Care should be taken to ensure that each statement describes a unique functionality that research participants will recognise as different from all other functionalities on the list. This is particularly important if choice-based methods are to be used (e.g., best-worst scaling, as mentioned below). Three research techniques are typically used: tick lists, ratings and best/ worst scaling. With simple tick lists, research participants are provided with a list of functional descriptions. The idea is to tick those functional attributes that

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they associate with the product or brand. (Often, a functional benefit would not actually be realised in the research scenario, so it would need to be anticipated, based on the functional and abstract conceptualisations.) Although this is a fairly unsophisticated process, this author has found the data to be useful, provided the sample is reasonably large (preferably 100 or more). The method has the advantages of being quick and that relatively long lists of 20–25 functional descriptors can be used without too much effect on data quality. Needless to say, the order and position of the terms in the tick list should be rotated using an appropriate design. An example of functional profiling of dark chocolate using tick lists is provided in Part 3. The resultant data sometimes fails to discriminate effectively across products and/or brands and is often correlated with liking. This is because: (i) in some categories, functional benefits are a property of the category rather than the product or brand; and (ii) research participants are inclined to believe that products which they like have more of the desirable functional benefits and fewer of the undesirable functional attributes. One can attempt to increase the discrimination of tick lists by forcing the consumer to pick a set number of items (n) from the list. This may be useful when many functional benefits are likely to be associated with all the brands or products in the study. As a rough guide, one should set n to about one-third of the total number of statements. With ratings, research participants use category scales or continuous line scales to estimate the extent to which they anticipate the product or brand will deliver the functional benefit in question. For most studies we would advocate a labelled 5-point scale in which the categories run from ‘extremely’ at one end to ‘not at all’ at the other (or words to that effect). Alternatively, scales labelled with 1–9 or 1–10 at the ends, can be used. However, in this type of research, such scales are less consumer-friendly and confer little, if any, advantage in discrimination over 5-point categorical scales, which usually yield more interpretable results. This is a fairly crude and un-engaging process for research participants and is more time-consuming than tick lists, so it’s recommended that no more than 10 functional benefits should be rated in this way. Although rating scales are widely used for functional profiling, they may require large sample sizes to give adequate discrimination and, in practice, may not be greatly superior to tick lists in this application. Best/worst scaling (maximum difference scaling – Finn and Louviere, 1992) has also been used by the author and his collaborators for functional profiling. (Best/worst scaling is described in detail towards the end of Part 2 and in Part 3.) Suffice to say at this stage, that the functional descriptors are presented to research participants in sets of four or five and he or she must choose which of the terms is most closely associated with (or most appropriate for) the product, and which term is least appropriate. Each participant would typically be presented with 10–15 sets of terms. These

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choice responses are used to calculate scale values. This allows the functional terms to be plotted on a scale, one for each product, effectively creating functional profiles. Although ideal for exploring emotional and abstract conceptualisations, it’s the author’s view that best/worst scaling is unnecessarily sophisticated for exploring functionality. Its main application has been in small consumer panels where tick lists and rating scales would not give sufficient sensitivity.

9.3.2 Capturing emotionality The process of capturing and measuring emotionality is much more difficult, largely because people aren’t always aware of the emotional impact that an object is having on them (or is likely to have on them). There are many definitions of emotion(s) and emotional state but ‘feelings’ and ‘the way you are feeling (within your own mind)’ serve as useful, if not particularly elegant, working definitions. People, places, events, brands, products and indeed, all objects, by virtue of the way that we conceptualise them, have the potential to impact on our emotions, either by prolonging the way we feel or by transforming us to a more or less desirable emotional state. The role of consumer goods is of particular interest in the context of this chapter. In the first place, we buy products to satisfy our basic functional needs: food, shelter, safety, health and hygiene, plus mobility and communication, these days. Satisfying these needs (or not) and the manner in which this is achieved, impacts on our emotions. Beyond these basic needs, most of what we purchase actually serves to satisfy our desires rather than our true needs and therefore is unnecessary by some definitions. Or perhaps not! Satisfying needs is about protecting and nourishing the body. Satisfying desires is about protecting and nourishing the soul. Given that the majority of us living in the developed economies of the world can readily satisfy our basic needs, much of our focus, whether we realise or not, is actually on satisfying our souls. The extent to which we can (or should) try to achieve this via the acquisition of worldly goods would be an interesting point to ponder (at another time)! Based on the foregoing, one way of viewing products would be as vehicles for perpetuating or changing emotions. Since the emotional consequences of products are obviously very important to us, perhaps more than we might have imagined previously, it’s clear that understanding and then measuring the capacity of consumer goods to cause emotional change is vital if we are to create new products and brands that have a real chance of success. The remainder of Part 2 is about capturing and measuring emotional change. Simplistically, this means that we would need to know about the emotional state of the individual at the start of the process; we would need to know where this individual wants to get to emotionally in a particular

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What is perceived

Object

Conceptual meaning

Emotional consequence

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Behaviour

(brand, pack and product)

Fig. 9.2

How objects influence behaviour.

set of circumstances (destination); we would need to know whether or not the brand or product has actually taken them there and, if not, where, if anywhere, it has taken them (the journey). Ideally, we’d also like to know which features of the branding, the packaging and the product are actually responsible for the emotional consequences, so that we can establish cause and effect. This is not easy! At the very least, it pre-supposes that people are aware of and can self-report their prevailing emotional state and also that the nature and extent of any emotional shift will be apparent to them. These are very big assumptions! The alternative is to shift emphasis away from capturing and measuring the emotional effect on the individual and focus on conceptual meaning; that is, the emotional, functional and abstract conceptualisations (described in Section 9.2.1 above) attached to the object in question. This is based on the hypothesis that the emotional consequence engendered by an object is driven partly by what it is perceived to be and partly by the conceptual meaning attached to the object by the individual (Fig. 9.2). The switch in emphasis away from measuring the effect of the object on the individual (which is often ethereal, transient and difficult to capture), to measuring what the individual ‘sees’ in the object, creates the possibility of a more practical research process. What’s more, marketers and product developers can have a fairly direct influence on how their product is perceived and conceptualised but only an indirect influence on its emotional consequence. This brings cause (brand/product) and effect (conceptual meaning) much closer together, with obvious benefits for brand and product development. The process of capturing the conceptual meaning of an object (which I call ‘conceptual profiling’) along with several case studies, are described in detail in Part 3. However, before doing so, several other research tools commonly used to measure emotion (or more probably, to measure conceptualisations with emotional connotations) are discussed briefly. 9.3.3 Emotion checklists Self-report emotion/mood word checklists have been used widely since the 1940s. King and Meiselman (2010) reviewed various of these and concluded

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that since most of the established emotion/mood vocabularies were created for psychiatric investigation, they typically comprise too many negative terms that are too extreme for the intrinsically pleasurable process of eating. As a consequence, they developed an alternative food-specific vocabulary comprising 39 emotion terms with associated magnitude scales (known as the EsSense Profile™). EsSense™ has detected small but significant differences in self-reported feelings engendered by eating pizza versus fried chicken versus mashed potato with gravy versus vanilla ice cream versus chocolate. King and Meiselman have also demonstrated that salty crackers differing only in flavour have slightly different EsSense Profiles™. Although self-report emotion/mood checklists have been used widely and are reported to be effective, some have questioned their sensitivity, their subtlety and their appropriateness. One of the main criticisms is that the subtlety and nuance of meaning of some emotion words may be beyond some members of the consuming public. Another criticism is that the processes of interpreting words, becoming aware of feelings and then relating words to feelings, is too rational and requires too much thought. In contrast, emotion is often transient, ephemeral, thoughtless and irrational. Going on to quantify feelings on a scale would obviously require even more thought! The implied incompatibility is clear. This is not the only issue! Undoubtedly, one of the biggest and most insidious problems encountered when trying to establish a cause and effect relationship between an object (cause) and the associated emotional consequence (effect), is that all of the emotional impact may not be immediate and, even if it was, it might not be apparent to the individual concerned. Even when listening to a familiar and highly emotive piece of music, for example, where there will often be an immediate and obvious emotional impact, it’s entirely possible that part of the total emotional effect may occur some time later and may never be apparent to you. Yet the total emotional consequence (i.e., immediate and eventual), whether apparent or otherwise, may go on to influence your emotions next time you hear the music. This problem is exacerbated greatly with objects that are less emotive. Imagine you’re a research participant attending a central location test on a wet, cold Saturday morning. You’re invited to taste unbranded coffee (for example) and record your feelings using a self-report emotion checklist. The coffee is devoid of all branding and the experience is far removed from the normal consumption context. Would you really expect your post-consumption emotions to reflect how you’d feel if you had consumed the coffee in a real-life consumption context? (Remember the role of situation and circumstance in creating our ‘circumstantially derived states of wanting’ – see Section 9.2.2). In addition, what are the prospects of you becoming aware of the eventual emotional consequences if you’ve only tried a small amount of the product once, and that was in a central location? Also, what’s the chance that moderate (but significant) sensory differences in the taste

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of two coffees would create a measurable difference in the way that you actually feel in a central location test and that this could be captured using a self-report emotion checklist? Bearing this in mind, it would seem unrealistic that researchers should sometimes ask people to record how they feel pre- and post-consumption. Some go even further by asking research participants to project how an unbranded product (or even a branded product) might change the way that they feel. It’s my view (albeit unsubstantiated at this stage) that self-report emotion checklists are much more likely to capture how the individual ‘sees’ the object rather than what the object has actually done to them, because this may not be immediately apparent, especially in a ‘sterile’ research environment. In other words, when King and Meiselman’s respondents self-reported higher levels of being ‘enthusiastic’ post-consumption of pizza versus ice cream (King and Meiselman, 2010), I wonder whether or not they were actually experiencing higher levels of ‘enthusiasm’ when consuming the former? Instead, perhaps they were merely conceptualising (‘seeing’) pizza as more ‘enthusiastic’ than ice cream? It is, of course, entirely possible that conceptualisation of a higher level of enthusiasm in pizza would cause higher levels of enthusiasm to be experienced, but not necessarily there and then.

9.3.4 Faces and figures In commercial new product development, research tools that use drawings or photographs of faces and figures (cartoons and avatars) are becoming increasingly popular as a means of relating to the emotionality projected by a particular product or a brand. Anecdotally, it is often said that our feelings are ‘written’ on our faces (e.g., ‘His guilt was written all over his face!’). This is the basis of a huge body of facial coding research, initially pioneered by Paul Ekman and colleagues back in the 1960s and 70s (see Ekman and Friesen, 1971, and Ekman, 2003, for example). The basic premise underpinning Ekman’s work is that we only ‘wear’ a limited number of core facial expressions (seven in fact) in response to the full gamut of emotions and feelings experienced by human beings and these are constant across cultures. According to Ekman’s scheme there’s one facial expression to reflect positive emotion (happiness), one expression for neutral emotion (surprise) and five expressions for negative emotion (fear, anger, sadness, disgust, contempt). Apparently, these expressions are innate rather than learned, as evidenced by the fact that very young children and people who are blind from birth exhibit the same core expressions (Hill, 2007). Other schemes sometimes add an eighth face to express neutrality. Either way, researchers can be trained to observe and classify facial expressions according to this categorisation process. In so doing, researchers are

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effectively ‘reading’ the feelings of individuals from their faces. Ekman’s work on facial coding and other forms of non-verbal expression (such as hand movements) have been applied widely in disciplines as disparate as criminology (‘Is s/he lying?’) and product development (‘What does s/he really think of this product?’). The commercial applications of facial coding to brand and product development have been championed in more recent times by Hill (2007) amongst others (see Emotionomics {2009}, www.sensorylogic.com and Noldus FaceReader {2009}, www.noldus.com). There are two basic applications: 1) Facial coding/reading (as described above) where researchers classify facial expressions as a means of ‘reading’ the feelings of others. 2) Facial scaling where research participants are shown seven (or eight) photographs of faces, each matched to one of the core facial expressions, and asked to choose the expression that best reflects the way they are feeling (in response to the object in question). Although facial coding and facial scaling are very well known and quite widely used, there are serious shortcomings with these approaches. One of the main problems stems from the fact that ‘facial expression’ and ‘emotion’ are used somewhat interchangeably, as if they are the same thing. They are not! For example, whilst there may be incontrovertible evidence that humans have just one facial expression to reflect the fact that they are experiencing a positive emotion, this does not mean that there is only one positive emotion. It probably means that experiencing a variety of positive emotions is likely to result in just one facial expression. Consequently, to imply that there is only one positive emotion, as many have done, is misleading and unhelpful. The same problem undoubtedly applies to the five negative facial expressions. However, it’s the existence of only one positive facial expression in particular, that severely limits the use of facial coding and facial scaling as diagnostic and evaluative tools within the creative and new product development processes. Another insidious problem associated with facial scaling is the possibility that some of the seven (or eight) faces are misconstrued as representing intervals on a scale of liking. For example, it is not inconceivable that facial expressions typically used to represent happiness and surprise could be construed as representing some degree of liking, and that sadness, anger, fear and disgust, in particular, could be construed as representing disliking (see Brainjuicer, 2006, as an example of a facial scale – via www.slideshare. net/IESA school of management/ measuring-emotional-engagement-withfacetrace). If this were the case, the facial scale might actually be (mis)used as a scale of liking. Another issue with facial scaling relates to the use of faces at all. It’s clearly implied by those who advocate their use, that each face has singularity of meaning (i.e., it communicates only happiness or only surprise or only disgust, etc.). This is never likely to be the case, for two reasons: firstly, each image may be interpreted differently by different people. For example,

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surprise (which is intended to be neutral) could be construed by some as expressing joy. Contempt could be construed as expressing silliness. Secondly, even if a particular facial expression does communicate happiness, for example, more clearly than any of the other six (or seven) feelings, the face itself carries other conceptual meaning by virtue of the fact that she (in Brainjuicer’s case) is quite obviously female, white-skinned and plain looking with hair combed tightly against her head and held in place with a hair grip. How meaningful would this face be to Afro-Carribeans, to Orientals or to south-east Asians living in a multicultural society such as the UK or the USA? How relevant or appropriate would this face be to an edgy, young brand such as Red Bull? Surely, it’s incumbent upon the originators of facial expressions to provide empirical evidence of singularity of meaning before advocating their use! Desmet (2002 and 2003) has developed an alternative system called PrEmo, comprising 14 animated cartoon figures (with sounds) to represent seven positive and seven negative emotions (www.premo-online.com). The claimed strength of PrEmo, and indeed all systems that use graphics, is that any associations made between the object in question and the cartoon figure operate at a non-conscious level. In other words, there’s no need for research participants to rationalise their thoughts or interpret the meaning of the cartoon character, in making the association as this all happens automatically, below the level of conscious awareness. Others have developed broadly similar systems (www.metaphorixuk.com) using avatars (cartoon images of the self). Whilst this may be an advantage of such systems, there’s also a problem! As with facial expressions, cartoon figures and avatars also communicate secondary conceptual meaning in addition to the primary emotional message. The very nature of cartoons means that these secondary conceptualisations are sometimes associated with words such as silly, trivial and juvenile, which is hardly appropriate for serious adult brands (Thomson, 2009 – unpublished confidential information). The use of faces, figures and cartoons as a means of capturing emotion, is becoming increasingly prevalent in commercial new product development. This may be due, at least in part, to the fact that such representations are engaging (to research participants and research buyers, alike) as are the associated research processes. However, for reasons expressed above, product developers need to be cautious about what these research processes are really telling us about their products and brands.

9.3.5 Imagery Pictures, photographs, illustrations and other forms of graphical imagery are greatly favoured these days as a means of capturing and communicating emotion. The assumed benefit of using graphics is that associations made between images and emotions need only be based on ‘gut instinct’, with no need for rational interpretation or cognitive processing on the part of the

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Fig. 9.3

Representation 1: ‘adventurousness’.

Fig. 9.4

Representation 2: ‘adventurousness’.

individual. To this end, many client organisations and research agencies have developed image banks. Figures 9.3 and 9.4 are fairly typical of the types of images that might be included from such commercial image banks. Again, these are interesting and engaging but what meaning do people really take from them? In other words, how are they conceptualised? The role played by pictures in capturing emotion is not always appreciated fully. Pictures serve two purposes; firstly to stimulate the imagination of the individual to think more widely and creatively about what they are experiencing; secondly, to act as the basis of association and hence the medium of communication as mentioned above. The former requires quite a fine balance because the objective is to ‘seed’ the thought processes without implanting ideas that wouldn’t otherwise exist. This balance is quite difficult to achieve with pictures because they are so evocative and emotive. In practice, qualitative or quantitative research procedures are used to select images that correspond with the emotions engendered by the object

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in question (i.e., branding, packaging, product, etc.). To capture the full extent of the emotional communication, the selected images are often assembled into a collage which may be supplemented with other media such as colours, textures (anything from silk to sandpaper), video clips, music, sound bites, words and even smells. Emotional profiles are often communicated and compared using these purely visual or multi-media collages. Part of the attraction in using graphics to communicate emotion is that brand developers, packaging designers and even flavourists and fragrance developers are familiar with briefs communicated graphically. Indeed, it’s often their preferred medium of communication. The extent to which graphics can actually capture emotional consequence (‘how it makes me feel’) versus emotional conceptualisation (‘how it seems to me’) is open to discussion. Both probably happen to some extent but my hunch is that research participants are much more likely to default to the latter, if only because it’s easier and they tend to opt for the path of least resistance. In the final analysis, it probably doesn’t matter provided we recognise what we’ve got! However, there is quite a tussle going on between the advocates of graphics versus the advocates of words, as to which is the best medium for capturing emotion. Both probably have their place but those who favour graphics usually argue their case on the basis that: • • • •

pictures transcend language ‘a picture’s worth a thousand words’, or so it’s often said words require rational interpretation whereas pictures don’t anybody can take meaning from a picture but the meaning of words may be too subtle for some.

Whilst pictures undoubtedly transcend language, they do not transcend culture. This means that the same image could allude to entirely different emotions or emotional conceptualisations in different cultures. Indeed, HSBC has built a global advertising campaign around exactly this point. It’s important that this is recognised, otherwise the consequences could be disastrous, especially for brand development. It’s a truism that a picture is undoubtedly ‘worth’ many words, if not a thousand. This reflects the fact that vast tracts of often disparate conceptual meaning are held within a single pictorial image. As a consequence, different people even from within the same tight cultural group may attend to different aspects of the total conceptual communication, thereby taking different meaning from the picture. Also, the conceptual meaning taken from an image, by a single individual, may differ according to occasion or context. In practice, this means that when research participants are presented with a gallery of images and asked to associate one or more with their emotional experiences (or emotional conceptualisations), we must concern ourselves about which aspect or aspects of the image they are actually referring to.

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For example, consider the two pictures shown in Figs 9.3 & 9.4 (above). Both were selected via preliminary qualitative screening as a possible means of representing and communicating ‘adventurous’. But which picture does it most effectively? Does either have the required degree of singularity of meaning that makes it unequivocally ‘adventurous’? As part of the ongoing process of creating an image bank for my own company, coloured representations of these pictures (along with many others) were submitted to quantitative evaluation, online. Research participants were given a list of 28 conceptual descriptors and asked to select all the terms that seemed relevant to the picture. They were then asked to select the single most relevant term. With the picture of children jumping into the water (Fig. 9.3), 81% of participants selected ‘adventurous’, 75% ‘youthful’, 72% ‘freedom’ and 52% ‘happiness’. However, only 34% selected ‘adventurous’ as the single most relevant term. With the picture of white water rafting (Fig. 9.4), 92% selected ‘adventurous’, 58% ‘freedom’, 44% ‘youthful’ and 28% ‘happiness’. Crucially though, 80% selected ‘adventurous’ as the single most relevant term. Clearly, the picture of white water rafting represents ‘adventurous’ with much greater singularity of meaning than the picture of children jumping into the water, making it the obvious choice. However, this still begs the question as to whether or not Fig. 9.4 is sufficiently unequivocal in representing ‘adventurous’ for research purposes. It’s difficult to be emphatic here. Figure 9.4 has one of the highest singularity ratings of any picture in our image bank, so we’ll continue to use it to represent ‘adventurous’ until we can find or create something with an even higher degree of singularity! There are many organisations worldwide that claim to have ‘validated’ image banks for research purposes. Just to be clear, validation in this context should mean the following: • The image should be associated with a particular emotion or conceptualisation (such as ‘adventurous’) much more strongly than any other emotion/conceptualisation (i.e., singularity of meaning). • It should only be associated weakly with other emotions or conceptualisations (i.e., it is unequivocal). • There should be a very high level of probability that this image would be selected over all other images in the image bank, to represent the emotion or conceptualisation in question (e.g., adventurous). Another issue sometimes encountered when using images, especially those that involve close-up photography of faces, is that the emotional meaning is sometimes misconstrued as perceptual description or even as liking. For example, there are several commercial image banks that include the image of a young girl ‘pulling a face’ (unfortunately, it cannot be reproduced here for reasons of copyright). This image only has weak and very equivocal associations with emotion (either emotional consequences or emotional conceptualisations) but it is very strongly associated

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with sourness and bitterness (perceptions) and with disliking (hedonic). It is frequently chosen by research participants, especially when investigating certain types of unbranded foods and beverages, but only because it represents sourness/bitterness and disliking. Such an image has no place in an ‘emotion’ image bank. None of this means that pictorial imagery and image banks should not be used for exploring emotion. Quite the contrary, in fact! However, it does mean that the images should be chosen very carefully and evidence of validation should always be sought before accepting them at face value. It’s also worth noting that pictures are invariably translated into words at some point!

9.3.6 Words and language As mentioned previously when discussing emotional checklists (Section 9.3.3) and pictorial imagery (Section 9.3.5), the use of words and language as a medium for capturing and communicating emotion and emotional conceptualisations is often criticised because of the assumed need for rational interpretation of words. Allied to this, three specific objections are often raised: • Understanding the meaning of words and, more particularly, appreciating the nuance of meaning, is thought by some to be beyond less welleducated and less intelligent people. It’s often further implied that this would effectively preclude such people from participating in word-based emotion research. • Detractors argue that in order to use language to describe emotions, we need to stop and think about the meaning of words and thereafter to make rational, cognitive (thought about) associations between words and emotions. • Words are inadequate to capture the subtlety and complexity of emotion. There’s much to disagree with here! These objections are based largely on the incorrect assumption that using words to capture emotion would require an extensive vocabulary of sophisticated and evocative terms, each with deep and subtle meaning, that the meaning of such emotion words should be interpreted and understood comprehensively by research participants and that the extent of the experience associated with each emotion word would need to be measured on a scale. Let’s deal with the ‘adequacy’ challenge first of all. Language has evolved over millennia, as the primary means of intimate communication amongst human beings. We use language to tell other people about what we are experiencing and to explain what we mean. I’ve used words to explain what I mean in this chapter; something that would have been much too subtle and complex for any other medium.

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The expression, ‘I cannot find words to express my feelings’ (or something of the like) is common in idiomatic English and this is often used to emphasise the inadequacies of words and language. However, this is a total misconception! Invariably, failure to describe feelings arises not because of the inadequacies of language but because of the inability of the individual concerned to crystallise his or her feelings. Once these are apparent, detailed and ‘colourful’ description is seldom a problem. The next issue concerns the adequacy of individuals’ vocabularies and their abilities in grasping nuance of meaning. In using the English language, people from all walks of life use remarkably few words for everyday communication. The same may also be true of other languages. We simply don’t need vast vocabularies. This isn’t a reflection of the poverty of the human spirit but a testimony to the richness of words and language. Much of this derives from the fact that words don’t just have narrow literal meaning but most also carry broad metaphorical meaning too. This was mentioned earlier in relation to sweet and sweetness, where much of the idiomatic use of the word is metaphorical and has nothing at all to do with gustatory sweetness (its literal meaning). One way of representing this would be to consider a word as having a nucleus of literal meaning, surrounded by a cloud of metaphorical meaning. As a consequence, a relatively small number of descriptive terms (say 100) actually provide us with a vast array of metaphorical meaning that we can use to describe our feelings (and emotional conceptualisations). Moreover, there’s no great need for metaphorical meaning to be especially well defined since feelings and emotional conceptualisations are often vague. Indeed, this makes the two phenomena especially compatible. To summarise, most of us do not need a large and sophisticated vocabulary to communicate effectively, comprehensively, elegantly and at a high level (should we wish to do so). This also means that we should be able to say everything we need to about an object, be it branding, a design for packaging or a product, using a relatively small and manageable number of words (estimated at between 15 and 30 – see later). There’s no doubt, however, that associating magnitude scales with words changes everything because it pushes the individual towards literal meaning and rational thought processes. Emotion is often ephemeral and irrational. The incompatibility is clear. Consider the word ‘trustworthy’, for example. Literally, it means ‘worthy of trust’. On the basis of a large amount of work conducted in recent months (some of which is presented below), we know for sure that dark chocolate, even when devoid of all branding, is conceptualised as being ‘trustworthy’. Does this literally mean that it’s ‘worthy of trust’? Of course not! It means that for some reason or other, consumers of dark chocolate make an association between the metaphorical meaning of the word ‘trustworthy’ and how they conceptualise dark chocolate (i.e., its conceptual meaning). We could go on and speculate that in tasting intensely bitter, dark chocolate is

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conceptualised as being strong and bold, and this is the basis of the assumed trustworthiness. Alternatively, it could be that chocolate (albeit milk chocolate) is often given by parents to reward children, and what could (or should) be more symbolic of trustworthiness than the loving bond between parent and child? Interesting as the aetiology of trustworthiness in chocolate might be, just recognising that chocolate is conceptualised as being trustworthy, recognising that trustworthiness in chocolate may be a driver of choice (for category and/or brand) and recognising that chocolates with different sensory profiles may differ in trustworthiness, are important steps forward. Of course, this begs the question as to how we know that dark chocolate is conceptualised as being trustworthy. For sure, we can’t ask consumers to taste chocolate and rate it on a scale of trustworthiness (‘How trustworthy does this chocolate taste?’). This would be counter-intuitive and quite absurd. We could modify the question and the scale to ask . . . ‘How trustworthy does this chocolate make you feel?’ . . . or something to that effect. Although this question sounds more intuitive, it makes the implicit assumption that individuals are aware of how they feel and how they conceptualise chocolate and also that, somehow or other, they are able to quantify these phenomena on a scale. None of these assumptions are necessarily true. This doesn’t mean that emotion word scales are useless but it does suggest that they have significant limitations. However, if research participants could be encouraged to think about the metaphorical meaning of words and measurement scales could be avoided completely, then words and language are likely to provide an excellent medium for capturing emotions and emotional conceptualisations. To this end, a new form of emotional/conceptual profiling has been developed which uses ‘best/worst scaling’, otherwise known as maximum difference scaling, in conjunction with lexicons. Both descriptions are unfortunate misnomers because measurement scales are avoided completely. As described previously (Section 9.3.1), when using best/worst scaling, words are typically presented in sets of four or five and research participants are requested to choose which word from the four or five matches their feelings (or emotional conceptualisations) best and which from the remaining three or four is worst (hence the name). The choice environment implicit in best/ worst scaling has two very positive attributes: i)

It avoids the use of a measurement scale. When words are associated with measurement scales, research participants need to think about the meaning of the word in order to quantify its magnitude in whatever it is that they are experiencing. This inevitably steers them towards the literal and rational meaning of the word and away from the ‘cloud’ of metaphorical and irrational meaning. It also assumes that the person concerned is actually capable of quantifying whatever they are experiencing.

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e tiv ip cr m es ter

D

Fig. 9.5

ii)

Co ua ncep lisa ttio n

Perception

Inter-relationships of words and thoughts.

Comparison encourages people to think about the subtlety and nuance of difference in the meaning of the words, which may steer them towards the metaphorical rather than the literal meaning.

Best/worst scaling is further described and then explored in detail in the two case studies that follow in Part 3. Before closing this section on the use of words and language, it’s worth emphasising that words, just like pictures, can be used to ‘seed’ thoughts as well as describe them. For this reason, it would be quite wrong to assume a unidirectional process that starts with perceptions then leads to conceptualisations which are finally described in words. It’s entirely possible that the whole process could happen in reverse where the mere mention or thought of a word triggers conceptualisation which then influences what is perceived. Alternatively, a word (e.g., strawberry) could trigger the perception of strawberry character which could then trigger associated conceptualisations (e.g., summer, sunshine, holidays, the tennis championships at Wimbledon, etc.). As a consequence, the three elements (percept, concept and descriptive term) should be envisioned not as a linear sequence but as forming a triangular arrangement (Fig. 9.5). This means that the process could be initiated at any corner of the triangle and then move in either or both directions.

9.4

Part 3: Conceptual profiling case studies

9.4.1 Case study 1 – emotional profiling of car marques This first case study involving car marques is presented specifically to demonstrate the emotional/conceptual profiling processes (which I call Brandphonics™) and to explain the data analyses procedures and outputs. It’s also rather interesting!

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This is followed by a case study on dark chocolate (Section 9.4.2) which compares the emotional and functional conceptual profiles of the chocolate brands (without tasting) versus the corresponding unbranded chocolate profiles and then explores brand/product consonance. In addition, the sensory profiles of the chocolates are superimposed on the emotional/conceptual profiles of the unbranded chocolate to determine which sensory characteristics are driving particular emotions/conceptualisations. Overview of methodology There are four basic requirements for this type of emotional/conceptual profiling: 1. Objects – in this first case study, 12 mid-range car marques from the UK market, represented on-screen as colour logos with brand names, as appropriate (Alfa Romeo, Audi, Citroen, Ford, Honda, Peugeot, Seat, Skoda, Toyota, Vauxhall, Volvo and VW). 2. Participants – 450 people aged between 21 and 75 years who are solely or jointly responsible for personal/household car purchase decisions and have bought a new or used car for private use in the past five years. 3. Emotional/conceptual lexicon – 22 words representing a mix of emotional and abstract conceptualisations, derived as described below: aggressive, approachable, bold, classy, confident, conservative, dull, easygoing, feminine, free-spirited, fun, irritating, masculine, powerful, pretentious, quirky, reassuring, sensual, simple, traditional, trendy and trustworthy. 4. Best/worst scaling protocol – emotional/conceptual terms presented in 11 sets of five (quins) based on a design controlled for position and context effects. We use partially balanced incomplete block designs, typically comprising 3 to 6 versions of the questionnaire. Each respondent profiled three car marques, yielding ca.112 individual conceptual profiles per marque. Profiling was conducted online. Emotional/conceptual vocabularies are developed specifically for a particular project/object. Three sources of information are typically used as inputs to the creation of such a lexicon; (i.) our own ‘master lexicon’ comprising >100 terms drawn from 28 emotional/conceptual territories; (ii.) terms supplied by clients, usually drawn from brand strategy documents and previous research; (iii.) the public domain, including websites, advertisements and other marketing materials. It’s important to include terms that have negative connotations because, in spite of what some brand-owners might wish to think, their brands and products (in particular) aren’t always conceptualised positively and this can often be a significant differentiator. The first draft of the emotional/conceptual lexicon will usually comprise approximately 40 to 50 terms. This is subsequently honed down to anything from 16 to 30 terms via qualitative screening. Whenever possible, the lexicon is subject to pilot-scale quantitative evaluation to determine whether or not

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Thinking about……, please choose the word which you would most strongly and then least strongly associate with this car brand

Traditional

Aggressive

Fig. 9.6

Free-spirited

Dull

Classy

Best/worst scalings.

it is sufficiently discriminatory and also to establish that there is minimal redundancy. Best/worst (or maximum difference) scaling was first described by Finn and Louviere (1992). The basic principle is very simple: conceptual terms (in this case) are presented in sets of four or five (which we refer to as ‘quads’ and ‘quins’, respectively). Research participants are shown the object in question and asked which of the conceptual terms in the quad (or quin) is most appropriate to describe what he or she has conceptualised (i.e. best) and which is least appropriate (i.e. worst). Precise wording of the question depends on the nature of the object. The form of questioning used in this study of car marques is shown in Fig. 9.6. The number of quads or quins presented depends on statistical factors (number of conceptual terms, sample size, and whether data are to be analysed at the individual level) and research factors (the object, the nature of the evaluation, and anticipated level of engagement); most studies use between 8 and 20 quads/ quins. The best/worst scaling evaluation process has several distinguishing features: • In choosing the most and least appropriate words from within the quads or quins, there’s no need for rational or literal interpretation of the meanings of the words. • Juxtaposing of words in this way actually encourages participants to consider and compare the full panoply of the conceptual meanings of the words before making their choices. • In spite of the foregoing, choice of words need only be based on a hunch or ‘gut instinct’ rather than a detailed analysis of meaning. • External measurement scales are unnecessary. These features address all of the substantive objections raised by others (see above) against the use of words as a medium for capturing emotion and conceptual meaning. In this study, 450 participants evaluated three car marques using 11 sets of five words (quins) per marque. Since there were 22 words and 12 marques,

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each word appeared ca. 280 times per marque. The basic metric obtained is the number of times each word is judged to be best and worst for a particular marque of car. However the method of analysis does not assume that every word appears in combination with every other word an equal number of times; this is seldom achievable, but it is good practice to balance the frequencies in the design as far as possible. Raw data takes the form of the number of times (proportion) that each of the 22 words is chosen as ‘best’ and the number of times it is chosen as ‘worst’. There are various ways of analysing this type of data, some of which are explained further in the chapter by Cardello and Jaeger (herein). We prefer to analyse this type of data using a multinomial logit model which yields a set of ‘scale values’ (utilities), one for each emotional/conceptual term for each object. These scale values have excellent properties on an interval scale. This effectively creates an emotional/conceptual profile for each car marque.

classy powerful

trustworthy confident

trendy bold masculine reassuring

sensual easygoing conservative aggressive pretentious traditional free-spirited approachable fun

simple

quircky

dull

Audi

irritating feminine

Conceptual profiles The profiles for Audi and Volvo are presented as examples in Fig. 9.7. Audi is conceptualised as being powerful, classy, confident and trustworthy. It is not characterised as simple or quirky nor is it feminine, irritating or dull (most especially). Volvo, on the other hand, is traditional, trustworthy, reassuring, conservative and confident but it isn’t considered to be trendy, sensual, fun, feminine or quirky. Clearly, both are trustworthy and aren’t feminine, but that’s where the similarities stop! For ease of interpretation and presentation, we often rescale the utilities (scale values) according to the underlying choice model to give a proportional scaling (Cohen and Neira, 2003). The output is a ‘share of profile’ for each product (Fig. 9.8). Generally we use the conceptual profiles (scale values) to define and illustrate the profile of each object but prefer ‘share of profile’ for making direct comparisons. Both representations are helpful in getting the full picture.

most

Fig. 9.7

reassuring traditional trustworthy

conservative

confident

masculine

approachable powerful

simple bold dull easygoing classy

free-spirited aggressive pretentious

Volvo

quircky feminine fun sensual trendy imitating

least

Scale values for conceptual profiles of Audi and Volvo car marques.

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100% Irritating Sensual Aggressive Feminine Pretentious Dull Quirky Trendy Masculine Bold Fun Free-spirited Simple Classy Conservative Powerful Easygoing Approachable Reassuring Traditional Confident Trustworthy

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Volvo

Ford Vauxhall

VW

Toyota Honda Peugeot Citroen Skoda

Seat

Audi Alfa Romeo

Fig. 9.8 ‘Share of conceptual profile’ for 12 car marques.

With the ‘share of profile’ data (Fig. 9.8), what matters is the amount (%) contributed by each conceptual term to the total for each marque (it always adds up to 100%). For example, and as might be expected from the foregoing, Audi’s conceptual profile is dominated by powerful (16.5%), classy (14.7%) and confident (10.4%). In other words, these three conceptualisations account for almost 42% of the conceptual message communicated by the Audi brand. By contrast, the same three conceptualisations only account for about 16% of the conceptual profile of Volvo! Conversely, Volvo’s conceptual profile is dominated by a combination of trustworthy (15.1%), traditional (14.9%), reassuring (12.5%) and conservative (10.7%), which account collectively for more than 53% of the total. The same conceptualisations account for a mere 20% with Audi. Volvo is the most reassuring of the 12 marques but it lacks the approachability of Toyota, for example. Alfa Romeo is interesting. Like Audi, it is dominated by powerful, classy and masculine but, unlike Audi, this is laced with an element of easygoing and fun. Alfa Romeo is considered to be the least trustworthy of the 12 marques! It’s testimony to the marketing skills of the Volkswagen Group that their four volume car marques (Audi, Seat, Skoda and VW) are well differentiated conceptually. Seat is characterised by fun (8.8%), free-spirited (8.8%), confident (8.2%), easygoing (7.2%), bold (6.8%) and trendy (6.1%). No other marque comes close in terms of fun and free-spirited. Clearly, Seat’s participation in the World Rally Championships, along with those lurid lemon-yellows and lime-greens, has made an impression. Unfortunately, it still hasn’t shaken off lack of trustworthiness. Skoda’s conceptual profile is predominantly simple (14.6%). Its nearest rival for simple is Citroen with a mere 6.1%. This, combined with traditional, approachable, easygoing,

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conservative and dull, gives Skoda a fairly unique, if not dynamic, profile. Audi has been described already. VW is conceptualised as the most trustworthy of the 12 marques although, beyond this, it seems to have a fairly eclectic, if not distinctive, profile. Ford is dominated by traditional (19.2%). This is the single largest conceptual term across any of the 12 marques. It seems that Citroen is the only marque with any appreciable, although still modest, degree of femininity. Honda and Toyota are not well differentiated. Conceptual maps (Biplots) A biplot (Gabriel, 1971) of objects and variables can be used to visualise similarities and differences across the conceptual profiles (Fig. 9.9). The nature of the data dictates that the car marques are treated as variables and the conceptual terms as observations. There are several commonly used types of biplot that reflect different scalings of the data. The type used here

1.0 Alfa Romeo 0.8 Classy

Audi

Powerful

0.6 Trendy

Dimension 2 (21.0%)

0.4

Masculine Fun Bold

Aggressive Sensual

0.2

Seat

Free-spirited

Pretentious

VW

Confident

Toyota Honda

0.0 −0.2

−0.4 Irritating Feminine

Simple

Dull −0.6

Fig. 9.9

−0.4

−0.2

0.0 0.2 0.4 Dimension 1 (65.0%)

Vauxhall Ford

Easygoing Conservative Traditional

Citroen

−0.6

−0.8 −0.8

Peugeot

Trustworthy Reassuring Volvo Approachable

Quirky

Skoda 0.6

0.8

1.0

Biplot for 12 car marques and 22 conceptual terms (D1 vs. D2).

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resembles an MDPREF graph (Carroll, 1972); something that should be familiar to most sensory and consumer scientists. The information presented in the biplot can be summarised are follows: i) Explained variance The degree to which the biplot approximates the total variance in the data is given by the sum of the variance explained by the two axes. In this case, 86% of the total variation is explained by the first two dimensions of the biplot. The degree of variance explained for individual car marques is proportional to the square of the length of the vector line. On this basis, Seat and Volvo are obviously much less well fitted in the biplot compared to the other 10 marques. Seat and Volvo require a third dimension (Fig. 9.10) that takes cognisance of the fact that Seat is more fun and free-spirited than any of the others and also that Volvo represents the most extreme combination of trustworthy, reassuring and traditional. The addition of dimension 3 brings the total explained variance to 95%.

0.8 Volvo

Traditional

0.6

Classy Conservative

Dimension 3 (9.1%)

0.4

Powerful Reassuring Masculine Trustworthy Audi

Dull Aggressive Pretentious

0.2 Sensual Irritating

Alfa Romeo Skoda

0.0

−0.2 −0.4

Feminine

Peugeot Citroen

Trendy

Quirky

Honda Toyota

Confident Approachable

Bold Simple

Vauxhall Ford VW

Easygoing

−0.6

Seat

Fun Free-spirited

−0.8 −0.6

−0.8

Fig. 9.10

−0.4

−0.2

0.0 0.2 0.4 Dimension 1 (65.0%)

0.6

0.8

1.0

Biplot for 12 car marques and 22 conceptual terms (D1 vs. D3).

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ii) Correlation between car marques This is approximated by the closeness of the directions of the vector lines. Thus Toyota and Honda are highly correlated. Naturally the strength of this inference depends on both marques being well explained by the map. If there were no differences between the car marques, all the vectors would align along dimension 1, which would explain 100% of the variance in the data. When there is some degree of commonality between the variables (car marques), as here, dimension 1 can be regarded as giving an average, or category profile. iii) Similarity of conceptual terms The overall dissimilarity between conceptual terms is approximated by the distance between them on the map. In the MDPREF type of biplot, this metric is represented less accurately than the differences between the car marques. An alternative scaling is available that represents (iii) more accurately than (ii): we often prefer the latter when overlaying sensory data (see below). iv) Approximation of a conceptual profile The conceptual profile of a particular car marque can be recovered, approximately, by projecting the conceptual terms at right angles onto the vector representing the marque. Thus Alfa Romeo scores highest on powerful and classy and lowest on dull, simple, irritating and feminine, in good agreement with the ranking of scale values or share of profile. For Seat and Volvo a better approximation is obtained using Fig. 9.10 (because of the need to include dimension 3). Alfa Romeo is strongly associated with dimension 2 on Fig. 9.9, which reflects the dominance of classy and powerful over trustworthy, traditional, reassuring, approachable and easygoing. Also, Alfa Romeo is neither dull nor simple. Skoda, which is associated with the opposite extremity of dimension 2, is the antithesis of Alfa Romeo. This isn’t necessarily a bad position for Skoda. Quite the contrary in fact! It is uniquely positioned as a functional and fairly utilitarian car brand at the budget end of the mid-range (and the Volkswagen Group’s brand portfolio). Audi is also uniquely positioned midway between Alfa Romeo and those paragons of reliability, VW, Honda and Toyota. Perhaps this accounts for Audi’s great success in recent years! Seat is also uniquely positioned because of its fun and easy-going persona (dimension 3 – Fig. 9.10). Either by design or default (but presumably the former), the Volkswagen Group has positioned the VW marque at the centre of its volume car brand portfolio, where it takes on Toyota and Honda, with Audi, Skoda and Seat arranged in different positions around this central hub on the basis of powerful/classy, simplicity and fun/easy-going, respectively. What clever and effective brand and product development! In taking an overview across dimensions 1–3 (Fig. 9.9 and 9.10), it seems that Alfa Romeo, Audi, Skoda, Volvo and Seat, are all differentiated

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conceptually from the rest of the pack. On the other hand, the remaining seven marques, VW, Honda, Toyota, Citroen, Peugeot, Vauxhall and Ford all occupy the same narrow conceptual footprint at the centre of the volume car market in the UK. It’s difficult to imagine that there’s room for all seven car brands in this crowded, central positioning! Some degree of differentiation is clearly necessary. It will be interesting to monitor the brand and new product development strategies of these car marques, and their market share, over the next three to five years. Individual differences One of the most compelling developments in the sensory optimisation of products over these past 15 years or so, has been the realisation that even in the narrowest product category, different people have different ‘tastes’ (i.e. liking is driven by different sensory characteristics for different people). Put another way; ‘there’s no such thing as the average consumer’. To this end, a variety of segmentation tools have been developed to explore these individual differences. My company (MMR Research Worldwide) has conducted many hundreds of ‘liking segmentation’ studies, across a wide range of consumer packaged goods, all around the world. In most instances, we find between three and seven ‘liking segments’. This is indicative of the degree of diversity of opinion in sensory enjoyment. The question arises therefore, as to whether or not we should anticipate a similar degree of diversity in how people conceptualise objects? To explore diversity in conceptual interpretation, we analysed each car marque separately at individual consumer level. An individual conceptual profile was estimated for each participant in the survey using a Hierarchical Bayes algorithm (Sawtooth Software Inc., 2009). These were mapped using a biplot of the MDPREF type, but on this occasion the participants are treated as variables and the conceptual terms as observations. The biplot for Audi is presented in Fig. 9.11. For reasons of clarity, participants are represented by circles and the vector lines linking them to the origin are omitted. This type of biplot is interpreted as explained previously, except that people should be substituted for car marques. What matters most is the relationships between the people and the conceptual terms. The concentration of people around the powerful/classy end of dimension 1 suggests that Audi is conceptualised in this way, quite consistently. However, beyond this theme, there is some diversity of opinion on dimension 2. About half of this population also conceptualises Audi as being somewhat masculine, aggressive and pretentious. The other half conceptualises Audi as being somewhat fun, free-spirited, easygoing and trendy. Taking this one step further, these data were segmented using latent class analysis. This yielded two equal-sized segments, represented here as the share of conceptual profile (Fig. 9.12). In Audi segment 1, the conceptual profile is dominated by powerful, classy, confident and trustworthy; amounting to almost 40% share of profile.

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1.0

0.5

Pretentious Masculine Aggressive

Dimension 2 (14.2%)

Irritating Dull

Powerful

Traditional Bold Conservative

0.0

Confident

Classy Trustworthy Reassuring Simple Approachable Sensual Trendy Quirky Easygoing Fun Free-spirited Feminine

−0.5

−1.0 −1.0

−0.5

Fig. 9.11

0.0 Dimension 1 (61.2%)

0.5

1.0

Biplot for Audi (D1 vs. D2).

Masculine, pretentious, bold and aggressive account for a further 30% (approximately). Fun, free-spirited and approachable, on the other hand, represent less than 7% share of profile. When the same groups of terms are evaluated in Audi segment 2, powerful, classy, confident and trustworthy add up to a staggering 64% (versus 40% for Segment 1) but masculine, pretentious, bold and aggressive amounts to a mere 6% (versus 30%). Fun, free-spirited and approachable equals 12% in total (versus 7%). Clearly, the only terms that differentiate these two segments of car buyers substantially, are the extent to which they conceptualise Audi as being masculine, pretentious, bold and aggressive. Otherwise, there is a very high degree of homogeneity of opinion. Not so with Citroen. The biplot (Fig. 9.13) shows car buyers arranged almost all around the periphery of the map. Further diversity of conceptualisation is captured in dimension 3 (not shown).

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100% Feminine Dull Irritating Quirky Simple Sensual Easygoing Conservative Traditional Aggressive Approachable Free-spirited Fun Pretentious Bold Trendy Reassuring Masculine Trustworthy Confident Classy Powerful

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% Segment 1

Fig. 9.12

Segment 2

Share of conceptual profile for Audi segments 1 & 2.

We were able to obtain a 3-segment solution via Latent Class Analysis (Fig. 9.14), although the sample size of 112 is too low to regard our threesegment solution as being particularly robust given the range of opinions. Nonetheless, in Citroen segment 1 (40% of the total), the dominant conceptualisations are dull (16.5%), irritating (11.7%), simple (11.6%), easygoing (7.2%), quirky (7.0%) and feminine (5.6%). This is the only segment that conceptualises Citroen as feminine; but none conceptualise it as masculine. Segment 2 (31% of the total) conceptualises Citroen as approachable (16.8%), trustworthy (15.4%), traditional (13.5%), reassuring (12.2%), easygoing (11.4%) and conservative (7.5%). In Citroen segment 3 (comprising the remaining 29% of the total), the main conceptual themes are free-spirited (13.2%), confident (12.9%), fun (12.1%) and trendy (10.2%). Clearly, Citroen means different things to different people. Perhaps this is driven by the diversity of their model portfolio, their early history of unusual and fairly quirky models (e.g., Citroen 2CV, Citroen DS) and their

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1.0

0.5

Dull

Simple

Dimension 2 (27.7%)

Conservative Irritating

Approachable Feminine

Reassuring Trustworthy Confident

0.0

−0.5

Traditional Easygoing

Masculine Free-spirited PretentiousQuirky Bold Fun Powerful Aggressive Classy Sensual Trendy

−1.0 −1.0

−0.5

Fig. 9.13

0.0 Dimension 1 (40.1%)

0.5

1.0

Biplot for Citroen (D1 vs. D2).

current rash of rather zany (‘transformer’) advertising. Whether or not this heterogeneity of meaning, with the implied lack of focus, is either desirable or sustainable is debatable. Needless to say, we could learn more about these car marques in a larger study comprising groups of people who are current owners, previous owners, potential owners and rejecters of specific marques. However, this case study serves to illustrate conceptual profiling very effectively. Implications for new product development This case study on car branding has demonstrated how robust, quantitative research and analysis tools can be used to generate brand conceptual profiles. Whilst the conceptual profiles of brands are, of course, very interesting in their own right, the next thing that needs to be considered is how these research tools and how conceptual profiling in general, fit into the bigger picture of new product development. This is addressed in the next case study.

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100% Aggressive Sensual Masculine Powerful Pretentious Classy Bold Feminine Trendy Irritating Quirky Conservative Fun Simple Dull Free-spirited Traditional Reassuring Confident Trustworthy Approachable Easygoing

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Segment 1

Fig. 9.14

Segment 2

Segment 3

Share of conceptual profile for Citroen segments 1, 2 & 3.

9.4.2

Case study 2 – conceptual profiling of dark chocolate (products and brands) It is, of course, generally recognised that the purpose of a brand is to communicate a strong emotional and functional message to target consumers, thereby increasing the ‘desire to acquire’. The more powerful the communication, the stronger the brand! In the earlier part of this chapter, it was discussed how unbranded product also has the potential to deliver emotional and functional messages too. Under most circumstances, it would surely be desirable that the product message should be aligned with that of the brand, or vice versa, so that they are mutually reinforcing (consonant) rather than contradictory (dissonant). This should be the aim of new product development. To this end, this case study explores: i) The application of conceptual profiling to unbranded product (i.e., products with no brand cues). ii) Relating the sensory profiles to the conceptual profiles of unbranded products, to establish cause and effect, where possible. iii) Direct comparison of the conceptual profiles of product and brand to identify areas of consonance and dissonance.

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iv) Functional profiling of the unbranded product (to complete the picture). The conceptual profile of an unbranded product such as dark chocolate is likely to be determined by the combination of three factors: i) Category conceptualisations – How individual consumers conceptualise the category at large (dark chocolate in this case). This will be subject to powerful background influences such as a lifetime of exposure to marketing communications (including the complete panoply of dark chocolate branding) superimposed on that individual’s lifetime of consumption experiences. ii) Sensory characteristics – What the individual actually perceives when he or she consumes a specific product (the sensory characteristics of the various chocolates). iii) Liking – whether or not the individual actually likes the taste of dark chocolate generally (in this case) and the extent to which he or she specifically likes each of the chocolates included in the study. Whilst these three factors can never be uncoupled completely, the way that a particular individual conceptualises the category will be a constant for him or her across all products in the study. This means that any differences in unbranded conceptual profiles will be determined primarily by differences across the sensory profiles of the individual products and by liking. On this basis, it is not unreasonable to hypothesise that there may be a partial cause and effect relationship between unbranded product sensory profiles and the corresponding conceptual profiles. This, of course, assumes that the products are discriminable and that the conceptualisations of the people concerned are influenced by these differences, even if they aren’t necessarily aware of them or attending to them deliberately. Conversely, it also follows that the more similar the sensory profiles of the unbranded products, the more likely that category conceptualisations will dominate the conceptual profiles of all of the objects. This means that differences in conceptual profiles may be relatively small, although still important. On a more practical level, it also assumes that there is a data analysis procedure that can effectively superimpose product sensory characteristics (explanatory variable) on the corresponding conceptual profiles of the unbranded products (dependent variable). Overview of methodology Since many of the processes used in this case study are similar in principle and practice to those used in the car marque study (above), most of the methodological detail is presented in brief and only the process for overlaying sensory and conceptual profiles is described in detail. For the unbranded study, nine sensorially differentiated dark chocolates were selected from the UK market (Cadbury’s Bournville Deeply Dark,

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Cote d’Or, Divine, Green & Blacks, Lindt, Montezuma, Seeds of Change, Tesco Finest and Waitrose). All of the chocolates were debranded, re-wrapped in foil and identified with a unique alphanumeric code. Tesco Finest and Waitrose are retailer brands. Green & Blacks, Seeds of Change and Montezuma are organic. All nine of these unbranded chocolates were subjected to conceptual profiling (emotional and abstract conceptualisations) using best/worst scaling, profiling of functional benefits using tick lists (as described in Section 9.3.1) and sensory profiling using a trained sensory panel. A subset of six of the nine chocolates were included in the conceptual profiling of brands (Seeds of Change, Divine and Waitrose were excluded). A common conceptual lexicon was developed for the unbranded and branded studies. In the first place, a small group of reasonably articulate consumers tasted and discussed the unbranded products under the guidance of a suitably qualified moderator. The group referred to a master list of ca. 100 emotional and abstract conceptualisations grouped into 28 emotional territories, but were permitted to add terms of their own. From an initial lexicon of 40 terms, a final selection of 24 was obtained on the basis of appropriateness to the brands and effectiveness in discriminating amongst the unbranded products (adventurous, aggressive, arrogant, comforting, confident, easygoing, energetic, feminine, fun, luxurious, masculine, ordinary, powerful, pretentious, sensual, serious, sociable, sophisticated, tacky, traditional, trustworthy, uncomplicated, warm and youthful). The unbranded product and the brand-only studies were conducted independently of each other; the former in a central location in the UK using an online interface, the latter using target consumers recruited from a UK-based online panel. All participants were regular or occasional consumers of dark chocolate (block bars). In the unbranded product study, each participant evaluated all nine unbranded chocolates in three batches of three on separate days using a balanced rotated design. Sixteen sets of five conceptual words (quins) were presented to each participant for each chocolate. For each quin, participants identified which of the five words is most and least suggested by the experience of eating the chocolate. In the brand-only study, the branding was presented as a twodimensional (flat) image of the pack front without price and photographed against a black background. Each participant profiled three brands. A balanced rotated design was used to determine which three brands were presented and their order of exposure. Data were analysed using the same procedures described above. Conceptual profiles of unbranded chocolate As an example, Fig. 9.15 shows the basic conceptual profile of Cadbury’s Bournville Deeply Dark. The conceptual terms positioned towards the right have the highest scale values (utilities) indicating that these conceptualisations (sociable and easygoing in particular but also trustworthy, comforting,

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Fig. 9.15

261

Sociable

Easygoing

Uncomplicated Comforting

Feminine

Warm Trustworthy

Traditional Youthful Fun Ordinary

Confident Sensual

Energetic Serious

Luxurious Adventurous

Sophisticated

Tacky

Masculine

least

Powerful

Pretentious

Arrogant Aggressive

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Conceptual profile of unbranded Cadbury’s Bournville Deeply Dark.

uncomplicated, warm and feminine) are the most prevalent. Conversely, arrogant, aggressive, pretentious and powerful have the lowest scale values, indicating that these conceptualisations are associated least with Cadbury’s Bournville Deeply Dark. The two-dimensional biplot in Fig. 9.16, derived and interpreted as described previously, provides an overview across all nine unbranded chocolates and 24 conceptual terms. Dimensions 1 and 2 explain 44.9% and 31.4% of the variation in the data, respectively. Dimension 1 juxtaposes sociable versus aggressive and arrogant. Dimension 2 juxtaposes energetic and adventurous against ordinary. Although over-simplifications, these interpretations still provide a reasonable overview of how the unbranded chocolates are conceptualised. The biplot of dimensions 1 vs. 2 reveals the fairly extreme conceptual differences between unbranded Cadbury’s Bournville Deeply Dark versus Montezuma and Lindt in dimension 2. Dimension 3 (Fig. 9.17), which explains 10.3% of the variation in the data, distinguishes Cote d’Or. Tesco, Waitrose and Divine are barely differentiated across dimensions 1 to 3, suggesting that their conceptual profiles are broadly similar. Likewise with Montezuma and Lindt. When the conceptual profiles of the unbranded products have a fairly high degree of similarity, dimension 1 can usually be interpreted as representing a ‘category conceptual profile’. The greater the proportion of variation captured by dimension 1 (44.9%, in this case), the more similar the conceptual profiles of the products. Conversely, the greater the proportion of variation in the data captured by dimensions 2 and 3 (41.7% in total), the greater the apparent differences in the conceptual profiles. Generally, this means that the overall differences across the products are best visualised in a biplot of dimension 2 vs. dimension 3 (Fig. 9.17). In this example, the proportion of variation explained by dimensions 2 and 3 is relatively high for unbranded products and both are discriminatory. Differences amongst individual products are illustrated in the share of profile data (Fig. 9.18).The differences across Montezuma, Cadbury’s Bournville Deeply Dark and Cote d’Or are readily apparent, as are

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262

Consumer-driven innovation in food and personal care products Montezuma Lindt

1.0

Seeds of Change

0.8

0.6 Energetic Powerful Adventurous

Confident Green & Blacks

Dimension 2 (31.4%)

0.4 Masculine Arrogant 0.2 Aggressive

Sophisticated

Serious

Pretentious 0.0

Traditional

Sociable

Luxurious Sensual

−0.2

Waitrose Divine Tesco

Feminine Easygoing Warm Trustworthy Fun Uncomplicated Cote d’Or

Youthful Comforting

−0.4

−0.6

Boumville

Ordinary Tacky

−0.8 −0.8

−0.6

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0

Dimension 1 (44.9%)

Fig. 9.16 Biplot for 9 unbranded dark chocolates and 24 conceptual terms (D1 vs. D2).

the similarities across Tesco, Waitrose and Divine and also Lindt and Montezuma. Sensory overlay The chocolates were profiled in triplicate using a trained sensory panel based at MMR’s sensory facility (the Sensory Science Centre) at the University of Reading, UK. Most of the important variation is captured by the first two dimensions of a sensory biplot of products and attributes (Fig. 9.19). The sensory data is not discussed further here, except to note that the spread and clustering of products on the sensory biplot shows broad similarities with the conceptual biplots (Figs 9.l6 and 9.17). For example, there is a loose clustering of Montezuma, Lindt, Green & Blacks and Seeds of Change. Also, Tesco and Divine are proximal. On the other hand, Cadbury’s

© Copyright MMR Research Worldwide 2010 – All rights reserved

Going beyond liking 0.8

Cote d’Or

0.6

Traditional Serious Pretentious

0.4 Dimension 3 (10.3%)

263

Ordinary

Arrogant

Green & Blacks

Uncomplicated Youthful

0.2

Confident Seeds of Change Masculine

Tacky Feminine Warm

0.0

Adventurous Sociable Montezuma Sophisticated Divine Aggressive Lindt Waitrose Powerful

−0.2

Bournville Trustworthy Comforting Easygoing Tesco

−0.4

Fun −0.6

Energetic

Luxurious

Sensual

−0.8 −0.8

−0.6

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0

Dimension 2 (31.4%)

Fig. 9.17 Biplot for 9 unbranded chocolates and 24 conceptual terms (D2 vs. D3).

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Green & Waitrose Blacks

Fig. 9.18

Tesco

Lindt

Bournville

Divine

Cote d’Or Seeds of Montezuma Change

Tacky Aggressive Arrogant Pretentious Luxurious Sensual Sophisticated Ordinary Youthful Masculine Feminine Fun Comforting Serious Powerful Adventurous Warm Traditional Trustworthy Easygoing Uncomplicated Energetic Confident Sociable

Share of conceptual profile for 9 unbranded dark chocolates.

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1.00 Sweet (O)

Vanilla (F)

Body (Mf) Initial bite (F)

Thickness (Ap)

0.75

Mouthcoating (Mf)

Waitrose

Sweet (At) Sweet (F) Creamy (Mf) Creamy (F)

Brown fruit (F)

0.50

Dimension 2 (26.1%)

Smooth (Mf)

0.25

Cocoa (F)

Brown (Ap) Cocoa (O)

Bournville

Montezuma

Cocoa (At) Salt (F)

Seeds of Change 0.00 Divine

Milk choc (F)

Green & Blacks

Tesco

Lindt

−0.25

Smoky/burnt (O)

Buttery/margarine (F)

Nutty/earthy (F)

Coffee (F) Red fruit (F) Bitter (F) Sour (At) Sour (F) Bitter (At) Molasses (F) Drying (Mf) Smoky/burnt (F) Astringent (Mf)

−0.50 Fatty/greasy (Mf) Cardboard/bland (F) Melt rate (Mf) Stale (F)

−0.75

Cote d’Or Waxy (Mf) Chemical (F)

−1.00 −1.00

−0.75

−0.50

−0.25

0.0

0.25

0.50

0.75

1.00

Dimension 1 (47.4%)

Fig. 9.19

Sensory biplot of 9 unbranded dark chocolates.

Bournville Deeply Dark and Cote d’Or each occupy fairly isolated positions on the sensory biplot. Only the positioning of Waitrose is somewhat at odds with the conceptual biplot. Perhaps the sensory characteristics that distinguish Waitrose are not conceptual drivers? Linking conceptual data generated using best/worst scaling with sensory data poses a challenge because of differences in the way the two data sets are scaled. As far as the author is aware, this hasn’t been attempted before. With best/worst scaling data, the lack of a unique origin to each product’s interval scale means that conceptual terms are comparable within but not between products. Conversely, with sensory data, sensory attributes are comparable between but not within products. In this study, we have used a graphical method in which sensory attributes are overlaid onto the conceptual biplot. Sensory attributes are standardised to zero mean and unit variance and scored on the dimensions of the conceptual biplot after application of suitable scaling factors. Details are given in Thomson et al. (2010).

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As mentioned previously, for performing the overlay we often use an alternative scaling of the biplot that maximises the accuracy of the representation of the conceptual terms at the expense of the representation of the products. However, to facilitate comparison with Fig. 9.16 we have retained the original scaling in this example. The variation in the sensory profiles (not the attributes) explained by each dimension of the conceptual biplot is calculated as the variance of the scores of the standardised attributes before application of any scaling factors. These figures are then expressed as a proportion of the total variance. Dimensions of interest are those that explain high proportions of conceptual and sensory variation. Because dimension 1 of the conceptual biplot represents the similarity rather than the differences between the products, it explains relatively little sensory profile variation (5%). Dimensions 2 and 3 together explain 45% of the variation in the sensory profiles (Fig. 9.20). Figure 9.21 highlights the sensory/conceptual associations. These show that cocoa (sensory characteristic) is associated with powerful and energetic (conceptualisations), bitter with confident, adventurous and masculine, smoky/burnt odour with arrogant, serious, traditional and pretentious,

0.8

Cote d’Or Chemical (F) Stale (F)

0.6

Dimension 3 (10.3%)

0.4

0.2

0.0

−0.2 −0.4

Traditional Serjous Melt rate (Mf) PretentiousSmoky/burnt (O, F) Fatty/greasy (Mf) Wishy (Mf) Green &Blacks Arrogant Salt (F) Sour (F, At) Ordinary Cardboard/bland (F) Red fruit (F) Astringent (Mf) Molasses (F) Uncomplicated Bitter (F, At) Youthful Drying (Mf) Confident Tacky Seeds of Change Nutty/earthy(F) Masculine Feminine Coffee (F) Warm Adventurous Buttery/margarine (F) Sociable Sophisticated Montezuma Brown fruit (F) Smooth (Mf) Divine Aggressive Lindt Milk choc (F) Cocoa (O) Waitrose Cocoa (Af) Powerful Thickness (At) Mouthcoating (Mf) Bournville Trustworthy Energetic Comforting Easygoing Cooca (F) TescoSweet (O) Body (Mf) Creamy (F, Mf) Sweet (F, At)

−0.6

Fun

Luxurious Initial bite (F)

Sensual Brown (Ap) Vanilla (F)

−0.8 −0.8

−0.6

Fig. 9.20

−0.4

−0.2

0.0 0.2 0.4 Dimension 2 (31.4%)

0.6

0.8

Overlay of sensory data on conceptual biplot D2 vs. D3.

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1.0

266

Consumer-driven innovation in food and personal care products 0.8

Cote d’Or Chemical (F) Stale (F)

0.6

Traditional Serjous Melt rate (Mf) Fatty/greasy (Mf) PretentiousSmoky/burnt (O, F) Waxy (Mf) Green &Blacks Arrogant Salt (F) Sour (F, At) Ordinary Cardboard/bland (F) Red frut (F) Astringent (Mf) Molasses (F) Uncomplicated Bitter (F,At)

Dimension 3 (10.3%)

0.4

0.2

Youthful Tacky

Nutty/earthy(F)

Drying (Mf) Confident Masculine

Feminine Warm Buttery/margarine (F)

Seeds of Change

Coffee (F) Adventurous Sociable Sophisticated Brown fruit (F) Aggressive Smooth (Mf) Divine Cocoa (O) Milk choc (F) Waitrose Cocoa (Af) Thickness (At) Powerful Mouthcoating (Mf) Bournville Trustworthy Energetic Comforting Easygoing Cooca (F) TescoSweet (O) Body (Mf)

0.0

−0.2 −0.4

Creamy (F, Mf) Sweet (F, At)

−0.6

Montezurna Lindt

Fun Luxurious Initial bite (F) Sensual Brown (Ap)

Vanilla (F)

−0.8 −0.8

Fig. 9.21

−0.6

−0.4

−0.2

0.0 0.2 0.4 Dimension 2 (31.4%)

0.6

0.8

1.0

Overlay of sensory data on conceptual biplot highlighting associations.

vanilla, brown and initial bite with sensual, fun and luxurious and creamy and sweet with fun, comforting and easygoing. Formal modelling processes are also being investigated but as yet, none has proved superior to the graphical method described herein. Comparison of brand vs. unbranded product Of particular interest in this study, is the degree to which the conceptual profiles of the unbranded products are congruent with (consonant) or different from (dissonant) the corresponding brands. To this end, the conceptual profiles of seven from nine of the corresponding brands from the unbranded study were obtained (Fig. 9.22). In interpreting brand profiles, we generally look for particular conceptualisations (or combinations of conceptualisations) that are dominant and also for conceptual terms that reflect uniqueness. With Lindt branding, for example, the dominant communication is a combination of luxurious and sophisticated (38.5%, in total). Green & Blacks comes close, with the same two conceptualisations accounting for 33.1% of the share of profile. In sharp contrast, luxurious and sophisticated

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267

Tacky Arrogant Aggressive Ordinary Pretentious Youthful Feminine Easygoing Fun Masculine Energetic Sociable Uncomplicated Serious Adventurous Traditional Warm Trustworthy Comforting Sensual Powerful Confident Sophisticated Luxurious

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Green & Blacks

Fig. 9.22

Lindt

Bournville Tesco Finest Cote d’Or Montezuma

Share of conceptual profile for 6 Dark Chocolate brands.

only account for 12.3% of the share of profile with Montezuma. On the other hand, the combination of confident, powerful, energetic, adventurous and masculine represent 40.8% of the share of profile for Montezuma but only 20.9% for Lindt. An overview of the similarities and differences in the conceptual profiles of the brands and the unbranded products are shown in the combined biplots of dimensions 1 vs. 2 (Fig. 9.23) and dimensions 1 vs. 3 (Fig. 9.24). These show quite clearly that only for Montezuma is there consonance between the brand and product sensory profiles. Cadbury’s Bournville Deeply Dark, on the other hand, shows fairly extreme dissonance between product and brand. Direct comparisons of the brand vs. product conceptual profiles for each of Montezuma and Cadbury’s Bournville Deeply Dark, are also shown in Figs 9.25 and 9.26, respectively, highlighting obvious consonance with the former and dissonance with the latter. Based on a large number of subsequent (but confidential) studies, it seems that brand vs. product conceptual dissonance is the rule rather than the exception, even across some of the best-known consumer brands in the world. This is surprising and has huge implications for new product development. Functional profiling Functional profiling was conducted on the nine unbranded chocolates using 14 functional conceptualisations: 12 positive (would provide a good end to a meal, would be good to share, would make a good gift/present, would help

© Copyright MMR Research Worldwide 2010 – All rights reserved

1.00

Bournville product Tesco product

0.75 Cote d’Or product

Tesco brand

Dimension 2 (24.1%)

0.50 Sociable Ordinary Uncomplicated Green & Blacks product Comforting Warm Traditional Easygoing Green & Blacks brand Trustworthy Feminine Fun Youthful Confident Lindt brand

0.25

0.00

Serious Sqphisticated Pretentious Sensual Energetic Luxurious Masculine Adverturous

Tacky −0.25

Cote d’Or brand Bournville brand Montezuma trand

Arrogant −0.50 Aggressive

Powerful

Montezuma product Lindt product

0.25

0.50

−0.75 −0.75

−0.50

−0.25

0.00

0.75

1.00

Dimension 1 (51.5%)

Fig. 9.23

Combined conceptual biplots for unbranded products and branding D1 vs. D2.

0.75

Green & Blacks product Cote d’Or product

0.50

Lindt product

Dimension 3 (11.4%)

Confident

0.25

0.00

−0.25 Tacky

Sociable Montezuma product Serious Energetic Montezuma brand Arrogant Masculine Adverturous Youthful Traditional Uncomplicated Pretentious Ordinary Feminine Bournville product Tesco product Powerful Fun Warm Bournville brand Easygoing Aggressive Tesco brand Cote d’Or brand Trustworthy Lindt brand Green & Blacks brand Comforting Sophisticated

−0.50 Sensual Luxurious

−0.75 −0.75

−0.50

−0.25

0.00

0.25

0.50

0.75

1.00

Dimension 1 (51.5%)

Fig. 9.24

Combined conceptual biplots for unbranded products and branding D1 vs. D3.

Going beyond liking

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100% Tacky Feminine Ordinary Traditional Pretentious Arrogant Sensual Aggressive Easygoing Warm Comforting Youthful Trustworthy Sociable Fun Serious Uncomplicated Sophisticated Masculine Energetic Luxurious Adventurous Powerful Confident

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Montezuma Brand

Fig. 9.25

Montezuma Product

Direct comparison of brand and product conceptual profiles: Montezuma.

me to relax/reduce my stress levels, would improve/enhance my mood, would be filling, would be nutritious, would be an easy snack, would give me a quick high, would be suitable for cooking, would be good as an aphrodisiac, would have health benefits) and two negative (would make me feel sick, would be bad for me). The list of functional conceptualisations was developed using broadly the same procedures described for the emotional/abstract conceptualisations (see pages 223–6). This functional study was conducted separately from the emotional/conceptual profiling using a sample of 280 consumers. Functional conceptualisations were presented in the form of a tick list (see Section 9.3.1). For each chocolate, the research participant was asked to decide whether or not the chocolate would deliver that particular functional conceptualisation and ‘tick’ it accordingly. The raw data takes the form of the percentage of people who ticked a particular functional conceptualisation for each chocolate. As mentioned previously (Section 9.3.1), simple tick lists are often adequate for eliciting functional conceptualisations, and in large samples there is usually no need for anything more elaborate. (Conversely, tick lists are

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Consumer-driven innovation in food and personal care products

100% Tacky Ordinary Pretentious Feminine Arrogant Youthful Easygoing Aggressive Uncomplicated Fun Traditional Sociable Serious Comforting Trustworthy Masculine Warm Energetic Adventurous Sensual Sophisticated Confident Powerful Luxurious

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Bournville Brand

Bournville Product

Fig. 9.26 Direct comparison of brand and conceptual profiles: Bournville Deeply Dark.

inadequate for emotional and abstract conceptualisations where best/worst scaling generates greatly superior conceptual profiles.) Figure 9.27 shows the individual functional profiles of the 9 unbranded chocolates. An overview is provided in the corresponding biplot (Fig. 9.28). All of the functional conceptualisations are discriminatory with the exception of filling, nutritious, good as an aphrodisiac and is bad for me. These were also the least often endorsed. Although significant discriminators, and quite heavily endorsed, suitable for cooking and would have health benefits were much less discriminatory than the remaining eight functional conceptualisations (7 positive and 1 negative – would make me feel sick). All eight of these are also highly correlated with liking and with each other although, as might be anticipated, would make me feel sick is negatively correlated with liking. This suggests that the sensory differences in the chocolate drive liking but they don’t drive functionality independently from liking. This is definitely not the case with the emotional and abstract conceptualisations. We have observed this same phenomenon in several other (but not all) unbranded product categories and have also established that it is invariant of the method used to capture the functional

© Copyright MMR Research Worldwide 2010 – All rights reserved

Proportion of respondents

50% Provide a good end to a meal Good to share Make a good gift/present Help me to relax/reduce stress Improve/enhance my mood Filling Nutritious An easy snack Give me a quick high Suitable for cooking Make me feel sick Good as an aphrodisiac Is bad for me Will have health benefits

40%

30%

20%

10%

G

re e

n

&

Bl ac W ks ai tro se Te sc o Li Bo nd ur t nv ille D iv in Se C ed ote e s of d’O r C ha M n g on te e zu m a

0%

Fig. 9.27

Individual functional profiles for 9 dark chocolates.

1.00 Green & Blacks

0.75 Make me feel sick

Suitable for cooking Lindt

Dimension 2 (20.9%)

0.50

Seeds of Change

0.25 Will have health benefits Cote d’Or

0.00

−0.25

Give me a quick high Provide a good end to a meal Improve/enhance my mood

Montezuma

Help me to relax/reduce stress Good to share Make a good gift/present

Waitrose Divine Tesco

An easy snack

−0.50 Bournville

−0.75 −0.75

−0.50

−0.25

0.00

0.25

0.50

0.75

1.00

Dimension 1 (75.4%)

Fig. 9.28

Biplot of functional profiles of 9 unbranded chocolates.

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conceptualisations (see Section 9.3.1). It is also definitely not the case for brands. This has four general implications for new product development: i) With some unbranded products, functionality is category driven. For example, in the case of chocolate, we have observed (but cannot present herein for reasons of confidentiality) that the functional profiles of dark, milk and white chocolate are different. ii) Seven of the positively oriented functional conceptualisations (provide a good end to a meal, good to share, good gift/present, help me to relax/ reduce my stress levels, improve/enhance my mood, easy snack, give me a quick high) are all key functional communications around which a strong, positive brand message can be created. This suggests therefore, that the key brand message should be built around emotional and abstract conceptualisations and that only those functional conceptualisations that reinforce and are consonant with these should be integrated into the brand message. iii) Random association of functional conceptualisations with emotional/ abstract conceptualisations, even if these functional conceptualisations are viewed very positively, could create dissonance. iv) As a consequence of ii) and iii), functionality cannot be ignored but it probably isn’t as important, interesting and useful as emotionality.

9.5 Conclusions As consumer goods brands battle with retailers’ and discounters’ products, in the constant struggle to justify their price premium, aligning the conceptual profile of the product per se with the conceptual profile of the brand (or vice versa), so that they are consonant, perhaps represents the last great unexploited opportunity for the optimisation and development of new products. Brand owners can afford to be single-minded about this! Retailers cannot, because of the need to stretch their brands across so many different products and categories. Having written this chapter, I have developed two personal maxims for new product development: ‘Consonance is king’ ‘Liking matters, but ‘wanting’ matters more’

9.6 Acknowledgements I am grateful to the following colleagues from MMR Research Worldwide for their help and collaboration in conducting the case studies, commenting on the manuscript and preparing the illustrations: Chris Crocker (most

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especially), Chris Marketo, David Howlett, Phiala Mehring, Pippa Bailey, Teresa Church and Toby Coates. I am also grateful to Compusense Inc., Guelph, Canada for generous support of the Sensory Science Centre at the University of Reading, UK.

9.7 Sources of further information and advice Ariely, D. (2008). Predictably irrational. The hidden forces that shape our decisions This is one of the most engaging books I’ve ever read. After reading it I arrived at the important observation that apparently irrational behaviour may not be irrational at all because, knowingly or otherwise, the individual concerned may be deriving some form of ‘hidden benefit’ from this behaviour. However, this behaviour may seem counter-intuitive simply because the observer (or even the individual him/herself) is not aware of the hidden benefit. Understanding how people conceptualise the world around them and how we all differ in this respect, is crucial to our understanding human behaviour and also to the process of creating new products that people actually want. Gladwell, M. (2005). Blink – the power of thinking without thinking I learned about Implicit Association Testing (IAT) from Blink. This opened my eyes and my mind to the fact that we aren’t aware of all of the things that influence our thoughts and our behaviour. It helps to explain why some things may seem outwardly irrational but may actually be driven by our own ‘hidden influences’. Why not participate in an IAT survey; it’s very revealing (and just a little disconcerting)? (www.harvard.edu). Marder, E. (1997). The Laws of Choice – predicting customer behaviour Eric Marder is the unsung champion of choice-based research; something I believe in passionately. It could make uncomfortable reading for some! Lindstrom, M. (2005). Brand Sense. New York: Simon & Schuster Martin Lindstrom’s book awakened many of us to the role of product sensory characteristics in branding and triggered a chain of events that led, at least in part, to the current widespread interest in emotionality. This is a fairly ‘light’ read but Brand Sense marks an important turning point in our understanding of the relationships between branding, packaging and product. Lindstrom deserves to be credited with some of this.

9.8 References carroll, j.d. (1972). Individual differences and multidimensional scaling. In, Shepard, R.N. & Nerlove, S.B. (eds.) Multidimensional Scaling: theory and applications in the behavioural sciences. New York: Seminar Press.

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cohen, c. & neira, l. (2003). Measuring preference for product benefits across countries. ESOMAR Latin American Conference, Uruguay. desmet, p.m.a. (2002). Designing emotion. Doctoral thesis (ISBN 90-9015877-4). desmet, p.m.a. (2003). Measuring emotions: development of an instrument to measure emotional responses to products. In Blythe, M.A., Overbeeke, K., Monk, A.F. and Wright, P.C. (eds.), Funology: from usability to enjoyment. Dordrecht, Boston, London: Kluwer Academic Publishers. ekman, p. (2003). Emotions revealed. New York: Times Books. ekman, p. & freisen, w.v. (1971). Constants across culture in the face and emotion. Journal of Personality and Social Psychology, 17, 124–129. finn, a. & louviere, j.j. (1992). Determining the appropriate response to evidence of public concern: the case for food safety. Journal of Public Policy & Marketing, 11(1), 12–25. gabriel, k.r. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58, 453–467. hill, d.v. (2007). Emotionomics: Winning Hearts and Minds. Edina: Adams Business & Professional. king, s.c. & meiselman, h.l. (2010). Development of a method to measure consumer emotions associated with foods. Food Quality & Preference, 21, 168–177. köster, e.p. & mojet, j. (2007). Boredom and the reasons why some new food products fail. In, MacFie, H.J.H. (ed.) Consumer-led food product development, pp. 262–280. Cambridge: Woodhead. lindstrom, m. (2005). Brand Sense. New York: Simon & Schuster. sawtooth software inc. (2009). The CBC/HB System for Hierarchical Bayes Estimation: Technical Paper’. Available from http://www.sawtooth.com schutz, h.g. (1988). Beyond preference: Appropriateness as a measure of contextual acceptance. In Thomson, D.M.H (ed.), Food Acceptability, pp. 115–134. London: Elsevier. thomson, d.m.h., crocker, c. & marketo, c.g. (2010). Linking sensory characteristics to emotion: an example using dark chocolate. Submitted to Food Quality and Preference. treasure, j. (2007). Sound Business. Management Books 2000 Ltd. yang, c.-c. (2009). A Study on Variable Selection for Kansei Engineering Systems – Applications for product design. Saarbrücken: Verlag Dr Müller.

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10 Consumer understanding and reaction to health claims: insights and methodology M. Rogeaux, Danone Research, France

Abstract: This chapter will first introduce the notion of “functional foods”. These specific foods are becoming more and more important in the food market but need to comply with a range of legislation on claims that are made for their effect on health and well being. From a consumer point of view, a very important point is consumer understanding of claims. Indeed this point is now mandatory in EU regulation and a key element to create a clear, precise and trusty relationship with the consumer. In the first part we will introduce the context. In the second part, we will discuss the factors that modulate the consumer claim understanding: individual factors and message factors. In the third part, we present a method that enables us to evaluate the level of understanding of a claim by consumers. The method presented, the Claim understanding test (CUT) enables us to obtain an objective and quantitative response on the evaluation of the level of claim misleading. The CUT test is based on two main principles (open question and a very strict process of codification to test the science alignment of the consumer verbatim). Key words: health claim, consumer, functional food, understanding, misleading.

10.1 Introduction “Let your food be your medicine”, said Hippocrates. Indeed in the minds of human beings there has probably always been a link between food and health. But recently, from the 20th century, we can observe more and more products positioning themselves to have a clear impact on health. This trend needs to be analyzed in relation to different facts: • The increasing life expectancy and changing lifestyle have led to an increase in the occurrence of chronic diseases (Ares, 2008) In a report from the World Health Organization (WHO, 2008), ageing has drawn attention to an issue that is of particular relevance to the organization of service delivery: the increasing frequency of multimorbidity. In the

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industrialized world, as many as 25% of 65–69 year olds and 50% of 80–84 year olds are affected by two or more chronic health diseases. The rapid advances in science and technology; that leads to new health food concepts for the consumer with associated scientific proofs. The increasing healthcare costs mean that consumption of healthpromoting products has an important role in prevention of disease (individual and societal issues). The fact that functional foods play an important part of the innovation portfolio (Datamonitor, 2008). The increase of functional foods consumption is impressive: In US market 18.1 MM$ (2002), 27.2 MM$ (2007) and a prevision of 36.7 MM$ for 2012. In European market: 6.3 MM$ (2002), 8.5 MM$ (2007) and a prevision of 10.7 MM$ for 2012. Regulations become more and more strict. For example, in Europe, legislation is proposed to require companies ask us to ensure that consumers are not being misled by the advertising or label messages. And a new consumer (more informed, more critical . . .) who wants to get precise information on products and joins an internet community to express opinions. In this context, we propose that:

• the positioning of foods as providing a health benefit will be more and more important in the future; • the need to effectively explain the benefit clearly and honestly to the consumer will be mandatory; • the regulatory authority will need evidence that our communication is not misleading. So, health companies consider that it is very important to build a trusting relationship with the consumer. In order to build this, we need to evaluate precisely consumer perception of functional foods. In this chapter we present three parts: • First, we’ll present functional foods: definition, market. • Second, we’ll focus on our knowledge of the factors that modulate claim understanding by consumers. • Third, we’ll focus on a method to measure claim understanding by consumers.

10.2 Functional foods 10.2.1 Definition For a long time food has been bringing a health benefit through a diet (ILSI 2008). For the first half of the 20th century, the focus on nutrition science

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has been on establishing the minimum requirements for essential nutrients in order to avoid deficiency diseases: this knowledge is the basis of a balanced diet that provides adequate intakes of nutrients taking into account energy intake. Based on this science, some new food products have been created to meet consumers’ nutritional requirements. Some improvements on the appreciation of the potential beneficial effects of nutrients in the diet have led to the possibility of creating food items with specific characteristics that improve body functions and well-being. These foods have come to be known as functional. A functional food can be defined as follows: “A food can be regarded as functional if it is satisfactorily demonstrated to affect beneficially one or more target functions in the body, beyond adequate nutritional effects, in a way that is relevant to either an improved state of health and well-being and/or a reduction of risk of disease” (DIPLOCK, 1999 after an ISLI work).

10.2.2 The scientific base of functional foods In order to regulate the usage of functional foods, more and more countries are formalizing the definitions of what is and what is not a functional food. We can notice that, in all cases, the main focus is the scientific benefit associated to the product (with effects proven by consumption of products) or to the composition of the product (with effects proven on compounds, ingredients contained in the final product, e.g. contains vitamin C). Regulations differ between countries. Japan Japan was the first country to recognize functional foods as a separate category when in 1991 it introduced the FOSHU (Foods for Specific Health Use) system to evaluate health claims. This system has valuable aspects: it regulates both safety and health, and it demands the food be analyzed for the amount of effective components. United States The United States had a solid system for disease reduction claims for foods, which were allowed only if there was “significant scientific agreement” that the claim was valid. However, the Food and Drug Administration’s (FDA) overview over health claims has eroded, and the United States now allow “qualified health claims” for which there is hardly any evidence, as long as a disclaimer is included. FDA regulates food products according to their intended use and the nature of claims made on the package. Three types of health-related statements or claims are allowed on food and dietary supplement labels: As described in FDA description, (FDA, 2003) the classification is as follows.

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I. Health claims “Health claims describe a relationship between a food, food component, or dietary supplement ingredient, and reducing risk of a disease or healthrelated condition.” A “health claim”, by definition, has two essential components: (1) a substance (whether a food, food component, or dietary ingredient) and (2) a disease or health-related condition. Dietary guidance statements used on food labels must be truthful and non-misleading. Statements that address a role of a specific substance in maintaining normal healthy structures or functions of the body are considered to be structure/ function claims. Structure/function claims may not explicitly or implicitly link the relationship to a disease or health-related condition. Unlike health claims, dietary guidance statements and structure/function claims are not subject to FDA review and authorization.

II. Nutrient content claims The Nutrition Labeling and Education Act of 1990 (NLEA) permits the use of label claims that characterize the level of a nutrient in a food (i.e., nutrient content claims) made in accordance with FDA’s authorizing regulations. Nutrient content claims describe the level of a nutrient or dietary substance in the product, using terms such as free, high, and low, or they compare the level of a nutrient in a food to that of another food, using terms such as more, reduced. An accurate quantitative statement (e.g., 200 mg of sodium) that does not “characterize” the nutrient level may be used to describe any amount of a nutrient present.

III. Structure/function claims Structure/function claims have historically appeared on the labels of conventional foods and dietary supplements as well as drugs. However, the Dietary Supplement Health and Education Act of 1994 (DSHEA) established some special regulatory procedures for such claims for dietary supplement labels. Structure/function claims describe the role of a nutrient or dietary ingredient intended to affect normal structure or function in humans, for example, “calcium builds strong bones.” In addition, they may characterize the means by which a nutrient or dietary ingredient acts to maintain such structure or function, for example, “fiber maintains bowel regularity,” or “antioxidants maintain cell integrity,” or they may describe general well-being from consumption of a nutrient or dietary ingredient. Structure/function claims may also describe a benefit related to a nutrient deficiency disease (like vitamin C and scurvy), as long as the statement also tells how widespread such a disease is in the United States. The manufacturer is responsible for ensuring the accuracy and truthfulness of these claims; they are not pre-approved by FDA but must be truthful and not misleading.

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European union December 20, 2006 marked the publication of regulation no. 1924/2006 on all nutrition and health claims concerning foods and beverages sold in the European Union. Three types of nutrition and health claims are authorized for food products sold in the European Union. All claims of these types will have to be submitted to the EFSA (European Food Safety Authority), which will evaluate all claims, according to scientific data. Type 1 Nutrition claims that state, suggest or imply that a food has particular beneficial nutritional properties due to its composition. Examples: “enriched”, “low”, “reduced”, “source of”, “light”, “no . . .”, “high” in calories or in another specific nutrient . . . Very often, the terminology “Article 13.1” is used to described this type of claim (in referring to the European regulatory). Type 2 Health claims that state, suggest or imply that a relationship exists between a food category, a food or one of its constituents and health. The claim specifies the physiological function of the component. Very often, the terminology “Article 13.5” is used to described this type of claim (in referring to the European regulatory). At the moment, only one claim received a positive opinion of EFSA (European Food Safety Authority) liking its tomato extract to blood circulation benefit (provided by Provexis company). Provexis has been handed final claim wording by the European Commission. The benefit is very medically oriented . . . The EFSA validation is on this claim “Impact of Provexis Natural Products Limited on Water-soluble tomato concentrate (WSTC I and II) and platelet aggregation”. The commission accepted a verbalisation more consumer oriented: “Helps maintain normal platelet aggregation, which contributes to healthy blood flow.” In the future, probably, others claims will be submitted and accepted in this category (Article 13.5). Type 3 Reduction of disease risk claim: These are health claims that state, suggest or imply that the consumption of a food category, a food or one of its constituents significantly reduces a risk factor in the development of a human disease. For example, “Phytosterols can reduce blood cholesterol and reduce the risk of heart disease”. Very often, the terminology “Article 14” is used to describe this type of claim (in referring to the European regulatory).

10.2.3

Consumer and functional food: the question of claim understanding For a functional food, the core point is the scientific facts on which we base the scientific claim. This scientific claim needs to be very precise and often this claim can be very difficult for the consumer to understand.

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In order to explain this benefit to the consumer, health food brand needs to express and communicate this benefit with different cues: consumer claim, logo, advertising. . . . These communication elements need to be adapted to the consumer, expressed in a simple language. We therefore often need to create a translation of the scientific term so that the consumer understands the product benefit. So for functional foods, we have always two elements that need to be in association: • Scientific fact that describes the health effect: scientific claim. • Communication elements that express the benefit: consumer claim + cues (logo, pictures, advertising). The two main questions are to: • determine how the consumer understands the benefit, and identify the factors that modulate this understanding; • define a method in order to determine if this understanding is in line with the scientific facts (regulatory concern).

10.3 The process of consumer understanding of the health benefit Behind this question to investigate consumer understanding, it is important to first define what is the information process performed by the consumer and then to specify the factors that modulate this response.

10.3.1 Global process of understanding The decision-making process used by the consumer to interpret the claim can be considered as an assimilation process. According to Moorman (1990), Fig. 10.1, this process is based on four fundamental steps: a)

The acquisition of information is the step during which the consumer searches for the information on the product’s packaging. b) The elaboration of the information proves its cognitive processing and its encoding in the consumer’s memory. c) The understanding is the meaning that the consumer gives to the information provided to him/her. d) Finally, the behavior is the type of decision made: e.g. purchase or non purchase of a product and also its re-purchase. In Moorman’s view, these four steps are affected by the consumer’s motivation and ability to process information. These two factors are themselves dependent on the characteristics of the information (type, familiarity), and also individual characteristics.

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Consumer understanding and reaction to health claims DETERMINING FACTORS Product- and purchase-related factors

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CONSUMER BEHAVIOR

Individual factors

Characteristics of information Format, Quantity, Type

Motivation to process nutritional information at time of purchase

Memorization / elaboration of information

Acquisition of information

Purchase-related characteristics

Behaviour

Individual consumer characteristics Socio-démographic Motivation, Expertise

Fig. 10.1

Ability to process nutritional information at time of purchase

Understanding of information

Model of decision taken presented by Moorman (1990).

DETERMINING FACTORS

CONSUMER BEHAVIOR Search Exposure

Interest

Perception: • Conscious • Subconscious

Knowledge Demographics

Liking

Understanding and inferences • Objective • Subjective

Label format Use • One-time, extended • Direct, indirect

Fig. 10.2

Model of decision taken presented by Grunert (2007).

Based on a more recent view (Fig. 10.2), Grunert (2007) provides a more complex view, dividing consumer behavior task into six steps: 1 2 3

The search for information. The exposure: only information to which consumers are exposed is likely to have an effect. The conscious and unconscious perception of the information. Although conscious perception is expected to have stronger effects on subsequent behavior.

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4

The label preference leads to a positive evaluation of the product (the color, the way the information is written). This preference doesn’t improve understanding but can reinforce the impact of a label and the fact it seems easier to understand. 5 The information understanding (at this stage, we need to distinguish the subjective understanding and the objective understanding). 6 The use of information in decision making: purchase and re-purchase with the impact of cumulative effect over time. According to this model, all six steps in the decision-making process are affected by four key factors: the consumer’s interest, knowledge, demographics and the format of the nutritional information. In both models, we find the same data process from research of information to decision making. We find also the impact of individual factors and characteristics of the information at every step to the process. In the Grunert Model, three specificities appear: difference between search step and exposure step, impact of linking and importance of subconscious perception. Both clearly determine that the consumers’ claim understanding can be subjective or objective. Subjective understanding refers to the personal interpretation that the consumer makes of the information. Objective understanding translates the initial intention of the manufacturer. The manufacturers’ work therefore consists in reducing this gap between subjective and objective understanding.

10.3.2 Factors that can impact understanding Consumers’ understanding of health claims depends on many factors. We will make a distinction between the factors relating to the consumers’ motivation and capacity, the socio-demographic factors and the factors peculiar to the characteristics of health claims. Then, it is necessary to remind us that consumers perceive the surrounding world through filters that can reduce their capacity to understand the messages sent to them: these cognitive biases are important to analyze. The individual factors: knowledge and motivation Nutrition expertise can be defined as: “the ability to transform and give sense to a stimulus” and directly echoes the consumers’ nutrition knowledge (Moorman, 1996). Most previous researches conclude that consumers have difficulties in understanding and using nutrition information, in particular in a digital form (Burton, 1994). It is an acknowledged fact that good nutrition knowledge favors a good interpretation and a better understanding of nutrition information (Burton, 1999). For example, expert consumers perceive more easily the link between the consumption of the food product and their daily nutrition needs. Besides, nutrition knowledge eases the memorization and encoding of the information acquired by consumers

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since, as mentioned above, they are more capable of understanding them (Balasubramanian, 2002; Moorman 1990). This aside, nutrition knowledge leads us to remember that the links between the consumers’ level of expertise and information are not one-sided, as they may seem. The consumer knowledge on nutrition is based on a learning process that associates education, scientific news, beliefs, discussions with peers, company information and institutional information (administration, health authority). All this information is not always aligned, so the consumer builds their own knowledge and representation with rational and non-rational explanations. Taking into account the interactions between the consumers’ pre-existing knowledge and the information communicated need to be integrated. Motivation also has a central role in the information research and processing process. As outlined by Jacoby (1977), without any motivation, it is impossible for individuals to enter into a sensible and efficient activity of information research. In the field of nutrition information, motivation has been studied through the concept of motivation to process nutrition information. It has been examined from two angles: sustainable and situational. As a disposition, it is defined as “a sustainable tendency to want to receive nutrition information” (Moorman, 1990). As regards situational motivation, it follows exposure to the stimulus. It is therefore created by a specific situation. Researches have shown that motivation plays a role at different levels of the information processing process. First, it has an indirect influence on understanding through the increased efforts that consumers make to search for information. Moorman (1990) thus shows that motivation increases the acquisition of information after a new labeling system of food products, which is clearer and more readable than before, is implemented. It also has a direct effect on the understanding and memorisation of nutrition information. For example, studies found that a sustainable motivation to process information contributes to a good and precise use of nutrition information (Burton, 1999). Many researches insist on the lack of motivation often shown by consumers as regards nutrition information processing as described by Cowburn, 2004; Jacoby, 1977; Mhurchu, 2007; Williams, 2005. We can highlight two main points: the health status and the parental status impact a lot on consumer awareness; the methodologies used have a great impact on the result (declarative questions always give more impact of claim than observational method!). The individual factors: the socio-demographic factors Age, gender and the level of education are often used to differentiate consumer understanding of claims. Although older consumers often have better nutrition knowledge and use nutrition information more to do their purchases (Govindasamy, 1999), the results of research tend to show that age is a factor having a negative impact on the capacities to process information and produce understanding

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quality. In fact, older consumers often tend to use nutrition information appearing on the food products’ labels in a less precise way (Cole, 1993) and spend more time to process it (Cole, 1990). This can be explained by the decrease in the cognitive functions (in particular the short-term memory) which typically affect older people. On the other hand, some studies have shown a relation between gender and the use of nutrition information because of a greater interest of women in their health and nutrition (Nayga, 2002). However gender does not seem to explain the differences of understanding of nutrition information (Burton, 1999). Likewise, the level of education is correlated with an increase in the use of claims and eases the understanding of nutrition information (Moorman, 1990). The factors linked to the characteristics of health claim: wording of health claims In order to improve the use of nutrition information, researchers have often been led to study how information is presented to consumers. Such research mainly concerns variables peculiar to the nature of the stimulus, such as the exhaustiveness, the format or the content of the information. With regards to the quantity of information, it seems that a happy medium should be found. In fact, marketing literature acknowledges that too much information tends to spoil the quality of the decisions taken by consumers (Malhotra, 1982). On the contrary, too little information can have negative effects. Russo et al. (1986) note that consumers reject a synthetic nutrition summary (based on amount of nutrients crossed with calories in order to reflect the product’s nutrition quality). The association of a short claim put on the front face of the packaging with a developing text put on the back face improves consumer use and understanding of the claim in comparison with the use of only one too long claim put on the front face (Wansink, 2003). Concerning the format of the information, several researches conclude that consumers are not capable of understanding nutrition information in its digital form: e.g. the sole information “200 Kcal” means little to most consumers (Burton, 1994). Nevertheless, if presented in certain forms, digital information can mean more to consumers. In fact, if they are given a reference framework (for example, expressing the quantity of nutrient in recommended daily intake or in comparison with the average of the product category) or if they are associated with oral information, this enables the consumers to improve the information understanding and memorization and the quality of the decision (Viswanathan, 2002). Lastly, the understanding of claims also varies according to their content (Van Trijp, 2007). It is thus more difficult for consumers to understand claims that put forward a cardiovascular benefit than those that promote a slimming benefit. Besides, claims describing the physiological action of a food are better understood than claims just mentioning the presence of a nutrient.

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Cognitive biases: a factor increasing the gap between subjective and objective understanding of health claims Cognitive biases are errors of interpretation peculiar to any human being. They express themselves in “a systematic gap between decisional behavior and rational standards of decision” (Pham, 1996). There are many of them. We will present the biases that are likely to appear most often when consumers interpret health claims. First, some biases drive us to only pay attention to some types of information and therefore to exclude the information deemed irrelevant. This is the case of the negativity bias that leads to giving more weight to negative than positive nutrition information during the assessment step. For example, researches have shown that consumers give more importance to fat than to fibres in their choices (Garretson, 2000). Another research has shown that consumers who do not have a specific health concern appear to use fat content as a decision rule (Basil, 2005). This phenomenon of negativity bias can be explained by the fact that once the nutrient is ingested, it is impossible to substitute for it, whereas it is always possible to make up for an insufficiency. For Garretson, the quality of the education of the consumer is key in order to help the consumer to have the right choice. Likewise, according to the confirmation bias, consumers are also motivated to interpret health claims in a way that confirms a pre-existing belief or behavior. One can therefore be led to give sense to information in a way that makes it consistent with a pre-existing opinion. Human beings also tend to reason by analogy, making comparisons between an object and another object typical of this category. That is the representativeness bias. This bias enables us to explain why the localisation of a product in a shop or the presence of an ingredient deemed beneficial in a recipe can make the evaluation of the food product favorable or unfavorable. According to the availability bias, the degree of familiarity of information can affect the consumers’ judgement. Moorman (1990) thus concludes that information relating to the most familiar nutrients is processed more by consumers than information relating to less familiar nutrients. Recently, the same result has been observed with a sample of 4612 respondents in the Nordic countries (Grunert, 2009). They demonstrate that the familiar ingredient can trigger health related associations in the consumer mind. These data are in line with the results of Urala (2003). The anchor bias provides an interesting theoretical framework to understand how consumers assess that a product slightly or strongly includes a nutrient. This anchoring effect strongly evolves depending on the information provided to the consumers. A 5% fat content in yoghurt may seem high to a consumer if he/she compares it with yoghurt with 0% fat content, while objectively it is low. The mass arrival of yoghurts with 0% fat content notably had the effect of moving the consumers’ anchoring point. Among the judgement biases, the halo effect often expresses itself in the consumers’ assessment. In some cases, consumers tend to attribute effects

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or characteristics to products that they do not have in reality (Roe, 1999). The source of halo is often linked to the presence of a claim and to the consumers’ incapacity to discriminate the other attributes. Taking these biases into account is essential to understand the reasons for the gap between subjective and objective understanding of health claims by consumers. As a conclusion, information understanding therefore appears as a complex process with the presence of many factors. Any predictive pattern of understanding appears unrealistic. In consequence, for each case: • a product and communication (e.g., Danacol, with a specific claim, pack, advertising) • a category (e.g., dairy product) • a benefit (e.g., reduction of cholesterol) • consumers (e.g., German; age; sex) • for a specific period (e.g., 2009). We need to test how consumers understand benefits with an ad-hoc evaluation. That is the reason why ad-hoc tests are necessary to validate understanding of each different claim and the way in which it is communicated.

10.4

How to evaluate consumer understanding with a consumer test?

In order to evaluate consumer understanding, some process can be set up with them. In the new context of the European regulation, we need to have a proper method dedicated to this point. We will first describe the European regulation’s requirements, then present the view of the literature in this field and present a possible method to evaluate it.

10.4.1 Focus on the regulatory requirements The EU Regulation on nutrition and health claims (No 1924/2006) defines precisely the way to use nutrition and health claims. While the first objective is focused on a rigorous scientific evaluation of claims, the second objective is focused on consumer understanding with an objective of protecting the consumer. Three parts of the document are important to notice. It is expressed that: . . . claims are not false, ambiguous or misleading to the consumer (article 3) . . . claims shall be permitted only if the average consumer can be expected to understand the beneficial effects as expressed in the claim (article 5) ‘claim’ means any message or representation, which is not mandatory under Community or national legislation, including pictorial, graphic or symbolic representation, in any form, which states, suggests or implies that a food has particular characteristics (article 2)

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Because of this new requirement we have formalized a dedicated method based on these three principles: • First, a detection of the misleading, with a precise evaluation (in response to article 3); • Second, a method easy to run that enables us to evaluate with all consumers (in response to article 5); • Thirdly a method including all the factors of communication (in response to article 2) with an evaluation of the product with its packaging and advertising. The language and the cultural points are fundamental in the way to express the benefit. It is fundamental to notice that we need to run this test in each country to take this point into account.

10.4.2 How can previous science help us to build this dedicated method? To help build a consumer methodology, we have valorized the synthesis of the available methods that was produced by a working group of ILSI (Leathwood, 2007). This work was followed up by a workshop in May 2006 in order to analyse the main points to be introduced to be in line with the European recommendation (ILSI report, 2007). The main recommendations to be followed in order to have a proper evaluation are as follows. Which consumer approach? In order to analyse how consumers examine the nutrition information, we can notice two main methods: • The qualitative approach: in the context of studies of consumer understanding of health claims, qualitative studies have a certain number of advantages. They enable us to go deeper into the different logic used by consumers to interpret the claims. The main weakness of qualitative methods lies in their representativeness. • The quantitative approach: quantitative methods have been used in previous surveys. It’s a good method to get some data on a representative consumer target. It is considered that if the consumer rightly mentions the benefit after having been exposed to the claim and does not make any wrong inferences (over-evaluation of the benefit), the claim is well understood. Through quantitative studies joined to experimental procedures, different forms of claims can be tested. As regards the methodology, quantitative methods also have some weaknesses; use of attitudinal indicators in most cases, based on consumer declarations and difficulty to build a questionnaire that expresses all the possible ways of consumer understanding. This last point is clearly a real barrier to using this method regularly: indeed the process of questionnaire creation needs to be strictly controlled.

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The ILSI works led to a consensus to prefer mixed qualitative and quantitative approaches with a two-step approach. The ILSI recommendation is to provide a “realistic method” in order to facilitate access for all companies. Which consumer? The European regulation states that health claims must be understandable for an “average consumer, who is reasonably well-informed, taking into account social, cultural and linguistic factors”. This notion of average consumer does not seem to be understood in the statistical sense. The most important objective seems to be able to protect all consumers from misunderstanding and to integrate cultural, social and linguistic factors. Which information needs to be validated? Article 3.a. reminds us to introduce to the consumer the communication elements. Consumer understanding will therefore not be assessed on the basis of health claims alone but also on the basis of the different means of expression of the health benefit (in particular, advertising and packaging). Which action standard? It’s important to define which level of misleading can be considered as acceptable in a regulatory view (notion of action standard). Is this threshold: 5%, 10 %, 20%, 40% . . . of verbatims? It’s delicate to define this threshold theoretically. It will be possible to define it based on real data with CUT test, with several situations that mix products with correct understanding (evaluation based on a consensual position of regulatory, administration and health authorities) and products with incorrect understanding. The action standard will be defined in order to discriminate product with correct understanding and product with incorrect understanding. Of course this level and the way to calculate it will be defined with endorsement of the regulatory authority. At the moment, based on internal data, we have observed for “correct understanding product” a level of misleading of 5% to 30% (depending on the benefit and the country).

10.5 Introduction of a new method: claim understanding test (CUT) After the ILSI workshop and based on the recommendations, and in conjunction with scientific experts and the IPSOS institute, Danone has developed a consumer understanding methodology in order to ensure that claims are not misleading. We will first introduce the process of the method and then present some applications.

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10.5.1 The CUT method and its implementation Our concern was to build a procedure that combines a scientifically rigorous view and a very structured process in order to respect the regulation issue. We have defined a method called CUT, Claim Understanding Test, focused on two main points: • the use of open questions; • and a precise codification defined on the link between science and consumer verbatim. We have done this methodological view, in order to avoid the construction of a quantitative questionnaire that appeared to us quite impossible to build and justify in a regulatory issue. We will detail the different steps of our methodology: Step Step Step Step Step

1: 2: 3: 4: 5:

Consumer recruitment Exposure to stimuli Questionnaire completion Data processing and coding Results analysis.

Step 1: Consumer recruitment The choice of the consumer target is particularly critical. In line with the European recommendations, we chose to consider the marketing target as being the average consumer. The sample therefore includes users and nonusers who are not reluctant to buy this product. We have defined working with 120 consumers, which seems a pertinent level to catch precise information but of course needs to be integrated in a global process of capitalization (per brand, per country). The consumers are recruited in the country we want to investigate. Basis for choosing this number of 120 consumers In all the tests already run (about 20), the individual factors have no impact on the way to understand the claim. In a recent survey (not yet published) we have set up a test with 720 consumers in order to test the impact of four variables: gender, age, education level and usage of the products, on the response to the CUT test. These four variables appear to have no statistical impact. This information leads us to confirm that we do not need to balance the recruitment on the factors and so 120 consumers are enough. Working with 120 consumers allows us to facilitate the organization of the test and to contribute to building a realistic method as recommended in the ILSI workshop. Our best practice is to integrate all the data in a database in order to be able to run some meta analysis in order to compare countries, to detect the evolution of the consumer understanding.

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Step 2: Exposure to stimuli Our purpose is to expose consumers to all the communication elements of the brand in order to have the most realistic configuration. At this stage, the objective is to expose the consumers to information communicated by the brand in the real life of the consumers (in the shop, in home and during consumption). Consumers are therefore exposed to the two main elements of communication of a brand before the next step (step 3): • The product packaging (by using the format which has sold the most in the country tested). They are given the possibility to see all the faces of the packaging. • The TV advertising. We choose the advertisement used most often in the country tested. Stimuli are sent to consumers on computer screens. No time constraint was imposed to consumers during the exposure to the packaging but there was only one presentation of the advertisement. We can observe that the response is based on both stimuli (pack and advertising). We use internet media to run the test. This media has been chosen for three reasons: • well controlled test (capacity to run all the test with the same design), • well adapted to open questions, • capacity to work with the average consumer who is a potential user of the product. Indeed, we observe an average level of internet penetration more than 50% in Europe. If the internet isn’t adapted to the country and the consumer target we want to investigate, it’s possible of course to use a classical hall test. Step 3: A protocol focused on open questions The protocol is focused on open questions. We chose “open questions” in order to allow maximum freedom to consumers and to avoid any bias. Information is collected through the internet. We have observed that the “internet media” enables us to obtain richer answers than with a traditional paper collection. With internet, we obtain longer citation with real sentences (not only single words) that enable us to accurately precis the consumer expression. We suppose that with internet, consumers decide when they want to fill out the survey. The question asked of the consumer is based on a classical question in the advertising evaluation domain (use by marketing institutes like IPSOS): After seeing this pack and commercial, if you had to tell a friend what xxx does, what would you say? Step 4: Verbatim coding and qualification: evaluation of the suitability of the consumer answers with the scientific dossier This step enables us to check and to qualify each consumer answer as regards its suitability with the real science. We consider that there is no

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The use of the code frame A filter to evaluate the data CONSUMER RESPONSE

SEMANTIC UNITS

CODEFRAME CHECK

“helps me to feel less bloated”

EVALUATION

SAFE

“Activia helps me to feel less bloated & prevents from being constipated” “prevents from being constipated”

RISKY

Fig. 10.3 The code frame, a filter of information.

Fig. 10.4 An extract of a claim compliance table.

misleading if the consumer verbatim is in line with the scientific facts. In order to do this check, we filter each verbatim through a very precise code: the claim compliance table (Fig. 10.3). It enables us to qualify any verbatim in a unequivocal way. The “claim compliance table” is the fundamental principle of this procedure (Fig. 10.4). We have therefore built a claim compliance table for all our brands. This claim compliance table translates the alignment of the scientific dossier and the consumer language in a specific country. We need to build a specific claim compliance table for each brand and for each country. How we build the claim compliance table? The claim compliance table is a way to express the alignment of consumer verbatim with science. The claim compliance table is built on the basis of all consumer verbatim observed. The consumer verbatim are expressed in a matrix [Notions × Actions].

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Each cell is coded with 3 possibilities: – S for safe: The consumer verbatim is in line with the scientific facts proven on the product e.g. “this fiber facilitates transit time”. – R for risky: The consumer verbatim is not in line with the scientific facts proven on the product and so creates a misleading impression e.g. “this probiobic dairy avoids me getting diarrhea”. – We add another codification other (vague): the expression is not misleading but is: A global and not precise enough description of a health concern: e.g. “Good for health” Not relevant, Integration of the description of a benefit other than health: e.g. “easy to eat”. A claim compliance table is used by the institute to code the consumer open answers. It needs to be precise and to integrate all possible synonyms in order to reduce any interpretation task. Credibility and validation The claim compliance table needs to be fully justified in order to be credible. It is mandatory! It is not a confidential document. We must be able to argue all the choices of codification with external experts, authority, regulatory bodies and we recommend validating this code frame with external science institutes. How to build it in a worldwide view For a brand, we need to have a complete alignment of all the claim compliance tables used in each country. We created two levels of code frame: • Master claim compliance table – A master is made by brand in English language in order to validate the main notions in comparison with scientific facts and the EFSA opinion. • Local claim compliance table – In each country the local code frame is first a translation of the master claim compliance table. In a second step we integrate all the specificities of language/culture. This task is done by local people aware of the knowledge of science and how local consumers express the benefit. Step 5: Results analysis The results are analysed according to two reading frameworks: • The rate of misinterpretations: We analyse the level of misunderstanding versus all the verbatim. In about 20 tests conducted over 2 years on several brands in several countries, this level is between 5% and 40%

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% Risky

65

26 Fig. 10.5

9

% Safe % Vague

Global view of the level of misunderstanding.

On Fig. 10.5 we can see an example of results with 26% of risky verbatim, 65% of safe verbatim and 9% of vague verbatim. • The allocation of misinterpretations depending on the benefit: We check if the misunderstandings are not focused on a specific benefit. For example, if we observed 20% of risky statements, we can have two situations. • Situation 1: all the risky statements concerned diarrhea. • Situation 2: the risky statements concerned diarrhea, but also bloating, transit. Situation 1 is probably linked to a real misunderstanding of the claim and we can guess that this result is due to a specific bias in our communication interpretation. Situation 2 is probably due to different errors of interpretation of the benefit. This situation is probably created by different factors (bias in our communication interpretation but also personal interpretation of the benefit). We consider that situation 1 is more risky than situation 2 and leads us to adapt our message. In both cases, it seems too early to define action standards. Our recommendation within the Danone Group is to build a database gathering numerous cases (multi-countries, multi-brands) and to exchange with external partners in order to draw some thresholds (Fig. 10.6). Practical points In a practical view, we have three stages: Stage 1)

Stage 2) Stage 3)

Preparation: elaboration and validation of the code frame; selection on the material (packaging, advertising); validation criteria of recruitment. Field survey with 120 consumers. Data process and recommendation

Timing The timing of stage 1 is driven by the elaboration and validation of the claim compliance table (minimum: 2 months). The timing of stages 2 and 3 is around one month. Budget 8–12 kæ for stages 2 and 3.

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split safe & risky answers regarding the dossier The main message is safe, well understood and spontaneously mentioned as the benefit of the product: “Feeling of fullness/for longer”. Composition is also safe. Diluted and low risky messages on appetite and duration. % Risky vs Dossier

% of answers

–3

APPETITE Appetite feelings

–3

Hunger feelings

% Safe vs Dossier

1

37 7

Feelings of fullness Desire to eat

–3 –1

DURATION Appetite & For longer

3

21 20

Feeling of fullness & For longer

–2 –1 –1 –1

Feeling fuller for longer than other yogurt/snack FOOD IN TAKE Food WEIGHT Weight management

–1 –1

Body shape & loss OTHER Other benefits Healthy eating

BENEFIT UNDERSTANDING

1 1 1 1 1

COMPOSITION

10 12 8

Protein composition Fiber composition Healthy snack

Fig. 10.6

26

30

% Risky % Vague % Safe 14

Split of the responses.

Some applications of CUT Application on products We use this test more and more within Danone in order to check our communication and now it has been introduced in our charter on Food, Nutrition & Health (DANONE, 2009). We have already used it 20 times on Actimel, Activia and Vitalinea in different countries in Europe (Germany, England, the Netherlands, Italy and Poland). The results are always useful and help us to validate our consumer communication and sometimes help us to detect some points of improvement. With multi-national surveys, we sometimes obtain for the same product, differences of understanding level linked to the local consumer capacity to understand and interpret the information. Therefore specific communication adapted to each country is constructed (specific packaging and advertising in line with local consumer knowledge). An example of a recent study. The material Product Shape in UK (from DANONE) (Fig. 10.7). Scientific CLAIM The scientific claim is “Shape Reduces Appetite Feeling” Consumer CLAIM It is scientifically proven to help you feel fuller for longer Present on the pack in the advertising

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Fig. 10.7 The pack.

10

88

2

Fig. 10.8 The level of understanding.

Material presented to the consumer The advertisement: with this message “Starting to feel empty . . . Shape is different to ordinary fat-free yogurts, with its exclusive hunger control formula containing fibre and protein and a delicious creamy texture It is scientifically proven to help you feel fuller for longer Try Shape . . . Hmm Danone! Test: 120 consumers, UK, June 2009 The results: On Fig. 10.8, we can see the level of responses. The benefit understanding is focused on the product effect “Feeling of fullness/for longer”. And in second position, the composition of the product: protein/fiber. The messages conveyed about the “Feeling of fullness and for longer” and the composition are coded as Safe. Very little spontaneous misunderstanding concerning: “appetite”/“duration”. Conclusion: Our communication (pack and advertising) can be considered well in line with science. This conclusion is not yet based on action standard and is still based on an interpretation view.

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Application on advertising A specific usage is also to introduce this CUT when we create an advertisement. We can detect which advertising is more in line with the scientific facts: We get 70% of statements in line with advertisement A versus 75% of verbatim in line without advertisement B (Actimel, in France, in August 2008). Other results We have compared the impact of the presence of the advertisement on the level of understanding. For the same product, the same country, we have compared two cells (CUT with and without advertising). We get 87% of statements in line with advertisement versus 63% of verbatim in line without advertisement (Actimel, in Germany, in November 2008). The effect appears important. The analysis of the raw data highlights that without advertising, consumers translate Actimel benefit in often inadequate technical terms. With advertising consumers simplify their interpretation and give correct terms. This point encourages us to always present the two elements (pack and advertising) even if we don’t understand how the average consumer understands and memorizes the advertising.

10.6 Future trends Based on the science presented in this chapter, we can proceed to conclude: • In today’s world, the need to evaluate the consumer claim understanding has become mandatory. This point is linked to the regulation but it is clearly integrated in the trusty relationship that brands need and want to have with consumers. In the future, this validation will become more and more integrated in the classic process of validation of functional foods in Europe but also worldwide. • The knowledge of the factors that modulate the claim understanding. The knowledge of the impact of the criteria is fundamental to reach an improvement of the quality of the claim understanding by detecting the ways to modulate the understanding. The first results show it is possible to define some first rules to create easy to understand claim. But the complexity and the permanent interaction product/consumer/benefit don’t allow us to predict consumer understanding. In the future, it is probably with precise surveys dedicated to measuring understanding of a product’s health benefit and with an interpretation

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taking into account the variability of the consumer that it will be possible to improve our claim communication. • The method about claim understanding. There is a real need to evaluate consumer claim understanding based on an exposure of communication elements. The method shared in this chapter, the CUT (Claim Understanding Test) allows this evaluation. This process is based on a simple consumer survey, based on open questions, but it is important to underline the core importance of the code frame that translates science into consumer words. In the future, the recommendation is to accumulate some data/facts in order to allow us to specify some threshold of the level of understanding. This method is quite new (2008). We have shared it with several partners: scientific, regulatory, companies, consumer association. The perception has always been positive. Consumers Based on CUT results, we plan to analyze the impact of individual factors. We want to analyze the impact of several psychological factors (knowledge on nutrition, motivation for functional foods, intuition . . .) on the CUT results. The hypothesis is that these criteria impact the motivation and the capacity to analyze the information and so can impact the CUT results. This knowledge allows us to better select the consumers and interpret the results in line with the spirit of the regulation to define the understanding of the average consumer. Information The strategy to use eye tracking presented by Clement (2008) on the claim understanding gives some pertinent way to analyze how a consumer analyzes the information of a label. By this method, we can analyze how information is read by consumers and in use introduce the information in the right position, with an adapted format. Another idea is to use the CUT as a way to challenge different communication. With this type of design we can challenge different communications and validate which verbalization of the claim is the most efficient. Others applications With the CUT test, we can challenge the claim understanding. This strategy of evaluation is based on: Open question + structured codification Can allow us to evaluate others paradigms in which we want to obtain a final decision: Is this notion well understood? For example, we think this design can be used to check these types of question:

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• Does the consumer misunderstand/understand what is a high intense sweetener? • Does the consumer misunderstand/understand a logo, an origin label? • Does the consumer misunderstand/understand the way to use a product (food or non food)? To recap, this test strategy can be used for all tests where we want to estimate consumer understanding.

10.7 Sources of further information and advice In current context, this claim understanding problem will probably be more and more explored in the future and more papers will probably be published in Food Quality & Preference, Appetite and Journal of Public Policy and Marketing. As a review of the situation, we can recommend: Grunert K, Wills J (2007), A review of European research of consumer response to nutrition information on food labels, Jnl of Public Health, 15, 385–399. ILSI report (2008), Functional foods from science to health and claim For a view of the trend on functional foods: Publication from New Nutrition Business http://www.new-nutrition.com/ logpageDev.asp or Datamonitor analysis For a view of on a new researches in order to integrate the interaction between consumer, type of information and product nature on consumer perception of health claim: Grunert K (2009), Perception of Health Claims Among Nordic Consumers, J Consum Policy 32, 269–287. AACLAIM project http://virtual.vtt.fi/virtual/acclaim/reports.htm Verbeke W, Scholderer J, Lahteenmaki L (2009), Consumer, appeal of nutrition and health claims in three existing product concepts, Appetite, 52(3), June, 684–692.

10.8 References ares g, gimenez a, gambaro a (2008) Influence of nutritional knowledge of perceived healthiness and willingness to try functional foods, Appetite, 51, 653–658. balasubramanian sk, cole ca (2002) Consumers’ search and use of nutrition information: the challenge and promise of the nutrition labelling and education act, Jnl of Marketing, 66(3), 112–127. basil d, basil m, desphande s (2005) The effect of specific health concerns on decision quality and search time; Marketing and public policy annual conference.

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burton s, biswas a, netemeyer rg (1994) Effects of alternative nutrition label formats and nutrition reference information on consumer perceptions, comprehension, and product evaluations, Jnl of Public Policy and Marketing, 13, 36–47. burton s, garetson ja, villequette am (1999) Implications of accurate usage of nutrition facts panel information for food products evaluations and purchase intentions, Jnl of the Academy of Marketing Science, 27, 470–480. clement j, & selsøe sørensen h (2008) Do average consumers read and understand food labels? Outline of a pilot study. Særnummer af Copenhagen Studies in Language (CSL 36) om brug af eye-tracking inden for humanistisk forskning: 145–155. cole ca, gaeth gj (1990) Cognitive and age related differences in the ability to use nutritional information in a complex environment, Jnl of Marketing Research, 27, 175–184. cole ca, balasubramanian sk (1993) Age differences in consumers’ search for information: public policy implications, Jnl of Consumer Research, 20, 157–169. cowburn g, stockley l (2004) Consumer understanding and use of nutrition labelling: a systematic review; Public health nutrition, 8, 21–28. danone (2009) Food, Nutrition & Health Charter, http://www.danone.com/images/ pdf/charte_alimentation_sante_en_2009.pdf (Accessed November 27th 2009). datamonitor (2008) Functional food, drinks and ingredients: consumer attitudes and trends, 1–97, Reference Code: DMCM4602, Publication Date: February 2008. diplock at, aggett pj, hornsra g, koletzko g, roberfroid m, salminem s, saris s (1999) Scientific concept of functional foods in Europe: Consensus document, British Journal of Nutrition, 81, S1–S19. fda (2003) Claims that Can Be Made for Conventional Foods and Dietary Supplements, http://www.fda.gov/Food/LabelingNutrition/LabelClaims/ucm111447.htm (Accessed November 27th 2009). garretson ja, burton s (2000) Effects of nutrition facts panel values, nutrition claims, and health claims on consumer attitudes, perceptions of disease related risks, and trust, Jnl of Public Policy and Marketing, 19, 213–227. govindasamy r, italia j (1999) Evaluating consumer use of food advertisements: the influence of socio-economic characteristics, Jnl of Nutritional Education, 4, 370–375. grunert k, wills j (2007) A review of European research of consumer response to nutrition information on food labels, Jnl of Public Health, 15, 385–399. grunert k, lähteenmäki l, boztug y, martinsdóttir e, ueland ø, åström a, and lampila p (2009) Journal of Consumer Policy, 32, 3 269–287. ilsi (2007) Consumer understanding of health claims, ILSI Europe Report Series, summary report of a workshop held in May 2006 organised by the ILSI Europe Consumer Science (http://www.ilsi.org/Europe/Publications/R2007Con_ Und.pdf). ilsi report (2008) Functional foods from science to health and claim. jacoby j , chestnut r, silberman w (1977) Consumer use and comprehension of nutrition information, Jnl of Consumer Research, 4, 119–128. leathwood p, richardson d, sater p, todd p, van trijp h (2007) Consumer understanding of nutrition and health claims: sources of evidence, British Journal of Nutrition, 98, 474–484. malhotra nk (1982) Information load and consumer decision making, Jnl of Consumer Research, 8(4), 419–430. mhurchu cn, gorton d (2007) Nutrition labels and claims in New Zealand and Australia: a review of use and understanding, Australian and New Zealand Journal of Public Health, 31, 2, 105–112. moorman c (1990) The effects of stimulus and consumer characteristics on the utilization of nutrition information, Jnl of Consumer Research, 17, 362–374.

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moorman c (1996) A quasi experiment to assess the consumer and informational determinants of nutrition information processing activities: the case of the nutrition labelling and education act, Jnl of Public Policy and Marketing, 15, 28–44. nayga r jr (2002) The impact of nutritional labels and health claims on consumers’ diets, Science des aliments, 22, 507–514. pham mt (1996) Cue representation and selection effects of arousal on persuasion, Jnl of Consumer Research, 22(4), 373–387. roe b (1999) The impact of health claims on consumer search and product evaluation outcomes: results from FDA experimental data, Jnl of Public Policy and Marketing, 18, 89–105. russo j, staelin r, nolan c, russel b, metcalf b (1986) Nutrition information in the supermarket, Jnl of Consumer Research, 13, 48–70. urala n, arvola a, lähteenmäki l (2003) Strength of health-related claims and their perceived advantage, International Journal of Food Science & Technology, 38, 7, 815–826. van trijp h, van der lans ia (2007) Consumer perception of nutrition and health claims, Appetite 48(3), 305–324. viswanathan m, hastak m (2002) The role of summary information in facilitating consumers’ comprehension of nutrition information, Jnl of Public Policy and Marketing, 21(2), 305–318. wansink b (2003) How do front and back package labels influence beliefs about health claims, Jnl of Consumer Affairs, 37, 305–313. who (2008) World Health Report 2008 Primary Health Care now more than ever, http://www.who.int/whr/2008/en/index.html (Accessed November 27th 2009). williams p (2005) Consumer understanding and use of health claims for foods, Nutrition Review, 63, 256–264.

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11 Pricing for new product development L. Lockshin and S. Mueller, University of South Australia, Australia

Abstract: Three different methods for setting the retail price for newly developed products are discussed in this chapter: heuristic, rule-of-thumb or competitive comparison method; hedonic price analysis; and discrete choice analysis. Each method is discussed with the help of an applied example to make the reader aware of the issues involved with it and strengths and weaknesses for their use are highlighted. Key words: hedonic price analysis, discrete choice experiments, rules of thumb, update of existing products vs. new to the world products.

11.1 Introduction The development process is only part of the battle when it comes to creating a successful new product. From the very beginning of the process, the company must be thinking about the buyer and what price the eventual user would be willing to pay for the new product. The eventual success of a new food or personal care product will rest squarely on the price the company receives in the marketplace, because all of the company’s development, overhead, and marketing costs must come from the difference between production costs and the price charged. This chapter explores how to determine the retail price for a new food or beverage product. We focus on the retail price and all of our methods will focus on the price the consumer pays for the product. Producers must work backwards from this retail price to calculate their price to their distributor or retailer. Before we can begin our discussion on pricing for new products, we first have to think about what makes a new product. There is an obvious difference between developing a new flavour for an ice cream, developing a new type of packaging for a juice or wine, or developing a new to the world product that has the potential to start a new category. No one pricing strategy will work for these two quite different situations. So first we will discuss

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the types of new product development and the problems they create for setting the price. 11.1.1 Changes and updates to existing products and categories Most new products are really changes to an existing product, such as adding a new flavour or ingredient or changing the packaging. In many cases these changes are based on what competitors have already done in the category. For example, one fruit juice producer comes out with a new flavour based on pomegranate juice and a major competitor decides to make their own version, perhaps with a slightly different blend of other juices. This is a new product, but to the consumer this is not much different than the others in the same category, and this reduces the flexibility a product developer has with regard to the price they can set. It is different if you are the first entrant in the market with a pomegranate-based juice. This will be discussed in ‘new to the world products’. The same thinking holds true with packaging changes. Developing a new colour scheme or updating your brand is important for remaining relevant to the buyers and to maintain shelf attractiveness, but these changes hardly demand (or result in) changes to the retail price. If a competitor has entered the market with a new type of packaging for the category, such as fruit juice in multi-serve pouch, rather than a Tetra Pak® box, this would constitute an innovation or a ‘new to the world’ product and perhaps the pricing would be a research issue. However, if the packaging is already in the market and a major competitor launches his or her own version of this packaging, there is not much call to do research on the pricing of this addition to an existing product category. There are some guidelines for pricing these new additions to one company’s product line and these will be covered in the section ‘heuristics or rules of thumb’ for pricing. A brand extension is when an existing brand name is applied to a product new to an existing manufacturer but in a different category. For example, a producer of fruit juices may decide to develop a fruit and nut health food bar for the healthy snacks category using their well-known brand name. The price they set in this new category will still depend on the prices of existing competing fruit and nut bars and to some degree the value their brand name brings to the potential buyers. The most usual approach is to use heuristics or rules of thumb to set the price for this new brand extension. In a few cases there would be a need to do more detailed research on the possible price depending on the range of existing prices and brands in the category. This will be discussed in the sections on hedonic price analysis and discrete choice experiments. 11.1.2 New to the world products The final type of new product is where either the product or some aspect of it is new to the world. Much of the research in the area of pricing new

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to the world products has been done in high technology categories, but there are also examples of these issues in food and personal care products. The fairly recent innovations in functional foods, where ingredients mainly for health purposes are added to normal foods, produce products with new to the world benefits, such as cholesterol lowering margarine or yogurts with special bacteria for digestion. Even when the benefits are not new to the world, new ingredients, new combinations of ingredients, or new manufacturing methods can be difficult to price using heuristic or hedonic price analysis, because there is nothing in the category to compare to. Here, discrete choice analysis provides a better and more accurate means to decide on the pricing strategy for new to the world products.

11.1.3 Organization of the chapter Three different methods of setting the retail price are discussed in this chapter: heuristic, rule-of-thumb or competitive comparison method; hedonic price analysis; and discrete choice analysis. Each of these has its applications in specific situations and we will consider each of these in turn. Our approach will be to present a specific example using real information and to work through the method step by step. The overall objective of this chapter is to make the reader aware of the issues involved with each method, not to become an overnight expert in what are often complicated analyses. We start with a brief section on heuristics, which should be familiar to most product developers, and then progress to hedonic price analysis and discrete choice analysis.

11.2 Rules of thumb for pricing new flavours, styles, and brand extensions 11.2.1 Additions to existing product lines Most new product developers are familiar with heuristics or ‘rules’ for pricing when a brand is updated with new packaging, new flavours, or extended into a new category. The major determinants for price are the category and the brand’s position relative to its competitors. The category provides the context for any pricing strategy. The first factor is the price range within the category for items of similar size. In some categories, like wine in 750 ml bottles, the price range between the cheapest and the most expensive items is large. Even in a supermarket there is usually a tenfold or greater difference between the cheapest wine ($3) and the most expensive ($30 or more). When a category has a wide price range, it indicates that all the items are not substitutes. There is the potential for a range of factors that must be taken into account when setting the price. Someone looking to buy a wine for a special occasion or for a gift will not see wines above $20 as substitutable for those under $10. The same holds true of someone

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buying perfume or cosmetics. In a sense this indicates what we call a partition or two separate categories. The same buyers often buy in both partitions, but not for the same occasion or purpose. In wine, some factors like the region or country of origin, or consistently high scores from wine writers provide an indication of the suitable price range. The same brand or company might have a range of wines in each of different price points in the wine category. The price in each different price range, even for a new flavour or grape variety would be the same price as their existing brand. For example, Penfolds brand wine has a Shiraz, a Chardonnay and a Cabernet in the $10 price point. If they add a new variety, Sauvignon Blanc, it would have to be about the same price. Penfolds might have a similar set of wines in their reserve range at $20 each and any new variety would be priced at the same level. In other categories brands may position themselves at budget, mainstream, and premium price levels. Within whatever level the brand is positioned, it is difficult to raise your price by more than ten per cent. Most categories have a much smaller price range, for example only ten per cent difference between the most and least expensive brands. This means that line extensions – adding a colour or flavour to an existing product – are tactics typically used to remain viable and to defend against competitors in a category; they do not require complex research to decide on the price. Pricing is determined based on the brand’s existing position in comparison to its competitor’s offer.

11.2.2 Brand extensions Brand extensions are when a company extends an existing brand into a new category for them. First we will discuss entering categories where there is typically a less than ten per cent price range among existing brands. Recently, Coca-Cola Australia extended an Australian fruit juice brand into the packed ice coffee market. This category is dominated by one of the large milk producers in Australia, which sells its brand mainly in convenience stores and take-away restaurants in cardboard milk cartons. The fruit juice brand recently purchased by Coca Cola Australia is a premium brand, which has a small price premium in the market compared to most of its competitors. Its brand extension into the iced coffee category was thus launched with about a nine per cent price premium to the market leader in order to maintain the positioning of the juice brand in this different category. Generally, prices are limited to within ten per cent of existing brands even in a category that is new for the producer. If the category has a much wider price range, like in the wine example above, in cosmetics, or in other specialty areas such as the growing gourmet or healthy/functional food sections of some categories, then the producer has to determine which price tier to enter. Again, the rule of thumb is to compete where you can be at least equal to and preferably better

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performing than your competitors. For example, an existing brand of wine competing in the under $15 price tier, might decide to develop a new wine to compete in the over $25 segment. Using a heuristic approach, the producers would taste existing wines in the upper price bracket, look at their packaging, and at the types of grape varieties and regions represented. They would then decide, given their own assets (vineyards, production system) and skills, how to develop a wine to compete successfully in this price bracket. However, their decisions are now based on their perceptions of what is important in this new price tier without any empirical evidence as to which of the features or attributes are those that consumers actually pay for as the price rises. There are ways to determine the value of individual or combinations of product attributes to the consumer. This brings us to the measurement of attribute importance and its value, i.e. price, to potential buyers.

11.3 Pricing for new to the world products or features We note at the beginning of this section that the two research-based methods we discuss, hedonic pricing and discrete choice analysis, can be used for existing products and existing features. The section on heuristics above should make it clear that in most instances the extensive efforts required to research the price of adding new attributes or features to an existing product in an existing category with competitors already marketing the feature are not worth the cost. However, when extending a brand into a higher price tier of a complex category, it bears consideration to assess the value of different levels of different attributes necessary to compete successfully, if these are not known. The two methods discussed can provide useful and cost-effective advice on these issues. Before delving into the two different methods, we first should understand the key terms we will employ. Products can be decomposed into attributes or features. We will use the terms interchangeably. Food and beverage products typically have intrinsic attributes – those that relate to the actual food or drink; and extrinsic attributes – those related to the packaging, brand, price and other information available to the buyer before he or she consumes the product. For example, an orange juice product may have intrinsic attributes such as sweetness, acidity, colour, intensity of fruit flavour; and external attributes, such as the container, its volume, the label, brand name, price, and origin. Each attribute can then have different levels, which can be categorical, like different colours on the label, or continuous, like levels of sweetness or acidity. Producers manipulate the attributes (presence or absence) and the levels in order to create a specific product. We and many other researchers believe that consumers buy ‘bundles’ of attributes, and by understanding the value of each attribute and the requisite levels, we can not only develop products that meet consumers’ needs,

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but price them so that consumers are willing to buy them. The difficult issue is what to include in the bundle and how to price the overall product. Much previous research has asked consumers to rate the importance of various attributes on typical Likert type scales: ‘How important is the brand name when buying orange juice?’ (1 = not important; 7 = very important). There are several problems with rating scales and these are discussed in detail elsewhere (Steenkamp and Baumgartner, 1998). The key point is that consumers do not evaluate attributes one at a time when buying a product, but instead they purchase the whole bundle at once for one price. The importance of brand name might vary with the other attributes and certainly with price. Methods that measure the importance of attributes in combination with others are more realistic and predictive of actual choice. Another problem with rating scales, and with ranking as well, is that the numbers are artificial. We do not have a 1–7 scale in our heads, so that what might be a 3 for one person is a 2 for another. Also, different people and even different cultures use only parts of the scale, never giving a 7 for some, or never a 1 for others, so it is difficult to accurately compare results between different people. There are two main difficulties with ranking. One is the limited number of items that can be ranked, and second, ranking does not provide the data necessary to compute the expected or potential price. The two methods we discuss have various strengths and weaknesses, which will be provided in the relevant section. However, both methods require one very important factor in order for them to produce useful pricing outcomes. The researcher or company must use the relevant and important attributes and the correct levels of those attributes. Even in the orange juice example above, there are ten attributes, each with multiple levels that could be tested. The more attributes used, the larger the sample size will have to be and the costlier the research. Thus, it is important to be able to identify the relevant attributes consumers use in their actual choices and to be able to translate these into measurable variables in the research. The two methods we discuss create measurable results differently, but neither method will provide actionable results if a key attribute is left out, misspecified, or measured incorrectly.

11.4 Hedonic price analysis (HPA) Hedonic price analysis is a technique based on multiple regression, where price is the dependent variable and we predict it based on a set of measured product attributes obtained in some way from the market. Some of the earliest uses were in agriculture, where auction prices for a product like strawberries were predicted based on the size and colour of the berries. The relative contribution of the size of the berry and the colour to the price could be assessed and then used by growers to decide which varieties to grow and which field practices would return the highest amount. Because

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hedonic price analysis requires data on existing prices for existing products and their attributes, is it is most useful for producers developing new brands for existing categories, especially when those categories have a wide range of prices. It is also useful for measuring the imputed value of specific attributes in order to ascertain whether these are worth adding to existing products. This section will continue as follows: first an example of hedonic price analysis will be presented in order to demonstrate the type of data, attributes and outputs used. A very brief interpretation of the results will be provided. The section will continue with a discussion of the pros and cons of hedonic price analysis.

11.4.1 Hedonic prices for wine in Australia: an example Background The following example is taken from Ling and Lockshin (2003). Wine is a product category with a wide range of prices and a number of attributes. Some of the relevant extrinsic attributes are: price, brand name or reputation, region of origin, grape variety, vintage, and producer size. While the intrinsic attributes are not easy to measure directly, quality judgements are often made by wine experts and writers. Producers involved at one price level are often interested in developing a wine for another typically higher price level, but it is not clear what are the important attributes and levels necessary for consumers to accept this new wine. The data The data for this model was obtained from a wine guide published annually in Australia by James Halliday. The wine guide lists hundreds of wineries and over 5000 wines. Each winery (brand) has a reputation indicator of 3–5 stars. Each wine in the book has a quality point score with the maximum of 100 points. There is a listed retail price of each wine, the grape variety, the region where the grapes are grown, the vintage, and the size of the producer. Many wines are blended from multiple grape varieties and multiple regions in Australia, but we wanted to measure the price effect separately for four major grapes grown in specific regions, so we only included wines of the four main grape varieties (Cabernet Sauvignon, Shiraz, Chardonnay and Riesling) where the grapes for each wine were grown in one region. We used the retail price as the dependent variable and developed hedonic equations for each of the four varieties using quality, region, winery size, and vintage as the predicting variables. We then developed four equations, one for each grape variety to test the effects of the predictor variables on the retail price. Results We provide only the results for the white wines (Table 11.1) as illustrative of the method and the outputs. Each of our attributes appears in the table.

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Table 11.1

Hedonic price equation for Australian wines by variety of wine Chardonnay

Riesling

Price (dependent variable) Coeff

t-value

Coeff

t-value

0.109**

(38.35)

0.093**

(36.29)

Sizel < 100 tonnes (very small) 100 ~ 499 tonnes (small) 500 ~ 2499 tonnes (medium) 2500 ~ 9999 tonnes (large) > 10 000 tonnes (very large)

0.259** 0.266** 0.217** 0.053** Base

(7.81) (8.47) (7.21) (1.55)

0.055** 0.049** 0.080** −0.011 Base

(2.80) (2.44) (4.23) (−0.37)

Agem (vintage year) 1 ~ 2 years (after 1997) 3 ~ 4 years (1996 and 1997) 5 ~ 6 years (1994 and 1995) 7 ~ 8 years (1992 and 1993) Over 8 years (before 1992)

0.182 0.217** 0.215** 0.263** Base

(2.16) (2.62) (2.59) (3.09)

0.019 0.010 0.019 0.040 Base

(0.50) (0.25) (0.50) (0.79)

Originnb Barossa Valley (W) Clare Valley (W) Great Southern (W) Hunter Valley (W) Margaret River (W) McLaren Vale (W) Adelaide Hills (C) Coonawarra (C) Mornington Penins (C) Tasmania (C) Yarra Valley (C)

−0.040 −0.051** 0.023 0.030 Base −0.015 −0.055 −0.108 0.021 −0.027 −0.008

(−0.99) (−0.93) (0.63) (0.94)

(1.16)

(−0.43) (−1.58) (−2.42) (0.61) (−0.74) (−0.24)

0.021 Base −0.000 0.074 −0.109* 0.019 0.069** −0.106** 0.032 0.139** −0.127**

(−0.01) (1.08) (−2.21) (0.71) (3.06) (−3.41) (0.79) (7.50) (−3.32)

Constant

−6.855**

(−24.62)

−5.511**

(−23.97)

QualityPRED a

2

R

0.777

0.865

Notes: * Significantly different from zero at the 5% level and ** at the 1% level. a Measured by the tonnes of wine grapes crushed of wine producers. b W refers to warm climate region and C to cool climate region.

Continuous attributes, those that have a numerical scale like quality, produce one coefficient. This coefficient provides a percentage increase or decrease for each dollar in price increase or decrease. The other attributes are categorical; they have discrete levels. Here, each level is compared to a base level of the attribute. For winery size the base is the largest size, and all other attributes have a coefficient that shows the price increase or decrease compared to the base. The same is true for vintage year (wine age) and for region of origin. We tested other attributes, but the table only shows those that had a significant effect on price.

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Since we used the recommended retail prices without modification, we can interpret the coefficients and the effect on the price for each level of each attribute. The constant can be interpreted as the base price, and the coefficients for each variable as the contribution to price above or below the baseline. The quality variable shows that for every increase of one quality rating point, the price in the market was 10.9% higher for Chardonnay and 9.3% higher for Riesling. A producer deciding to launch a new Chardonnay could compare it to others in the potential price category using wine experts and adjust the price based on the comparable quality. We can also see there is a positive price effect if the wine comes from a smaller winery, especially for Chardonnay. The baseline is the largest sized wineries, and anything smaller results in a substantial price premium of over 20% for Chardonnay and 5–8% for Riesling. Thus, a smaller producer would obtain a price premium in the market, but a bigger brand would not be able to charge as much. This might indicate to a large winery to launch a new brand in an existing category, rather than use the same brand name as one in a lower price tier. We also see a difference in the price based on the region of origin. For each grape variety, we used the region with the highest reputation based on Halliday’s book. All other regions were either equivalent or reduced the price accepted in the market. For example, a Chardonnay from Clare Valley would be priced 5.1% less than one from Margaret River, given equivalent quality; or a Riesling from Coonawarra would be priced 10.6% lower for equivalent quality than one from the baseline region, Clare Valley. So how do we put this together? We first must understand that the hedonic pricing model, as developed through linear regression is additive; each effect is added to the next in order to get the overall price increase/ decrease. Let’s take, for example, a new small producer, who wishes to launch a Chardonnay into the market. If the standard price in the price tier is $15, and the average quality level is 85 points, than launching a 90 point wine, would allow a price increase of 10.9% * 5 points or 55% ($8.25). If the wine comes from a small winery of 250 tonnes, then the price will increase by 26.6% or $3.99. If it is a new vintage, then there is no price premium or discount for the wine. Finally, if the Chardonnay was from Adelaide Hills, there is also no price premium or discount. So our new Chardonnay should be able to enter the market at a price of $15 + $8.25 + $3.99 or about $27.24. Of course the price might really end up at $26.99 or some other relevant price near our calculated one.

11.4.2 Pros and cons of hedonic price analysis Hedonic price analysis is a relatively straightforward technique to use to model the impact of individual attributes on the price. It can be done with any statistical program that has a regression module. Like any statistical

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technique it is only as good as the data used in the model. It is important to have real sales data for a period of time that allows such things as stockouts, discounts and price changes to average out. We would recommend at least a quarter (three months) of data and better yet a year. The data also has to have the important attributes that consumers use in deciding between different products. This means that before analysis, the researchers should conduct focus groups, interviews, or have strong secondary research that identifies the important product features necessary to model what impacts on price. One of the issues that troubles hedonic price analysis is the fact that the prices measured represent supply and demand effects. Auction prices, for example, are a pure measure of demand, except that the greater the supply, the lower the price. This law of demand holds for many food and personal care products, except icon brands where a high price can signal exclusivity. On the other hand, it is reasonable to assume that companies supply what the market will bear and what the stores will stock, so excess supply does not result in decreasing prices in the long term. As noted earlier, each product category will often have price tiers, where brands positioned differently compete. It is important to decide which brands and prices to include in the hedonic analysis. If you narrow your sample too much, there will not be enough price variation to calculate the contribution of each feature. If you include brands that you do not compete with at all because their price is so much higher or lower, then the results will not represent market reality. In most categories where the prices do not differ by more than 10–15%, it is fine to use all the products. Where there are strong partitions, with some products five to ten times higher in price, it is best to concentrate on those that compete more directly. As with any statistical technique there are technical issues that must be understood in both the modelling and the interpretation. These can be quite complex and we recommend using trained statisticians familiar with regression and modelling to conduct the analysis. One of the key issues is that many of the features or attributes are often highly correlated, which make the coefficients or measures of the price impact unreliable. Another issue is that the relationship between price and the features is not always linear, so the analyst must transform the prices in various ways to find the best fit. Then the coefficients must be interpreted with care. Persons familiar and experienced with multiple regression analysis can overcome most of these issues. The biggest issue with hedonic price analysis is that it can only measure the impact on price of features available in the market. If there are no products with the new feature already for sale, the impact of that new additive, health claim, or functional attribute cannot be measured. The next section of this chapter presents a different method for measuring the impact on price of new to the world products or features – discrete choice experiments.

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11.5 Basic discrete choice experiments 11.5.1 What is it, and how does it ‘work’? Discrete choice experiments (DCEs) let consumers make repeated choices from pre-specified alternatives (product bundles) and thereby mimic their real-world decision behaviour. The choice options available to respondents differ in their attributes and levels as selected by the researcher. The combinations of these attributes and levels are controlled by an experimental design. In their choices consumers are forced to make trade-offs between features of competing products because they can choose only one bundle at a specific price. From respondents’ repeated choices from different sets of bundles at different prices researchers can find out which attributes and levels are driving consumers. The preference for a product is thereby separated into part worth utilities (also called part worth values) for each attribute level, which can be quantified in monetary value. The higher such a part worth utility for an attribute level, the more likely consumers will choose a product. Choice experiments have some similarities to conjoint studies, which are already widely applied in new product development (Moskowitz and Silcher 2006, van Kleef et al., 2005). Conjoint studies also combine attribute levels into stimuli, but are then usually rated or ranked by respondents. Choice experiments do not require consumers to rate or rank competing products but elicit consumers’ discrete choices, thereby replicating their realistic decision-making process. DCEs are based on random utility theory and are proven to be a highly valid method for prediction of real life consumer behaviour and product market shares (Louviere et al., 2000). Choice modelling is an appropriate method for a wide range of marketing and managerial decisions to investigate what drives consumer choice and covers many marketing phenomena such as: • Product design: How should I design or optimize my new or existing product /service? • Pricing: How should I price my product in a competitive market place to maximize profitability? • Line optimization: Which products of my product line cannibalize or complement each other? • Brand positioning: What do customers want from my product that differs from what they want from my competitors’ products? • Brand equity quantification: What monetary value does my brand have? Despite the fact that our focus here is pricing for new product development, we want to mention that a DCE can be set up to answer more than one of these questions at the same time. For instance, in addition to deriving product price sensitivities to set the optimal market price, the same DCE can be used for brand positioning to find those attributes that customers specifically value for products of a certain brand. Because of the large array

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1

• Research design • Questions to be answered by DCE

2

• Experimental design • Determine attributes and levels and derive statistical design

3

• Design the choice task • Stimulus and choice set generation

4

• Data collection • Sample size, survey mode

5

• Data analysis • Data check, cleaning and coding; model estimation

6

• Interpretation of results • Simulation of preference or market shares

Fig. 11.1 Typical process of a discrete choice research project.

of problems that can be approached with a DCE, it is important to tailor it to the specific research questions it needs to address. There is no blueprint DCE existent that is able to solve all problems; rather every experiment has to be designed specifically for every project. An overview of the typical process of a choice experiment can be found in Fig. 11.1. The six-step process starts with the specification of the research design, which is based on the particularities of the product and market to be analysed, and the research questions to be answered. In the second step the product attributes and levels to be varied in the experiment are determined jointly with the experimental design that combines them into choice stimuli. During the data collection respondents choose from different sets of alternatives. Sampling can be accomplished via online surveys, paper and pencil questionnaires or with real products in central location tests. The data set will have to be controlled and cleaned up before it can be analysed and interpreted. Finally, choice simulators can be built based on the results of the estimated choice models to allow an intuitive interpretation by visualizing how changes in attribute levels cause changes in preference or market shares. In the next section we will walk through an example choice experiment for prawns, where some new to the market attributes are combined with

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others as a way to illustrate these six steps in more detail. Choice experiments require advanced knowledge in experimental design and discrete multivariate analysis methods and will very likely be sourced from an outside service provider if a company has no expert specialized in this field. Accordingly, our focus in the next section is to create a general understanding of choice modelling and to give advice on options to be specified between a marketing department and an external service provider. This introductory example will not enable the reader to undertake a DCE on his or her own.

11.5.2 Example DCE for new to the market attributes We use the example of a choice experiment that examines if prawns with environmental and health claims can achieve a price premium in the Australian market as a means to understand the process of developing a discrete choice experiment in the food category (Mueller et al., 2009b). At the time of the study in early 2008, prawns with specific production, health or environmental claims were not yet sold in the Australian market. Because of the lack of available retail data, the first two methods described above are not applicable. Hence, a choice experiment was selected as the appropriate research method for these new to the market attributes. As we will later see, this method not only allows us to quantify the relative impact of new attributes on choice, but is also able to find consumer segments that differ in their preferred product attributes to potentially target them with different product solutions. Also sociodemographic attributes can be incorporated into the segmentation as active or passive variables to better locate and target the different preference segments. While their passive inclusion only looks for potential ex-post differences in segments solely determined by choice differences (see also page 326), their active integration in the model forces segments to also differ in these sociodemographic characteristics of high importance to the product developers. Being able to model differences in consumer preferences (consumer heterogeneity) is a strong advantage of DCE over a hedonic price analysis which only finds price premiums or discounts on the aggregated level, representing the average consumer. Research design The first phase of a choice experiment sets the frame for all following steps and has to make sure that the experiment is tailored to the product and the research questions. Accordingly, the saying, ‘measure twice but cut once’ is recommended. For conceptualizing the choice process it is important to understand the specific characteristics of the product, the market it competes in, and the occasion and location where consumers purchase it, in order to integrate this knowledge into the experiment. In our case we wanted to know if consumers would pay a price premium for health and environmental claims of prawns and if they discriminated

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between wild caught and farmed prawns. Most prawns in Australia sold in supermarkets and seafood shops are sold in bulk by the kilogram, and brands are unimportant to the consumer in this market. Thus, we were not interested in looking at specific prawn brands but only examined generic prawns. For other new product development projects brands are likely to play an important role and it needs to be decided if the experiment should include alternative specific effects (different brands) or interactions between the brand and other attributes. The following examples will clarify these two special research designs. If the aim is to optimize brand positioning and to measure if an attribute is valued differently by consumers for different brands, then the research design needs to include an interaction effect. The basic discrete choice model using orthogonal main effects plans is additive and estimates a part worth value for each attribute level independent of other attributes. If a NPD team of a personal care product with different brands wants to analyse the effect of a claim such as ‘includes essence of aloe vera’ then the basic model results in one part worth value for this claim, which is independent of the brand it is combined with. If they want to analyse if the brand is more likely to gain from the claim than the competitor brands, then the research design has to include an interaction between the brand and the claim. There are also products where certain attributes are restricted or should not be combined. One example is a beer producer who wants to test if they should include a light beer into the product line. Light beers always have lower alcohol levels than standard beers and it would be implausible for a consumer to choose between a light beer with a high alcohol level and a standard beer with a low alcohol level, which would occur if beer type and alcohol level were be combined without restriction. This can be solved with a nested or alternative specific research design where the alcohol level in the choice stimuli depends on the beer type. A similar example is a DCE for cars with different brands and prices where an unlimited combination would result in a Kia sold for $80 000 and a Mercedes Benz offered for $15 000. Here the price levels should be tied to different market segments. Alternatively, the experiment could also be limited to products within a price tier, which are competing directly against each other, modelling the price segments separately. We need to consider in the research design if there are attributes consumers must have or a product should not have. These are attributes consumers would not be willing to trade off against better or worse levels of other attributes, thereby violating an assumption of compensatory choice models. Compensatory models assume that an undesirable attribute level can be compensated with a very desirable level of another attribute, such as a low price. As an example of non-compensatory decision rules, diet beverage consumers would not be willing to trade off ‘tastes good’ for lower calories. It is a must-have in this category, and other superior product features cannot compensate its absence. Even though compensatory models

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have been found to be very robust approximations of non-compensatory decisions, attributes that are a ‘must-have’ or a ‘should-not-have’ should either not be varied in the DCE or special estimation procedures need to be applied to capture non-compensatory decision behaviour. It should also be considered if consumers are forced to make a choice from the alternatives or if they can opt-out by choosing no option to capture the case that all alternatives are unappealing and none was chosen in practice (Ryan and Skatun, 2004). To measure the uptake of a new product compared to an existing alternative, a status quo option could be included in each choice set (Louviere et al., 2000). To sum up, in this first phase the product developers and the research provider need to discuss as many insights as are already known about the product from previous research. The experiment outcomes will not reflect real market behaviour if important choice drivers are omitted. For the respondent the choice task should be set up as close as possible to the real purchase situation to elicit his or her true choice behaviour. As much as possible needs to be known about the purchase occasion, purchase location and mindset of the consumer to tailor a DCE to the specific research question. For instance, choice drivers for seafood in a restaurant will be very different from those in a retail setting; both cannot be captured in the same choice experiment. The number and level of detail of the questions a potential research provider asks a NPD team can be a good indicator about their quality and ability to tailor the experiment to the actual problem. Attributes and levels – experimental design As a rule of thumb all attributes that are important to the consumer should be included in the experiment. If an important attribute, such as brand for perfumes, is omitted in the experiment, then the impact of the other attributes will be overestimated and market predictions will be biased. The price premium estimated for desirable attributes (those with a positive part worth) and the price discounts resulting from the model for undesirable characteristics (with a negative part worth utility) will both be too high and mislead product developers. Trying to include all attributes can result in the problem of varying too many attributes in the DCE. When deciding how many and which attributes to include, new product developers should keep in mind that they have a much more detailed understanding of the product category than consumers, who have much less product involvement. Including too many characteristics in the experiment increases the danger of overwhelming and overloading respondents and forces them to use heuristics to get through the task (DeShazo and Fermo, 2002). In detailed and complex categories where consumers spend more effort on the purchase decision such as cars, computers and TVs, consumers can usually understand about a dozen attributes (Severin, 2000). However, consumers only consider a limited number of

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Table 11.2

List of attributes and levels

Attribute

Levels Level 1

1 2 3

Price Region of origin Health claim

4 4 4

4

Environmental claim Production Storage

4

5 6

2 2

$12.50/kg Australia Rich in Omega 3 Sustainable fishery Wild caught Fresh

Level 2

Level 3

Level 4

$19.00/kg Spencer Gulf Low in fat

$25.50/kg Thailand –

$32.00/kg China –

–

–

–

Farmed Frozen

attributes for most food and personal care products because their purchases are often habitual or impulsive. For the prawn study we selected six attributes with two or four levels each, covering those attributes known to be important choice drivers (price, region and storage) and those of special interest for our study (production method, health and environmental claims); see Table 11.2 for the list of attributes and levels. We decided to not include other potential prawn attributes such as their colour and size, if they were sold cooked, uncooked, peeled or unpeeled, which would have increased the experimental design and subsequent cost. We focused on unpeeled raw prawns and assumed that if claims were found to be important for these than the same would be true for cooked or peeled prawns. The price range should cover the least and most expensive offers in the market, because valid predictions are only possible within the selected price range but not outside of it. We also recommend modelling price with more than two price levels to be able to account for non-linear price effects. We ensured that price levels were evenly spaced to allow a better interpretation of the effects and the estimation of a linear price function, if appropriate (Louviere et al., 2000). Our design considers the limited availability of prawns sold with any extra claim by only allowing every second and fourth choice option to have a health or environmental claim. Generally the attribute levels should be formulated in a language familiar to respondents (e.g., from qualitative interviews), technical jargon needs to be translated and ambiguity can be decreased by using graphics. The larger the number of attributes and levels, the larger the experimental design that combines them into stimuli. A respondent has to complete a larger number of choice tasks to be able to elicit all required information. Most researchers limit the number of choice sets to between 12 and 18 tasks per respondent to avoid respondent fatigue, random, and heuristic responses.1 1

The appropriate number of choice sets is context specific and might require less than 12 choice sets in very complex and large choice sets. On the other hand, some researchers have already used up to 32 choice sets per respondent (Louviere and Meyer, 2007).

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Large designs with many attributes and levels have to be split onto several respondents (blocked into versions), with each person only completing a subset of all choices (Lancsar and Louviere, 2008). This increases the sample size required for the experiment. The attributes and levels selected for our prawn study could be accommodated in a main effects experimental design of 16 choice sets with four options each (Street and Burgess, 2007), which can be easily completed by an individual respondent. Previously DCEs have almost exclusively been applied to non-sensory attributes for which they proved to be highly predictive. The first attempts to integrate actual product tastings into choice models have so far resulted in mixed results (Enneking et al., 2007; Hein et al., 2008; Mueller et al., 2009a). Choice experiments were found not to be suitable for food products with low visual discrimination, high sensory interactions between stimuli, and fatiguing tasting effects, such as repeated sampling of alcoholic beverages. The experimental design and hence the number of attributes and levels in an experiment with sensory stimuli has to be very limited so as to not overstrain respondents who are required to experience the sensory properties of every stimulus in each choice set. Some suitable sensory attributes, such as the appearance of food or broad taste descriptors (e.g., dry or sweet for wine) can be incorporated into DCEs as verbal and pictorial presentations (for an example see Reisfelt et al., 2009). Design the choice task Once attributes and levels have been combined into choice stimuli and choice sets it has to be decided how these will be presented to respondents. Historically choice alternatives were predominantly presented in tables, listing the levels of attributes for each choice option. Recent research found that the influence of visual attributes such as product packaging can be validly measured in choice experiments if they are presented in visual form, such as by photographs, graphics or product prototypes (Mueller et al., 2010). It was also found that choice instructions and the presentation of the choice task should set consumers in a realistic purchase situation to closely capture their real decision process. This requires the researcher to specify a purchase situation and to simulate a realistic purchase environment. The choice context, composition of the choice sets and framing of choice questions and instructions must be incentive compatible to encourage respondents to reveal true preferences (Carson et al., 2000). For our prawn experiment we chose a graphical attribute presentation (see Fig. 11.2), mimicking the product presentation found in many retail stores as closely as possible (Lancsar and Louviere, 2008). We instructed respondents to imagine that they were purchasing prawns for a meal with family, friends or relatives to set them in a realistic purchase situation and to reduce the variance between respondents caused by different imagined purchase occasions, such as everyday use versus a festive occasion or a gift.

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Which prawns would you be most likely to purchase?

A

B

C

Would you realistically buy the prawns you chose?

Fig. 11.2

D Yes

No

Example of choice task for prawn study.

Data collection Generally choice experiments can be flexibly adjusted to any data collection method, such as brief consumer intercept polls, telephone surveys, paper and pencil surveys or online surveys (Dillman and Bowker, 2001). As consumer intercepts in shopping malls are limited to only a few choice sets per respondent, and telephone interviews cannot use graphical stimuli, paper and pencil and online surveys are the most frequent data collection methods used for DCEs. In the last few years there has been an increasing trend towards online surveys with the wide availability of broadband internet connections in the general population making the use of a large number of graphical stimuli possible. The emergence of high quality online panel providers allows a fast and reasonably priced access to representative population samples. Research comparing traditional paper and pencil conjoint surveys with online data collection has found online sampling to be superior regarding internal consistency and predictive (face) validity (Sethuraman et al., 2005). For our prawn study we decided on an online survey using a large Australian online panel provider that actively manages a pool of more than 300 000 panellists to be representative for the Australian population. When choosing a panel provider it is important to check the size and representativeness of the panel as well as if the panel is actively managed to exclude professional repliers and double responses from the panel. The required sample size depends on the specific research question of the experiment and the desired precision of the estimates (Louviere et al.,

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2000). If individuals complete a full experimental design then from our experience a minimum of about 200 to 300 respondents are necessary to estimate an aggregated model. A larger number of respondents is required to model consumer heterogeneity with stable segments of a minimum size of at least 30 respondents for the smallest segment (segment size is usually not symmetric). From our experience a minimum of 600 to 800 respondents is a good rule of thumb if you want to consider whether segments exist and estimate significant covariate effects. Sample size has to be increased further if the experimental design is blocked into versions over several respondents. It should also be considered that the net sample size usable for analysis decreases by about 5% to 15% due to random, incomplete or invalid responses. Before sampling, the survey and choice experiment should be piloted to ensure that respondents understand the choice context as well as attributes and levels, to check task complexity, survey length and the correctness of the statistical design (Lancsar and Louviere, 2008). Our study collected responses from 1198 Australian seafood consumers, representative for the Australian population, who had purchased seafood in the last four weeks. We wanted only those respondents to qualify who had recent purchase experience to ensure that they had already established product preferences. Data analysis As already mentioned before, the raw data needs to be coded2 and controlled and cleaned for random, incomplete or invalid responses. An experienced research provider should have established quality control procedures to check for straight liners (always choose the same position in the choice task) or other response patterns. Also the time taken for the choice experiment can be a further indication of response quality (Bonsall and Lythgoe, 2009). Respondents who score high on several indicators should be excluded from the data set to avoid biased estimates. Accordingly, in our study 52 respondents (4%) were excluded from the sample, which resulted in a net sample size of 1146. The modelling approach to be used depends on the research design (were interactions or brand specific alternative specific effects included, see page 315) and assumptions about consumers’ behaviour. Here we cannot present the multiplicity of existing analysis and choice modelling approaches in detail but only give a very brief overview. The interested reader should use the references given at the end of this section for further details. The multinominal logit model, for which McFadden won the Nobel Prize in 2000, is the workhorse of choice modelling. This classical model is widely used for aggregated modelling or for looking at differences between 2

Instead of dummy coding we recommend effects coding which avoids correlations of the estimated effects with the intercept. For more details see Lancsar and Louviere (2008) and Bech and Gyrd-Hansen (2005).

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a-priori specified groups (e.g., younger versus older consumers, users of my brand versus non-users). The standard model has three key assumptions that might not be valid for the specific choice behaviour to be analysed and are relaxed by later developments: a) no random preference variation, b) proportional substitutability across alternatives3 and c) uncorrelated choices.4 One of its key assumptions relevant for new product development is that preference heterogeneity can only be considered if it is related to observed characteristics, such as sociodemographic variables. In other words, it assumes that the majority of respondents have similar preferences, and segments unrelated to observable characteristics do not exist. Recent research found that this is often the case; differences in choice and preferences are rarely related to other observable consumer characteristics (Mueller et al., 2010). There are two major choice model groups that can deal with consumer heterogeneity, mixed logit models and latent class models. While mixed logit models5 assume consumer preferences to be a continuum over each attribute level, latent class choice analysis6 assumes that there are certain lumps or groups of consumers with similar preferences to each other but different to everyone else. The most recent developments in choice modelling also consider the consistency of how consumers act in choice experiments by including a preference consistency scale factor.7 Many research providers use one specific software for their analysis and are thereby often limited to one type of model they can use. A high quality research provider should be able to understand and question the underlying assumptions of the different models and should have the flexibility to use the model most appropriate for a specific research project. For the prawn project we first estimated an aggregated model with multinominal logit and then modelled preference differences with a latent class choice model that also takes differences in response consistency into account.8 The use of a latent class model was motivated by our expectation that there are rather distinct consumer groups or segments of prawn 3

Proportional substitutability is caused by the assumption of independence of irrelevant alternatives (IIA); for details and empirical tests see Train (2003). Models that relax IIA are nested logit (IIA relaxed between nests but assumed within nests), multinomial probit, mixed logit and latent class models. For more details see Louviere et al. (2000), Train (2003) and the literature cited there. 4 Correlations of repeated choices within a person can be modelled with mixed logit models; for an introduction see Hensher et al. (2005). 5 Mixed logit is implemented in estimation program LIMDEP & NLOGIT (Hensher et al., 2005) or with a Hierarchical Bayes estimation procedure in Sawtooth software. 6 Latent class models can, for instance, be estimated with Latent Gold software. 7 For a detailed discussion see Fiebig et al. (2010), Louviere et al. (2002) and Louviere and Eagle (2006). An empirical comparison of models with and without considering response consistency can be found in Colombo et al. (2009). 8 The scale extended latent class model was estimated in Latent Gold Syntax 4.5.

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consumers who have similar preferences within a group rather than a continuum of consumers who are all different from each other. Interpreting results and simulation We will first present the results of the aggregated model before we look at the contribution of a model that also takes preference differences between consumers into account. The results of the aggregated model, assuming identical consumers, are presented in Table 11.3. Estimates represent relative part worth utilities for each product level that add up to zero within each attribute. Positive or larger values indicate levels that increase the utility and hence the probability of a product being chosen whereas negative or smaller estimates decrease it. In our case Australian seafood buyers most highly value prawns of Australian origin over a specific local Australian origin (Spencer Gulf) and have a low preference for imported prawns. The price effect is almost linear with lower prices being preferred over higher prices. Fresh prawns are preferred over frozen and ‘low in fat’ is chosen more often than ‘rich in Omega3’ or prawns with no health claim. The effect of the environmental claim is the smallest and only marginally significantly different from zero. How can these utility estimates be used to predict if consumers would be willing to pay a higher price for a health or environmental claim? The Table 11.3 (n = 1146)

Part worth utility estimates for aggregated multinominal logit model

Attributes

Levels

Estimate

Wald

p-value

Region of origin

Australia Spencer Gulf (AUS) Thailand China

1.36 0.80 −1.04 −1.12

8008

0.00

Price

$12.50 $19.00 $25.50 $32.00

0.99 0.24 −0.26 −0.97

3787

0.00

Storage

fresh frozen

0.41 −0.41

1816

0.00

Health claim

rich in Omega3 low in fat none

0.02 0.09 −0.11

50.7

0.00

Production method

wild caught farmed

0.05 −0.05

24.1

0.00

Environmental claim

sustainable none

0.04 −0.04

5.9

0.02

Pseudo R2 = 0.365

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Importance

Region of origin Price Storage Health claim Production method Environmental claim

44.0% 35.0% 15.0% 3.5% 1.6% 1.3%

underlying principle of compensatory choice models is that consumers compensate good characteristics with bad ones when they make a choice. Our estimates confirm that higher prices are clearly a bad characteristic. The part worth estimate of 0.09 for the health claim ‘low in fat’ indicates that it can only compensate for a very small price premium if it is compared to the drop in utility of −0.75 for a price increase from $12.50 to $19.00. A more advanced model treating price as continuous variable results in a price premium of $2.26/kg for ‘low in fat’ and $1.86/kg for rich in Omega3, whereas Australian consumers are only willing to spend a small price premium of $0.90/kg for prawns from a sustainable fishery. The Wald statistic in the fourth column of Table 11.3 is an indicator of the contribution of an attribute in explaining choices; the larger it is the higher is the impact of the attribute on consumer choice.9 The resulting attribute sensitivity of the model is listed in Table 11.4, where it becomes clear that the two claims and the production method have only a very small influence on consumer choice. It has to be mentioned that these importance weights are specific to the attribute levels chosen in the experiment. For instance, if a wider price range had been chosen, then price would likely be more important and vice versa. Also, if there were an attribute we did not include in the experiment, which was very important to consumers, our attribute importances would be overestimated. In other words, their real importance to consumers in the market would be smaller than estimated and also the price premiums and discounts from the model would be inflated. These examples demonstrate how important it is to select the right attributes and levels to be able to make valid market predictions. In the next step of the analysis we allowed consumers to have different preferences and estimated a scale extended latent class model for which the results are shown in Table 11.5. The segmented model does better in explaining respondents’ choices as indicated by the higher explained 9

For a detailed discussion of reliable ways to derive relative attribute importance see Lancsar et al. (2007) and Louviere and Islam (2008).

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325

Part worth estimates for segmented model Levels

Attributes

Segment size Characterization

Region of origin

Class 1

Class 2

Class 3

Class 4

Class 5

31%

30%

15%

15%

9%

Australia + price

region + storage

Australia + claims

low price

fresh

Australia Spencer Gulf (AUS) Thailand China

2.32 1.73

2.07 2.08

0.74 0.04

0.79 0.59

0.46 0.13

−1.96 −2.09

−2.68 −1.47

−0.42 −0.36

−0.79 −0.59

−0.28 −0.31

Price

$12.50 $19.00 $25.50 $32.00

1.38 1.01 −0.02 −2.37

0.11 0.18 0.03 −0.32

0.16 0.13 −0.07 −0.22

2.80 1.48 −1.10 −3.18

0.80 0.39 −0.13 −1.06

Storage

fresh frozen

0.46 −0.46

0.37 −0.37

−0.01 0.01

0.12 −0.12

1.37 −1.37

Health claim

rich in Omega3 low in fat none

0.10 −0.06 −0.04

−0.01 0.01 0.00

0.17 −0.01 −0.16

0.02 0.24 −0.26

0.24 −0.17 −0.08

Production method

wild caught farmed

0.03 −0.03

0.03 −0.03

0.00 0.00

0.00 0.00

0.07 −0.07

Environmental claim

sustainable none

0.07 −0.07

0.23 −0.23

0.21 −0.21

0.33 −0.33

0.03 −0.03

Pseudo R2 = 0.557

variance (pseudo R2 of .36 versus pseudo R2 of .55).10 It results in a solution with five different consumer segments (classes) that differ in the relative importance of attributes for their choice and in the relative preference for attribute levels (Table 11.6). The results cannot be discussed here in full detail; a brief characterisation of the drivers of each segment is given in the third row in Table 11.5. When interpreting the results it has to be observed that the part worth utilities of an attribute can only be compared within a segment (class) but not between different segments. For instance from the part worth utilities it appears that class 1 prefers $12.50 to $19.00, but it cannot be concluded 10

It should be noted that the pseudo R2 of a choice model is different to the R2 of a linear regression model. Hensher et al. (2005, p. 338) discuss the relationship between both measures and according to their experience a pseudo R2 of 0.3 represents a decent model fit for a discrete choice model. Accordingly, the aggregated model already fits the data well but the model fit is further improved by the latent class model.

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Table 11.6 Attribute importance for consumer segments Segment size Region of origin Price Storage Health claim Production method Environmental claim

Class 1 31%

Class 2 30%

Class 3 15%

Class 4 15%

Class 5 9%

Overall 100%

81% 16% 3% 0% 0% 0%

82% 2% 15% 0% 0.3% 0.5%

93% 3% 0% 1.4% 0% 2.9%

5% 94% 0% 0.3% 0% 0.5%

4% 12% 83% 0.4% 0.5% 0%

62% 21% 12% 0.3% 0.1% 0.6%

that class 1 prefers $19.00 more than class 2. The part worth utility estimates of 1.01 and 0.18 are measured on different segment specific scales and cannot be compared directly.11 Only the relative attribute importance in Table 11.6 can be compared between the segments. How does the segmented model provide us with new insights? First of all we find that consumers do not react identically to price. There appears to be a very price sensitive segment (class 4) whose strong preference for low prices can hardly be compensated by other attributes. About a third of consumers prefer medium price levels ($19.00) over lower prices, indicating that they prefer higher quality products. All other segments prefer low prices but also value other attributes such as region or freshness, which can partially compensate for the disutility of higher prices. In average over all segments, region becomes a stronger choice driver, whereas the overall importance of production method, health and environmental claims decreases. By modelling distinct segments it becomes clear that only one segment (class 3), representing 14% of all consumers, values environmental and health claims. Interestingly, this segment values ‘rich in Omega3’ higher than ‘low in fat’. Only the low price segment prefers ‘low in fat’ prawns but is not strongly impacted by health claims in general. The production method is unimportant to all consumers, indicating that Australian seafood producers do not have to discount farmed prawns relative to wild caught prawns. Estimating a model which is able to consider differences in consumer behaviour adjusts the implications for price premiums for health and environmental claims compared to the overall model. It was found that only a small market segment slightly values both claims, which is modestly price inelastic. For the pricing of prawns with health and environmental claims this means that only a small segment of the market would actually be willing to pay a price premium, not the whole population as suggested by the aggregated model. In the next step, the segments can be characterized by their sociodemographics and purchase behaviour to find ways to target them specifically with products they prefer. For instance, it was found that the segment that values 11

For more details see Lancsar et al. (2007).

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Pricing for new product development Ehrenberg-Bass

Preference shares for total sample:

Institute for Marketing Science

Option 1

Option 2

Option 3

Option 4

$12.50/kg China Frozen Farmed -

$12.50/kg Thailand Frozen Farmed -

$25.50/kg Spencer Gulf (AUS) Fresh Farmed Rich in Omega3 -

$19.00/kg Australia Fresh Farmed -

Preference share total sample

8%

7%

27%

59%

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

1% 1% 16% 30% 3%

1% 0% 15% 25% 3%

18% 46% 25% 3% 35%

81% 53% 45% 41% 59%

Price Region of origin Storage Production Health claim Sustainability Australia & price Region & storage Australia & claims Low & price Fresh

Cluster 1 31%

327

Cluster 2 30%

Fig. 11.3

Cluster 3 15%

Cluster 4 15%

27%

59% 7% 8%

Cluster 5 9%

Preference share simulator for prawn experiment.

health and environmental claims is younger than the average consumer, more likely to be female and overrepresented in Victoria and Tasmania. Choice experiment results can be incorporated into simulators that allow a more intuitive understanding of their implications. Simulators allow the product developers to play ‘what if’ market scenarios.12 Product attribute levels of several competing offerings can be changed and the resulting impact on preference shares observed. Figure 11.3 shows an example of a preference share simulator with four competing products and the ability to adjust the attribute levels varied in the experiment. The simulator calculates the preferences for each of the five segments (lower part of the table and small pie charts) and aggregates them to total market shares (right pie cake). Most simulators estimate preference shares, assuming that these alternative products are available to all consumers in the market place. Simulators can also be calibrated for availability of product levels and asymmetric market information to predict market shares under different circumstances.

11.5.3 The value of DCE in new product development Strengths Choice modelling is an extremely versatile tool that can provide answers to a wide range of questions related to preference measurement. Specifically for new product development it is able to quantify preferences for new to the world products or attributes and to predict their preference or market shares. Product preferences are separated into part worth utilities for each 12

To predict beyond the sample requires that DCE results be calibrated with market data, if available (Louviere et al., 2000; Orme and Johnson, 2006).

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attribute level, which can then be quantified in monetary value. These partial product prices give product developers advice on how to price new products to be introduced into the market. With preference or market share simulators NPD teams can also estimate the relative share their product will achieve under different pricing options of their own and competing products. Choice models have proven to be highly predictive for real markets and are found to give valid estimates. Compared to hedonic price analysis DCEs have the advantage of being able to model consumer heterogeneity and target specific consumer segments. Furthermore they do not require existing sales data and can clearly separate demand effects from supply effects. Limitations Choice models measure outcomes of consumer choice behaviour but shed only limited light on the reason for these behavioural outcomes. Consumers are seen rather as black boxes and additional methods are necessary to understand the reasons for their choice behaviour. Often this can be easily accomplished by combining DCEs with questions about a respondents’ usual behaviour, their last purchases, and their beliefs and attitudes. Recent progress in choice theory is also focussing on a more detailed modelling of consumer decision processes and information processing strategies during choice.13 At the moment, choice models provide useful and valid estimates of likely purchases at different prices, but do not tell us much about why this behaviour occurred. The numbers of attributes and levels that can be integrated in choice models is limited by respondents’ information processing, computer screen size, the costs for realistic stimulus creation and the costs of larger samples to cover extensive experimental designs. Because of the size limitations of paper or computer screens most DCEs use relatively small choice sets of only two to four products from which consumers have to choose. These are often not representative of real world consideration or choice sets, such as supermarket shelves with dozens of products within a product category. New developments mimic simulated real shopping environments with a multitude of products presented on simulated choice shelves. One disadvantage of DCEs is that they require a highly specialized knowledge in statistical design, survey tools and discrete multivariate data analysis, which often have to be sourced from external research service providers.

11.6 Summary Table 11.7 summarizes the three methods discussed in this chapter regarding their best use for pricing in new product development. 13

For instance see Louviere and Meyer (2007), Adamowicz et al. (2008), and Hensher (2009).

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329

Summary of three methods

Required data

External service provider required Costs

Applicable for changes of existing products Applicable for new to the world products Provides insights into pricing for different consumer or retail segments Provides insights for potential new consumers

Rules of thumb

Hedonic price analysis

Discrete choice analysis

Prices of competing similar products, brand strength no

Prices and detailed product composition in relevant market possibly

Product attributes and levels to be analysed

low, only for market observation yes

low to medium, for product characterization and analysis yes

extensive, for research design, sampling and analysis yes

no

no

yes

no

no

yes

no

no

yes

yes

11.7 Sources of further information and advice Lancsar and Louviere (2008) provide a short but comprehensive user guide to discrete choice experiments for healthcare decision making that is also applicable in many points to new product development. They provide a detailed checklist of questions to be considered from conceptualising the choice experiment until the interpretation of its outcomes. Louviere et al. (2003) give a review of the application of discrete choice experiments in market research over the last twenty years. The book by Hensher, Rose and Green (2005) is a very good comprehensive introduction into choice modelling including the statistical basics and many applied examples. Despite new developments since its publication Louviere, Hensher and Swait (2000) is still the fundamental reference book for choice modelling covering a wide range of model types and details of experimental design. Train (2003) is an advanced but very readable book on all choice model families, which also focuses on estimation procedures. An excellent overview of optimal DCE designs, including most recent

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developments, can be found in Street and Burgess (2007) who developed theory to produce optimally or near optimally efficient designs for strictly additive choice models. All these books provide detailed references to academic papers in choice modelling.

11.8 References adamowicz, w., bunch, d., cameron, t.a., dellaert, b.g.c., hanneman, m., keane, m., louviere, j., meyer, r., steenburgh, t. & swait, j. (2008), Behavioral frontiers in choice modelling, Marketing Letters, 19, 215–228. bech, m. & gyrd-hansen, d. (2005), Effects coding in discrete choice experiments, Health Economics, 14, 1079–1083. bonsall, p. & lythgoe, b. (2009), Factors affecting the amount of effort expended in responding to questions in behavioural choice experiments, Journal of Choice Modelling, 2(2), 216–236. carson, r.t, groves, t. & machina, m.j. (2000), Incentive and informational properties of preference questions, San Diego (CA): University of South California, 2000. colombo, s., hanley, n. & louviere, j. (2009), Modeling preference heterogeneity in stated choice data: an analysis for public goods generated by agriculture, Agricultural Economics, 40, 307–322. deshazo, j.r. & fermo, g. (2002), Designing choice sets for stated preference methods: the effects of complexity on choice consistency, Journal of Environmental Economics and Management, 44, 123–143. dillman, d.a. & bowker, d.k. (2001), Mail and internet surveys: the tailored design method, New York: Wiley. enneking, u., neumann, c. & henneberg, s. (2007), How important intrinsic and extrinsic product attributes affect purchase decision. Food Quality and Preference, 18, 133–138. fiebig, d.g., keane, m.p., louviere, j. & wasi, n. (2010), The Generalized Multinominal Logit Model: Accounting for Scale and Coefficient Heterogeneity, Marketing Science, DOI:10.1287/mkc.1090.0508. http://mkt.sci.journal.informs. org/cgi/content/abstract/mksc.1090.0508. hein, k.a., jaeger, s.r., tom carr, b. & delahunty, c.m. (2008), Comparison of five common acceptance and preference methods, Food Quality and Preference, 19, 651–661. hensher, d.a. (2009), Attribute processing, heuristics and preference construction in choice analysis. Keynote paper for choice modelling conference, Leeds March 30–April 1 2009, in Hess, S. And Daly, A. (eds.), Choice Modelling, Emerald Press, UK. hensher, d.a., rose, j.m. & green, w.h. (2005), Applied choice analysis: A primer, Cambridge University Press. lancsar, e. & louviere, j. (2008), Conducting discrete choice experiments to inform healthcare decision making: A user’s guide, Pharmaeconomics, 26, 661–677. lancsar, e., louviere, j. & flynn, t. (2007), Several methods to investigate relative attribute impact in stated preference experiments, Social Science & Medicine, 64, 1738–1753. ling, b.-h. & lockshin, l. (2003), Components of wine prices for Australian wine: how winery reputation, wine quality, region, vintage and winery size contribute to the price of varietal wines, Australasian Marketing Journal, 11(3), 19–32. louviere, j. & eagle, t. (2006), Confound it! That pesky little scale constant messes up our convenient assumptions, 2006 Sawtooth Software Conference Proceedings, Sequim (WA).

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louviere, j.j. & islam, t. (2008), A comparison of importance weights/measures derived from choice-based conjoint, constant sum scales and best-worst scaling, Journal of Business Research, 61, 903–911. louviere, j. & meyer, r.j. (2007), Formal choice models of informal choices: what choice modelling research can (and can’t) learn from behavioural theory, Review of Marketing Research, 4, 3–32. louviere, j.j., hensher, d.a. & swait, j.d. (2000) Stated Choice Methods: Analysis and Application, Cambridge, Cambridge University Press. louviere, j., street, d.j., carson, r., ainslie, a., deshazo, j.r., cameron, t., hensher, d., kohn, r. & marley, t. (2002), Dissecting the random component of utility, Marketing Letters, 13, 177–193. louviere, j., street, d.j. & burgess, l. (2003), A 20+ years retrospective on choice experiments, in: Market Research and Modelling: Progress and Prospects, Wind, Y. & Green, P.E. (eds.), New York: Kluwer. moskowitz, h. & silcher, m. (2006) The applications of conjoint analysis and their possible uses in sensometrics. Food Quality and Preference, 17, 145–165. mueller, s., francis, l. & lockshin, l. (2009a) Comparison of best-worst and hedonic scaling for the measurement of consumer wine preferences, Australian Journal of Grape and Wine Research, 15(3), 205–215. mueller, s., danenberg, n. & remaud, h. (2009b), Are health or environmental claims important to Australian prawn consumers?, Global Aquaculture Advocate, Sep/Oct, 28–29. mueller, s., lockshin, l. & louviere, j. (2010), What you see may not be what you get: Asking consumers what matters may not reflect what they choose. Marketing Letters, in press. DOI:10.1007/511002-009-9098-x. http://www.springerlink.com/ content/cr45366326204142/?p=8633a954887c45.79eac3a0918c12753&pizB orme, b. & johnson, r. (2006), External effect adjustments in conjoint analysis, Sawtooth software research paper series, Sequim (WA). reisfelt, h.n., gabrielsen, g., dall aaslyng, m.d., schmidt bjerre, m. & moller, p. (2009), Consumer preferences for visually presented meals, Journal of Sensory Studies, 24, 182–203. ryan, m. & skatun, d. (2004), Modelling non-demanders in choice experiments, Health Economics, 13, 397–402. sethuraman, r., kerin, r.a. & cron, w.l. (2005), A field study comparing online and offline data collection methods for identifying product attribute preferences using conjoint analysis, Journal of Business Research, 58, 602–610. severin, v. (2000), Comparing statistical efficiency and respondent efficiency in choice experiments, Sydney (NSW): University of Technology Sydney, 2000. steenkamp, j.-b.e.m. & baumgartner,h. (1998), Assessing measurement invariance in cross-national consumer research, Journal of Consumer Research, 25 (June), 78–90. street, d. & burgess, l. (2007), The Construction of Optimal Stated Choice Experiments: Theory and Methods, Hoboken, New Jersey, John Wiley & Sons, Inc. train, k.e. (2003), Discrete choice methods with simulation, Cambridge University Press. van kleef, e., van trijp, h.c.m. & luning, p. (2005), Consumer research in the early stages of new product development: a critical review of methods and techniques, Food Quality and Preference, 16, 181–201.

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12 Experimental auction markets for studying consumer preferences J. L. Lusk, Oklahoma State University, USA

Abstract: This chapter discusses a nascent new product development tool, experimental auction markets, in which people bid to buy real products using real money in a setting employing rules that provide incentives for people to truthfully reveal their value for each product up for auction. A brief introduction to experimental auction markets is presented, and the usefulness of the approach is illustrated by showing how bids can be used to address a host of questions that arise in new product development research. The chapter concludes with a discussion of frontier research in experimental auction markets. Key words: auction, experimental economics, willingness-to-pay.

12.1 Introduction Which new product will consumers like the best? Which new product or line extension will be most profitable? How large a market share will a new product garner? A host of quantitative tools have emerged to help sensory analysts, marketers, and new product developers answer such questions including conjoint analysis, hedonic rating scales, and purchase intention questions. The popularity of such tools can be attributable to the utility they bring in answering key marketing questions. Alas, no research method is a panacea, and it is prudent to continually re-evaluate current practices and explore other alternatives. One alternative that has come to the forefront in recent years is the use of experimental auction markets. In an experimental auction, people bid to buy real products using real money in a setting employing rules that provide incentives for people to truthfully reveal their value for each product up for sell. The bidding environment can be constructed to provide market feedback to participants which re-enforces the truth-telling bidding strategy and promotes individual reflection on their value for the goods. The bids

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obtained in the experimental auctions are interpreted as the maximum amount people are willing to pay for the new good, and as such, experimental auctions can be (and have been) fruitfully combined with traditional sensory methods to yield measurements of the desirability of products using a money metric. In a business environment, the task of new product development is often separated from the task of product pricing and marketing. One set of researchers conducts sensory analysis to identify which products taste best, and another team of researchers takes the newly developed product and determines marketing and pricing strategies. Unfortunately, this approach can lead to suboptimal decisions. For example, sometimes people’s responses to hedonic scales from taste tests are not significantly related to people’s willingness-to-pay for a new product (Jaeger and Harker, 2005). A key advantage of the experimental auction approach is the ability to integrate the new product development and pricing tasks, and to supplement the information gained through traditional sensory and consumer testing with the economic information contained in auction bids. The key distinguishing feature of experimental auctions is that they are not hypothetical. The questions asked in the first paragraph of this chapter arise, in a sense, because we want to know what would have happened in a market that is different than the one that actually exists. Traditional sensory and marketing research methods attempt to address these questions by constructing hypothetical markets. By contrast, experimental auctions represent an attempt to create the counterfactual by actually creating the missing market. Whereas responses to conjoint or purchase intention questions provide an indication of stated preferences, bids in experimental auctions are revealed preferences. One of the primary reasons experimental auctions have emerged as a useful tool in new product development research is the increasing recognition of the drawbacks associated with hypothetical willingness-to-pay and purchase intention questions. There is now a wealth of evidence that people tend to significantly overstate the amount they are willing to pay for goods in a hypothetical setting as compared to when real purchases are made (see, for examples, the Meta analyses in Little and Berrens, 2004, List and Gallet, 2001, or Murphy et al., 2005). Moreover, it has long been recognized that stated purchase intentions fail to accurately predict actual purchases (see, for examples, Kalwani and Silk, 1982 and Morwitz, 1997, 2001). Economists have long been skeptical of responses to stated preference questions, and exhibit keen interest in people’s incentives when answering survey-type questions. Thus, it is perhaps not too surprising that the use of experimental auctions began more than 40 years ago with economists eliciting people’s monetary values for lotteries to characterize preferences for risk (e.g., Becker et al., 1964). Then, in the 1980s economists began applying these methods to study demand for the environment. Such investigations were bolstered by theoretical work on auctions and growth in the academic

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field of experimental economics. Researchers started applying what they learned in so-called induced value experiments to elicit people’s homegrown values: those values that people bring into an experiment for realworld goods. Eventually, marketers and sensory scientists also recognized the value of experimental auctions to develop new products as reflected by the works of, for example, Hoffman et al. (1993), Lange et al. (2002), and Wertenbroch and Skiera (2002). Today, experimental auctions are used around the world by applied economists, psychologists, marketers, and sensory scientists interested in developing and valuing new products and technologies. As we showed in Lusk and Shogren (2007), there are well over 100 academic studies published using experimental auctions in applications ranging from valuing food safety (e.g., specific pathogens, biotechnology, pesticides, traceability, and growth hormones), food attributes (e.g., meat tenderness, meat color, fat content, and packaging), a variety of foods (e.g., kiwis, apples, chocolates, potatoes, corn chips, cookies, milk, and sandwiches), and a variety of nonfood, high-value goods ranging from sports cards to firm business records to used cars to gasoline to Christmas gifts. The purpose of this chapter is to provide a brief introduction to experimental auction markets and to illustrate the usefulness of experimental auctions in addressing a host of questions that arise in new product development. In such a short space, I do not claim to provide an exhaustive treatment of the issue; however, interested readers are referred to Lusk and Shogren (2007) where we provide an extensive introduction and reference guide to using experimental auctions. As alluded to earlier, no research method is a panacea – including experimental auctions. In the final section, I mention how research is progressing to deal with some of the weaknesses of experimental auctions.

12.2 Experimental auctions in action There is perhaps no better way to describe how data from experimental auctions can be used than through example. Often published studies on experimental auctions focus on methodological or theoretical issues, and as such, it is often difficult to see how auction data can be used in an applied new product development setting. This section provides illustrations of the varied uses of experimental auctions using a single data set obtained from a series of experiments I conducted in 2001 related to consumer preferences for beef steaks. Beef steaks are a particularly interesting application because they represent a commodity for which there had been little product differentiation or branding in the market place, at least at the time the initial study was conducted, and there was significant interest among cattle producers and beef processors/retailers in people’s preferences for new beef products with different beef quality attributes and brands. The next

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sub-section describes the experiment and the data, and the remaining subsections use the data to answer a host of applied new product development questions.

12.2.1 Description of application Subjects were recruited from the general population of Manhattan, Kansas, USA using random digit dialing techniques, and were offered $40 cash to participate in a “steak preference experiment” conducted at the meat laboratory on the Kansas State University campus. About 48% of contacted individuals agreed to participate in a research session, and 85% of individuals who agreed to take part in a session actually showed. Because one of the original objectives of the research was to study the extent to which bidding behavior was influenced by a host of factors such as auction mechanism and the use of endowments, subjects were randomly assigned to one of 11 treatments. Here, I focus only on the treatments where people submitted bids to buy one of five different beef ribeye steaks. The first steak was a “conventional generic” steak with no label or indicator of quality, and the four potential new products were: (1) a “guaranteed tender” (GT) steak that was guaranteed to be of adequate tenderness based on shear force measurements, (2) a “natural” steak produced without growth hormones or antibiotics, (3) a “USDA Choice” steak corresponding to the US Department of Agriculture grading system, and (4) a “Certified Angus Beef” (CAB) steak that advertised several quality attributes related to product taste. As will be described momentarily, treatments differed in terms of the auction mechanism used to elicit values; however, for sake of exposition, I explain the procedures used to elicit bids in the second price auction, a mechanism which is incentive compatible – meaning people have an economic incentive to truthfully reveal their value for the good. Once subjects arrived at a session, they were shown and provided an opportunity to examine each of the five types of uncooked steaks, each in identical wrapping. An information sheet describing each of the beef steaks was read aloud and distributed to participants. Then, the auction procedures were described, instructions and examples were given, and individuals participated in a non-hypothetical auction for candy bars to familiarize them with the procedures. Then consumers participated in an auction for each of the beef steaks. The procedures for the second price auction were as follows: Step 1: Each subject simultaneously submitted five sealed bids for each of the five steaks: generic, guaranteed tender, natural, USDA Choice, and Certified Angus Beef. Step 2: The bids were collected by the monitor and ranked from highest to lowest for each steak. The highest bidders’ ID numbers and the market prices (i.e., the second highest bid) for each of the five steaks were posted in the front of the room.

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Step 3: Steps 1 and 2 were repeated for four additional rounds. Step 4: A random drawing determined which of the five bidding rounds was binding. Step 5: A random drawing determined which of the five steaks was binding. Step 6: The winning bidder for the randomly selected steak in the randomly selected round paid the market price (i.e., second highest bid) for the steak. We randomly drew a binding round and a binding steak so that we could elicit demand for a single unit – i.e., we did not want people to alter their bids in the fear that they might have to purchase multiple steaks. Five bidding rounds were used so that subjects could gain experience with the auction mechanism and their value for the steaks. At this point, it is worth pointing out why the second price auction is incentive compatible, i.e., why the best strategy is to bid exactly what each steak is worth. Consider the following: if someone bids more than a steak is worth to them, they might end up having to buy a steak for more than they really want to pay. Conversely, if someone bids less than a steak is really worth to them, they may end up not winning the auction even though it they could have bought a steak at a price they were actually willing to pay. Thus, the best strategy is to bid exactly what each steak is worth to each individual. A more formal proof is given in Lusk and Shogren (2007). Second price auctions are not the only incentive compatible mechanism. In this study, we also made use (in other treatments) of three other incentive compatible mechanisms: the English auction, the random nth price auction, and the Becker, DeGroot, and Marschak (BDM) elicitation mechanism. In an English auction, participants offer ascending bids until only one participant, the one with the highest bid, is left in the auction. In a random nth price auction, participants submit sealed bids for a good. Then a random bid (the nth bid) is drawn from the sample and the (n − 1) highest bidders bought one unit of the good at a price equal to the nth bid. With the BDM elicitation procedure, participants received an item if their sealed bid exceeds a subsequently drawn random number (from a known distribution), where the randomly drawn number became the market price. Although the mechanisms are distinct in their make-up, theoretically, they are all theoretically incentive compatible. Each of the mechanisms was adapted to elicit demand for the five steaks used in this application. In total, the data set I use in this chapter consists of 35 subjects who participated in a second price auction and 29, 31, and 28 subjects who took part in random nth, English, and BDM auctions, respectively. In the data analysis that follows, I simply pool the data across auction treatments to create a sample size of 119 subjects and focus on bids in round one. I do this for two reasons. First, this chapter is designed to illustrate how auction bids can be used and pooling the data helps ease the exposition. Second, all mechanisms are theoretically demand revealing and each have their own

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advantages and disadvantages. A literature exists that shows that composite forecasts from various models or people generally outperform a single forecast (e.g., Clemen, 1989). It is reasonable to expect that estimates of willingness-to-pay (e.g., forecast of true willingness-to-pay in the population) from several mechanisms might outperform estimates from any single mechanism. Interested readers are referred to Lusk et al. (2004) or Lusk and Schroeder (2006) for more information on how the auctions were conducted. Exact instructions used in the auctions can be found in Lusk and Shogren (2007).

12.2.2 Summary statistics and relative importance of product attributes Table 12.1 provides summary statistics associated with bids for each of the steaks segregated by auction mechanism. As shown in the last column, we fail to reject the hypothesis that the means are equal across auction mechanisms for each of the steaks; a finding which re-enforces the idea that the optimal bidding strategy is the same in each of the mechanisms: submit truthful bids. The fact that different types of auctions gave basically the same results also provides some reassurance that the values obtained are indeed reflective of people’s true willingness-to-pay for the products. One of the primary reasons people conduct new product development research is to determine the relative preferability of product attributes and results from experimental auctions provide such measures. A simple analysis of the summary statistics yields a great deal of insight in this regard. For example, focusing on the pooled data, it is apparent that the CAB steak is the most preferred type, generating bids that are almost twice that of the generic steak with no quality distinction ($2.19 vs. $4.37). That is, people are willing to pay double the price to have a CAB steak rather than a conventional generic steak. Certainly such information would be useful to developers of new products as it indicates not just which product is more preferred, but how much more CAB is preferred in the units of dollars, which can be compared directly against costs. Results also reveal that although a guarantee of tenderness is valued by most consumers over no such guarantee, consumers are also interested in other quality attributes as evidenced by the fact that mean bids for USDA Choice and CAB are higher than the mean bid for GT steaks. Somewhat surprisingly, natural steaks were not highly valued by this sample of participants; however, as noted by the relatively high standard deviation of bids ($1.97) and the difference in the minimum and maximum bids (minimum = $0; maximum = $10), there is a significant amount of variability in people’s preferences for this steak. That some people bid zero on some steaks simply means that they would not be willing to buy the particular steak at any price (at least at the time the experiment was conducted). Another way to look at the relative desirability of different steak attributes is to plot the inverse cumulative-distribution function of bids for each

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Table 12.1

Summary statistics Second price

Random nth price

English

BDM

Pooled

P-valuea

$2.46 $2.00 8.6% $6.75 $1.94

$1.78 $2.00 14.3% $4.00 $1.14

$1.85 $1.88 18.2% $3.75 $1.31

$2.67 $3.00 25.9% $6.11 $2.02

$2.19 $2.00 16.0% $6.75 $1.68

0.103

Guaranteed tender (GT) Mean $3.28 Median $3.00 % Zero 2.9% Max $12.00 St. Dev. $2.44

$2.57 $2.76 8.6% $5.00 $1.23

$2.67 $3.25 9.1% $4.25 $1.26

$3.41 $3.00 18.5% $10.00 $2.57

$2.99 $3.00 9.2% $12.00 $2.01

0.259

Natural Mean Median % Zero Max St. Dev.

$2.91 $2.50 8.6% $8.50 $2.18

$2.29 $2.50 8.6% $4.50 $1.35

$2.48 $2.75 18.2% $5.00 $1.70

$2.70 $3.00 33.3% $10.00 $2.53

$2.60 $2.50 16.0% $10.00 $1.97

0.606

Choice Mean Median % Zero Max St. Dev.

$3.60 $3.20 0.0% $10.00 $2.19

$3.49 $3.00 0.0% $7.00 $1.49

$3.77 $3.75 4.5% $6.25 $1.59

$4.58 $4.50 11.1% $13.00 $3.20

$3.82 $3.75 3.4% $13.00 $2.21

0.237

Certified Angus Beef (CAB) Mean $3.97 $3.96 Median $4.00 $4.00 % Zero 0.0% 0.0% Max $11.50 $10.00 St. Dev. $2.43 $1.83

$4.53 $4.63 4.5% $6.50 $1.53

$5.31 $4.50 11.1% $15.00 $3.86

$4.37 $4.25 3.4% $15.00 $2.58

0.146

# of Obs.

22

27

119

Steak Generic Mean Median % Zero Max St. Dev.

35

35

a

P-value from ANOVA; null hypothesis is that means are equal across auction mechanisms.

steak, which can be interpreted as inverse unit demand curves for each steak (i.e., curves which show at a given price the share of people that would buy a steak assuming that steak were the only option available). These demand curves are shown in Fig. 12.1. The curves indicate, for example, that at a price of $4, only about 20% of people would buy a generic steak (assuming it were the only available), but at the same price of $4, about 60% of consumers would buy a CAB steak (again, assuming it were the only available). Moreover, the figure shows that there are only a few people (about 10% or less) willing to pay more than $8 for any steak. Figure

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$14 Generic Guaranteed tender Natural Choice Certified Angus Beef

Price (willingness-to-pay)

$12 $10 $8 $6 $4 $2 $0 0%

20%

40%

60%

80%

100%

Quantity demanded (percentage of consumers)

Fig. 12.1

Unit demand curves for five steaks.

12.1 also shows that at high price levels, demand for the GT steak is virtually indistinguishable from that for natural steak; however, at lower price levels, a larger share of people would buy the GT steak. One drawback to the data shown in Fig. 12.1 is that it illustrates demand for each steak assuming it is the only option available. To allow for demand inter-relationships (i.e., the fact that people make a choice between competing steaks at different prices), it is useful to calculate predicted market shares at given price levels, and this is the issue we take up in the next sub-section.

12.2.3 Market share predictions, demand elasticities, and optimal pricing When developing new products, it is often useful to know more than which product obtains a higher average sensory rating than another, but also to be able to understand how many people would buy a particular product, and how sensitive people’s choices are to changes in the product’s price. Managers making new product development decisions would almost certainly welcome such information in addition to the data that is typically collected as a part of new product development research. That is, managers are interested in determining how consumers respond to a price change and in forecasting the market share of a new product. Bids from experimental auctions can be readily employed to make such predictions. To illustrate, denote the utility derived from purchasing steak type j as: U ij = WTPij − pj ,

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where WTPij is the auction bid or maximum willingness-to-pay for steak j and pj is an assumed price of good j. To convert this money-metric utility function to market share, a “first choice” or “highest utility” rule is assumed in which each person is assumed to choose the product producing the highest utility from a choice set with J brands. Person i is assumed to choose steak type j if Uij > Uik for all k ≠ j. Let Iij be an indicator variable that takes the value of 1 if individual i is predicted to choose steak type j and 0 otherwise. Further, let wi be the number of units individual i purchases once a choice between brands has been made (a measure which was ascertained in response to a post-experiment questionnaire). Now, in a sample of N individuals choosing between J steaks, the market share of steak type j (MSj) is: N

∑I w ij

MS j =

i =1 j N

i

∑ ∑ Iik wi

.

12.2

k =1 i =1

Because of the term wi in the summation, the percentage of people predicted to choose a particular brand need not match the market share if, for example, “heavy users” are more likely to prefer one brand/type than another. Market share will also differ from the frequency of individual choices because some consumers may choose not to purchase at all if prices and values are such that equation (12.1) is negative for all brands. Given the market share equation in equation (12.2), one can easily calculate profit maximizing prices. Consider a retailer facing constant per-unit marginal costs of cj for steak-type j. The profit function for such a retailer is: N

J

i =1

j =1

π = ∑ wi ∑ MS j ( pj − c j )

12.3

where the first summation is simply the total amount of beef steaks sold, and the second term is the summation of per-unit profit resulting from each brand/type. The goal of the retailer is to choose the prices pj to maximize equation (12.3). There is no closed form solution to this profit maximization decision because the underlying demand curves (i.e., the distribution of bids for each steak) are not characterized by a parametric function; however, it is a straightforward task to find the profit maximizing prices using a simple grid search. Demand elasticities, which indicate how the market share of a product changes as the product’s price changes, are easily calculated based on equation (12.2). Because we have a finite sample of auction bids, there are dollar ranges in which few WTP values lie. Because of this, we calculate arcelasticities. For example, the own-price elasticity of the demand for steak j is [(MS1j − MS0j )/MS0j ]/[(p1j − p0j )/p0j ], where the superscript 0 denotes an

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initial base-line pricing condition, and the superscript 1 denotes predictions when the price of j increases from p0j to p1j . These elasticities indicate how the market share of product j will change (in percentage terms) when the price of product j increases by 1%. Unlike the elasticity estimates that arise from the well-known multinomial logit, these estimates do not suffer from the independence of irrelevant alternatives problem, which has a number of unfortunate consequences such as: a) forcing the ratio of the market shares of any two goods to remain constant regardless of prices or available brands and b) forcing equality of cross-price elasticities across all brands. Equation (12.2) does not rest on the independence of irrelevant alternatives assumption, and it does not impose the aforementioned restrictions on substitution patterns. Moreover, unlike many other prediction methods, equation (12.2) does not assume homogeneity in preferences across people and does not depend on an assumed parametric form to characterize preferences. Table 12.2 reports the results of several market share simulations. First, a base-line market scenario was created in which people were assumed to only have a choice between a 12 oz. generic steak at $2.50, a 12 oz. Choice steak at $3.50, and no purchase. In this base-line scenario, 16.81% of people are predicted to choose the generic steak, 47.06% are predicted to choose the USDA Choice steak, and 36.13% are predicted to refrain from purchasing (i.e., 36.13% of people placed bids on the generic and Choice steaks that were both less than the assumed prices). Translating these values into market share (which accounts for differences in total consumption across people) indicates that Choice steak would garner about 76% of the market share, with the remainder going to generic. In this initial scenario, a retailer’s expected profit, for this sample of 119 people, is $154.75. To see how this profit figure is calculated, first note that given the pricing and cost assumptions, the retailer makes $0.50 for each steak sold. 16.81% of people are predicted to choose generic steak, which means 20 people in our sample (0.1681*119 = 20) will chose generic. Our data reveal that those 20 people purchase an average of 3.725 steaks per month; thus, the retailer would earn $0.50*20*3.725 = $37.26 from this group of people. Likewise, 47.06% of people are predicted to choose the Choice steak, which means 56 people in our sample (0.4706*119 = 56) will choose Choice. Our data reveal that those 56 people consume an average of 4.196 steaks per month; thus, the retailer would earn $0.50*56*4.196 = $117.49 from this group of people. Combining the profit from the people who purchase generic and Choice, the retailer would expect to earn $154.75 ($37.26 + $117.49 = $154.75). Now, we can ask, if a retailer only had the option of choosing one new product to introduce, which should it be: GT, natural, or CAB? The results in Table 12.2 clearly show that whereas GT and natural would only pick up very small market shares (0.65% and 1.29%, respectively), CAB steaks would account for a third of all purchases. Introducing CAB at a price of

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Table 12.2

Market share simulations Scenarios

Steak type

Baselinea

Introduce Guaranteed tender (GT)b

Introduce naturalc

Introduce Certified Angus Beef (CAB)d

Baseline with optimal prices

Percent of individuals choosing Generic 16.81% 15.97% Choice 47.06% 46.22% GT – 1.68% Natural – – CAB – – None 36.13% 36.13%

15.97% 45.38% – 3.36% – 35.29%

14.29% 26.89% – – 28.57% 30.25%

26.89% 17.65% – – – 55.46%

Market share Generic 24.07% Choice 75.93% GT – Natural – CAB –

22.78% 75.93% – 1.29% –

19.41% 47.26% – – 33.33%

49.77% 50.23% – – –

Profite

$154.75

23.75% 75.61% 0.65% – – $155.75

Change in market share from baseline Generic – −0.32% Choice – −0.32% GT – 0.65% Natural – – CAB – –

$156.75 −1.29% 0.00% – 1.29% –

$255.00 −4.66% −28.67% – – 33.33%

$323.00 25.41% −25.41% – – –

a In baseline scenario, the prices of generic and Choice $2.50 and $3.50 12 oz. steak, respectively; the guaranteed tender, natural, and CAB steaks were assumed unavailable. b This scenario is the same as the baseline except the price of GT is available at $4.00 per 12 oz. steak. c This scenario is the same as the baseline except the natural steak is available at $4.00 per 12 oz steak. d This scenario is the same as the baseline except the price of CAB is available at $4.00 per 12 oz. steak. e Assumes constant per-unit marginal cost of $2 for generic and $3 for all other steaks.

$4.00 would increase profit from $154.75 in the baseline case to $255.00. This is an enormous increase in profit (a 65% increase), and such an increase would almost be sure to be noteworthy to managers currently dealing with small profit margins. The last column asks how the situation in the base-line scenario might change simply by altering prices to maximizing profit given in equation (12.3). As can be seen, the retailer could make more profit ($323) simply by better choosing prices than by introducing a new brand (optimal prices in baseline case are $3 for generic and $5 for Choice). Of course, the retailer might consider introducing a new brand and optimize pricing, and the framework outlined here provides a straightforward means of determining the effects of such a strategy.

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Table 12.3 Arc elasticities of demand for baseline case 1% change in price of . . . Percentage change in market share of . . . Generic Choice

Generic

Choice

−0.906 0.287

3.992 −1.265

Note: arc elasticities are calculated by determining how the baseline market shares given in Table 12.2 change as the price of generic steak increases from $2.50 to $3.00 (a 20% increase) and then subsequently as the price of Choice steak increases from $3.50 to 4.00 (a 14.29% increase).

Rather than conducting full-scale market share simulations, it is often sufficient to consider elasticities of demand, which indicate how the market share of one product changes as either its own price or the prices of other products increase by 1%. Table 12.3 shows the elasticities of demand for the baseline market scenario outlined in Table 12.2. The diagonal elements in Table 12.3 show the own-price elasticities of demand, which indicate how the consumption of each good changes as it own price increases. The diagonal values are negative, which means that people choose to buy less of a good when the good’s own price rises. The off-diagonal elements in Table 12.3 show the cross-price elasticities, which indicate how the market share of one good changes when the price of the good increases. The cross-price elasticities are positive which means people choose to buy more of a good when the price of a similar substitute good increases. Results in Table 12.3 show that the own-price elasticity of demand is −0.906 for generic steak and −1.265 for Choice steak. This implies that a 1% increase in the price of generic steak is predicted to cause a 0.906% fall in the market share for generic and a 1% increase in the price of Choice steak is predicted to cause a 1.265% fall in the market share for Choice. These results reveal that generic steak demand is apparently more inelastic than Choice over these price ranges; that is, the market share of generic steak is less sensitive to changes in the price of generic than the market share of Choice steak is to changes in the price of Choice steak. Cross price elasticities indicate that changes in the price of higher quality Choice beef have a more pronounced effect on sales of generic than the reverse. Generic sales benefit greatly from increases in the price of Choice, but Choice sales are relatively unaffected by generic steak price changes. In fact, increasing the price of generic by 1% only causes a 0.287% increase in Choice market share.

12.2.4 Determinants of willingness-to-pay The preceding analysis was conducted with very little attention devoted to the question of why certain people bid more for some steaks than others. However, for new products introductions to be successful in the

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marketplace, it is useful to identify the motivations behind people’s preferences for competing products. To investigate this issue, one can use regression analysis to determine how bids for each of the five steaks vary by demographic characteristics and responses to other questions collected in a post-experiment survey. Because a non-trivial fraction of the bids were zero (see Table 12.1), a traditional ordinary least squares regression would be inappropriate. Moreover, because people submitted bids on five steaks, it is likely that the regression error terms are likely to be contemporaneously correlated across steak types. Both considerations led to the use of a simultaneous tobit (or censored) regression model. To illustrate the model, the following latent variable equations can be specified: WTPi*, generic = β′1X i + ε i, generic WTPi*,GT = β′2 X i + ε i ,GT WTPi*,natural = β′3 X i + ε i ,natural WTPi*,Choice = β′4 X i + ε i ,Choice WTPi*,CAB = β′5 X i + ε i ,CAB where Xi is a vector of variables for individual i hypothesized to affect auction bids, βj is a conformable vector of coefficients, WTP*i,j is a latent variable equal a person’s actual auction bid for steak j if WTP*i,j > 0 and zero otherwise. The residuals, εij, are assumed to follow a multivariate normal distribution such that: 2 ⎛ ⎡0 ⎤ ⎡ σ 11 ⎡ε generic ⎤ ⎜⎢ ⎥ ⎢ ⎢ ε ⎥ ⎜ ⎢0 ⎥ ⎢ ρ12σ 1σ 2 ⎢ GT ⎥ ⎢ε natural ⎥ ∼ N ⎜ ⎢0 ⎥ , ⎢ ρ13σ 1σ 3 ⎜⎢ ⎥ ⎢ ⎢ ⎥ ⎜ ⎢0 ⎥ ⎢ ρ14σ 1σ 4 ⎢ ε Choice ⎥ ⎜ ⎢0 ⎥ ⎢ ρ σ σ ⎢⎣ ε CAB ⎥⎦ ⎝ ⎣ ⎦ ⎣ 15 1 5

ρ12σ 1σ 2 2 σ 22 ρ23σ 2σ 3 ρ24σ 2σ 4 ρ25σ 2σ 5

ρ13σ 1σ 3 ρ233σ 2σ 3 2 σ 33 ρ34σ 3σ 4 ρ35σ 3σ 5

ρ14σ 1σ 4 ρ24σ 2σ 4 ρ34σ 3σ 4 2 σ 44 ρ45σ 4σ 5

ρ15σ 1σ 5 ⎤ ⎞ ⎟ ρ25σ 2σ 5 ⎥⎥ ⎟ ρ35σ 3σ 5 ⎥ ⎟ ⎥⎟ ρ45σ 4σ 5 ⎥ ⎟ 2 ⎥⎦ ⎟⎠ σ 55

The parameters of this model can be estimated using maximum likelihood estimation with Monte Carlo integration used to evaluate the multiple integrals in the likelihood function. More details on such a model can be found in Huang (1999). Table 12.4 reports the maximum likelihood parameter estimates. Results reveal that there is little relationship between bidding behavior and most of the demographic variables. The exception to this statement is income. Higher income individuals tended to bid higher for steaks than lower income individuals, which is consistent with expectations. The parameter estimates can be interpreted as marginal effects of the respective variable on uncensored willingness-to-pay. For example, a $1000 increase in household income is associated with a roughly $0.14 increase in willingness-topay for the generic steak. That income was not generally related to bids for

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Determinants of auction bids; simultaneous tobit regressions

Variable

Definition

345

Equation/dependent variable Generic Intercept Female Age Child College Degree Income Steak Chance 1 Chance 3 Chance 4 Chance 5 σ ρ1,j ρ2,j ρ3,j ρ4,j ρ5,j

1.589*a (0.537)b 1 if female; 0 if male −0.413 (0.342) age in years −0.019 (0.018) 1 if children under 12 0.281 in household; else 0 (0.341) 1 if BS degree or 0.614 (0.371) higher; else 0 Annual household 0.135* (0.056) income in $1000s steak purchases −0.040 (lbs/month) (0.039) likelihood generic 1.272* steak is tender (%) (0.602) likelihood generic will 0.192 (0.433) cause illness (%) likelihood Choice steak is tender (%) likelihood CAB steak is tender (%) standard deviation of 1.683* (0.122) error term error correlation with generic steak residual error correlation with 0.835* (0.03) GT steak residual error correlation with 0.573* (0.066) natural steak residual error correlation with 0.656* (0.055) Choice steak residual error correlation with 0.652* CAB steak residual (0.055)

GT

Natural

Choice

CAB

3.147* (0.816) −0.088 (0.404) −0.016 (0.021) 0.334 (0.403) 0.261 (0.434) 0.150* (0.066) −0.025 (0.046) 0.582 (0.555)

2.564* (0.558) −0.163 (0.412) −0.021 (0.021) 0.084 (0.412) 0.341 (0.439) 0.039 (0.066) −0.013 (0.047)

1.947* (0.801) −0.596 (0.411) −0.009 (0.022) 0.206 (0.409) 0.545 (0.446) 0.146* (0.068) −0.008 (0.047)

2.111* (0.982) −0.713 (0.499) −0.021 (0.026) 0.013 (0.496) 0.572 (0.537) 0.119 (0.082) 0.019 (0.057)

−0.177 (0.834) −0.623 (0.796) 1.993* (0.14) 0.835* (0.03)

1.997* (0.703) 2.062* (0.716) 2.005* (0.146) 0.573* (0.066)

2.053* (0.139) 0.656* (0.055)

2.912* (0.849) 2.473* (0.167) 0.652* (0.055)

0.646* (0.057)

0.79* (0.036) 0.531* (0.068)

0.805* (0.034) 0.557* (0.066)

0.646* (0.057) 0.790* 0.531* (0.036) (0.068) 0.805* 0.557* (0.034) (0.066)

0.911* (0.016) 0.911* (0.016)

a

One asterisk represents statistical significance at the 0.05 level or lower. Numbers in parentheses are standard errors. Number of observations in each regression = 117. b

the natural steak implies that there are other factors, rather than income, that explain which people tended to bid high or low on this particular product. That is, poorer households were just as likely to bid high (or low) on a natural steak as were richer households.

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The post-experiment survey contained several questions related to people’s beliefs about the tenderness and safety of each of the steaks, and Table 12.4 shows that these beliefs were significantly related to auction bids. For example, people were asked, “If you were to purchase a generic steak, what is the likelihood that this steak would be tender? For example, a 0% chance would mean there is NO chance the generic steak would actually be tender; whereas, a 100% chance would mean that the generic steak would be tender for certain.” Results reveal for each additional percentage increase in the belief generic steaks were tender, bids for the generic steak increased $0.0127 such that going from an assuredly tough generic steak (0% chance of tenderness) to a guaranteed tender generic steak (100% chance of tenderness) increased bids by $1.272. The value associated with a 100% increase in the likelihood of tenderness was even higher for Choice and CAB steaks: $2.062 and $2.912, respectively. We also asked people, “If you were to purchase a generic steak, what is the likelihood that you, at some point in the future, will become ill due to the possible use of added growth hormones or antibiotics?” Results reveal that people who assigned a higher likelihood of becoming ill from the use of growth promotants in beef production bid significantly higher amounts for the natural steak. These results illustrate that people’s bids are rationally related to their beliefs in predictable and intuitive ways, suggesting bids are not capricious but rather reflect an underlying coherent belief structure. Finally, the correlation estimates indicate that people who bid relatively high on one steak were also likely to bid relatively high on other steaks; the highest correlation is between Choice and CAB steaks, suggesting people viewed these two products similarly. These correlations suggest a potential relationship between steak-types – information that might be exploited with further analysis, the likes of which are considered in what follows.

12.2.5 Characterizing product space When developing new products, interest often lies in determining how competing products relate to one another in the mind of the consumer. Several techniques are often used to group products on one or more latent dimensions. In this sub-section, we consider two techniques. The first is perceptual mapping, in which multidimensional scaling methods are applied to the matrix of bid difference. The approach identifies how different steaks may relate on one or more underlying dimensions. These dimensions are determined by subjects’ bids differences which are essentially judgments about the similarity of the steaks. The second technique we consider is exploratory factor analysis, which aims to represent a larger set of observable variables (bids for all five steak types) in terms of a smaller set of constructed variables or factors, which are assumed to represent theoretical constructs explaining patterns of bidding behavior. Both techniques can help identify which goods people view similarly and might aid sensory and

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marketing researchers in identifying new products that could be strong competitors in existing markets and new goods with few competitors. Perceptual mapping Using the bidding data described in Sections 12.2.1 and 12.2.2., I calculated, for each person, a matrix of bid differences, which was then used to calculate Euclidian distances between bids for steak types. For example, suppose an individual submits the following bids for the generic, GT, natural, Choice, and CAB steaks: $0, $1, $2, $3, and $4. These bids suggest that the consumer perceives generic and GT as more similar (in dollar space) than generic and CAB. These similarities/dissimilaritiesare characterized by the matrix of bid differences, which for this set of hypothetical bids are shown in Table 12.5. Using metric multidimensional scaling techniques, and assuming only two dimensions in the data, I identified how similar or dissimilar each of the steaks were on the two underlying dimensions using a least squares estimation technique (see Schiffman et al. (1981) or Young and Hamer (1994) for more complete discussions on multidimensional scaling). The results are shown in Fig. 12.2. The plot reveals an interesting pattern. The USDA Choice and CAB steaks are very similar on the x-dimension (i.e., both steak types have an x-coordinate of about 0.2), but they are quite different on the y-dimension (i.e., the y-coordinate for CAB is about −0.1, but is +0.1 for Choice). Likewise, whereas the generic and natural steaks are quite similar on the x-dimension, they are quite different on the y-dimension. Just the opposite is true for the Choice and natural steaks; they are similar in the y-dimension but dissimilar in the x-dimension. These data seem to suggest that the x-dimension relates to perceptions of fuller taste or preferences for fat content. Both the CAB and Choice steaks have higher level of intramuscular marbling, which produces higher levels of juiciness and flavor. Whereas CAB and Choice steaks score high in this dimension, generic and natural do not. Guaranteed tender falls roughly in the middle; people view it as tastier than generic and natural, but perhaps less so than Choice and CAB. It is a little less clear what is being measured along the y-dimension, but I speculate that it might have something to do with perceptions of safety. That the natural steak scores highest Table 12.5

Generic GT Natural Choice CAB

Matrix of bid differences Generic

GT

Natural

Choice

CAB

$0 $1 $2 $3 $4

−$1 $0 $1 $2 $3

−$2 −$1 $0 $1 $2

−$3 −$2 −$1 $0 $1

−$4 −$3 −$2 −$1 $0

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USDA Choice

0.1

0.05 Guaranteed tender 0 –0.3

–0.2

–0.1

0

0.1

0.2

0.3

-0.05

-0.1

CAB

Generic -0.15

Fig. 12.2 Perceptual map from multidimensional scaling applied to auction bid differences.

and generic scores lowest in this y-dimension would support this notion. This conjecture is also supported by the fact that the USDA steak scores highly in the y-dimension and people might have seen the “USDA” label associated with “Choice” as indicating something about the level of safety assurance provided by the government agency (note: such belief would be mistaken as all steaks undergo the same USDA inspection, but of course people’s beliefs do not have to match reality). One observation that emerges from Fig. 12.2 is that the guaranteed tender steak is the most dissimilar to the other four steaks, at least in these two dimensions. Exploratory factor analysis A related approach that can be used to identify groupings of steak/brand types is factor analysis. Exploratory factor analysis can be used to determine whether there might be common dimensions of preference or some theoretical constructs that explain bids for some steaks, but not others. Using the matrix of the correlation coefficients (or the covariance matrix) between bids for the five steaks, I carried out an exploratory factor analysis (for more details on the approach see Hatcher (1994) or Kline (2002)). The results revealed that one factor explains the vast majority of the variance in the underlying bids (over 90% to be exact). However, in the results that follow,

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Table 12.6 Standardized regression coefficients from exploratory factor analysis; results from Promax/oblique rotation Variable

Factor 1

Factor 2

Factor 3

Standardized regression coefficients Generic bid Guaranteed tender bid Natural bid Choice bid CAB bid

0.79*a 0.69* 0.14 0.27 0.01

0.06 0.20 0.07 0.76* 0.70*

0.06 0.13 0.52* −0.07 0.28

Inter-factor correlations Factor 1 Factor 2 Factor 3

1.00 0.71 0.60

0.71 1.00 0.65

0.60 0.65 1.00

a

For expositional reasons, standardized coefficients greater than 0.50 were indicated with a *.

I retained the first three factors as they each contributed positively to the percentage of variation explained, and because a three-factor solution yielded results that are interpretable (i.e., each variable exhibited high loadings with only one factor). Results from the rotated three factor solution are shown in Table 12.6. The results indicate that the generic and guaranteed tender steaks load most highly onto the first factor, the Choice and CAB steaks load most highly onto the second factor, and the natural steak is the only to load highly onto the third factor. Similar to the argument in the preceding subsection, the data suggest one latent factor related to taste and preferences for fat content – i.e., the second factor driving bids for Choice and CAB. The third factor is most likely related to safety concerns as the natural steak is the only steak to load highly on the third factor. This leaves the first factor, which perhaps simply relates to an overall taste for beef irrespective of safety/quality. 12.2.6 Characterizing consumer space Whereas Section 12.2.5 discussed methods to identify relationships between goods (or steaks), here we consider a method for organizing people into similar response categories based on their bidding behavior. More precisely, cluster analysis might be used to identify groups of people with similar bidding behavior. Such knowledge can be useful for new product developers interested in uncovering market segments. Many companies use segmentation strategies to cluster people with similar purchasing behavior and tailor their marketing approaches to each segment. Numerous methods are used to create clusters; the most common approach being k-means clustering which creates clusters to minimize the variability within clusters and maximize the variability between clusters. Here, I use an explicit likelihood function approach to identify clusters in

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the data, and because of the censored nature of the bid data (i.e., there are several $0 bids) a censored (or tobit) regression formulation is used. Let WTPij represent individual i’s bid on steak j. For K latent classes, the probability density function for a sequence of J bids can be written as follows: ⎛ −WTPjk ⎞ f ( λi ) = ∑ P ( k ) ∏ Φ ⎜ ⎟ k =1 j =1 ⎝ σ jk ⎠ k

J

WTPij =0

⎛ WTPijk − WTPjk ⎞ φ⎜ ⎟ σ i2j ⎝ ⎠

WTPij >0

,

12.4

where φ is the standard normal probability density _ function, Φ is the standard normal cumulative distribution function, WTPjk is the mean estimated bid for steak j in cluster k; σjk is the cluster- and steak-specific standard deviation; and P(k) is the probability of belonging to cluster k, which I _ e αk . The parameters in equation 12.4, αk, WTPjk, parameterize as P ( k ) = K ∑ e αm m =1

and σjk, are estimated by maximizing the log value of (12.4) summed across the N individuals in the sample. Analysis indicates that AIC and BIC criteria are minimized at k = 6, and as such, the results from the 6-cluster solution are presented in Table 12.7. Table 12.7

Results of cluster analysis applied to auction bids Class/Cluster

Estimated parameters Generic mean bid GT mean bid Natural mean bid Choice mean bid CAB mean bid Overall average bid Sigma Class probability

1

2

3

4

5

6

5.096 6.879 3.970 8.225 9.523 6.739 2.245 0.098

1.485 1.827 1.762 2.203 2.458 1.947 0.494 0.117

0.499 0.743 0.676 0.939 1.091 0.790 0.430 0.081

−0.321 1.236 0.556 2.685 3.323 1.496 2.601 0.256

2.085 3.037 2.649 3.573 4.172 3.103 0.650 0.217

3.361 3.984 4.169 5.080 5.483 4.416 0.869 0.231

0.63 0.94 0.86 1.19 1.38

−0.21 0.83 0.37 1.80 2.22

0.67 0.98 0.85 1.15 1.34

0.76 0.90 0.94 1.15 1.24

0.400 26.100 0.400 0.300 2.300 0.600

0.533 37.800 0.200 0.433 4.033 0.300

0.615 32.808 0.308 0.346 3.846 0.462

0.407 35.741 0.296 0.518 5.519 0.333

Ratio of steak means to class average Generic 0.76 0.76 GT 1.02 0.94 Natural 0.59 0.90 Choice 1.22 1.13 CAB 1.41 1.26 Mean demographics Female Age Children College degree Income Student

0.333 35.667 0.333 0.583 5.583 0.417

0.571 28.214 0.071 0.286 2.500 0.714

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$10 Generic GT $8

Natural Choice

Mean bid

CAB $6

$4

$2

$0 1

2

3

4

5

6

Class

Fig. 12.3

Mean bid for each steak type by cluster/class membership.

The top portion of Table 12.7 shows the estimated cluster parameters, and the bottom portion of the table reports means of several demographic variables by cluster membership (note: cluster membership was determined by assigning people to the cluster for which they had the largest posterior probability of belonging). Figure 12.3 displays the results in graphical form. The last three classes were the largest, with 25.6%, 21.7%, and 23.1% of respondents falling into classes 4, 5, and 6, respectively. Results reveal that people in segment/cluster/class 1 (making up about 9.8% of the sample) tended to bid the highest on average for all steaks, whereas cluster 3 bid the lowest on average. Clusters 2 and 3 exhibited low variation in bids, i.e. bids on all steak types were relatively similar, but by contrast, people in class 1 and especially in class 4 differentiated their bids markedly across the five types of steaks. Although class 1 exhibited the highest average bid across all five types of steaks, this class of people did not place a high value on the natural steak; by contrast, the people in class 6 preferred natural steaks relatively more than any other segment. The last row of Table 12.7 reports the means of several demographic variables (which are defined in Table 12.4) by class membership. There were many more females (61.5%) in class 6 than there were in class 1 (33.3%). Members of class 3 (the low bidders), were younger on average and tended to be more likely to be students than members of other clusters. Class 1 members (the high bidders) had the highest income on average.

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12.3

Frontier research in experimental auction markets

The primary purpose of this chapter was to give readers a feel for the varied uses of experimental auction data. As can be seen, auction data are quite versatile and can be used to address a wealth of questions of relevance in new product development. In this last section I briefly outline some of the frontiers of experimental auction research and mention some potential areas for future research.

12.3.1 Field experiments Recent experimental work has begun to move from laboratory settings to the field to elicit values at the point of purchase. As discussed by Harrison and List (2004), this is an important methodological change. Moving the auction to more familiar territory such as grocery store or mall might be advantageous for a number of reasons. First, subjects self-select into the market, and as such, sample-selection biases are of less concern as the population of interest is directly intercepted. Second, in a field setting, subjects are able to bring all their learned knowledge to bear on the task at hand. To the extent that individuals develop heuristics to make purchase decisions in the marketplace, conducting an experiment in a field setting allows individuals to use such heuristics. Many times, field experiments provide interesting tests of the effect of market experience on behavior. The effect of market experience on behavior is non-trivial and has been shown to have important economic consequences in a variety of settings. Field valuation can reduce the costs of experimental work, and the associated biases that go along with the compensatory fees that must often be paid to subjects to attend a laboratory session. For example, we have recruited participants by offering a free unit of a conventional good and elicited a bid to exchange the endowed good for a “new” counterpart, and we have used bids and choices employing store coupons/gift cards to establish people’s values in a retail establishment. In many field auctions no inducement is needed; subjects select into the market because they are interested in purchasing the good. In summary, there are reasons to believe that moving the experimental auction markets in field settings might enhance predictive validity, but more work is needed to identify the effect of elicitation setting on behavior and to determine which settings exhibit higher levels of external validity.

12.3.2 Hybrid elicitation methods that combine auction and conjoint One drawback to experimental auction methods are that they involve procedures that can be unfamiliar to people. Even though people, theoretically, have an incentive to submit bids equal to true values, they may not do so

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even if informed of the logic of this strategy. Thus, we highly recommend auction participants be given adequate training with the mechanisms and have time to learn. Another potential drawback is that people’s bids in auctions might be influenced by a host of factors unrelated to their value for the good. In an attempt to address both these concerns, researchers are beginning to consider hybrid methods that combine the advantages of experimental auctions (binding commitments, incentive compatibility, responses in dollarspace, etc.) with the advantages of more traditional methods such as conjoint analysis (ease of administration, direct links to product attributes, etc.). For example, Ding et al. (2005) employed a typical conjoint approach where people were shown a series of product profiles that differed along several attributes (e.g., brand, price, package size, etc.), but rather than asking people to (hypothetically) rate or rank the desirability of each product, people stated their maximum buying price for each product profile, one of these profiles was randomly chosen, and a BDM mechanism was used to determine whether the profile was actually purchased. In some ongoing research with Bailey Norwood, I have developed a hybrid approach we refer to as a calibrated auction-conjoint method (CACM), in which people rate the desirability of product attributes and attribute levels (as in selfexplicated conjoint analysis), but where the ratings are inputted into a computer to construct bids for product profiles which are then submitted in an experimental auction. The CACM involves an iterative and interactive decision making task where people’s auction bids are inextricably linked to an underlying attribute-based utility function. The method is “selfcalibrating” because the only way for people to alter their bids for a product is by changing their utility coefficients via responses to rating scales. By combining such methods, people are able to see directly the economic consequences implied by their conjoint-ratings (i.e., conjoint-ratings are placed in an economic context where choices have consequences that might otherwise be less transparent), and likewise, the auction bids are placed in a context where it is clear that the valuations correspond in a systematic way to underlying product attributes.

12.3.3 Data combination strategies Rather than creating new methods, researchers are beginning to think about how to combine data from different value elicitation approaches to exploit the advantages of each. For example, we have studied whether data from experimental auctions and data from discrete choice experiments might be merged into a single prediction model. Moreover, just as researchers have attempted to combine stated preference data from surveys with scanner data in an effort to improve predictions, auction data might similarly be employed. Such methodological studies would also be useful in carrying out further tests of the external validity of auction data.

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12.3.4 Psychology and bidding behavior One relatively unexplored area worthy of future research is a deeper investigation into the determinants of bidding behavior that may relate to personality or emotions. For example, previous research using psychometric scales has identified competitiveness as a personality trait. Are more competitive people more likely to attend and participate in experimental auctions? What would such a finding imply about the validity of elicited values? Alternatively, why not use experimental auctions to study competitiveness? Of course competitiveness is but one of many personality traits that might be studied when conducting experimental auctions, and experimental auctions represent a useful platform in which to test hypotheses about the effect of personality differences. In addition to personality traits, there is some research suggesting that emotions can affect willingness-to-pay. For example, Lerner et al. (2004) showed that emotions induced by having people watch movie clips carried over to valuation exercises can have a significant effect on people’s bids. There is also ongoing research being conducted at Cornell University studying the relationship between people’s temperatures and heart rate (measured by infrared devices) on bidding behavior in experimental auctions for food products. 12.3.5 On-line auctions Finally, there has been an increasing trend in using the Internet to carry out new product development and consumer testing. Experimental auctions are readily amenable to such an environment, and one only has to turn to examples such as e-Bay to see that real-money auctions can thrive on the Internet. One advantage to moving to an on-line setting is that a much broader and more diverse subject pool can be used in the experimental auction. It might be possible to work within the context of existing online auction web sites, such as e-Bay, to conduct new product development research by simultaneously offering competing products and comparing bids across products. One difficulty with such an approach is that one does not have as much ability to control the other substitutes that may be available on e-Bay, and moreover, unlike the case with more traditional experimental auctions, the types of auctions used by e-Bay may not be incentive compatible and will not yield bids from each person who may be interested in the products. However, such concerns could be alleviated by recruiting a random sample of people to log-on to a specially designed web site which requires each participant to submit bids on each competing product in an auction environment that is designed to give people incentives to truthfully reveal their values. 12.3.6 Concluding comments Experimental auctions possess several advantages over other approaches used to determine consumers’ preferences: they force people to think

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carefully about their responses by employing binding commitments, they yield measurements that are in money-space and are directly interpretable without having to rely on statistical models, and they do not require restrictive assumptions about the functional form for utility. To this point, one of the factors that has hindered more widespread adoption of experimental auction methods is a lack of familiarity with the method and a lack of exposure to examples of cases in which auction bids can be fruitfully employed. This chapter represents an explicit attempt to narrow the gap between those of us who routinely use auction methods and those who are interested in expanding their toolkit.

12.4 Sources of further information and advice Readers interested in learning more about experimental auctions may find the following references useful. For a more exhaustive list of over 100 studies using experimental auctions, see table 1 in Lusk and Shogren (2007). The following sources provide general guides and advice on using experimental auctions: Loheac, Y., B. Brest, S. Issanchou. (2007). “Using Auctions to Estimate Prices and Value of Food Products.” Consumer-Led Food Product Development, H. MacFie, ed. Cambridge, UK: Woodhead Publishing. Lusk, J.L. and J. Shogren. (2007). Experimental Auctions: Methods and Applications in Economic and Marketing Research. Cambridge, UK: Cambridge University Press. Shogren, J.F. (2005). “Experimental Methods and Valuation.” in Handbook of Environmental Economics, K.G. Mäler and J. Vincent, eds. Amsterdam: North-Holland. The following papers integrate experimental auction methods with food sensory evaluations: Lange, C., C. Martin, C. Chabanet, P. Combris, and S. Issanchou. (2002). “Impact of the Information Provided to Consumers on Their Willingnessto-Pay for Champagne: Comparison with Hedonic Scores.” Food Quality and Preference 13:597–608. Lusk, J.L., J.A. Fox, T.C. Schroeder, J. Mintert, and M. Koohmaraie. (2001). “In-Store Valuation of Steak Tenderness.” American Journal of Agricultural Economics 83:539–550. Jaeger, S. and R. Harker. (2005). “Consumer Evaluation of Novel Kiwifruit: Willingness-to-Pay.” Journal of the Science of Food and Agriculture 85:2519–2526. Kassardjian, E., J. Gamble., A. Gunson, and S.R. Jaeger. (2005). “A New Approach to Elicit Consumers’ Willingness to Purchase Genetically Modified Apples.” British Food Journal 107:541–555.

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Noussair, C., S. Robin, and B. Ruffieux. (2004). “A Comparison of Hedonic Rating and Demand-Revealing Auctions.” Food Quality and Preference 15:393–402. Platter, W.J., J. D. Tatum, K. E. Belk, S. R. Koontz, P. L. Chapman, and G. C. Smith. (2005). “Effects of Marbling and Shear Force on Consumers’ Willingness to Pay for Beef Strip Loin Steaks.” Journal of Animal Science 83:890–899. The following papers are seminal or influential papers on experimental auctions: Brookshire, D.S., D.L. Coursey, and W.D. Schulze. (1987). “The External Validity of Experimental Economics Techniques: Analysis of Demand Behavior.” Economic Inquiry 25:239–250. Hayes, D.J., J.F. Shogren, S.U. Shin, J.B. Kliebenstein. (1995). “Valuing Food Safety in Experimental Auction Markets.” American Journal of Agricultural Economics 77:40–53. Hoffman, E., D. Menkhaus, D. Chakravarti, R. Field, and G. Whipple. (1993). “Using Laboratory Experimental Auctions in Marketing Research: A Case Study of New Packaging for Fresh Beef.” Marketing Science 12:318–338. Wertenbroch, K., and B. Skiera. (2002). “Measuring Consumers’ Willingness to Pay at the Point of Purchase.” Journal of Marketing Research 39:228–241.

12.5 References becker, g.m., m.h. degroot, and j. marschak. (1964). “Measuring Utility by a SingleResponse Sequential Method.” Behavioural Science 9:226–232. clemen, r. (1989). “Combining Forecasts: A Review and Annotated Bibliography.” International Journal of Forecasting 5:559–583. ding, m., r. grewal, and j. liechty. (2005). “Incentive-Aligned Conjoint Analysis.” Journal of Marketing Research 42:67–83. harrison, g. and j.a. list. (2004). “Field Experiments.” Journal of Economics Literature 42:1009–1055. hatcher, l. (1994). A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equations Modeling. Cary, NC: SAS Institute. hoffman, e., d. menkhaus, d. chakravarti, r. field, and g. whipple. (1993). “Using Laboratory Experimental Auctions in Marketing Research: A Case Study of New Packaging for Fresh Beef.” Marketing Science 12:318–338. huang, h.c. (1999). “Estimation of the SUR Tobit model via the MCECM Algorithm.” Economics Letters 64:25–30. jaeger, s. and r. harker. (2005). “Consumer Evaluation of Novel Kiwifruit: Willingness-to-Pay.” Journal of the Science of Food and Agriculture 85:2519– 2526. kalwani, manohar u. and alvin j. silk. (1982). “On the Reliability and Predictive Validity of Purchase Intention Measures.” Marketing Science 1:243–283. kline, p. (2002). An Easy Guide to Factor Analysis. New York, NY: Routledge.

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lange, c., c. martin, c. chabanet, p. combris, and s. issanchou. (2002). “Impact of the Information Provided to Consumers on Their Willingness-to-Pay for Champagne: Comparison with Hedonic Scores.” Food Quality and Preference 13:597–608. lerner, j., d. small, and g. loewenstein. (2004). “Heart Strings and Purse Strings: Carry-Over Effects of Emotions on Economic Transactions.” Psychological Science 15:337–341. list, j.a. and c.a. gallet. (2001). “What Experimental Protocol Influence Disparities Between Actual and Hypothetical Stated Values?” Environmental and Resource Economics 20:241–254. little, j., and r. berrens. (2004). “Explaining Disparities between Actual and Hypothetical Stated Values: Further Investigation Using Meta-Analysis.” Economics Bulletin 3:1–13. lusk, j.l. and t.c. schroeder. (2006). “Auctions Bids and Shopping Choices.” Advances in Economic Analysis & Policy Vol. 6, No. 1, Article 4. lusk, j.l. and j. shogren. (2007). Experimental Auctions: Methods and Applications in Economic and Marketing Research. Cambridge, UK: Cambridge University Press. lusk, j.l., t. feldkamp, and t.c. schroeder. (2004). “Experimental Auction Procedure: Impact on Valuation of Quality Differentiated Goods.” American Journal of Agricultural Economics 86:389–405. morwitz, v.g. (1997). “Why Consumers Don’t Always Accurately Predict Their Own Future Behavior.” Marketing Letters 8:57–70. morwitz, v.g. (2001). “Methods for Forecasting from Intentions Data.” In Principles of Forecasting: A Handbook for Researchers and Practitioners, J.S. Armstrong, ed. Norwell, MA: Kluwer Academic Publishers, pp 34–56. murphy, j.j., p.g. allen, t.h. stevens and d. weatherhead. (2005). “A Meta-Analysis of Hypothetical Bias in Stated Preference Valuation.” Environmental and Resource Economics 30:313–325. schiffman. s. s., reynolds, m. l., and young. f. w. (1981). Introduction to Multidimensional Scaling. New York: Academic Press. wertenbroch, k., and b. skiera. (2002). “Measuring Consumers’ Willingness to Pay at the Point of Purchase.” Journal of Marketing Research 39:228–241. young. f. w. and hamer. r. m. (1994). Theory and Applications of Multidimensional Scaling. Hillsdale, NJ: Erlbaum Associates.

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13 Doing consumer research in the field C. R. Payne, New Mexico State University School of Business, USA and B. Wansink, Cornell University, USA

Abstract: Why consider field contexts to understand consumer behavior? Conventional wisdom suggests laboratory contexts are more convenient, offer a greater degree of control, and are generally cheaper. New product failures, however, suggest a lack of consumer information in the contexts that they actually behave. Field contexts can provide this essential information, which can be a catalyst for new product success. This chapter outlines the nature of field research, showing how to navigate through potential landmines that can result in consumer field data being data far afield. Specifically, we discuss four different types of field research based on combinations of control and realism. Within each of these four fields, variables allowing conclusions of not only when they work, but also why, whether to test these variables during one occasion or many, questionnaire design, and field data analysis are considered. We conclude the chapter by discussing common field mistakes regarding a field study’s product, placement, and promotion to research gatekeepers. Key words: field study, consumer research, field study types, realism, control.

13.1 Introduction 13.1.1 The need for field research Why is consumer field research important? Time, effort, and resources needed to gather information from field settings provide little incentive. Laboratory research – on the other hand – is convenient, controlled, and relatively cheap. Internal reliability of laboratory research, however, comes at the cost of external reliability: consumers do not live, eat, shop, and make decisions in laboratories. The true context in which consumers live is essential to understand how and why they behave. This chapter outlines the nature of field research with the intent of providing guidance to avoid costly mistakes that could limit success for a new product in the marketplace. Consider Persil Power. In 1994 (remember Forrest Gump?), Unilever launched what it thought would be a breakthrough laundry detergent. After

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10 years, 35 patents, and significant monetary investment, Persil Power made its debut throughout Europe (Knox, 2002). Laboratory tests confirmed that manganese, Persil Power’s key ingredient, cut through stains quicker than anything currently on the market. Yet, Persil Power failed horribly in the marketplace after only a few weeks. What happened? Even though Unilever took their findings from the laboratory to the field, their tests were not really field research. They did not test their new product in the true contexts in which their consumers behaved. If they had, they would have found that many consumers frequently washed their dyed garments at high temperatures, despite the garment’s washing instructions. Persil Power’s key ingredient, manganese, did very well at low and high temperatures with white garments, but became volatile at high temperatures with dyed garments. How volatile? – Volatile enough to actually destroy a garment. Once consumers experienced the “power” of Persil and shared their stories with others, Unilever pulled the product from the marketplace losing hundreds of millions of dollars in new product development. If the unfortunate fate of Persil Power represented the exception to what can happen in doing consumer field studies, there would be no need for this chapter. However, stories of field study failure in new product development are legion (see Haig, 2005). This suggests two things. First, people recognize the importance of field studies. Second, however, they may not recognize how to do them well. Is it possible to execute a perfect field study? No. Is it possible to conduct a field study that results in information from which you and your organization can be confident represents your consumers’ behavior? Absolutely! We attempt to provide guidance regarding the nature of field research, what to consider when doing field research, and common mistakes to avoid while doing field research – but first, an overview of the chapter.

13.1.2 Planting, irrigating, and harvesting: a field study overview In broad terms, consumer field research has many similarities to the work of farming – not a sexy metaphor, but stay with me. Farmers work long strenuous hours, get their hands dirty, and deal with unexpected environmental events. As consumer field researchers, you will too. Both authors have spent many sleepless nights preparing for consumer field studies, have weighed hundreds – if not thousands – of pounds of saliva soaked food, and have been surprised by county food inspectors, a fire, a medical emergency, and genuinely creepy people. In specific terms, if field research is like farming, then conceptualization and setup of field research is like “planting,” execution of the study like “irrigating,” and analysis of field data like “harvesting.” It is not enough to be proficient at only one of these aspects of consumer field research. All three must be done well to produce information that can be leveraged to increase the likelihood of producing actionable results.

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Planting Conceptualization and setup of field studies begin months before – hopefully. The planning process should consider the most appropriate context to answer the research question of interest. In addition to consideration of contexts, the field study planning process should include decisions regarding its design such as choice of variables that allow conclusions of not only “when” they work, but also “why” (i.e., moderation and mediation) and whether to test these variables during one occasion or many (i.e., between and within subject designs). The use and measurement of these variables necessitate the creation of a questionnaire that is comprehensive enough to include all variables that a researcher believes will be useful in explaining “when” and “why” of consumer behavior, yet succinct enough to not be confusing or too time consuming. Finally, the field study planning process should consider procurement of equipment and supplies, Institutional Review Board permission (if applicable), and staffing. While these latter aspects of field study planning are neither “fun” nor intellectually engaging, they are essential in making sure that field studies run smoothly. The extent to which you can minimize deviations from the carefully planned field study script, the more likely the study will produce meaningful data. Irrigating Much like a field must rely on timely and frequent nourishment to avoid crop failure, so too must a field study have timely and frequent nourishment to keep it from failing. It is impossible to anticipate all potential problems. However, there are certain irrigation tools that you can use during the field study execution to avoid terminal catastrophes. We like to think of these tools as encompassing three broad categories: product, place, and promotion. Product simply refers to making sure that – not only do you have enough of it – but also making sure you have a staff member dedicated to the sole purpose of replenishing it during the field study. Place refers to awareness of the global and specific environment of your field study, becoming aware of potential problems that could or are occurring and being willing to make changes. Promotion refers to the “pitching” of your study idea to research gatekeepers (i.e., organizational management, ethics review boards, management of field locations, and study participants). For all gatekeepers, your study should emphasize careful consideration of your ethical obligation to study participants. Product, place, and promotion are all discussed in depth later. Harvesting The field study has now been completed: the proper context has been chosen, the questionnaire created and then used, and you have been as flexible as Gilligan (read: no spine) in adapting to what the field study environment has thrown at you. You can sit back and relax, right? Yes and

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no. Make sure you congratulate yourself and others for helping complete one of the most challenging aspects of research: the field study! Go to a movie, go out with friends, or buy (and eat?) caviar. You deserve it. Harvesting the data is next. Field study data is unique in many respects. Unlike laboratory studies, there is much more missing data, the potential for coder error is high, and the wrong analysis can yield null results. The way in which a researcher can describe field data, use predictive analytics, and target specific sub-segments of customers is unique too. Specific statistical computer programs are built to deal with some of the limitations of field data (generalized estimating equations, classification and regression trees, etc.) and can be used to maximize the probability of finding actionable information.

13.2 The nature of the field 13.2.1 The realism-control (RC) matrix The purpose of field research is to maximize both consumer realism (external validity) and control (internal validity). However, given limitations based on conceptualization, execution, and data type, some fields are more appropriate than others. For example, a field researcher who is interested in understanding how consumers use a product would more than likely use an observational study that captures consumer behavior in its natural context whether that context be a restaurant, a home, or the workplace. This would be in contrast to other field contexts – such as internet, marketplace, and hybrid field studies – that would also be useful, but do not approach the realism needed to understand the research question. The RC matrix suggests four domains in which these fields can operate (see Fig. 13.1). High control

Hybrid

Marketplace

Low realism

High realism

Internet

Observational

Low control

Fig. 13.1 The RC matrix for consumer research.

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13.2.2 Internet fields The internet is considered a context for field research because data can be collected in the exact environment in which consumers interact with a (virtual) product that then can lead to purchase and consumption. Internet fields have less control than other field contexts simply because you do not have direct access to what is happening around consumers as they interact with your research. Also, internet fields may be less realistic than other fields because it is difficult to know how representative obtained consumer samples are (see Fig. 13.1). If done correctly, however, internet fields can provide relatively quick answers using fewer resources than other field contexts. While internet-based research has been in the field researcher’s tool-box for quite some time, only recently has the capability of this context proved to be an asset in terms of believability of results. Lists purchased from market researchers (i.e., www. infousa.com) can target consumer populations of interest and internet consumer panels can help relieve concerns of inadequate response rates. Part of the reluctance of researchers using this context – besides obtaining representative samples – is the previous inability of internet-based field research to incorporate experimental methodology. Because of this, most internet-based research resulted in survey type executions. This is not the case anymore. Using www.qualtrics.com, for example, you can randomly assign consumers to single or multiple exposures to variables in complex factorial designs. As an example, to test the idea that positioning food in certain ways on plates results in different ratings of acceptability and willingness to pay, the authors used the internet to execute a 4 × 2 × 2 mixed model design (Shimizu et al., 2009). It included two within subject factors (type of food and type of food in different combinations) and one between subject factor (positioning of plate). The great thing about creating this design in this context was that once it was loaded into the internet-based research engine and study links were sent via email to potential participants, the actual execution of the work was complete.

13.2.3 Observational fields Observational fields include those which researchers attempt to become part of the marketplace periphery to examine consumer behaviors “in the real world.” This field – relative to all others – is the most realistic; in what other field can you record behavior in its exact marketplace context without contamination that could occur from consumer–researcher interaction? However, complete realism comes at the cost of not being able to systematically test variables thought to influence behavior; in other words, observational fields lack experimental control (see Fig. 13.1). Despite this, observational fields can provide rich data from which interesting associations can be found and tentative conclusions can be drawn.

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Those who actually observe should collect data systematically (i.e., fixed time intervals over the span of specific days) and be blind to the hypotheses of the work. Otherwise, a study may be influenced by unique characteristics of a particular day or time (i.e., only Friday or Saturday consumption or mid-afternoon dining) or observers choosing particular people for whom they feel a study is most appropriate. An example is given to illustrate. Wansink and Payne (2008) conducted an observational field study wherein 216 people were observed at Chinese buffets around the United States over the span of one week (Monday through Sunday) from 11:00 am to 1:00 pm. Twelve researchers were trained to estimate buffet patrons’ height and weight (to later calculate body mass index). Demographic variables, such as sex, age, height and weight and eating behaviors, such as number of chews per bite, choice of small or large plate, feet from buffet, use of chopsticks or fork, food selection strategy, were observed, estimated, and recorded on a 1-page code sheet. Results indicated that higher body mass indices were associated with the use of forks versus chopsticks, smaller versus larger plates, fewer versus more chews per bite, sitting closer versus farther away to the buffet, and immediately serving versus “scouting out” the buffet. Even though we did not tell observers the goal or intent of the study, we also statistically controlled for the possibility of individual observer bias by creating a series of dummy variables representing the presence of a particular coder. In this way, we could be more confident about the results obtained. Confidence in results was also obtained by knowing that the data was collected systematically specifying both the days and times observers were to collect data.

13.2.4 Marketplace fields Marketplace consumer research fields are those in which consumers interact with a product in its natural context, but do so under the experimental design of the field researcher. These fields include malls, stores, preschools, restaurants, and homes – to name a few. Marketplace fields – relative to internet and observational – are more realistic and provide a greater degree of control (see Fig. 13.1). This is because marketplace field researchers can incorporate experimental designs into the exact context in which consumers operate. A major obstacle that can arise in this field is getting permission to conduct your study. Field researchers at universities will have to contend with getting permission from their respective institutional review boards. Field researchers at universities and otherwise may also need to get permission from school district institutional review boards and top-level management at malls and stores. In a recent study for preschoolers, for example, we needed university consent, preschool director consent, teacher consent, parental consent, child assent, and anonymity assurances for quantitative and qualitative data before we were allowed to step one foot into the preschool. Ideas for getting permission to work in this field are discussed later.

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Once permission is obtained for the study, execution of the field study can begin. Experimental designs can be incorporated into all marketplace fields. For example, in a recent study, the authors gained access to a community preschool where they incorporated a simple between subject design that randomly assigned children to receive one of two differently sized bowls. In each case, field researchers asked the child how much of a sweet snack they would like. Researchers used a small serving spoon to give them 2–3 ounces of cereal followed by the question “Is that enough or do you want more?” or “Do you want more or is that enough?” The goal was to understand if bowl size influenced preschool children’s requests for more food. Indeed, bowl size did influence children’s requests – larger bowls led to requests for more food.

13.2.5 Hybrid fields Hybrid fields are those that export laboratory type controls to field studies. Hybrid fields incorporate the most control in their designs as compared to other fields, yet have relatively lower realism than marketplace and observational fields (see Fig. 13.1). While marketplace fields incorporate experimental designs in “messy environments,” hybrid fields attempt to leverage “built” marketplace contexts. These built marketplace contexts are designed so that specific precautions can be made to limit the influence of unimportant environmental variables on variables hypothesized to influence behavior. In field research parlance, central location tests are very similar. In contrast to central location tests, however, hybrid fields attempt to incorporate as much realism into the consumption context as the controlled experiment allows. A school cafeteria, for example, was used to build a laboratory type experiment. A portion of the cafeteria was enclosed with portable dividers so regular cafeteria patrons could not see what was happening inside. Participants recruited from the school campus entered through a gap in the portable dividers, were given either cash or a meal card, and allowed to choose and consume whatever foods they wished that were on a display. The goal of the hybrid field study was to understand better how different payment systems affected what foods students chose and how much they consumed. Had we conducted the experiment in the cafeteria without portable dividers, non-recruited students would have noticed that we were giving away free money and meal cards. You can imagine what would have happened to us and our study had we done this. Because we included a believable context in which to test the study’s hypotheses, we could be relatively confident that results obtained would replicate in the exact context of the study. Also, because we were careful to limit unnecessary distractions (free money!) that could have occurred because the environment was not controlled, we could be reasonably confident that the variables of interest actually worked (Just et al., 2008).

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13.3 Consumer field study considerations Table 13.1 illustrates four key aspects of field research that should be considered for each field type. Consideration of moderation/mediation variables entails testing contexts and processes that may affect consumers’ relationship with a product. Consideration of within/between designs entails deciding if single or multiple exposures to variables of interest are necessary to understand consumer behavior. Consideration of questionnaire design entails constructing the appropriate questionnaire that matches field type. Lastly, consideration of analysis entails awareness of types of analytical tools available as a function of field type. We discuss each of these considerations in turn and provide a reading list at the end of the chapter for curious readers.

13.3.1 Moderation or mediation? A key component to understanding how new food products will fare in the real world is to understand in what contexts and by what processes consumers’ relationship with that product is affected. In field research parlance, emphasis on context – whether physical, psychological, or demographic – necessitates use of “moderating” variables, while emphasis on process necessitates use of “mediation” variables. The use of moderating or mediating variables can occur in any field type. Moderating variables such as food labeling, packaging, placement, variety, and consumers’ mood, dietary restraint, gender, and socioeconomic status have all been shown to modify evaluation and consumption of food. For example, Wansink et al., (2001) found that by simply changing the labels of the same food (i.e., chocolate cake to Belgian Black Forest chocolate cake) in a restaurant setting, consumers evaluated it as having better taste and texture and consumed more of it. Similarly, presenting food on ornate versus plain tableware, it is rated more favorably and consumers are willing to pay more. Different food labels and tableware “moderate” because they directly control consumers’ reaction to food (i.e., evaluation, consumption, and willingness to pay). Thus, moderation refers to the extent of direct control that different contexts have on consumer behavior. While moderating contexts “work” in terms of providing results that researchers intended, it is generally not known by what means or “why” they do. The long-term consequence of not knowing “why” may result in many resources used on product development targeting a particular market segment only resulting in market failure. Consider a food packaging example. A food company may want to stimulate sales of a product by introducing new packaging. They test consumer preferences for this new packaging against the original and find that it does indeed increase sales. The food company can now say that packaging seems to moderate sales. Because they are so successful with new packaging of

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•

•

Marketplace

Hybrid

•

•

•

•

Moderation and mediation most powerful here because of controlled context

Moderation and mediation easily incorporated here, but not as robust as hybrid

Non-experimental. However, pseudo moderation possible Mediation possible, but very difficult to obtain

Experimental moderation occurs as a function of visual stimulus or non-manipulated variables Mediation models best supported because of large sample size

Moderation or mediation?

• •

•

•

•

Both are possible Best place to implement

Within subject design difficult if not impossible

Non-experimental

Both easily executed here, but high drop-out rates for multiple

One occasion or many?

Considerations for different consumer field study types

Observational

Internet

Table 13.1

Progress bar Easy navigation of web page

•

•

•

•

Researcher and consumer use Manipulation checks

Researcher and consumer use Manipulation checks

• 1-page code sheet • Categories

• •

Questionnaire design

All analysis types possible Path analysis/ Structural equation modeling

•

•

•

•

Classification and regression trees Analysis of variance/ regression

Classification and regression trees Analysis of variance/ regression

Correlated observer data • General estimating equations • Path analysis/ Structural equation modeling

•

•

•

Analysis

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the first product, they immediately change the packaging of a second. Unfortunately, sales actually drop this time. What happened? Changing package type indeed moderated sales in the first and the second product, but not predictably. It happens that the new packaging that the food company introduces for the first product is taller and skinnier than the original, while new packaging for the second is shorter and wider than the original. Food or drink packaged in tall skinny containers is perceived to contain more than the same amount of food or drink packaged in short wide containers because people focus on the height rather than the width when judging quantity (Wansink and van Ittersum, 2005). Given that consumers appreciate value – in this case, getting a lot of product for their money – any perception of “more” will result in increased sales. Had the company included a questionnaire that assessed consumers’ perceptions of quantity (a mediating variable), they could have known that it was not the introduction of a “new” package that increased or decreased sales, but the perception that the “new” package contained more in the first case and less in the second. Now knowing “why” packaging influences sales, the food company can be confident that changing package shape in numerous contexts will result in increased sales. Should field research use moderating or mediating variables? It is difficult to imagine a field study in which a researcher would not want to use both. In both cases you can get “second chances” regarding multiple means by which you can achieve actionable results. With moderation, even if a variable does not moderate as intended, it may in combination with other potential moderators (i.e., interactions or fractional factorial designs) and/ or once controlling for irrelevant variables that may skew the results of the current study, but are not important to the real world execution of the deployment of the new product into the marketplace (ANCOVA and regression designs). With mediation, even if a variable does not mediate as intended, a multitude of other variables may. While field researchers should use both moderating and mediating variables, Table 13.1 suggests different considerations when using them in different field types. Internet fields, for example, can easily incorporate mediating variables, but have the added feature of producing mediation results that are very statistically robust results given large sample size requirements for tests of mediation. However, internet fields can only include visual moderating variables – because of the constraints of the research medium – or other non-manipulated moderating variables (e.g., gender). In contrast, while observational fields cannot incorporate experimental moderation, they can include non-manipulated moderating variables. Incorporating mediation variables in observational fields is almost impossible because mediation data is usually gathered exclusively through consumer self-report attitude and observation. Fields in which moderation and mediation variables are most easily deployed are marketplace and hybrid. Both of these fields can seamlessly

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incorporate these variables generally without limitation. However, moderation/mediation results obtained from hybrid fields – versus marketplace fields – are more believable. This is because hybrid fields contain a greater degree of experimental control limiting the influence of irrelevant external variables that can confound results of a field study.

13.3.2 One occasion or many? It is important for field researchers to understand how consumers respond to a particular product in different contexts, but should this response be measured once or multiple times? In other words, is a “snap-shot” of consumers’ behavior enough to be confident of how they will interact with the product in the future? Successful product life cycle prediction may rest on field research that allows for many consumer interactions with the product over time in different contexts. Rarely, however, are resources available to do so. This is because it takes tremendous amounts of time, energy, and money to track large groups of people over time in experimental designs. This does not mean that a field researcher should not attempt to do it, but rather should be aware of field types that help facilitate or discourage the execution of multiple measurements of consumer behavior. One way to think about this issue is to understand the difference between within and between-subject experimental designs. These experimental designs imply how frequently consumers interact with a product. Within subject designs usually include consumers interacting with a product over time, while between subject designs are consumer behavior “snapshots.” Both within and between subject designs assume that people – either randomly assigned to treatment groups or treatment order – are similar in demographic, psychographic, and behavioral characteristics; that is, the only reason for a change in consumer behavior is due to different levels of a moderating variable not because of subject differences. The use of within or between experimental designs can occur in any field type except observational. Field research using within-subject designs exposes each consumer to all levels of a moderating variable (i.e., short wide package and tall skinny package) to know how consumers’ relationship with a product potentially changes. For example, wanting to assess consumer likeability of a product over time, a researcher may use marketplace fields to both study how different contexts affect likeability of a cookie and how this likeability changes as a function of time. Random assignment of consumers to different contexts in which they will experience the cookie is important. This is done to limit unintended effects on consumer behavior. That is, it could be that the order of cookie contexts – instead of cookie contexts themselves – is responsible for their behavior. Consumer one, for example, will evaluate the cookie at a sampling at the grocery store on week one, then at home on week two,

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then at restaurant on week three, while consumer two will evaluate the cookie at a restaurant on week one, at the grocery store on week two, then at home on week three. Between subject designs are more frequently used field research because of their convenient, controlled, and time and cost saving features – you only have to prepare and execute the study design once. To test the same idea as mentioned in the within-subject example, a between-subject design would randomly assign each consumer to one of three groups: evaluation of the cookie at home, evaluation of the cookie at the grocery store, and evaluation of the cookie at the grocery store. Each consumer only evaluates the cookie once. In the end, the researcher will be able to understand the effect of context, but not time on the likeability of the cookie. While field researchers will probably use between versus within subject designs because of their convenient, controlled, and time and cost saving features, Table 13.1 suggests different considerations when using each in different field types. Internet fields, for example, can easily incorporate within and between subject designs. However, when within-subject designs are used in internet fields, drop-out rates are usually high unless incentivized consumer panels are used (Dillman et al., 2008). Observational fields use neither within nor between subject designs because they are not experimental. Marketplace fields can easily incorporate between subject designs, but it is almost impossible to implement within subject designs. This is because in marketplace fields you are usually “pulling people off the street” to participate in your study. Getting them to come back to the same location at the exact time you wish would take very attractive incentives. This is not likely. Last, hybrid fields can easily incorporate both within and between designs. This field is the best place to implement within subject designs because consumers have already been recruited to participate in this field and have committed to coming to a marketplace-type context to participate. If they initially show up, it is much more likely that they will be willing to come again given the right incentives.

13.3.3 Questionnaire design One of the most important tools in consumer field research is the questionnaire. It is the script by which consumer and researcher wind their way through the study. If done correctly, the questionnaire can reduce experimenter confusion, increase consumers’ speed of completion, and promote easy transfer from questionnaire to electronic data. Questionnaires are essential in all field types (see Bradburn et al., 2004 for a thorough discussion). Confusion Experimenter confusion can occur when the questionnaire is not easily interpretable for quick coding by researcher or consumer. Consider Fig. 13.2.

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Date

Day

Time

Place

Operation: AYCE Height:

Weight

M

F

Time left:

Time seated: Body types of those at table

,

# items taken each trip: Trip1)

Seating Distance to walk to buffet Table Booth Face to food Back to food

Small plate Went up alone

,

,

,

Side

Both Both

Large plate Both Went with others

,

%

# at table

3)

Order Buffet Soda

ft

Manners Napkin on lap Not on lap Chewed mouth closed Open Didn’t add salt Added Bus Casual Very casual

Body type

% occupancy of restaurant

2)

How did they serve/eat Scouted out Dig-in Chopsticks Fork

Age

4)

5)

Both Water

Ala cart Tea/coffer

# Drink refills # Soup trips # Salad trips # Dessert trips # Rice

0 0 0 0 0

1 1 1 1 1

2 2 2 2 2

3+ 3+ 3+ 3+ 3+

# Noodles

0 1 2 3+

Plate Average plate fill level

%

Average leftovers

%

Average # chew/bite Insights:

Fig. 13.2

Field questionnaire for observational study.

A different version of this questionnaire was used for an observational field study mentioned previously in this chapter concerning eating behaviors at Chinese buffets. The first row asks for the date, day, time, and place. In pretests, some confusion occurred with what to enter for place; that is, was the entry asking for the name of the Chinese buffet restaurant, from where in the restaurant researchers were observing eating behaviors, or the actual city and state in which the Chinese buffet was located? While it may seem trivial as to what observers put in this field entry, it was not. The study could be confounded by observations that were unique to overrepresentation

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of only one type of buffet restaurant or one type of buffet restaurant in a particular area of the country. If we were able to obtain both the name of the buffet restaurant and city, state information, we could statistically accommodate for this possibility. Speed of completion Quick questionnaire completion in consumer field studies is important for two reasons. First, consumer self-reports of attitudes and behaviors pertaining to food are more likely to mirror reality if deliberation on these questions is kept at a minimum. This is because interactions with food are generally low involvement (Wansink, 2004). Second, quicker completion of a questionnaire results in more consumers completing the field study. This is important because statistical power – or the ability to detect a significant difference if there is one – is a direct function of sample size. Consider Fig. 13.3. This first page of a questionnaire was used in a hybrid field study in a school cafeteria. Instead of asking participants to write what they chose (and ate) for lunch, we provided them with the possibilities so that they could simply check the appropriate box and move quickly through the questionnaire.

Food and Brand Lab Lunch Survey Week 2 Please check[x] each of the foods you selected today. Next to the ones you selected indicate what percentage of the food you ate and how many calories you thought was in the portion you ate.

Entrees

% You ate

# Calories eaten

Bacon Cheese Burger Chicken Breast Turkey Sandwich Chicken Fingers

Sides Salad Baked Potato-Chips Macaroni and Cheese French Fries

Desserts Brownie Peaches

Drinks Skim Milk Soft Drink Water

Fig. 13.3

Food questionnaire for hybrid study.

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Data transfer Easy transfer of paper to electronic data is essential to reducing coding error, which can wreak terror into the validity of field data results. One obvious way to do this is to create a code sheet from the questionnaire that clearly delineates variable names and the number scheme by which you will record consumer responses. For example, what would be the best way to create a code sheet for Fig. 13.3? There are many good ways, but consider labeling each food as 1, 2, 3, 4; that is bcheese1, bcheese2, bcheese3, bcheese 4, salad1, salad2, salad3, salad4, etc. The person doing data entry can then quickly go row by row entering in a zero if consumers did not choose the food and a one if they did, then enter in the perceived percentage of food consumed, perceived calories consumed, followed by the actual calories consumed – obtained and written by the researcher in the empty box near the food. While all field researchers should use questionnaires, Table 13.1 suggests different considerations when using them in different field types. Internet fields, for example, should incorporate questionnaires that are easy to navigate on the web and include progress bars. If questionnaires on the web are difficult to read, get through, or generally cumbersome, people will simply shut it down. Further, including a progress bar has shown to increase completion of a survey simply because it will indicate “how much more” of the questionnaire they need to complete instead of assuming a never-ending series of questions. In contrast, a questionnaire for an observational study should only be one page and include categorical responses. This is because observational studies are very difficult to code suggesting the degree of difficulty only increases as additional pages are added. Also, because observational studies are so difficult to code, the ability to simply indicate a consumer behavior with a “check” – instead of either having to write the behavior or deliberate on a magnitude scale – makes it easier for observers to capture all necessary information. Questionnaires for hybrid and marketplace fields are unique in that both field researcher and consumer use it. This means that the questionnaire needs to be built so that the consumer can work on the questionnaire without being influenced by items meant for the researcher. Figure 13.3, for example, includes blank boxes next to foods. This was done so that after the consumer was finished eating, a researcher could record how much of the food was remaining. Even though the questionnaire was given after the participant finished their meal, identification of the boxes as “weight remaining” could have influenced them to respond differently to subsequent questions. Last, because hybrid and marketplace fields are usually interested in testing moderating and mediating variables, it is obviously important that they include questions to see if these variables actually influenced consumers in the intended direction. For example, if I wanted to understand if consumers chose hedonic foods – versus utilitarian – when they didn’t have

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the ability to deliberate, I would include questions in the questionnaire specifically pertaining to the moderating variable. For example, if I manipulated ability to deliberate by requesting that a consumer remember a series of numbers as they chose a food, then I would put questions into the questionnaire that specifically assessed this (e.g., “What was the number sequence you were asked to remember;” “What were the two foods presented to you”) as well as mediating variables that assessed process (e.g., “When choosing a food, I could couldn’t think clearly”).

13.3.4 Field data analysis Field analysis resulting in description of field data, prediction of consumer response, and targeting consumers for a particular product all are somewhat unique. This is because field study contexts – in contrast to laboratory contexts – demand execution of relatively simple moderating and mediating variables due to the tremendous amount of money, time, and effort involved. This results in description, prediction, and targeting that are specifically tailored to field contexts. Describing While clustered bar-charts are not unique to field studies, they probably are the most frequently used figure in this context to convey meaning. Consider Fig. 13.4. It represents results from a hybrid field study where consumers where introduced to two different television programs that either elicited boredom or excitement. In addition, some of the participants could obtain food by a nearby kitchen or the table in front of them. After two minutes, the television program was stopped and consumers were allowed to obtain food. From Fig. 13.4, it can be clearly seen that television show type affected those obtaining food from the kitchen more so than those obtaining food

60

Grams consumed

50 40 Kitchen

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Table 20 10 0 Exciting

Fig. 13.4

Not exciting

Clustered bar chart for field study.

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from the table: going to the kitchen after a short break resulted in more consumption when watching a boring show than exciting show. Predicting A particularly troubling problem (is there another kind?) can occur frequently in gathering field study data – especially from observational fields. Observers, even when blind to the hypotheses of the study, may unknowingly bias observations of consumer behavior. Researchers call this problem “correlated observer data.” Unless researchers account for this bias in analysis of the data, results may simply imply idiosyncratic tendencies of observers rather than the actual tendencies of the consumer. Consider a real world example. We had observational data from 12 people who observed multiple eating behaviors at Chinese buffets across the United States. Our goal was to predict categories of behavior (i.e., whether they used chopsticks or not) from an estimated body mass index score controlling for perceived age, and sex. Because there may have been observers who tended to focus on some behaviors at the expense of others in a systematic way (i.e., observers tending to observe patrons who were more likely to use chopsticks than forks) standard regression and chi-square models would not work. This is because standard regression models do not control for correlated data (i.e., biased observer information). In our analysis, we did not want to remove variables indicating observer bias. If we did, our analysis results could simply be a statistical artifact of observer bias. The solution to this is to use statistical procedures, such as generalized estimating equations and multi-level modeling, which take into account possible random effects (i.e., within-observer correlation) (Harden & Hilbe, 2002). These statistical procedures allow for robust estimation that includes adjustment of correlated observations of observers. In other words, using these random effect statistical procedures, we could now account for observer bias and still get robust estimates of a consumer’s buffet behavior. Targeting Targeting is a broad analysis term meant to suggest techniques allowing for identification of consumers for whom a particular product would be most attractive. In field study settings, research leveraging experimental methodologies (random assignment to groups) – until recently – has not had analysis techniques allowing for systematic consumer targeting. It is not enough that hypothesized moderating variables don’t “work.” In other words, if researchers expect a particular context (e.g., dimmed lighting) to result in specific consumer behavior (e.g., willingness to pay greater amounts for a meal), but it does not – what should be done? Too much time, money, and effort has been put into a field study for it not to yield actionable results. The conclusion that a field study doesn’t work may be because – traditionally – field study analyses have focused on the “average person.” For

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example, it may be the case that for the “average person” dimmed lighting did not result in willingness to pay greater amounts for a meal. However, instead of the “average person,” what if we could instead find groups of people for whom dimmed lighting increased willingness to pay more for a meal? Classification and regression trees (C&RT) is a statistical targeting technique that searches for different groups of people in your field study sample who are most sensitive to variables expected to affect their behavior. C&RT, for example, will find the subgroups in the sample who are most affected by dimmed lighting in terms of their willingness to pay more for a meal. These subgroups will have particular characteristics that were measured at the time of the field study (e.g., # days patron eats out, sex of participant, eating alone or with others) (see Izenman, 2008 for a thorough discussion). Consider Fig. 13.5. It is a result of a hypothetical C&RT analysis that identifies groups of people whose willingness to pay more for a meal is

Eating with others Male patron

$4.80

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N = 70

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Restaurant provided great service = yes $4.70

$4.45

N = 21 KEY $ = Mean WTP for Group N = # People in Group

N = 33 N = 66

Restaurant provided great service = no $4.30 N = 33

Fig. 13.5

$4.00

Sample classification and regression tree.

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affected by combinations of different variables. To execute this analysis, C&RT first splits the field study sample into the two field study groups (dimmed lighting and regular lighting). While willingness to pay more for a meal is greater in dimmed lighting ($4.55) than normal lighting ($4.45), the difference is probably not significantly different – but you already know this from traditional analyses. Now, instead of swearing at yourself because you spent thousands of dollars on a field experiment that didn’t work, you begin to feel euphoria (o.k., maybe I am describing myself). This is because you know what C&RT will do next. C&RT now searches for subgroups of people within each field study group that are more or less willing to pay more for a meal. For example, in the dimmed lighting group, C&RT found that if you target people that eat out more than four times a month, on average, they are willing to pay $5.01 for their meal instead of $4.55 that regular lighting people would be willing to pay – not bad, a 13% increase. Now, C&RT searches for the next variable on which subgroups differ in their willingness to pay. It found that if you segment the remaining people on their sex (a biological state, not activity), their willingness to pay more for a meal increases even more. In this example, males who eat out more than four times a month and exposed to dimmed lighting were willing to pay $5.60 for their meal – a 26% increase compared to the regular lighting group. Thus, including dimmed lighting seems to work best for those who eat out more than four times a month and are male. In contrast, dimmed lighting does not work well for those who eat out four times or less a month who are willing to pay $4.21 for their meal – a decrease of 5% from what people are willing to pay with regular lighting. While many analysis types are appropriate for all fields (e.g., regression), some types are better suited to particular fields. Table 13.1 suggests as much. For example, because of the flexibility of what you can do in internet fields, all analysis types are possible. The possibility, however, of achieving large sample sizes makes path analysis and structural equation modeling more robust. Path analysis is simply a special case of structural equation modeling where the constructs measured in path analysis have one indicator (i.e., the observed variable), while constructs in structural equation modeling have many (e.g., the latent variable). In both cases, field researchers attempt to model a “causal flow” of constructs leading to other constructs resulting in a response variable. Observational fields, as mentioned previously, have the common problem of correlated observer data. Thus, if an observational field researcher wishes to predict consumer behavior, random effect statistical procedures should be used. Path analysis and structural equation modeling may also be leveraged here because of the ease of obtaining large samples. Finally, marketplace and hybrid fields – because of their emphasis on experimental designs – traditionally use analysis of variance and regression techniques. However, in both cases, the use of classification and regression

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trees may help a struggling field study find actionable results. That is, by allowing C&RT techniques to find unique combinations of variables, specific groups of consumers can be targeted making an initial unsuccessful study successful. Traditional techniques – such as ANOVA and regression – do not have this ability.

13.4 Field mistakes A field study’s product, place, and promotion are fertile ground for mistakes. Believe us, if there is a mistake to be had in field research we have made it. We discuss some of the more egregious errors and how we have corrected for them in subsequent field studies. The bottom line is this: it is impossible to plan, execute, and analyze a perfect field study; something always goes wrong.

13.4.1 Product Not having or running out of a product during a field study is probably the worst failure you can encounter – except possibly starting a field study table on fire, which actually happened! We conducted a field study at a community kitchen where the product to be tested were chicken wings. Ten minutes before the field study was to begin, the catering service still had not arrived with the product. Questionnaires had been printed, other resources had been obtained, consumers were already recruited, space had been reserved, and staff had been assigned. What happened? We found out that the staff member responsible for ordering the chicken simply said “show up at noon.” Noon came and went. Finally, the delivery guy showed up with our chicken. We still had to cut the chicken and prepare it with a special sauce. By the time we were ready, our first recruited group had to leave. No big deal right? We had other consumers. We lost a valuable number of people for analysis and incentives that we had to give the consumers for coming even though they did not complete the study. The field research goal is to gather as much response data as you can on a product in the allotted time. The rule of thumb is this: always have the food prepared a half an hour before the study should begin and always bring twice as much product as you think you will need. In addition, have someone be specifically responsible for obtaining the product and replenishing it during the study.

13.4.2 Place Contexts such as fast-food restaurants, sit-down restaurants, buffet restaurants, kitchens, malls, cafeterias, preschools, and high schools all have been used as locations for our field studies. Not living or working in these

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Fig. 13.6 The local environment of a field study.

environments on a consistent basis makes anticipation of potential catastrophes difficult. Consider a field study that was completed at a college cafeteria. We prepared all food and placed it in food warmers and chillers during the study. Sometime during the study, a cafeteria employee approached one of us to warn us that the county food inspector was on his way. How can you prepare for a surprise like that? You can’t. Luckily, the inspector never showed up – not that we believed our food was tainted, but who knows the secret ways of the county food inspector? The global environment is impossible to control. You may just have to smile when the equivalent of the county food inspector comes for a visit. The local environment of the field study, however, is more controllable. Table positioning, experiment layout, placement of food, pens, and questionnaires are all under the control of the field researcher (see Fig. 13.6). When doing field research in a mall, for example, never put food on a table unless you are ready to begin the study. In one field study experience in a mall, food was placed on a table in an initial attempt to get the study set up. Unfortunately, food on a table located in a major pedestrian thoroughfare in a mall is code for “try some.” After only a few minutes of exposure, people began snacking on the food with their bare hands. Not only was the staff member embarrassed, but the consumer also after he was told he couldn’t do that.

13.4.3 Promotion The ability to conduct your study is directly dependent on promotion of it to research gatekeepers. Research gatekeepers can be members of an ethical review board, upper-level management at your organization, or

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those responsible for the field context (e.g., mall managers). Best practices for promoting your study to these individuals should always provide evidence of careful consideration of your ethical obligation to study participants. In many countries (see http://www.hhs.gov/ohrp/international/ HSPCompilation.pdf for a list), any organization – public or private – that receives governmental funding is required by law to submit their human participant-based research to an ethical review panel. Even if the organization for which you work does not require ethical review, your proposed study should at least conform to principles of informed consent. This means, at minimum, that your participants know the study’s procedure, its risks and benefits, whether or not compensation is provided, that information they provide will be held strictly confidential, and that their involvement in your study is voluntary. These principles are usually summarized in a “consent form” to be signed and dated by the participant (see Fig. 13.7 for an example). Promoting your study taking care to emphasize ethical obligations to participants is not only about being a responsible researcher. The reputation and legal stability of you, your organization for whom you work, and managers of the location of your field research all rely on you successfully navigating through potential ethical landmines. If all the aforementioned gatekeepers approve of your study once it is examined closely, rest assured that you have complied with the “gold standard” of consumer field research. Whether you are a grizzly veteran, but would like a gentle reminder, or a newbie of consumer research, we include 10 tips regarding interaction with research gatekeepers. The perspective is taken from an academic setting where Institutional Review Boards (also known as human participant ethics approval) assure ethical compliance. But, even if you don’t have a formal process for obtaining human participants ethics approval, the principles behind these tips still apply with the intent of fulfilling ethical obligations to study participants. One of the authors is a member of their university’s Institutional Review Board for Human Subjects. The 10 tips we give do not represent any policy or any bias of any committee. They are simply ideas we have learned over the past 10 years. We hope they will make it easier for you to do consumer field research. 1. It helps to know the rules. Make sure that you first complete your institution’s ethics training or ethics training provided by the institution that is responsible for the context in which you will be conducting research (e.g., public school districts). Most institutions require successful completion of this training before they even consider your application. If your institution doesn’t have ethics training, suggest to management that they consider it. 2. Work considerately and patiently with the person who processes your proposal. This well-meaning, but stressed-out person can help you navigate through the institution’s unique requirements. In addition,

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Consumer-driven innovation in food and personal care products Consent form

You are invited to participate in a research study that seeks to understand better how and why people prefer specific tastes. You were selected as a possible participant because of your interest in this project. We ask that you read this form and ask any questions you may have before agreeing to be in the study. Background information: People prefer tastes for a variety of reasons. However, there is still much to be known about why people prefer some tastes more than others. This study involves understanding better how and why people prefer specific tastes. By understanding better how and why people prefer tastes, it may be possible to understand how to eat better. Procedures: You will be asked to drink Cola or Seltzer water that may or may not contain high fructose corn syrup, aspartame, or table sugar. If you are allergic to any of these, we ask that you DO NOT DRINK THE BEVERAGE GIVEN TO YOU. After drinking it, you will be asked to provide information about your experience and yourself such as your height, weight, age, etc. You may skip any question that you may feel uncomfortable answering. Your entire experience here today should take approximately 20–30 minutes. Risks and benefits of being in the study:There are no known benefits to participating in this study. Although considered generally harmless, you may be allergic to high fructose corn syrup, aspartame, or table sugar. If this is the case, please do no not drink the beverage. Also, If you do have food allergies to the snacks offered, we will ask you not to continue with the study. Compensation: You will receive $5 for participating. Voluntary nature of participation: Your decision whether or not to participate will not affect your current or future relations with Cornell University or the researchers conducting this study. Participation in this research study is voluntary. If you decide to participate, you have the right to withdraw at anytime or refuse to participate. You may skip any questions you feel uncomfortable answering. Confidentiality: The records of this study will be kept private. In any sort of report we might publish, we will not include any information that will make it possible to identify you. Research records will be kept in a locked file; only the researchers will have access to the records. Contacts and questions: The researcher conducting this study is (name). Please ask any questions you have now. If you have questions later, you may contact him at (phone); (mailing address); (email). If you have any questions or concerns regarding your rights as a subject in this study, you may contact the Institutional Review Board at (phone) or access their website at (website) You will be given a copy of this form to keep for your records. Statement of consent: I have read the above information, and have received answers to any questions I asked. I consent to participate in the study. Signature ___________________________________ Date ________________________ This consent form will be kept by the researcher for at least three years beyond the end of the study and was approved by (organization) (date).

Fig. 13.7

Example consent form.

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this person can help novice researchers become familiar with silent but crucial intricacies of the ethics process that is not stated on any formal documents. This next tip sounds like a lot of work, but you’ll have to do it eventually when you submit a report or paper of the study. Here it is: Give a 5–6 page mini-write-up of the entire study including: a short introduction, a punchy objective to the study, a short justification of why you are conducting the study, a specific description of participants, your best thoughts about the methodology (if experimentation is used, explanation of specific design), and a short description of anticipated results. The more detailed, the more likely you won’t have to go through rounds and rounds of revisions. Even if your institution does not require this, create it anyway and give copies to those in charge. This way you and others involved have documentation of the ethical intent of the study. Taking this action can ensure that your study is safeguarded from unscrupulous people who wish to damage the reputation of you and your institution. Be very detailed in both your description of who you will recruit to participate, how you will recruit them, why you are recruiting the participants you are, and when you will recruit them. Make sure you complete all necessary human participant protection forms or, if this is not required, abide by aforementioned principles of informed consent. This shows your dedication and seriousness to the care of the participants from whom you will benefit. Know when the ethics committee meets and how often. Some applications that involve very little intrusiveness are processed relatively quickly (10–14 days). Applications that involve more moderate participant intrusiveness usually take much longer (4–8 months). If there isn’t an ethics committee, get approval from the institution’s lawyer or legal representative. Pay attention to all of your institution’s instructions for submitting forms to them (number of copies, appendices, etc.). Make sure to submit ALL forms. When you receive your application back, respond to all comments by the ethics committee (or those responsible for review) as quickly as possible. It is better to get the application back soon while the application is still fresh in the minds of the committee and before you get tied up in other matters. When your application is finally approved, thank the person responsible for processing your proposal and ask them to please thank the committee – or those responsible – on your behalf. Comply with all requests from the ethics committee to provide updates on your research if asked and submit any progress reports if requested. Even if there is no committee, due diligence for your study implies submitting short reports of its progress.

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13.5 Conclusion Doing field research is not easy, but if done carefully, it can provide results that are far more actionable that data collected in the laboratory. Internet, observational, marketplace, and hybrid all have their place as appropriate contexts for execution of a field study. Considerations for field research include the use of moderation and mediation, single or multiple occasion measurement, questionnaire design, and field analysis. Many, many, many problems can occur – and will – with field studies. However, focusing on product, place, and promotion can help reduce the probability for failure, increasing the likelihood of obtaining actionable results. The future of field research is promising, but not in a technological sense. Field research requires the work of a farmer: you will work long strenuous hours, get your hands dirty, and deal with unexpected environmental events. This will never change. However, what will change is the popularity of field research as data gathered from here is found to be more actionable than other contexts.

13.6 Sources of further information and advice Mediation/moderation analyses Aguinis, H (2004), Moderated regression, New York: Guilford. • Great resource on how to conduct moderation analyses in regression. MacKinnon D (2008), “Introduction to statistical mediation analysis,” New York, Routledge Academic. • Everything you need to know about mediation – first of its kind. Urdan, T C (2005), Statistics in plain English, New York: Lawrence Erlbaum Associates, Inc. • See pp. 117–126. Moderation is synonymous with “interaction” in this text. http://davidakenny.net/kenny.htm (David Kenny – University of Connecticut) • Warehouse of information from one of the great researchers on mediation/moderation. “Statistical Mediation and Moderation Analysis” group on Facebook (Andrew Hayes – Ohio State University) • Get your questions answered from an expert on the topic. http://www.comm.ohio-state.edu/ahayes/ (Andrew Hayes – Ohio State University) • Great resource for information on mediation/moderation and downloadable computer programs to help. http://www.public.asu.edu/~davidpm/ripl/mediate.htm (David MacKinnon – Arizona State University) • Everything you wanted to know about mediation and more.

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http://www.victoria.ac.nz/psyc/staff/paul-jose.aspx (Paul Jose – Victoria University of Wellington) • Downloadable graphing computer program for mediation/moderation. Within/between subject designs Field A, Hole G J (2003), How to design and report experiments, Thousand Oaks, CA: Sage Publications. • Great resource for designing, executing, and reporting your study. See chapter 3 for specifics of within/between designs. Mitchel M L, Jolley J M (2009), Research design explained, Belmont, CA: Wadsworth Publishing. • Everything you would want to know about within/between subject designs. http://www.wadsworth.com/psychology_d/special_features/ext/workshops/ between1.html • Review of within/between designs with examples. http://web.mst.edu/~psyworld/within_subjects.htm • Quick review of within/between designs. Questionnaire design Bradburn N, Sudman S, Wansink B (2004), Asking questions: the definitive guide to questionnaire design, San Francisco: Jossey-Bass. • Everything you wanted to know about questionnaire design: the questionnaire bible. Dillman, D (2007), Mail and internet surveys: the tailored design method, Hoboken: John Wiley & Sons, Inc. • The classic guide to questionnaire design with significant material on internet questionnaires. Sudman S, Wansink B (2002), Consumer Panels, Chicago: American Marketing Association. www.qualtrics.com • A “questionnaire university.” This site helps with learning, creating, deploying, data capture, and analysis of simple or complex online questionnaires. http://www.ed.uiuc.edu/SPED/TRI/questionnaire.htm • Quick overview of questionnaire design by Tobey Fumento of University of Illinois @ Urbana-Champaign. Analysis Breiman L, Friedman J, Stone C J, Olshen R A (1984), Classification and regression trees, Boca Raton, FL: Chapman & Hall. • Classic text on Classification and Regression Trees. It is moderately technical.

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Field, A (2009), Discovering statistics using SPSS, Thousand Oaks, CA: Sage Publications • A great primer not only for using the SPSS statistics program, but also for learning statistics. Hardin J W, Hilbe J M (2003), Generalized estimating equations, Boca Raton, FL: Chapman & Hall. • The gold-standard text of GEE. Kline, R B (2005), Principles and practice of structural equation modeling, New York, NY: Guilford Press. • Thorough explanation of structural equation modeling. Norusis, M (2009), SPSS 17.0 advanced statistical procedures companion, Englewood Cliffs, NJ: Prentice Hall. • Step-by-step instructions on how to complete a GEE analysis in the SPSS computer program platform. Olobatuyi, M E (2006), A user’s guide to path analysis, Lanham, MD: University Press of America • Good overview of path analysis. Rokach L, Maimon O (2008), Data mining with decision trees: theory and applications, Hackensack, NJ: World Scientific Publishing Co. • Less technical text on Classification and Regression Trees. Good resource for learning the basics. http://onlinestatbook.com/index.html • Incredible online resource for learning basic statistics. Interactive activities that make learning stats actually fun?

13.7 References and further reading agresti a (2006), An introduction to categorical data analysis, Hoboken, NJ, John Wiley & Sons. bradburn n, sudman s, and wansink b. (2004), Asking questions, San Francisco, CA, Jossey Bass. dillman d, smyth j d, and christian l m (2008), Internet, mail, and mixed mode surveys: The Tailored Design Method, Hoboken, NJ, John Wiley & Sons. haig m (2005), Brand failures: the truth about the 100 biggest branding mistakes of all time, Sterling, VA, Kogan Page Limited. harden j w, and hilbe, j m (2002), Generalized Estimating Equations, Boca Raton, FL, Chapman & Hall/CRC. izenman a j (2008), Modern multivariate statistical techniques: regression, classification, and manifold learning, New York, Springer. just d, wansink b, mancino l, and guthrie j (2008) “Behavioral economic concepts to encourage healthy eating in school cafeterias,” USDA, ERS, ERR 68. knox s (2002), “The boardroom agenda: developing the innovative organization,” Corporate Governance, 2:1, 27–36. shimizu m, payne c, and wansink b (2009) “Healthy foods on the left side of a plate,” Annual Meeting of the Association for Psychological Science, San Francisco. wansink b (2004), Environmental factors that increase the food intake and intake volume of unknowing consumers, Ann Review Nutr, 24, 455–479.

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wansink b, and van ittersum k (2005), “Shape of glass and amount of alcohol poured: Comparative study of effect of practice and concentration,” BM J, 331, 1512–1514. wansink b, and payne c r (2008) “Eating Behavior and Obesity at Chinese Buffets,” Obesity, 16:8 (August), 1957–1960. wansink b, painter j, and van ittersum k (2001), ‘Descriptive Menu Labels Effect on Sales’, Cornell Hotel Restaur Adm Q, 42, 68–72.

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14 The importance of consumer involvement and implications for new product development I. Lesschaeve, Vineland Research and Innovation Centre, Canada and J. Bruwer, The University of Adelaide, Australia

Abstract: Understanding consumer needs, desires or preferences has led to an extensive body of literature in two traditionally independent disciplines: business and science. There is an increasing interest by both marketing researchers and consumer scientists to better assess the moderating role of consumer involvement on product choice and purchase and to understand its influence on consumer attitudes. This chapter defines the concept of involvement based on the theoretical background, describes the methodologies to measure it, and illustrates its influence on consumer purchase and consumption behaviour in both food and non-food contexts. The implications for new product development and innovation are also discussed. Key words: involvement, decision-making process, antecedents, consumer behaviour, purchase intent.

14.1 Introduction New product developers may conceive the most innovative idea, design the most technologically advanced product or the most pleasing food; the success of their creation on the market will always depend on consumer favourable and sustainable responses. Available statistics are, however, not encouraging. According to Saguy and Moskowitz (1999), 90% of new food products fail. This data was confirmed recently, indicating that the success rates of new product launches have not changed for the past 30 years (Conroy et al., 2009). Many new product development processes have been proposed in the business literature to mitigate the risk of failure, e.g. the Stage Gate® process (Cooper, 1983; Cooper and Edgett, 2008). Successful innovation can

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have its source in superiority at the supply side or it can have its source in superior understanding of consumer demand, or both. Whatever the source, ultimately market success depends on the degree to which the new product reflects unmet consumer needs (Atuahene-Gima, 1995; Kahn, 2001). Cooper and Edgett (2005) elaborated the importance of utilizing a customerfocused method, where this approach increases the chances of releasing a winning product into the market. To be successful, companies need to shift their innovation process from being production or marketing driven to consumer centric as discussed by Conroy et al. (2009). Understanding consumer needs, desires or preferences has led to an extensive body of literature in two traditionally independent disciplines: business and science. The study of consumer behaviour is a stream of business research investigating the interactions between consumers and the marketing mix defining the product. Consumer science studies the interaction between consumer and the physical properties of the product. So many factors may influence consumers’ acceptance of new products (MacFie and Thomson, 1994; Siegrist, 2007). The product itself, characterized by physical and intrinsic attributes (e.g., sensory properties, nutritional value, or benefits), affects consumption by generating physiological and psychological effects that consumers memorized as either positive or negative experiences. Consumer personal characteristics, whether they are of biological or psychological sources, affect choice and consumption behaviours. For example, their ability to perceive product sensory attributes, their personality, social status, or familiarity with the product can affect their preference and may contribute to the formation of specific attitudes toward the product. Finally, social-economic factors such as price, availability, brand or other social and cultural factors may influence the formation of attitudes towards the product, and create particular beliefs. There is an increasing interest by both marketing researchers and consumer scientists to better assess the moderating role of consumer involvement on product choice and purchase and to understand its influence on consumer attitudes. Involvement is an individual trait that can play a significant role in the consumer decision-making process. The aim of this chapter is to better define the concept of involvement, by describing the methodologies to measure it, and illustrating its importance on consumer behavior in both food and non-food contexts. The implications for new product development and innovation will also be discussed.

14.2 Theoretical background of the involvement construct The consumer involvement construct is rooted within human learning theory. The process how individuals learn and become involved with products is of great importance to researchers and marketing practitioners alike. Despite the fact that this learning process has been studied for decades by

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researchers, there is still no single, universal theory of how people learn (Schiffman et al., 2008). From a marketing perspective, consumer learning can be regarded as “the process by which individuals acquire the purchase and consumption knowledge and experience that they apply to future related behaviour” (Schiffman and Kanuk, 2006, p.207). There are two general categories of consumer learning theory: behavioural learning theory and cognitive learning theory. The importance of these theories to marketers is in the insights they offer them on how to shape their messages to bring about the desired (purchase) behaviour in consumers. Behavioural learning theories, namely classical conditioning and instrumental conditioning, are rooted in the fact that observed behaviour to specific stimuli has taken place, for example a proven reduction in teenage smoking in response to an advertised anti-smoking campaign on television. Cognitive learning theory, on the other hand, has as its premise that learning takes place as a result of consumer thinking and problemsolving. This type of learning therefore involves mental processing of information. Information processing is in turn related to the consumer’s individual ability and the complexity of the information to be processed (Schiffman et al., 2008). There is a relationship between experience with a product category and the ability of consumers to use the information they have learned – the more experience consumers have with a specific product category, the greater their ability to make use of that information. Also, consumers with higher cognitive ability acquire more information and are more capable of integrating information on several product attributes (Schiffman and Kanuk, 2006, p.226). Added to this the fact that products differ in terms of the degree of personal relevance they have for consumers and the so-called Involvement Theory of learning, its context, meaning and applications call for further discussion in this chapter.

14.2.1 Premise of Consumer Involvement Theory (CIT) The conceptualization of involvement in consumer behaviour owes much to social psychology, where the historical roots of involvement research can be found. The basic ideas about the content, nature and functioning of involvement, as well as a great deal of the confusion surrounding the concept, can be traced back to theorizing and empirical research conducted by social psychologists more than fifty years ago (Sherif and Cantril, 1947; Laaksonen, 1997). The origin of involvement research and the ways in which consumers process information first began in 1947 with the development of the so-called ‘Social Judgment Theory’ (Stiff and Mongeau, 2003) that evolved into the theories of learning as discussed. It was, however, not until about 30 years ago researchers began to understand that consumers with different characteristics vary in their level of involvement, with this variation affecting consumer attitudes and behaviour (Traylor and Joseph, 1984; Slama and Tashchian, 1985). As a

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result of this finding, researchers began to understand the importance of studying involvement for the purpose of segmenting consumer markets. Involvement theory evolved from the research efforts that became known as the so-called hemispheral lateralisation or ‘split-brain’ theory. The premise of this theory is that the left and right hemispheres of the human brain specialize in the kind of information they process. The right hemisphere is mainly concerned with the processing of holistic information such as pictorial and non-verbal, whereas the left side is primarily responsible for cognitive activities such as reading, speaking and attribute information processing. The right brain is therefore emotional, impulsive and intuitive and the left side is rational, realistic and active (Schiffman and Kanuk, 2006). The theory was first enthusiastically embraced in various advertising media strategies and soon became known as Consumer Involvement Theory (CIT). CIT has been increasingly used to explain elements of the consumption process and it has been widely agreed that there are high and low involvement consumers, but also high and low involvement purchases of products or services (Schiffman et al., 2008; Schiffman and Kanuk, 2006; Kapferer and Laurent, 1993; Rothschild and Kinnear, 1984). Therefore in a nutshell, high involvement purchases are very important to the consumer while low involvement purchases are not very important (Schiffman and Kanuk, 2006). Low involvement products are generally frequently purchased, widely distributed, low-priced consumer non-durables. On the other hand, all high involvement products are not technologically complex, high-priced, consumer durables. Often, there is a degree of hedonism linked to high involvement products (Chaudhuri, 2000). For example, wine is an experiential product (Kolyesnikova et al., 2008) and therefore fits the tenet of hedonic motivation well. It was not long before the focus turned to the consumer’s involvement with products and purchase situations led to the premise that a consumer’s involvement level is dependent on the degree of “personal relevance” (Zaichkowsky, 1985, p.342) that the specific product has for the consumer. Products mean different things to different people, but if the product is personally relevant to consumers, they are more likely to become involved in gathering information (knowledge) about the product and with the product itself. Whereas the intensity of a consumer’s involvement with a product is simply referred to as either low or high, it is strictly speaking a continuum. For example, Charters and Pettigrew (2006) identified a group of mediuminvolvement consumers in their study. However, it is the persistence of this intensity of involvement that differentiates certain product categories and types of consumers from one another. The more enduring type of (high) involvement persistence “is usually accompanied by a large body of knowledge about the product category acquired over time” (Schiffman et al., 2008, p.206).

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14.2.2 Defining the involvement construct Different perspectives There is a lack of consensus in the literature regarding how to define the involvement construct. For the purposes of the discussion in this chapter, a fairly general definition of involvement has been used. As defined by Rothschild (Rothschild and Kinnear, 1984, p.217), “involvement is an unobservable state of motivation, arousal or interest. It is evoked by a particular stimulus or situation and has drive properties. Its consequences are types of searching, information-processing and decision making.” This definition has been the basis of many involvement-related research projects and defines involvement in a way suitable to support the discussion that follows. Not only is there a lack of consensus regarding how to define the involvement construct, there is also significant debate regarding the conceptualization of involvement, with multiple classifications existing in the literature. The relationship between marketing and involvement is also rather complex in nature with varying opinions, models, classifications, dimensions and methods of measurement. These warrant a closer look in what follows. Categorisations There is general consensus that involvement can be classified into three broad areas, namely enduring, situational and response (Houston and Rothschild, 1977; Laaksonen, 1997; Richins et al., 1992). Enduring involvement relates to an established and long-lasting connection with a product or brand. A person might, for example, only buy a particular car brand such as Toyota and in the process establish an enduring relationship with the brand. A temporary connection to a product or brand is considered situational (Michaelidou and Dibb, 2006). Enduring involvement is also referred to Product Class Involvement (Drichoutis et al., 2007) or Personal Involvement (Laaksonen, 1999). Situational involvement is thought to be goaloriented in that once a goal is achieved, involvement with the product of focus is no longer necessary (Bloch, 1981). For example, a man purchasing an engagement ring for his girlfriend is considered highly involved leading up to and during the purchase, but after the purchase has been made, he is no longer connected to engagement rings as a product category; Situational involvement is also described as Purchasing Decision Involvement in the literature (e.g., Brennan and Mavondo, 2000; Lockshin et al., 1997). Response involvement combines enduring and situational involvement as a result of a bond formed during a situational involvement circumstance (Arora, 1982; Houston and Rothschild, 1977; Lin and Chen, 2006; Richins et al., 1992). The response involvement literature characterizes the level of involvement by describing different static or dynamic responses of an individual when facing an information processing or a choice task (Laaksonen, 1999). However this view has been criticized, arguing that responses are the result of the effect of involvement rather than involvement itself.

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Models assessing how situational involvement and enduring involvement integrate differ and therefore no common mode of assessment currently exists. Richins et al. (1992) attempted to establish a single model to measure how these two types together affect response involvement. Findings showed minimal connection between the two types of involvement, validating past research that both affect overall response involvement (Celsi and Olson, 1988; Houston and Rothschild, 1977; Peter and Olson, 1987; Richins et al., 1992). Therefore, most researchers agree that the two major classification areas of involvement are situational and enduring involvement and this will therefore be the basis of the further discussion that follows. Minimal research has been conducted targeting the relationship between enduring involvement level and consumer perceived risk (Richins et al., 1992). However, Venkatraman (1989) distinguished between both enduring and instrumental (akin to situational involvement), but contextualized the involvement concept in terms of risk. He suggested that while enduring involvement precedes risk, instrumental (situational involvement) is actually intertwined with risk. Lacey et al. (2009) also support the contention that risk is ‘intertwined’ with involvement. Dimensions The single- or multi-dimensional nature of involvement is also a heavily debated topic among researchers. Some researchers such as Zaichkowsky, Traylor and Joseph believe involvement is one-dimensional, focusing on the intensity of the personal relevance itself, while others such as Bloch, Kapferer and Laurent view involvement as multi-dimensional, the intensity of involvement being obtained by summing up the different dimensions or by creating a profile across each dimension (Bloch, 1981; Laurent and Kapferer, 1985; Traylor and Joseph, 1984; Zaichkowsky, 1985). Despite the divergence in views there is still consensus in that most researchers now view involvement as a multi-dimensional construct (Michaelidou and Dibb, 2006). Five dimensions are identified by Kapferer and Laurent and include; interest, sign, perceived pleasure, risk importance and risk probability (Kapferer and Laurent, 1985b). Interest as a dimension is further supported by other scholars (Van Trijp et al., 1996). Importance (Jensen et al., 1989; Lastovicka and Gardner, 1979) and self-expression serve as additional dimensions not directly defined by Kapferer and Laurent, but related to one or more of the five defined dimensions (Higie and Feick, 1989). In relation to branding, risk can be replaced with consumer brand competence and consumer product difference (Kapferer and Laurent, 1985b). Dimensions play a role in consumers’ level of involvement with a product or brand. Despite this ‘guideline’ one of the main challenges in the measurement of involvement remains the identification of the relevant individual dimensions of this construct.

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14.3 Measurement methods 14.3.1 Reflective versus formative scale measurement perspective A construct has been described as “an abstract idea inferred from specific instances that are thought to be related” (Burns and Bush, 2010, p.128). A construct is therefore a concept measured by multiple variables, for example brand loyalty, attitude, socio-economic status, etc. Business researchers often identify structural relationships among latent, unobserved constructs. In the social sciences the most common measurement theory “has its basis in classical test theory and the factor analytic perspective, wherein observable indicators are reflective effects of latent constructs” (Howell et al., 2007, p.205). Once researchers and practitioners know the construct to be measured, they should determine the proper way to go about this measurement. In most cases, there is a need for researchers to justify, both theoretically and empirically, their choice of measurement model (Coltman et al., 2008). To effectively carry out any measurement one needs to use some form of a scale. It has now been 30 years since Churchill (1979) criticized the field of marketing for not paying enough attention to construct validity and other measurement errors. More recently this criticism has continued in the extant literature why so little attention has been given to construct validity and associated measurement issues (Coltman et al., 2008; Howell et al., 2007; Jarvis et al., 2003). These criticisms led to increasing attention to construct validity in general and more rigorous assessment of the measurement properties of constructs (Borsboom et al., 2003; Jarvis et al., 2003), as well as for researchers to make the difficult choice of considering reflective or formative indicators defining a construct. Theoretical considerations Three broad theoretical considerations are important in deciding whether the measurement model should be reflective or formative. These include: (1) the nature of the construct, (2) the direction of causality between the variables (indicators) of the latent construct, and (3) the characteristics of the variables used to measure the construct (Coltman et al., 2008). 1. Nature of the construct: in a reflective model the latent construct (e.g., Involvement) exists independent of the measures. Nearly 95% of latent constructs with multiple items assume reflectivity in their measurement (Coltman et al., 2008). Attitude and personality measurements are good examples of using the reflective perspective. In a formative model by contrast, changes in the measures are hypothesized to cause changes in the underlying construct and the measures are therefore causal or referred to as formative (Jarvis et al., 2003). For example, when measuring a company’s performance, a set of measurements can be created that measure disparate elements such as profitability, market share and so on. These items are not interchangeable and are therefore formative.

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2. Direction of causality: reflective models assume that causality flows from the construct to the indicators, while in formative models, the exact opposite is the case. This means that in reflective models a change in the construct causes a change in the indicators, while in formative models it is the other way around (Coltman et al., 2008). 3. Characteristics of variables: in the case of formative models, the indicators define the construct and hence the domain of the construct is sensitive to the number and types of variables that a researcher chooses. In a reflective model, the variables all share a common theme and are interchangeable (Coltman et al., 2008). Involvement – reflective or formative measurement? A construct such as involvement could be measured either reflectively or formatively. It should be remembered that constructs are not necessarily (inherently) reflective or formative. Most researchers view involvement as a multi-dimensional construct, as previously mentioned. The dimensions may be reflectively or formatively related to the involvement construct. Whereas it is not possible (at least at this stage) to suggest whether measuring involvement from a formative perspective is more desirable, most if not all studies in involvement have used the reflective model. Perhaps the suggestion of Howell et al. (2007) is the most sensible one, namely that researchers should strive to measure their constructs reflectively with as many as possible, strongly correlated indicators that are unidimensional for the same construct. In the case of the involvement construct, formative measurement does not, at this stage, appear to be an equally attractive alternative, at least not until more research work has been done in this field. The following discussion therefore shows the measurement of involvement as reflective.

14.3.2 Methodological issues Methodological concerns such as creating an accurate scale with which to measure the construct are not the only issues that affect involvement and involvement research. Indeed, there is significant debate as to exactly how involvement as a construct should be measured. For instance, some researchers suggest that it cannot be measured directly – antecedent constructs like price, risk, and personal meaning must be used to measure it instead (Kapferer and Laurent, 1985a). This in turn relates back to the concurrent debate revolving around whether involvement should be measured using a single or multi-dimensional construct. Selecting appropriate measurement methods for data is critical in returning accurate and valid results (Stevens et al., 2006). While some concepts are easily measured, such as demographics, accurately capturing data encompassing attitudes, beliefs, motivation and involvement are far more difficult. Data accuracy can be greatly improved through the use of appropriate measures as well as through minimizing measurement errors. It is therefore

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important that researchers (and marketers) understand the various types of measurement and apply the appropriate measure as the circumstance requires (Stevens et al., 2006). Development of a psychometric scale Following a measurement development process aids researchers in developing appropriate measures for data collection. Concepts must be clearly identified and defined prior to selecting measurement methods. Following measurement development, a sample data must be collected to pretest questions and/or statements and analysis must be conducted to determine accuracy of data. Finally, data can be collected and analyzed (Stevens et al., 2006). Figure 14.1 illustrates the measurement development process of a psychometric scale, which has been applied in several studies aiming at developing involvement scales (Laurent and Kapferer, 1985; Lockshin et al., 1997; Bell and Marshall, 2003). It is important to be mindful of the fact that the nature of every product is different and therefore presents an individual challenge in terms of the process of developing the measurement method of a specific product involvement. Scale properties A common feature of consumer research is the attempt to have respondents communicate their feelings, attitudes, opinions, and evaluations in some measurable form. The best measures are those that are accurate, precise, lucid, and timely (McDaniel and Gates, 2006). In other words, they are those that accurately measure the construct (e.g., involvement) with as few

Identify concepts to be measured

Develop operational definitions of concepts

Develop measures (questions or observational techniques)

Collect data: instrumental pretest

Assess validity and reliability

Collect data for analysis (Source: Stevens et al., 2006)

Fig. 14.1

Measurement development process.

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random or systematic errors as possible. It is systematic errors that are the most worrying, with these faults stemming from the measurement instrument or process involved (McDaniel and Gates, 2006). Overall, there are two main criteria for good measurement. First, the measurement scale must be reliable. That is, it must generate consistent results over time. Not surprisingly, reliability also implies that the scale be free from random error. The second criterion for good measurement is that it is valid. Put more simply, the scale must measure what it set out to measure. Several criteria have been proposed to test for reliability and validity of a multi-item scale aiming at measuring individual traits, attitudes or behaviours: these are content validity, scale dimensionality, reliability, convergent validity, discriminate validity, and nomological validity (Bearden et al., 1993, pp. 3–5; Mittal, 1989). • Content validity or face validity assesses the degree to which a measure reflects how the items relate to the characteristics of the construct; it is usually performed a priori by the researchers or a group of experts to determine if the statements are meaningful, and if the type of scale is appropriate (Peter, 1981). • Dimensionality of the construct is determined to ensure that the proposed scale is not contaminated by other constructs. Multivariate statistical techniques such as exploratory or confirmatory factor analysis have been used in the literature to identify the items loaded on independent factors, each factor thus representing one construct. • Reliability of the scale is assessed two ways: first by testing the stability of responses of the same group of respondents over two consecutive tests and second by evaluating the internal consistency of the items defining the scale. The reliability coefficient Cronbach’s alpha (Carmines and Zeller, 1979) has been widely used to assess internal consistency of scale items. Consistency is deemed satisfactory for alpha equal to 0.7 and higher (Nunnally, 1979 in Bearden et al., 1993) or sometimes as low as 0.6 (Robinson, 1991 in Bearden et al., 1993). Other approaches consist of examining the inter-item correlation matrix or the corrected item to total correlation matrix. • Convergent validity assesses the degree to which two scales measuring the same construct are correlated. • Discriminant validity reflects the degree to which two measures designed to measure similar but conceptually different constructs are related. • Nomological validity assesses the degree to which predictions from a formal theoretical network containing the concept are confirmed empirically. Types of scales Four measurement scales are widely used in consumer research; they vary greatly in levels of sophistication. Listed from least to most comprehensive,

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these scales include; nominal, ordinal, interval, and ratio (Khurshid and Sahai, 1993; Meilgaard et al., 2007). A nominal scale is the most basic scale type mainly focused on capturing points of difference such as male or female, indeed it is also often referred to as a categorical scale. It simply involves a count of the frequency of the cases assigned to the various categories. An ordinal scale involves the ranking of individuals, attitudes or items along the continuum of the characteristic being scaled, for example, if a researcher asked consumers to rank five wine brands in order of preference. An ordinal scale helps establish levels of rank, but does not take into consideration the degree in which the rankings occur. An interval scale takes into account the level of degree. It is only with interval scaled data that the use of the arithmetic mean as the measure of average can be justified. The interval or ordinal scale has equal units of measurement, thus making it possible to interpret not only the order of scale scores but also the distance between them. A ratio scale is the highest level of measurement and takes into account all aspects of the first three scales with the addition of computing absolute magnitudes (Khurshid and Sahai, 1993). The various types of scales used in consumer research fall into two broad categories, namely comparative and non-comparative. In comparative scaling, the respondent is asked to compare one brand or product against another. With non-comparative scaling respondents need only evaluate a single product or brand which is most often the case when product class involvement (e.g., red wine or virgin olive oil) is measured. Non-comparative type scaling is most often used when measuring psychological variables, Likert and Semantic differential scales in particular. Both are common methods used in an effort to measure involvement (Zaichkowsky, 1985). The Likert scale is what is termed a summated instrument scale and offers five to seven or more response choices urging a respondent to select the most suitable option – see Table 14.1. A typical set of responses to a statement include (1) strongly agree (2) agree (3) undecided (4) disagree (5) strongly disagree. This means that the items making up a Likert scale are summed to produce a total score. In fact, a Likert scale is a composite of itemized scales. The semantic differential offers a seven-point scale providing opposite meanings on either end of the scale. This type of scale makes extensive use of words rather than numbers. Respondents describe their feelings about the products or brands on scales with semantic labels. When bipolar adjectives are used at the end points of the scales, these are termed semantic differential scales. For example, the restaurant provided excellent service and the restaurant provided poor service with options in-between these extremes. The respondent then makes a selection on the appropriate side of the scale. Zaichkowsky (1985) tested various measurement scales and found the semantic differential scale was most suitable to use across product categories. Scales such as Likert were too specific to cross various categories.

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Likert scale example

Statement If the price of raw materials fell firms would reduce the price of their food products Without government regulation the firms would exploit the consumer Most food companies are so concerned about making profits they do not care about quality The food industry spends a great deal of money making sure that its manufacturing is hygienic Food companies should charge the same price for their products throughout the country

Strongly agree

Agree

Neither agree nor disagree

Disagree

Strongly disagree

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

(Source: FAO, 1997)

Table 14.2 displays the semantic differential 7-point scale used across three product categories with the response options of paired opposites to include: unimportant-important; interested-uninterested and exciting-unexciting (Zaichkowsky, 1985).

14.4 Consumer involvement scales Several methods for measuring consumer involvement currently exist. What follows outlines the dimensions and scale strategies used by the developers of some of the most used scales which are summarized in Table 14.3. Lastovicka and Gardner (1979), the creators of the 22-item Component of Involvement (CP) scale in 7-point format, view involvement as having two major components, namely normative importance (earlier described as relevance) and commitment. Commitment refers to the binding of an individual to his or her brand choice. The Component of Involvement scale is composed of three factors that encompass the two major components. These are familiarity, commitment and importance.

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Table 14.2

Semantic differential scale example

Relationship between the scale scores and the open-ended responses Judges’ ratings

Collapsed for chi-square (Total)

Scale scores Low

Medium

High

Low

Medium

High

11 0

13 4

7 10

12 8

1 10

0 14

19 9

3 11

0 3

Low Medium High (Total)

7 4 0 (11)

35 mm Camerasa 1 0 (8) 12 7 (23) 4 10 (14) (17) (17) (45)

Low Medium High (Total)

12 8 0 (20)

1 9 1 (11)

Red wineb 0 8 6 (14)

Low Medium High (Total)

19 9 0 (28)

3 9 2 (14)

Breakfast cerealsc 0 (22) 1 (19) 2 (4) (3) (45)

(13) (25) (7) (45)

a χ2 = 10.4, df = 2, p < 0.01. b χ2 = 17.0, df = 2, p < 0.001. c χ2 = 11.2, df = 2, p < 0.01. NOTE: as more than 20% of the expected cell frequencies dropped below 5, either the low or high row was collapsed into the medium row to compute the statistic. (Source: Zaichkowsky, 1985)

Bloch (1981) developed the Involvement with a Product Class (IPCA) scale focused on people’s involvement with cars. However, the scale has been repeatedly re-used for the purpose of measuring involvement with other products. The scale was composed of 17 Likert-type items scored using a 6-point format (strongly agree to strongly disagree) and encompasses six dimensions: enjoyment of driving and using the car, readiness to talk about cars, interest in car racing activities, self-expression through one’s car, attachment to one’s car and interest in cars. As discussed previously, Laurent and Kapferer (1985) introduced the concept of involvement as a multi-dimensional construct along five dimensions. They had a scale for levels of involvement and another for dimensions of involvement and combined it (as shown in Table 14.4) and called it the Consumer Involvement Profile scale. In a more recent study done by Jain and Sirnivasan (Jain and Sirnivasan, 1990), the Likert scale measuring the Consumer Involvement Profile was adapted to a semantic differential format. The Personal Involvement Inventory (PII) scale developed by Zaichowsky (Zaichkowsky, 1985) consists of 20 semantic differential items which were

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CPI (changed)

Jain and Srinivasan, 1990 Zaichkowsky, 1985

Wine retailing segmentation

Source: Kaasin-Montgomery, 2009.

Lockshin et al., 1997

Mittal and Lee, 1989 Mittal and Lee, 1988

CPI

Laurent and Kapferer, 1985

Personal Involvement Inventory (PII) PDI

CP

IPCA

Bloch, 1981

Lastovicka and Gardner, 1979

Scale name

21 scenarios

5-item

16 semantic differential format 20 semantic differential items

16

22

17

Number of scale items

Exploratory

7-point bipolar phrases

7-point Likert

6-point Likert strongly agree to strongly disagree 5-point Likert totally disagree to totally agree

6-point Likert strongly agree to strongly disagree

Measure used

Involvement measurement scales and dimensions in literature base

Researcher, Year

Table 14.3

Three: utilitarian, sign, hedonic value Three: interest, enthusiasm, excitement Enthusiasm, excitement

Uni-dimensional

Five: perceived importance and risk of product class, probability of making a mispurchase, symbolic or sign value, hedonic/pleasure, interest Same as above

Six: enjoyment, readiness to talk, interest in related activities, selfexpression through product, attachment, interest in product Three: familiarity, commitment, normative importance

Dimensions

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48 128

47 43

56 50

65 65 64 120

Undramatized risk

107 75

76 77 92 41

Small pleasure

57 123

91 130 123 120

Conformist pleasure

Name of type

Ten involvement types: description

Note: Entries are standardized indices (mean = 100, standard deviation = 50). (Source: Kapferer & Laurent, 1985a)

34 59 40 87

Functional differentiation

24 59 38 19

Minimal involvement

Involvement types

Interest Sign Pleasure Risk importance Competence Perceived difference

Table 14.4

127 92

113 142 111 79

Riskless involvement

114 121

121 73 47 124

Functional involvement

97 59

137 93 141 138

Need for expertise

145 142

144 164 144 133

Total involvement

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scored on a 7-point scale. The scale is uni-dimensional. The scale was later reduced from 20 items to 10 items and the revised Personal Involvement Inventory then broken into 2 subscales representing a cognitive and affective grouping (Zaichkowsky, 1994). Mittal and Lee (1989) created the Purchase Decision Involvement (PDI) scale which measures both product and brand involvement. They adapted an empirical scale developed by Laurent and Kapferer (1985) to better differentiate between product involvement and brand involvement. Indeed Mittal and Lee (1988) argued that only one dimension of the five proposed by Laurent and Kapferer (the interest in the product) described product involvement, the other four being antecedents of involvement. Their scale therefore utilizes the involvement profile formulated on product importance, perceived risk of purchasing a product, symbolic (sign) value and hedonic (interest) value while separating product involvement and brand involvement. Only five scale items were used in this scale and they were scored with the use of 7-point bipolar phrases. To the authors’ knowledge, only self-reported measures have been used to assess the involvement level of an individual. Drichoutis (Drichoutis et al., 2007) used “time spent to do grocery shopping” as a variable that could be moderated by food purchase involvement, similarly did Vieira (2009) for “time spent for shopping for clothes”. Although in these particular examples the measure was self reported, one can envision recording such information through observational and behavioural research.

14.4.1 Low and high involvement products The role of involvement is described as an interactive variable determined by the characteristics of both the product and the individual. Therefore some products can be seen as low involvement, such as pesticides, but some consumers can become highly involved in detergents should their personal interest involve environmental or health consequences (Laaksonen, 1999). Whereas the level of product involvement is an individual difference rather than being linked inextricably with certain products, past research suggests that certain products do lend themselves more readily to involvement; for example, fashion clothing, food and more recently, wine (O’Cass, 2004; Bell and Marshall, 2003; Lockshin et al., 1997). Food products across a broad spectrum are good examples of lower involvement association in general (Marshall and Bell, 2004). This can be seen in relation to products used on a regular basis negating personal engagement during grocery shopping (Kuenzel and Musters, 2007). Laurent and Kapferer (1985) reported the average Consumer Involvement Profile for 14 products assessed by housewives. Among them a categorization could be made between utilitarian products (washing machine, iron, vacuum cleaner, oil, toothpaste, shampoo, facial soap, and detergents) and hedonic products (dress, bras, TV Set, chocolate, Champagne, yogurt).

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Utilitarian products do not generate any pleasurable experience when purchasing them or using them. An experience product such as wine may engender involvement more easily than purely utilitarian products by virtue of its hedonic, ‘quasi-aesthetic’ (Charters and Pettigrew, 2006, p. 181) nature. Laurent and Kapferer’s findings allowed confirming some a priori assumptions: detergents obtained low scores for 4 dimensions (importance of negative consequences, subjective probability of mispurchase, pleasure value, sign value). Some a priori utilitarian products like washing machine and facial soap were rated high on the pleasure dimension of involvement; in addition the choice of the washing machine was found associated to a symbolic value. These nuances would not have been captured should product involvement be considered as a one-dimensional concept.

14.4.2 Antecedents of involvement Enduring involvement is believed to be stable and not affected by situational variations such as purchase context. Laaksonen (1999) suggested that the intensity of enduring involvement to increase as the strength, number and centrality of the personal consequences or implications associated with a product increases. It is therefore valuable to question what these potential consequences or the antecedents of product involvement can be. Behaviours related to consumer involvement development can be characterized by information processing, evaluative activity, and physical efforts (Olshavsky and Granbois, 1979 in Laaksonen, 1999). It has been suggested that the antecedents of enduring involvement can be found in the utilitarian, hedonic, personal and social importance of the product. Several factors have been listed in the literature as potential sources for strengthening the linkages between a product and a consumer and are described below. Knowledge of these factors could become an asset when launching a new product by attempting to create strong linkages between the new product and its potential customers. Personal importance or interest is one of the primary dimensions of involvement as most definitions of involvement include interest in the first place. The term involvement has been used diversely in the literature; however, despite difference in nuances, there seems to be a common thread, in that most researchers agree with the statement that a person’s interest is something that directly relates to his/her level of involvement (Mittal and Lee, 1989). Bloch (1986) believed that interest in a product motivates people to seek further knowledge about the product (Park and Moon, 2003). Creating interest and reaching consumers’ values that would make a new product meaningful or important to them is a strategy that advertisers use often. Similar to interest, pleasure is another common antecedent for strengthening involvement. Pleasure was considered as one of the five facets of involvement in Kapferer and Laurent’s Consumer Involvement Profile and was defined as “the hedonic value of the product, its ability to provide

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pleasure and enjoyment”. The more the hedonic value is, the more the likelihood for a product to generate involvement with consumers. According to D’Hauteville (2003), studies have shown that wine interest and pleasure elements were highly correlated in the case of wine. Similar results were found in assessment of Champagne wines and chocolate gift boxes by French consumers (Kapferer and Laurent, 1993), indicating that for hedonic products, pleasure could be the primary driver of involvement. However, Rodgers and Schneider (1993) suggested that this association could be culturally dependent since they observed a similar correlation on 11 applications of the Consumer Involvement Profile scale on US consumers for products varying in their hedonic value. We will discuss culture influence later in this section. Some research indicated that subjective product knowledge was related to motivational factors such as confidence in decision making (Park and Moon, 2003; Veale, 2008). Highly involved consumers develop, through past experiences, general knowledge and beliefs about associations between the product category and their personal aims and values (Celsi and Olson, 1988). According to Celsi (Celsi et al., 1992) it is important to distinguish constructs of high levels of involvement and knowledge, although they are clearly correlated. Product knowledge comprises familiarity and expertise, where familiarity is defined as the number of product-related experiences that have been accumulated by the consumer and expertise is defined as the ability to perform product-related tasks successfully (Alba and Hutchinson, 1987 in Broderick, 2007). Broderick (Broderick, 2007) showed that product knowledge, expressed by product-related experiences accumulated by the consumer, was negatively correlated to risk involvement. Educating consumers has been a strategy widely used by the US wine industry to create interest from infrequent wine drinkers. Tasting seminars, food and wine pairing workshop, ‘how to’ guides have flourished in the last two decades. The impact of these activities on overall wine consumption has not been tracked per our knowledge. Risk is another common dimension for measuring involvement and relates to the risk perceived by consumers of committing an error and the subsequent consequences of this poor choice. Laurent and Kapferer (1985) put risk in an important position in their Consumer Involvement Profile and developed two types of risk, risk importance and risk probability. Wine involvement was also linked to five generic risks, functional, social, financial, physical and time (Johnson and Bruwer, 2003; Lacey et al., 2009). Ritual is a relatively new dimension developed by Bruwer and Li (2007) and relates to specific processes or actions taken in relation to wine including the storage of wine and preparation for drinking wine. Whereas the behaviour dimension revealed the statement that high involvement wine consumers often match food and wine, this dimension upgrades involvement to a higher level, namely to match wine and wine glasses. Other products could be subject to ritual practices, for example cigar smoking.

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The impact of consumer culture on involvement for a product class has not been extensively studied (Coulter et al., 2003). Broderick (2007) investigated potential differences in the concept of involvement due to cultural differences. She surveyed food purchasers in five European countries (Italy, Spain, UK, France, and Germany) and found that the measurement of involvement in food was invariant among nations, allowing generalizations of relationships between involvement and consequences. Indeed, results showed positive evidence of psychometric equivalence and therefore commonality in the concept of food involvement measured on the five country sample of food purchasers.

14.5 Moderating role of involvement on consumer purchase and consumption behaviours 14.5.1 Involvement as a segmenting variable The construct of involvement has been found to influence brand loyalty, product information search processing, responses to advertising communications, diffusion of innovations, and ultimately, product choice decisions. There is also a great deal of consensus regarding how involvement relates to other constructs, specifically in the way that it informs purchasing patterns, informational and decision-making processes, as well as consumption behaviours. For instance, Barber et al. (Barber et al., 2007; Barber et al. 2008) report a link between wine purchasing and involvement, with different buyers reacting differently to informational cues. Others characterize involvement as it relates to informational processing, and how it impacts the choice process, the willingness to reach satisfaction levels, the extent of the information search, and the receptivity to advertising (Laurent and Kapferer, 1985). Lee (1994) demonstrated that highly involved consumers use a step-by-step process which involves a complex analysis on the information attributes of the particular product, while lowly involved consumers simply categorize the product through its brand and undertake their evaluation on product category. Similarly, Celsi and Olson (1988) focus on the relationship between involvement and decision-making/informational processing, suggesting that high involvement persons differ from low involvement persons in the amount and direction of attention they expend, the effort they use in comprehension, where they focus of their attention, and in the depth and breadth of semantic elaboration during the comprehension process. Many authors have recommended the use of involvement as a segmenting tool to better target marketing strategies to the specific consumer segments (e.g., Lockshin et al., 2001; Lockshin et al., 1997; Taylor-West et al., 2008; Vieira, 2009) and study the moderating role of involvement on consumer behavior and attitudes. The literature mostly investigated consumers belonging to the two ends of the involvement continuum: the low and highly

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involved consumers. The most common method to segment respondents according to their level of involvement (e.g., Quester and Smart, 1998) considers the distribution of one item scores or summed item scores. Several protocols have been described in the literature: the composite score median split (Lockshin et al., 2006); the composite score average split (Joreskog and Sorbom, 1982 in Prenshaw et al., 2006); and the tripartite procedure, keeping the higher and lower end of the spectrum as high and low involved consumers respectively, as described by Quester and Smart (1998) and Lockshin et al. (2001). Some authors have used consumer involvement profiles, i.e. several involvement components, to segment consumers (Kim, 2005; Lesschaeve and Rossi, 2009) and therefore capture more nuances and information on the factors engendering enduring involvement.

14.5.2 The case of food products Food has been considered as a low involvement product category, implying that consumers were not making additional cognitive efforts to select food items or do not develop particular interest in such commodities (Beharrell and Dennison, 1995; Verbeke and Vackier, 2004). Recent food safety incidents and increased awareness of the importance of food for health have raised consumers’ concerns in their food selection. Bell and Marshall (2003) argued that food involvement could vary between individuals and may affect food choice and preferences. Some authors have also demonstrated that considering food products are of low involvement cannot be supported in the case of products with unfavourable image or products related to high perceived risk. That would be the case of fresh mussels (Bello et al., 2000), food products with high cost and social group status membership (e.g., wine) where the risk to fail when purchasing is also high (Mitchell and Greatorex, 1989). This could also apply to food products protected by an appellation ensuring both origin and product typicality (e.g., Espejel and Fandos, 2009), where both the brand and symbolic side of consumption play a relevant role, and therefore could lead to the development of interest and personal relevance to consumers. Per our knowledge, two scales have been proposed in the literature, based on two different conceptualizations of food involvement and are described below. The particular case of wine involvement is also worth discussing considering the abundant literature generated since the late 1990s in the field of wine business research. The food involvement scale (FIS; (Bell and Marshall, 2003)) The Food Involvement Scale assesses involvement holistically through the food provisioning process inspired by Goody’s five stages of food life cycle: acquisition, preparation, cooking, eating, and disposal (Goody, 1982). Using a scale development process similar to what was described previously, Bell and Marshall (2003) proposed a 12-item scale to measure food involvement,

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each item being rated on a 7-point Likert scale. Results showed that the concept of food involvement was two dimensional: the first dimension was related to ‘Set and Disposal’ and the second dimension was related to ‘Preparation and Eating’. In this first paper, Bell and Marshal (2003) posited that food involvement could explain consumer perceptual and behavioural differences. They tested consumers who were low and high involved in food for their ability to discriminate between foods altered in sweetness, sourness, saltiness, and flavour strength. Individuals with higher levels of food involvement (‘Preparation and Eating’ subscale) perceived greater differences between samples for sweetness, saltiness, and flavor strength, but not for perceived sourness. Individuals with higher levels of food involvement (Preparation and Eating subscale) also exhibited greater differences in hedonic ratings for the food samples tested. Older individuals and females had higher FIS scores. This result provided face validity for the measure as it can be argued that older individuals have more experience with aspects of the food lifecycle and have created more robust linkages with food, hence higher food involvement scores. In most cultures females are responsible for food procurement and preparation; therefore higher food involvement scores could be expected relative to males. In a second paper, Marshall and Bell (2004) compared the Food Involvement Scale with other scales used to explain food choice behaviour: the Personal Involvement Inventory described in a previous section, the VARSEEK Scale (Van Trijp and Steenkamp, 1992), the Food Neophobia Scale (Pliner and Hobden, 1992), and Perceived Dietary Variety (Bell and Meiselman, 1995). A significant but weak correlation was found between the Food Involvement Scale and the Personal Involvement Inventory, disconfirming the convergence validity of the Food Involvement Scale to measure the construct of involvement. It can be argued, however, that the Personal Involvement Inventory measures a global personal involvement and is not specific to a product class. Moreover the Food Involvement Scale was found two-dimensional, contrary to the Personal Involvement Inventory which is one dimensional by construct. Interestingly, the Food Involvement Scale was found correlated to the Variety Seeking scale, high food involvement being related to variety seeking. This finding provides a new perspective on food involvement; not only consumers highly involved in food will engage in higher information seeking and processing, they may also seek for more variety in their food selection and may be driven to make healthier food choice (as suggested by Marshall and Bell, 2004). Previous studies found that basically the same was the case for high involvement wine consumers (Lockshin et al., 2001; Lockshin et al., 1997). Product class involvement in food-purchasing behaviour (Drichoutis et al., 2007) The proposed scale aimed at measuring food involvement in the context of purchasing; therefore the authors were more interested by the situational © Woodhead Publishing Limited, 2010

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involvement in food rather than the enduring linkage between food and the individual that Bell and Marshall (2003) measured with the Food Involvement Scale. Drichoutis (Drichoutis et al., 2007) conceptualized food purchasing involvement by considering four factors, namely Individual characteristics, Situational and attitudinal factors, Product knowledge and Level of information search. Food purchasing involvement is measured indirectly by asking respondents to rate the importance on a five-point scale of five attributes when grocery shopping, namely: price, taste, nutrition, ease of preparation, and brand name. The validity of this scale was not tested in this study; however, the authors investigated how socio-economic variables, nutrition knowledge and the importance of following the Dietary Guidelines for Greeks affected food purchasing involvement defined by these five attributes. Results indicated that younger consumers, those with higher education and income who engage in nutritional label use behaviour and do not prepare food for their household are more likely to have low involvement with food. This particular result has some commonality with Bell and Marshall’s findings that older consumers were more highly involved in food (Bell and Marshall, 2003; Marshall and Bell, 2004). Different consumer profiles were also associated with different aspects of food involvement based on importance attached to price, ease of preparation, nutrition, taste, and brand name. The authors suggested that the indirect measure of food purchasing involvement could be used for segmenting consumers according to their perceived importance of price, taste, nutrition, ease of preparation, and brand name when food shopping. Applications: effect of food involvement on behaviours related to food consumption and purchase Kahkonen and Tuorila (1999) included a measure of involvement to determine how this personal characteristic affected consumer acceptability of regular and reduced fat versions of margarine, frankfurters, chocolate and yogurt. They used a revised version of the Personal Involvement Inventory scale proposed by McQuarrie and Munson (1991). Consumers were asked to rate the importance of 10 items on a 7-point scale, for each product category. The level of involvement was rated the highest for yogurts and chocolate bars, two products with some hedonic value, and the lowest for margarines, a utilitarian product which calls for low involvement. Finally, increasing involvement increased the pleasantness and buying probability ratings for all the products, confirming that the ‘pleasure’ dimension can be the driver in food involvement, for hedonic products as posited by Kapferer and Laurent (1993). These findings demonstrate that food could not be considered as a single category; consumers can exacerbate different levels of involvement for different food categories. This was later confirmed by Kuenzel and Muesters (2007) who investigated consumer purchase involvement for 16 food products using Mittal’s questionnaire (Mittal, 1989). © Woodhead Publishing Limited, 2010

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Barker (Barker et al., 2008) investigated the relationships between level of education and food involvement as measured by the Food Involvement Scale, hypothesizing that involvement could affect diet and healthy choices. Their study revealed that women with low food involvement, tended to have lower educational attainment than other women. The low education attainment of these respondents led to a lower frequency of fruits and vegetables consumption. Food involvement did not account, however, for this relationship between education attainment and quality of diet as measured by the consumption of fruits and vegetables. Conversely, Marshall and Bell (2004) showed a trend correlating high food involvement with healthier choice among their student sample. This result suggests that at a higher education attainment and likely similar income level, food involvement might moderate healthy choice and the quality of consumers’ diet. The level of involvement was found to affect the quality signals consumers would rely on when making a food choice and a purchase. Petty (Petty et al., 1983) showed that the manipulation of the extrinsic signals of a product quality has higher impact on the attitudes and feelings of satisfaction and loyalty for highly involved consumers. This moderating role of involvement has been extensively investigated in the wine domain for the last decade. Wine consumer involvement has been conceptualized as the interest, enthusiasm and excitement that consumers exhibit towards wine. Wine is an information-intensive product (Watson et al., 1999), one that has potential for high involvement (Bruwer and Reilly, 2006; Bloch and Bruce, 1984; Hollebeek et al., 2007). Wine is therefore a product with which consumers can form a personal connection and this is the nexus of the so-called involvement theory of consumer learning (Schiffman et al., 2008). It also allows for a reasonably equal sample of both high and low involvement clusters (Quester and Smart, 1998). It is a product with a large range of brand choices from many price segments and many other attributes allocated to the product allowing for other variables to easily be tested with involvement. Charters and Pettigrew (2006) found that highly involved consumers conceptualized wine quality more objectively, by using more cognitive dimensions (interest or complexity) than lower involved consumers who conceptualized wine quality more subjectively; they focused indeed more on sensory dimensions of quality. Low involvement consumers rely more on the brand, the bottle packaging (closure, label) (Barber et al., 2008) and less on information about the winery, the grape varietal, country of origin, vintage and style of wine (Goldsmith, 2000; Lockshin et al., 1997; Lockshin et al., 2001; Quester and Smart, 1998). In a recent study, Lesschaeve and Mathieu (2009) showed that the label style was the most important cue for assessing Riesling wine expected quality among the 9 tested elements (i.e. geographical indication, sub appellation indication, font size of regional indications, closure type, label style, logo VQA (quality standard), reserve mention, vintage and price range). However, the fact that most of these

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involved consumers preferred the traditional label style is in accordance with the previous findings. Barber (Barber et al., 2008) and Goldsmith and d’Hauteville (1998) believed that wine purchasing and wine consumption plays an important role in determining one’s level of wine involvement. Their research indicated that high involvement consumers consume better quality wine on a regular basis (Barber et al., 2008). Lockshin (Lockshin et al., 2001) claimed that high involvement consumers drink wine on a regular basis while low involvement consumers only drink wine on special occasions. It is also worth noting that the place where wine is purchased has a direct link to involvement (Lockshin et al., 2001). High involvement consumers normally prefer buying wine from specialty stores, and they will visit big retail chains as well. On the other hand, low involvement consumers seldom visit specialty stores. Involvement is also linked to price sensitivities (Bloch, 1986). Barber et al. (2008) indicated that high involvement consumers are willing to spend more money per bottle than low involvement consumers. One possible reason for this phenomenon is that high involvement consumers know how to appreciate wine and therefore are willing to spend more money per bottle. In a study conducted in New Zealand with consumers of Sauvignon blanc wines, Hollebeek et al. (2007) found that low involvement consumers considered price as a strong signal cue whereas high involvement consumers would focus on other cues such as the region of origin. Along these lines, Mathieu and Lesschaeve (2009) found that highly involved wine consumers experienced higher wine quality when the wine was presented with a high price tag ($16–20 CDN) and a well known sub-appellation (Beamsville bench). In the same study, however, another group of highly involved consumers were driven by lower price. Differences exist therefore among highly involved wine consumers, supporting the concept of involvement as a continuum. The dichotomic segmentation in high and low involvement groups might not be sufficient to explain consumer behaviours; the characterization of consumer involvement profiles may bring more strategic information to marketers to reach out to these different classes of involved consumers (Lesschaeve and Rossi, 2009). These authors applied the Consumer Involvement Profile (Laurent and Kapferer, 1985) to better understand which dimensions of involvement had led to developing a strong linkage between consumers and wine as a product class. The perceived hedonic value of wine, its symbolic value, and the importance and probability of risks in making a poor choice were found important to explain higher wine involvement. The hedonic and symbolic values of wine impacted differently the highly involved wine consumers tested. The largest proportion of these consumers (71%) considered pleasure and status recognition/symbolic as equally important sources of their involvement; a similar finding was shown for involved consumers of a tradition Spanish delicatessen (Espejel and Fandos, 2009). This study suggested

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that consumers interested in the symbolic values associated with the product will attach more importance to the hedonistic side of their consumption. In the case of highly involved consumers, the intrinsic and extrinsic quality attributes reinforce the pleasure experienced through the purchase and consumption. Several research studies have been done in the field of wine tourism. Charters and Ali-Knight (2001) described wine-related travel as a pleasure seeking activity. Others also asserted that that wine-related travel gives people pleasure (Alant and Bruwer, 2004; Bruwer, 2004; Bruwer, 2002; Charters and O’Neil, 2000; Galloway et al., 2007). Pleasure being a strong antecedent of involvement especially for hedonistic products, it is not surprising that they discovered that high involvement consumers are active in participating in wine events and enjoy visiting winery tasting rooms. Espejel and Fandos (2009) showed that involvement increased the impact of both intrinsic and extrinsic perceived quality on consumers’ loyalty levels. The authors posited that highly involved consumers develop loyalty as high perceived quality is experienced with a product from an appellation of origin. This result is in line with Olsen’s findings that product involvement is a complete mediator between satisfaction and repurchase loyalty (Olsen, 2007). Implications for new food product development The above literature review showed that enduring involvement with a new food product is likely to develop when the product provides a pleasant experience, when it becomes personally relevant to the consumer who will seek more information and develop an on-going interest which will minimize the perceived risk of making a wrong choice at purchase. Motives driving relevance are diverse and could be related to intrinsic (being healthy) or symbolic (being successful) benefits. It should be noted that the pleasantness of the experience of consuming the new product not only includes its sensory properties but also any extrinsic cues that could signal and reinforce the message of product relevance to the consumers. When the consumption experience is pleasant, the food choice theory calls for the reinforcement of consumer expectations for future purchase that will strengthen again if the experience is positive and will lead to consumer satisfaction and purchase loyalty.

14.5.3 The case of non food products Most of the literature pertaining to the moderating role of involvement on purchase and consumption of non food products addressed the case of apparel, clothing and more specifically fashion clothing (O’Cass, 2004; O’Cass, 2000; Vieira, 2009; Kim, 2005; Michaelidou and Dibb, 2006). Few studies looked at cars (Bloch, 1981; Taylor-West et al., 2008), and services (Prenshaw et al., 2006).

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In all cases, the measurement scales had either been proposed by others (Kim, 2005; Prenshaw et al., 2006; Taylor-West et al., 2008; Vieira, 2009) or were developed such the Fashion clothing involvement scale (O’Cass, 2000). The case of clothing will be discussed below as an example of non-food products; however, we anticipate that findings are transferable to the domains of household or personal care products. Fashion clothing involvement scale (O’Cass, 2000) In his introductory argument, O’Cass justifies the need for a new measurement scale due to the poor alignment among authors in the definition of the involvement construct; it resulted in one definition encompassing different forms of involvement. He proposes to consider four types of involvement, all related to fashion clothing: product, purchase decision, advertising, and consumption. These four types purport “to represent basic types of involvement relevant to a consumer’s environment and maintain involvement as an enduring relationship between a consumer and an object, not a temporary or situational one”. Therefore O’Cass rejects the notion that involvement could be led by a situation contrarily to what was presented previously in the theoretical background section of this chapter. After reviewing the literature and conducting several interviews, the author compiled a list of more than 100 items, which was submitted to a pilot test for further purification of the scale. Results led to a scale including 42 items and measuring the four types of involvement. This questionnaire was administered to 450 students which allowed testing the scale properties. Reliability and consistency were deemed satisfactory. To assess the relationships between the four types of involvement, several models were studied using structural equation modelling. The theoretical model fitting the best the data considered an overarching construct named ‘consumer involvement’ encompassing product involvement, purchase decision involvement, advertising involvement, and consumption involvement. This consumer involvement profile allows for investigation of the moderating role of the four types of involvement on specific behaviours. This is different from the profile proposed by Laurent and Kapferer (1985) who were using different facets to explain enduring/product involvement. Clothing involvement as a multidimensional construct (Michaelidou and Dibb, 2006) This 15-item scale was proposed as a tool to measure five dimensions of clothing involvement, four of which are similar to the Consumer Involvement Profile: importance, pleasure, interest, self-expression, and sign value. Dimensions related to risks have been removed here, arguing that this dimension pertain to the individual not its relation with the product. The 15 items were selected using the theoretical literature to select items with face validity to describe a fashion involvement construct. The scale was administered on-line to over 500 participants, representing a convenience

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sample. Respondents indicated their degree of agreement with the 15-item involvement scale using a 5-point Likert scale. Internal consistency was validated. The study of discrimination validity indicated that involvement with clothing was in fact bi-dimensional: the first dimension related to pleasure and enjoyment from shopping; the second dimension describing the importance consumers attach to the product, which relates to the symbolic nature of clothing as a means of self-expression. Effect of non food involvement on behaviours related to consumption and purchase Remaining in the fashion clothing domain, several studies investigated the moderating role of consumer involvement on their behaviours related to fashion apparels. O’Cass (2004) examined the effect of materialism and self-image/product-image congruency as potential antecedents of consumers’ involvement in fashion clothing. Purchase decision involvement, subjective fashion knowledge and consumer confidence were assessed as potential consequences, i.e. these would be affected by consumer involvement levels. Findings confirmed relationships between involvement and consumer personal characteristics or values, previously described in the literature. Fashion clothing involvement was found significantly affected by consumer’s degree of materialism, gender and age. Materialism relates to valuing possessions and using them to project an image to others, to signal particular values of lifestyles. We see materialism as a synonym of the “symbolic value”, an antecedent of product involvement proposed by Laurent and Kapferer (1985), although this has not been tested by the author. Supporting the importance of the symbolic value as an antecedent of clothing involvement, the second dimension of the clothing involvement scale proposed by Michaelidou and Dibb (2006) was explained by self expression, a means to express oneself through clothing and the symbolic value of clothing. O’Cass (2004) found that fashion clothing involvement influenced fashion clothing knowledge. Finally, the results indicate that fashion clothing knowledge influences consumer confidence in making purchase decisions about fashion. The latter results reinforced previous findings that subjective product knowledge is related to motivational factors such as confidence in decision making (Park and Moon, 2003) and negatively correlated to risk involvement (Broderick, 2007). Similar conclusions were reported recently (Vieira, 2009). Expanding on the application of their Fashion clothing involvement scale, the same research group found that the level of involvement of young Chinese students (referred as Generation Y in the paper) affected positively their perception of brand status and brand attitude. (O’Cass and Choy, 2008). Highly involved consumers perceived premium brands with higher status and held an overall positive attitude toward those high status

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brands. This was not true for mass brands. Further brand status and brand attitude were found to have positive impacts on consumers’ willingness to pay a premium for a specific brand. Time spent deciding was identified by Vieira (2009) as affected by consumer degree of fashion clothing involvement; the more involved the consumer is the more time will be spent to try on and browse in the store. The author reported positive relations between involvement and continuity commitment, i.e. the more fashion involved the more committed the consumer will be incline to invest regularly in fashion clothing. The fashion literature recognizes the need to better characterize the predictive variables of involvement. Vieira suggested theorizing as antecedents the five dimensions of fashion adoption-related behaviour proposed by Tigert et al. (in Vieira, 2009), i.e. (1) fashion innovativeness and time of purchase; (2) fashion interpersonal connection; (3) fashion interest; (4) fashion knowledge ability; and (5) fashion awareness and reaction to changing fashion trends. Implications for new non-food product development Commonalities exist between the antecedents and consequences of involvement for food and non-food products. Interest, pleasure and symbolic values tend to drive fashion clothing involvement. These involved consumers are seeking more knowledge about fashion and develop a purchase loyalty or continuity commitment with the repeated experiences. The fashion brand is the main quality signal valued by these consumers. How do these findings apply for household and personal care products? As stated before, involvement is likely to occur more with hedonic products than utilitarian ones. Fashion clothing is one example. However, perfume was also described with a high hedonic value (Kapferer and Laurent, 1993) and could involve consumers more than, for example, anti-perspirant sticks. Experienced pleasure is not enough, however; other drivers related to consumer values, beliefs and perceived benefits associated to the usage of the new product certainly play a role.

14.6 Implications for consumer-driven innovation The previous sections provided substantiation regarding the formation of consumer involvement: it is built over time, as the consumer becomes familiar with its features and benefits while the product engages the consumer to look for further information or requires specific efforts to get acquired. Consumer involvement with a high involving product impacts significantly his or her purchase and consumption behaviour. So how can companies launching an innovative product engage quickly potential consumers to create strong psychological linkages with them? How do innovative technologies applied to familiar products impact consumers’ acceptance and

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endorsement? These questions are fundamental to be posed in the context of consumer-driven innovation and will be discussed in what follows.

14.6.1 The challenge of innovative products Human beings tend to be conservative and cautious when presented with novelty, whether it is a food or non-food product. All parents have experienced at least once this situation with their children when they tried to introduce a new food into their diet. Neophobia is the trait describing this initial reaction and several scales have been developed to assess it (Pliner and Hobden, 1992; Cox and Evans, 2008). Assessing the introduction of a novel fruit on the market, the yellow flesh kiwifruit, Jaeger et al. (2003) did not observe a moderating role of neophobia on consumers’ acceptance of this novel fruit. It could be argued that neophobic consumers tend not to participate in consumer studies and therefore this segment of population is rarely surveyed. Repeated exposure to the novel product was shown to diminish this first apprehension (Pliner, 1982), the novel product becoming more familiar to the individuals. Product knowledge including familiarity expertise about the product was found to impact consumers’ involvement with products as discussed earlier. It seems, therefore, strategic for companies to capitalize on familiarity and knowledge/consumer education to increase consumers’ acceptability of their new product. Familiarity with a product can become a deterrent when a new concept is introduced in the well known category, for example the launch of an organic variant of the well known product. Tarkiainen and Sundqvist (2009) investigated the moderating role of involvement in the purchase of four organic products: coffee, bread, fruit, and flour available in Finnish supermarkets. Their findings showed that the reason why consumers do not buy organic food regularly despite their positive attitudes is that such ideologically formed attitudes are not present in habitual, low-involvement shopping activities with limited problem-solving needs as in food shopping from grocery stores. This behaviour however, seemed to depend on the consumer brand loyalty. Shoppers tend to develop a routine when buying products in which they are highly involved (e.g., coffee); switching to an organic alternative is unlikely if their habitual brand does not offer it. For product category without strong brands, the switch to an organic alternative is more likely (e.g., bread) to occur. Heiskanen et al. (2007) studied consumers’ reaction and adoption to radical innovations, which are naturally subject to neophobic reactions from potential users. These authors argued that consumers’ conservatism cannot only be attributed to the lack of consumer experience with the product or an irrational resistance to change. Their studies revealed that consumers’ lack of interest for new products is often based on the failure of the product concepts to meet important needs and contextual requirements, or on their

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potential external effects. These findings strongly support the development of innovative products using consumer inputs for guidance and definition of the new product concept, i.e. listening to the voice of the consumers. “Failure to define the product before development begins is a major cause of both new product failure and serious delays in the development cycle” (Cooper and Kleinschmidt, 2007). Several methods have been proposed in the literature (e.g., Meilgaard et al., 2007; Moskowitz and Gofman, 2007).

14.6.2 Proposed strategies to involve consumers in a new product The positive consequences of relying on involved consumers to maintain or grow the market share for a given product have been discussed in the previous sections. As a conclusion, the following considerations are proposed to engage consumers and create that deep linkage between themselves and the new product. 1. Offer tasting/testing opportunities to initiate a first pleasurable experience and minimize risk Wine purchasers claimed that taste was the most decisive factor for making a choice at point of purchase (Thompson and Vourvachis, 1995; Thomas and Pickering, 2003). Having tasting the wine previously was found the most important purchase factor cited by New Zealand consumers (Jaeger et al., 2009), independently of their level of wine involvement. The wine finding can easily be transferred to other food and non-food domains and actually implemented by many companies considering the frequent demonstration stand available in supermarkets or department stores. However, as stated previously, the pleasantness of the experience is not sufficient by itself to stimulate involvement. 2. Create personal relevance This step might be the most challenging one especially for a new product without any comparative on the market. Knowing consumer interest or personal relevance implies running appropriate studies to better characterize the target consumers: whether it is to determine the attribute driving preference, to describe their lifestyle or understand the values motivating the interest in specific product attributes. Several methodologies have been proposed in the literature such as concept testing (e.g., Moskowitz, 1996, Moskowitz et al., 2005), laddering techniques (e.g., Brunso and Grunert, 2007; Pieters et al., 1995), the value scaling (e.g., Kamakuta and Novak, 1992); the attitudes and beliefs (e.g., Shepherd, 1986) or lifestyle segmentations (e.g., Bruwer et al., 2002). Knowing what turns on the consumer creates the communication features required to link to consumer personal relevance. 3. Use of new technologies and medias It is well known in the marketing literature that word of mouth or ‘consumer to consumer communication’ is an effective (and free) way to

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promote a product, service or company. Word of mouth has been shown to influence a variety of conditions: awareness, expectations, perceptions, attitudes, behavioural intentions and behaviour (Buttle, 1998; Bruwer and Reilly, 2006). Sheth (1971) (in Buttle, 1998) concluded that word of mouth was more important than advertising in raising awareness of an innovation and in securing the decision to try the product. In a recent study Bruwer and Lesschaeve found that 50% of visitors of a winery tasting room were recommending the winery products to friends or family (Lesschaeve et al., 2009). The emergence of social media and on-line communities provides new avenues for companies to engage their consumers and build the involving relationship between them and their new product. Whether consumers share with their friends promotional email messages (aka viral marketing), comment their experience with the product on company websites or become fan of a brand on their social network, consumers become more involved in sharing opinions which can influence potential aspects. Debate exists among market researchers to consider these consumers as opinion leaders, early influencers or loud consumers who need to speak up (see, for example, discussions threads on that topic on www.linkedin.com/Next Gen Marketing Research group). As for any new research methodology, common sense is required to determine the best use of these new media and technologies. By consulting consumers for recipe development, offering feedback opportunities, allowing them to comment on the strengths and weaknesses of the new product, companies increase their opportunities to enhance interest in the new product, the brand and company. As discussed earlier, product involvement tends to lead to brand loyalty and therefore market success.

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15 Statistical design of experiments in the 21st century and implications for consumer product testing B. T. Carr, Carr Consulting, USA

Abstract: Design of experiments (or DOE) is a powerful research tool that can be applied productively at any stage of the product development cycle. DOE is more efficient, more sensitive and more robust than traditional research methods. DOE consists of four primary classes of experimental designs: factorial experiments, factor-screening experiments, optimization experiments and mixture experiments. Each type of experiment is best suited to answer a specific type of research question. The chapter discusses the structure of each type of experiment, the questions that each type of experiment answers and characteristics that distinguish one type of experiment from another. A numerical example is presented for each type of experiment. Information is provided on how to select experimental variables and their ranges. Traditional approaches to constructing experimental designs are compared to computer-aided design techniques. Lastly, the implications of conducting consumer tests on samples from DOEs is discussed. Key words: design of experiments, DOE, factorial, fractional factorial, response surface methodology, RSM, mixture experiment, optimal design, D-Optimal, A-Optimal, G-Optimal, V-Optimal.

15.1 Introduction 15.1.1

Brief description of statistical design of experiments (DOE) and alternative approaches Design of experiments (or DOE) is a powerful research tool that can be applied productively at any stage of the product development cycle. DOE consists of four primary classes of experimental designs, each of which is best suited to answer a specific type of research question and which applies best at a different stage of the cycle.

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For example, in product development, factor-screening experiments can be used early in the research cycle to identify formula and process variables that have high impacts on the characteristics of the product and distinguish them from variables that have little or no impact on the product. That allows researchers to focus their efforts productively on the variables that will change the product’s characteristics and to not waste their time studying variables that do not change the product. Once the high-impact variables have been identified, factorial experiments can be used in the middle of the product development cycle to study them in detail. It is seldom the case that the absolute levels of the ingredients in a formula or the specific process settings, considered in isolation from each other, lead to the best product. Instead, the best product results from achieving the correct balance of ingredients and process settings. Achieving the right balance requires an understanding that the best level of one ingredient in the formula may depend on the level at which another ingredient is being used. For example, the best level of a sweetener on a formula may depend on the level of acid being used. Alternatively, the best temperature for processing a product may depend on its moisture level or the through-put rate being used in the manufacturing process. Factorial experiments provide researchers with the means to understand these complex balancing acts by allowing them to model both the individual and interactive effects of the formula and process variables in their products. Ultimately, the goal of product development is to deliver the “best” formulation, process and package to consumers. What is “best” depends on many things – the types and levels of ingredients in the formula, the types of equipment and processing set points used during manufacturing and the types and performance properties of the packaging materials used. All of these product features can be efficiently studied using product optimization experiments. Like factorial experiments, product optimization studies allow researchers to understand the individual (or “main”) and combined (or “interactive”) effects of the experimental variables. In addition, product optimization experiments allow researchers to interpolate between the levels of the quantitative variables included in the experiment, thus allowing them to identify the optimal ingredient levels or process settings, even if they were not levels specifically included in the study. There is a special type of product optimization study, called a mixture experiment, that can be applied when all of the variables in the experiment are levels of ingredients and the total concentration of the ingredients being studied is a fixed constant. Mixture experiments have all of the predictive power of a standard optimization study but, because of the constraint on the total concentration of the ingredients, mixture experiments are much smaller (in terms of the number of experimental samples) than standard optimization studies. For example, approximately fifteen experimental samples are required for a standard three-variable product optimization

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study, while only seven experimental samples are required for a threecomponent mixture experiment. By being aware of the different types of experimental designs that are available and the types of research questions they are best suited to address, researchers maximize their product understanding while minimizing the experimental resources required during the product development cycle.

15.1.2

Distinguishing the design of the panel from the design of the samples The topic of this chapter is DOE related to a product’s formula, process and package. It addresses experimental designs including factor-screening studies, factorial experiments, product optimization studies and mixture experiments. Those familiar with sensory evaluation or consumer product tests may have encountered the term DOE in a different context. In that context, experimental designs such as complete-block designs, balanced incomplete block designs, Latin squares, etc., occur. Although it is not a completely reliable distinction, it is convenient to think of the experimental designs that are the topic of this chapter as the “designs of the samples” and to think of the latter types as the “designs of the panels.” The design of the panel relates to such matters as: how many samples need to be evaluated, how many samples can be evaluated in a single session, how many total evaluations are required to achieve the desired level of precision, etc. While the design of the samples, on the other hand, relate to the comparisons of interest among the samples, such as: what is the impact of increasing the concentration of flavour in the product or how does the impact of processing temperature change when through-put increases. The distinction is worth being aware of because it is often the case that sensory and consumer studies will be conducted on samples that have been produced according to a designed experiment. So, for example, a balanced incomplete block design may be used to collect data on a set of samples that were produced according to a factorial experimental design.

15.2 Advantages of statistical design of experiments (DOE) 15.2.1 Ease and depth of interpretation Designed experiments focus on the individual and combined effects of the experimental variables included in the study rather than on the performance of specific test samples, each of which may represent a hodgepodge of variable changes. By focusing on the main effects of the experimental variables and their interactions, researchers can understand why performance changes when the levels of the experimental variables are manipulated. This information can be used to answer many important questions

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about the system they are studying without having to make new test samples for each set of questions. Based on the information obtained from the DOE, researchers can identify the combination of variable levels that best satisfy the action standards of the project. They will know which variables are most important to control to deliver a high quality product and which ones do not matter as much. Learnings of this type have highly practical implications. For example, they may allow the researcher to select a combination of variables that are nearly optimal in terms of product performance but are far less costly.

15.2.2 Efficiency Researchers sometimes choose not to use DOE under the misconception that designed experiments require more resources than alternative approaches. In fact, just the opposite is true. Designed experiments yield more information with greater precision than the traditional one-variableat-a-time approach. Examples of factorial experiments and factor-screening studies will be presented later in the chapter to illustrate this point. Although incorrect, the notion that designed experiments require more resources is understandable. Two factors contribute to the misconception. First, DOE is front-end loaded. Researchers must commit in advance to running the entire DOE. There are no options to stop early and still obtain the full benefits available from the design. Second, researchers are sometimes overly optimistic about the possibility of obtaining a quick solution to the research objective. With the ever present tight timelines that always confront researchers it is tempting to pursue a promising hunch that may lead to a quick solution requiring only three or four test samples and one round of testing. However, in the vast majority of cases, the first round of testing does not yield a satisfactory solution. Subsequent rounds of sample preparations and testing are conducted often ending only when either the testing resources or the project schedule are exhausted. In cases like this, researchers will have taken more time to prepare more test samples and conducted more sample evaluations than would have been required had a designed experiment been used. Further, the designed approach allows the researcher to make informed decisions about why certain combinations of the experimental variables are better than others. And the results of the DOE can be applied in future studies without having to relearn the effects of the experimental variables. When a non-design approach is used, the researcher may have no other option but to select the best performing sample that had been prepared thus far without necessarily knowing why it performed well or if there are other options available that would have performed better. Both in terms of the resources required for a single study and long-term learnings that result from them, designed experiments are more efficient than other research options.

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15.2.3 Sensitivity Designed studies are more sensitive than non-designed approaches because the levels of the experimental variables are replicated as a natural part of the experimental design. These “hidden replications” add precision to the study without adding more test samples. Consider, for example, the two test plans in Table 15.1. Test Plan A is a one-at-a-time approach for studying the individual effects of seven experimental variables. Test Plan A consists of eight test samples – one baseline sample and seven experimental samples that each differ from the baseline on a single variable. Note that the impact of each experimental variable is measured by the difference between the baseline sample and a single experimental sample. Test Plan B is a designed factor screening study for the same seven experimental variables. Test Plan B also consists of eight test samples but note that for each experimental variable, four of the test samples are set to the low level of the variable and

Table 15.1 One-at-a-time versus DOE approaches. Both approaches require eight experimental samples. The one-at-a-time approach provides one estimate of the effect of each experimental variable. The DOE approach provides four estimates of the effect of each experimental variable a) Traditional one-at-a-time approach: eight samples; one replication per experimental variable Run Diameter 1 2 3 4 5 6 7 8

Small Large Small Small Small Small Small Small

Mold Sweet Temperature Moisture Sprinkles Color Position Aromatic 0.4 0.4 0.0 0.4 0.4 0.4 0.4 0.4

2.25 2.25 2.25 4.50 2.25 2.25 2.25 2.25

0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0

7.0 7.0 7.0 7.0 7.0 4.0 7.0 7.0

Open Open Open Open Open Open Closed Open

Low Low Low Low Low Low Low High

b) Statistically designed approach: eight samples; four replications per experimental variable Run Diameter 1 2 3 4 5 6 7 8

Small Small Large Large Small Small Large Large

Mold Sweet Temperature Moisture Sprinkles Color Position Aromatic 0.4 0.0 0.4 0.4 0.4 0.0 0.0 0.0

2.25 2.25 2.25 4.50 4.50 4.50 2.25 4.50

0.0 0.5 0.5 0.5 0.0 0.5 0.0 0.0

7.0 7.0 3.5 7.0 3.5 3.5 3.5 7.0

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four are set to the high level of the variable. All eight test samples are used to assess the effect of each of the experimental variables. The hidden replications in Test Plan B provide four times the amount of information about the impact of each of the experimental variables than is available from the same sized Test Plan A. 15.2.4 Robustness The findings from designed studies are more likely to reproduce themselves in the future even when some uncontrolled changes are made to the system. The reason for this can be seen by comparing the two test plans in Table 15.1. The findings about the impact of the experimental variables in Test Plan A are all dependent on the choice of the baseline sample. Had a different baseline sample been chosen, the impact of changing the level of some of the experimental variables may have been different. In Test Plan B, on the other hand, no one sample plays a special role in the design. All samples contribute equally to the findings about the impact of the experimental variables. Further examination of Test Plan B reveals that within the four samples that are at the low level of one experimental variable and the four samples that are at the high level of the variable, the levels of the remaining six experimental variables are churning away in the background. If the signal associated with the difference between the high and low levels of an experimental variable rises above the noise created by the background changes of the other experimental variables, a researcher can be more confident that those changes will reproduce themselves in the future even if some small changes have been made to the system.

15.3 Factorial experiments 15.3.1 Structure Factorial experiments are the backbone of DOE. Other types of experimental designs are either subsets of full factorial experiments (e.g., factor screening studies) or they are factorial experiments that have been augmented with additional experimental samples to allow for more complex statistical models to be fit to the data (e.g., some classes of product optimization studies). Although factorial experiments tend to be used toward the middle of the product development cycle, it is appropriate to discuss them first to provide a perspective on what is gained or lost when a standard factorial experiment is modified. Factorial experiments always consist of two or more experimental variables studied at two or more distinct levels. The levels of the variables may be categorically distinct from each other (e.g., different suppliers of the same ingredient) or they may vary along a continuum on which the levels of the experimental variables can be distinguished numerically (e.g., the concentration of an ingredient in a formula). Factorial experiments often

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High

Variable 3 Mid

High Variable 2

Low

Low High

Low Variable 1

Fig. 15.1 The distribution of the experimental samples in a 2 × 2 × 3 factorial experiment.

consist of combinations of categorical and continuous variables. Each level of one variable is studied at all possible combinations of the levels of the other variables in the design. One complete replication of a factorial experiment consists of all possible combinations of the levels of the experimental variables. The number of experimental samples that make up one replication is the product of the levels of all of the experimental variables. For example, in a three-variable experiment in which two of the variables are studied at two levels and one is studied at three levels (see Fig. 15.1), the number of experimental samples in one full replication is 2 × 2 × 3 = 12.

15.3.2 Questions they answer The learnings obtained from a factorial experiment (or any DOE for that matter) focus on the effects of the experimental variables rather than on the performance of any one sample in the study. The questions that factorial experiments are best suited to answer are, for example, How does changing the level of sweetener impact product quality? (I.e., the “main effect” of sweetener.) or Does the impact of processing temperature change when the moisture level of the product is increased? If so, in what way and by how much? (I.e., the temperature-by-moisture-level “interaction.”)

15.3.3 Main effects and interactions Factorial experiments provide clean estimates of the main effects of the individual experimental variables and their interactions. The estimates are “clean” in the sense that the estimated effect of one variable is not influenced by any other variable in the experiment. Similarly, in a full factorial experiment, the estimated interactions among variables are not influenced

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by any other main effects or interactions in the study. This has an important practical benefit. The results of factorial experiments can be reliably expected to reproduce themselves in the future even if some of the experimental conditions change because the effects are measured “cleanly” from other variables in the study as opposed to being measured relative to some baseline sample, as is done in the traditional one-variable-at-a-time approach. A bit of notation will be helpful to illustrate how factorial effects are estimated. Consider the simplest of factorial settings, a two variable experiment (variables A and B) in which each of the variables is studied at two levels (arbitrarily identified as “low” and “high”). There are 2 × 2 = 4 experimental samples in the design. The four experimental samples will be named according to the levels of the experimental variables that were used to produce them. If the “high” level of an experimental variable is used in a sample that sample’s “name” will include the lower case letter of that variable. If the high level of variable A is used in a sample, the letter “a” will be in the sample’s name. If the high level of variable B is used in a sample, the letter “b” will be in the sample’s name. If the “low” level of a variable is used in a sample that sample’s name does not include that (lowercase) letter. The name of the experimental sample for which all of the variables are at their low levels is “(1)”. The four experimental samples and their names using the a-b-c notation are presented in Table 15.2. Also presented in Table 15.2 is the +/− notation that will indicate how the factorial main effects and interactions are estimated. Whenever a variable is at its high level, that sample receives a “+” for the main effect of that variable. Whenever a variable is at its low level, that sample receives a “−” for the main effect of that variable. The +/− levels for interactive effects are obtained by cross-multiplication (i.e., + × + = +; − × − = +; + × − = − and − × + = −). Using the +/− notation from Table 15.2 it can be seen that the main effect of variable A is [(a − (1)) + (ab − b)]/2 and the main effect of variable B is Table 15.2 The a-b-c and +/− notations for a three-variable factorial experiment. The a-b-c naming convention and the +/− notation are used to estimate the main effects and interactions in factorial experiments Treatment combination (1) a b c ab ac bc abc

Factorial effect I

A

B

C

AB

AC

BC

ABC

+ + + + + + + +

− + − − + + − +

− − + − + − + +

− − − + − + + +

+ − − + + − − +

+ − + − − + − +

+ + − − − − + +

− + + + − − − +

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[(b − (1)) + (ab − a)]/2; with the averaging being done based on the number of differences included in the estimate. So even in the smallest of all factorial experiments, there are two hidden replications of each experimental variable. Also note that the main effect of variable A is assessed at both the low and high levels of variable B and vice versa. This adds to both the efficiency and robustness of the factorial approach compared to the onevariable-at-a-time alternative. A main effect reveals if changing the level of an experimental variable has a noticeable effect on a measured characteristic of the product. It is equally important to understand if the effect of one variable is constant across the levels of other variables in the DOE – that is, do the variables “interact” in their impact on the product. That information is available directly from factorial experiments. Using the +/− information in Table 15.2, it is seen that the interaction of variables A and B is estimated as [(ab − a) − (b − (1))]/2. As written the formula determines if the effect of variable B at the high level of variable A (i.e., (ab − a)) is the same as the effect of variable B at the low level of variable A (i.e., (b − (1))). If the difference between the two estimates is zero, then there is no interaction between the variables. If the difference between the two estimates is not zero then the impact of one variable depends on the level at which the other variable is being used – the variables interact. Interactions are often displayed graphically as in Fig. 15.2. The impact of one variable is graphed separately at each level of the other variable being considered. If the lines on the graph are parallel as in Fig. 15.2a, there is no interaction between the experimental variables. Any lack of parallelism as, for example, in Fig. 15.2b, indicates that the two variables interact. By understanding the nature and magnitude of the individual and combined effects of the experimental variables on the product, researchers can

b) Interaction

a) No interaction

Slow

Fast Agitation

Conc. Low

Conc. Low

Conc. High

Conc. High

Slow

Fast Agitation

Fig. 15.2 Interaction charts. The parallel lines in the graph of (a) indicate that there is no interaction between agitation and concentration. The absence of parallel lines in the graph of (b) indicates the effects of agitation and concentration interact. Agitation has no impact on the product at high concentration. Fast agitation increases the response compared to low agitation at low concentration.

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efficiently and confidently advance their product improvement initiatives. The insights that result from factorial DOEs provide a foundation of understanding how the experimental variables affect critical product characteristics. Because of the robustness of the results obtained from factorial DOEs, the understandings can be applied with confidence in the future without the need to repeat any primary research.

15.3.4 Factorial experiment example Objective Assess the individual and combined effects of four production variables on the consumers’ acceptance of a shampoo. Design of the samples The four experimental variables and their ranges are: Silicone Type: Silicone Level: Pearlizer: Polymer Level:

A or B 0.1% or 2.0% No or Yes 0.1% or 1.0%

The factorial experiment for the four experimental variables each being studied at two levels is presented in Table 15.3. The factorial experiment is comprised of the 16 possible combinations of the low and high levels of the four experimental variables.

Table 15.3 Sixteen experimental samples and overall liking for the four-variable factorial experiment example Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Run order

Silicone type

Silicone level

Pearlizer

Polymer level

Overall liking

5 8 2 15 9 10 1 7 13 6 14 3 16 11 4 12

A A A A A A A A B B B B B B B B

0.1 0.1 0.1 0.1 2.0 2.0 2.0 2.0 0.1 0.1 0.1 0.1 2.0 2.0 2.0 2.0

No No Yes Yes No No Yes Yes No No Yes Yes No No Yes Yes

0.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0

58 71 58 68 85 91 85 81 50 62 51 59 80 81 78 85

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Design of the panel The experimental samples are evaluated in an in-home use test (IHUT) involving 400 respondents, each of whom evaluates four of the sixteen samples, yielding 100 evaluations per sample. The four samples that each respondent evaluates are determined according to a BIBD design (see Section 15.6). The average overall liking ratings of the test samples are presented in the last column of Table 15.3. Data analysis The Overall liking data are submitted to an Analysis of Variance (ANOVA) procedure. The model used in the analysis estimates the main effects of each of the four experimental variables and all of the two-way interactions among the experimental variables. The three-way and four-way interactions are treated as experimental error in the analysis. All testing is conducted at the 5% level of significance. The ANOVA table (Table 15.4) reveals that there are significant effects on Overall liking due to Silicone type, Silicone level and Polymer level. (Whenever the P-value in Table 15.4 is less than 0.05, the effect is significant at the 5% level.) Further, there is a significant Silicone-level-by-Polymerlevel interaction. Interpretation of results The interactive effect is studied first because the significance of the interaction indicates that the conclusions concerning Polymer level may depend on which Silicone level is being used (or vice versa). Fig. 15.3 is a graph of the Silicone-level-by-Polymer-level averages. It can be seen that the high level of Silicone is better liked than the low level of Silicone, regardless of the level of Polymer. Further, it can be seen that there is no meaningful difference due to Polymer level at the high level of Silicone. However, at the low level of Silicone, the low level of Polymer is significantly better liked. Table 15.4 The ANOVA table for the four-variable factorial experiment example Source A-Silicone type B-Silicone level C-Pearlizer D-Polymer level AB AC AD BC BD CD Residual Total

Sum of squares

df

Mean square

F

P-value

162.56 2232.56 10.56 175.56 14.06 10.56 0.56 0.56 68.06 7.56 45.31 2727.94

1 1 1 1 1 1 1 1 1 1 5 15

162.56 2232.56 10.56 175.56 14.06 10.56 0.56 0.56 68.06 7.56 9.06

17.94 246.35 1.17 19.37 1.55 1.17 0.06 0.06 7.51 0.83

0.0082 <0.0001 0.3296 0.0070 0.2681 0.3296 0.8132 0.8132 0.0408 0.4029

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Overall liking

82

mer

Poly

0.1% 72

er

lym

%

1.0

61

Po

50 0.10

0.57

1.05

1.52

2.00

Silicone level

Fig. 15.3 Silicone-level-by-Polymer-level interaction. The lack of parallelism between the two lines is evidence of an interaction. At the high Silicone level, Polymer level has no significant effect on Overall liking. At the low Silicone level, the low Polymer level is significantly better liked than the high Polymer level.

The interaction is significant because different conclusions would be reached regarding the effect of Polymer level depending on which level of Silicone is being used. However, the overriding conclusion is the high Silicone is better liked and at high Silicone there is no meaningful effect due to Polymer level. Examination of the average ratings of the two Silicone types reveals that Type A is significantly more well liked than Type B. Recommendations In order to maximize consumer acceptance among the variables and ranges studied in this experimental design, the shampoo should be made with Silicone Type A at 2.0%. At 2.0% Silicone, either the high or low level of Polymer can be used. The shampoo can be made with or without Pearlizer. If for whatever reason, it is necessary to make the shampoo at 0.1% Silicone, the Polymer level should be set to 0.1% also.

15.4 Screening experiments 15.4.1 Structure Screening experiments are subsets of full factorial experiments. The specific subset of experimental samples that make up a screening study is chosen to provide clean estimates of the main effects of the experimental variables (and, in some cases, clean estimates of some interactions). Screening experi-

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ments are much smaller than a full factorial experiment conducted on the same number of experimental variables. For example, one replication of a full factorial experiment with seven experimental variables each studied at two levels comprises 128 experimental samples, while a screening experiment on seven variables at two levels requires only eight experimental samples. What is given up in order to achieve this much economy is discussed later in the section. The experimental variables in a screening experiment can be either categorical or numeric. Often screening experiments include only two levels of each experimental variable. The primary purpose of a screening experiment is to identify the experimental variables that have high impacts on the product’s characteristics and distinguish them from the experimental variables that have little or no impact on the product. Two levels of a variable are sufficient to determine if the variable has an effect on the product and how the magnitude of its effect compares to the effects of the other variables in the design. Screening experiments are widely used in conjoint studies. In these types of studies a product, concept or service is described based on a number of experimental variables, each of which is studied at several levels. In conjoint studies, the number of levels of each variable is typically greater than two because the researchers are interested in the levels themselves rather than in determining only if changing from one level to another has a noticeable effect on the product. Conjoint studies rely on screening experiments called orthogonal arrays, which provide clean estimates of the individual effects of each variable and the magnitude of the effect of each level of each variable relative to an arbitrarily defined baseline condition. The orthogonal arrays comprise the minimum number of combinations of variable levels that is needed to be able to estimate each of the effects independently of one another. The total utility (e.g., overall liking) of a particular combination of variable levels is assumed to be accurately estimated by the sum of the values of the individual parts (called part-worths in the terminology of conjoint research). Conjoint designs are also very economical in term of the number of combinations required to perform the analyses. For example, a conjoint study comprised of four variables, with 3, 4, 5 and 7 levels, respectively, requires only 16 distinct experimental samples, while the full factorial version of the design requires 420 samples. 15.4.2 Questions they answer In product research, screening experiments answer the questions like: “What are the high-impact variables in the experiment?” “Which variables have little or no impact on the product?” “How should the experimental variables be ranked in terms of their impacts on the product, from largest to smallest?”

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In most cases, screening experiments are run early in the product development cycle. They identify the high-impact variables that will provide the greatest learnings in subsequent designed studies (either full factorial or product optimization experiments). The benefit of screening studies is that they protect researchers from wasting time and resources performing detailed experimentation on variables that have little or no impact on the product. This is especially useful at the early stages of product development, especially when research is being conducted on a new product category. When there are many potentially important variables but the researcher has little direct experience upon which to decide which variables should be studied first, screening experiments efficiently identify the highest impact variables that should be studied early on and distinguish them from the lower impact variables that should be set aside initially but potentially studied in detail at a later time, if resources and timing permit. Screening experiments accelerate the product development process by ensuring the research is focused on variables that will have a noticeable effect on the product.

15.4.3 Aliasing The small size of screening experiment comes with a price. In order to keep the number of samples small, screening experiments give up information on interactions. Specifically, any information in the data related to interactions is inextricably mixed up with information on the main effects of the experimental variables. In statistical terminology, interactive effects are said to be “aliased” with main effects. Table 15.5 can be used to show how this aliasing occurs. Table 15.5 presents the +/− notation for a full factorial experiment with three factors in which each factor is studied at two levels. It is easily seen that there are four samples with +’s and four samples with −’s for each main effect and interaction. As was discussed, above, it is the contrast of the samples with +’s and the samples with −’s that provide the estimate of the main effect or interaction. Suppose in order to eliminate half of the experimental samples the research chooses to run only the four samples that have +’s for the threeway, ABC interaction – that is, samples a, b, c and abc. These four samples form a half-fraction of a 23 factorial experiment (see Fig. 15.4). (The researcher could have equally well chosen the four samples with −’s on the ABC effect, which is the other half-fraction of the experimental design. For purposes of illustration, only the selected half fraction will be discussed.) It is standard practice to create a half-fraction of a full factorial experiment by selecting the samples that have either the +’s or the −’s for the highest order interaction in the DOE. Note in Table 15.5 that for the four chosen samples there are two samples with +’s and two samples with −’s for each main effect and each two-way

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Table 15.5 Table 15.2 rearranged to illustrate how fractional factorials are constructed. Note that the pattern of +’s and −’s for the four samples with +’s for ABC are the same for the pairs A & BC, B & AC and C & AB, indicating that these effects are aliased in the fractional factorial screening experiment Treatment combination a b c abc (1) ab ac bc

Factorial effect I

A

B

C

AB

AC

BC

ABC

+ + + + + + + +

+ − − + − + + −

− + − + − + − +

− − + + − − + +

− − + + + + − −

− + − + + − + −

+ − − + + − − +

+ + + + − − − −

High

C

High B Low

Low Low

High A

Fig. 15.4 1/2-fraction of a 23 factorial experiment. The distribution of the experimental samples in a half fraction of the full 23 factorial experiment. There are two samples at the high and low levels of each of the experimental variables, providing two hidden replications to estimate the effect of each of the experimental variables.

interaction. So even though the design consists of only four experimental samples, there are still two hidden replications for each effect. This is further evidence of the sensitivity and efficiency available from statistically designed experiments. However, returning to the discussion of aliasing, consider the estimates of each main effect in detail. The main effect of experimental variable A is [(abc + a) − (b + c)]/2. Similarly, the estimates of the main

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effects of B and C are [(abc + b) − (a + c)]/2 and [(abc + c) − (a + b)]/2, respectively. Now, consider the estimates of the two-way interactions. Note that for the BC interaction, the pattern of +’s and −’s yields the estimate [(abc + a) − (b + c)]/2, which is identical to the estimate of the main effect of A. In statistical terms, the main effect of A is said to be aliased with the BC interaction. Similarly, the estimate of the AC interaction is identical to the estimate of the main effect of B and the estimate of the AB interaction is identical to the estimate of the main effect of C, so the main effect of B is aliased with the AC interaction and the main effect of C is aliased with the AB interaction. With the information available, there is no way to separate the effects of the aliased terms. It is critical to remember that just because the effects of the interactions cannot be estimated it does not mean that the interactions are not present. Had the full factorial experiment been run, all of the effects could have been estimated separately. By running only four of the eight experimental samples the ability to separate the main and interactive effects is lost. The practical implication of this is that when a significant effect is identified in the study it is necessary to assume that the effect can reasonably be interpreted to result from the main effect of the experimental variable instead of any interaction that main effect is aliased with. There is no way to confirm this assumption from the experimental data, so the researcher’s understanding of the product system becomes critical to the correct interpretation of the experimental results. For example, if the researcher knows that two experimental variables (B and C) have been shown to interact in similar product systems in the past, then in a screening experiment, when the main effect that is aliased with that interaction (variable A, say) is significant, the researcher may choose to interpret the result as the interactive effect of B and C rather than the main effect of A. In general main effects tend to dominate interactions, so in most cases (apart from the warning in the next paragraph) it is safe to interpret the significant effects observed in a screening study as resulting from the main effects of the experimental variables as opposed to the interactive effects of the aliased terms. A final note of caution before moving on to the next type of experimental design: the three-factor experiment, above, was chosen for illustrative purposes only. It is a good choice because it provides an easy to interpret example of how aliasing occurs in fractional factorial designs but in another important sense it is a poor choice because it aliases main effects with two-way interactions. All screening experiments that are based on a subset of full factorials, whether they are for product research or concept optimization/conjoint studies, contain aliased effects. The smaller the number of runs in the screening experiment, the higher the level of aliasing. In general, researchers should try to avoid running DOEs in which main effects are aliased with two-way interactions. Two-way interactions are so common that

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the risk of obtaining misleading results is uncomfortably high when two-way interactions are aliased with main effects.

15.4.4 Screening experiment example Objective Determine the relative importance of six production variables on the consumers’ impressions of a sweet snack. Design of the samples The six experimental variables and their ranges are: Diameter: Sweet aromatic: Moisture: Sprinkles: Color: Mold position:

0.875″ or 1.750″ 0.0% or 0.4% 2.25% or 4.50% 0.0 gram or 0.5 gram 3.6% or 7.2% Closed or Open

The screening experiment is constructed using a 1/8-fraction of a 26 factorial experiment. The eight experimental samples (from among the total of 64 possible combinations) that make up the design are presented in Table 15.6. Design of the panel A total of 108 consumers evaluate all eight experimental samples in one test session. The samples are served in a balanced, randomized order using a William’s Square Design to account for any positional bias and carry-over effects that might be present. The consumers rate the experimental samples on a variety of liking and intensity measures (see Table 15.7). Table 15.6 example Sample 1 2 3 4 5 6 7 8

Eight experimental samples for the six-variable screening experiment

Run order

Diameter (in.)

Sweet aromatic (%)

Moisture (%)

Sprinkles (gm.)

Color (%)

Mold position

2 7 1 3 6 8 5 4

0.875 1.750 0.875 1.750 0.875 1.750 0.875 1.750

0.0 0.0 0.4 0.4 0.0 0.0 0.4 0.4

2.25 2.25 2.25 2.25 4.50 4.50 4.50 4.50

0.5 0.0 0.0 0.5 0.5 0.0 0.0 0.5

7.2 3.6 7.2 3.6 3.6 7.2 3.6 7.2

Closed Closed Open Open Open Open Closed Closed

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Table 15.7 example

Responses measured in the six-variable screening experiment

Number

Response

Units

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Like Color Like Size Like Thickness Like Coating Like Appearance Like Overall Purchase Intent Like Flavor Like Mouthfeel Like Crispness Like Crunchiness Color Size Thickness Amount of Coating Fruity Flavor Creamy Mouthfeel Crispness Crunchiness

9 Point 9 Point 9 Point 9 Point 9 Point 9 Point Top-2-Box % 9 Point 9 Point 9 Point 9 Point (Light/Dark) (Small/ Large) (Thin/Thick) (None/Extreme) (Weak/Strong) (None/Extreme) (None/Extreme) (None/Extreme)

Data analysis Because of the small numbers of samples involved in screening experiments, ANOVA is not always a sensitive approach for analyzing the data. Instead, graphical techniques are used to identify the experimental variables that have significant effects on the responses. The graphics most commonly used are the normal probability plot and the half-normal probability plot. The logic of the probability plot is straightforward. The observed effects of the experimental variables are plotted on the x-axis of the chart. The values that would be expected if none of the experimental variables have a significant effect are plotted on the y-axis of the chart. If the experimental variables actually have no impact on the response, then the observed effects (x-axis values) and the expected effects (y-axis values) will fall on a straight line. Any observed effects that fall off of the straight line (either high and to the right or low and to the left) represent significant effects. The half-normal probability plots for two of the responses are shown in Fig. 15.5. None of the observed effects fall off of the line (high and to the right) for the Colour Liking response (Fig. 15.5a). This indicates that none of the experimental variables have a significant impact on Colour liking. On the other hand, the observed effects of Mold position and Diameter fall off the line (high and to the right) for the Size liking response (Fig. 15.5b). This indicates that Mold position and Diameter have significant effects

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Half-normal % probability

Half-normal % probability

(a)

445

95 90 80 70 50 30 20 10 0

0.00

0.13

0.26 0.39 Color liking

0.53

99

Diameter

95 90

Mold position

80 70 50 30 20 10 0

0.00

0.48

0.95 1.43 Size liking

1.90

Fig. 15.5 Probability plots used to analyze data from screening experiments. If none of the observed effects (x-axis values) fall off of the line, high and to the right (as in a), then none of the experimental variables have a significant effect on the response. Any observed effects (x-axis values) that fall off of the line, high and to the right (as in b), have significant effects on the response. In b, Diameter and Mold position have significant effects on Size liking.

on Size liking. All of the measured responses are analyzed in a similar fashion. Interpretation of results The purpose of a screening experiment is to prioritize the experimental variables according to the impacts they have on the measured responses. Table 15.8 summarizes the variables that have significant effects on each of the responses. It is clear that Mold position and Diameter have the greatest impact. Moisture and Color have significant effects on a few of the responses. Sweet aromatic and Sprinkles did not have significant effects on any of the responses. It is clear from Table 15.8 that the closed Mold position is better liked than the open Mold position. The larger Diameter is better liked for Size, Thickness and Appearance but the smaller Diameter is better liked Overall and for Mouthfeel. The lower Moisture is better liked Overall and for Crispness and Crunchiness. The concentration of Color has a noticeable effect on perceived Color and Amount of coating but does not have a significant effect on any of the liking responses. Recommendations Hold Mold position constant in the closed position. Conduct additional research on Diameter and Moisture to fine-tune the values of the two variables. Hold Color at its current level. Do not add any Sweet aromatic or Sprinkles to the product.

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Table 15.8 Significant effects in the six-variable screening experiment example. A “+” means that the variable had a significant effect on the response and that the high level of the variable yielded a higher value for the response. A “−” means that the variable had a significant effect on the response and that the high level of the variable yielded a lower value for the response. Mold position and Diameter have the greatest impacts on the product. Moisture and Color have some significant effects. Sweet aromatic and Sprinkles do not have significant effects on the product Response Acceptance Color Size Thickness APP Coating OVR Appear OVR Liking PI Flavor Mouthfeel Crispness Crunchiness Intensity Color Size Thick Amt Coating Fruitiness Creaminess Crispness Crunchiness

Diameter

Sweet aromatic

Moisture

Sprinkles

Color

− −

+ + + + + −

Mold position

− − −

−

− − −

− −

+ + +

+

− −

+ + +

+ +

15.5 Optimization experiments 15.5.1 Structure Product optimization designs are the direct link between fundamental product characteristics, such as formula, process and packaging, and key measures of product quality, including physical, chemical, sensory and consumer measures. The advantage offered by product optimization designs is that the product characteristics are under the direct control of the product developer. Consumer product research sometimes generates a consumer description of an ideal product. The consumer research itself does not provide direct guidance on how to achieve the optimal product and, in some cases, the optimal product cannot be realized because it represents unachievable combinations of product characteristics. In designed product optimization studies, when an optimal formula, process or package is identified, it can be realized because the types and levels of the ingredients in the

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formula, the equipment and settings used to make the product and the types and performance characteristics of the packaging materials are under the direct control of the product developer. If the developer was able to make the test samples for the experimental design, he or she will be able to make the optimum product identified by the design. Product optimization designs use response surface methodology (or RSM) (Box and Wilson, 1951). RSM is a designed regression analysis. The systematic changes made to the levels of ingredients, process settings and packaging materials are used as predictor variables in the analysis. The quality characteristics measured on the resulting experimental samples are the dependent (i.e., predicted) variables in the analysis. The property that most distinguishes RSM designs from the factorial and screening experiments that have already been discussed is that the predictions are not limited to the levels of the experimental variables that were studied in the design. Predictions can be obtained for any combination of levels within the minimum to maximum ranges studied in the experiment. There are two levels of RSM designs. The simplest RSM designs are first-order (or linear) designs. Two-level factorials or fractional factorials with experimental variables whose levels vary along a scale are examples of first-order RSM designs. First-order designs can point in the direction of the optimum product but they cannot pinpoint the location of the optimum because the first-order (linear) models cannot bend. In order to locate the optimum levels of the experimental variables, a second-order (or quadratic) design is required. The second-order designs provide estimates of the curvilinear and interactive effects of the experimental variables so, if the optimum levels of the variables fall within the ranges studied in the experiment, the second-order model will be able to pinpoint the optimal levels. Second-order designs are required for optimizing a product, so the remainder of the section focuses on second-order designs only. There are two popular classes of “classical” response-surface optimization designs – central composite RSM designs and Box-Behnken RSM designs. In order to fit a second-order model, each experimental variable must be studied at at least three levels. Central-composite designs and BoxBehnken designs differ in how they achieve the multiple levels of the experimental variables. Central-composite designs use five levels for each experimental variable (see Fig. 15.6). Central composite designs are two-level factorial or fractional factorial designs with additional “axial” points and centre points added. The axial (or star) points extend beyond the ranges of the factorial points in the design. There are two axial points for each experimental variable. For each experimental variable one axial point is set to an extra-low level of the variable and the other axial point is set to an extra-high level of the variable. The levels of the other experimental variables are held at their midpoints. The factorial portion of the central composite design provides estimates of the linear main effects and interactions among the

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Fig. 15.6 A three-variable central composite RSM design. The design is a combination of a full 23 factorial experiment, for estimating the linear and interactive effects of the three variables augmented by six axial points and a center point that provide estimates of the curvilinear effects of the experimental variables.

experimental variables. The axial and centre points provide estimates of the quadratic effects of the experimental variables. The distance that the axial points extend from the centre of the design space and the number of times the centre points are replicated in the experiment have an impact on the statistical properties of the regression models and the predictions obtained from the designs. It is valuable to know that the choices are not entirely arbitrary but it is not necessary for researchers to know the details of the statistics because the statistical software packages that are used to generate central composite designs will automatically select the preferred levels. Researchers who are generating the designs manually should refer to a text on DOE (e.g., Montgomery, 2005) for the details on these choices. Box-Behnken designs (Box and Behnken, 1960) use three levels for each experimental variable. Instead of producing samples at the low and high levels of the experimental variables, as in a two-level factorial experiment, Box-Behnken designs consist of experimental samples that are produced at the mid-points of the edges of the experimental region (see Fig. 15.7). Each experimental variable is present at low, middle and high levels, so the linear, quadratic and interactive effects of the variables can be estimated. Central composite designs and Box-Behnken designs each have their own set of advantages and disadvantages. Central composite designs include samples that represent the extremes of the experimental region, while BoxBehnken designs do not include the “corners” of the experimental space. On the other hand, central composite designs require five levels of each experimental variable but only one sample is produced at the lowest level and one at the highest level of each variable. Running five levels of each

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Fig. 15.7 A three-variable Box-Behnken RSM design. Each variable is studied at three levels, providing estimates of the linear, curvilinear and interactive effects of the three experimental variables.

experimental variable may be prohibitive for some products or processes. Also, especially in the case where the lowest level of a variable is zero, for example when an ingredient is being removed completely from a formula, producing only one sample at the lowest level may not provide an adequate level of assurance that it is safe to produce a sample at that level. BoxBehnken designs run multiple samples at the lowest and highest level of each experimental variable, thus providing more data to use when deciding if it is safe to produce a product at the extremes of the experimental region. Neither the central composite design nor the Box-Behnken design is uniformly superior in all situations. The choice on which type of design to use will depend on the resources available for the study and the specific variables and levels that are being considered. 15.5.2 Questions they answer Optimization experiments can be used to identify the levels of the experimental variables that maximize desirable product characteristics or minimize undesirable product characteristics. The predictive models obtained from the designs also can be used to assess how sensitive the product is to changes in the levels of the experimental variables. For example, it may be found that the level of an expensive ingredient can be reduced substantially from the “optimal” level without incurring a meaningful drop in the overall acceptance of the product. More than just identifying the optimal conditions, optimization studies allow researchers to make informed decisions about practical compromises that can be made without jeopardizing the ultimate quality of the product.

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15.5.3 Predictive models Product optimization studies use second-order polynomial equations of the form: Y = β0 + β1 X 1 + β2 X 2 + β11 X 12 + β22 X 2 2 + β12 X 1 X 2 where Y = a quality characteristic, such as Overall liking, Xi = the level of experimental variable i and βj = the values of the coefficients whose values are estimated in the regression analysis. The models can be built using standard model-selection regression techniques to insure that only those effects that have a significant impact on the quality of the product are included in the model. The three most popular model-selection techniques are forward inclusion, backward elimination and stepwise selection. The forward inclusion technique begins with no terms in the model. At each step, the term with the highest level of significance is added to the model. The process continues until there are no terms whose addition would significantly improve the model. The backward elimination technique begins with all of the candidate terms in the model. At each step, the term with the lowest level of significance is removed from the model. The process continues until only statistically significant terms remain in the model. Stepwise selection is a combination of forward selection and backward elimination. Initially, terms are added to the model based on their levels of significance. In designs in which the factor levels are not completely independent of each other it can happen that a term which is initially significant may become non-significant as other terms are added. When that occurs in the stepwise selection procedure, the non-significant term is removed from the model. Terms are added and removed until all of the terms in the model are significant and no further improvement would occur if another term was added. The backward elimination technique is regarded as the most robust model-selection method because all of the terms get a chance to be included in the model, which is not the case with forward inclusion and stepwise selection techniques. The impact of the experimental variables on the quality characteristics of the product can be depicted graphically in a response-surface chart (Fig. 15.8) or in a contour chart (Fig. 15.9). Both charts can be used to illustrate the location of the optimum product in the experimental region and to illustrate the sensitivity of the product to deviations from the optimal levels. Also, by overlaying multiple responses on a single contour chart (Fig. 15.10), areas of the experimental region that satisfy multiple action standards can be identified. The form of the model used in the RSM analysis is an approximation to the unknown model that relates the quality characteristic to the levels of the experimental variables. It is an empirical model whose estimated regression coefficients are obtained from the experimental data. It is not a

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Liking 10.5

8.5

6.5 200% 100% Acid

4.5 200%

100%

50% 50%

Sweetener

Fig. 15.8 A response surface plot of Overall liking on sweetener and acid levels. A response surface plot is a 3-D depiction of the effects of two experimental variables on a response measured on the product. The plot illustrates that for both sweetener and acid, Overall liking increases between 50% and 100%, reaches a maximum and then begins to decrease between 100% and 200%.

Liking 200

Acid

130 10

100 9.5 9 8.5 8 50 50

100

150

200

Sweetener

Fig. 15.9 A contour plot of Overall liking on sweetener and acid levels. A contour plot is a 2-D depiction of the effects of two experimental variables on a response measured on the product. The contour plot illustrates that Overall liking is maximized at 150% sweetener and 130% acid.

theoretical model of the relationship between the quality characteristic and the levels of the experimental variables. If the regression model fits the experimental data well, the model predictions will be accurate within the range of the levels studied in the design. However, it is impossible to tell at what point outside of the experimental region the predictions begin to

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Consumer-driven innovation in food and personal care products Overlay plot

Flavor

0.5

Calories = 100

0.3

Cost of goods = $0.35 Overall liking = 6.0

0.1 0

5 Sugar

10

Fig. 15.10 Contour plot with multiple action standards. By overlaying multiple responses on a single contour plot, it is possible to identify a region for which all of the action standards are satisfied.

depart from the true values. Because of this, RSM models (as with all empirical models) should not be used for extrapolating results beyond the range of the data used to generate the model. This fact has strong implications on the choice of experimental variables and the ranges over which they are studied, which will be discussed later in the chapter.

15.5.4 Optimization experiment example Objective Identify the levels of sucrose, flavor and acid that maximize consumer acceptance of a ready-to-drink beverage. Design of the samples The three experimental variables and their ranges are: Sucrose: Flavor: Acid:

7% to 12% 0.05% to 2.5% 3.0 pH to 4.0 pH

A 15-run Box-Behnken design (see Fig. 15.7) is constructed to study the effects of sucrose, flavor and acid. The design is comprised of twelve distinct “non-center point” formulas and three replicated center points. The 15 experimental samples that make up the design are presented in Table 15.9.

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Table 15.9 Fifteen experimental samples and Overall liking for the three-variable optimization experiment example Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Run order

Sucrose (%)

Flavor (%)

Acid (pH)

Overall liking

14 5 2 12 7 9 3 8 15 11 1 6 13 4 10

9.5 7.0 12.0 9.5 9.5 12.0 9.5 7.0 7.0 9.5 9.5 7.0 12.0 12.0 9.5

2.5 0.5 1.5 2.5 1.5 2.5 1.5 1.5 2.5 0.5 1.5 1.5 0.5 1.5 0.5

3.0 3.5 3.0 4.0 3.5 3.5 3.5 4.0 3.5 3.0 3.5 3.0 3.5 4.0 4.0

6.8 6.7 7.1 6.7 7.2 6.9 7.1 6.8 6.6 6.7 7.0 6.8 6.9 7.1 6.7

Design of the panel A total of 102 consumers evaluate five samples per day across three days. A center-point sample is served once on each of the three days. The remaining 12 non-center point samples are allocated across the days according to a BIBD design (see Section 15.9). The samples are served in a balanced, randomized order using a William’s Square Design to account for any positional bias and carry-over effects that might be present. The Overall liking ratings of the 15 samples are presented in Table 15.9. Data analysis A full quadratic regression model, including the three linear effects of the experimental variables, the three quadratic effects of the experimental variables and the three two-way cross-product terms, is fit to the Overall liking data. A backward elimination procedure is used to reduce the full model to the reduced model that contains only predictor variables that are significant at the 10% level. The reduced model contains the linear and quadratic effects of sucrose, flavor and acid. None of the cross-product (i.e., interaction) terms are statistically significant (see Table 15.10). The model explains 93% of the variability in the Overall liking data. Interpretation of results The effects of the three experimental variables can be depicted graphically in a perturbation chart (see Fig. 15.11). A perturbation chart is constructed by plotting the predicted values of the response for one experimental variable while it is varied from its low level to its high level while all of the other experimental variables are held constant. The perturbation chart illustrates

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Table 15.10 The ANOVA table for the three-variable optimization experiment example Source

Sum of squares

df

Mean square

F

P-value

0.1401 0.0000 0.0008 0.0114 0.2619 0.0443 0.0310 0.0136 0.0174 0.4663

1 1 1 1 1 1 8 6 2 14

0.1401 0.0000 0.0008 0.0114 0.2619 0.0443 0.0039 0.0023 0.0087

36.20 0.01 0.20 2.94 67.67 11.43

0.0003 0.9140 0.6676 0.0945 <0.0001 0.0096

0.26

0.9153

A-Sucrose B-Flavor C-Acid A^2 B^2 C^2 Residual Lack of fit Pure error Total

7.2 Sucrose

Overall liking

7.1 Acid 7.0 Flavor

6.9

6.8 Low

Medium

High

Factor levels

Fig. 15.11 Perturbation chart of Overall liking. Perturbation charts illustrate the effects of all of the experimental variables on one graph. The plots reveal the relative impact of each experimental variable and the level of the variable that maximizes (or minimizes) the response.

the relative impact of each of the experimental variables in a single chart. Figure 15.11 shows that Overall liking is maximized at high sucrose (12%) and medium levels of flavor and acid (1.5% and 3.5 pH, respectively). Figure 15.11 also reveals that sucrose and flavor have much greater effects on Overall liking than does acid, which is evidenced by the large vertical range of the sucrose and flavor curves compared to the acid curve. A contour plot of flavor and acid with sucrose held fixed at 12% (Fig. 15.12) also shows the greater impact of flavor compared to acid, while also clearly illustrating that Overall liking is maximized at the medium levels of both of these experimental variables.

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Overall liking @12% Sucrose 4.00 6.9

6.9

Acid

3.75

7.0

3.50

7.0

7.2 3.25 6.9

69

7.1 3.00 0.50

1.00

1.50

2.00

2.50

Flavor

Fig. 15.12 Contour plot of Overall liking. The contour plot illustrates that at 12% sucrose, Overall liking is maximized at 1.5% flavor and 3.5 pH acid.

Recommendations Overall liking is maximized at 12% sucrose, 1.5% flavor and 3.5 pH acid. It is most important to ensure that the product is produced with sucrose and flavor levels as close as possible to their optimal levels.

15.6 Mixture experiments 15.6.1 Structure Mixture experiments are a special class of optimization studies. The distinguishing characteristics of mixture experiments are that all of the experimental variables are concentrations of ingredients AND the total concentration of the experimental variables is constant for all of the experimental samples. The total concentration may be 100% of the product or it may be a fixed value less than 100%. For example, the total concentration of preservatives in a product may be constrained to be no more than 0.1%. A mixture experiment could be used to study the effects of varying the relative proportions of different preservatives in the product under the constraint that every test sample has 0.1% total preservatives. The constraint that the sum of the concentrations is a constant effectively reduces the dimensionality of the experimental design. For example, in a three component mixture experiment, once the concentrations of two of the components are known, the concentration of the third component is

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Consumer-driven innovation in food and personal care products X1

X2

X3

Fig. 15.13 An unconstrained three-component mixture experiment. In an unconstrained mixture experiment, all ingredients vary from 0% to 100% of the mixture. Because of the constraint that the sum of the three components must always total 100%, the experimental region for a three-component experiment can be covered adequately with only seven distinct experimental blends.

automatically known because the sum of the three components is a constant. The practical impact of this fact is that a mixture experiment involving a given number of experimental variables is much smaller than a standard RSM design involving the same number of variables. For example, a central composite design with three variables will have at least 15 samples where a three-component mixture experiment could have as few as 7 samples (see Fig. 15.13). The concentrations of the components in a mixture experiment can be unconstrained – that is, each ingredient can vary from 0 to 100% of the total concentration (as in Fig. 15.13), or the components can be constrained within specific limits. For example, in a three component mixture experiments, involving ingredients A, B and C, it may be necessary to limit the concentration of the ingredients to specific ranges due to reasons related to standard of identity, cost or functionality. For example, the concentrations of A, B and C may be limited to the following: 20% < A < 50% 10% < B < 80% 0% < C < 100% The constraints affect the shape of the experimental region and the number of samples necessary to cover the experimental region in a uniform and sensitive manner (see Fig. 15.14).

15.6.2 Questions they answer The questions answered by mixture experiments are the same as those answered by standard optimization experiments. Mixture experiments can

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A 0.90

0.00

0.80 B

0.10

0.20

0.70 C

Fig. 15.14 A constrained three-component mixture experiment. Applying constraints to the levels of the components in a mixture experiment affects the shape of the experimental region. The experimental samples are distributed at the extremes, the mid-points and the interior of the region in order to provide precise estimates of the effects of the experimental variables.

be used to identify the relative proportions of the ingredients that maximize desirable product characteristics or minimize undesirable product characteristics. As with standard optimization experiments, the predictive models obtained from the designs can be used to assess how sensitive the product is to changes in the relative proportions of the experimental ingredients. Standard optimization studies and mixture experiments differ in how the results are interpreted. In a standard optimization study the levels of the experimental variables are controlled independently, so it is possible to identify a specific variable that is responsible for a change in the product. In a mixture experiment, on the other hand, the components cannot be varied independently. Any change in the concentration of one ingredient will automatically change the concentrations of the other ingredients on the mixture. Therefore, the results of mixture experiments are interpreted in terms of how the relative proportions of the ingredients affect the product.

15.6.3 Predictive models The constraint associated with mixture experiments also affects the form of the regression equation used to model the product quality characteristics. For simplicity, assume that the sum of the levels of the mixture variables is 100% (or 1). For a three-component mixture experiment, with variables X1, X2 and X3, then 1 = X1 + X2 + X3. From this it is easily seen that X12 = X1(1 − X2 − X3) = X1 − X1X2 − X1X3, so quadratic terms in the regression

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equation for a standard optimization study can be replaced by a combination of linear and cross-product terms. Also, note that β0 = β0(1) = β0(X1 + X2 + X3) = β0X1 + β0X2 + β0X3, so the intercept in the equation for a standard optimization study is absorbed into the linear effects in the equation for a mixture design. By substitution, the standard optimization model: Y = β0 + β1 X 1 + β2 X 2 + β3 X 3 + β11 X 12 + β22 X 2 2 + β33 X 3 2 + β12 X 1 X 2 + β13 X 1 X 3 + β23 X 2 X 3 becomes Y = β1*X 1 + β2*X 2 + β3*X 3 + β12*X 1 X 2 + β13*X 1 X 3 + β23*X 2 X 3 The fact that there are fewer unknown parameters in the regression equation for a mixture experiment is further evidence for why mixture experiments can be smaller than standard optimization studies with the same number of experimental variables. Standard stepwise regression techniques are used to determine which terms in the mixture model have significant impacts on the product quality responses. The linear effects of each of the mixture variables are always included in the model.

15.6.4 Mixture experiment example Objective Three sweeteners: APM, ACE-K and Swt-X are of interest to an ingredient supplier. The supplier wants to know if the sweeteners exhibit synergy – that is, more intense sweetness in a blend of sweeteners than would be expected from the potencies of the individual sweeteners. Blends that exhibit synergy require lower concentrations in applications and thus reduce costs. Design of the samples A three component mixture experiment comprised of nine experimental samples is constructed. All three of the experimental variables: APM, ACE-K and Swt-X are varied from 0% to 100% of the total expected sweetness. The target sweetness of each experimental sample, based on the concentration-response curves of the individual sweeteners is 4.5 SE (i.e., sucrose equivalence). The nine experimental samples that make up the design are presented in Table 15.11. Design of the panel The nine experimental samples are evaluated by twelve assessors who are members of a trained descriptive panel that specializes in the evaluations of sweeteners. All nine sweetener blends are evaluated in aqueous solutions targeted to deliver an expected sweetness of 4.5 on the panel’s sweet taste intensity scale. The order of evaluations is randomized individually for each assessor. Evaluations are performed in triplicate.

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Table 15.11 Nine experimental samples and sweet taste intensity for the threecomponent mixture experiment example Sample 1 2 3 4 5 6 7 8 9

Run order

APM

ACE-K

Swt-X

Sweetness

2 4 7 5 3 6 1 8 9

1.00 0.00 0.00 0.50 0.50 0.00 0.33 0.33 0.33

0.00 1.00 0.00 0.50 0.00 0.50 0.33 0.33 0.33

0.00 0.00 1.00 0.00 0.50 0.50 0.33 0.33 0.33

4.5 5.0 4.5 7.5 7.0 6.0 7.5 8.5 8.0

Data analysis The average sweet taste intensity ratings of the nine experimental samples are fit to a full quadratic three-component mixture model. As discussed earlier, the full model is comprised of the linear terms for each of the three sweeteners and the three two-way cross-product terms among the three sweeteners. All linear terms are always included in the model, so the backward elimination procedure used to identify the significant predictors applies only to the cross-product terms. Modeling is done at the 10% level of significance. All cross-product terms are significant at the 10% level, so the final model is: Sweetness = 4.5APM + 5.0ACE-K + 4.5Swt-X + 12.0APM*ACE-K + 11.0APM*Swt-X + 6.0ACE-K*Swt-X The model explains 96% of the variability in the sweet taste data. Interpretation of results The coefficients of a mixture model are difficult to interpret individually because any change in the level of one of the components in the mixture is accompanied by a change in at least one of the other components. As a result, mixture models are better interpreted using contour plots. Figure 15.15 shows that there is substantial sweet taste synergy in the sweetener blends because the predicted sweet taste intensity is often far above the expected sweetness of 4.5. The contour plot reveals that maximum synergy occurs at a blend of 42% APM, 36% ACE-K and 22% Swt-X. Recommendations In order to maximize perceived sweetness with as low a total sweetener concentration as possible, the ingredient supplier should prepare blends of sweeteners with approximately 42% APM, 36% ACE-K and 22% Swt-X.

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Max. Sweet at 42% APM, 36% ACE-K & 22% Swt-X

5.0 6.0 7.0

0.00

0.00 7.9

7.5

3

7.0 6.0

6.0 100.00 ACE-K

0.00

5.0 100.00 Swt-X

Sweetness

Fig. 15.15 Contour plot of sweet taste intensity. The contour plot from the threecomponent mixture example illustrates that significant sweet taste occurs among the sweetener blends because the observed sweet taste intensity exceeds the expected intensity of 4.5 SE. Synergy is maximized at a blend ratio of 42% APM, 36% ACE-K and 22% Swt-X.

15.7 Selecting experimental variables and their ranges 15.7.1 Keys to a successful DOE The two factors that are most important to the success of a statistically designed study have nothing to do with statistics. They are: 1. picking the correct experimental variables to study, and 2. studying the variables across the correct range of values. An experimental design cannot reveal information about variables that are not included in the study. If the DOE is conducted on a set of variables that do not affect the quality of the product, nothing will be learned and resources will be wasted. Researchers should select experimental variables that are known to affect the quality of the product. This information may be available from the technical literature or from prior internal research. In the absence of any prior knowledge of this type, screening experiments can be used to identify the experimental variables that have meaningful effects on the product and, therefore, merit more in depth study. In setting ranges for the experimental variables in a DOE the watch words are, “Be bold but not foolhardy.” The range chosen for each experimental variable must be broad enough to cause a noticeable effect. In an optimiza-

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tion study, for example, the low and high levels of a variable should be noticeably different from the midpoint. The differences should be noticeable to a general observer, not just to the product developer working on the project. Some restraint needs to be exercised in setting ranges also. The ranges cannot be so broad as to cause product failures. A pudding is not a pudding if it is as thin as water or as hard as a rock. A cereal is not a cereal if it is roasted at a temperature that turns it into a blackened cinder. Also, when the experimental samples will be tested with consumers, the ranges cannot be so broad that they cause a shift in the consumer’s context of measurement. For example, if one of the objectives of a DOE was to determine the optimal lemon : lime ratio in a carbonated soft drink, the ranges should not be set to 100% lemon, 50% lemon : 50% lime and 100% lime. Even if the consumers are instructed to rate the products as lemon : lime beverages, if high quality lemon and lime flavors are used, it is likely that the 100% lemon samples will receive high ratings (as lemonades) and the 100% lime samples will receive high ratings (as limeades). It would better to set slightly narrower ranges, such as 90% lemon : 10% lime to 10% lemon : 90% lime, so that all of the experimental samples are actually lemon : lime beverages. When setting ranges for experimental variables it is important to remember that the purpose of a DOE is to study the effects of the experimental variables. It is not to make good test samples. Although somewhat discomforting, it is important for researchers to recognize that most, if not all, of the experimental samples in a DOE will be sub-optimal. In optimization studies it is standard practice to select the midpoint of the experimental variable to be close to the expected optimal level. By default then, the low and high levels of the variable will be sub-optimal. By having both high scoring and low scoring samples in the DOE, the statistical models can determine combinations of levels of the experimental variables that are predicted to produce the optimum product. If all of the samples in the DOE are “pretty good” the responses will be flat and no learning will take place.

15.8

Traditional designs and computer-aided optimal designs

15.8.1

Optimal designs: economizing on resources while preserving the advantages of the traditional approaches The types of designs discussed thus far represent the traditional approaches to DOE. The designs possess many desirable statistical properties and are, in many sense, optimal designs for their intended uses. However, for some studies practical considerations make it impossible to use the traditional designs. Some reasons for this include: insufficient resources to run all of the samples associated with the traditional design, the traditional design

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includes infeasible combinations of variable levels and interest in fitting a regression model that does not fit the traditional form. To address situations when non-traditional designs are needed, statisticians have invented an approach to developing experimental designs that are “optimal” for the constraints that the researcher has to operate within. There are a variety of “optimal designs”, varying in the criterion they optimize. No one criterion is uniformly superior to any other. Some of the most popular criteria are: A-Optimal Designs: Minimize the average variance of the parameter estimates. D-Optimal Designs: Minimize the generalize variance of the parameter estimates. G-Optimal Designs: Minimize the maximum variance of the predicted values from the regression model. V-Optimal Designs: Minimize the average variance of the predicted values from the regression model. D-Optimal designs tend to be the most widely used in practice. The process of developing an optimal design is the same regardless of the criterion being used. The process has three components: 1. Candidate set: The researcher specifies a set of candidate points – that is, combinations of the levels of the experimental variables that are feasible or of interest to the researcher. 2. Model for the design: The researcher specifies the full model to be used to predict the responses based on the levels of the experimental variables. The full model in a traditional optimization study includes the linear and quadratic effects of each of the experimental variables and all of the two-way interactions (cross-products) between the experimental variables. There are many practical situations in which a researcher may not want to fit the traditional model. One example is when a researcher wants to determine only if a new ingredient has an effect on the product or not. The researcher chooses to study the ingredient at only two levels, out and in (i.e., 0% and the supplier’s recommended usage level). Since there are only two levels for the ingredient, only the linear effect of the ingredient can be studied, so in the model for the design, the researcher would leave out the quadratic effect of the ingredient. (That reduces the required number of experimental samples in the DOE by one.) Another example might be that prior research has shown that a particular processing variable never interacts with any ingredient variable in the product. In a five-variable DOE that includes the process variable and four ingredients, the researcher may choose to leave out any interaction/ cross-product term that involves the process variable. (Once again, the number of experimental samples required in the DOE decreases by one for every term that is removed from the model.)

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3. Number of experimental samples: The researcher specifies the number of experimental samples to include in the DOE. The number must be greater than the number of terms in the regression model specified in the previous step. Optimal designs are gaining wide usage for three reasons. Firstly, computer programs are available that make it easy to construct optimal designs. Secondly, time and resources can be saved because optimal designs that are smaller than their traditional counterparts can be constructed. Thirdly, optimal designs offer greater flexibility than their traditional counterparts. Many widely used statistical packages include modules for constructing optimal designs. Making no attempt to be exhaustive, some of the packages are: Design Expert, JMP, Minitab and SAS. Optimal designs may be feasible when the number of samples in the traditional design is too large. For example, the minimum number of experimental samples in a four-factor (full factorial) central-composite design is 25. There are only 15 terms in the full four-factor regression model. An optimal design involving including any number of runs between 16 and 24 could be constructed. The precision of any experimental design increases with the number of experimental samples in the design. However, the optimal design approach will generate the best design for the number of runs the researcher can afford to include in the experiment. Often researchers want to study more than two levels of a variable, even in a screening experiment. The optimal design approach provides the flexibility of studying as many levels of the experimental variables as desired without having to include more experimental samples than needed.

15.9 Implications of product testing with consumers Ideally, all of the experimental samples should be evaluated by each respondent in a single test session. However, the number of samples in a statistically designed experiment often exceeds the number of samples that can be evaluated in a single session before physiological or psychological fatigue sets in. Every effort should be made to maximize the number of experimental samples evaluated in a single session. Adjusting standard test controls, such as extending inter-stimulus waiting periods, ensuring that the respondents follow the prescribed rinsing/cleansing procedures, etc., may allow for additional samples to be evaluated before physiological fatigue becomes an issue. Keeping the ballot as short as possible may allow for additional samples to be evaluated before psychological fatigue begins. Even with these additional measures, the number of samples in the experimental design may be greater than the number that can be evaluated in a single session. If so, three approaches are available to deal with the situation. The first approach is to have each respondent evaluate only a subset of the total number of experimental samples in a single test session. The second approach is to

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have each respondent evaluate all of the samples across multiple test sessions. The advantages and disadvantages to both of the approaches need to be evaluated on a study by study basis. The third approach is a combination of the first two. Have each respondent evaluate a subset of the total number of experimental samples across multiple sessions.

15.9.1 Incomplete serving designs If each respondent evaluates only a subset of the experimental samples in a single session, it is important to ensure that by the end of the study all of the samples are evaluated by an equal number of respondents. Further, to account for context effects, it is valuable to have every pair of experimental samples evaluated by an equal number of respondents. That will ensure that all samples are measured with equal precision and that the comparisons of any two samples will be equally sensitive. Balanced incomplete block designs (BIBD’s) deliver both of these properties. Catalogs of BIBD’s (e.g., Cochran and Cox (1957)) and computer programs that generate BIBD’s (e.g., Design Express (2007)) are available. Once the subsets of samples that make up the BIBD are defined, the serving rotations that will be used in the study need to be constructed. The serving orders should be constructed so that every sample is evaluated equally often in each serving position to nullify the effects of any servingposition bias that may exist. Also, each experimental sample should be evaluated before and after every other experimental sample equally often to nullify any carry-over effects that may exist. In consumer tests this is easily achieved by serving each block of the BIBD in a different order according to a William’s Square serving protocol (MacFie and Bratchell (1989), Wakeling and MacFie (1995)). A simple example of this approach is presented in Tables 15.12 and 15.13. A three-of-four BIBD is presented in Table 15.12. Each respondent will evaluate three of the four samples. In a single pass through the BIBD, which consists of four respondents, each

Table 15.12 One replicate of a 3-of-4 balanced incomplete block design (BIBD). In the design, each respondent (i.e., block) evaluates three of the four samples. In one replicate of the BIBD each sample is evaluated three times and every pair of samples are evaluated together twice Sample Block 1 2 3 4

1

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X X X

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Table 15.13 Serving orders balanced for position and carry-over effects. For every 24 respondents, each sample is evaluated in each serving position six times and each sample is evaluated before and after every other sample six times Serving position Respondent 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1

2

3

1 1 2 2 3 3 1 1 2 2 4 4 1 1 3 3 4 4 2 2 3 3 4 4

2 3 1 3 1 2 2 4 1 4 1 2 3 4 1 4 1 3 3 4 2 4 2 3

3 2 3 1 2 1 4 2 4 1 2 1 4 3 4 1 3 1 4 3 4 2 3 2

experimental sample will be evaluated three times and every pair of samples will be evaluated by two respondents. A balanced set of serving positions is presented in Table 15.13. Each block from the basic BIBD is presented to six respondents with each respondent receiving the samples in a different order. In the example in Table 15.13, all possible serving orders for all four blocks of samples can be evaluated by 24 respondents. The rows of Table 15.13 have not been randomized to make it easy to see how the balance of the serving positions across the blocks is achieved. In practice, the 24 rows should be assigned at random to the first 24 respondents in the test and then reassigned using separate randomizations to each subsequent group of 24 respondents until the desired number of evaluations per sample is achieved. It is not always necessary to serve all possible serving orders of the samples in a BIBD to achieve the desired balance in serving positions and

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carry-over effects. Computer programs, such as Design Express (2007), are extremely helpful for developing balanced serving orders for statistically designed studies of sizes that commonly occur in practice. 15.9.2 Multi-day consumer tests Increasingly, studies are being conducted in which respondents return for several test sessions. Within each session each respondent evaluates a subset of the total number of samples. In some cases, this approach allows each respondent to evaluate all of the samples in the statistical design. For example, if there are fifteen samples in the experimental design, each respondent could evaluate a different subset of five samples in each of three sessions. The subsets would be constructed so that after the three sessions, each respondent would have evaluated all fifteen experimental samples. This approach is especially useful for collecting consumer responses from low-incidence product categories because it maximizes the information obtained from every qualified respondent. In order to account for session effects and to maximize the information obtained on every experimental sample, it is advisable to use a BIBD design within each session. Each respondent receives a completely unique block of samples from one session to another until in total, each respondent evaluates all of the samples once. All of the test controls described above for balancing serving position and carry-over effects should be used within each session. Every effort should be made to have each respondent evaluate as many of the experimental samples as possible, so a multi-session test should be considered even when it is not possible for respondents to participate in enough sessions for them to evaluate all of the experimental samples. Consider again the example of fifteen experimental samples where each respondent evaluates five samples per session. If for whatever reason, only two evaluation sessions can be conducted, each respondent should evaluate ten of the fifteen samples according to a BIBD design. The ten samples would be split equally across the two sessions. Balancing the serving positions and carry-over effects (for all ten of the samples that each respondent evaluates) will ensure that every sample is evaluated equally often within each session. 15.9.3 Incorporating instrumental and sensory information DOE is a powerful tool for helping researchers to understand the impact that changing formula, process and packaging variables have on the consumer’s perceptions of their products. DOE analyses also can be applied to instrumental and sensory data to determine how the changes in the formula, process and packaging properties affect these responses also. Often, it is important to understand additionally how the instrumental and sensory characteristics of the products impact the consumer’s perceptions them-

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selves. The statistical tools available to address the second objective vary from simple to complex. A well-designed experiment will consist of clearly differentiated experimental samples. In all likelihood the samples will be rated differently by consumers and will possess distinctly different instrumental and sensory characteristics. Some simple techniques for understanding which instrumental and sensory measures relate systematically to the consumer ratings include: X-Y graphs of consumer responses with instrumental and sensory measures, correlation analyses and simple and multiple linear regression analyses. More complex modelling techniques can be applied to assess how the entire array of instrumental and sensory measures impacts consumer perceptions of the experimental samples. Broadly speaking these techniques belong to a class of analyses called preference mapping (Meilgaard et al. (2007)). There are a variety of preference mapping methods, each with its own set of advantages and disadvantages. It is beyond the scope of this chapter to pursue a detailed discussion of how and when preference mapping techniques can be applied. The interested reader should seek out additional information started with the cited reference. In closing it is worth mentioning that DOE and preference mapping are a powerful pair of methods with which all researchers should be familiar. Where DOE identifies the formula, process and packaging drivers in a product category, preference mapping identifies the sensory and instrumental drivers. The two methodologies complement each other. Preference mapping can be applied to any well-differentiated set of products to identify the sensory and instrumental measures that affect consumer acceptance. Preference mapping also identifies how much the sensory and instrumental measures have to change in order to have a meaningful impact on consumer acceptance. This information can be used in subsequent DOE studies to select the experimental variables (i.e., those that impact the key sensory and instrumental measures) and to establish the ranges over which the experimental variables will be studied (i.e., the ranges over which the key sensory and instrumental measures will vary enough to have a meaningful impact on consumer acceptance). On the other hand, the results of a preference mapping study are reliable only within the range of sensory and instrumental characteristics spanned by the products that are available to be included in the study. DOE can be used to create entirely unique products that go beyond anything that is currently available. In most exploratory product research one or the other of these methods will be applicable.

15.10 Further reading There are many good examples of statistically designed experiments in the product research literature. A short list of examples worth reviewing follows:

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Full Factorial Experiments: Fischer and Frewer (2009), Martin et al. (1999), De Souza et al. (2007) and Vazquez et al. (2009). Screening Experiments: Hough et al. (1992) and Johansen et al. (2010). Optimization Experiments: Brossard et al. (2002), Chu and Resurreccion (2004), Schantz and Bowers (1993) and Villegas et al. (2010). Mixture Experiments: Deshpande et al. (2005). D-Optimal Experiments: Petit et al. (2007).

15.11 References box, g e p and behnken, d (1960), ‘Some new three level designs for the study of quantitative variables’, Technometrics, 2, 455–475. box, g e p and wilson, k b (1951), ‘On the experimental attainment of optimum conditions’, Jour Roy Stat Soc B, 13, 1–45. brossard, c, rousseau, f, llamas, g and dumont, j-p (2002), ‘Odor perception over liquid emulsions containing single aroma compounds: effects of aroma concentration and oil volume fraction’, Journal of Sensory Studies, 17, 511–525. chu, c a and resurreccion, a v a (2004), ‘Optimization of a choclate peanut spread using response surface methodology (RSM)’, Journal of Sensory Studies, 19, 237–260. cochran, w g and cox, g m (1957), Experimental Designs, 2nd Ed., New York, Wiley. deshpande, r p, chinnan, m s and mcwatters, k h (2005), ‘Nutritional, physical and sensory characteristics of various chocolate-flavored peanut-soy beverage formulations’, Journal of Sensory Studies, 20, 130–146. design express (2007), Design Express: Presentation Orders for Consumer Trials, Ruscombe Berkshire UK, Qi Statistics. de souza, e a m, minim, v p r, minim, l a, coimbra, j s r and da rocha, r a (2007), ‘Modeling Consumer Intention to purchase fresh produce’, Journal of Sensory Studies, 22, 115–125. fischer, a r h and frewer, l j (2009), ‘Consumer familiarity with foods and the perception of risks and benefits’, Food Quality and Preference, 20, 576–585. hough, g, bratchell, n and wakeling, i (1992), ‘Consumer preference for dulce de leche among students in the United Kingdom’, Journal of Sensory Studies, 7, 119–132. johansen, s b, næs, t, øyaas, j and hersleth, m (2010), ‘Acceptance of caloriereduced yoghurt: Effects of sensory characteristics and product information’, Food Quality and Preference, 21, 13–21. macfie, h j h and bratchell, n (1989), ‘Designs to balance the effect of order of presentation and first-order carry-over effects in hall tests’, Journal of Sensory Studies, 4, 129–148. martin, n c, skokanova, j, latrille, e, beal, c and corrieu, g (1999), ‘Influence of fermentation and storage conditions on the sensory properties of plain low fat stirred yogurts’, Journal of Sensory Studies, 14, 139–160. meilgaard, m, civille, g v and carr, b t (2007), Sensory Evaluation Techniques, 4th Ed., Boca Raton, CRC Press. montgomery, d c (2005), Design and Analysis of Experiments, 6th Ed., New York, Wiley. petit, c e f, hollowood, t a, wulfert, f and hort, j (2007), ‘Colour–coolant–aroma interactions and the impact of congruency and exposure on flavour perception’, Food Quality and Preference, 18, 880–889.

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schantz, r and bowers, j (1993), ‘Response surfaces of sensory characteristics for reduced sodium chloride and phosphate salts in emulsified turkey sausage’, Journal of Sensory Studies, 8, 283–300. vazquez, m b, curia, a and hough, g (2009), ‘Sensory descriptive analysis, sensory acceptability and expectation studies on biscuits with reduced added salt and increased fiber’, Journal of Sensory Studies, 24, 498–511. villegas, b, tárrega, a, carbonell, i and costell, e (2010), ‘Optimising acceptability of new prebiotic low-fat milk beverages’, Food Quality and Preference, 21, 234–242. wakeling, i n and macfie, h j h (1995), ‘Designing consumer trials balanced for first and higher orders of carry-over effect when only a subset of k samples from t may be tested’, Food Quality and Preference, 6, 299–308.

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16 Data handling in cross-cultural studies: measurement invariance J. Scholderer, Aarhus University, Denmark

Abstract: Sound marketing research can significantly reduce the risk associated with new product decisions. In a globalised economy, this often requires the collection of market and consumer data across different countries, cultures, and language communities. However, are such data actually comparable? This chapter will familiarise the reader with a set of statistical techniques by which the cross-cultural comparability of data – their measurement invariance – can explicitly be assessed. The statistical framework (multi-group confirmatory factor analysis) is described in detail, including data requirements, model specification, estimation, testing, and interpretation. The chapter includes a worked example, complete with command syntax for three different software packages. Key words: cross-cultural research, measurement invariance, data quality, response bias, psychometrics, reliability, validity, factor analysis, structural equation modelling.

16.1 Introduction For companies supplying global markets, it is strategically important to know in which countries a new product is likely to succeed. Introducing a new product is a costly affair: national regulations have to be met, the packaging of the product has to be adapted to local languages, distribution has to be developed, advertising and promotion spending is likely to be high. In such a situation, sound marketing research is of utmost importance. However, conducting cross-cultural marketing research – research that involves different countries, cultures, and language communities – is fraught with technical difficulties. How can we be sure that the items in a survey questionnaire still mean the same to respondents after they were translated from one language to another? How can we be sure that they measure the underlying constructs – be they attitudes, preferences, or behavioural intentions – in the same way as in the language in which the questionnaire

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was originally developed? How can we be sure that response category labels such as “completely agree” or “extremely likely” still express the same degree of agreement or likelihood as in the original language? Taken together, how can we know that our measurements are invariant across cultures? Sound marketing research starts at the product development stage. Suppose, for example, that you work in a packaging company that has developed a new type of material for food products packaging. Part of your material is a film containing silicate nanoparticles that increases the shelflife of the product. You wonder whether recent stakeholder disputes about the potential hazards of nanoparticles have negatively affected the attitudes of consumers in your five biggest markets. You conduct an attitude survey and find that consumers in Japan report significantly more positive attitudes towards nanotechnology than consumers in France, Germany, the UK, and the US. However, you have also heard that it is considered impolite in Japanese society to bluntly state negative opinions. You present your survey results to your sales director, and he asks you whether you think that only food companies in Japan should be targeted as potential customers. You wonder what to say: do you really know what to conclude from the results of your survey? Do Japanese consumers really have a more positive attitude towards nanotechnology, or should the result rather be interpreted as a “politeness bias”? Should a target market decision really be based on results that are based on potentially biased measurements? Consider another example. You work in a consumer products company that has developed a new laundry detergent, targeted at the major consumer markets in Asia, North America, and Western Europe. The brand manager asks you whether the same fragrance descriptions can be used globally in the positioning of the product or whether the fragrance descriptions should be adapted to local consumer perceptions. You conduct a consumer test in China, India, Japan, South Korea, the US, Canada, France, Germany, Italy, Spain and the UK. Each consumer is presented with a set of detergent samples and rates them in terms of eleven perceptual dimensions taken from the fragrance wheel that you normally use with your sensory panel (“sweet spices”, “citrus”, etc.). However, unlike the members of your sensory panel, the participants in your consumer test have not been trained using reference substances. Can you meaningfully compare the consumer ratings across the different cultures? Do consumers in, say, India have the same understanding of a perceptual dimension like “sweet spices” as consumers in Spain? Do they use the intervals of the rating scale in the same way? Is their absolute threshold for the detection of the fragrance the same? In other words, are the measurement characteristics of the rating scales invariant across the different cultures in which they have been applied? The aim of this chapter is to introduce modern statistical methods for assessing whether data collected in different countries, cultures, or language communities can be meaningfully compared or not. We will start with a

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short discussion of the measurement invariance problem. Then, we will outline the most flexible statistical framework for assessing measurement invariance, multi-group confirmatory factor analysis. We will discuss how different levels of measurement invariance can be formulated as constraints that are imposed on a factor analysis model, and how to test whether they hold in a given data set. Finally, we will discuss different strategies for dealing with situations in which common assumptions about measurement invariance are violated. In an appendix to the chapter, we will discuss available software packages for multi-group confirmatory factor analysis, including example syntax.

16.1.1 Sense and nonsense in existing practice The academic marketing, consumer science, and new product development literature is traditionally dominated by studies that were conducted in one particular cultural setting but are usually interpreted in such a way as to suggest that the conclusions would also apply in any other cultural setting. In applied research, traditional practice is often different. Even the most miniscule differences between countries, regions, demographic groups, and times of measurement are analysed and evaluated. Observed differences are rarely examined for significance – not to mention corrected for multiple comparisons – but taken as true indications of substantial differences between the respective populations (e.g., consumer attitudes in North America versus consumer attitudes in Europe) or situations (e.g., consumer attitudes to premium-priced products before, during, and after the financial crisis of 2008/2009). In the last twenty years, researchers have become more sensitive to the assumptions that are routinely made in research practice and the problems that may be caused if these assumptions are violated. The objective of measurement invariance analysis is to explicitly assess whether one particular set of routinely made assumptions – the comparability of data between groups – actually holds. He, Merz and Alden (2008) systematically reviewed all articles published in 16 leading marketing journals between 2000 and 2005 that involved comparisons between respondents from different countries. Of these articles 27% reported measurement invariance analyses, whereas 73% did not. In a follow-up survey among the authors of the articles that did not report measurement invariance analyses, the most frequently mentioned reasons were “I (we) didn’t think that measurement invariance assessment was necessary” (32%), “the data in my (our) study weren’t conducive to measurement invariance assessment” (32%), and “I (we) didn’t have enough familiarity with measurement invariance techniques to conduct measurement invariance assessment” (28%). These numbers suggest that a cavalier disregard for issues of cross-cultural validity is the norm rather than the exception in marketing research. Even worse, roughly three quarters of all published papers in cross-cultural marketing may contain conclusions whose validity is entirely unknown.

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16.1.2 Measurement and meaningfulness In comparisons of means (and, by extension, unstandardised regression coefficients), the basic problem is the following. Suppose we have collected measurements of an observed variable x in two consumer populations A and B and are interested in differences between the expected values of x. The variable x may be any type of survey response, for example a consumer’s attitude towards a particular brand of laundry detergent, his or her liking of a food sample in a preference test, stated willingness to pay for additional food safety controls, or purchase intent for a product sold in a new type of packaging. A direct test of the hypothesis μx(A) – μx(B) = 0 would rest on the assumption that, in both populations, x measures an underlying quantity ξ on a common interval scale f: x = τ + λξ with invariant location and scale parameters τ and λ such that differences in ξ can be meaningfully inferred from differences in x. When populations A and B are different cultures, and the observed variable x is a questionnaire item, it becomes unreasonable to simply assume a common interval scale for responses on x. Different languages may imply differences in the semantics of item wording or response category labels, so that the existence of systematically biased location parameters τ (A) and τ (B) (additive bias) and scale parameters λ(A) and λ(B) (multiplicative bias) has to be considered. In comparisons of correlations (and, by extension, standardised regression coefficients), the problem is slightly different. Suppose we have collected measurements of two observed variables x and y in two consumer populations A and B and are interested in differences between the two populations in terms of the correlation between x and y. The variables x and y may stand for any two variables whose interrelationship the researcher is interested in, for example the relationship between a consumer’s attitude towards a particular product (x) and the frequency with which he or she actually buys the product (y). A direct test of the hypothesis ρxy(A) = ρxy(B) would rest on the assumption that, in both populations, x and y measure their underlying quantities ξ and η with the same reliability, i.e. that the variances of the measurement errors in x and y are invariant across populations A and B. If this assumption is violated, it cannot be decided whether a difference between the observed correlations rxy(A) and rxy(B) is due to differences in the correlations between the underlying true scores or whether it is due to different degrees of attenuation caused by differences in measurement error variances.

16.1.3 The general measurement invariance framework The general statistical theory of measurement invariance has been formulated by Meredith (1993). A random variable x is said to be measurement invariant with regard to selection on a group variable g if and only if the cumulative distribution function of any realisation of x is locally independent of any realisation of g when conditioned on the underlying quantity ξ

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such that F(x | ξ, g) = F(x | ξ) for all x, ξ, and g. The general theory has a number of special cases that include earlier formulations within specific psychometric frameworks such as common factor analysis (factorial invariance; Meredith, 1964), confirmatory factor analysis (multi-group confirmatory factor analysis; Sörbom, 1974), and item-response theory (differential item functioning, multi-group item response theory; Shealy and Stout, 1993, Muthén and Lehman, 1985).

16.2 Assessing measurement invariance The most flexible statistical framework for assessing measurement invariance is multi-group confirmatory factor analysis. Before we describe the framework, some words about its background. Classical psychometric theory (Spearman, 1904) assumes that the variance of any observed variable x can be additively decomposed into true score variance (the “signal” component) and measurement error variance (the “noise” component). The ratio of the true-score variance to the total variance of the observed variable x is the reliability of the measure x. Common factor analysis, a technique most readers will be familiar with, is a simple generalisation of this basic model. Instead of assuming a single true-score variable, factor analysis assumes that there may be several true-score variables underlying a set of observed variables. These are the common factors. The ratio of the variance of an observed variable x that is accounted for by the common factors to the total variance of the variable x is the communality. The residual variance (1 − communality) is the measurement error variance. Common factor analysis makes three restrictive assumptions. It assumes that the common factors are uncorrelated, that each observed variable is jointly determined by all common factors, and that there is only one population from which observations have been selected. Multi-group confirmatory factor analysis is a generalisation of common factor analysis that does not make these restrictive assumptions.

16.2.1 Multi-group confirmatory factor analysis Multi-group confirmatory factor analysis (Sörbom, 1974) represents the observed responses to P items (p = 1, . . . P) as a linear function of M latent factors (m = 1, . . . M, M ≤ P) and P random errors: x(g) = τ(g) + Λ(g)ξ(g) + δ(g),

16.1

where x is the P × 1 vector of observed variables in group g, τ is a P × 1 vector of intercepts, ξ(g) is the M × 1 vector of latent factors, Λ(g) is a P × M matrix of factor loadings, and δ(g) is a P × 1 vector of random measurement errors, assumed to be uncorrelated with the latent factors and to have zero expectation. The group variable g stands for the different (g)

(g)

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populations from which observations have been sampled. In the context of cross-cultural research, g typically stands for countries, cultures, or language communities. The model-implied means μx(g) and covariances Σxx(g) are: μx(g) = τ(g) + Λ(g)κ(g), Σxx

(g)

= Λ Φ Λ′ (g)

(g)

(g)

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+Θ , (g)

16.3

where κ(g) is the M × 1 vector of latent factor means, Φ(g) the M × M covariance matrix of the latent factors, and Θ(g) the P × P covariance matrix of random measurement errors.

16.2.2 Levels of measurement invariance Across groups, the measurement model defined in Equation (16.1) can be invariant with respect to each of its parameter matrices τ(g), Λ(g), Φ(g), Θ(g), and κ(g). In a paper that has become a standard reference in measurement invariance testing, Steenkamp and Baumgartner (1998) have proposed a hierarchical model comparison procedure. Seven consecutive levels of invariance can be distinguished: • Configural invariance. The pattern of zero and non-zero factor loadings is assumed to be invariant across groups g = 1, 2, . . . G. Configural invariance implies that the same underlying constructs are measured in all groups. • Metric invariance. The factor loadings are assumed to be invariant across groups g, implying the additional constraint Λ(1) = Λ(2) = . . . = Λ(G). Metric invariance implies that the observed variables are measured according to the same scale units in all groups. • Scalar invariance. Factor loadings and intercept terms are assumed to be invariant across groups g, implying the additional constraint τ(1) = τ(2) = . . . = τ(G). Scalar invariance implies that the observed variables are measured according to the same scale units and scale locations in all groups (i.e., on common interval scales). • Factor covariance invariance. Factor loadings, intercept terms and factor covariances are assumed to be invariant across groups g, implying the additional constraint φmn(1) = φmn(2) = . . . = φmn(G) for all factors m, n (m ≠ n). Factor covariance invariance implies that interrelationships among the underlying constructs are the same in all groups. • Factor variance invariance. Factor loadings, intercept terms, factor covariances and factor variances are assumed to be invariant across groups g, implying the additional constraint φmm(1) = φmm(2) = . . . = φmm(G) for all factors m. Factor variance invariance implies that variability of the underlying constructs is the same in all groups. • Error variance invariance. Factor loadings, intercept terms, factor covariances, factor variances and error variances are assumed to be invariant across groups g, implying the additional constraint θpp(1) = θpp(2) = . . .

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= θpp(G) for all items p. Error variance invariance implies that the item reliabilities are the same in all groups. • Identity. In addition to invariance of all measurement model parameters, the vector κ of the latent factor means is assumed to be invariant across groups g, implying the additional constraint κ(1) = κ(2) = . . . = κ(G). Identity implies that there are no differences between groups in the means of the underlying constructs. Of these seven levels, only four refer to measurement properties of the observed variables. These are configural invariance (dimensionality of the observed variables), metric invariance (comparable scale units of observed variables), scalar invariance (comparable scale locations of observed variables), and error variance invariance (comparable reliabilities of observed variables). The other three invariance levels concern properties of the latent variables: factor covariance invariance (comparable interrelationships), factor variance invariance (comparable heterogeneity), and identity (comparable means).

16.2.3 Identification, estimation, and testing A confirmatory factor analysis model is usually formulated in such a way that the pattern of factor loadings has “simple structure”, that is, each item loads on one factor only. A model will only be identified if it has nonnegative degrees of freedom, i.e. if the number of parameters does not exceed the number of sample moments based on which the parameters are to be estimated. In confirmatory factor analysis, the total number of sampling moments equals the number of observed means plus the number of distinct elements in the observed covariance matrices, summed up over groups. Hence, for G groups and P items measured in each group, the total number of sampling moments is GP + G(P(P + 1)/2). Furthermore, since latent variables do not have a pre-defined scale, identification constraints have to be imposed on the model. This is usually done by fixing the loading of one item per factor to λpm(g) = 1 in each group and by fixing the intercept of one item per factor to τp(g) = 0 in each group. If these guidelines are followed, a confirmatory factor analysis model with only one latent factor will be identified if at least three items are measured in each group, and a model with two or more factors will be identified if at least two items per factor are measured in each group. A confirmatory factor analysis model can be estimated by a variety of methods. The most common ones are maximum likelihood (Jöreskog, 1970) and robust maximum likelihood (Satorra and Bentler, 1988), both of which are implemented in widely used structural equation modelling software such as AMOS, LISREL, and Mplus. In measurement invariance analysis, one would typically estimate a series of seven increasingly constrained models:

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Model Model Model Model Model Model Model

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1: Configural invariance 2: Metric invariance 3: Scalar invariance 4: Factor covariance invariance 5: Factor variance invariance 6: Error variance invariance 7: Identity.

In addition to the parameter estimates, the estimation will yield an overall goodness-of-fit χ2 for each model which tests whether the modelimplied means and covariances deviate significantly from the observed means and covariances, and a large number of descriptive fit measures. Descriptive fit measures are often more useful for assessing model fit. Unlike the overall goodness-of-fit χ2, which gains excessive statistical power in large samples, they do not depend on sample size and are therefore easier to interpret. The most popular of these measures is the root mean squared error of approximation (RMSEA; Steiger, 1998), a relative non-centrality index that measures how well the model-implied means and covariances reproduce the observed means and covariances per degree of freedom. RMSEA values below .080 are usually interpreted as an indication of acceptable fit, and values below .050 as an indication of close fit. Using these guidelines, one way of assessing the level of measurement invariance in a given data set is to: • fit the seven increasingly constrained models indicated above, • inspect the RMSEA values for each model, • decide for the model with the highest level of invariance for which the RMSEA can still be regarded as acceptable. 16.2.4 Model comparisons A more stringent way of assessing measurement invariance is based on hierarchical model comparisons. The seven models outlined above are hierarchically nested: models with higher levels of measurement invariance are more constrained, special cases of models with lower levels of measurement invariance. Steiger, Shapiro and Browne (1985) have shown that the difference Δχ2 = χ2(M1) − χ2(M0)

16.4

obtained from comparing the fit of a more restricted target model M1 to the fit of a less restricted baseline model M0 is asymptotically χ2-distributed with Δdf = df1 – df0 degrees of freedom1. In the literature, this test is 1 Note that the difference between two Satorra-Bentler scaled χ2 statistics obtained from robust ML estimation of hierarchically nested models is not χ2 distributed. The necessary correction is described in detail in Satorra and Bentler (2001). An Excel spreadsheet that performs the calculations can be obtained from the author.

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commonly referred to as the χ2-difference test (or, equivalently, the likelihood ratio test) for incremental model fit. Monte-Carlo studies have shown that the test adequately controls Type I error rate and has sufficient power to detect group differences (French and Finch, 2006; Meade and Bauer, 2007; Meade and Lautenschlager, 2004) as long as the sample sizes are sufficiently large, the observed variables are normally distributed, and the communalities are at least moderate. However, it has the same disadvantage as the overall goodness-of-fit χ2. The test gains excessive power in large samples, often yielding a significant result even when the improvement or deterioration in model fit is so small as to be negligible in practice. Hence, the results of the test should always be interpreted in the context of descriptive fit statistics such as the RMSEA. If, for example, the χ2-difference test yields a significant result in the comparison of two models but the RMSEA only changes from .03 for the first model to .04 for the second model, the fit of the second model can still be regarded as excellent.

16.3 Numerical example of data handling in cross-cultural studies We will now demonstrate how measurement invariance is assessed in practice. The demonstration will be based on parts of the data that Scholderer, Brunsø, Bredahl and Grunert (2004) used for assessing the cross-cultural validity of the food-related lifestyle instrument (FRL). The FRL consists of altogether 69 items measuring 23 dimensions of consumer lifestyle. For the sake of simplicity, we will only use six items in the example analysis, measuring the dimensions “snacks versus meals” and “social event”. The items are listed in Table 16.1. All items are answered on seven-point scales ranging from 1 (completely disagree) to 7 (completely agree). We will compare two groups: a representative sample of French consumers (N = 1000) and a Table 16.1

FRL items used in the example

Item number

Item formulation

Snacks versus meals FRL10 I eat before I get hungry, which means that I am never hungry at meal times. FRL65 I eat whenever I feel the slightest bit hungry. FRL23 In our house, nibbling has taken over and replaced set eating hours. Social event FRL27 FRL45 FRL42

Going out for dinner is a regular part of our eating habits. We often get together with friends to enjoy an easy-to-cook, casual dinner. I enjoy going to restaurants with my family and friends.

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Observed means and covariances Item FRL10

FRL65

FRL23

FRL27

FRL45

FRL42

3.557 1.706 1.213 0.136 0.122 0.107

4.043 1.122 0.432 0.365 0.070

2.116 0.538 0.186 0.080

3.823 1.532 1.198

4.568 1.008

4.802

2.504

2.991

1.869

3.068

4.353

4.202

3.864 1.192 0.979 0.099 −0.003 0.209

4.018 1.110 −0.101 −0.019 −0.225

3.355 −0.113 0.063 −0.104

4.071 1.925 1.664

4.503 1.219

5.221

3.007

3.379

2.275

3.155

3.310

3.691

French sample (N = 1000) Covariances

Means

UK sample (N = 1000) Covariances

Means

Table 16.3 Measurement invariance analysis: goodness-of-fit and model comparison statistics Model Configural invariance Metric invariance Scalar invariance Factor covariance invariance Factor variance invariance Error variance invariance Identity

χ2

df

p

RMSEA

Δχ2

Δdf

p

43.321 47.767 186.250 193.242 202.565 309.748 394.934

16 20 24 25 27 33 35

.000 .000 .000 .000 .000 .000 .000

.041 .037 .082 .082 .081 .092 .101

4.446 138.483 6.992 9.323 107.183 85.186

4 4 1 2 6 2

.349 .000 .008 .009 .000 .000

representative sample of UK consumers (N = 1000). In an initial step, the within-group means and covariances of the six observed variables were computed. The means and covariances are shown in Table 16.2. Based on the means and covariances, seven increasingly constrained models were estimated, using the maximum-likelihood method in LISREL 8.71. Goodness-of-fit and model comparison statistics are shown in Table 16.3. The results are not unusual. For all seven models, the overall goodnessof-fit χ2 indicates significant deviations of the model – implied from the observed sampling moments. In a similar manner, the χ2-difference test

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indicates significant deterioration of model fit in all model comparison steps except for the first. As discussed above, this is mainly a consequence of the large samples (N = 1000 in each group) involved in the present analysis and therefore of limited usefulness. Descriptive fit measures such as the RMSEA are usually more interpretable when sample sizes are large. Researchers typically apply the following conventions when interpreting RMSEA values: • RMSEA > .08: unacceptable fit, • RMSEA < .08: acceptable fit, • RMSEA < .05: close fit. The RMSEA indicates close model fit for the first two models, the configural invariance and the metric invariance model. Based on the RMSEA values, it can be concluded that the factor loadings are invariant across groups. This is already a very good result: metric invariance means that the items vary according to the same scale units in both groups. The RMSEA values for the next three models are just outside the range that would conventionally be regarded as acceptable. A pragmatic decision would be to accept these models “with a grain of salt”; after all, conventions are merely rules of thumb und small deviations from rules of thumb may still be justifiable in certain situations. If one would follow such a pragmatic approach, one would conclude that the intercepts, the factor covariances, and the factor variances are reasonably stable across groups. This would be an extremely good result: scalar invariance means that the absolute values of the items are comparable across groups. Factor covariance invariance means that the two underlying constructs measured here have the same interrelationship in both groups. Factor variance invariance means that the two underlying constructs have the same variability in both groups. The RMSEA values for the last two models, on the other hand, are clearly outside the range that would conventionally be regarded as acceptable. The unacceptable fit of the error variance invariance model indicates that the items measure their underlying constructs with different reliability in the two groups (more specifically, that the amount of measurement error variance is different even though the amount of reliable variance is the same in both groups). The unacceptable fit of the identity model indicates that there are substantial differences between the two groups in terms of the means of the underlying constructs.

16.4 Correcting for bias: three strategies 16.4.1 Removing the offending items The simplest way of dealing with violations of metric invariance, scalar invariance, or error variance invariance is to identify the items that are responsible for the problem and remove them. Of course, this strategy is

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only feasible if enough items remain after the culling to identify the model. Nevertheless, it seems to be the most common strategy employed by researchers (even if this is rarely reported in the resulting publications), and it is probably also the most robust strategy. All other ways of dealing with biased data require considerably more involved computations and are therefore also more error-prone.

16.4.2 Partially constrained multi-group models A second strategy is to formulate the research hypothesis as a multi-group structural equation model, constrain those parameters to be invariant across groups for which measurement invariance could be established, and allow the remaining parameters to vary between groups. From a psychometric perspective, this is probably the most elegant solution to the problem, at least as long as the research hypothesis can in fact be expressed as a structural equation model (i.e., it must be linear), the samples are sufficiently large, and the distributions of the observed variables do not deviate too much from normality.

16.4.3 Numerical estimation of bias It has long been known that the existence of two items per factor with invariant loadings and intercepts is already sufficient to invoke a common interval scale on which the latent factor means are measured (“partial measurement invariance”; Byrne, Shavelson and Muthén, 1989). Scholderer, Grunert and Brunsø (2005) have shown that such a partially invariant measurement model can also be used to estimate the magnitude of bias in the observed variables and correct for it. The procedure is cumbersome and requires considerable experience in the use of structural equation modeling software, but can be extremely useful when subsequent analyses are planned that cannot be formulated as structural equation models (e.g., cluster analyses).

16.5 Conclusion The purpose of this chapter was to familiarise the reader with a set of statistical techniques that can help decide if data collected in different countries, cultures, or language communities can be meaningfully compared. The techniques are general. However, due to the high financial risk that is associated with all new product launch decisions, we believe that these techniques are particularly relevant to all researchers involved in new product launch decisions, either in terms of product development, consumer science, or marketing research. Precise, valid, and comparable measurements can substantially reduce risks, and the techniques discussed here can

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help achieve that. Our recommendations can be summarised in the following five points: • Do not pool data from a cross-cultural study before you have established a level of measurement invariance that makes your statistics meaningful. Pooled means are only meaningful if scalar invariance holds. Pooled variances are only meaningful if metric invariance holds. Pooled correlations are only meaningful if error variance invariance holds. • Do not trust cross-cultural comparisons, either in your own or in other people’s research, unless the techniques discussed in this chapter have been applied and a level of invariance has been established that is required by the particular type of cross-cultural comparison. • Do not assume that well-established measures do not suffer from measurement invariance problems. Experience shows that measurement invariance rarely holds in practice, even when the instrument is a standard questionnaire that has extensively been used in previous research. • Always make sure that each construct you are interested in is measured at least by two, better by three observed variables. The techniques discussed in this chapter are only applicable when several measurements of the same construct are available. • Do not throw out the baby with the bath water. Even if measurement invariance does not hold for all measures that were collected, it is often possible to identify biased measures and exclude them, or to use statistical techniques that do not assume high levels of invariance. Of course, the techniques discussed here also have their drawbacks. They make demands on the data that come at a cost (relatively large samples, multiple measurements of the same constructs), and they require a certain level of statistical expertise in the user (intimate familiarity with regression and factor analysis, and at least basic user experience with structural equation modelling software). However, we believe that these drawbacks will almost always be outweighed by the increased research quality that will results from the techniques.

16.6 References blunch, n. (2008). Introduction to structural equation modelling using SPSS and AMOS. London: Sage. byrne, b. m., shavelson, r. j. & muthén, b. o. (1989). Testing for the equivalence of factorial covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456–466. french, b. f. & finch, w. h. (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling, 13, 378–402. he, y., merz, m. a. & alden, d. (2008). Diffusion of measurement invariance assessment in cross-national empirical marketing research: Perspectives from the literature and a survey of researchers. Journal of International Marketing, 16, 64–83.

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jöreskog, k. g. (1970). A general method for analysis of covariance structures. Biometrika, 57, 239–251. jöreskog, k. g. & sörbom, d. (1999). LISREL 8: User’s Reference Guide. Lincolnwood, IL: Scientific Software International. meade, a. w. & bauer, d. j. (2007). Power and precision in confirmatory factor analytic tests of measurement invariance. Structural Equation Modeling, 14, 611–635. meade, a. w. & lautenschlager, g. l. (2004). A Monte-Carlo study of confirmatory factor analytic tests of measurement invariance. Structural Equation Modeling, 11, 60–72. meredith, w. (1964). Notes on factorial invariance. Psychometrika, 29, 177–185. meredith, w. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525–543. muthén, b. o. & lehman, j. (1985). Multiple group IRT modeling: Applications to item bias analysis. Journal of Educational Statistics, 10, 133–142. muthén, l. k. & muthén, b. o. (2006). Mplus user’s guide (4th Ed.). Los Angeles, CA: Muthén & Muthén. satorra, a. & bentler, p. m. (1988). Scaling corrections for chi-square statistics in covariance structure analysis. Proceedings of the American Statistical Association, 308–313. satorra, a. & bentler, p. m. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507–514. scholderer, j., brunsø, k., bredahl, l. & grunert, k. g. (2004). Cross-cultural validity of the food-related lifestyles instrument (FRL) within Western Europe. Appetite, 42, 197–211. scholderer, j., grunert, k. g. & brunsø, k. (2005). A procedure for eliminating additive bias from cross-cultural survey data. Journal of Business Research, 58, 72–78. shealy, r. & stout, w. (1993). A model-based standardization approach that separates true bias/DIF as well as item bias/DIF. Psychometrika, 58, 159–194. sörbom, d. (1974). A general method for studying differences in factor means and factor structures between groups. British Journal of Mathematical and Statistical Psychology, 28, 229–239. spearman, c. (1904). “General intelligence,” objectively determined and measured. American Journal of Psychology, 15, 201–293. steenkamp, j. b. e. m. & baumgartner, h. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25, 78–90. steiger, j. h. (1998). A note on multiple sample extensions of the RMSEA fit index. Structural Equation Modeling, 5, 411–419. steiger, j. h., shapiro, a. & browne, m. w. (1985). On the multivariate asymptotic distribution of sequential chi-square statistics. Psychometrika, 50, 253–264.

16.7 Appendix Measurement invariance analysis requires structural equation modelling software that can fit multi-group confirmatory factor analysis models. The relevant software packages are LISREL, AMOS and Mplus2. LISREL is the pioneer product, originally developed by Karl Jöreskog and Dag 2

The SAS procedure CALIS and the R package SEM are currently unable to estimate multi-group confirmatory factor analysis models.

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Sörbom. It is commercially available from Scientific Software International Inc. (www.sscicentral.com; the price of a single-user license was $495 at the time of writing). AMOS is distributed by SPSS Inc. (www.spss.com; single-user license: $1199 at the time of writing). Mplus is available from Muthén and Muthén (www.statmodel.com; single-user commercial license for the base programme: $695 at the time of writing). All software suppliers offer training courses (mainly in the United States; details on their websites). Many university departments have introductory courses, too. The author of the present chapter offers tailor-made courses for companies and university departments (for details, contact Joachim Scholderer at [email protected]).

16.7.1 Example syntax in LISREL LISREL has by far the most efficient syntax for conducting measurement invariance analyses. The different groups in a multi-group analysis are specified after each other (details in Jöreskog & Sörbom, 1999). The syntax below defines the scalar invariance model that was estimated in the above example. The key line is the model definition for the second group, “MO LX = IN TX = IN PH = PS TD = PS KA = PS”. MO stands for model, LX for the matrix of factor loadings, TX for the vector of intercepts, PH for the matrix of factor covariances, TD for the covariance matrix of the measurement errors, and KA for the vector of latent factor means. The specifications “LX = IN” and “TX = IN” constrain the complete matrix of factor loadings and the complete vector of intercepts to be invariant across groups, whereas “PH = PS”, “TD = PS”, and “KA = PS” specify the matrix of factor covariances, error covariances, and the vector of the latent factor means to have the same pattern of fixed and free elements in all groups, and instruct the numerical estimation routine to use the same starting values for the estimation of the free elements. Group 1: France DA NI=6 NO=1000 NG=2 LA FRL10 FRL65 FRL23 FRL27 FRL45 FRL42 / CM FI=frl_f.cov ME FI=frl_f.mea MO NX=6 NK=2 LX=FU,FI TX=FI PH=SY,FR TD=DI,FR KA=FR VA 1 LX 1 1 LX 4 2 FR LX 2 1 LX 3 1 LX 5 2 LX 6 2 VA 0 TX 1 TX 4 FR TX 2 TX 3 TX 5 TX 6 PD OU ME=ML ND=3 ALL Group 2: UK DA NO=1000

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LA FRL10 FRL65 FRL23 FRL27 FRL45 FRL42 / CM FI=frl_uk.cov ME FI=frl_uk.mea MO LX=IN TX=IN PH=PS TD=PS KA=PS PD OU

16.7.2 Example syntax in AMOS Unlike in LISREL, there is no keyword in AMOS by which whole matrices of parameters can be constrained to be invariant across groups. Each parameter in the model has to be labelled with an arbitrary name, separately for each group. Once all parameters are labelled, they can be constrained across groups (for details, see Blunch, 2008). Since this can become very error-prone and time-consuming when the model is large, AMOS is not the software of choice for multi-group structural equation modelling. However, AMOS has one advantage: it allows the user to specify a whole sequence of models which are then automatically compared by the software. The following syntax will estimate all seven models from the example above. #Region “Header” Imports System Imports System.Diagnostics Imports Microsoft.VisualBasic Imports AmosEngineLib Imports AmosGraphics Imports AmosEngineLib.AmosEngine.TMatrixID Imports PBayes #End Region Module MainModule Public Sub Main() Dim Sem As New AmosEngine Try Sem.Title (“Measurement invariance analysis”) Sem.TextOutput() Sem.Standardized() Sem.Smc Sem.ModelMeansAndIntercepts Sem.BeginGroup(“FRL.xls”, “France”) Sem.GroupName(“France”) Sem.AStructure(“FRL10 = (0) + (1) ksi1 + (1) delta1”) Sem.AStructure(“FRL65 = (tau12) + (lambda121) ksi1 + (1) delta2”) Sem.AStructure(“FRL23 = (tau13) + (lambda131) ksi1 + (1) delta3”)

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Consumer-driven innovation in food and personal care products Sem.AStructure(“FRL27 = (0) + (1) ksi2 (1) delta4”) Sem.AStructure(“FRL45 = (tau15) + (lambda152) ksi2 (1) delta5”) Sem.AStructure(“FRL42 = (tau16) + (lambda162) ksi2 (1) delta6”) Sem.AStructure(“ksi1 (phi111)”) Sem.AStructure(“ksi1 <-> ksi2 (phi121)”) Sem.AStructure(“ksi2 (phi122)”) Sem.AStructure(“delta1 (theta111)”) Sem.AStructure(“delta2 (theta122)”) Sem.AStructure(“delta3 (theta133)”) Sem.AStructure(“delta4 (theta144)”) Sem.AStructure(“delta5 (theta155)”) Sem.AStructure(“delta6 (theta166)”) Sem.MStructure(“ksi1 (kappa11)”) Sem.MStructure(“ksi2 (kappa12)”) Sem.BeginGroup(“FRL.xls”, “UK”) Sem.GroupName(“UK”) Sem.AStructure(“FRL10 = (0) + (1) ksi1 (1) delta1”) Sem.AStructure(“FRL65 = (tau22) + (lambda221) ksi1 (1) delta2”) Sem.AStructure(“FRL23 = (tau23) + (lambda231) ksi1 (1) delta3”) Sem.AStructure(“FRL27 = (0) + (1) ksi2 (1) delta4”) Sem.AStructure(“FRL45 = (tau25) + (lambda252) ksi2 (1) delta5”) Sem.AStructure(“FRL42 = (tau26) + (lambda262) ksi2 (1) delta6”) Sem.AStructure(“ksi1 (phi211)”) Sem.AStructure(“ksi1 <-> ksi2 (phi221)”) Sem.AStructure(“ksi2 (phi222)”) Sem.AStructure(“delta1 (theta211)”) Sem.AStructure(“delta2 (theta222)”) Sem.AStructure(“delta3 (theta233)”) Sem.AStructure(“delta4 (theta244)”) Sem.AStructure(“delta5 (theta255)”) Sem.AStructure(“delta6 (theta266)”) Sem.MStructure(“ksi1 (kappa21)”) Sem.MStructure(“ksi2 (kappa22)”) Sem.Model(“A”) Sem.Model(“B”, “lambda121=lambda221”, “lambda131=lambda231”, “lambda152=lambda252”, “lambda162=lambda262”) Sem.Model(“C”, “B”, “tau12=tau22”, “tau13=tau23”, “tau15=tau25”, “tau16=tau26”) Sem.Model(“D”, “C”, “phi121=phi221”)

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+ + +

+ + + + + +

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Sem.Model(“E”, “D”, “phi111=phi211”, “phi122=phi222”) Sem.Model(“F”, “E”, “theta111=theta211”, “theta122=theta222”, “theta133=theta233”, “theta144=theta244”, “theta155=theta255”, “theta166=theta266”) Sem.Model(“G”, “F”, “kappa11=kappa21”, “kappa12=kappa22”) Sem.FitAllModels Finally Sem.Dispose End Try End Sub End Module

16.7.3 Example syntax in Mplus Mplus has very efficient command syntax and offers more analytical options than other structural equation modelling packages. A multi-group analysis begins with the specification of the overall model, followed by group-specific statements. The default setting in Mplus is that all parameters defined in the overall model statement (MODEL: . . .) are invariant across groups. The user then simply re-states all parameters in the group-specific model statements (MODEL g1: . . . , MODEL g2: . . .) that are not constrained across groups (for details, see Muthén & Muthén, 2006). The syntax below defines the scalar invariance model that was estimated in the example. TITLE: DATA:

VARIABLE: ANALYSIS: MODEL:

MODEL g1:

MODEL g2:

OUTPUT:

Scalar invariance model FILE = frl.dat; TYPE = MEANS COVARIANCE; NGROUPS = 2; NOBSERVATIONS = 1000 1000; NAMES = frl10 frl65 frl23 frl27 frl45 frl42; TYPE = MEANSTRUCTURE; ksi1 BY frl10@1 frl65 frl23; ksi2 BY frl27@1 frl45 frl42; [frl10@0 frl65 frl23 frl27@0 frl45 frl42]; ksi1 WITH ksi2; ksi1 ksi2; frl10 frl65 frl23 frl27 frl45 frl42; [ksi1 ksi2]; ksi1 WITH ksi2; ksi1 ksi2; frl10 frl65 frl23 frl27 frl45 frl42; [ksi1 ksi2]; ksi1 WITH ksi2; ksi1 ksi2; frl10 frl65 frl23 frl27 frl45 frl42; [ksi1 ksi2]; STANDARDIZED MODINDICES (1);

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17 Bayesian networks for food science: theoretical background and potential applications V. A. Phan and M. A. J. S. van Boekel, Wageningen University, The Netherlands and M. Dekker and U. Garczarek, Unilever Food and Health Research Institute, The Netherlands

Abstract: Although Bayesian networks have gained popularity in many fields, they have just recently emerged in food-related problems. This technique can be used as a tool for prediction, explanation, exploration or decision making under uncertainty. The chapter mainly gives a theoretical background of Bayesian networks through a food example. It also discusses the advantages and challenges, as well as potential applications of Bayesian networks in the food area. Key words: Bayesian network theory, uncertainty, prediction, modeling in food science, food design.

17.1 Introduction Food research is highly complex. Food technologists and researchers need to take into account not only physical chemical interactions between food ingredients under processing, but also biological interactions between food and microorganisms, and between food and the human body. Owing to its nature, we need to consider variability and uncertainty of the system. Variability reflects natural variation whereas uncertainty represents the lack of human knowledge (van Boekel, 2008, page 2–5). For instance, perception responses to the same odorant can vary between human subjects, or even within one subject at different psychological or physiological states (variability). Besides this, the mechanism of how odorants trigger olfactory receptors has not yet been fully understood (uncertainty). Therefore, we humans build models to simplify and approximate the real world, i.e. to handle complex problems.

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One of the challenges of the food industry in the 21st century is to reformulate commonly eaten foods. This task has been defined in response to the dietary recommendations for lower intake of saturated fat, trans fat, sugar and salt (van Raaij et al., 2008). The reduction of these components requires a huge research effort to re-create the conventional flavor and texture. In this situation, prediction of sensory attributes and consumer acceptance, while modifying physical chemical properties of foods, is a valuable tool. Deterministic models essentially ignore uncertainty and variability of complex problems. Stochastic or probabilistic approaches, however, suggest possible solutions by expressing uncertainty and variability through probability distributions (Fearn, 2004). Recent food research has witnessed an increasing application of modern measurement techniques. Hence, more and more data are generated and food scientists need to work with large datasets. The capability of data analysis techniques to provide efficient explanation of data and exploration of implicit information is then of importance. Cunningham (1995) has discussed this point while bringing together classical and modern statistical approaches. In classical statistical analysis, the analyzer must formulate and test each hypothesis individually. The information discovery process becomes time-consuming and difficult to manage. In response, machine learning techniques, which are the convergence of artificial intelligence and statistics, have been intensively developed over the last decades. These techniques can automate both hypothesis generation and testing processes. Bayesian networks, also referred to as Bayesian belief networks, belief networks, Bayes nets, or causal probabilistic networks, are one machine learning technique based on a probabilistic approach. This technique can be used as a tool for prediction, explanation, exploration or decision making under uncertainty (Heckerman, 1995, Kjaerulff and Madsen, 2008). Bayesian networks are growing in popularity with numerous applications covering a variety of areas, such as finance, medical diagnosis, robotics, genetics and ecology. General introductions to Bayesian networks as well as real-life case studies in these domains are presented by Pourret, Naïm, and Marcot (2008). One example is the early application of Bayesian logic in medical diagnosis (Barnett et al., 1998). A model system was developed from a database of a thousand clinical findings such as symptoms, laboratory data and associated diseases. This model can predict the most likely diseases when provided with a description of new patients’ data. Despite of the wide use of the Bayesian networks technique in various fields, its presence in food-related problems has just recently emerged (van Boekel, 2004). Modeling with Bayesian networks has mostly focused on microbial risk assessment in the food production chain (Barker et al., 2005, Barker et al., 2002, Carlin et al., 2000). This kind of model was shown to add new information in a structured and simple manner (Barker et al., 2005). To the authors’ knowledge, the first published effort in designing food was to build Bayesian networks relating sensory features to consumer

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preference (Corney, 2000). Bayesian networks were shown to be a valuable addition to food design and could be built from small data sets. In short, Bayesian networks are able to handle variability and uncertainty in explaining and exploring information and particularly in predicting behaviors of systems. Although promising in solving problems in food research, Bayesian networks have not yet gotten enough attention among the food science community. This is probably because available tutorials on this technique often require an advanced mathematical background that few food experts have. The present paper aims to make ideas and techniques of Bayesian networks accessible to food scientists by describing a Bayesian network model using a food example (Section 17.2); showing benefits of the model once it has been built (Section 17.3); explaining the theories behind Bayesian networks (Sections 17.4, 17.5; 17.6). We will discuss in general advantages and challenges, as well as potential applications of this technique in the food area (Section 17.7), and finally provide sources for further reading (Section 17.8). Hypothetical examples of Bayesian networks are used throughout the paper. Variables, probability values and dependent relations were suggested based on knowledge and beliefs of the authors. Terminologies and concepts (formatted bold) concerning Bayesian networks are gently introduced while the paper focuses on the examples.

17.2 Concepts of Bayesian networks Suppose we conducted a consumer test on snack consumption among teenagers (n = 200). There were four treatment conditions of two levels of snack types: sweet and salty, and two levels of eating environments: with friends and without friends. In each condition, teenagers first tasted snack samples and scored their liking on a continuous line hedonic scale ranging from 0 (not at all) to 100 (very much). They then could eat the snack as much as they wanted. The total amount of snack consumed (intake) by each teenager was recorded. Data were generated by Hugin software (http://www. hugin.com/), and a sample of 20 cases is shown in Section 17.10. We are interested in four variables: ‘Snack type’, ‘Liking’, ‘Eating with friends’ and ‘Intake’, and want to examine their relationship using the technique of Bayesian networks. A Bayesian network has two aspects: qualitative and quantitative (Fig. 17.1). The qualitative aspect is a graph formed by a set of labeled nodes (labeled circles, implying respective variables) linked to each other by a set of arcs. These arcs imply dependence relations among variables. Each node in the graph is associated with a table called Conditional Probability Table (CPT). The set of these CPTs represents the quantitative aspect of the model. They allow quantification of relations among variables through probability values.

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Fig. 17.1 A hypothetical Bayesian network of snack consumption among teenagers. Labeled circles (nodes) represent respective variables of interest. Arcs indicate dependent relations between the two linked variables. The table associated to each node identifies different states that the variable can take, and the probability that the variable takes a specific state (given or not certain conditions). Probabilities associated with ‘Snack type’ and ‘Eating with friends’ were fixed by the experimental design. Probabilities associated with ‘Liking’ and ‘Intake’ resulted from the hypothetical data.

Definition 17.1. Probability of an event A is the likelihood or chance that A will occur, denoted as P(A) The arc pointing from parent node to its child node suggests a possible cause-effect relationship. For instance, in Fig. 17.1, the node ‘Snack type’ is a parent of ‘Liking’, i.e., the type of snack could influence liking scores. The node ‘Intake’ has two parents: ‘Liking’ and ‘Eating with friends’, i.e. snack consumption is supposedly affected by these two variables. These interactions (placement of the arcs) were suggested by the present authors. In Bayesian networks, the graph is directed, i.e. nodes are connected by arcs, and acyclic, i.e. following the arcs, there is no way from one node back to itself. This Directed Acyclic Graph (DAG) is considered as the structure of the Bayesian network model. In snack consumption data, values of two variables ‘Liking’ and ‘Intake’ are typically treated continuous because they can be given by any real number, e.g., between 0 and 100 for ‘Liking’ and any record for ‘Intake’. In principle, Bayesian networks can handle both continuous and discrete variables. Many general-purpose algorithms, however, only deal with

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models containing discrete variables. Therefore, continuous data used in Bayesian networks are often discretized, i.e., creating a countable set of values. Continuous variables can be converted into discrete variables by setting categories (here called states). In this case, two states of ‘Liking’ could be ‘Very much’, which is used to label liking scores greater or equal to 70; and ‘Not very much’ to label the rest (Appendix–Section 17.10).The intervals and related names of states are generally suggested by domain experts, and preferably based on earlier empirical findings. Values of ‘Intake’ in our hypothetical network were also set into three states in the same manner, being ‘Low’, ‘Medium’ and ‘High’. The data of ‘Snack type’ and ‘Eating with friends’ were categorical themselves (set by experimental design), and their states were ‘Sweet’ and ‘Salty’ for ‘Snack type’; and ‘Yes’ and ‘No’ for ‘Eating with friends’. When one variable takes a specific state, i.e. its value is defined, it is treated as an event. For example, (‘Liking = ‘Very much’) and (‘Snack type = ‘Sweet’) are two events. In the DAG, if a node has no parent, each value in its associated CPT represents the probability of the respective variable taking a specific state. For instance, the CPT of ‘Snack type’ says P(‘Snack type’ = ‘Sweet’) = 0.5 and P(‘Snack type’ = ‘Salty’) = 0.5; and that of ‘Eating with friends’ says P(‘Eating with friends’ = ‘Yes’) = 0.5 and P (‘Eating with friends’ = ‘No’) = 0.5. These probabilities reflect the randomization process of the experiment: ‘the chance of a teenager receiving a sweet or salty snack is equal, and his/ her chance for eating snacks alone or with friends is also the same’. If a node has parent(s), the associated CPT indicates the probability of the respective variable taking a specific state, given that the state of its parent variable(s) has been specified. For instance, having ‘Snack type’ as the unique parent, the CPT of the node ‘Liking’ is read as follows: P(‘Liking’ = ‘Very much’ | ‘Snack type’ = ‘Sweet’) = 0.7, or in words: ‘given that a snack is sweet, the probability of this snack being liked very much is 0.7’. P(‘Liking’ = ‘Not very much’ | ‘Snack type’ = ‘Sweet’) = 0.3 P(‘Liking’ = ‘Very much’ | ‘Snack type’ = ‘Salty’) = 0.4 P(‘Liking’ = ‘Not very much’ | ‘Snack type’ = ‘Salty’) = 0.6 The probabilities above were accessed by counting the frequency of liking score values labeled as ‘Very much’ or ‘Not very much’ given by teenagers when (‘Snack type’ = ‘Sweet’) and when (‘Snack type’ = ‘Salty’). The node ‘Intake’ has two parents: ‘Liking’ and ‘Eating with friends’. Probabilities in its CPT were determined as the frequency of intake values being labeled as ‘Low’, ‘Medium’ or ‘High’ for each of 2 × 2 state combinations of the two parent variables. For instance, we can say that teenagers consume a lot of snack if they like it very much and while eating with friends from the probabilities below:

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P(‘Intake’ = ‘Low’ | ‘Liking’ = ‘Very much’, ‘Eating with friends’ = ‘Yes’) = 0.1 P(‘Intake’ = ‘Medium’ | ‘Liking’ = ‘Very much’, ‘Eating with friends’ = ‘Yes’) = 0.2 P(‘Intake’ = ‘High’ | ‘Liking’ = ‘Very much’, ‘Eating with friends’ = ‘Yes’) = 0.7 Probabilities in the CPTs of ‘Liking’ and ‘Intake’ are called conditional probabilities, because they are conditioned to the state(s) of their parent(s). All values of the set of CPTs of a Bayesian network are recognized as parameters of the model. Definition 17.2. Conditional probability is the probability of an event A given that another event B has occurred, denoted P(A | B)

17.3 Use of Bayesian networks We have obtained a Bayesian network comprising of its structure (a set of nodes linked to each other by a set of arcs, i.e. DAG, known as the qualitative aspect), and its parameters (a set of conditional probability tables CPTs, known as the quantitative aspect). Then, what we can do is to perform inference. Probabilistic inference is the computation of probabilities of interest given the model (Heckerman, 1995). For example, from the network of snack consumption (Fig. 17.1), we want to compute the probability that teenagers eat a low (or medium, or high) amount of a snack, given that they are eating sweet snacks with friends. This computation is equivalent to predicting the snack consumption when certain information is available. We used Hugin software to illustrate the inference within Bayesian networks. On the Hugin interface, probabilities are represented in percentage and visualized using horizontal bars.

17.3.1 Initial probability distribution The initial probability distribution of the snack consumption network is presented in Fig. 17.2. Compared with the network in Fig. 17.1, the DAG stays the same, whereas overall marginal probability values are shown instead of conditional probability tables. Definition 17.3. Overall marginal probability is the probability of one variable taking a specific state while not knowing the values of all other variables in the network Overall marginal probabilities of one variable were automatically calculated based on the CPT of that variable and the CPT(s) of its parent node(s). For instance, from conditional probability values of the CPTs

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Eating with friends 50.00 Yes 50.00 No

Intake Intake 21.75 Low 41.75 Medium 36.50 High

Fig. 17.2 Initial probability distribution of Bayesian network describing snack consumption among teenagers (Hugin interface). Overall marginal probabilities of each variable are represented by horizontal bars and by percentages. These percentage values were calculated by the software from the associated CPT of the variable, and the CPT(s) of its parent node(s).

associated with ‘Intake’, ‘Liking’ and ‘Eating with friends’, we obtained overall marginal probabilities: P(‘Intake’ = ‘Low’) = 0.21, P(‘Intake’ = ‘Medium’) = 0.41, and P(‘Intake’ = ‘High’) = 0.36. This set of overall marginal probabilities specifies the overall marginal probability distribution of the variable ‘Intake’, denoted as P(‘Intake’). Similarly, P(‘Eating with friends’) includes (‘Yes’ = 0.50; ‘No’ = 0.50), and P(‘Liking’) includes (‘Very much’ = 0.55; ‘Not very much’ = 0.45).

17.3.2 Reasoning from cause to effect Suppose we want to know how ‘Snack type’ influences ‘Intake’. Once the initial probability distribution of the network is given (Fig. 17.2), in order to answer this question, we need to set evidence for the variable ‘Snack type’. Evidence could be the information we observed or potential evidence about one hypothesis to be tested. It is hypothesized that snacks are sweet, i.e. P(‘Snack type’ = ‘Sweet’) is set equal to 1.0 (Fig. 17.3a). Computation of probability distributions of other variables is performed by the software, and all probabilities are conditioned by event (‘Snack type’ = ‘Sweet’). Overall marginal probability distribution for each variable is replaced by its conditional marginal probability distribution.

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Fig. 17.3 Inference on the snack consumption network: influence of ‘Snack type’. When it is certain that ‘Snack type’ = ‘Sweet’ (a), it is very likely that teenagers like it ‘Very much’, and that the amount of snack consumed is higher than when ‘Snack type’ = ‘Salty’ (b). This results from the greater probability of ‘Intake’ taking ‘High’ state and the lower probability of ‘Intake’ taking ‘Low’ state when the evidence is ‘Snack type’ = ‘Sweet’. The type of snack has no influence on whether or not teenagers are having snacks with their friends.

Definition 17.4. Conditional marginal probability is the probability of one variable taking a specific state while knowing the value of at least one other variable in the network When no information about the type of snack is given, the (overall) marginal probability P(‘Liking’ = ‘Very much’) = 0.55, but when it is certain that the eaten snack is sweet, its (conditional) marginal probability P(‘Liking’ = ‘Very much’ | ‘Snack type’ = ‘Sweet’) becomes 0.7. The (overall) marginal probability P(‘Intake’ = ‘High’) = 0.36 also increases when being conditioned with (‘Snack type’ = ‘Sweet’): P(‘Intake’ = ‘High’ | ‘Snack type’ = ‘Sweet’) = 0.41. This shift in probability distributions gives us more ‘confidence’ to say that teenagers will like a snack very much and consume quite a lot when they are given sweet snacks. Besides, the evidence P(‘Snack type’ = ‘Sweet’) = 1.0 does not change the probability distribution of the variable ‘Eating with friends’, which means ‘Snack type’ has no influence on the consumption environment. This conclusion is obvious because these two variables are independent variables by experimental design. Considering the evidence P(‘Snack type’ = ‘Salty’) = 1.0, resulting probability distributions are presented in Fig. 17.3b. Getting back to the question how ‘Snack type’ influences ‘Intake’, it is enough to compare the probability distribution of the variable ‘Intake’ when the evidence is P(‘Snack type’ = ‘Sweet’) = 1.0 (Fig. 17.3a) and when P(‘Snack type’ = ‘Salty’) = 1.0 (Fig. 17.3b). The distribution of ‘Intake’ has more weight on ‘High’ state and less weight on ‘Low’ state when P(‘Snack type’ = ‘Sweet’) = 1.0 than when the other evidence was set. We

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can conclude from this hypothetical network that teenagers are more likely to have high intake when the snacks are sweet than salty. In the same manner, the influence of ‘Liking’ and ‘Eating with friends’ on ‘Intake’ could be tested by setting new evidence on these variables.

17.3.3 Combined influence of variables Bayesian network models permit to visualize the combined effect of two variables. Fig. 17.4 shows probability distributions of ‘Liking’ and ‘Intake’ when evidences were set for ‘Snack type’ and ‘Eating with friends’. Let us consider Fig. 17.4a (eating sweet snacks) as the baseline of Fig. 17.4b (eating sweet snacks with friends) and Fig. 17.4c (eating sweet snacks without friends). Adding the information of the eating environment (with friends or alone) either increases (Fig. 17.4b) or decreases (Fig. 17.4c) the probability that teenagers consume a high amount of sweet snacks. The same trend was observed when comparing the marginal probability distribution of ‘Intake’ in three input evidences: (1) eating salty snacks; (2) eating salty snacks with friends and (3) eating salty snacks alone (illustration not shown). Hence, the combined effect of ‘Snack type’ and ‘Eating with friends’

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Fig. 17.4 Inference on the snack consumption network: combined evidences. Knowing that teenagers are eating sweet snacks (a), the probability that they consume a high amount is much greater when added that they are eating with friends (b) than knowing that they are eating alone (c).

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is present. ‘Eating with friends’ enhances the influence of ‘Snack type’ on ‘Intake’. The goal of this section is that we want to predict the snack consumption when it is known that teenagers are eating sweet snacks with friends. The answer is indeed the probability distribution of the node ‘Intake’ when it was set that P(‘Snack type’ = ‘Sweet’) = 1.0 and P(‘Eating with friends’ = ‘Yes’) = 1.0 (Fig. 17.4b). In the same manner, we can set evidence for more variables. For example, we want to predict the intake when teenagers are eating salty snacks with friends and they all like salty snacks very much.

17.3.4 Reasoning from effect to cause Inferences performed in Fig. 17.3 and Fig. 17.4 are reasoning forward, i.e. from cause to effect. Bayesian network models also allow reasoning backward, i.e. from effect to cause. Suppose the only information we know is the amount of snacks consumed. If this amount is low (Fig. 17.5a), the eaten snacks were more likely to be salty than sweet (P = 0.55 vs. P = 0.45) and it is very likely (P = 0.77) that teenagers ate snacks alone. Opposite trends are found when the intake is high (Fig. 17.5b). In short, predictions can be made with Bayesian networks through inference. The backward reasoning is a particular strength of these models. It could be useful in product design. For instance, a model relating input attributes to output attributes can deduce the most likely states of how input attributes should be in order to obtain the desired output attributes.

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Fig. 17.5 Inference on the snack consumption network – backward reasoning. If the ‘Intake’ is known to be ‘Low’, the ‘Snack type’ is deduced to be rather ‘Salty’ than ‘Sweet’ and teenagers seem to eat snacks alone (a) and if the ‘Intake’ is known to be ‘High’, it is very likely that teenagers consumed sweet snacks together with friends (b).

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17.4 Inference in simple models Applying Bayesian networks was demonstrated in the previous section. This section aims to explain how the calculations of the probabilities are done in a simple Bayesian network model. Suppose we work on a network only relating ‘Liking’ to ‘Snack type’ (Fig. 17.6). This network was extracted from the hypothetical network on snack consumption among teenagers (Fig. 17.1). We want to know how likely a snack is to be ‘Sweet’ (or ‘Salty’) if it is observed that teenagers like the given snack ‘Very much’. Thus, we need to compute two conditional probabilities: P(‘Snack type’ = ‘Sweet’ | ‘Liking’ = ‘Very much’) and P(‘Snack type’ = ‘Salty’ | ‘Liking’ = ‘Very much’). The variable ‘Snack type’ has two states: ‘Sweet’ and ‘Salty’, and is quantified by its overall marginal probability distribution P(‘Snack type’), i.e. (0.5, 0.5) (Fig. 17.6). We do not know yet the overall marginal probability distribution of the variable ‘Liking’. However, the relationship between ‘Snack type’ and ‘Liking’ is quantified through the conditional probability distribution P (‘Liking’ | ‘Snack type’) = (0.7, 0.3, 0.4, 0.6) (Fig. 17.6). In order to compute the overall marginal probabilities of ‘Liking’, we need to know the joint probability distribution of the given Bayesian network. Definition 17.5. Joint probability of two events A and B is the probability that both events occur together, denoted as P(A, B) For instance, the joint probability of two events (‘Snack type’ = ‘Sweet’) and (‘Liking’ = ‘Very much’) is P(‘Snack type’ = ‘Sweet’, ‘Liking’ = ‘Very

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Fig. 17.6 A Bayesian network model relating ‘Snack type’ (either ‘Sweet’ or ‘Salty’) to ‘Liking’ (either ‘Very much’ or ‘Not very much’). Overall marginal probability distribution of ‘Snack type’ resulted directly from the experimental design, i.e. half of tested snacks were sweet and the other half were salty. Conditional probabilities associated with ‘Liking’ were determined by the frequency of the liking scores being labeled ‘Very much’ or ‘Not very much’.

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much’), which represents the probability that one snack is found to be both sweet and liked very much. Definition 17.6. Joint probability distribution of two discrete variables X and Y, denoted as P(X,Y), is the set of joint probabilities P(X = x, Y = y), where x and y are any state of X and Y, respectively For instance, the joint probability distribution of two variables ‘Snack type’ and ‘Liking’, i.e., P(‘Snack type’, ‘Liking’), consists of four joint probabilities: P(‘Snack P(‘Snack P(‘Snack P(‘Snack

type’ type’ type’ type’

= ‘Sweet’, ‘Liking’ = ‘Very much’), = ‘Sweet’, ‘Liking’ = ‘Not very much’) = ‘Salty’, ‘Liking’ = ‘Very much’) = ‘Salty’, ‘Liking’ = ‘Not very much’).

17.4.1 Calculation of joint probabilities The fundamental rules of probability allow calculating joint probability from marginal probability and conditional probability: P(A, B) = P(A | B) ∗ P(B) = P(B | A) ∗ P(A)

17.1

Applying directly Equation [17.1], the joint probability distribution of P(‘Snack type’, ‘Liking’) can be obtained from P(‘Snack type’) and P(‘Liking’ | ‘Snack type’), for example: P(‘Snack type’ = ‘Sweet’, ‘Liking’ = ‘Very much’) = P(‘Liking’ = ‘Very much’ | ‘Snack type’ = ‘Sweet’) ∗ P(‘Snack type’ = ‘Sweet’) = 0.7 ∗ 0.5 = 0.35 Four joint probabilities of the distribution P(‘Snack type’, ‘Liking’) are shown in the joint probability table in Fig. 17.7a.

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Fig. 17.7 Calculating the marginal probability distribution of the Bayesian network relating ‘Snack type’ (either ‘Sweet’ or ‘Salty’) to ‘Liking’ (either ‘Very much’ or ‘Not very much’). Marginal probabilities of ‘Liking’ are obtained by summing up all rows of the joint probability table (a) and those of ‘Snack type’ found by summing up all its columns. The same results given by Hugin software are shown in (b).

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17.4.2 Calculation of overall marginal probabilities Besides, the law of total probability says for any event A, that if there is a set of n mutually exclusive and exhaustive events [1]Ei (i = 1,..,n), then: n

P ( A ) = ∑ P ( A, Ei )

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i =1

which enables us to calculate the marginal probability distribution P(‘Liking’) based on the two mutually exclusive and exhaustive events of ‘Snack type’: P(‘Liking’ = ‘Very much’) = P(‘Liking’ = ‘Very much’, ‘Snack type’ = ‘Sweet’) + P(‘Liking’ = ‘Very much’, ‘Snack type’ = ‘Salty’) = 0.35 + 0.20 = 0.55 P(‘Liking’ = ‘Not very much’) = P(‘Liking’ = ‘Not very much’, ‘Snack type’ = ‘Sweet’) + P(‘Liking’ = ‘Not very much’, ‘Snack type’ = ‘Salty’) = 0.15 + 0.30 = 0.45 The rule of this calculation is to sum up all rows of the joint probability table (Fig. 17.7a). If summing up all columns, the marginal probability distribution P(‘Snack type’) is again found. The same results given by Hugin software are shown in Fig. 17.7b. 17.4.3 Calculation of conditional probabilities of interest At this stage, our conditional (marginal) probabilities of interest can be computed using the derived form of Equation [17.1]: P(‘Snack type’ = ‘Sweet’ | ‘Liking’ = ‘Very much’) = P(‘Snack type’ = ‘Sweet’, ‘Liking’ = ‘Very much’) / P(‘Liking’ = ‘Very much’) = 0.35 / 0.55 = 0.6364 P(‘Snack’ = ‘Salty’ | ‘Liking’ = ‘Very much’) = P(‘Snack type’ = ‘Salty’, ‘Liking’ = ‘Very much’) / P(‘Liking’ = ‘Very much’) = 0.20 / 0.55 = 0.3636 Note that the probability P(‘Snack’ = ‘Salty’ | ‘Liking’ = ‘Very much’) can also be derived from P(‘Snack type’ = ‘Sweet’ | ‘Liking’ = ‘Very much’) because all marginal probabilities of one variable sum up to 1. These outcomes are also given automatically by Hugin software when setting evidence (‘Liking’ = ‘Very much’) (Fig. 17.8a). Similar steps allow us to obtain the probability distribution of ‘Snack type’ when the evidence ‘Liking’ = ‘Not very much’ is set (Fig. 17.8b). In short, the joint distribution of a Bayesian network is the key to do inference.

17.5 Inference in complex models So far, we have considered only inference in the network containing two variables (‘Snack type’ and ‘Liking’) and each variable has two states. The joint probability distribution of this network consists of four joint probability values and only three of those need to be specified (the last one [1]

n events E1, E2, . . . , En are said to be mutually exclusive and exhaustive if no two of them occur at the same time (Ei ∩ Ej ≠ ∅ with i, j ∈ n and i ≠ j) and their individual n

probabilities sum up to 1 ( ∑ P ( Ei ) = 1). i =1

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Snack type 63.64 Sweet 36.36 Salty

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Snack type 33.33 Sweet 66.67 Salty

Liking 0.00 Very much 100.00 Not very much

Fig. 17.8 Inference in the Bayesian network relating ‘Snack type’ and ‘Liking’. The distribution of the variable ‘Snack type’ given evidence on the variable ‘Liking’ is found by calculating the joint probability P (‘Snack type’, ‘Liking’).

is dependent of the rest). In real world problems however, we are typically interested in looking for relationships among a large number of variables (Heckerman, 1995). Consider, for example, a network connecting n variables (X1, X2,..., Xn). Assuming each variable of this network takes only 2 states, its joint probability distribution P(X1, X2,..., Xn) is specified by (2n − 1)[2] joint probability values. This exponential relationship can result in an enormous number when n is large. If the variables have more than two states, this number grows even more rapid. In order to simplify the calculation of the joint probabilities, assumptions on probabilistic relations, e.g. dependence and conditional independence, are used in Bayesian networks. 17.5.1 Example of problem Here we use again the network on snack consumption. The variable ‘Purchase intention’ is now included (Fig. 17.9). When teenagers tasted and gave liking scores for snack samples, they also stated whether or not they have the intention to purchase the product. Values of ‘Purchase intention’, being either ‘Yes’ or ‘No’, were assumed to be only influenced by the variable ‘Liking’. The structure of Bayesian networks can be read by typical connections linking a group of three nodes. Three kinds of connections are distinguished: serial (X→Y→Z) or (X←Y←Z), diverging (X←Y→Z), and converging (X→Y←Z). In the ‘Purchase’ network (Fig. 17.9), for instance, (‘Snack type’ → ‘Liking’ → ‘Intake’) and (‘Snack type’ → ‘Liking’ → ‘Purchase intention’) are two serial connections, (‘Purchase intention’ ← ‘Liking’ → ‘Intake’) is a diverging connection, and (‘Liking’ → ‘Intake’ ← ‘Eating with friends’) is a converging connection. These kinds of connections will be referred to while examining probabilistic relations in the network. [2]

The number of joint probabilities of the network is 2n. However, as all these probabilities have to sum up to 1, the last one is dependent on the other values, which results in the number (2n − 1).

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Snack type

Eating with friends

Liking

Purchase intention

Fig. 17.9

Intake

Network of snack consumption including ‘Purchase intention’.

In order to perform inference on this network, we need to specify the joint probability distribution over the network, i.e. P(‘Intake’, ‘Purchase intention’, ‘Liking’, ‘Snack type’, ‘Eating with friends’), or abbreviated as P(‘Int’, ‘Pur’, ‘Lik’, ‘Sna’, ‘Eat’). Applying the fundamental rules of probability of Equation [17.1], the joint probability distribution of the network BN of n variables (X1, X2,..., Xn) can be decomposed into product of conditional and marginal probability distributions: P(X1, X2,..., Xn) = P(X1 | X2,..., Xn) ∗ P(X2,..., Xn) = P(X1 | X2,..., Xn) ∗ P(X2 | X3,..., Xn) ∗ P(X3, X4,..., Xn) = P(X1 | X2,..., Xn) ∗ P(X2 | X3,..., Xn) ∗ ... ∗ P(Xn−1 | Xn) ∗ P(Xn) 17.3 which allows us to rewrite the joint probability distribution of the open snack consumption network as follows: P(‘Int’, ‘Pur’, ‘Lik’, ‘Sna’, ‘Eat’) = P(‘Int’ | ‘Pur’, ‘Lik’, ‘Sna’, ‘Eat’) ∗ P(‘Pur’ | ‘Lik’, ‘Sna’, ‘Eat’) ∗ P(‘Lik’ | ‘Sna’, ‘Eat’) ∗ P(‘Sna’ | ‘Eat’) ∗ P(‘Eat’) Thus, we can calculate the joint probability distribution through the conditional probability distributions. These conditional probability distributions can be simplified when specific assumptions about probabilistic relations among the five variables are defined: assumptions about their dependencies.

17.5.2 Independence and conditional dependence Let us consider the node ‘Eating with friends’. It is linked directly to ‘Intake’, indirectly to ‘Liking’ through a converging connection, indirectly to ‘Snack type’ and ‘Purchase intention’ through one converging connection and one serial connection (Fig. 17.9).

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Snack type 50.00 Sweet 50.00 Salty

Liking 55.00 Very much 45.00 Not very much

Eating with friends 100.00 Yes 0.00 No

Intake 10.00 Low 38.00 Medium 52.00 High

Purchase intention 57.50 Yes 42.50 No

(c)

Snack type 42.11 Sweet 57.89 Salty

Liking 28.95 Very much 71.05 Not very much

Purchase intention 44.47 Yes 55.53 No

Eating with friends 100.00 Yes 0.00 No

Intake 0.00 Low 100.00 Medium 0.00 High

(b)

Snack type 50.00 Sweet 50.00 Salty

Liking 55.00 Very much 45.00 Not very much

Purchase intention 57.50 Yes 42.50 No

(d)

503

Eating with friends 0.00 Yes 100.00 No

Intake 33.50 Low 45.50 Medium 21.00 High Snack type 51.65 Sweet 48.35 Salty

Liking 60.44 Very much 39.56 Not very much

Purchase intention 60.22 Yes 39.78 No

Eating with friends 0.00 Yes 100.00 No

Intake 0.00 Low 100.00 Medium 0.00 High

Fig. 17.10 The variable ‘Eating with friends’ is independent to ‘Snack type’, ‘Liking’ and ‘Purchase intention’ because modifying values of ‘Eating with friends’ (a, b) does not lead to any changes on the marginal probability distributions of the other three variables. However, when prior information on ‘Intake’ is provided, for example ‘Intake’ = ‘Medium’, modifications of ‘Eating with friends’ (c, d) affect marginal probability distributions ‘Snack type’, ‘Liking’ and ‘Purchase intention’. Thus, ‘Eating with friends’ becomes conditional dependent to ‘Snack type’, ‘Liking’ and ‘Purchase intention’ given values of ‘Intake’.

On one hand, when no information about the values in the network are given, changing the marginal probability distribution of ‘Eating with friends’ does not affect those of ‘Snack type’, ‘Liking’ and ‘Purchase intention’ (Fig. 17.10a,b). In turn, different evidences on these three variables do not lead to any modification in values of ‘Eating with friends’ (illustrations not shown). In general, information cannot be transmitted through a converging connection. In probability theory, two events (variables) are said to be independent if the probability (distribution) of one event (variable) does not change whether or not provided with information about the other. Definition 17.7. Two events A and B (P(A) ≠ 0 and P(B) ≠ 0) are independent if P(A | B) = P(A) Definition 17.8. Two discrete variables X and Y are independent if P(X = x | Y = y) = P(X = x) for any state x, y of X and Y, respectively; or simply expressed by probability distribution if P(X | Y) = P(X) From the definitions of probabilistic independence, it can be interpreted that ‘Eating with friends’ is independent of the three variables ‘Snack type’,

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‘Liking’ and ‘Purchase intention’. Consequently, P(‘Sna’ | ‘Eat’) = P(‘Sna’); P(‘Lik’ | ‘Eat’) = P(‘Lik’); P(‘Pur’ | ‘Eat’) = P(‘Pur’). On the other hand, when knowing the value of the middle node of the converging connection (‘Liking’ → ‘Intake’ ← ‘Eating with friends’), changing the marginal probability distribution of ‘Eating with friends’ appears to affect those of ‘Purchase intention’, ‘Liking’ and ‘Snack type’ (Fig, 17.10c,d). In this situation, ‘Eating with friends’ becomes conditional dependent to ‘Purchase intention’, ‘Liking’ and ‘Snack type’ given values of ‘Intake’. Thus, it is said that information can be transmitted through a converging connection only if information about the middle node is provided.

17.5.3 Dependence and conditional independence Consider now the node ‘Purchase intention’. It is linked directly to ‘Liking’, indirectly to ‘Snack type’ through a serial connection, indirectly to ‘Intake’ through a diverging connection; and indirectly to ‘Eating with friends’ through one diverging connection and one converging connection. When no information about the values in the network are given, changing the marginal probability distribution of ‘Purchase intention’ affects those of ‘Snack type’, ‘Liking’, ‘Intake’ (Fig. 17.11a,b). When evidence, however, is set for ‘Liking’, e.g. ‘Liking’ = ‘Very much’, added information (a)

(b)

Snack type 56.52 Sweet 43.48 Salty

Liking

Eating with friends 50.00 Yes 50.00 No

76.52 Very much 23.48 Not very much

Purchase intention 100.00 Yes 0.00 No

Intake 18.52 Low 38.52 Medium 42.96 High

(c)

Snack type 63.64 Sweet 36.36 Salty

Liking 100.00 Very much 0.00 Not very much

Purchase intention 100.00 Yes 0.00 No

Eating with friends 50.00 Yes 50.00 No

Intake 15.00 Low 35.00 Medium 50.00 High

Snack type 41.18 Sweet 58.82 Salty

Liking 25.88 Very much 74.12 Not very much

Purchase intention 0.00 Yes 100.00 No

(d)

Eating with friends 50.00 Yes 50.00 No

Intake 26.12 Low 46.12 Medium 27.26 High Snack type 63.64 Sweet 36.36 Salty

Liking 100.00 Very much 0.00 Not very much

Purchase intention 0.00 Yes 100.00 No

Eating with friends 50.00 Yes 50.00 No

Intake 15.00 Low 35.00 Medium 50.00 High

Fig. 17.11 Information can be transmitted from ‘Purchase intention’ to ‘Snack type’ through a serial connection (X→Y→Z) and to ‘Intake’ through a diverging connection (X←Y→Z) (a,b). However, this flow of information is blocked when evidence is set for ‘Liking’, the middle node in serial and diverging connections (c,d).

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on ‘Purchase intention’ has no more effect on (conditional) marginal probability distributions of ‘Intake’ and ‘Snack type’ (Fig. 17.11c,d). Similarly, given ‘Liking’ = ‘Very much’, added information on ‘Intake’ (or ‘Snack type’) does not influence either of the probability distributions of the other two nodes (illustrations not shown). Thus, information can be transmitted through serial and diverging connections. This flow of information, however, can be blocked by giving evidence on the middle node of these two connections. In brief, although three variables ‘Snack type’, ‘Purchase intention’ and ‘Intake’ do not link directly to each other, they are not independent. New information about one variable can lead to changes in values of the other two variables through the updated information on the middle variable ‘Liking’. However, when the value of ‘Liking’ is known, new information about one of the three variables ‘Snack type’, ‘Purchase intention’ and ‘Intake’ does not change the values of the other two. This observation ia an example of the concept of conditional independence in probability theory. Definition 17.9. Two events A and B are conditionally independent given event C if P(C) ≠ 0 and P(A | B,C) = P(A | C) Definition 17.10. Two discrete variables X and Y are conditionally independent given another random variable Z if P(X = x | Y = y, Z = z) = P(X = x | Z = z) for any state x, y, z of X, Y and Z, respectively; or simply expressed by probability distribution P(X | Y,Z) = P(X | Z) or P(X | Y,Z) = P(Y | Z) Being consistent with the definition of conditional independence, three variables ‘Snack type’, ‘Purchase intention’ and ‘Intake’ are conditional independent to each other given ‘Liking’. In this case, we can simplify some conditional probabilities, for example, P(‘Int’ | ‘Pur’, ‘Lik’) = P(‘Int’ | ‘Lik’); P(‘Int’ | ‘Sna’, ‘Lik’) = P(‘Int’ | ‘Lik’). To summarize, two dependent variables X and Y can become conditionally independent if there is a third variable Z forming a serial connection (X→Z→Y or X←Z←Y) or a diverging connection (X←Z→Y). Besides, two independent variables X and Y can become conditionally dependent if there is a third variable Z forming a converging connection (X→Z←Y). 17.5.4 Joint probability distribution in Bayesian networks Having defined probabilistic relations in the network, let us come back to the calculation of the joint probability distribution: P(‘Int’, ‘Pur’, ‘Lik’, ‘Sna’, ‘Eat’) = P(‘Int’ | ‘Pur’, ‘Lik’, ‘Sna’, ‘Eat’) ∗ P(‘Pur’ | ‘Lik’, ‘Sna’, ‘Eat’) ∗ P(‘Lik’ | ‘Sna’, ‘Eat’) ∗ P(‘Sna’ | ‘Eat’) ∗ P(‘Eat’) Since ‘Intake’, ‘Snack type’ and ‘Purchase intention’ are conditional independent given ‘Liking’; and ‘Eating with friends’ is independent to ‘Snack type’, ‘Purchase intention’ and ‘Liking’, we have:

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P(‘Int’ | ‘Pur’, ‘Lik’, ‘Sna’, ‘Eat’) = P(‘Int’ | ‘Lik’, ‘Eat’) P(‘Pur’ | ‘Lik’, ‘Sna’, ‘Eat’) = P(‘Pur’ | ‘Lik’, ‘Eat’) = P(‘Pur’ | ‘Lik’) P(‘Lik’ | ‘Sna’, ‘Eat’) = P(‘Lik’ | ‘Sna’) P(‘Sna’ | ‘Eat’) = P(‘Sna’) resulting in: P(‘Int’, ‘Pur’, ‘Lik’, ‘Sna’, ‘Eat’) = P(‘Int’ | ‘Lik’, ‘Eat’) ∗ P(‘Pur’ | ‘Lik’) ∗ P(‘Lik’ | ‘Sna’) ∗ P(‘Sna’) ∗ P(‘Eat’) Visibly, the joint probability distribution P(‘Int’, ‘Pur’, ‘Lik’, ‘Sna’, ‘Eat’) is rewritten by the product of conditional probability distributions of each variable given its parent(s) (if it has parents) and marginal probability distributions of variables that have no parents. To generalize, the joint probability distribution of the network BN having n variables (X1, X2,..., Xn) in Equation [17.3] can be computed as the product of conditional probability distributions of each node given its parent(s): n

P ( X 1 , X 2 ,..., X n ) = ∏ P ( X i | parents ( X i ) )

17.4

i =1

If the node Xi has no parent, its conditional probability distribution is actually its marginal probability distribution. In short, identifying independence and conditional independence relations among the set of variables of interest is essential to compute the joint probability distribution, which in turn enables us to perform inference on the network. Above, inference in the network is performed in order to illustrate the probabilistic relations among its variables. In practice, however, if the structure is defined by domain experts, it also implies probabilistic relations through the identification of serial, diverging and converging connections. If the structure is not known yet, these probabilistic relations could be examined based on data, and the structure is then built from these relations. This learning process will be discussed briefly in the next section.

17.6 Learning Bayesian networks 17.6.1 Definition of Bayesian networks Most papers on Bayesian networks begin with giving the definition, which is difficult to relate to real world problems in food science. We hope, after having introduced basic terminologies and concepts, that the definition below can be now more easily connected to content: Definition 17.11. A Bayesian network is a graphical model for probabilistic relationships over a set of variables. It consists of a qualitative aspect, encoding (conditional) dependence and independence among variables; and a quantitative aspect, encoding the joint probability distribution over these variables

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17.6.2 Learning Bayesian networks To construct (or to learn) a Bayesian network model, we need to specify its structure (DAG, a set of nodes linked by a set of arcs) and its parameters (all conditional probabilities forming the Conditional Probability Table for each node). The input that can be used in learning Bayesian networks are the so-called prior knowledge and data (new observations). Prior knowledge could be common knowledge of the domain, or collected from published scientific papers, or even beliefs of domain experts. Data involved may be complete, i.e. all values are observed, or incomplete, i.e. containing missing values. In some cases, the structure of the network can be elicited from domain knowledge, i.e., domain experts stating relevant variables and interactions among them (Corney, 2000). Hence, the structure is considered as known. Theoretically and practically, prior knowledge also allows specifying network parameters, i.e., probability values, in the case of expert systems (Heckerman, 1995). Otherwise, these probabilities are to be estimated from data. In some cases, however, the network structure is not known, or incomplete. Therefore, data is the only input for inducing structure and estimating parameters. Learning the structure of a Bayesian network from data is a challenge pursued within the machine learning domain. The task is even harder with incomplete data. Due to its complexity and as it remains an open problem, we do not discuss about structure learning in this introductory paper.

17.6.3 Known structure, complete data Consider the network on snack consumption among teenagers (Fig. 17.1); assume that its structure was defined by using prior knowledge. Conclusions from various studies provided prior knowledge for this network, such as: (1) flavor of a food product is an important factor determining the liking for it; (2) the more we like a product, the more we eat it; (3) the social interaction during a meal also influences the amount of food we eat. Based on these commonly accepted statements and the experimental design, the structure of snack consumption was drawn by hand. The data of snack consumption study is complete (a sample of the dataset is shown in Appendix–Section 17.10). Conditional probabilities of the CPT for each variable were simply the frequency of its specified state given the state of its parents. Having obtained the complete structure and all the parameters, inference can be performed.

17.6.4 Known structure, incomplete data In reality, data is often not complete, e.g. some variables were not observed for all cases. Then, the frequency cannot be accessed. To solve this

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problem, the missing data could be assigned to certain expected values based on available data using the EM- (Expectation-Maximization) algorithm (Lauritzen, 1995). This algorithm uses an iterative method to maximize the probability of the observed data given the (estimated) parameters of the network.

17.7 Discussions Bayesian networks, as well as other machine learning techniques, are rather complementary than contradictory to classical statistical approaches in analyzing data (Cunningham, 1995). Up to date, not many applications of Bayesian networks in the food area have been published. Hence, in this section, we discuss the general advantages and disadvantages of this approach in the view of using food data, as well as potential applications of Bayesian networks in food areas.

17.7.1 End-user friendly communicator Visually, Bayesian networks provide a good communication tool of mathematical relations to end-users through graphical representation. They can give fast responses to queries (inferences) once the model is completed.

17.7.2 Handle complex problems Assumptions on probabilistic dependence and independence permit Bayesian networks to model complex problems. In a large network, it would be enough to examine relations of each variable with its parent variables. Then, reasoning on learnt causal relationships can be done to predict behavior of the whole system. First, we can estimate and visualize how the “cause” influences its “effect”, i.e. forward reasoning. This feature serves to explain, as well as explore information from our system. Second, backward reasoning reveals how the “cause” should be to obtain certain desired values of its “effect”. This feature of Bayesian networks is valuable in designing food products driven by any desired characteristics or consumer demands.

17.7.3 Use of prior knowledge Learning the structure of a network is the most difficult task, especially from small datasets. Fortunately, Bayesian networks enable us to combine prior knowledge with data. In food-related problems, existing knowledge could provide information to define (at least partly) dependence relations between variables of interest.

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17.7.4 Handle incomplete datasets Gathering food data, particularly concerning human responses, is very expensive and time-consuming. Thus, typical features of food datasets are small and often incomplete (Corney, 2000). The EM-algorithm, which is one among several possible solutions, allows approximating the missing observations of one variable through the state of other variables (Heckerman, 1995). Apparently, the larger the dataset is, the more reliable are the estimated probabilities. However, there is no such criterion describing “enough data” to perform the analysis. The performance of the networks is best validated when testing with new data.

17.7.5 Discretization of continuous variables While food data often have continuous values, Bayesian network software can deal with continuous variables in only a limited manner. Hence, we need to convert continuous variables into discrete variables. This is a disadvantage of Bayesian networks due to a huge information loss, especially in linear relationships (Myllymäki et al., 2002). Furthermore, finding “the appropriate” way to discretize data is another issue. The number of intervals and the division points can lead to different results (Myllymäki et al., 2002). On one hand, the bigger the number of intervals is, the better the real relationships of variables can be captured. On the other hand, the increase of this number requires larger amount of data to estimate all the probabilities. Generally, this step is performed by domain experts based on specific goals of the modeling or other relevant information. Bayesian networks, however, are evolving very fast and promise more flexible uses of continuous data.

17.7.6 Potential applications of Bayesian networks in the food area In the food area, most models are related to chemical kinetics and microbial growth and, are based on deterministic approaches. Applications of Bayesian networks in modeling are at the early stage, and mostly concern microbial risk assessment. van Boekel (2004) has discussed Bayesian solutions with respect to the inherent variability and uncertainty in foodscience problems, from food quality–safety management to food design aspects. Food quality and safety management often involves a large number of variables, and these variables are not always observed or measured due to economical or technological constraints. Bayesian networks are suitable to handle these problems, and could be applied in building models to control different dimensions of quality, as well as to detect potential risk factors along the food chain.

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Food design is driven by consumer preference, which can be generally accessed by sensory attributes of a product. Conventional flavor and texture are widely accepted and constitute the so-called “balance” of a food. Recent efforts of the food industry, however, are to remove a large portion of saturated and trans fats, and to reduce amount of salt and sugar from food products without losing the balance in flavor and texture. This problem involves not only physical, chemical interactions of different ingredients at the food level, but also the multi-modal perception at the brain level. We can practically handle interactions at the food level. Huge uncertainty due to the lack of knowledge at the brain level, however, does not allow us to control the perception integration. Therefore, deterministic food design limits itself within various isolated contexts. Bayesian networks might be valuable in product design. First, this technique is capable to deal with uncertainty. Second, it provides a possibility to combine different related studies, which enable us to consider a complex problem as a whole. Particularly, consumer and marketing research is giving more and more attention to Bayesian networks beside Structural Equation Modeling as a conventional technique (Blodgett and Anderson, 2000, Gupta and Kim, 2007, Repères research). These two techniques have been shown to be complement each other (Gupta and Kim, 2007). The number of observations in consumer and marketing research is rather large, which enables parameter learning and possibly structure learning in Bayesian networks. From this point of view, sensory studies may encounter challenges when using Bayesian networks due to the limited sample size. However, the possibility to use prior knowledge may be of help in these cases. More modeling work with sensory data is expected to be conducted in future in order to examine the application of Bayesian networks in this field.

17.8 Sources for further information and advice “Learning Bayesian Networks” written by Neapolitan (2003) is highly recommended to readers who want to get an in-depth understanding on Bayesian networks. Besides, Heckerman (1995) wrote “A Tutorial on Learning with Bayesian networks” which highlighted well the main features and discussed technical problems. For readers who go toward applications, a short and gentle introduction “Bayesian Networks without Tears” given by Charniak (1991), or a more detailed introduction written by Murphy (1998) are advisable. Technical approaches are described in detail in “Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis” (Kjaerulff and Madsen, 2008). Particularly, the textbook “Bayesian Networks: A Practical Guide to Applications” (Pourret, Naïm and Marcot, 2008) brings in many applications in various fields.

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There are a considerable number of software packages available in open source or commercially to build Bayesian networks. They were listed and given a detailed description in (Korb and Nicholson, 2004). Here are some examples: Hugin (http://www.hugin.com/, Hugin Expert A/S) is a commercial product that supports an easy use by click-and-point procedures. Hugin can learn structure and parameters from discrete data, and also support inference on Bayesian networks having continuous variables. Hugin version 7.2, however, cannot learn parameters from continuous data. Besides, decision and utility nodes can be added to Bayesian networks, resulting in the so-called “Influence diagrams”, to support the decision-making process. Netica (http://www.norsys.com, Norsys Software Corp.) is also a widely used commercial software that supports Bayesian networks and Influence diagrams. Netica can learn only parameters and work only with discrete nodes. BayesiaLab (http://www.bayesia.com/ , Bayesia Ltd) is commercially available to learn Bayesian networks, both parameters and structure. However, discretization of continuous variables is also required. This tool does not support utility and decision nodes. Bayes Net Toolbox (http://people.cs.ubc.ca/~murphyk/Software/BNT/bnt. html, Murphy K) is a widely used and powerful mathematical software package, and runs only on Matlab. This free software supports both parameter and structure learning. gR (http://www.ci.tuwien.ac.at/gR/), a language and environment for statistical computing and graphics, provides free packages to learn Bayesian networks. Package deal (Bøttcher and Dethlefsen, 2003) can deal with both discrete and continuous variables in learning structure and parameters. This package also allows transferring information to Hugin interface.

17.9 References barker gc, talbot nlc and peck mw (2002), ‘Risk assessment for Clostridium botulinum: a network approach’, International Biodeterioration and Biodegradation, 50, 167–75. barker gc, malakar pk, del torre m, stecchini ml and peck mw (2005), ‘Probabilistic representation of the exposure of consumers to Clostridium botulinum neurotoxin in a minimally processed potato product’, Int J Food Microbiol, 100, 345–57. barnett go, famiglietti kt, kim rj, hoffer ep and feldman mj (1998), ‘DXplain on the Internet’, Proc AMIA Symp, 607–11. blodgett jg and anderson rd (2000), ‘A Bayesain network Model of the Consumer Complaint Process’, Journal of Service Research, 2, 321–38.

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bøttcher s and dethlefsen c (2003), ‘deal: A package for learning Bayesian Networks’, Journal of statistic software, 8 (20), Dec 2003. carlin f, girardin h, peck mw, stringer sc, barker gc, martinez a, fernandez a, fernandez p, waites wm, movahedi s, van leusden f, nauta m, moezelaar r, torre md and litman s (2000), ‘Research on factors allowing a risk assessment of sporeforming pathogenic bacteria in cooked chilled foods containing vegetables: a FAIR collaborative project’, Int J Food Microbiol, 60, 117–35. charniak e (1991), ‘Bayesian Networks without Tears’, AI Magazine, 12, 50–63. corney dpa (2000), ‘Designing food with Bayesian Belief Networks’, In Parmee IC, Evolutionary Design and Manufacture ACDM2000, London, Springer-Verlag, 83–94. cunningham sj (1995), ‘Machine learning and statistics: a matter of perspective’, Hamilton, New Zealand, University of Waikato, Department of Computer Science. Available from: http://waikato.researchgateway.ac.nz/handle/10289/1089 [Accessed 10 September 2009]. fearn t (2004), ‘Bayesian statistics and the agro-food production chain’, In van Boekel MAJS, Stein A and van Bruggen AHC, Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, Dordrecht Kluwer Academic Publishers, 11–16. gupta s and kim hw (2007), ‘Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities’, European Journal of Operational Research, 190, 818–33. heckerman d (1995), ‘A tutorial on learning with Bayesian networks’, Technical report MSR-TR-95-06, Microsoft Research. Available from: http://research. microsoft.com/apps/pubs/default.aspx?id=69588 [Accessed 10 September 2009]. kjaerulff ub and madsen al (2008), Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Springer. korb kb and nicholson ae (2004), ‘Appendix B – Software packages’, Bayesian Artificial Intelligence, Chapman & Hall/CRC. Available from: http://www.csse. monash.edu.au/bai/book/appendix_b.pdf [Accessed 10 September 2009]. lauritzen sl (1995), ‘The EM algorithm for graphical association models with missing data’, Computational Statistics & Data Analysis, 19, 191–201. murphy kp (1998), ‘A Brief Introduction to Graphical Models and Bayesian Networks’, Berkeley, CA, Department of Computer Science, University of California. Available from: http://people.cs.ubc.ca/~murphyk/Bayes/bnintro.html [Accessed 10 September 2009]. myllymäki p, silander t, tirri h and uronen p (2002), ‘B-Course: a web-based tool for Bayesian and causal data analysis’, International Journal on Artificial Intelligence Tools, 11, 369–408. neapolitan re (2003), Learning Bayesian Networks, Englewood Cliffs, NJ, Prentice Hall. pourret o, naïm p and marcot b (2008), Bayesian Networks: A Practical Guide to Applications, Wiley. repères research. Available from: http://reperes.eu/index.php?n=32&p=chap5 [Accessed 10 September 2009]. van boekel majs (2004), ‘Bayesian solutions for food-science problems?’, In van Boekel MAJS, Stein A and van Bruggen AHC, Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, Dordrecht, Kluwer Academic Publishers, 17–27. van boekel majs (2008), Kinetics modelling of reations in foods, CRC Press. van raaij j, hendriksen m and verhagen h (2008), ‘Potential for improvement of population diet through reformulation of commonly eaten foods’, Public Health Nutrition, 8, 1–6.

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17.10 Appendix A consumer test (n = 200) was assumed to be completed. A sample (20 cases) of the hypothetical data is shown in Table 17.1. The test was designed by 2 × 2 treatment combinations, which comprised two snack types: sweet and salty, and two eating environments: alone and with friends In each treatment condition, teenagers scored their liking for the snack, and their ad lib intake was recorded. Data in Table 17.1 were generated by Hugin software and cases (1 case = results of one subject per treatment) are listed randomly, i.e., not necessarily in order of subject, test product or eating environment. Table 17.1

Sample of snack consumption data

Case

Snack type

Eating with friends

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ..

Salty Sweet Sweet Sweet Salty Sweet Salty Sweet Salty Sweet Sweet Sweet Sweet Sweet Salty Salty Salty Sweet Sweet Salty ..

No No No No No Yes Yes Yes No Yes Yes No No No No Yes No No No No ..

Liking 90 62 50 56 75 82 88 72 81 49 69 73 91 78 55 54 83 92 71 80 ..

Liking(1) (discretized) Very much Not very much Not very much Not very much Very much Very much Very much Very much Very much Not very much Not very much Very much Very much Very much Not very much Not very much Very much Very much Very much Very much ..

Intake (g) 65.5 47.0 70.3 39.5 69.6 72.0 80.0 65.4 30.2 74.0 67.6 56.3 54.0 18.0 40.8 86.1 69.0 73.5 90.3 82.1 ..

Intake(2) (discretized) High Medium High Low High High High High Low High High Medium Medium Low Low High High High High High ..

(1) Liking scores were obtained by subjective ratings on a continuous line hedonic scale ranging from 0 (‘Not at all’) to 100 (‘Very much’). These continuous data were converted into two categories: ‘Not very much’ (value < 70), and ‘Very much’ (value > = 70). (2) Intake data are also continuous and were discretized into three categories: ‘Low’ (value < 45), ‘Medium’ (45 < = value < 65), and ‘High’ (value > = 65).

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18 Corporate social responsibility – does it matter to consumers? S. C. Beckmann, Copenhagen Business School, Denmark

Abstract: This chapter addresses the central question of what we know so far as to when, why and how consumers respond to which corporate social responsibility (CSR) activities? In order to find answers, English-language academic journals were scrutinized for the period 1990–2008i. This assessment was then used to identify crucial research issues that are of specific relevance for organizations willing to engage in CSR activities and interested in communicating their engagement to one or more of their main stakeholders. Additionally, the analysis presents an evaluation of the role of CSR in purchase decisions vis-à-vis other attributes such as price, taste, convenience, and health, and addresses the issue of consumer segmentation. The role of CSR in new product development is also addressed. A brief introduction to the history of CSR-related thinking in consumer and marketing management research provides the background necessary to understand the status quo and to assess what the future may bring in terms of consumer response to CSR. Key words: consumer behaviour research, CSR effects, new product development, literature review.

“How selfish soever man may be supposed, there are evidently some principles in his nature, which interests him in the fortune of others, and render their happiness necessary to him, though he derives nothing from it except the pleasure of seeing it.” Adam Smith, The Theory of Moral Sentiments, 1759

i

Business & Society, Journal of the Academy of Marketing Science, Journal of Business Ethics, Journal of Communication, Journal of Communication Management, Journal of Consumer Affairs, Journal of Consumer Marketing, Journal of Consumer Policy, Journal of Consumer Psychology, Journal of Consumer Research, Journal of Marketing, Journal of Marketing Communications, Journal of Public Policy & Marketing.

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18.1 Introducing the topic One of the main arguments for corporate commitment to social responsibility (CSR) is the assumption that consumers reward corporate engagement in social activities. It is embedded in the general stakeholder argument: a socially responsible company is supposed to address the concerns and satisfy the demands of its main stakeholders (e.g., Donaldson and Preston, 1995; Jones, 1995; Maignan et al., 1999; Waddock, 2000) – those actors who can, directly or indirectly, affect, or be affected by, corporate activities such as customers, suppliers, employees, shareholders, the media, investors, regulators, and interest organizations (cf., Freeman, 1984). One of the key stakeholders of companies in the marketing exchange process certainly are consumers (Folkes and Kamins, 1999; Hunt and Vitell, 1992). Yet, why do consumers perform altruistic acts such as financial contributions to charitable organizations, paying more for environmentally responsible products or even donating organs? One of the explanations is the desire to experience a “warm glow” (Andreoni, 1990), which contradicts the traditional economists’ view of people as selfish utility maximizers. The question is whether consumers also experience a “warm glow” vis-à-vis companies that perform altruistic acts and reward them through enhanced corporate reputation, improved brand image and stronger customer loyalty? However, research on the relationships between CSR activities and consumers-as-stakeholders’ perceptions, attitudes and behaviours has only been addressed in recent years. Moreover – as will be seen below – the studies investigating consumers and marketing management in this context are concerned with a wide and not necessarily coherent range of issues. Moreover, quite different approaches to what constitutes CSR are used, which not only makes it difficult to compare results across studies but also to draw a coherent picture. Additionally, studies explicitly investigating consumers’ responses to the communication of CSR are scarce, while some studies more implicitly address consumer responses or non-responses, which often are conceptualized as purchase intentions or brand evaluations.

18.2

What constitutes corporate social responsibility (CSR)?

18.2.1 Marketing and management perspectives Traditionally, and put very simply, marketing managers have conceptualized marketing performance in terms of sales, profit, and/or market share goals in relation to a particular product or service within a particular time period, taking a shareholder perspective. However, a stakeholder perspective is increasingly gaining ground, and companies have been put under growing pressure to exhibit good corporate citizenship in each country in which they operate (Pinkston and Carroll, 1994), both in marketing and general managerial terms. Public discourse indicates that companies are today more than

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ever supposed to fulfil their economic, legal, ethical, and discretionary obligations not only vis-à-vis their shareholders but increasingly also towards employees, customers, other stakeholders, and the community at large (Sen and Bhattacharya, 2001). Corporate social responsibility (CSR) has thus become a popular concept with practitioners as well as academics (Brown and Dacin, 1997; Handelman and Arnold, 1999; Osterhus, 1997) who strongly advocate that CSR activities should be regarded as the entry ticket to doing business in the 21st century (e.g., Altman, 1998). However, this suggestion is by no means new. Both the management and marketing literature have discussed social responsibility for many decades, dating back to at least the 1930s (in the USA, e.g., Berle and Means, 1932). Especially the 1960s and 1970s witnessed a strong interest that re-surfaced in regular intervals until to date. For instance, Austin (1965) argued that business leadership had to appraise the social effects of its strategic policy decisions and technological advances, not least to prevent too much governmental interference through regulations. Along similar lines, Grether (1969) suggested that social involvement of private business was necessary and should occur through the open competitive market system, thus meeting the requirements of both social performance and competitive market performance: “Inevitably, large, diversified national and multinational corporations interlinked so broadly and deeply at so many levels carry very heavy social responsibilities” (p. 41). Similar interest and concerns were raised in the marketing literature. For instance, Lazer (1969) called for a much broader understanding of the marketing concept that sees marketing responsibilities extending beyond the profit realm and as “an institution of social control instrumental in reorienting a culture from a producer’s to a consumer’s culture” (p. 3). This perspective later found resonance in the concept of market orientation (Jaworski and Kohli, 1993; Kohli and Jaworski, 1990). Similarly, Lavidge (1970) claimed that marketing not only had become broader in function and scope, but also was increasingly confronted with requests to redress irresponsibility. He also underlined the dynamics of requirements: “. . . history suggests that standards will be raised. Some practices which today are generally considered acceptable will gradually be viewed as unethical, then immoral, and will eventually be made illegal” (p. 25) – a statement that certainly holds true if one looks at the past three decades since. Another strand of the marketing literature was concerned with social marketing, i.e., the applicability of marketing concepts to the advancement of social causes (e.g., Kelley, 1971; Kotler and Zaltman, 1971). Along similar lines, cause-related marketing became a popular topic, defined as the “the process of formulating and implementing marketing activities that are characterized by an offer from the firm to contribute a specified amount to a designated cause when customers engage in revenue-providing exchanges that satisfy organizational and individual objectives” (Varadarajan and Menon, 1988, p. 60; see also Cornwell and Smith, 2001; Lafferty and

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Goldsmith, 2005; Strahilevitz, 1999). The 1980s and 1990s then saw a more managerial approach to marketing, social responsibility and business ethics (e.g., Drumwright, 1994; Robin and Reidenbach, 1987; for a meta-analysis of the marketing and consumer research literature with focus on environmental issues see Kilbourne and Beckmann, 1998). The marketing literature mainly uses the same understanding of the rationale of CSR as do other disciplines: a socially responsible company is supposed to address the concerns and satisfy the demands of its main stakeholders (e.g., Donaldson and Preston, 1995; Jones, 1995; Maignan et al., 1999; Waddock, 2000). Stakeholders in turn are defined as those actors who can, directly or indirectly, affect, or be affected by, corporate activities such as customers, suppliers, employees, shareholders, the media, investors, regulators, and interest organisations. However, what elements actually constitute CSR is less agreed upon, stretching from Carroll’s (1998) “four faces of corporate citizenship” embracing economic, legal, ethical and philanthropic components to Lantos (2001, 2002) who argues for rejecting altruistic (philanthropic) CSR, but including ethical and strategic objectives of CSR.

18.2.2 Recent perspectives on a fuzzy concept Following the majority of managerial studies and drawing on the above history of the concept, among marketers, communication scholars, and social scientists there is a general agreement that CSR is defined as the organization’s status and activities with respect to its perceived societal obligations (Brown and Dacin, 1997). Again: such a definition turns our attention to the perceptions of corporate CSR engagement in terms of how well the corporation is able to engage with its stakeholders, rather than to assessments of individual CSR activities. Hence, from a motivational viewpoint, CSR is about the extent to which a company is prepared to examine and improve its impact on all those affected by its activities and to view its long-term reputation within the context of the social and ecological sustainability of its operations (Frankental, 2001, p. 23). Many authors have highlighted the fuzziness of the concept of CSR (Morsing and Langer, 2006) and the overlaps with concepts such as responsible corporate governance (Kuhndt et al., 2004), the social enterprise, sustainability management (Kaptein and Wempe, 2001), corporate citizenship (Matten and Crane, 2005; Mirvis and Googins, 2006), corporate social responsiveness (Ackerman and Bauer, 1976), and corporate sustainability (Dyllick and Hockerts, 2002). Others have discussed whether the individualistic concept of responsibility can be applied to institutions and corporations at all (Goodpaster and Matthews, 1982). As one response, business ethics has developed a “modern” dialogue-based concept of responsibility (e.g., Homann and Blome-Drees, 1992): Accepting responsibility means to give answers to inquiries of internal and external stakeholders, to give

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account for business behaviour, and to develop a culture of transparency, reliability, and trustworthiness in a dialogue on equal terms of sovereign partners. From a managerial view, CSR can be viewed as the corporate implementation of the concepts of sustainable development and stakeholder management, and herewith as one element of sustainable management (Loew et al., 2004). Multinational institutions (such as IAO, UNO, AI and OECD), stakeholder round tables, industry associations, and corporate initiatives (e.g., the Global Compact) have worked since more than a decade on the ascertainment of this umbrella concept. They have developed indicators, measurement instruments, industry reporting standards (e.g., AA1000, SA 8000, ISO 14001), and auditing schemes. In academia, several European research consortia on CSR issues are trying to improve the theoretical foundations of the concept, to further empirical knowledge on CSR practice, and to map out policy recommendations based on that research. In a nutshell: beyond conceptual debates, political debates, and management talk, CSR is in practice well entrenched and amply funded today, and most large corporations have accepted to respond to it. The reasons for engaging in CSR and for how to communicate CSR are rooted in different factors, however.

18.3

Mapping the field of consumers’ response to corporate social responsibility (CSR)

Again, a brief historical overview assists in understanding the roots of the consumer perspective on CSR. Similar to the management and marketing literature, consumer behaviour studies – in the Anglo-Saxon literature – can be traced back to the 1970s, most of them referring to Berkowitz’s and Lutterman’s (1968) profiling of the “traditional socially responsible personality”. Typical for the marketing interest at that time, most studies focused on first demographic, later also sociographic and psychographic criteria in order to pinpoint viable consumer segments for socially responsible marketing efforts (Anderson and Cunningham, 1972; Brooker, 1976; Kinnear and Taylor, 1973; Kinnear et al., 1974; Mayer, 1976; Webster, 1975; Scherhorn and Grunert, 1988). Results of these studies were frequently inconclusive and sometimes contradictory. The “green” segment research stream nonetheless precipitated, at least for a short period, a flurry of green products, green ads, and interest in energy conservation, waste handling and recycling. Another major stream of research, beginning in the early 1980s, investigated the antecedents of socially responsible behaviours such as recycling or buying of “green” products, sometimes with the objective to develop communication campaigns to support such purchase decision and disposal behaviour. Again, results were inconclusive in developing the link between environmental attitudes and environmentally responsible

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behaviour (Balderjahn, 1988; Beckmann, 2005a, 2005b). This stream also introduced other concepts such as knowledge measurements, motivation, peer influence, cost-benefit analysis, and financial incentives as variables to the study designs (see both Kilbourne and Beckmann, 1998, and Ölander and Thøgersen, 1995 for reviews).

18.3.1 CSR in consumer behaviour studies The majority of consumer behaviour research is grounded in the cognitive information-processing paradigm and assesses – more or less explicitly – the antecedents, correlates and consequences of the following principal stages in the consumer decision-making process: need recognition, information search, evaluation of alternatives, purchase, post-purchase activities and evaluation of experiences with the product or service. In the context of CSR, these stages are conceptualized as follows: • Need recognition refers to consumers’ awareness of and interest in companies’ CSR activities as an additional product attribute. Reasons for interest could be, for instance, pro-environmental attitudes, human rights concerns or beliefs that purchase decisions have political impact. • Both information search, actively and passively, and the evaluation of alternatives are influenced by attitudes and beliefs concerning product, brand and/or company. Attitudes and beliefs, in turn, are certainly influenced by personal, non-commercial and commercial sources of information. • Purchase is most often measured as purchase intentions, in this case of products and services from companies engaging in CSR activities. • Experiences with purchased products and services are insofar relevant, as negative experiences for instance concerning quality expectations might counterbalance pro-CSR attitudes and hence decrease consumer loyalty. Very few studies in the consumer/CSR context, however, address this sequence explicitly. In most cases, one or two stages and a selection of their corresponding concepts are investigated – by both qualitative and quantitative methods. It is also important to point out that the stages can be iteratively linked and that some of the concepts are not necessarily related in a clear-cut cause-effect sequence. Both aspects are rarely addressed. Some other limitations also apply: in many of the studies only certain aspects of CSR activities are investigated, thus providing a limited picture of consumer responses to CSR. Rarely is the whole spectrum of activities addressed, which could either indicate that most companies do not engage in the full range of CSR or that they are only known for a limited set of CSR activities. From a methodological perspective, the reason could also be that study design becomes too complicated to deliver valid and reliable results if the full range were to be studied. Another important caution

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concerns the fact that most studies reviewed here have been conducted in the USA, which for cultural, political and historical reasons limits the generalizability of their findings to a European or even Scandinavian setting (cf. Morsing and Langer, 2006, see also Matten and Moon, 2004). The central findings from the literature review are summarized in Table 18.1. Admittedly, this is a rather coarse and simplified picture since especially experimental findings do not lend themselves easily to a summarizing exercise. Moreover, it was not possible to group these studies according to the above outlined consumer decision-making model. The table therefore reflects the somewhat muddy state of affairs of current research into consumers’ response to CSR (and thereby the complexity not only of the concept at hand but also of human beings). Nonetheless, the various findings can be – in terms of the above model of decision-making stages – summarized as follows: • ”Need recognition” (awareness, knowledge and interest): the majority of respondents confess to interest in CSR issues, but there is considerable heterogeneity among consumers in terms of awareness and knowledge of companies’ CSR activities. The majority of consumers seem not to be aware that by and large many companies engage in at least some kind of CSR activities. And other consumers are sceptical or even cynical about companies’ CSR communication. • Information search and evaluation of alternatives/attitudes and beliefs: in general, consumers have a favourable attitude towards companies that engage in CSR. Several aspects, however, complicate the picture – overall company reputation, the fit between company and cause, personal connection to the cause that is represented by the company’s CSR activity, distinction between proactive and reactive CSR initiatives, product quality and price. And it goes for almost all instances that the relationship between expressed attitudes and active consumer choice is weak. • Purchase (intentions): most consumers are unwilling to compromise on core attributes such as price and quality. However, a company’s proactive stance towards CSR functions as an “insurance policy” in, for instance, product-harm crises. Similarly, consumers appear to be more resilient to negative information about a CSR committed company and stay loyal when there is an occasional lapse on its part. Additionally, consumers are obviously more sensitive to unethical than to responsible behaviour, i.e. “doing bad” hurts more than “doing good” helps. • Post-purchase experiences: Since the majority of consumers, as stated above, trade off CSR features for “traditional” attributes, a negative experience with product or service quality will in most cases backfire and thus prevent re-purchase despite CSR activities. All these findings are complicated by the fact that there are individual, social and national differences that cut across the stages and concepts

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Surveys from MORI databases Questionnaire cum choice task

Britain USA

In-depth interviews Questionnaire cum choice task Field experiment (web survey) Scenario design cum questionnaire Questionnaire cum 3 conditions Questionnaire cum 3 conditions Telephone interview (survey) Experiment Questionnaire cum 3 conditions Questionnaire cum 3 conditions Experiment Experiment

USA

USA USA USA

USA

USA USA ? ? USA USA USA

Corporate associations is an important aspect

CSR as “insurance policy” in crisis situations

USA

Questionnaire lab-type Questionnaire lab-type Experiment in shopping mall Survey Experiment Experiment cum questionnaire Experiment cum questionnaire Experiment

USA

Survey Web-based questionnaire Experiment in shopping mall

USA Slovenia USA

Consumers are aware of and interested in CSR

CSR increases positive attitudes towards the company and/or the brand

Method(s)

Country

Central findings from the literature review

Main findings

Table 18.1

Einwiller et al., 2006

Coombs & Holladay, 2006 Dawar & Pillutla, 2000 Klein & Dawar, 2004 Ricks, 2005

Undergraduate students (n = 49) Undergraduate students (n = 81) Instant coffee consumers (n = 178) Undergraduate students (n = 171) Mall shoppers (n = 150) Mall shoppers (n = 150) Non-student adults (n = 293) Non-student adults (n = 159)

Murray & Vogel, 1997 Mohr et al., 2001 Mohr & Webb, 2005 Sen et al., 2006

Lichtenstein et al., 2004

Mall shoppers (n = 210)

Consumers (n = 48) Non-student adults (n = 194) Students (n = 1075)

Undergraduates (n = 163) Undergraduates (n = 127) Mall shoppers (n = 229) Supermarket customers (n = 508) Students (n = 61) Not revealed “Subjects” (n = 115) MBA students (n = 82)

Brown & Dacin, 1997

Creyer & Ross, 1997 Golob et al., 2008 Handelman & Arnold, 1999 Lewis, 2003 Mohr & Webb, 2005

Parents (n = 280) Company employees (n = 350) Mall shoppers (n = 216) general population (n > 1000) Non-student adults (n = 194)

Source

Respondents

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Survey cum choice experiment

Depth interviews, focus groups and video ethnography 2 Focus groups 2 Focus groups

USA ? ?

Australia, Hong Kong

8 countries

UK

Consumers’ attitudes are more affected by unethical behaviour than by pro-CSR behaviour

Lack of knowledge, awareness and/or concern – and very little knowledge about which companies are CSR committed or not

UK

Questionnaire

USA

Questionnaire

USA

CSR activities have positive spill-over effects to strategic alliances (sponsorships, co-branding, not-for profit) Experiment

Internet-based experiment

The Netherlands

Corporate ability and CSR trade-off

USA

Web-based questionnaire Telephone interviews Survey

USA Spain Spain

CSR may increase loyalty

Auger et al., 2003

MBA students (Australia, n = 162) Undergraduate students (Hong Kong, n = 111) Amnesty International supporters (Australia, n = 172) Consumers (n = 8 × 20)

(n = 10), divided by gender

Not revealed

Boulstridge & Carrigan, 2000 Carrigan & Attalla, 2001

Belk et al., 2005

Folkes & Kamins, 1999

Undergraduate students (n = 172) Not revealed (n = 52) Students (n = 42)

Non-student adults (n = 225)

Cornwell & Smith, 2001 Lafferty & Goldsmith, 2005 Ross et al., 1992

Berens et al., 2007

Students (n = 112) Breast cancer survivors and race participants (n = 196) Students (n = 463)

Du et al., 2007 Marin et al., 2009 Salmones et al., 2005

Panelists (n = 3465) Bank customers (n = 400) Mobile phone users (n = 689)

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USA

Consumers’ support of the CSR domain chosen by the company matters

Web-based questionnaire Open-ended questionnaire Survey experiment Questionnaire Questionnaire (vignette design) Questionnaire 2 Focus groups 2 Focus groups In-depth interviews Questionnaire cum choice task Experiment Questionnaire cum experiment Experiment Field experiment Survey Survey

USA USA USA

7 countries Denmark

Denmark UK

UK

USA

USA USA USA

USA USA

CSR has a positive effect on purchase intention

Trade-off effects in favour of traditional decision criteria (“old habits die hard”)

Product category and/ or price play a role

Little willingness to pay more: the effect of personal costbenefit analyses

?

Method(s)

Country

Continued

Main findings

Table 18.1

Lichtenstein et al., 2004

Supermarket customers (n = 508) Students (n = 61) Not revealed “Subjects” (n = 115) Students (n = 277)

Andreu et al., 2005 Beckmann et al., 2001 Beckmann, 2004 Boulstridge & Carrigan, 2000 Carrigan & Attalla, 2001 Mohr et al., 2001 Mohr & Webb, 2005 Strahilevitz, 1999 Strahilevitz & Myers, 1998 Creyer & Ross, 1997 Osterhus, 1997

Students (total n = 152) Convenience sample (n = 106)

Non-student adults (n = 194) Undergraduate students (n = 208) Undergraduate students (n = 150) Undergraduate students (n = 264) Undergraduate students (n = 120) Parents (n = 280) Non-student adults (n = 1128, 798)

Consumers (n = 48)

(n = 10), divided by gender

Graduate students (n = 839) Not revealed

Du et al., 2007 Ellen et al., 2006

Panelists (n = 3465) Students (n = 281) University employees (n = 193)

Sen & Bhattacharya, 2001

Source

Respondents

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National and cultural differences do exist

Pro CSR consumers do exist, but profiling them is difficult

Consumers distinguish between personal and social consequences of ethical/unethical company behaviour

Scepticism and cynicism concerning corporate CSR (communication) Questionnaire cum choice Consumer decisions questionnaire Consumer decisions questionnaire Questionnaire Survey cum choice experiment

Survey In-depth interviews Survey Questionnaire Depth interviews, focus groups and video ethnography Survey

Europe

WWW WWW

Australia, Hong Kong

USA

USA USA

7 countries 8 countries

USA, France, Germany

?

In-depth interviews Survey

USA ?

Baron, 1999

“Subjects” (n = 50) “Subjects” (n = 48)

Andreu et al., 2005 Belk et al., 2005 Maignan & Ferrell, 2003

Students (total n = 152) Consumers (n = 8 × 20) Employees in financial sector (n = 145 USA, n = 169 France, n = 94 Germany)

Consumers (n = 48) Consumers (n = 582)

Auger et al. 2003

MBA students (Australia, n = 162) Undergraduate students (Hong Kong, n = 111) Amnesty International supporters (Australia, n = 172) Women (n = 912)

Hustad & Pessemier, 1973 Mohr et al., 2001 Roberts, 1995

Pitts et al., 1991

Students (n = 257)

Students (n = 379)

Mohr et al., 2001 Sen & Bhattacharya, 2001 Swaen & Vanhamme, 2004

Consumers (n = 48) Students (n = 277)

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associated with them. And unfortunately they do not relate in a simple, straightforward manner to, for instance, demographics such as gender and age or one’s socioeconomic position in society. Nor can they be predicted conclusively from situational factors such as the product, price or purchase environment. Moreover, individual differences also may involve selectively ethical interests: the same consumer choosing a brand because it is environmentally responsibly produced may be unaware of or disinterested in issues such as fair worker treatment and racial discrimination. In terms of cross-national differences, the few studies that looked into this issue indicate that the differences to a large extent relate to the development stage of CSR in the respective countries. While for instance Dutch consumers regard CSR as a hygiene factor (Meijer and Schuyt, 2005) and Danish students reject an annual environmental report as significant CSR activity (Beckmann, 2004), South American students consider all kinds of CSR actvities as highly significant (Andreu et al., 2005). One interesting result, which emerged in several studies, indicates that an important predictor of ethical consumer behaviour is past behaviour relating to social causes – in other words an anti-nuclear energy activist in the late 1970s becomes an organic produce consumer in the 1980s and a CSR rewarding customer in the 1990s. Consumer attitudes are then one possible foundation on which to construct a segmentation that may inform marketing and strategic CSR communication. Along similar lines, and on the basis of their focus groups with UK consumers, Carrigan and Attalla (2001) suggest that there are four consumer segments. “Caring and ethical” consumers are those who seek out information on CSR and act according to their attitudes. They are the most likely to respond to strategic CSR communication. Engaging the “confused and uncertain” in a dialogue is probably rather difficult, as they are interested but remain bewildered by the lack of guidance and contradictory information about CSR. The “cynical and disinterested” are also hard to address, since they are not convinced that companies are truly socially responsible and, moreover, value other attributes such as price, quality and convenience as least as high as CSR. Finally the “oblivious” are mainly a lost cause since they are unaware of CSR as such. However, a change in life circumstances may trigger interest, for instance new mothers who have been previously unaware of Nestlé’s activities in relation to baby food may then seek out other brands and companies. This segmentation corresponds to some degree to findings from the US where Mohr et al. (2001) explored how much consumers really care about a company’s level of social responsibility. On the basis of 44 semistructured interviews they grouped consumers into four categories following Andreasen’s (1995) model of stages in behaviour change: pre-contemplation, contemplation, action, and maintenance. While precontemplators (34% of the sample) are not considering CSR in purchase decisions (and a few of them are even opposed to CSR), contemplators (26%) are thinking about

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it or have done so in the past, but CSR is still not an important criterion. The action-oriented (18%) have decided to base at least some of their buying on CSR considerations, and maintainers (22%) use the CSR criterion in much of their purchasing. Precontemplators are similar to the “cynical and disinterested” and “oblivious” consumers of Carrigan and Attalla, contemplators resemble the “confused and uncertain” and action-oriented and maintainers look quite like the “caring and ethical”. In a nutshell, as with all other aspects of consumer feeling, thinking and acting, there is a plethora of attitudes, beliefs and behaviours vis-à-vis CSR, which renders simple segmentation, especially one based on sociodemographic characteristics, basically impossible. Hence, capitalizing on consumers’ interest in CSR is only possible with careful market and target group analysis that takes into account the complexity of consumers’ responses to various initiatives. And which does not neglect that in general, consumer interest in CSR seems not to be very strong (Sen et al., 2006), despite opinions expressed in polls and media hype. This conclusion has of course consequences for corporate decisions on how to deal with this complexity and to what extent to engage in CSR activities. By and large, CSR basics are regarded as a standard company task without being given special credit – and any additional activities need to be credible and fit with consumers’ corporate associations and expectations. Any new developments in products and services or brand extensions thus have to be plausible in the light of a company’s history and image.

18.4

New product development and corporate social responsibility (CSR)

An increasing number of industry actors increasingly seek to integrate humane, ethical, transparent and sustainable practices as general components into their business activities (Van Marrewijk, 2003; Schmidt, 2001). Not least the increased greening of markets (Pujari et al., 2004), the focus on fair trade (Nicholls, 2002, 2004) and environmental technology innovation, leads businesses to change their organizational modus operandi, where product and service strategies are more and more converted into life-cycle strategies (Menon and Menon, 1997; Schmidt, 2001). This shift is illustrated through a comparison of the 1990’s management systems that focused on doing things right the first time, e.g. TQM (Total Quality Management) and EFQM (European Foundation for Quality Management) systems, with CSR related systems that are oriented towards doing the right things (Zwetloot, 2003). Hence, notions within the CSR concept such as triple-bottom lines (people, planet and profit) and sustainable development (Steurer et al., 2005) are gradually becoming part of the firm’s value chain activities. CSR, when used actively, was formerly at the heart of firm specific activities such as marketing communication and production processes (Partidário et al.,

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2006). However, with an increasing demand on sustainable long-term innovation, more attention is now given to new product development issues. In the context of CSR, new product development should seek to integrate socially responsible business behaviour with corporate innovation processes. NDP is a process of information transmission and coordination among knowledge intensive units to create a ‘complex synthesis of specialized technologies and knowledge domains which result in products that comprise a value for customers’ (Graafland, 2003; Kogut and Zander, 1992; Krogh et al., 2000; Shani et al., 2003). Interestingly, one of the first articles on this topic was already published by Varble (1972) who emphasized the necessity of minimizing negative externalities. NDP within the CSR framework also depends on the firm’s ability to engage external stakeholders whose expertise complements those of the organization (Polonsky and Ottman, 1998). Furthermore, NPD in conjunction with CSR relies on the premise that the investment in socially responsible and ethical product innovations undertaken by the organization will cause increased sales, profit and customer satisfaction (Luo and Bhattacharya, 2006). Thus, in order for NDP to affect corporate performance requires the intricate task of addressing and balancing stakeholder demands (Orlitzky et al., 2003). Within the area between NDP and CSR activities, recent academic studies on the field have investigated product development for consumers and organizational buying of green products and services (Polonsky and Ottman, 1998; Polonsky et al., 1998). Most of the research has been oriented towards socially responsible consumers and focused on the environmental part of CSR. Limited attention, however, has been given to study how industrial firms, whose purchases outnumber those of consumers, address CSR areas such as sustainability in their NPD processes (Pujari et al., 2004). The management processes that reflect the most appropriate approach to NDP also vary. As suggested above, most often changes in corporate strategies focus on long-term life cycle solutions. Schmidt (2001) argues that the most appropriate method assumes both heavy corporate investment in value chain activities such as production and R&D, but also more soft approaches such as networking and stakeholder management, since they are pivotal in creating sustainable and financially feasible NDP outcomes. Finally, it should be noted that no empirical studies could be found in our literature research that assess consumers’ response to NPD activities within the CSR framework. Given the increasing numbers of companies inviting their customers to actively participate in new product development, often via online communities, filling this void obviously is only a question of time.

18.5. Future trends Reviewing the past decade of research into consumers, marketing and CSR, it can safely be stated that the effects of CSR initiatives are anything but

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straightforward and depend on a number of factors that are intertwined in a complex manner: consumers’ interest in some CSR and disinterest in other CSR activities (which in turn is grounded in values held by citizen-consumers), information and knowledge level, consumer-company congruence, relevance of other product/brand attributes, evaluations of trade-offs between CA (corporate associations) and CSR, and perceived credibility of information source. Furthermore, there are national and/or cultural differences that suggest a strong influence of the economic, technological, political and social context within which any assessment of the effects of CSR activities on consumers’ responses, be it attitudes or behaviour, need to be analysed. So the answer to the introductory question of whether consumers experience a “warm glow” vis-à-vis CSR committed companies is: yes, quite a few consumers feel positive, and yes, they will reward these companies, though much more in an intangible – enhanced corporate reputation and brand image – than a tangible manner that is directly reflected in the bottom-line. The influence of CSR activities on consumer behaviour is much more complex and tentative than its effects on their attitudes and beliefs. Moreover, consumers are more sensitive to negative CSR information than to positive CSR information, thus increasing the risk of boycott in events of perceived social irresponsibility (Beckmann and Langer, 2003). A review by Valor (2008) even concludes that current market failures prevent consumers from rewarding CSR activities, mainly because consumers see responsible consumption as time consuming, economically disadvantageous and stressful. Based on the above discussion, there is so far no definite answer as to what marketing management and marketing communications can contribute in the context of CSR and consumers. It is, however, evident that consumers do react to CSR initiatives and that they even expect companies to act socially responsible. As regards CSR-based new product development, the general trend of increasing environmental awareness, climate change concerns and apprehension of human rights’ violations certainly enhances the acceptance of new products and services that take these issues into account. Since acceptance is the necessary though not sufficient precondition for image improvement followed by purchase intention, the future for CSR-based new product development looks rather bright. Yet there is more we do not know than what we know, and many questions arise that essentially refer to the three dimensions outlined below. Who are they? Valid knowledge about the target group and target audiences is necessary in order to establish whether there is congruence between actual CSR efforts and consumers’ interests and concerns. Better understanding is needed of the antecedents and context of consumer’s potential ethical purchase behaviour and the extent to which they are prone to make trade-offs

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as regards other product/service attributes. This is particularly important in the context of innovative CSR-based new product development. Recognizing the effects of other influential stakeholders like the media that may impact perceptions about CSR efforts is equally important. What should be communicated and how? As suggested above, knowledge about the target group is relevant in establishing which ethical issues are top-of-mind for a given segment and how they rank these issues in importance. Similarly, it is important to establish under which conditions CSR has positive, negative and neutral effects on the purchase behavior of the target group. Moreover, it has to be determined which media channels offer the greatest credibility and trustworthiness when conveying messages about CSR efforts. Finally, it needs to be assessed how consumers will react to communication about CSR-based NPD such as sourcing of ingredients that are Fair Trade or environmental concerns in production and distribution. How well aligned is company performance, communication performance and CSR performance? Company performance relates to several different measures and the challenges therefore are to secure a good reputation through consistency across performance indicators. This entails the assessment of whether the fit between the provided value and CSR cause chosen by the organization is logical, trustworthy and convincing. Similarly, NPD has to fit into this picture. Lack of consistency may also be caused by a negative company history that colours the perception of the present alignment or attempts for alignment by the organization.

18.6 References and further reading ackerman, r. w. & bauer, r. a. (1976). Corporate social responsiveness: The modern dilemma. Reston, VI: Reston Publishing Company. altman, b. w. (1998). Transformed corporate community relations: A management tool for achieving corporate citizenship. Business and Society Review, 102/103, 43–51. anderson, t. w. & cunningham, w. h. (1972). The socially conscious consumer. Journal of Marketing, 36(3), 23–31. andreasen, a. r. (1995). Marketing Social Change – Changing Behaviour to Promote Health, Social Development and the Environment. San Francisco, CA: Jossey-Bass. andreoni, j. (1990). Impure altruism and donations to the public good – A theory of warm glow giving. Economic Journal, 100(401), 464–477. andreu, l., beckmann, s. c., bigné, e., chumpitaz, r. & swaen, v. (2005). An international comparison of CSR perceptions. In: DeMoranville, Carol W. (Ed.), Proceedings of the 12th World Marketing Congress – Marketing in an interconnected World: Opportunities and challenges, Academy of Marketing Science, Münster.

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auger, p., burke, p., devinney, t. m. & louviere, j. j. (2003). What will consumers pay for social features? Journal of Business Ethics, 42, 281–304. austin. h. w. (1965). Who has the responsibility for social change – business or government? Harvard Business Review, July-August, 45–52. balderjahn, i. (1988). Personality variables and environmental attitudes as predictors of ecologically responsible consumption patterns. Journal of Business Research, 17, 51–56. baron, j. (1999). Consumer attitudes about personal and political action. Journal of Consumer Psychology, 8(3), 261–275. beckmann, suzanne c. (2004). Corporate Social Responsibility – Danish business students’ perspective. In: Proceedings of the 33rd European Marketing Academy (EMAC) Conference. Murcia: University of Murcia. beckmann, s. c. (2005a). In the eye of the beholder: Danish consumer-citizens and sustainability. In: Grunert, Klaus G. & Thøgersen, John (Eds.), Consumers, policy and the environment, pp. 265–300. New York, NY: Springer. beckmann, s. c. (2005b). Information, consumer perceptions, and regulations: The case of organic salmon. In: Krarup, Signe (Ed.), Environment, information and consumer behaviour, pp. 197–215. Cheltenham, UK: Edward Elgar. beckmann, s. c. & langer, r. (2003). Consumer-citizen boycotts: Facilitators, motives and conditions. In: Proceedings of the 32nd European Marketing Academy (EMAC) Conference. Glasgow: University of Strathclyde. beckmann, s. c., christensen, a. s. & christensen, a. g. (2001). “Myths of nature” and environmentally responsible behaviours: An exploratory study. In: Proceedings of the 30th European Marketing Academy Conference. Bergen: Norwegian School of Management. beckmann, s. c., morsing, m. & reisch, l. a. (2006). Strategic CSR communication: An emerging field. In: Morsing, Mette & Beckmann, Suzanne C. (Eds.) (2006). Strategic CSR information, pp. 11–36. Copenhagen: DJØF Publisher. belk, r. w., devinney, t. & eckhardt, g. (2005). Consumer ethics across cultures. Consumption, Markets and Culture, 8(3), 275–289. berens, g., van riel, c. b. m. & van rekom, j. (2007). The CSR-Quality trade-off: When can corporate social responsibility and corporate ability compensate each other? Journal of Business Ethics, 74, 233–252. berkowitz, l. & lutterman, k. g. (1968). The traditional socially responsible personality. Public Opinion Quarterly, 32, 169–182. berle, a. a. & means, g. c. 1944 (1932). The modern corporation and private property. NY: Macmillan. bhattacharya, c.b. & sen, s. (2004). Doing better at doing good: When, why, and how consumers respond to corporate social initiatives. California Management Review, 47(1), 9–24. boulstridge, e., & carrigan, m. (2000). Do consumers really care about corporate responsibility? Highlighting the attitude-behaviour gap. Journal of Communication Management, 4(4), 355–368. brooker, g. (1976). The self-actualizing socially conscious consumer. Journal of Consumer Research, 3, 107–112. brown, t. j., & dacin, p. a. (1997). The company and the product: Corporate associations and consumer product responses. Journal of Marketing, 61(1), 68–84. carrigan, m. & attalla, a. (2001). The myth of the ethical consumer – do ethics matter in purchase behaviour? Journal of Consumer Marketing, 18(7), 560–577. carroll, a. b. (1998). The four faces of corporate citizenship. Business and Society Review, 100/10, 1–7. coombs, w. t. & holladay, s. j. (2006). Unpacking the halo effect: reputation and crisis management. Journal of Communication Management, 10(2), 123–137.

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cornwell, t. b. & smith, r. k. (2001). The communications importance of consumer meaning in cause-linked events: findings from US events for benefiting breast cancer research. Journal of Marketing Communications, 7, 213–229. creyer, e. h., & ross, w. t. (1997). The influence of firm behavior on purchase intention: Do consumers really care about business ethics? Journal of Consumer Marketing, 14(6), 421–432. dawar, n. & pillutla, m. (2000). Impact of product-harm crisis on brand equity: The moderating role of consumer expectations. Journal of Marketing Research, 37(May), 215–226. donaldson, t., & preston, l. e. (1995). The stakeholder theory of the corporation: Concepts, evidence, and implications. Academy of Management Review, 20(1), 65–91. drumwright, m. e. (1994). Socially responsible organizational buying: Environmental concern as a noneconomic buying criterion. Journal of Marketing, 58, July, 1–19. du, s., bhattacharya, c. b. & sen, s. (2007). Reaping relational rewards from corporate social responsibility: the role of competitive positioning. International Journal of Research in Marketing, 24(3), 224–241. dyllick, t. & hockerts, k. (2002). Beyond the business case for corporate sustainability. Business Strategy and the Environment, 11(2), 130–141. einwiller, s., fedorikhin, a., johnson, a. r. & kamins, m. a. (2006). Enough is enough! When identification no longer prevents negative corporate associations. Journal of the Academy of Marketing Science, 34(2), 185–194. ellen, p. s., webb, d. j. & mohr, l. a. (2006). Building corporate associations: consumer attributions for corporate socially responsible programs. Journal of the Academy of Marketing Science, 34(2), 147–157. folkes, v. s. & kamins, m. a. (1999). Effects of information about firms – ethical and unethical actions on consumer attitudes. Journal of Consumer Psychology, 8(3), 243–59. frankental, p. (2001) Corporate social responsibility – a PR invention? Corporate Communications, 6(1), 18–23. freeman, r. e. (1984). Strategic Management: A Stakeholder Approach. Academy of Management Review, 24, 233–236. golob, u., lah, m. & jani, z. (2008). Value orientations and consumer expectations of corporate social responsibility. Journal of Marketing Communications, 14(2), 83–96. goodpaster, k.e. & matthews, j.b. jr. (1982). Can a corporate have a conscience? Harvard Business Review, 60(1), 132–142. graafland, j. j. (2003). Distribution of responsibility, ability and competition. Journal of Business Ethics, 45, 133–147. grether, e. t. (1969). Business Responsibility toward the Market. California Management Review, 12(1), 33–42. handelman, j. m., & arnold, s. j. (1999). The role of marketing actions with a social dimension: Appeals to the institutional environment. Journal of Marketing, 63(July), 33–48. homann, k. & blome-drees, f. (1992). Wirtschafts- und Unternehmensethik. Göttingen, UTB. hunt, s. d. & vitell, s. j. (1992). The general theory of marketing ethics: a retrospective and revision. In: Smith, N.C. and Quelch, J.A. (Eds.), Ethics and Marketing, pp. 775–784. Homewood, IL: Irwin. hustad, t. p. & pessemier, e. a. (1973). Will the real consumer activist please stand up: An examination of consumers’ opinions about marketing practices. Journal of Marketing Research, 10(Aug), 319–24.

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jaworski, j. & kohli, a. k. (1993). Market orientation: Antecedents and consequences. Journal of Marketing, 57(3), 53–70. jones, t. m. (1995). Instrumental stakeholder theory: A synthesis of ethics and economic. Academy of Management Review, 20(2), 404–437. kaptein, m. & wempe, j. (2001). Sustainability management. Balancing conflicting economic, environmental and social corporate responsibilities. The Journal of Corporate Citizenship, 2(Summer), 91–106. kelley, h. h. (1971). Attribution in Social Interaction. New York: General Learning Press. kinnear, t. c. & taylor, j. r. (1973). The effect of ecological concern on brand perceptions. Journal of Marketing Research, 10, 191–197. kinnear, t. c., taylor, j. r. & ahmed, s. a. (1974). Ecologically concerned consumers: Who are they? Journal of Marketing, 38, 20–24. kilbourne, w. e. & beckmann, s. c. (1998). Review and critical assessment of research on marketing and the environment. Journal of Marketing Management, 14(6), 513–532. klein, j., & dawar, n. (2004). Corporate social responsibility and consumers’ attributions and brand evaluations in a product-harm crisis. International Journal of Research in Marketing, 21(3), 203–217. kohli, a. k. & jaworski, j. (1990). Market orientation: The construct, research propositions, and managerial implications. Journal of Marketing, 54, April 1–18. kogut, b. & zander, u. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383–397. kotler, p. & zaltman, g. (1971). Social Marketing: An approach to planned social change. Journal of Marketing, 35 (July), 3–12. krogh, v. g., ichijo, k., & nonaka, i. (2000). Enabling Knowledge Creation. New York, NY: Oxford University Press. kuhndt, m., tuncer, b., snorre andersen, k. & liedtke, c. (2004). Responsible corporate governance. An overview of trends, initiatives and state-of-the-art elements. Wuppertal Papers No. 139. Wuppertal: Wuppertal Institute of Climate Environment Energy. lafferty, b. a. & goldsmith, r. e. (2005). Cause-brand alliances: does the cause help the brand or does the brand help the cause? Journal of Business Research, 5, 423–429. lantos, g. p. (2001). The boundaries of strategic corporate social responsibility. Journal of Consumer Marketing, 19(3), 205–230. lantos, g. p. (2002). The ethicality of altruistic corporate social responsibility. Journal of Consumer Marketing, 18(7), 595–630. lavidge, r. j. (1970). The growing responsibilities of marketing. Journal of Marketing, 34(Jan), 25–28. lazer, w. (1969). Marketing’s changing social relationships. Journal of Marketing, 33(Jan), 3–9. lewis, s. (2003). Reputation and corporate responsibility. Journal of Communication Management, 7(4), 356–364. lichtenstein, d. r., drumwright, m. e. & braig, b. m. (2004). The effect of corporate social responsibility on customer donations to corporate-supported nonprofits. Journal of Marketing, 68(4), 16–32. loew, t., ankele, k., braun, s., & clausen, j. (2004). Bedeutung der CSR-Diskussion für Nachhaltigkeit und die Anforderungen an Unternehmen. Final Report. Berlin: future e.V. & IÔW.r luo, x. & bhattacharya c. b. (2006). Corporate social responsibility, customer satisfaction, and market value. Journal of Marketing, 70, 1–18.

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maignan, i. & ferrell, o. c. (2003). Nature of corporate responsibilities: Perspectives from American, French, and German consumers. Journal of Business Research, 56(1), 55–67. maignan, i., ferrell, o. c. & hult, g.t.m. (1999). Corporate Citizenship: Cultural antecedents and business benefits. Journal of the Academy of Marketing Science, 27(4), 455–69. marin, l., ruiz, s. & rubio, a. (2009). The role of identity salience in the effects of corporate social responsibility on consumer behaviour. Journal of Business Ethics, 84, 65–78. matten, d. & crane, a. (2005). Corporate citizenship: Toward an extended theoretical conceptualization. Academy of Management Review, 30(1), 166–179. matten, d. & moon, j. (2004). A conceptual framework for understanding CSR. In: Habisch, A., Jonker, J., Wegner, M. and Schmidpeter, R. (Eds.), Corporate Social Responsibility Across Europe, p. 335–356. mayer, r. n. (1976). The socially conscious consumer – another look at the data. Journal of Consumer Research, 3(2), 113–115. meijer, m. & schuyt, t. (2005). Corporate social performance as a bottom-line for consumers. Business & Society, 44, 442–461. menon, a. & menon, a. (1997). Environmental marketing strategy: The emergence of corporate environmentalism as market strategy. Journal of Marketing, 61, 51–67. mirvis, p. h. & googins, b. (2006). Stages of Corporate Citizenship. California Management Review, 48(2), 104–126. mohr, l. a. & webb, d. j. (2005). The effects of corporate social responsibility and price on consumer responses. The Journal of Consumer Affairs, 39(1), 121–147. mohr, l. a., webb, d. j. & harris, k. e. (2001). Do consumers expect companies to be socially responsible? The impact of corporate social responsibility on buying behavior. The Journal of Consumer Affairs, 35(1), 45–72. morsing, m. & langer, r. (2006). CSR communication in the business press: Advantages of strategic ambiguity. In: Proceedings of the 11th International Conference on Corporate and Marketing Communications. Ljubljana/Slovenia: Faculty of Social Sciences, University of Ljubljana. murray, k. b., & vogel, c. m. (1997). Using a hierarchy-of-effects approach to gauge the effectiveness of corporate social responsibility to generate goodwill toward the firm: Financial versus non financial impacts. Journal of Business Research, 38, 141–159. nicholls, a. (2002). Strategic options in Fair Trade retailing. International Journal of Retail and Distribution Management, 30(1), 6–17. nicholls, a. (2004). Fair Trade New Product Development. The Service Industries Journal, 24(2), 102–117. ölander, f. & thøgersen, j. (1995). Understanding of consumer behaviour as a prerequisite for environmental protection. Journal of Consumer Policy, 18(3), 345–385. orlitzky, m., schmidt f. l., & rynes s. l. (2003). Corporate social and financial performance: A meta-analysis. Organization Studies, 24(3), 403–441. osterhus, t. l. (1997). Pro-Social Consumer Influence Strategies: When and How Do They Work? Journal of Marketing, 61(4), 16–29. partidário, p. j., lambert j., & evans, s. (2006 ). Building more sustainable solutions in production-consumption systems: The case of food for people with reduced access. Journal of Cleaner Production, 15, 513–524. pinkston, t. s., & carroll, a. b. (1994). Corporate citizenship perspectives and foreign direct investment in the US. Journal of Business Ethics, 13(2), 157–169. pitts, r. e., wong, j. k. & whalen, d. j. (1991). Consumers’ evaluative structures in two ethical situations: A means-end approach. Journal of Business Research, 22, 119–130.

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19 Anti-consumption: a cause for concern in the food and personal care products sectors? M. S. W. Lee, The University of Auckland Business School, New Zealand

Abstract: Anti-consumption is a concept that has been around for as long as consumption has existed; however, research focusing on reasons against consumption has been scarcer than its counterpart. This chapter explores four reasons motivating anti-consumption: innovation resistance, risk aversion, undesired self and voluntary simplicity. Two case studies in food packaging (bottled water) and food technology (genetic modification) are used to illustrate the practical relevance of these four anti-consumption literatures to the fast moving consumer goods industry. This chapter argues that anticonsumption is a cause for concern amongst new product developers, who often have the dominant perspective that consumers want increased consumption and innovation. It further argues that even knowledge of fringe movements such as anti-consumption is of substantial importance to product developers. Key words: anti-consumption, innovation resistance, risk aversion, undesired self, voluntary simplicity.

19.1 Introduction This chapter aims to inform new product developers that anti-consumption perspectives do exist amongst some consumers. This fresh perspective will enable new product developers to understand consumers who may not desire innovative products and increased consumption but, rather, may prefer to reduce or resist consumption of new products. Knowledge gained from examining anti-consumption will help new product developers to improve existing products or create more appropriate products, thereby reducing the resistance towards, and failure of, new products.

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The first section introduces the anti-consumption concept. The second section takes an in-depth look at the various reasons motivating anticonsumption. These reasons are explored by looking at four anticonsumption literatures that the author believes are most relevant to the fast moving consumer goods (FMCG) industry. These four streams of literature, divided into four sub-sections, are: innovation resistance, risk aversion, undesired self and voluntary simplicity. The third section examines two case studies in order to illustrate the practical relevance of the anti-consumption literatures to industry. The first case study is bottled water and the second case study is genetically modified (GM) food. The fourth section summarises why anti-consumption may be a cause for concern for product developers and other practitioners in the food and personal product industries. Finally, this chapter concludes with a commentary on likely future trends, and provides the reader with some sources for further information.

19.1.1 What is anti-consumption? Anti-consumption literally means “against consumption” (Lee, Fernandez, and Hyman, 2009). Anti-consumption research, then, aims to discover why people are actively against the consumption of certain products (including services, brands, experiences, technologies, and lifestyles). In particular, anticonsumption researchers are interested in the reasons for anti-consumption that are above and beyond the simple preference of one item over another (Zavestoski, 2002), for instance brand loyalty to Pepsi instead of Coca Cola. Furthermore, incidents where products are rejected because they are considered too expensive, unavailable or inaccessible (for example, when a consumer simply does not have the financial resources to purchase a BMW) are also of less interest, since these are obvious reasons for anti-consumption that do not increase our understanding of consumers (Lee et al., 2009). As a phenomenon, anti-consumption has been less researched compared to the phenomenon of consumption. Nevertheless, anti-consumption covers a variety of topics ranging from an individual’s negative perception of one product, to the boycotting of companies (for example, the anti-consumption of Nike owing to sweatshop factories) and sometimes, entire countries, for example, the anti-consumption of American brands in the Middle East (Sandikci and Ekici, 2009). Scholarly and anecdotal evidence suggests that anti-consumption attitudes and behaviours have become more extensive and diversified (Sandikci and Ekici, 2009). An increase in anti-consumption material has emerged on all forms of popular media, including books, magazines, blogs, anti-consumption websites, YouTube videos, social network websites and even in movies. Disney’s sci-fi animation movie “WALL-E” presents a daring attack on the culture of consumption (Climate Progress, 2008), while a quick perusal on the internet will quickly demonstrate no lack of antibrand sites (Kucuk, 2008).

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19.1.2 The reasons behind anti-consumption This section examines four streams of the anti-consumption literature that may offer insights as to why some consumers practice anti-consumption. These four streams are: innovation resistance, risk aversion, undesired self and voluntary simplicity. Innovation resistance The first reason for anti-consumption, which is of obvious relevance to new product developers, is innovation resistance. Defined as “the resistance offered by consumers to changes imposed by innovations” (Ram, 1987, p.208), this definition is supported by Schein (1985, as cited in Ellen, et al., 1991) who states that it is not the technology or product that is resisted but rather, the changes caused by the technology. Similarly, Higgins and Shanklin (1992) assert that the technological sophistication of innovations is often ahead of an individual’s technological understanding. These findings indicate that most consumers are not drawn to products by their technological sophistication but rather their ease of use. For example, the microwave was a successful innovation, not because of the inherent technology, but because of the convenience that microwaves provided. Interestingly, while convenience was directly responsible for the technology’s current pervasiveness, there is now anti-consumption activity drawing attention to the perceived risk of microwaves. Thus there is a constant trade-off between the convenience an innovation provides and its perceived risks. In cases where innovations are not perceived by consumers as overtly beneficial, innovation resistance is likely to occur, impeding product adoption and increasing anti-consumption. Ram (1987) argues that resistance to change is a normal consumer response when faced with an innovation. He further argues that not all change is necessarily good and that resistance is therefore innate in such situations. Similarly, Bagozzi and Lee (1999), state that resistance to change is inevitable, that despite better products and services resulting from innovation, resistance is an instinctive consumer response that has to be overcome before adoption of the innovation occurs. The more radical an innovation the greater the resistance, and often, unless the benefits are seen to outweigh risk, innovation resistance will continue to cause anti-consumption of the new technology. In some cases, consumers cannot be convinced that the benefits outweigh the risks and the innovation will simply fail, in other cases innovations that seem particularly risky still succeed; because of the complex social system in which consumption resides it is difficult to predict which innovations will fail and which will succeed. Cosmetic surgery is one such innovation/practice. Initially seen as controversial, the risks were seen to outweigh any potential benefits, but a look at today’s media environment indicates that the benefit of looking “beautiful” is beginning to outweigh the risks of invasive procedures. Genetic modification (GM) is an innovation travelling a similar path to cosmetic surgery. However,

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innovation resistance against GM may be stronger because of the perceived environmental and personal health risks, and people’s opposition to large multinational corporations privatising life by patenting the genetic code of various genetically modified organisms (GMOs). Instinctive consumer resistance of innovations can be explained by theory from psychology, which reveals that consumers have an intrinsic desire for psychological equilibrium (Newcomb, 1953; Osgood and Tannenbaum, 1955). Changes imposed on a consumer’s behaviour by an innovation may disturb this equilibrium, since the consumer must adjust their current beliefs and behaviour to meet the characteristics of an innovation (Ram, 1989). Extremely novel innovations, such as GM, often demand the greatest efforts of readjustment in a consumer’s behaviour due to their complicated nature (Moreau, Lehman and Markman, 2001). Hence, resistance is an instinctive response to innovations, as consumers often prefer to resist changes rather than disrupt their psychological equilibrium. Innovation researchers (Watson, 1971; Sheth, 1981; Ram 1989, Bagozzi and Lee 1999) also agree that “habit” is one of the main reasons for innovation resistance. For instance, Sheth (1981) conceptualises perceived risk and habit as the two main factors driving innovation resistance with habit being the single most powerful determinant in generating resistance. Habit is an individual’s established practice or fixed behaviour of doing things (Sheth, 1981). Consumers preserve their habits to maintain psychological equilibrium, therefore habit plays a strong role in consumer decision making, especially when those decisions involve new product innovations (Bagozzi and Lee, 1999). Consumers may resist an innovation because it conflicts with their current habits or because it threatens to create changes within those well established habits. The powerful influence of habit is evident in consumer culture; many consumers resist innovations simply because of inertia, even though these innovations are “better” than the products they currently use. The Dvorak typewriter/computer keyboard is a salient example of how habit contributes to innovation resistance. The Dvorak keyboard is argued to be an ergonomically superior keyboard to the more common QWERTY keyboard, and the failure of the Dvorak keyboard has been attributed to the fact that is was patented nearly 70 years after the introduction of the QWERTY keyboard (1860s). By then, existing typists had already developed the habit of using the QWERTY layout and most businesses preferred the status quo QWERTY keyboard. Thus, despite the potential to be a more effective innovation, the Dvorak keyboard could never overcome the habit of its main target group. Other research indicates that individuals face several barriers preventing their adoption of innovations. These barriers are categorised as either functional barriers or psychological barriers (Ram and Sheth, 1989). There are three functional barriers consisting of product usage patterns, product value, and risks associated with product usage (Ram and Sheth, 1989). Issues involving product usage patterns arise when the innovation is not

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compatible with consumers’ existing habits. Product value barriers are triggered when consumers do not perceive a relative advantage against existing alternatives. Finally, risk barriers are caused by uncertainties. The Dvorak keyboard is a good example of an innovation failing to overcome these three functional barriers. On the other hand, psychological barriers arise from two factors: the traditions and norms of the consumer, and perceived product image (Ram and Sheth, 1989). Tradition barriers occur when an innovation conflicts with consumers’ social norms and values. The higher the conflict between an individual’s beliefs, values or norms with an innovation, the higher the resistance (Ram, 1987, 1989; Ram and Sheth, 1989). Perceived product image barriers occur when the innovation is linked with negative associations due to its product category, country of origin or industry affiliation. GM is an innovation predominately facing psychological barriers. Resistance to innovation is also dependent on the psychological characteristics of the consumer, with an individual’s personality being a major determinant of innovation resistance (Szmigin and Foxall, 1998). For instance, innovators welcome new products for the sake of the experience and therefore have lower resistance to innovations, while other personality traits such as self confidence also play an important role in consumer resistance of innovations (Sheth, 1981). In contrast, people scoring high on dogmatism, which is a trait characterised by close-mindedness and a rigid assertion of opinion or belief as if it were fact, are less innovative than people scoring low on dogmatism. Therefore, the more anxious or threatened dogmatics feel by innovations the more closed minded they become, resulting in resistance of innovations and their potential risks (Coney, 1972). Another factor influencing innovation resistance is communication. Rogers and Shoemaker (1971) emphasise the importance of communication that successfully conveys product benefits to consumers thereby increasing the potential of innovation adoption. A key criticism of the failure of the Dvorak keyboard was because the benefits of the innovation, and the innovation itself, were not communicated very well to the relevant people. On the other hand, several authors (Ellen, et al., 1991; Ram, 1987), found that innovation modification rather than communication has the potential to reduce innovation resistance. If an innovation cannot be modified according to consumer preferences, resistance to the innovation cannot be overcome (Ram, 1989), regardless of the communication efforts. In other words, some scholars argue that innovation resistance may be overcome by ensuring that consumers are made aware of the benefits, while other scholars argue that innovations resistance can only be overcome by making the innovation more compatible with consumers’ requirements. Of course, the problem with the latter suggestion is that some innovations cannot be modified easily, and furthermore, when it comes to truly radical innovations, consumers may not always be aware of what they want, and therefore are unable to indicate how they would like an innovation to be modifed.

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Lastly, in the context of technology-based food innovations, a study on the acceptance of technology-based food innovations found it had many similarities with innovations in other areas such as computing and the internet, the only difference being that, unlike other innovations, food is ingested by the consumer (Ronteltap et al., 2007). Owing to the ingestion of the product, technology-based food innovations create the most sensitive consumer concerns about innovations (Ronteltap et al., 2007), and may be one of the reasons why GM of food has been met with heavy innovation resistance. Risk aversion The second reason for the practice of anti-consumption is risk aversion, defined as an individual’s preference for a guaranteed outcome over a probabilistic one, in avoidance of uncertainty (Machina 1987; Mitchell 1999; Mandrik and Bao, 2005; Gneezy et al., 2006). Hence, to avoid risks, individuals may practise anti-consumption of options which harbour some uncertainty. Fundamental to understanding risk aversion are the concepts of risk attitude and risk perception (Weber and Milliman, 1997). Risk attitude reflects an individual’s general or consistent predisposition towards risk and how much he or she dislikes risk (Pennings and Wansink, 2004). On the other hand, risk perception is an individual’s assessment of the risk inherent in a particular situation (Pennings and Wansink, 2004). In addition, the concept of risk perception involves both the perceived uncertainty of outcomes and the perceived severity of negative consequences (Mitchell, 1999). Hence, individuals avoid products they perceive to be risky due to the uncertain outcomes of its usage. The nature and degree of risk consumers perceive, and the manner in which they deal with perceived risk, are important determinants of the decision to resist rather than adopt innovations, such as food technology (Henson et al., 2008). Anticipating and understanding consumers’ risk perceptions is especially important in technological industries, as risk continually fluctuates with the frequent introduction of innovations (Henson et al., 2008; Tucker et al., 2006). Knowledge of consumer risk perceptions is also crucial in industries where unpredicted events, such as food safety concerns or product recalls, may dramatically influence consumer demand of the products (Pennings, et al., 2002). A study by Henson et al. (2008) on consumer attitudes to food and non-food technologies in Canada highlighted the role of perceived risk and perceived benefit in determining their acceptability. The study found that perceived risk and perceived benefit are significantly and negatively related to one another, which suggest particular technologies are positioned on a perceived risk-benefit continuum, with higher perception of risk contributing to higher resistance of the technology (Henson et al., 2008). Situated at one extreme of the continuum are widely accepted food technologies that are perceived to have low risk and high

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benefit, such as pasteurisation (Henson et al., 2008). At the other extreme are food technologies that are perceived to be high risk and low benefit and which consumers are more sceptical about, such as hormones in food (Henson et al., 2008). Furthermore, individuals differ with respect to the amount of risk they are willing to incur in a given situation (Mandrik and Bao, 2005). Individuals may be classified as either risk seekers or risk averters. Risk seekers are more likely to choose risky or uncertain products; due to underestimation of risk, they tend to overestimate gains and underestimate losses (Mukherji et al., 2008). On the other hand, risk averters are more attentive to scrutinize and track the consequences of their decisions and as a result, risk averters tend to demand more information on probabilities, hence, adopting worst case scenarios (Mukherji et al., 2008). Risk averters typically overestimate risk in that they tend to overestimate losses and underestimate gains (Mukherji et al., 2008), focus more on the likelihood of losses or the potential for loss (Schecter, 2007), and have a habitual disposition for loss focused behaviours that result in a risk averse mentality (Weber and Milliman, 1997). Risk averse consumers often feel threatened by ambiguous and novel situations, and perceive new products as risky due to the unknown certainty of its performance compared to established products and, as a result, are reluctant to try new products (Tucker et al., 2006). Hence, instead of searching for new information or taking risks by trying new products when new purchases are made, consumers with high levels of risk aversion avoid the possible loss of trying new products by using a simplifying strategy, which involves staying loyal to the well-established products they frequently purchase. Previous studies have shown that consumers perceive aspects of risk differently and that their predictive value for total risk and risk aversion behaviour often depends on the product category (Matzler et al., 2008). For instance, perceived health risks associated with computers may be less than perceived health risks associated with food products. The risks associated with consuming food products is internal, as it is digested by the human body, whereas risks associated with computers are seen as external because, for the time being, they predominately remain separate from the body. However, as the human and machine boundary becomes more blurry this century, it will be interesting to see if risk aversion towards cybernetic and prosthetic technology becomes as severe as it has been towards food-technology. There is also debate of how important food safety really is, when compared to other social issues. A study by Finn and Louviere (1992) suggests that while consumers often express a concern for food safety, they were actually the least concerned about food safety, when forced to choose between the importance of that construct and other issues such as the environment, medical care, crime, poverty, taxes, etc. However, that study compared food safety to other political issues, it did not compare importance

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of food safety to the safety of other products; therefore from the perspective of a product developer, it still makes sense that food should be treated with more sensitivity than non-consumable products. Finally, different aspects of risk (financial, performance, and social risk) are often also perceived differently as they arise from various sources (Matzler et al., 2008). For instance, performance risk described by consumers who have actually used the product may be considered more accurate than performance risk described by advertisers of the product. Additionally, risk aversion may also differ for the same product across different consumers in different situations (Mandrik and Bao, 2005). For instance, financial risk may be the dominant risk for one consumer purchasing an expensive skin product while health risks may be dominant for a different consumer; therefore, a consumer’s risk aversion is dependent on the consumer and his or her unique situation. Undesired self The third reason for anti-consumption is the consumer’s undesired self. Ogilvie (1987) describes the undesired self as symbolising what an individual wants to avoid being or hopes to never become. Thus, the undesired self contains feared aspects and attributes of one’s self-concept which an individual does not want to be (Hogg and Banister, 2001). The undesired self is “a representation of the self at its worst, it thus acts as a central avoidance goal” (Phillips et al., 2007, p.1037). The cosmetic industry frequently utilises the concept of the undesired self by claiming that the use of certain products will allow consumers to “push” themselves away from their undesired self; for instance, anti-wrinkle products that fight the signs of aging, or cleansers that clear up pimply skin. These two common messages target very different groups, but both utilise the idea of providing the consumer with a means of distancing themselves away from an undesired self-concept. In addition to negative attributes, the undesired self is also characterised by memories of fearsome events, embarrassing situations and emotions that are unwanted by the individual (Ogilvie, 1987), hence, motivating the individual to consistently avoid such situations. Aspects of a person’s undesired self may even include feared future possibilities, regardless of whether the person has actually evidenced such qualities in the past or not (Cheung, 1997). Individuals use the undesired self as a guide to assess their well-being; in fact, undesired self has been found to be more effective and accurate in evaluating an individual’s well-being than the ideal self (Ogilvie, 1987). Consistent with Ogilvie’s (1987) argument, Banister and Hogg (2004) declare that various aspects of negative and rejected selves (undesired self) can be considered reference points or standards used by an individual to assess how close or distant they are from being like their negative self (undesired self). Similarly, Phillips et al. (2007) study on the prediction of

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negative emotions using self theory found that unlike the ideal and ought selves, the undesired self strongly predicted positive and negative affect, with negative affect declining as people moved farther from the undesired self. These results illustrate that the undesired self has a stronger and more influential effect on the evaluation and measurement of a person’s wellbeing and life satisfaction than ideal and ought selves. Additionally, the undesired self is a more concrete measurement of one’s well-being because it is grounded in an individual’s previous lived experiences therefore less conceptual and more experiential, and contains immovable standards against which an individual judges his or her current state of well-being (Ogilvie, 1987). On the other hand, the ideal self is grounded in guesswork, apparitions worth striving for, consequently a composite of abstract unrealised future states (Ogilvie, 1987). The undesired self represents a negative self or negative event the individual has experienced, therefore, linked with negative emotion that the individual does not want to associate with themselves. The concreteness of the undesired self in measuring well-being may be attributed to the fact that negative events or negative attributes are more likely to be remembered than events with positive effect, as a sort of evolutionary survival mechanism (Ogilvie, 1987). By being able to invoke memories and images from a negative past experience, an individual has a better chance of recognising similar situations in subsequent experiences. The undesired self is also seen to reflect an emotional value when the self is measured within an evaluative ethic (Lewis and Haviland, 2000). Thus emotions (negative emotions) are relevant to the conceptualisation of the undesired self. Negative emotions have a role in that they give the undesired self the task of defining the boundaries of an individual’s self, which is “that sort of person I will not be” (Lewis and Haviland, 2000). The undesired self is closest to the devalued side of emotion, and acts as a motivation to refrain from being close to the negative self to which negative emotions are resident. Similarly, Banister and Hogg (2001) assert that an individual’s undesired self is of particular relevance when they fill or associate products with negative meanings. The undesired self (“so not me”) embodies the most extreme notions of what is “not me” and could be linked to feelings of repulsion in the rejection of products (Banister and Hogg, 2001). While the undesired self may be harnessed by the cosmetic, food, health and well-being sectors to promote their products, as a concept, it also causes the anti-consumption of many products and services. Consumers avoid products and services that they associate with negative product-user stereotypes in order to enhance or support their self concept (Hogg and Banister, 2001). Products and services that consumers choose not to consume contribute to their self-concepts and define their social reference groups just as the products and services they choose to consume do (Banister and Hogg, 2001; Englis and Solomon, 1995, 1997; Hogg, 1998; Hogg and Banister, 2001). Studies on avoidance groups show that when individuals associate a

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certain lifestyle with a social group that they want to avoid, they tend to negatively assess consumption items that they believe are stereotypical representations of the behaviour associated with the particular avoidance group (Englis and Solomon, 1995, 1997). For example, the avoidance of Rolex and BMWs, not because of cost, but because those brands represent an ostentatious yuppie lifestyle that some consumers find symbolically undesirable. Hence, consumers avoid consumption of products typically identified with an avoidance group. What a person chooses to consume and not consume is therefore an important aspect of both individual (self) and group identity and indicates what “social type” an individual is and what “social type” he or she is not. A study by Hogg and Banister (2001) found that in order to maintain a positive self-identity, it is essential to avoid products which represent an undesirable consumer identity for that individual. Consumers communicate positive messages about themselves and the ways they avoid the undesired end state through their consumption choices in an attempt to approach their desired end state (Hogg and Banister, 2001). Hence, the undesired self (Ogilvie, 1987) is of particular relevance to consumers because it involves a strong motivational drive to protect self-identity and self-esteem from what the individual is afraid of becoming. As a result, the undesired self is an important motivational driver in an individual’s choice to practise anti-consumption. Voluntary simplicity The fourth and last reason, this chapter examines, for anti-consumption is voluntary simplicity. Voluntary simplicity (VS) is a way of living that is “outwardly simple and inwardly rich” (Elgin and Mitchell 1977, p.2). Or as Etzioni (2004) defines it, voluntary simplifiers (VSers) are individuals using their free will to minimise their consumption in order to focus on nonmaterialistic channels for satisfaction and meaning in their lives. VS exists in three variations, from moderate levels (in which people downshift their consumptive rich lifestyle but not necessarily into a low level), to strong simplification (in which individuals significantly restructure their lives) to holistic simplification (individuals adjust their whole life patterns according to the ethos of VS) (Etzioni, 1998). Hence, VS is defined dissimilarly by individuals according to the variation of VS to which they belong. Despite the existence of distinct definitions, one common descriptor of VS is mindful living (Johnston, 2008). Mindful living refers to consumers’ satisfaction with the way their everyday activities put forward a less materialistic lifestyle as a more personally fulfilling, socially beneficial, spiritually enlightening and environmentally friendly lifestyle (Johnston, 2008). For instance, being satisfied with the resources they currently have and purchasing products that are environmentally friendly. Based on a survey of readers of Co-Evolution Quarterly, Elgin and Mitchell (1977) proposed that VSers hold a set of five basic values: material simplicity, self-determination, ecological awareness,

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human scale, and personal growth which will now be briefly described. The value of material simplicity refers to the purchase and consumption of items that are essential and needed for survival rather than unnecessary materials that are luxurious. For instance, purchase of food is a need and required for life; on the other hand ice-cream will not be consumed because it is not essential for life. The second value, self-determination, refers to the desire of VSers to have greater control over their own future rather than unnecessarily depend on others. This may be accomplished by growing their own food, and living without dependence on possessions such as televisions. The third value, ecological awareness, recognises the limited resources on the earth and seeks to preserve the natural environment and reduce environmental pollution. The fourth value, human scale emphasises that a small scale is better for living and working environments. For instance, cutting back on long hours at work restores the basics and perspectives of simple living. Finally, personal growth refers to achieving material simplicity, self sufficiency, human scale and ecological awareness in order to create time and space away from everyday incidents to grow both spiritually and psychologically. These values motivate consumer’s decision-making efforts and affect their choice to practise anti-consumption behaviours. Though the number of people practising VS fluctuates over the years, the lifestyle has never disappeared (Huneke, 2005). In fact, over the past thirty years, environmental and sustainability concerns have escalated worldwide (Huneke, 2005), therefore VS has become more visible as a feasible alternative to modern living. An estimated 60 million adults were living a life of VS worldwide in the year 2000 (The Simple Living Network, 2009). Increased interest in VS over the past ten years can be seen as a result of individuals’ desire to reduce levels of stress and the rapidity of daily life (Huneke, 2005). Furthermore, the internet not only connects likeminded VSers across the globe, but it has also made their practices and thoughts more accessible to mainstream consumers; thus the opinions of VSers and other similar lifestyles may gain even more influence over the coming years. VS is significantly forward looking in its perspective as it asks individuals to consider the consequences of their lifestyles and actions on their wellbeing and the environment (Elgin and Mitchell, 1977; Shaw and Newholm 2002; Zavestoski 2002; Bekin et al., 2005). Motivations for being part of the VS movement range from acutely personal concerns such as moral obligations to the environment to critical national problems such as the effects of consumption and production on the environment (Elgin and Mitchell, 1977). Individuals that explore VS do so in hope of instilling their lives with meaning, shifting their priorities for the “good”, and finding alternatives to the consumer culture as a means of building a sense of self with which they are satisfied (Zavestoski, 2002). VSers have been found to include various anti-consumer beliefs. Etzioni (1998) and Iwata (2001) reported that VSers fluently described a philosophy that was clearly anti-consumerism and

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anti-materialistic. VSers reduced their commitment to the consumer culture by believing that function, necessity and performance of a product are more important than style (Shama and Wisenblit, 1984; Craig-Lees and Hill, 2002). In terms of measuring VS, various quantitative methods exist. LeonardBarton (1981) devised a scale consisting of 18 behavioural items (e.g., recycle newspaper, composting, make gifts instead of buying, ride bicycle to work and second-hand purchases). Following on from Leonard-Barton’s (1981) work, Shama and Wisenblit (1984) developed a scale measuring the five motivational dimensions of VS: material simplicity, human scale, selfdetermination, ecological responsibility, and personal growth, as first proposed by Elgin and Mitchell (1977). Iwata (1997) later developed a 20-item scale of VS lifestyles (e.g., I do not do impulse buying, I prefer products with simple functions to those with complex functions, and I usually try not to pollute or destroy the environment), referring to the items used by Shama and Wisenblit (1984). These scales have been used by various studies (Huneke, 2005; McDonald et al., 2006) as foundations for exploring VS. Using interviews, Craig-Lees and Hill (2002) addressed some of the specific consumption habits of VSers. Their research demonstrated that VS participants purchased items which had the least amount of packaging. In addition, Craig-Lees and Hill (2002) found that VSers preferred to purchase local and organic produce. Huneke (2005) also found that VSers performed several behaviours that were associated with environmental and social responsibility, such as only purchasing environmentally friendly products and only purchasing from socially responsible producers. Ethical concerns often guide consumption of VSers and include environmental degradation, mistreatment of animals at factory farms and the use of pesticides and excessive landfill use (Shaw and Newholm, 2002). Voluntary simplifiers are more likely than others to recycle, build compost heaps, and engage in other civic activities that indicate stewardship toward the environment because simplifiers draw more of their satisfaction out of such activities than out of conspicuous consumption (Shama and Wisenblit 1984; Etzioni, 1998; CraigLees and Hill 2002; Huneke 2005). Most VSers have a strong moral commitment to products they consume; they associate commercialised culture with bad values and are attentive to its devastating effects on the environment (Schor, 1998); in this sense, most hardcore VSers practise the anticonsumption of mainstream hyper-marketed goods. This perspective is consistent with a content analysis conducted by Grigsby (2000, as cited in Zavestoski 2002) on VS books which found environmental decline and the degradation of human fulfilment from consumerism as two of the main themes associated with VS. Similarly, Shama and Wisenblit (1984) and Iwata (1997) described VS as possessing the characteristics of self sufficiency, low consumption, and ecological responsibility. According to Piacentini and Banister (2009), VSers do not have a goal to exit the consumption market completely, but seek to reduce the various

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effects of being a consumer. Piacentini and Banister (2009) further state that VS may be more accurately described as the altering of consumers’ consumption choices. For instance, some VSers reduce consumption in a few areas but may actually increase consumption of goods and practices they consider as more acceptable (Craig-Lees and Hill, 2002). Similarly, Shaw and Newholm (2002) position VS consumers on a scale from consumers that fully support ideas about reducing consumption to consumers that merely refine their consumption towards ethical standards. The latter continues to maintain usual levels of consumption, but consumes according to some of their ethical concerns in comparison to the VSer that acts by reducing their level of consumption overall.

19.2 Anti-consumption and personal care products and innovation in food This section will illustrate the practical relevance of the four literatures: innovation resistance, risk aversion, undesired self and voluntary simplicity, to the anti-consumption of food packaging and food innovation. These four literatures will be applied to two case studies, first, the case of bottled water which is classified as a packaged food product and, second, the case of genetically modified food which is a technology based food innovation.

19.2.1 The case of bottled water Bottled water is drinking water packaged in bottles and sold for human consumption and is classified as a packaged food product (World Health Organisation, 2000). In previous years bottled water was one of the top beverages sold around the world (Larsen, 2007), and chosen over tap water. The most common reason stated to explain the growing use of bottled water is dissatisfaction with the taste of local tap water and concerns over the safety of tap water, but ironically, in blind taste tests consumers are often unable to accurately label bottled water from normal tap water (Gleick, 2004). Furthermore, over the past few years, an increasing number of reports on the water quality and environmental problems of bottled water have arisen (Gleick, 2004). More than 1.5 million tons of plastic are used to bottle water, and most water is bottled in PET (polyethylene terephthalate) which is created from crude oil, a non-renewable resource (Gleick, 2004; Owen, 2006). Although less energy is required to recycle PET than glass or aluminium, and smaller amounts of emissions are released into the atmosphere, most plastic bottles are not being recycled, leading to large amounts of waste and pollution to the environment (Gleick, 2004). According to studies by Corporate Accountability International, millions of plastic bottles end up in landfills, incinerators and as rubbish on land, in streams, rivers and in the sea (Naidenko

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et al., 2008). Plastic bottles can take between 400 and 1000 years to degrade (Owen, 2006; Sorrick, 2008), hence the existence and large amount of plastic bottles in the environment today, and plastic pollution affects both the present health and future health of the environment. Studies have also found that plastic bottles may leach chemicals into water the longer they are stored in the bottle (The Canadian Press, 2006; Sorrick, 2008). Concerns have also been raised by some communities about the removal of thousands of litres of water a day and its impact on the environment and the community (Clarke, 2008). Impact on the environment starts when various local streams and underground aquifers become depleted due to excessive withdrawal by water-bottling plants of natural mineral or spring water to produce bottled water (Zabarenko, 2007). In addition to the water in bottles, the most conservative estimates suggest that about twice as much water is used in the production process, alone (Koutsoukis, 2007). For instance, a one litre bottle requires about three litres of water during the manufacturing process (Koutsoukis, 2007). Moreover, dire effects on the environment are caused by the significant amounts of energy and resources required to produce, package, store and transport bottled water (Block, 2008). Bottled water companies’ advertisements give consumers the impression that bottled water is safer and healthier than tap water (Morgan, 2006). However, science has yet to prove that bottled water is any safer or healthier for consumption than tap water (Morgan, 2006; Wilk, 2006). According to Boldt-Van Rooy (2003), about 75 percent of bottled water sold in the United States comes from natural underground sources, such as rivers, lakes and artesian wells. The remaining 25 percent is just tap water in a bottle (Owen, 2006). However, bottled water is much more expensive than tap water and even fuel, with costs being driven up by plastic bottles, bottling, marketing, transportation (Owen, 2006; Wilk, 2006), and a large profit margin (Gleick, 2004). In addition, there are also health risks associated with bottled water; previous studies have found chemical pollutants, contaminants and bacteria in various brands of bottled water (Gleick, 2004; Naidenko et al., 2008). Unknown to most consumers, tap water is actually more strictly and frequently tested than bottled water; for instance, tap water in Canada is inspected daily, whereas bottled water plants are inspected at three-year intervals (Organic Principle, 2008). In many countries such as the United States, bottlers often do most of the sampling and testing themselves, without independent supervision; this can result in biased reporting of results. Furthermore, although there are national standards for bottled water in most countries, a universally accepted international standard certification scheme does not exist yet (Gleick, 2004). As a result of the above issues anti-consumption of bottled water is increasing and is clearly illustrated by San Francisco, United Kingdom (UK), New York, Paris and Toronto, where city and country governments,

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restaurants, schools and religious groups have banned the selling of bottled water in favour of tap water (Larsen, 2007). For instance, Leeds University in the UK has banned the sale of bottled water in the university shops, in favour of free water fountains and reusable water bottles (Haydock, 2008). Another example is Toronto which has banned bottled water sales on city premises in favour of access to tap water in all city facilities (Clarke, 2008). According to PepsiCo and industry analysts, consumers are choosing tap water over other beverages at restaurants and at home to save the environment and their money (Martin, 2008). Similarly, green minded consumers choose tap water over bottled water due to concern that most of the plastic bottles will end up in landfills and pollute the environment (Hein, 2008). Water has meaning far beyond simple thirst quenching; water is viewed by some individuals as one of the most important substances that comes from nature (Wilk, 2006). The importance of water is seen in Christian traditions of baptism, libation, bathing and drinking through which people are viewed to connect to the power of nature (Wilk, 2006). Hence, religious groups are just one of the many groups of consumers who are practising anti-consumption of bottled water for moral reasons (Larsen, 2007). These groups regard the packaging and sale of water, which is naturally free, at a high price as unethical; they are also concerned by the negative impact that bottled water has on the environment (Larsen, 2007). In the United States, which has the highest bottled water consumption, sales rose just one per cent in 2008, which is small compared to 11 per cent in 2007 (The Sun, 2008). France, home to popular bottled water Evian has also experienced slow sales of bottled water (Larsen, 2007). Concerns about the high energy use to produce the plastic bottles and the associated contribution to climate change and waste are motivating individuals to go back to tap water (Larsen, 2007). The issues discussed above can relate to anti-consumption literature and the reasons behind the increasing anti-consumption of bottled water. These are risk aversion, undesired self and voluntary simplicity. Risk aversion The act of avoiding purchase and consumption of bottled water due to uncertainties of the effects it has on the environment and human health is attributable to risk aversion (Machina 1987; Mitchell 1999; Mandrik and Bao 2005; Gneezy et al., 2006). Hence, consumer anti-consumption of bottled water takes place in order to avoid its associated risks. Risk averters are attentive to, and more likely to scrutinize, the consequences of their decisions (Mukerji et al., 2008). As a result, risk averters tend to demand more information on probabilities, hence adopting worst case scenarios (Mukerji et al., 2008). Due to assessing the worst possible consequences of purchasing bottled water and scrutinizing the long term impact on the environment, risk aversion will become a salient reason for anti-consumption of bottled water.

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Risk averters have strong risk perceptions (Pennings and Wansink, 2004) about the hazards inherent in plastic bottles. Plastic bottles take between 400 and 1000 years to degrade (Owen, 2006) and the risks involved within that time period are long term, hence consumers’ risk perception (Pennings and Wansink, 2004) of bottled water is severe leading to their decision to anti-consume bottled water. Concerns about the leaching of chemicals from the plastic bottles into the water may cause further risk averse consumers to avoid bottled water (Sorrick, 2008). Ironically, the success of bottled water may be due to risk perceptions of municipal water supply. However, as more attention is directed at the environmental and occasional health risks of bottled water, as well as the unregulated nature of the industry, risk aversive consumers may begin to practise anti-consumption of bottled water, choosing, instead, to filter their own tap water as the safest alternative. Understanding consumers’ risk perceptions is important in the bottled water industry where unpredicted events such as contamination or product recalls may occur and influence consumers’ demand of the product (Pennings et al., 2002). Similarly, understanding risk perceptions is also equally important for local government, environmentalists and other groups interested in promoting tap water, since it is the perceived risk of municipal water that maintains the consumption of bottled water. The decision to purchase or resist bottled water also portrays an image of one’s self to others (Matzler et al., 2008) and decisions are carried out accordingly to avoid the undesired self (Ogilvie, 1987) which the following section now examines. Undesired self As previously discussed, most plastic bottles end up in landfills, incinerators and as rubbish on land, in streams, rivers and in the sea (Naidenko et al., 2008). Risk averters avoid these consequences by anti-consuming bottled water. However, the reason for avoiding bottled water may be further explained through the concept of the undesired self, which Ogilvie (1987) describes as a symbol of the self that an individual avoids becoming. By purchasing and consuming bottled water, an individual contributes to the increasing waste and long-term detrimental effects of plastic bottles on the environment. For many individuals, a common undesired self involves being seen as a person who contributes to society’s negative impact on the environment. Since the undesired self is a central avoidance goal (Phillips et al., 2007), by anti-consuming bottled water, an individual avoids becoming like his or her undesired self. Furthermore, Banister and Hogg (2001) assert that an individual’s undesired self is of particular relevance when they fill or associate products with negative meanings. The undesired self is closest to the devalued side of an individual’s emotion, and acts as motivation to refrain from being close to the negative self, which holds negative emotions. Over the recent years,

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bottled water is quickly being constructed in society as a negative product, as evidenced by governments, restaurants, religious groups and schools banning the sale of bottled water (Larsen, 2007), owing to the negative impacts associated with its consumption. By choosing not to consume certain products, individuals form their self-concepts and define their social reference groups just as the products they choose to consume do (Banister and Hogg, 2001; Englis and Solomon, 1995, 1997; Hogg, 1998; Hogg and Banister, 2001). Hence, the undesired self is an important motivational driver in an individual’s decision to anti-consume bottled water and, instead, to consume tap water. Certainly, the success of bottled water may also be due, in some part, to the influence of the undesired self. Currently, consumers who choose to consume bottled water may do so to distance themselves from being portrayed as someone who drinks unhealthy fizzy drinks such as Pepsi or supports “greedy” multinational companies like Coca Cola. Thus, bottled water appears to be a healthier substitute for other bottled beverages. However, as more people begin to feel that drinking bottled water is perceived as equally undesirable, and that the companies behind bottled water are just as profit driven, greater anti-consumption of bottled water may occur. The act of people in governments, restaurants, schools and religious group’s anti-consuming bottled water and choosing to consume tap water portrays a self-concept that cares about the environment. The social reference group most appropriate to explain the self-concept of caring for the environment or being green-minded are voluntary simplifiers, which will now be examined. Voluntary simplicity Individuals that explore VS do so in hope of instilling their lives with meaning and finding alternatives to the consumer culture as a means of building a sense of self with which they are satisfied (Zavestoski, 2002). As seen from the case study, the most significant concern about bottled water consumption is the impact it has on the environment. One of the main values of a VSer is ecological awareness (Elgin and Mitchell, 1977) and they perform various behaviours that are associated with environmental and social responsibility, such as recycling and buying organic and local produce (Huneke, 2005). The values of VSers motivate their consumer decisionmaking efforts and affect their choice of whether or not to consume bottled water. As seen, most plastic bottles end up in landfills (Naidenko et al., 2008) and excessive landfill use is one of environmental concerns of VSers (Shaw and Newholm, 2002). Most VSers have a strong moral commitment to products they consume and are attentive to the negative effects certain products have on the environment (Schor, 1998). Hence, VSers that seek to reduce the effects of being a consumer (Piacentini and Banister, 2009), and the number of plastic bottles in landfills, choose to avoid bottled water and to drink tap water instead.

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In countries where tap water is clean and readily available, consumption of bottled water is a luxury rather than a necessity (Ortega, 2005). At the core of VS is the philosophy of simple living (Elgin and Mitchell, 1977) which involves minimising consumption and focusing on non-materialism (Elgin and Mitchell, 1977; Etzioni, 2004) to satisfy a way of living that is “outwardly simple and inwardly rich” (Elgin and Mitchell 1977, p.22). Hence, bottled water is a luxurious or materialistic item that is fundamentally against the VSers’ philosophy of simple living. In countries where tap water is unavailable or where the quality of municipal water is more questionable, the growth in bottled water is increasing (Gleick, 2004). However, both VS philosophy, and common sense, would suggest that bottled water should only ever be considered a temporary solution, rather than a substitute, for a reliable municipal water supply. Only a fraction of the money involved in the bottled water industry is necessary to provide most developing countries with clean municipal water (Wilk, 2006). Thus, the success of bottled water consumption in developing countries in the absence of adequate infrastructure equates to someone buying bottled water for his or her household instead of establishing a more dependable plumbing system. That an industry could exploit such a lack of self-reliance is most displeasing to most VSers, and some mainstream consumers, thus also contributing, at a moral level, to the anti-consumption of bottled water. VS is significantly forward looking in its perspective as it asks individuals to consider the consequences of their lifestyles and actions on their well-being and the environment (Bekin et al., 2005). Although VS is a fringe movement, there is mounting evidence that even mainstream consumers are becoming more aware of the long-term effects that millions of plastic bottles in landfills, have on today’s, as well as tomorrow’s environment.

19.2.2 The case of genetically modified (GM) food This chapter now examines the anti-consumption of technology based food innovation, using GM as a case study. It is well established that the introduction of GM food has been met with great resistance by consumers (Christoph, et al., 2008; Curtis and Moeltner, 2007; Falkner, 2007; Klerck and Sweeney, 2007; Rontelap et al., 2007; Townsend and Campbell, 2004). Controversy about GM often refers to the modern GM techniques rather than traditional GM techniques (Bredahl, 2001). Thus, the modern concept of GM will be used throughout this case study. Modern GM is defined as the alteration in the genetic composition of an organism, which can be transferred within a related organism or to an unrelated organism (Knight et al., 2005). On the other hand, traditional genetic modification only allows transference between organisms that are closely related (Knight et al., 2005).

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The knowledge on which the techniques of GM are based dates back to the 1950s (Uzogara, 2000). In the 1970s it became possible to isolate genes, alter them and copy onto cells, which created many new commercial opportunities (Uzogara, 2000). Application of the GM technology to medicine was quite rapid, whereas application to plants took much longer. In 1994, the first GM whole food, tomatoes, was introduced into the market (Uzogara, 2000); since then GM has been applied to a variety of products, such as corn, oil, potatoes and apples. As individuals became more aware about the process of GM, concerns over the use and safety also rose. Potential benefits of GM include increased food supply for growing populations, improved resistance to disease, pests and herbicides, increased nutrients and yields (Uzogara, 2000). Despite these potential benefits of genetic modification of foods, the technology has been surrounded by controversy and negative consumer attitudes since the 1990s (Bredahl, 2001; Sparks et al., 1995; Christoph et al., 2008; Curtis and Moeltner, 2007; Falkner, 2007; Klerck and Sweeney, 2007; Rontelap et al., 2007). Concerns surrounding GM food were sparked by events such as the cloning of Dolly the sheep in Scotland, which created several controversial debates, scepticism and assumptions about the process and ethics of cloning as well as other aspects of GM (Uzogara, 2000). Other issues linked to the area of food, science, and technology include the cloning of other farm animals and the incidence of “mad cow disease” in Great Britain in the early 1990s, as well as the beginning of the “terminator seed” technology and the decision by the Food and Drug Administration (FDA) to classify GM foods and irradiated food as organic foods (Uzogara, 2000). Other controversies include the case of (Bt) toxin versus the Monarch butterflies, the Basmati rice patent controversy, as well as the effect of herbicide and pest resistance on the environment (Uzogara, 2000). Critics such as organic farmers, consumer and health advocacy groups, the public and some apprehensive scientists and environmentalists believe that applying GM techniques to human food production could have several negative consequences (Bredahl, 2001). For these critics, safety, ethical, religious and environmental concerns outweigh the interest in improved food quality, increased food production, and improved agriculture which may be possible with GM techniques. Individuals view GM as an uncertain innovative technology that threatens world agriculture, health and the environment. As a result, critics often label GM foods as Frankenfood, indicating the relationship between humans, science, and nature, and themes such as “playing God” and “unintended consequences”, all of which were highlighted centuries ago in Mary Shelley’s famous novel Frankenstein (Bredahl, 2001). As the above events show, GM food is still associated with being a relatively new phenomenon, issues on food safety, environmental risks, social and ethical concerns associated with GM foods need to be addressed, as do the consumer’s right to be informed and to choose between GM foods and non-GM foods.

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In an attempt to explain the resistance of GM food, the literatures on innovation resistance, risk aversion, undesired self and voluntary simplicity will be applied to previous research by Christoph, Bruhn and Roosen (2008). The study was conducted on 1510 (n = 1510) randomly selected German adults through a mail survey. This study was specifically chosen because “German consumers refuse genetically modified food” (Christoph et al., 2008, p.58) making it relevant to the phenomenon of anti-consumption. The main aim of the study was to analyse consumer attitudes towards GM, the knowledge about it and its acceptability in different application areas. The respondents (n = 1510) were given 22 items on a five-point Likert scale about risks and benefits of GM products, the production process, technological development as well as overall questions on food and nature (Christoph et al., 2008). The results revealed that five main factors described respondents’ attitudes towards GM. These factors are: (1) Support (reflects respondent’s opinion about possible benefits of GM products for humans and the environment, the production processes and his/her tolerance regarding possible risks). (2) Criticism (includes all associated risks of GM products). (3) Trust (reflects consumer trust in scientists, government and food industry). (4) Attitude towards progress (respondent’s attitude to progress in general). (5) Scepticism towards innovation (respondent’s attitude towards the technical component of GM). They also found that rates of acceptability were lowest for applications of GM to food products in comparison to non-food products, which was more acceptable (Christoph et al., 2008). Unforeseen risks and moral concerns regarding biotechnology were also evident. These findings can be related to issues explored in the anti-consumption literature of innovation resistance, risk aversion, undesired self and voluntary simplicity. Innovation resistance The fact that rates of acceptability were lowest for applications of GM to food can be explained by the difference between technology-based food innovations and all other innovations such as cars, computers and internet banking. To trial innovations such as GM food it must be eaten; this creates higher perceptions of risk because of the intimate relationship consumers have with food, which is ingested into the human system (Rontelap et al., 2007). Once ingested, the unknown effects on the human body may be irreversible; thus, there are perceptions of fatal, or highly detrimental, events occurring if GM food is ingested. The perception of unpredictable and potentially negative events leads to resistance of GM food by the consumer.

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Sheth (1981) asserts that perceived risk of adopting an innovation is one of the major factors contributing to resistance of an innovation. Food technologies typically possess many of the risk characteristics that provoke the greatest concern amongst consumers (Cardello, 2003). Perceived risks of GM food include safety risks such as potential toxicity and health risks such as allergenicity from the consumption of GM food. In addition, perceived environment risks may include unintentional gene transfer to wild plants. Furthermore, the lower acceptability of GM technology applied to food can be explained by Ram and Sheth’s (1989) functional and psychological barriers. The risks associated with the consumption of GM food (functional barrier) as discussed above: the internal intake of the product creates risk perceptions amongst individuals as to the possible negative outcomes that may occur due to its consumption. On the other hand, the individual traditions, norms or beliefs could be acting as a psychological barrier to consumption of food with the application of the GM technology. As discussed earlier, communication is essential in motivating consumers to adopt innovation (Rogers and Shoemaker, 1971). The lack of communication regarding consumer benefits of the first generation of GM foods may have shaped consumers’ perceptions of the credibility of benefits that are now being communicated about GM food. One of the benefits communicated by GM is its ability to generate extra food to provide for the poor (Uzogara, 2000). This benefit has not yet eventuated; thus consumers may ignore communication because past benefits have not been delivered. Furthermore, even in scenarios where GM has been utilised successfully, the results of publicising the benefits of GM could be positive or negative. Promoting the previous “successes” of GM may cause anti-consumers to reconsider the benefits of GM as outweighing the risks. Alternatively, drawing attention to GM processes that have been, so far, “flying below the radar” may cause current consumers to pay more attention to the topic and/ or product category in general, and may actually create new anti-consumers. For instance, a consumer may be happily using soy products, unaware that the majority of soy is GM; however, upon hearing that news, he or she may start rejecting most soy products, opting instead for organic soy. Thus, when an innovation is as controversial as GM, announcements about its potential benefits still need to be considered carefully, as even positive communications may be met with staunch resistance. Innovation resistance literature also suggests that consumers adopt products for their ease of use rather than their technological sophistication (Higgins and Shanklin, 1992). GM technology is highly sophisticated and this is illustrated through its ability to create products which are unable to form naturally, such as the creation of an organism from both animal and plant origins (Uzogara, 2000). Following this argument, it should be obvious then, that resistance of GM will only be overcome when the general public finds its uses highly convenient. Indeed, most of the successful instances of GM in food have been so convenient that they have simply been taken for

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granted by the end consumer. For example, long life tomatoes, and pesticide resistant soybeans. The technology behind those creations are still highly sophisticated, but the convenience of having a tomato that lasts longer or a more successful crop yield, both of which translate to a more easy and cost effective source of food, demonstrates that innovation resistance diminishes when consumers find it more convenient to use an innovation than to resist it. Ram (1989) also discusses the importance of modifying an innovation to prevent its resistance. However, once GM is applied to food, there is no way to take the ingredients out: either the individual eats it or not. Hence, once GM is applied to food it cannot be altered to suit individuals’ safety concerns. The irreversibility of GM, and the perceived risk to the individual and the environment, may also explain consumer scepticism or risk perception towards the innovation. Risk aversion Consumers’ criticism towards GM food can be explained by Matzler et al.’s (2008) argument that predictive value for total risk depends on the product class. In this case, the application of GM to food products has a higher total risk than applications of GM to non-food products owing to the sensitive nature of food consumption. Therefore, consumers avoid the uncertain outcome of consuming GM food by resisting it. This finding is supported by Mitchell (1999); outcomes and consequences of consuming GM food are often perceived to be uncertain thus GM food is avoided by consumers to prevent the possibility of unwanted consequences. Other research suggests that consumers believe the negative impact of GM outweighs its positive benefits (Gamble et al., 2000). Consumers’ perceptions concerning the negative impact of GM may be heightened by the existing negative information regarding GM, in the media and discussed amongst consumers. Typically consumers purchase products for benefits; however, GM food has perceived negative impacts that outweigh their positive benefits. Hence GM food is not an attractive option for consumers. However, research has found that a considerable amount of consumers are willing to purchase GM food when there is a price advantage (Knight et al., 2005). This view is consistent with the fact that GM negative outcomes outweigh positive benefits. Therefore, the perception that product or cost benefits will outweigh negative outcomes may help to reduce resistance of GM food. Attitudes towards GM are not only restricted to the benefits and disadvantages of the technology but are also of moral relevance (Sparks et al., 1995; Bredahl et al., 1998). GM is perceived as a technology that produces goods that do not form naturally, such as plants with animal genes, thus the end-product is unnatural and individuals feel the use of GM in food is tampering with nature or “playing God”. Hence, the use of GM is perceived as morally irresponsible and some consumers avoid the risk of being

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associated with a product that conflicts with their morals, by practising anticonsumption (Gamble et al., 2000; Rozin 1990; Saba et al., 2000). Undesired self The undesired self is a central avoidance goal (Phillips et al., 2007), and may be used to further understand the reasons why certain consumers practise anti-consumption of GM. A consumer who identifies himself as being morally responsible by recycling and consuming only organic products would have an undesired self of consuming environmentally unfriendly products. Individuals who believe that GM food poses potential risks to their health, the environment and even society, refrain from purchasing such products that are associated with their undesired self (Hogg and Banister, 2001). For instance, a “green” consumer would anti-consume GM products in order to maintain their self-concept of all things natural. Any negative emotions a consumer has towards GM food will define the boundaries of their self concept (Lewis and Haviland, 2000). A consumer evaluates GM food and decides whether or not its characteristics fit with his or her undesired self. Negative emotions attached to the undesired self motivate the consumer to stay away from their undesired end state. Hence, a consumer with negative emotions towards the GM technology is most likely to resist the GM product to prevent experiencing their undesired self. Additionally, products that consumers do not purchase define their social reference groups just as the products they choose to consume do (Banister and Hogg, 2001; Englis and Solomon, 1995, 1997; Hogg, 1998; Hogg and Banister, 2001). A consumer who rejects GM products may be using this act of anti-consumption to define a social reference group to which they belong, such as voluntary simplifiers or a green community group; such conspicuous anti-consumption is further evidenced by the popular display of anti-genetic engineering stickers on car windows. Voluntary simplification The final stream of literature that may shed light on why some consumers practise anti-consumption of GM is VS. Ethical and environmental concerns often guide consumption of VSers (Shaw and Newholm, 2002). VSers have strong moral commitment to products they consume and often prefer to grow their own or purchase local, environmentally friendly, organic products. GM food is genetically altered, therefore not organic, and from a VSer’s perspective, is unethical. Artificially modifying a gene to make a plant more resistant to pesticides and then increasing the amount of pesticides to ensure a larger more profitable crop, is at odds with the VSers’ ideal scenario of food production. Therefore it is no surprise that people identifying themselves as VSers should also choose to practise anti-consumption of GM products. VSers perform several behaviours that are relevant to social and environmental responsibility (Huneke, 2005). VSers live an alternative way of life to the mainstream consumer, are very attentive to the effects their

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consumption has on the environment, and are also more likely to recycle than others. However, even mainstream consumers that reject GM may do so from a similar viewpoint as VSers, that is, they too are concerned with GM’s negative impact on areas such as: simplified living, environmental sustainability, and ethics.

19.3 Summary Anti-consumption is a cause for concern for new product developers and other practitioners in the food and personal products industry, because not all consumers want to increase their consumption, and not all consumers value innovation. The dominant perspective amongst new product developers is that every consumer wants increased consumption and innovation; however, this chapter has provided two salient examples to the contrary. Anti-consumption can ultimately result in failure of an existing or new product if its reasons are not adequately appreciated. By examining and understanding anti-consumption, new product developers are able to determine the reasons behind anti-consumption and make the appropriate changes without creating further costs or unnecessary new products. For instance, when bottled water sales decrease, new product developers may create a new flavour of water or enlist the help of an advertising agency to launch a new marketing campaign. The bigger picture suggests that they need to understand why bottled water was resisted in the first place. The flavour could be fine and the market may already be aware of the brand; perhaps what product developers should be focusing on is developing a new biodegradable plastic bottle to reduce waste in the environment or altering their existing processes to reduce the environmental impact of their product. In terms of GM, knowing that there is a growing resistance in particular groups of people to GM creates new opportunities for organic and local farmers. On the other hand, realising that people practise anti-consumption of GM because of innovation resistance, undesired self, risk aversion, and philosophical VS reasons gives GM advocates a framework for improving acceptance of their innovation.

19.4 Future trends It is likely that over the next five, ten, and twenty years we will see instances of anti-consumption phenomena translating to mainstream consumer behaviour. It is progress at the edges of society that moves civilisation forwards; hence it is knowledge of the fringe that allows product developers to innovate. Knowledge of anti-consumption can provide many opportunities both for competitors, in terms of realising what consumers avoid in your rivals, and for the sector/brand suffering from anti-consumption, in terms

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of knowing what aspects of your own offering need addressing. The introduction of salads and “healthy choice menus” by McDonald’s is a prime example of progress in a mature market, which would not have occurred without attention being paid to anti-consumption. The idea of adding a completely counter intuitive menu to the fast food giant’s staple of burger and fries, was only possible through the detection of an anti-consumption trend and reacting to it appropriately rather than denying it, or passing off the threat as the mindless ramblings of a few subversive consumers. In the case of bottled water, current slowing sales and the increasing number of individuals choosing to anti-consume bottled water may continue as campaigns against bottled water increase and gain more awareness, and support, from governments and the general public. However, on the flip side, unless countries take action now to correct any faults in their public water supplies, such as taste issues, and educate their people on their water systems and regulatory processes, bottled water consumption will increase again. Alternatively, new product developers may choose to create more environmentally friendly plastic bottles to replace the current plastic bottles, as the company GoodWater has attempted to develop with their bio-polymer water bottles (http://www.goodwater.org.nz/). In the case of GM food, acceptance of the innovation may begin to increase as more thorough studies are carried out by scientists and food specialists to confirm the safety of consuming GM food. However, consumer scepticism of both governmental agencies and research integrity may mean that such communications will be heavily scrutinised. Therefore, it is likely that more consumers will demand that food be labelled accurately and clearly. Communicating the benefits of GM food will help increase its consumption, but due to the controversial nature of GM, it would be wise to consider the pros and cons of publicly announcing GM involvement in any product category, regardless of its success. With regards to opportunities targeting anti-consumption of GM, resistance is high in consumers who have an interest in the area of natural health and food, and resistance in Europe is much higher than America. However, as the idea of simple living and slow food catches on globally and greater attention is, once again, directed at the environment (due to the sudden surge of interest in sustainability and global warming), we may see more GM being rejected and more consumers favouring organic and local sources of food. This scenario is already evident with the sudden resurgence of farmer’s markets and the growing success of organic food stores and fair trade retailers. These trends, and others such as vegetarianism, yoga, and surfing, demonstrate the importance of studying fringe movements. What were once the social issues and the consumption choices of hippies, religious zealots, and alternate life-stylers, is now the stuff of multimillion dollar industries. Consequently, product developers and managers need to realise that what is today’s anti-consumption may be tomorrow’s consumption choice.

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19.5 Sources of further information and advice International Centre for Anti-consumption Research (ICAR): www.icar. auckland.ac.nz ICAR is hosted by The University of Auckland Business School; the website contains existing and ongoing research on anti-consumption. Think outside the bottle: www.thinkoutsidethebottle.org Corporate Accountability International’s Think Outside the Bottle Campaign encourages consumers to choose tap over bottled water and support the efforts of local elected officials to do the same at the city, state, and national level. Inside the Bottle: www.insidethebottle.org Inside the Bottle is a Polaris Institute campaign designed to stimulate awareness and action about the bottled water industry. The campaign highlights the environmental, health, social and economic impacts of bottled water and calls for the rebuilding and maintenance of public tap water systems.

19.6 References and further reading allen, l. and darby, j. l. (1994), “Quality control of bottled and vended water in California: A review and comparison to tap water”, Journal of Environmental Health, 56 (8). arnold, e. (2006), “Bottled water: Pouring resources down the drain”, Washington, DC: Earth Policy Institute. bagozzi, r. p and lee, k. h. (1999), “Consumer resistance to, and acceptance of, innovations”, Advances in Consumer Research, 26 (1), 218. banister, e. n. and hogg, m. k. (2001), “Mapping the negative self: From ‘so not me’ to ‘just not me’, Advances in Consumer Research, 28, 242–248. banister, e. n. and hogg, m. k. (2004), “Negative symbolic consumption and consumer’s drive for self esteem: The case of the fashion industry”, European Journal of Marketing, 38 (7), 850–868. bekin, c., carrigan, m. and szmigin, i. (2005), “Defying marketing sovereignty: Voluntary simplicity at new consumption communities”, Qualitative Market Research: An International Journal, 8 (4), 413–429. block, b. (2008), “Bottled water demand may be declining”, Worldwatch Institute. bogo, j. (2001), “Consider the source (for bottled water)”, Earth Action Network, Inc. 12 (2), 12. boldt-van rooy, t. (2003), “Bottling up our natural resources: The fight over bottled water extraction in the United States”, Journal of Land Use, 18 (2), 278–281. bosnjak, m. and rudolph, n. (2008), “Undesired self-image congruence in a low involvement product context”, European Journal of Marketing, 42 (5/6), 702–712. bredahl, l. (2001), “Determinants of consumer attitudes and purchase intentions with regard to genetically modified foods: Results of a cross-national survey”, Journal of Consumer Policy, 2 (1), 23. bredahl, l., grunert, k. g. and frewer, l. j. (1998), “Consumers’ attitudes and decision-making with regard to genetically engineered food products–a review of the literature and a presentation of models for future research”, Journal of Consumer Policy, 21, 251–277.

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cardello, a. v. (2003), “Consumer concerns and expectations about novel food processing technologies: Effects on product liking”, Appetite, 40, 217–233. cbcnews.ca (2006), “Plastic bottles leach chemicals into water: Study”, The Canadian Press. cheung, s. k. (1997), “Self-discrepancy and depressive experience among Chinese early adolescents: significance of identity and the undesired self”, International Journal of Psychology, 32 (5), 347–359. christoph, i. b., bruhn, m. and roosen, j. (2008), “Knowledge, attitudes towards and acceptability of genetic modification in Germany”, Appetite, 51, 58–68. clarke, t. (2008). Toronto stood up to bottled water industry, Polaris Institute. climate progress (2008), “Wall-E is an eco-dystopian gem – an anti-consumption movie (from Disney!)”, http://climateprogress.org/2008/07/15/wall-e-is-an-ecodystopian-gem-an-anti-consumption-movie-from-disney/ coney, k. (1972), “Dogmatism and innovation: A replication”, Journal of Marketing Research, 9 (4), 453–455. craig-lees, m. and hill, c. (2002), “Understanding voluntary simplifiers”, Psychology and Marketing, 19 (2), 187–210. curtis, k. r. and moeltner, k. (2007), “The effect of consumer risk perceptions on the propensity to purchase genetically modified foods in Romania”, Agribusiness, 23 (2) 263–278. datamonitor (2008), “Global bottled water industry profile”, www.datamonitor.com elgin, d. and mitchell, a. (1977), “Voluntary simplicity”, Co-Evolution Quarterly, 1–40. ellen, p. s., bearden, w. o. and sharma, s. (1991), “Resistance to technological innovations: An examination of the role of self-efficacy and performance satisfaction”, Journal of the Academy of Marketing Science, 19 (4), 297–307. englis, b. g. and solomon, m. r. (1995), “To be or not to be: Lifestyle imagery, reference groups, and the clustering of America”, Journal of Adverting, 24, 13–28. englis, b. g. and solomon, m. r. (1997), “Special session summary: I am not therefore, I am: The role of avoidance products in shaping consumer behavior”, Advances in Consumer Research, 24, 61–63. etzioni, a. (1998), “Voluntary simplicity: Characterization, select psychological implications, and societal consequences”, Journal of Economic Psychology, 19 (5), 619–643. etzioni, a. (2004), “The post affluent society”, Review of Social Economy, 62 (3), 407–420. falkner, r. (2007), “The global biotech food fight: Why the United States got it so wrong”, Journal of World Affairs, 1, 99–110. finn, a. and louviere, j. j. (1992) “Determining the appropriate response to evidence of public concern: The case of food safety”, Journal of Public Policy and Marketing, 11 (1), 12–25. gamble, j., muggleston, s., hedderley, d., pariminter, t. and richardson-harman, n. (2000), “Genetic engineering: The public’s point of view”, The Horticulture & Food Research Institute of New Zealand, 1–78. gleick, p. (2004), “The myth and reality of bottled water”, The Worlds Water 2004– 2005. Chapter 2. Pacific Institute. Island Press. gneezy, u., list, j. a. and wu, g. (2006), “The uncertainty effect: when a risky prospect is valued less than its worst possible outcome”, The Quarterly Journal of Economics, 1283–1309. gourville, j. t. (2006), “Eager sellers and stony buyers”, Harvard Business Review, 84 (6), 98–106. grigsby, m. e. (2000), “Buying time and getting by: The voluntary simplicity movement”, Dissertation, University of Missouri-Columbia. haydock, s. (2008), “Ban bottled water”, www.guardian.co.uk

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20 Genetic variation in taste and odour perception: an emerging science to guide new product development R. D. Newcomb, J. McRae, J. Ingram, K. Elborough and S. R. Jaeger, The New Zealand Institute for Plant and Food Research Limited, New Zealand

Abstract: The human genetic revolution is upon us, and while at this stage, it is restricted to providing fundamental insights on human traits, the notion underpinning the present chapter is that an understanding of human genetic variability, in the not too distant future, will be turned into insights that can systematically inform innovation of desirable products in the food and beverage, and personal care industries. Acceptability is a key driver of product consumption and use, and the extent to which products are liked/disliked is informed by liking/ disliking for the sensory attributes of the product. Sensory perception, in turn, is a complex phenomenon controlled by genetic and environmental influences. To date more is known about the latter type of influences, but the human genetic revolution has presented the opportunity to identify the genetic determinants of sensory perception, and by proxy, the extent to which liking has a genetic basis. The purpose of the present chapter is to introduce this emerging science, by way of reviewing: what is currently known about the genetic determinants of sensory acuity (notably taste and odour), what is known about the impact of genetic variation for these traits on food preference and consumption, and finally what realistically is the ability of using this knowledge to inform new product development. In the chapter emphasis is placed on discussing industry opportunities, both in the food and beverage, and personal care sectors, and also on identifying barriers to industry application of this emerging science. Key words: human genetic variation in sensory perception, genotype-phenotype associations, gastronomics.

20.1 Introduction How we as humans interact with our foods is of great cultural significance. Different cultures display many rituals concerning food, from how to grow

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and prepare food to how, when and in what circumstance it should be eaten. Clearly culture must be a major influence on what foods we prefer and choose to eat. In today’s world more commercial factors such as brand and cost are becoming increasingly important in influencing consumer choice for food and personal care products. While these, and a multitude of other external drivers for food preference and consumption, are very important, the question we are addressing here is: Are there internal factors that influence food perception, liking and more generally product choice? More specifically, do genetic factors that impact our ability to perceive flavours and tastes therefore play a role in determining what products we find pleasurable and choose? And if so, can we use these genetic factors as predictors of preference for different markets to guide the development of novel foods, beverages and other personal products? This chapter will critically review the case for genetic factors as drivers for food, beverage and associated new product preference and explore if and how an understanding of the genetic basis of taste and odour perception and variation in these sensory abilities could be harnessed to guide the development of novel products. In this chapter we will briefly overview the rapidly increasing state of knowledge around the genetics of taste and odour perception. We will then go on to speculate how this new understanding could be used to develop new foods, beverages and other aroma-based product lines (e.g., perfumes and toiletries) and finally comment on how feasible and realistic these new concepts actually are from both scientific and commercial viewpoints.

20.1.1 Brief introduction to human genetics The sequencing on the human genome was largely completed in 2001 (International Human Genome Sequencing Consortium, 2001; Celera Genomics Sequencing Team, 2001). The unveiling of the genome has opened a world of possibilities to link knowledge of the genetic variation between individuals to various different attributes or traits, whether these be disease, physiology or behaviour-related states. This ability to analyse variation in the human genome and link it through to different traits was hailed as the breakthrough of the year in 2007 by Science magazine (Pennisi, 2007). The initial target for much of this research has been the contribution of genes to understanding disease states from diabetes to Alzheimer’s disease in what are known as genome-wide association studies (Donnelly, 2008). For the food and beverage industry this has presented the opportunity of targeting those individuals that share a similar version of a gene or genotype with a specific food tailored for them. The human genome is made up of about three billion nucleotide basepairs, distributed across 23 chromosomes and contains approximately 21 000 protein-encoding genes (International Human Genome Sequencing Consortium, 2001; Celera Genomics Sequencing Team, 2001). The four different

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nucleotides, adenine, thymine, guanine and cytosine are often represented by the letters A, T, G and C, respectively. Within a gene groups of three of these nucleotides code for an individual amino acid of which there are 20 different types. Proteins formed from strings of amino acids coded by genes are made by ribosomes in the cell and go on to perform many of the structural and functional roles in a cell. For the ability to perceive odours and tastants receptor proteins are important in the initial detection of the compounds. Odorant receptors are found within the olfactory epithelium expressed within odorant sensitive neurons while taste receptors are expressed in specific taste sensitive neurons within taste buds on the tongue. Within the genome there are large families of genes encoding both odour and taste receptors (Fig. 20.1). These will be reviewed in detail later in this chapter. While 99.5% of nucleotides in the human genome do not vary between individuals (Sherry et al., 2001), there are some differences in people’s genetic make-up and these genotypic differences can alone, or as a result

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Fig. 20.1 The locations of clusters of genes encoding active bitter-taste and odorant receptors on the chromosomes making up the human genome. The locations of bitter-taste receptor clusters are indicated to the left of the numbered chromosomes, and the locations of odorant receptor clusters are shown on the right. The numbers of genes in each cluster are indicated alongside. Note that the sizes of the genes plotted in the clusters are not drawn to scale.

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of genotype-by-environment interactions, manifest in variable phenotypes or characteristics such as eye-colour and ear-shape or to certain behaviours and disease states. A familiar example to sensory scientists is that of phenylthiocarbamide (PTC) and its derivative 6-n-propylthiouracil (PROP). To some people PROP and PTC taste intensely bitter, and to others they have virtually no taste at all. It has long been known that the ability to detect PTC is heritable (Snyder, 1931), but only recently have the underlying genetic controls been identified. Kim et al. (2003) located the region of the genome responsible for PTC sensitivity to chromosome 7, and Bufe et al. (2005) identified the specific gene: TAS2R38. Of the 1002 nucleotides that encode this taste receptor, three affect PTC sensitivity, located at the 145th, 785th and 886th nucleotides in the coding region of the TAS2R38 gene sequence. This type of genetic variant, at individual nucleotide positions are known as single nucleotide polymorphisms (SNPs; Fig. 20.2), the most common type of genetic variants within the human genome (Sherry et al., 2001). SNP genotypes are represented as nucleotide pairs, such as GG. Each diploid cell in our body contains two paired sets of 23 chromosomes, one from each parent. SNP genotypes contain a nucleotide from both chromosomes. Each nucleotide in a genotype is referred to as an allele. Thus, assuming only two allelic states, individuals have one of three possible genotypes for each SNP, and given A and G as the two alleles for a SNP, then the potential genotypes are AA, AG and GG. Individuals who cannot detect PTC have a GG genotype for rs7135398 (i.e., the reference SNP number that uniquely identifies the SNP in TAS2R38 at the 145th nucleotide), a TT

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Fig. 20.2 A single nucleotide polymorphism (SNP) on homologous chromosomes. The double helix backbone of DNA is shown by the curved ribbons with nucleotides lying between the backbone of the DNA double helix of each chromosome. The SNP is the difference between homologous chromosomes at one nucleotide with other surrounding nucleotides matching between the chromosomes. [Adapted from http://www.snipscreen.com/img/snp_dna_by_David_Hall.png]

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genotype for rs1726866, and a GG genotype for rs10246939. To date, nearly 12 million unique human SNPs have been assigned a reference SNP (rs) number in the National Centre for Biotechnology Information’s dbSNP database (http://www.ncbi.nlm.nih.gov/projects/SNP) (Wheeler et al., 2007).

20.2 The genetics of human taste perception Taste receptor cells are activated by distinct classes of compound resulting in different behavioural and physiological responses depending on the nature of the compounds. Taste receptor cells are found mainly on the tongue in groups of 50–100 to form a taste bud, which in turn cluster to form higher order structures such as papillae (Bachmanov and Beauchamp, 2007). There are at least five modalities to taste including sweet, umami, salt, sour and bitter, with each modality mediated by a distinct mechanism and set of receptors. The genes encoding the various classes of taste receptors and their associated signalling pathways are beginning to succumb to investigation. For some of these genes there is also knowledge of whether variation within them encodes differences in the ability to perceive the different compounds within each class of tastants.

20.2.1 Genetic determinants of bitterness perception Bitter compounds are typically noxious and toxic. The ability to detect these compounds likely evolved to avoid the ingestion of the many toxins produced by plants (Drewnowski and Gomez-Carneros, 2000). Even so, mildly bitter tasting foods are common in diets and even for some foods it is perceived as a positive attribute. Bitter compounds are detected by taste receptors of the TAS2R family, members of the G protein-coupled receptor superfamily (Chandrashekar et al., 2000). In humans this family contains 24 putatively functional bitter taste receptors. Some of the bitter taste receptors seem specific for individual compounds, while others can detect a wide range of chemistries (Bachmanov and Beauchamp, 2007). Interestingly, activation of all of these different taste receptors results in the same perception, what we call bitterness. There is substantial variation in the ability to detect bitter compounds among individuals (Reed et al., 2006). Differences in ability to detect compounds including bitter tastants is typically determined by estimating the critical threshold concentration; that is the concentration at which a person can just detect the difference between background (water) and bitter compound-containing samples. There is also much genetic variation within members of the TAS2R family (Kim et al., 2005; Ueda et al., 2001; Wang et al., 2004) and this variation is likely to underpin individual differences in the ability to perceive bitter compounds. Perhaps the best characterised example within the bitter taste receptors which illustrates

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how such genetic variation encodes different states of sensory perception is variation in PROP taste perception encoded by variants of TAS2R38 described above in Section 20.2. More recently links between variation in the other bitter taste receptors and variation in the perception of bitterness by various compounds are being uncovered. For example, variation in the ability to detect the bitterness of the sulfonyl amide sweeteners, acesulfame K and saccharin among Europeans is associated with a specific allele of the TAS2R44 gene (Roudnitzky et al., 2009). An interesting aside to the story of genetic variation in bitter taste perception is the determination of the genotype of a Neanderthal sample at the 145 position of the TAS2R38 gene. Lalueza-Fox et al. (2009) were able to determine that the individual was heterozygous at this position and therefore likely a PROP taster. This example shows that variation in this gene is very old, at least as old as the split between Homo sapiens sapiens and neanderthal of approximately half a million years ago and suggests of the importance of the ability to detect phytonutrients, which at high concentrations may be toxic (Drewnowski and Gomez-Carneros, 2000).

20.2.2 Genetic determinants of sweetness perception The ability to perceive sweet-tasting compounds is encoded by the TAS1R family of taste receptors. Sugars and sweet-tasting amino acids such as glycine are largely recognised by receptors containing TAS1R2 and TAS1R3 subunits. There is substantial variation in the ability to detect sweetness among humans with much of the variation explained by heritable factors (Keskitalo et al., 2007; Reed et al., 2006). Similarly at the molecular level there is variation in the TAS1R1 and TAS1R3 receptor genes but not until very recently has this genetic variation been linked to sweet taste sensitivity. Fushan et al. (2009) found that two variants within the promoter region upstream of the TAS1R3 gene are strongly associated with sweet taste sensitivity, explaining 16% of population variability. Whether this genetic difference also accounts for differences in sweet food consumption is yet to be determined.

20.2.3 Genetic determinants of umami perception The ability to perceive umami-tasting compounds are also encoded by members of the TAS1R family of taste receptors. Umami taste is encoded by receptor complexes containing TAS1R1 and TAS1R3 subunits that detect L-glutamate and L-aspartate. Umami tastants, such as mono sodium glutamate (MSG), have the interesting property that on their own or in combination with other tastants they are not highly pleasant, but in combination with volatile flavours they are able to enhance the pleasurable experience associated with the odours (McCabe and Rolls, 2007). Variation has been observed in the TAS1R3 receptor that is associated with differences

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in umami perception. One particular variant in the TAS1R3 subunit confers a doubling in the savoury rating of a certain concentration of monopotassium glutamate (Chen et al., 2009).

20.2.4 Genetic determinants of saltiness and sourness perception The most important dietary salt is sodium chloride or NaCl, essential for many physiological processes in the body and often perceived as a positive taste attribute. Sourness, on the other hand, is often negatively associated with unripe fruit or soiled foods. Only recently has information regarding the molecular basis of how saltiness and sourness are perceived begun to come to light. Saltiness or more specifically sodium, is perceived by the epithelial sodium channel ENaC (Chandrashekar et al., 2010), while sourness, the perception of acidity, is likely encoded by a specific subfamily of calcium channel (PKD1 family of TRP channel). Variation in these genes is yet to be studied in any detail. However, a twin study does suggest a genetic basis to differences in the ability to recognise sourness but not saltiness (Wise et al., 2007).

20.2.5 Genetic determinants of other sensory modalities There are a number of other minor modalities of taste including the ability to detect fats, coolness, astringency and spices including cayenne. Again only very recently has any information on their likely reception systems come to light. Fat taste or the ability to detect fatty acids is thought to be encoded by the CD36 family of transporters (Laugerette et al., 2005). Cayenne pepper is recognised by the pain and temperature receptor TRPV1, while other spices are thought to be perceived through other members of the TRPV family of ion channels (Gerhold and Bautista, 2009). Encoded by another member of the TRP family of ion channels (TRPM8) is cold sensation, often associated with cooling agents such as menthol (McKemy et al., 2002). While there is currently no link to genetic difference for these thermal tasting sensations there is evidence of variation in the population for them (Bajec and Pickering, 2008). The sensation of astringency is due to the interaction of protein in saliva and phenols found in various food and beverages such as tannins in red wine. While there is evidence for differences among individuals in the perception of this sensation, the genetic basis for this variation is not yet known (Dinnella et al., 2009).

20.3 Genetics of odour perception Aroma is the major determinant of food flavour. While taste contributes five basic modalities to flavour, plus perhaps a few more, the potential contributions of aroma or smell to flavour are vast and arguably limitless. While

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the firing of any bitter taste receptor, even though they are responding to different compounds, is the same, bitterness; the olfaction system interprets both the level of activation and the combinations of receptors that are activated resulting in the many perceived aromas. We tend to give the aromas names based on associated foods, plants and other emitters of the volatiles.

20.3.1 A brief background to odour perception Aromas that enter our nose, whether through the nasal openings (orthronasal) or via the back of the throat (retronasal), are detected in the olfactory epithelium by olfactory sensory neurons. At the surface of each neuron is expressed a single type of odorant receptor that is tuned to a range of structurally related odorants. Binding of odorants to these receptors triggers signals in the sensory neurons that are transmitted through to the olfactory bulb. Here the signals from all the sensory neurons expressing the same receptor converge and are transmitted on to the olfactory cortex within the brain where the signals from all the different populations of sensory neurons are integrated and translated into information about detection and recognition of the odour. Odorant receptors are thought to bind odours under a simple lock and key model with the size and shape of the binding pocket determining what odours can bind (Fig. 20.3). Since the receptors are able to bind many odorants and one odour can be bound by many receptors, a combinatorial coding system based on the integration of these multiple signals allows us to distinguish many thousands of odours using a repertoire of only hundreds of receptors. For additional background information Nobel Laureate Richard Axel offers a concise and accessible account of how the nose and brain perceive odours (Axel, 2006).

20.3.2 Genetic determinants of odour preception Humans are highly variable in their olfactory sensitivity and quality perception (Ayabe-Kanamura et al., 1998). These range from differences in general olfactory ability (e.g., Wysocki and Gilbert, 1989) through to variability in sensitivity towards particular odorants. Variation in the ability to detect individual odours has been described for many compounds found in foods, beverages and personal care products (Snyder, 1931; Amoore et al., 1968; Lison et al., 1980; Pelosi and Pisanelli, 1981; Hirth et al., 1986; Meilgaard, 1993; Kendal-Reed et al., 1998; Bremner et al., 2003; Plotto et al., 2006; Lunde et al., 2008). These studies show variation in the ability to detect such compounds as β-ionone, which has a violet aroma, through to the sulphurous metabolites of asparagus that are excreted in urine. There is an incredible range among individuals in their ability to detect different odorants. For the violet-smelling compound β-ionone, for example, threshold concentrations, or the concentration at which the compound can just be detected,

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Fig. 20.3 The lock and key model of odour binding to odorant receptors. Odorants may contain multiple structural moieties that are recognised by different receptors. These have been stylised so that each odorant contains two detectable structural moieties, so that each odorant is detected by two receptors. The set of activated receptors discriminates one odorant from another even though all odorants are bound by more than one receptor. [Adapted from http://www.colorado.edu/intphys/ Class/IPHY3730/image/figure8-1.jpg]

vary by as much as one hundred million times (Plotto et al., 2006). Furthermore, the distribution of thresholds neatly falls into two major groups, those that can and those that can’t smell the compound. The ability to smell all compounds, however is not that extreme with many distributions of threshold concentrations forming a typical normal or bell-shaped distribution over a hundred or thousand-fold range. However, even for these compounds this results in groups that can and can’t smell the compounds; it is

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just that the distribution is more a continuous grading throughout the concentration range. Twin studies by, for example, Wysocki and Beauchamp (1984), Segal et al. (1995), and Finkel et al. (2001) indicate that this variation is heritable. Then more recently, the first reports describing a genetic basis for differences in human olfactory phenotypes have emerged (Keller et al., 2007; Menashe et al., 2007; Jaeger et al., 2010; Eriksson et al., 2009). In all cases the genetic variation underpinning variation in the ability to detect these odorants lies in or close to genes encoding odorant receptors. The genes for odorant receptors (ORs) are one of the largest gene-families in the human genome, having 855 members (Glusman et al., 2001; Olender et al., 2008; Hasin-Brumshtein et al., 2009). These genes are located throughout the genome often in clusters (Fig. 20.1). The nucleotide sequence of each odorant receptor gene is approximately 1000 base pairs long. Alteration of one of the nucleotides within the gene may encode a different protein sequence and alter the function of the encoded protein (e.g., Keller et al., 2007). Only about 390 of the 855 odorant receptor genes encode functional receptors (Krautwurst, 2008). The rest have lost their function over evolutionary time and are termed pseudogenes (Niimura and Nei, 2007). Interestingly other mammals typically have in the vicinity of one thousand odorant receptor genes (e.g. mice, dogs) with very few pseudogenes, suggestive of a much stronger selection pressure to maintain a large diversity of receptors. This shrinking of the active repertoire of odorant receptor genes in humans seems to have occurred early in primate evolution with monkey and ape species also predicted to have a large proportion of non-functional genes within their genomes (Gilad et al., 2004). The authors go on to note that this loss of functional odorant receptor genes coincides with the evolution of three-colour vision in primates, perhaps indicating a shift to a more visually orientated way of interacting with the environment and each other. Pseudogenes of odorant receptors, however, may still play a role in olfactory acuity (Menashe et al., 2003). At least 61 odorant receptor pseudogenes contain SNPs in their sequence that switch the receptor between functional and non-functional forms, so called segregating pseudogenes (Menashe et al., 2006). Thus, it is possible that individuals with only the inactive form of the segregating pseudogene would not be able to perceive an odorant (Menashe et al., 2006), whereas individuals with functional forms of the receptor would be able to detect the odorant. Menashe et al. (2007) looked at the association between threshold concentration for four aroma compounds and genetic variation in 43 odorant receptor segregating pseudogenes. They found a significant association between the ability to detect the cheesy, sweaty smelling compound isovaleric acid and the segregating pseudogene OR11H7P. Menashe et al. (2007) went on to show using in vitro studies that the active form of the receptor is able to respond to isovaleric acid while the inactive form is not. In another example Keller et al. (2007) described the molecular basis for the variable

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ability to detect and describe androstenone. This steroidal derivative can register as pleasant (sweaty), unpleasant (sweet, floral) or as undetectable among individuals. Using a cell-based screening technique Keller et al. (2007) assessed the ability of a number of human odorant receptors for their ability to detect the compound, finding one (OR7D4) that gave a high level of response to androstenone. They then found that two single amino acid changes within the receptor, common in natural populations, impaired the ability of the receptor to be activated by androstenone. Furthermore, genotypes containing these substitutions were highly associated with whether the compound was regarded as pleasant or not and the amplitude of the perception of the compound among individuals.

20.3.3 A case study on the genetic basis of cis-3-hexen-1-ol preception Jaeger et al. (2010) used a genome wide association (GWA) approach to look for genetic associations with the ability to detect the grassy smelling compound cis-3-hexen-1-ol. This approach is distinct from those employed by Menashe et al. (2007) and Keller et al. (2007) as it makes no assumptions about where in the genome the genetic variation underpinning the trait might be located. Threshold concentrations were estimated for cis-3-hexen1-ol using various concentrations of the odorant in headspaces created in a wine glass. These estimated concentration values were then tested for association with genotypes at over 900 000 SNPs within the human genome determined using a commercial SNP chip and DNA from each of the participants (Jaeger et al., 2010). While only ~50 participants were used in this study, it is more desirable to have numbers over 100 to ensure sufficient statistical power. A number of associated genetic variants were found, but of special interest was a cluster of associated variants that fell together in a small region of chromosome six (Fig. 20.4). Individuals possessing different genotypes for one of these SNPs (rs9295791) had markedly different perception abilities for cis-3-hexen-1-ol (Fig. 20.5). Individuals with an AA genotype for rs9295791 had the lowest cis-3-hexen-1-ol perception threshold (smellers), while AG heterozygous individuals had higher perception thresholds and the individual with the GG genotype had the highest cis-3-hexen-1-ol threshold (non-smellers). If this SNP was the one that controlled functionality in the OR responsible for cis-3-hexen-1-ol perception, then we would, on the basis of this single piece of genotype information, be able to predict people’s sensitivity to cis-3-hexen-1-ol. Those with an AA genotype at rs9295791 would be expected to be very sensitive to the odour of cis-3hexen-1-ol, whereas those with the GG genotype would be expected to be somewhat unable to smell this odorant. Interestingly this SNP is very close to an odorant receptor gene, OR2J3, and overall this set of significantly associated SNPs overlays a region containing not just one odorant receptor gene, but a cluster of 25 odorant receptor genes. Work is now underway to

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A 8

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Fig. 20.4 Genome wide association plot for SNPs associated with cis-3-hexen-1-ol perception on chromosome six. Associations are plotted as –log10 P values for each SNP on (A) the entire chromosome 6 and (B) within the associated region on chromosome 6. Each SNP is plotted according to its chromosomal location. The size of the plotted point is proportional to the strength of association. The locations of odorant receptor genes are plotted at the bottom of (B), beneath the P value plot as vertical lines. Reproduced from Jaeger et al. (2010).

confirm these results and determine which one/s of these genes is involved in detecting cis-3-hexen-1-ol and what the nature of the genetic variation is within the genes that underpins the difference in ability to detect the compound (Jaeger et al., 2010). Cis-3-hexen-1-ol is an important flavour component of many fruit and vegetables from green kiwifruit to tomatoes and is also an important flavour compound in many white wines. Therefore it will be of significant interest to test whether variation within this region of the genome might account for the differences in descriptor and liking of these foods and beverages containing the compound among individuals.

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Concentration (ppm)

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Fig. 20.5 Detection thresholds for cis-3-hexen-1-ol by genotype at the SNP rs9295791. Error bars indicate the standard error of the thresholds. No error bar is provided for the threshold for genotype GG, since n = 1. Reproduced from Jaeger et al. (2010).

While these types of studies are just the beginning of finding the genetic basis to differences in sensory ability for aromas they do show much promise. To actually be able to find such associations suggests that only a few genes, or even only one gene of major effect, are responsible for the trait. Of course we are only seeing the publication of associations that find a result and we don’t know how many studies are not. Even so, the major limitation to finding associations seems most likely to be the lack of research projects rather than the inability to find significant gene associations. Undertaking such studies is not trivial requiring careful design to obtain accurate threshold concentration estimates, often difficult for odour compounds, and collect these threshold data and derive genotype data from sufficient participants to allow a reasonable chance of finding statistically significant associations. We’d expect to see many more associations over the coming few years especially for industrially relevant aroma compounds and flavour-associated sensations (e.g., vanilla, cream, buttery, smoky, pine, lavender, citrus).

20.4 The impact of genetic variation on food preference and consumption So far in the chapter we have covered some background on human genetics to enable a review of the scientific literature on the genetic basis of sensory perception, particularly of tastes and odours. However, the big question is whether the associations of genetic variation to sensory acuity flows through to food preference or even food consumption or product purchasing behaviours.

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Many studies have explored links between food preferences pertaining to different modalities (e.g., bitter, sweet, salty, sour, flavour, etc.) to PROP taster/nontaster status. This research has been extensively reviewed in Drayna (2005), Wooding (2006) and most recently by Tepper (2008), where she concluded that PROP status is a useful marker for food preferences and diet selection. An inverse relationship has been demonstrated in young women between PROP sensitivity and the acceptance of tart citrus, Brassicas, spinach and coffee. Female PROP tasters also show a decreased acceptance of sweet and fatty foods. People carrying these different genotypes of TAS2R38 perceive different levels of bitterness in vegetables such as broccoli and turnips that contain glucosinolates that are structurally similar to PTC. Therefore, if regarded negatively, people that detect these foods as strongly bitter may avoid them and miss out on any beneficial components found in these foods (Garcia-Bailo et al. 2009). One study has found that TAS2R38 taster genotypes associate with a preference for sweettasting foods in children, but not in adults (Mennella et al., 2005). While these studies overall do support a relationship between PROP genotypes and food preferences, they also highlight the difficulties in undertaking these studies particularly in their design to take into account other variables that influence food preference and selection such as repeat purchasing habits, culture, etc. (Tepper, 2008). Another approach to looking at relationships between genotype and food preference is the genome wide association approach. Rather than looking at known sensory acuity genotypes, all genetic variants are tested for association for food preferences. Using this strategy Mäestu et al. (2007) found that higher consumption of sweet foods is associated with a particular variant within the adrenergic alpha 2A receptor gene within Estonian children. This particular variant is also associated with increased levels of sugar metabolism and increased daily calorie intake. In a similar study Eny et al. (2008) found an association between a genetic variant in the type 2 glucose transporter and sugar intake among Canadians. They found that members of one particular genotype class ate on average 16 grams more than the opposite genetic variant. In a separate study, variation in the serotonin receptor 5-HT2A was associated with levels of protein consumption in Brazilians (Lima et al., 2006). What is interesting about these studies is that significant associations were found in genes that are not involved in the peripheral sensing of these compounds such as the receptors associated with detecting sugars and amino acids. Instead genes involved in uptake, metabolism or higher level perception were associated. These studies suggest that we need to keep an open mind as to what genes may impact product preference and not just focus on initial reception. Currently there is no information regarding any of the other taste modalities and any aroma genotypes and relationships to food preferences. While a number of studies are underway in this area this remains a significant gap that needs filling before the field will have confidence to fully promote the

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use of genetic variation information in the development of products where the consumer reaction to taste and aroma characteristics are important.

20.5 Industry opportunities and issues We have summarised the recent advances in the emerging science of the genetics of flavour and taste perception and links to product preference and selection. While there are increasing examples of these linkages, how industry will use this information is not clear. Here we outline the opportunities for industry as well as discuss some of the issues around the implementation of the knowledge.

20.5.1

Opportunities to harness knowledge of the genetic basis of sensory perception in new product development The high level vision for the application of this new knowledge is the ability to specifically target products to various segments in the market predicted to like the taste/aroma of the product. Let us step through some possible scenarios. If, for example, the genetic basis of the ability to smell, say, vanilla is determined to be encoded largely by a variant in a single gene, with one genotype able to smell vanilla well and another unable to detect the flavouring at levels used in the product of interest, then gene marker technology could be used to quickly survey a potential market and determine the proportion of the market that could smell vanilla and likely enjoy the product based on the flavouring. If the proportion of smellers is high in a test market then you’d likely target this market for release and even hold back on release in other markets where the proportion of smellers was lower. This logic could be further extended to target the use of different flavours or tastants in a product range for different markets based on predicting which flavours can be detected by the majority of the people in each of the different markets. So for one market the product might be flavoured with vanilla and in a second with another flavour. But, I hear you say, why not just test the various markets with vanilla. Well of course you could do this. However, the real power comes from where, say, a novel flavour is developed, or at least a new to the market flavour is commercialised, a major driver of the major flavour houses. If the genetic basis of its sensory acuity is determined then the markets where this new flavour will be most successful can be predicted. This is becoming even more relevant as the databases of human genetic variation expand (e.g., International HapMap Consortium 2005, 2007; The Human Genetic Diversity Project) and estimates of frequencies of particular genetic variants in different regions of the world become more accurate and fine-scale. Such a strategy might work best in markets that are ethnically simple in their composition rather than ethically diverse. This is because ethnically

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diverse markets are more likely also genetically complex with the ability/ inability to detect and therefore like/dislike certain compounds encoded by different genes in different genetically related groups. While this may be an issue it is too early to tell whether such a strategy will be less successful in diverse markets. Of course you could argue why not just make a range of products available differentiated by flavour and see which do best. However, this approach may not always be practical or financially viable. Certainly though, if a genetic marker is available for a flavour it could be used to get some preliminary objective data on what variability in sensitivity occurs in the population and make early decisions in the new product development cycle on whether to keep working on that flavour. It may be that there is high variability in perception which could mean more people potentially not detecting the flavour in the focal product. Another possibility that this technology might bring is to genotype children to allow predictions of their flavour preferences. If their genotypes are significantly different within a market from those of adults this might signal a shift in the flavour preference. This might also give time to develop any new products for adults based on knowledge gained from the cohort as children. The ultimate personalisation of products is the ‘market of one’ approach where each person is targeted individually. For a genetic approach to personalisation to be feasible this would require a large proportion of the population to have personal genetic information which they could use to determine their most suitable product, say using an internet site. There are companies, such as Navigenics and 23andMe, offering personalised genotyping services and these are being used by members of the public already for questions of ancestry and risks, among other things. Indeed 23andMe already offer advice on whether you are likely to enjoy bitter foods based on the TAS2R38 polymorphism. A future possibility is that products could be differentiated by flavour with recommendations for which flavour to choose based on a consumer’s genotype. At the very least, the ability to proportion variation in sensory data by genotype to help uncover other types of variation associated with hedonics or purchasing behaviour will be a very important use of this new knowledge. This is, such data may be used as a way of portioning data much like is currently done with variables such as age, gender, lifestyle, etc. The percentage of variation that is explained by genotype could then be factored into a study attempting to determine the factors driving food selection. More detailed knowledge of the compounds that particular receptors can detect may allow the development of sets of associations based on genotype. That is if a person has high sensitivity to a particular flavour, they may also have high sensitivity to another that also binds the same receptor. If these olfactory associations were known then we might be able to predict flavours that would be suitable to a market segment based on chemical composition. This approach would be even more useful when designing new odour molecules. There are other types of useful associations that may

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emerge from the genetics. Since genes are positioned in a genome within chromosomes and many of the genes encoding receptors involved in sensory perception are clustered, there may be associations in sensory perception or even with other consumer focused product attributes that are derived from genetic linkage (that is genes being in close proximity with each other). For example, if a gene that confers high sensitivity for spearmint flavour is next to a gene that encodes low sensitivity to chocolate then knowing the sensory perception ability for one flavour can be used to infer the ability to detect the second and also to help in the design of products. The development of spearmint chocolate for this group may not work or may need different ratios of the two compounds than that determined by the noses of the product development team who may have a different genotype. In fact this raises another point worth mentioning: that with more knowledge of the genetic bases of sensory perception, genotyping the members of your product development team and running them through a number of simple sensory acuity tests for compounds that are being used in product development becomes a very sensible thing to do, especially if their sensory acuity is very different from the consumers the products are being developed for. This is also applicable to linkage with genes involved in other consumer traits. For example, if a gene for a particular sensory acuity is close to a gene which contains variation associated with health and wellness that is impacted by the food or beverage. This field of linking genotype to health and wellness associated with foods is known as nutrigenomics and is exactly parallel to the approach outlined here for gastronomics. Therefore products that have been developed for health based on genotype could simultaneously be personalised for flavour through knowledge of whether the genes are close on the chromosome. The fact that consumers are already being genotyped to allow them to purchase targeted health-based food shows that some people have already bought into the concept of personalised foods and therefore will be likely to also purchase personalised food for flavour. Bitterness is an important issue for many additives in food or in the development of new foods with enhanced consumer attributes. Many artificial sweeteners for example have a bitter aftertaste for some people (Roudnitzky et al., 2009). Being able to predict for which markets and people this will be an issue or using this knowledge to help in the development of novel sweeteners that cannot bind bitter taste receptors will be useful. For the development of new foods and beverages with enhanced health benefits, many of the compounds that are targeted, such as flavanols, are themselves also bitter. However it is not known whether there is significant variation in populations for the bitterness perception associated with these compounds. It must be pointed out here again that knowledge of the genetics of sensory perception not only applies to food and beverages but also many other types of product ranges including toiletries, perfumes and many

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household cleaning products. In fact many different products are scented and for household products a current trend is to use food-related flavours such as chocolate in personal care products such as shampoo. With this in mind this new genetic knowledge will apply equally well. Indeed some of these products contain only a single compound-based aroma and therefore knowledge of the genetics of target market’s sensory acuity of this single compound would be very useful for predicting its value in addition and also the level of compound that should be included in the product.

20.5.2

What is happening and what needs to happen to realise these opportunities? There are many opportunities for using knowledge of the genetics of sensory perception in new product development, but there are also many things that need to happen before new product developers can take full advantage and within their financial and temporal limitations. Certainly the cost of genotyping is coming down very quickly, to the point where a genotype will soon be obtainable for an individual for one hundred US dollars or even less. This level of cost will likely bring the technology within the reach of the medical industry for medical diagnostics or for members of the public for personal interest reasons whether that be for health or wishing to know about ancestry. What is likely going to be more limiting to the uptake of this overall approach is the identification of the genetic associations with sensory acuity and product selection. To date there are only a few examples where associated genotypes have been identified as outlined above in Sections 20.3 and 20.4. Many more will be required as the diversity of flavourings and scents used by industry in products is large. The research is limited currently by the speed of phenotyping. For example, to determine a critical threshold concentration for one compound, testing across many concentrations with replication is required for each individual. These tests have a high setup cost and are time-consuming. They also need to leave sufficient time between tests and breaks to reduce the risk of olfactory fatigue setting in. Efforts to reduce the level of replication while maintaining statistical rigor and streamline phenotyping methodologies and analysis will help here. Furthermore, for these gene association studies typically high numbers of participants are required to provide the levels of statistical power necessary to find the associations. Some power is provided by the fact that the data is quantitative but numbers over one hundred are still typically recommended. Qualitative studies such as those undertaken for disease associations require much larger sample sizes of 1000 or even up to 10 000 participants. The number of participants, time required for phenotyping and current cost of genotyping all add up to a reasonable level of cost associated with this kind of project. This together with the number of flavours to find genetic association for mounts up to a large cost required for this baseline

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information. Much of this cost will likely be borne by government granting agencies and relevant industries with large research budgets. Another issue is whether the genotype associations found in one population equate to a second population. This needs to be true for the ability to extrapolate the findings to other markets. It may be possible that in a second population a variant of a gene occurs that offers even higher levels of sensory acuity compared with any seen in the original test population. However, in this case it is not necessary to undertake the full genetic test but just use the genetic variant identified in the first population and test for association between this one variant and the sensory perception in the second or third population. Typically this would involve using a single SNP test which would just require taking a buccal swap from the participants and using much simpler discriminating dose tests to determine sensory acuity. For uptake and successful utilisation of these research results and incorporation into new product development industries will need to have staff familiar with the basic principle of sensory science and genetics. While they may not actually undertake the research described above they may support such research or at least have the skills and know how to use this information successfully. People with these skills in industry may not be there today but need to either be developed in house or hired in. The overall pace of the genetic revolution is almost frightening and having gene savvy personnel in residence to translate these developments and help industries make decisions in this area will become more important.

20.5.3 Assumptions, issues and ethics Without wanting to detract from the potential of this new area, there are a number of important assumptions that need to be tested and potential issues that need to be addressed for this research field to yield improved product innovation. Arguably we need to know a lot more about valence; that is whether we like or dislike a flavour, before we can implement this new knowledge. Just because you can smell or taste something well doesn’t mean you are going to like it. In fact, it may well be the exact opposite in some cases. This is still an area we know little about, certainly in terms of whether genetics has a strong influence. Genetic predictors of valence would arguably be even more valuable than genetic predictors of sensory acuity as these are more likely to lead directly to preference. A major assumption of the field is that knowledge of single compound sensory acuity or the basis for preferring a single compound will be strong enough to have impact in a complex mixture. For many non-food products where there is the opportunity to use single or simple blends this translation may well hold, or for foods and beverages where single or simple blends dominate. But in foods and beverages with complex blends such as wines it is going to be interesting to see whether simple genetic traits are going

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to explain a significant portion of the variation in preference or product purchasing. Similarly cultural practice is going to be a huge influence in the foods people choose to eat and the products people buy. Even though an individual may show a clear range of sensory acuities for different compounds, these may not be a good predictor of preference due to repeated cultural practice. It is quite possible that many genes will be involved in these traits and that these genes may be different for different populations. In some cases it could well be that the genetic basis for sensory acuity will have to be determined for each market. There is little data on this at present to know whether this is likely. Furthermore, the trend towards more multicultural societies will likely mean that overall market characteristics based on gene frequency information are less likely to be useful. There will be no market average, if you like. Therefore for market evaluation this technology might be best for markets that are dominated by a single ethnic grouping such as Japan. Even in highly multicultural societies however, the idea of personalisation of the product would still hold even with complete admixing. While cost is also an issue it is becoming less and less so. Within ten years cost will be the least important of all these issues and barriers to implementation of the technology, with factors such as access to skills and ability to translate this new knowledge into product development being more important. The use of genetic information to guide product development, be it for foods and beverages, or personal care products is likely to come under scrutiny from an ethical perspective – and rightly so. Ethics is the systematic reflection on the moral aspects of life and its conflicts and plainly there are ethical issues to be considered in the genomics era, for example with regards to how personal genetic information will be handled and whether organisations engaged in product innovation will be ethical with the use of this information. That questions be asked about information use and confidentiality does not mean that commercial use of this emerging science is completely doomed, but it is appropriate that sound systems be put in place for how such information is safeguarded and it is appropriate to allow the opportunity for public discussion of scientific developments in this area. The area of personalised nutrition, which is informed by molecular nutrition research, is more mature than the use of genetic information to link sensory perception to acceptability as an approach to guiding new product development. It therefore offers some insights to some of the ethics questions that may surface. Briefly, personalised nutrition identifies individual nutritional needs based on genetic make-up (Williams et al., 2008) and builds on the promise that a deeper understanding of how nutrition influences the activity of human genes may contribute to improved quality of life by providing opportunities to develop food products or dietary advice tailored

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on the nutritional needs of groups in society, or even individuals (e.g., Ronetaltap and van Trijp, 2007). Ethical questions that can be asked in connection with this development are illustrated by Görman (2007), who noted: “Personalised nutrition will be based on genetic information. It is a common understanding that such information is perceived as special, problematic and perhaps even frightening for people in general. Parallels have been drawn with genetically modified foods. Will personalised nutrition face the risk of being regarded as controversial in the same way as genetically modified food has been?” (p. 56). Furthermore, he asked: “Should healthy people without identified health risks who ask for genetic tests be offered opportunistic screening? . . . In most cases the result of the test will be an alleviation of anxiety, but when the test is ‘positive’, the person in question is turned into a patient, perhaps for the rest of his or her life. The future will be characterised by a consciousness of an actual, or possible, infirmity. In this way the well-being of a person who considers himself or herself as normal, may not be supported, but instead decreased” (p. 57). It should not be forgotten that food means much more than nutrition. A meal is a social event, an important manifestation of the relationship with others. This means that food is an important aspect of human happiness and well-being, and not only an instrument for health. Will personalised nutrition contribute to a good life? Or will personalised nutrition instead limit the role of some or all food to medicine and transform eating to a lifelong medication? (p. 56). In a similar vein: “A major concern when it comes to tailor-made diets is that normal healthy food may be overlooked. Such a development may diminish the health of those concerned. Instead directions should be found for using knowledge achieved by nutritional genomics in such a way that the welfare of those in need can be increased.” (p. 58). At a more operational level, the issue of voluntary participation and informed consent to genetic participation is extremely important, and it is already clear that better procedures are needed in this area (e.g., Marshall et al., 2006). Certainly it is clear ethical considerations need to be addressed and worked through for the use of genetic information in developing new products personalised for flavour.

20.6 Summary The human genetic revolution has the potential to transform new product development in the food/beverage and personal care industries. The present chapter has introduced the emerging science of identifying genetic determinants of human sensory acuity and understanding how genetic variability impacts consumption and use behaviours. The extant literature already holds examples of genes that control variability in odour and taste perception. Similarly, there is some evidence to suggest that human genetic

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variation does indeed exert an influence on food preference and consumption. Thus, the scientific foundations for ability to harness the genetic revolution to inform new product development already exist to a limited extent. More fundamental research is needed before industry will be able to benefit directly from the emerging science, and a number of issues will need to be addressed before this new science will be used routinely in new product development.

20.7 Sources of further information and advice For background on the genetic basis of variation in odour acuity try: Garcia-Bailo, B., Toguri, C., Eny, K. M. and El-Sohemy, A. (2009) Genetic variation in taste and its influence on food selection. OMICS 13: 69–80. or Tepper, B. J. (2008) Nutritional implication of genetic taste variation: The role of PROP sensitivity and other taste phenotypes. Annual Review of Nutrition 28: 367–388. For background on the genetic basis of variation in odour acuity try: Hasin-Brumshtein, Y., Lancet, D. and Olender, T. (2009) Human olfaction: from genomic variation to phenotypic diversity. or Jaeger, S. R., MacRae, J. F., Salzman, Y., Williams, L. and Newcomb, R. D. (2010) A preliminary investigation into a genetic basis for cis-3-hexen1-ol odour perception: a genome-wide association approach. Food Quality & Preference 21: 121–131. For background on human genetics as it enters the post-genome era, try a textbook like: “Human Molecular Genetics” by Tom Strachan and Andrew P Read (3rd edition), 2004 Garland Science, New York. ISBN 0-8153-4182-2. Also note that http://www.ncbi.nlm.nih.gov/genome/guide/human/resources.shtml is an excellent human genome resource. For background on sensory threshold testing, which is used to obtain phenotype meaures on human sensory acuity, try a text book like: Lawless, H. T. and Heymann, H. (1999) Sensory Evaluation of Food. Principles and Practices. Aspen Publishers, New York. or ASTM E679-04 (2004) Standard Practice for Determination of Odor and Taste Thresholds by a Forced-Choice Ascending Concentration Series Method of Limits. ASTM International, West Conshohocken, PA, USA.

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For background on genome-wide association studies, try texts like: Chanock, S. J., Manolio, T., Boehnke, M., Boerwinkle, E., Hunter, D. J., Thomas, G., Hirschhorn, J. N., Abecasis, G., Altshuler, D., Bailey-Wilson, J. E., Brooks, L. D., Cardon, L. R., Daly, M., Donnelly, P., Fraumeni Jr, J. F., Freimer, N. B., Gerhard, D. S., Gunter, C., Guttmacher, A. E., Guyer, M. S., Harris, E. L., Hoh, J., Hoover, R., Kong, C. A., Merikangas, K. R., Morton, C. C., Palmer, L. J., Phimister, E. G., Rice, J. P., Roberts, J., Rotimi, C., Tucker, M. A., Vogan, K. J., Wacholder S., Wijsman, E. M., Winn, D. M. and Collins, F. S. (2007) Replicating genotype-phenotype associations. Nature 447: 655–660. or Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14 000 cases of seven common diseases and 3000 shared controls. Nature 447: 661–678. or Pearson, T. A. and Manolio, T. A. (2008) How to interpret a genome-wide association study. Journal of the American Medical Association 299(11): 1335–1344. or http://www.genome.gov/26525384, for a Catalog of Published GenomeWide Association Studies. or McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A. and Hirschhorn, J. N. (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics 9: 356–369. or Hirschhorn, J. N. and Daly, M. J. (2005) Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics 6: 95–108.

20.8 References amoore, j. e., venstrom, d., & davis, a. r. (1968). Measurement of specific anosmia. Perception and Motor Skills, 26, 143–164. axel, r. (2006). The molecular logic of smell. Scientific American, 16(3), 69–75. ayabe-kanamura, s., schicker, i., laska, m., hudson, r., distel, h., kobayakawa, t., & saito, s. (1998). Differences in perception of everyday odors: a JapaneseGerman cross-cultural study. Chemical Senses, 23, 31–38. bachmanov, a. a. & beauchamp, g. k. (2007). Taste receptor genes. Annu. Rev. Nutr., 27, 389–414. bajec, m. r. & pickering, g. j. (2008). Thermal taste, PROP responsiveness, and perception of oral sensations. Physiology & Behaviour, 95, 581–590. bremner, e. a., mainland, j. d., khan, r. m., & sobel, n. (2003). The prevalence of androstenone anosmia. Chemical Senses, 28, 423–432. bufe, b., breslin, p. a. s., kuhn, c., reed, d. r., tharp, c. d., slack, j. p., kim, u.-k., drayna, d., & meyerhof, w. (2005). The molecular basis of individual differences

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in phenylthiocarbamide and propylthiouracil bitterness perception. Current Biology, 15, 322–327. celera genomics sequencing team (2001). The sequence of the human genome. Science, 291, 1304–1351. chandrashekar, j., kuhn, c., oka, y., yarmolinsky, d. a., hummier, e., ryba, n. j. p., & zuker, c. s. (2010). The cells and peripheral representation of sodium taste in mice. Nature, 464, 297–301. chandrashekar, j., mueller, k. l., hoon, m. a., adler, e., feng, l., guo, w., zuker, c. s., & ryba, n. j. p. (2000). T2Rs function as bitter taste receptors. Cell, 100, 703–711. chen, q. y., alarcon, s., tharp, a., ahmed, o. m., estrella, n. l., greene, t. a., rucker, j., & breslin, p. a. (2009). Perceptual variation in umami taste and polymorphisms in TAS1R taste receptor genes. Am J Clin Nutr., 90(3), 770S–779S. dinnella, c., recchia, a., fia, g., bertuccioli, m., & monteleone, e. (2009). Saliva characteristics and individual sensitivity to phenolic astringent stimuli. Chemical Senses, 34, 295–304. donnelly, p. (2008). Progress and challenges in genome-wide association studies in humans. Nature, 456, 728–731. drayna, d. (2005). “Human taste genetics.” Annu Rev Genomics Hum Genet, 6, 217–235. drewnowski, a. & gomez-careros, c. (2000). Bitter taste, phytonutrients, and the consumer: a review. Am. J. Clin. Nutr., 72, 1424–1435. eny, k. m. et al. (2008). Genetic variant in the glucose transporter type 2 is associated with higher intakes of sugars in two distinct populations. Physiol Genomics, 33(3), 355–360. eriksson, n., macpherson, j. m., tung, j., hon, l., naughton, b., saxonov, s., avey, l., wojcicki, a., pe’er, i., & mountain, j. (2009). Web-based, participation-driven studies yield novel genetic associations for common traits. American Society of Human Genetics Annual Meeting. finkel, d., pedersen, n. l., & larsson, m. (2001). Olfactory functioning and cognitive abilities: A Twin Study. Journal of Gerontology: Psychological Sciences, 56B(4), 226–233. fushan, a. a., simons, c. t., slack, j. p., manichaikul, a., & drayna, d. (2009). Allelic polymorphism within the TAS1R3 promoter is associated with human taste sensitivity to sucrose. Curr. Biol., 19, 1288–1293. garcia-bailo, b., toguri, c., eny, k. m., & el-sohemy, a. (2009). Genetic variation in taste and its influence on food selection. OMICS, 13, 69–80. gerhold, k. a. & bautista, d. m. (2009). Molecular and cellular mechanisms of trigeminal chemosensation. Ann. N.Y. Acad Sci, 1170, 180–189. gilad, y., przeworski, m., & lancet, d. (2004). Loss of olfactory receptor genes coincides with the acquisition of full trichromatic vision in primates. PLoS Biol., 2, E5. glusman, g., yanai, i., rubin, i., & lancet, d. (2001). The complete human olfactory subgenome. Genome Research, 11, 685–702. görman, u. (2007). Some ethical issues raised by personalised nutrition. Genes and Nutrition, 2, 55–58. hasin-brumshtein, y., lancet, d., & olender, t. (2009). Human olfaction: from genomic variation to phenotypic diversity. Trends in Genetics, 25, 178–184. hirth, l., abadanian, d., & goedde, h. w. (1986). Incidence of specific anosmia in Northern Germany. Human Heredity, 36(1), 1–5. international hapmap consortium. (2005). A haplotype map of the human genome. Nature, 473(7063), 1299–1320. international hapmap consortium. (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature, 449(7164), 851–861.

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international human genome sequencing consortium (2001). Initial sequencing and analysis of the human genome. Nature, 409, 860–921. jaeger, s. r., mcrae, j. f., salzman, y., williams, l., & newcomb, r. d. (2010). A preliminary investigation into a genetic basis for cis-3-hexen-1-ol odour perception: A genome-wide association approach. Food Quality and Preference, 21, 121–131. keller, a., zhuang, h., chi, q., vosshall, l. b., & matsunami, h. (2007). Genetic variation in a human odorant receptor alters odour perception. Nature, 449, 468–472. keskitalo, k., knaapila, a., kallela, m., palotie, a., wessman, m., sammalisto, s., peltonen, l., tuorila, h., & perola, m. (2007). Sweet taste preferences are partly genetically determined: identification of a trait locus on chromosome 16. Am. J. Clin. Nutr., 86, 55–63. kim, u., wooding, s., ricci, d., jorde, l. b., & drayna, d. (2005). Worldwide haplotype diversity and coding sequence variation at human bitter taste receptor loci. Hum. Mutat., 26, 199–204. kim, u.-k., jorgenson, e., coon, h., leppert, m., risch, n., & drayna, d. (2003). Positional cloning of the human quantitative trait locus underlying taste sensitivity to phenylthiocarbamide. Science, 299, 1221–1225. kendal-reed, m., walker, j. c., morgan, w. t., lamacchio, m., & lutz, r. w. (1998). Human responses to propionic acid. I. Qualification of Within- and Betweenparticipant variation in perception by Normosmics and Anosmics. Chemical Senses, 23, 71–82. krautwurst, d. (2008). Human Olfactory Receptor Families and Their Odorants. Chemistry & Biodiversity, 5, 842–852. lalueza-fox, c., gigli, e., de la rasilla, m., fortea, j., & rosas, a. (2009). Bitter taste perception in Neanderthals through the analysis of the TASR38 gene. Biol. Lett., 5, 809–811. laugerette, f., passilly-degrace, p., patris, b., niot, i., febbraio, m., montmayeur, j. p., & besnard, p. (2005). CD36 involvement in orosensory detection of dietary lipids, spontaneous fat preference, and digestive secretions. J. Clin. Invest., 115, 3177–3184. lima, p. s., cruz, i. b. m., schwanke, c. h. a., netto, c. a., & licinio, j. (2006). Human food preferences are associated with a 5-HT2A serotonergic receptor polymorphism. Mol. Psychiatry, 11, 889–891. lison, m., blondheim, s. h., & melmed, r. n. (1980). A polymorphism of the ability to smell urinary metabolites of asparagus. British Medical Journal, 281, 1676–1678. lunde, k., skuterud, e., nilsen, a., & egelandsdal, b. (2008). A new method for differentiating androstenone sensitivity among consumers. Food Quality and Preference, 20, 304–311. mäestu, j. et al. (2007). “Human adrenergic alpha 2A receptor C-1291G polymorphism leads to higher consumption of sweet food products.” Mol Psychiatry, 12(6), 520–521. marshall, p. a., adebamowo, c. a., adeyemo, a. a., ogundiran, t. o., vekich, m., strenski, t., zhou, j., prewitt, t. e., cooper, r. s., & rotimi, c. n. (2006). Voluntary participation and informed consent to internaltional genetic research. American Journal of Public Health, 96(11), 1989–1995. mccabe, c. & rolls, e. t. (2007). Umami: a delicious flavor formed by convergence of taste and olfactory pathways in the human brain. Eur. J. Neurosci., 25, 1855–1864. mckemy, d. d., neuhausser, w. m., & julius, d. (2002). Identification of a cold receptor reveals a general role for TRP channels in thermosensation. Nature, 416, 52–58.

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meilgaard, m. (1993). Individual differences in sensory threshold for aroma chemicals added to beer. Food Quality and Preference, 4, 153–167. menashe, i., man, o., lancet, d., & gilad, y. (2003). Different noses for different people. Nature Genetics, 34, 143–144. menashe, i., aloni, r., & lancet, d. (2006). Variations in the human olfactory receptor pathway. Cellular and Molecular Life Sciences, 63, 1485–1493. menashe, i., aaffy, t., hasin, t., goshen, s., yahalom, v., luetje, c. w., & lancet, d. (2007). Genetic elucidation of human hypersomia to isovaleric acid. PLoS Biology, 5(11), e284. mennella, j. a., pepino, m. y., & reed, d. r. (2005). Genetics and environmental determinants of bitter perception and sweet preferences. Paediatrics, 115, 216–222. niimura, y. & nei, m. (2007). Extensive gains and losses of olfactory receptor genes in mammalian evolution. PLoS One, 2, e708. olender, t., lancet, d., & nebert, d. (2008). Update of the olfactory receptor gene (OR) superfamily. Human Genomics, 3, 87–97. pelosi, p. & pisanelli, a. m. (1981). Specific anosmia to 1,8-cineole: the camphor primary odour. Chemical Senses, 6, 87–93. pennisi, e. (2007). Breakthrough of the year: Human genetic variation. Science, 318(5858), 1842–1843. plotto, a., barnes, k. w., & goodner, k. l. (2006). Specific anosmia observed for β-lonone, but not for α-ionone: Significance for flavor research. Journal of Food Science, 71, 401–406. reed, d. r., tanaka, t., & mcdaniel, a. h. (2006). Diverse tastes: genetics of sweet and bitter perception. Physiol. Behav., 88, 215–226. ronetaltap, a. & van trijp, h. (2007). Consumer acceptance of personalised nutrition. Genes and Nutrition, 2, 85–87. roudnitzky, n., bufe, b., wooding, s., thalman, s., engel, a., & meyerhof, w. (2009). Phenotype-genotype correlation in individuals’ sensitivities to the bitter off-taste of sulfonyl amide sweetners. Pangborn abstract, O3.1. segal, n. l., topolski, t. d., wilson, s. m., brown, k. w., & araki, l. (1995). Twin analysis of odor identification and perception. Physiology & Behavior, 57(3), 605–609. sherry, s. t., ward, m.-h., kholodov, m., baker, j., phan, l., smigielski, e. m., & sirotkin, k. (2001). dbSNP: The NCBI database of genetic variation. Nucleic Acids Research, 29, 308–311. snyder, l. h. (1931). Inherited taste deficiency. Science, 74, 151–152. tepper, b. j. (2008). Nutritional implications of genetic taste variation. The role of PROP sensitivity and other taste phenotypes. Annual Review of Nutrition, 28, 367–388. ueda, t., ugawa, s., ishida, y., shibata, y., murakami, s., & shimada, s. (2001). Identification of coding single-nucleotide polymorphisms in human taste receptor genes involving bitter tasting. Biochem. Biophys. Res. Commun., 285, 147–151. wang, x., thomas, s. d., & zhang, j. (2004). Relaxation of selective constraints and loss of function in the evolution of human bitter taste receptor genes. Hum. Mol. Genet., 13, 2671–2678. wheeler, d. l., barrett, t., benson, d. a., bryant, s. h., canese, k., chetvernin, v., church, d. m., dicuccio, m., edgar, r., federhen, s., feolo, m., geer, l. y., helmberg, w., kapustin, y., khovayko, o., landsman, d., lipman, d. j., madden, t. l., maglott, d. r., miller, v., ostell, j., pruitt, k. d., schuler, g. d., shumway, m., sequeira, e., sherry, s. t., sirotkin, k., souvorov, a., starchenko, g., tatusov, r. l., tatusova, t. a., wagner, l., & yaschenko, e. (2007). Database resources of the National Centre for Biotechnology Information. Nucleic Acids Research, 36, D13–D21.

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williams, c. m., ordovas, j. m., lairon, d., hesketh, j., lietz, g., gibney, m., & van ommen, b. (2008). The challenges for molecular nutrition research 1: linking genotype to healthy nutrition. Genes and Nutrition, 3, 41–49. wise, p. m., hansen, j. l., reed, d. r., & breslin, p. a. (2007). Twin study of the heritability of recognition thresholds for sour and salty taste. Chem. Senses, 32, 749–754. wooding, s. (2006). “Phenylthiocarbamide: a 75-year adventure in genetics and natural selection.” Genetics, 172(4), 2015–2023. wysocki, c. j. & beauchamp, g. k. (1984). Ability to smell androstenone is genetically determined. Proceeding of the National Academy of Sciences of the United States of America, 81(15), 4899–4902. wysocki, c. j. & gilbert, a. n. (1989). National geographic smell survey. Effects of age are heterogeneous. Annals of the New York Academy of Science, 561, 12–28.

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21 Neuroimaging of sensory perception and hedonic reward M. G. Veldhuizen, The John B. Pierce Laboratory and Yale University School of Medicine, USA

Abstract: A multitude of factors drive preferences, and not all aspects of preferences are open to conscious evaluation, and this is why neuroimaging may provide insight into the measurement of preferences of food and personal care products. In this chapter we discuss different neuroimaging techniques and their appropriateness for measuring neural response to food and personal care products. We review the neural correlates involved in the encoding of food reward and the processing of the pleasantness of personal care products. Next we discuss the neural processes involved in decision making. We then explain common pitfalls for new product developers and neuroscientists interested in studying the neural correlates of preference behavior. We conclude this chapter with a review of recent advances in the study of preferences. Key words: taste, smell, food, chemosensory, food reward, neuroimaging. Abbreviations: ACC anterior cingulate cortex; MEG magnetoencephalography; EEG electroencephalography; PET positron emission tomography; fMRI functional magnetic resonance imaging; BOLD blood oxygenized level dependent; fNIRS response functional near infrared spectroscopy; mOFC medial orbitofrontal cortex; lOFC lateral orbitofrontal cortex; mPFC medial prefrontal cortex; DLPFC dorsolateral prefrontal cortex; prACC pregenual anterior cingulate cortex.

21.1 Introduction Why do we eat specific foods? There seem to be a multitude of factors that drive food choice. Do we eat to replenish ourselves? Certainly, we like foods that alleviate physiological needs. Variations in perceived pleasantness of stimuli may be mediated by the changes in the balance of the internal environment of an organism, a process termed “alliesthesia” (Cabanac, 1971). For example, after consumption of sugar, pleasantness ratings of a sweet stimulus decrease. In contrast, injection of insulin, which leads to

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subjective hunger, causes pleasantness ratings of a sweet stimulus to increase (Cabanac et al., 1968; Jacobs, 1958). This demonstrates that homeostatic signals influence perceived pleasantness of stimuli to achieve approach behavior and subsequent nutrient intake. Do we eat what we eat because it is a habit? The perceived pleasantness of foods is largely determined by experience (Cardello, 1996, Mela, 2001), so we will like what our mother ate during the months that we were breastfed (Mennella and Beauchamp, 2002), what our parents and others with the same cultural background gave us to eat in the past (Laing et al., 1994, Prescott et al., 1996) and any new food that we were courageous enough to try multiple times (De Houwer et al., 2001). Then, do we also eat because it is pleasurable? Yes, we do. An increased perceived pleasantness of food is related to increased intake of that food, at least in the short run in laboratory settings (Sorensen et al., 2003). Also, unpleasantness ratings generally predict in an accurate fashion a lack of consumption and market success (De Castro and Plunkett, 2001, Rudolph, 1995). However, it has become clear that positive pleasantness ratings may not always be predictive of subsequent consumption as reflected in sales (Rudolph, 1995) and pleasantness may explain only a small part of variation in intake (De Castro and Plunkett, 2001). This demonstrates that directly asking a subject about their liking of food may not always be predictive of behavior. Simply put, we do not always know what we like. This was elegantly demonstrated in a study in which subliminal presentations of happy and angry faces cause corresponding effects on subsequent evaluation and intake of a beverage, despite the absence of a change in reported feelings (Winkielman et al., 2005). Also, a sweet drink can influence preference learning in the absence of taste sensation simply by having positive postingestive effects (De Araujo et al., 2008). Similarly, one can ask about preference for personal care products (like perfume, shampoo or lotions), but subjects may not always have insight into their preferences or are unaware of any response bias. Thus, not all preferences are conscious or open to introspection. Asking subjects directly to evaluate a stimulus in terms of pleasantness does not always give us the answers we are looking for, and it is for this reason that neuroimaging may provide an important contribution to the development and marketing of new products. In other words, can we let the brain speak for itself, and tell us what we like? The (incomplete) list of determinants of food choice listed above makes it clear that the study of food preferences through neuroimaging will involve many different processes. For this reason the neuroimaging of food preferences will be a challenging but important task. The neural correlates of preference for personal care products may prove somewhat easier to solve, and promising advances have recently been made in this direction. At the same time, it seems that despite the multifaceted nature of food reward a few brain areas seem to be consistently involved when it comes to choosing from multiple food products (Rangel et al., 2008).

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In this chapter we will first discuss the different neuroimaging techniques that can be used to study preference behavior, their advantages and disadvantages, particularly in relation to evaluating food in such a specific experimental setting. Next we will review the neural correlates of pleasantness of food and personal care products, product choice and discuss some of the new insights into food reward that came from neuroimaging. Then we will discuss what is needed and what should be avoided if neuroimaging techniques are used for the study of preferences. We will end this chapter with an overview of what neuroimaging promises for new product development and a list of recommended resources.

21.2 Neuroimaging techniques There is a diversity of techniques available to study neural response in humans. To evaluate the advantages and disadvantages of the different techniques, often concepts like temporal and spatial resolution are used. With temporal resolution we mean the precision of the measurement in time; a high temporal resolution means there are many datapoints per temporal unit. Similarly, a high spatial resolution means a high precision of spatial information or many observations per spatial unit. Neuroimaging techniques differ in temporal and spatial resolution and comfort level for the subject. However, they are matched in functional resolution; as the appropriateness of the technique depends on the type of question one wishes to answer. That being said, there are some difficulties with each of the techniques specific to the nature of reward processing in general and to food perception specifically. We will discuss five different techniques here.

21.2.1 Mageneto- and electroencephalography Electrical and magnetic activity produced by neural events in the brain can be measured with magnetoencephalography (MEG) or electroencephalography (EEG). We will discuss these techniques together here, because they (methodologically) have a lot in common. In Fig. 21.1 and Fig. 21.2 we depict an overview of the neural basis, measurement, the measured signal, and the results of MEG and EEG respectively. Both methods employ averaging the signal over multiple trials (across a time window that starts with the onset of a relevant event, like the presentation of a stimulus or the instruction to perform a specific task). This technique improves the signal-to-noise ratio by averaging out random activity, leaving the relevant signal that resulted from the event, which is then compared to signal in response to a baseline stimulus or baseline task. Signal from EEG and MEG reflects the activation of approximately 50 000 neurons. As these signals are measured from the scalp, only those bundles of neurons that lie closely to the surface of the head generate measurable signal. This means that these

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Fig. 21.1 An overview of magnetoencephalography (MEG). A. The neural basis of the signal measured by MEG originates from the electrical activity generated by similarly oriented tens of thousands of pyramical cells in sulci. B. A big dewar that covers the subject’s head, leaving the face and neck free, contains superconducting quantum interference devices (SQUIDS) that measure the magnetic signal. The dewar can be oriented two ways, such that the subjects can sit upright, or lie down. C. Because of the favorable temporal resolution of MEG, the signal is averaged over the multiple repeats of a task or stimulus, time-locked to the onset of stimulus presentation. D. The resulting averaged time-courses from one of the detectors (at a specific location) can be compared with those at a different location or with those during a different task or a different stimulus. Owing to the high temporal resolution, claims can be made with regards to timing and seriality of processes. Although there is a fairly low spatial resolution, the origin of the signal measured at the detectors can be computed with a process called dipole location estimation. Activity originating from brain areas that are not on the surface of the cortex makes it hard to measure the signal and as a result the dipole estimation reliability decreases.

techniques are not able to reliably measure neural activity from many areas in the brain that are buried deeper than the folds and ridges that lie close to the skull, and as such are functionally not a whole-brain measure. The source of activation can be estimated with a resolution of about ~1 cm. MEG has a better spatial resolution and is less affected by distortion from

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Fig. 21.2 An overview of electroencephalography (EEG). A. The neural basis of the signal measured by EEG originates from the electrical activity generated by similarly oriented tens of thousands of pyramical cells in gyri. B. A cap with electrodes filled with a conducting substance is attached to the subject’s head. Because of this the subject can move, although muscle activity will distort the small electrical signal from the brain. C. Like with MEG, the EEG signal is averaged over the multiple repeats of a task or stimulus, time-locked to the onset of stimulus presentation. D. The resulting averaged time-courses from one of the electrodes can be compared with those during the presentation of a different stimulus (or task). Owing to the high temporal resolution, claims can be made with regards to timing and seriality of processes. Also, there are characteristics in the time-course of the EEG signal that are informative. Between 50 and 150 ms post-stimulus there are two small negative peaks that are indicative of early and low-level sensory processing, whereas the large positive peak around 300 ms post stimulus is thought to reflect late cognitive component in the stimulus processing stream. Despite EEG’s low spatial resolution, the origin of the signal measured at the electrodes can be depicted by mapping the activity measured across the scalp in bins. Like with MEG dipole location estimation (albeit with limited reliability) can also be performed.

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surrounding tissue than EEG, but reflects activity from slightly more superficial layers of cortex than EEG. It is thought that the signal measured by MEG and EEG originates from similarly oriented pyramical cells in the cortex, but since the magnetic signal is orthogonal to the electrical signal, MEG is more likely to reflect activity from sulci (depressions or furrows in the folded cortex), whereas EEG reflects activtiy from the gyri (the ridges of the folded cortex) (see Fig. 21.2A and B). The big advantage of these techniques compared to other techniques is the temporal resolution; MEG and EEG can resolve events with a precision of 10 milliseconds or less. As a result, these techniques can be very useful to answer questions about the timing and sequence of information processing in the brain. Responses to chemosensory stimuli have been measured with both MEG (Kettenmann et al., 1997, Kobayakawa et al., 1996, 1999, 2008) and EEG (Kettenmann et al., 2005, Rombaux et al., 2006). MEG has also been applied to distinguish separable sequential processes in shopping experiences (see also Section 21.4 Product choice and neuroeconomics) (Ambler et al., 2004). With regard to the study of food processing, these techniques have a number of great advantages: they are generally comfortable for the subject, there is no sound from the equipment, the subject can sit upright and the signal is not as sensitive to movement as other neuroimaging techniques, allowing for a more naturalistic (eating) situation. An important disadvantage is that we know from animal studies and from studies with other imaging techniques that many key areas in reward and food processing are buried deep within the brain, like the basal ganglia, the cingulate and the orbitofrontal cortex. This makes these techniques less suitable for studying some aspects of processes that involve midline structures deep inside the skull, like reward learning and pleasantness coding. However, for the study of the processing of higher-order or cognitive aspects of food perception, processes that generally take place in brain areas closer to the skull, these techniques are very appropriate.

21.2.2 Positron emission tomography Positron emission tomography (PET) measures the regional distribution of radioactive tracers that have been injected into the bloodstream of a subject. The radioactive tracers can bind to oxygen or glucose, and areas that emit more radioactivity reflect an increased blood flow or glucose consumption, a proximal measure of increased brain activity. PET has a very low temporal resolution (on the scale of minutes), and a fast decay of radioactivity, which necessitates the use of designs (block designs) where a task is performed during a longer time-window or a same stimulus is presented multiple times during a long time-window. See Fig. 21.3 for an overview of the neural basis, measurement, the measured signal and the results of PET. Neural response to unimodal chemosensory stimuli (e.g., an odor or a gustatory stimulus presented by itself) habituates fast (Poellinger et al., 2001, Wagner et al.,

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Fig. 21.3 An overview of positron emission tomography (PET). A. A radioactive isotope that binds to, for example, glucose or oxygen is injected into the subject’s bloodstream. When the radioactive isotope decays, it emits a positron which collides with a nearby electron, which produces two gamma rays. Brain areas that consume more oxygen/glucose will emit more gamma rays and thus the signal measured by PET reflects a proxy of neural activity. B. Detectors surround the subject head in a circular array. C. As a result of the low temporal resolution of PET, the signal is averaged over time-windows of 60 seconds to 60 minutes. D. The results from PET measurement are often depicted as activation maps (thresholded at a certain value of a statistic) of a condition or a contrast map of signal during task A vs. task B.

2006). It is unknown how strong habituation to complex multimodal chemosensory stimuli (like food) is. As neural response is averaged over a longer period of time in PET studies, a disadvantage of PET is that the design has to be optimized specifically for the study of odor and/or food perception. However, PET has the advantage of being able to image radiotracers that bind to specific neuroreceptors in the brain, like dopamine receptors. As the mesolimbic dopamine pathway is thought to play an

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important role in reward learning, PET studies have contributed greatly to insights into individual differences of food reward processing (Volkow et al., 2003, Wang et al., 2001) and the role of dopamine in the coding of meal pleasantness (Small et al., 2003b). The amount of radioactive materials injected into the subject is considered safe, but there are strict limitations on how often a single subject can participate in a PET scan. The major disadvantages of PET are that it is invasive, a lot of personnel is required to do a scan, and as a result it is very expensive.

21.2.3 Functional magnetic resonance imaging Functional magnetic resonance imaging (fMRI) employs the different magnetic properties of oxygenized hemoglobin and deoxygenized hemoglobin and provides an indirect measure of regional blood flow in the brain (see Fig. 21.4). The BOLD (blood oxygenized level dependent) response is thought to reflect local field potentials of groups of several millions of neurons (Logothetis, 2002, 2008, Logothetis et al., 2001). FMRI can be used with block designs and event-related design, in which the signal is averaged over multiple trials time-locked to a relevant event. Usually a subtraction method is employed, in which a baseline stimulus or baseline task is subtracted from a stimulus or task of interest. This relies on the assumption that a cognitive process can be inserted into a task and does not affect other processes that are going on (pure insertion), which is an assumption that is not necessarily true (Logothetis, 2008). Since there is a delay in blood flow to tissue having higher metabolic needs, temporal resolution of fMRI is somewhat slow, on a scale of about 1 second. Spatial resolution of fMRI is about 1 to 3 mm3. FMRI is non-invasive although usually somewhat unnerving to subjects, because of the small bore that the subject is inserted into from the head down to about the waist (this disadvantage can be partially diminished with head-only scanners), and the loud noises that are produced during scanning. FMRI allows imaging of most structures, although there is the problem of susceptibility artifact and signal drop-out around cavities, problematic mostly around the anterior medial temporal lobe (amygdala) and orbitofrontal cortex. Because of its reasonable temporal resolution, its good spatial resolution, non-invasiveness, and its ability to image the entire brain, fMRI poses a safe technique that has advantages over MEG, EEG and PET. Unfortunately, fMRI is very susceptible to movement artifacts and movement is usually restricted to 1–3 mm in any direction. This poses a serious challenge for studying intake behavior in food perception, since chewing and swallowing is generally associated with significant movement of the head. Several methods have been developed that have succesfully been able to combine in-mouth stimulation and swallowing in the fMRI scanner without creating unacceptable levels of concurrent movement (Haase et al., 2009, Marciani et al., 2006, Ogawa et al., 2005, Veldhuizen et al., 2007).

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Fig. 21.4 An overview of functional magnetic resonance imaging (fMRI). A. In a magnetic field molecules in the brain orient themselves in a similar direction. Then if a pulse (a change in magnetic field by gradient and radiofrequency coils) is given, the spinning state of molecules is perturbed. The molecules return to their original spinning state, but critically, this happens at a different speed for molecules with different magnetic properties, emitting different amounts of electromagnetic energy. Since the magnetic properties of oxygenated and deoxygenated blood are different, a proxy of oxygenated blood flow (indicative of local energy consumption by neural activity) can be measured by the radiofrequency head coils from the MR signal. B. The subject is inserted, lying down on a table, to the waist into the bore of the magnet. The outer layer is the magnet itself producing a static magnetic field. The inner layer consists of gradient coils and the cage around the subject’s head contains the radiofrequency coils. C. Signal intensity is measured over time for each small spatial unit (a voxel) in the measured volume (part of or the entire brain). Like with MEG and EEG signal is averaged over the multiple repeats of a task or stimulus, time-locked to the onset of stimulus presentation. Sometimes (like with PET), the signal is averaged over longer time-windows. D. The resulting averaged time-courses are often depicted for different conditions or stimuli to indicate a difference in the peak intensity of the BOLD response across conditions, or in the timing of the BOLD response across areas. Similarly, the spatial distribution of the signal in response to a task or a stimulus is telling, and often brain activation or contrast maps (comparing two conditions) are given.

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21.2.4 Functional near infrared spectroscopy An emerging noninvasive neuroimaging technique is functional near infrared spectroscopy (fNIRS). FNIRS uses light to measure the the amount and oxygen content of hemoglobin in the outermost 3–4mm of the brain and is an indirect measure of neuronal activity, with similar temporal properties as fMRI. This technique is promising, because the subject can sit upright, and move somewhat, but like ERP and MEG has the disadvantage of not being able to measure signals from deep brain structures. fNIRS has been used to study taste and flavor, but because of its ability to measure only cortically superficial neural response, will be most useful to study cognitive processes (like decision making) in food perception (Okamoto et al., 2006a, Okamoto and Dan, 2007, Okamoto et al., 2006b).

21.2.5 Common limitations of neuroimaging techniques One disadvantage that all of these methods have in common is that power is usually dependent on the number of stimulus presentations that can be repeated within a limited window of time (usually 1.5 to 2 hours). This is problematic for chemosensory stimuli, because these systems need a relatively long time in between stimulus presentations to avoid adaptation and habituation. Another important limitation is that reverse inference is not possible with any of these techniques. When we present an unpleasant stimulus we can ask whether this leads to an activation in brain region x or faster temporal component y of the neural response, but that does not mean that we can draw conclusions about the pleasantness of a stimulus if we see a change in brain region x or temporal component y. This fundamental mistake is made frequently in scientific reports and in the media, and we will return to this matter in Section 21.4, which discusses the promises and pitfalls of neuroimaging of food pleasantness.

21.2.6 Summary of neuroimaging techniques Table 21.1 summarizes the advantages and disadvantages for each of the neuroimaging techniques discussed in this paragraph. So far, PET and fMRI have been used most to study processing of food reward in the brain. This is partly because both techniques are able to measure signal from (almost) all areas in the brain. Relative to PET, fMRI combines reasonable temporal resolution and good spatial resolution. FMRI has proven to be able to answer many questions about the brain processing of food reward, and more informative techniques are in development. However, fMRI is not very comfortable to the subject and there is a lot of concurrent noise. For MEG, fMRI and PET, a medical infrastructure (for knowledge, skills and equipment) is needed and these generally come at very high cost. This is not necessarily the case for EEG and fNIRS, for which relatively low-cost equipment is available, and which have the great advantage of allowing for

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a much more naturalistic environment, but unfortunately do not allow imaging of many of the relevant structures in the study of food and reward processing. As a result of only partial brain coverage of neural response in MEG, fNIRS and EEG, the use of these techniques is somewhat underrepresented in the field of studying reward processing. However, these techniques are able to give detailed information into the sequence of cognitive processes that are involved in perception and decision making. Overall, fMRI provides the best information/cost ratio, but it should be kept in mind that the information obtained in the restricted, uncomfortable environment of an fMRI scanner may not always translate to natural behavior.

21.3

Key neural substrates of pleasantness

21.3.1 Organization of the brain The brain is thought to be the physiological substrate of the mind as well as the structure that regulates behavior that promotes the welfare of an organism. To achieve this, it is thought that there are subareas in the brain that are functionally specialized towards, for example, extracting information about the environment, about the balance in the organism’s internal milieu, and when action needs to be undertaken. Initially the predominant thought was that subareas would be responsible for a specific function and that each area has its own function. By now, it has become clear that many areas partake in a great variety of behaviors and that their function cannot be studied in isolation of other brain areas, e.g. networks of brain areas have become the functional unit. Important functional subsystems of the brain are the sensory systems, that serve to extract information from our environment, which consist of unimodal processing areas that encode stimulus features, higher order processing unimodal areas and multimodal integrative areas that encode objects or patterns. Unimodal sensory areas can be found in the occipital cortex (vision), the temporal cortex (audition), the frontal cortex (gustation in the insula and overlying operculum), paralimbic cortex (olfaction in piriform cortex), and parietal cortex (somatosensation in the postcentral gyrus) (see Fig. 21.5). On the other end of the spectrum of functional subsystems there is the motor-system that enables an organism to engage in goal-directed action based on information extracted from the environment. Additionally there are functional subsystems that are thought to be intermediaries between the extraction of information and the production of action, and those are involved in emotion, memory, learning and motivation. This is a very widespread network, including areas in (para)limbic cortex (striatum, amygdala, hypothalamus), the medial orbitofrontal cortex (mOFC), lateral orbitofrontal cortex (lOFC), medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (DLPFC), and anterior cingulate cortex (ACC).

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Fig. 21.5 A simplified overview of important neural substrates in the perception and evaluation of products. The sensory systems are important for encoding stimulus features and are recruited depending on the peripheral senses the stimulus impinges on. The higher-order areas subserve functions that are generally not very dependent on contact with peripheral sensory systems. They help an organism learn, adapt to a changing environment, and engage in goal-directed behavior. Note that this map of representation of functions is heavily simplified and that it is not exhaustive of the functions that these areas are involved in.

21.3.2

Overview of neural correlates of food and personal care product perception Many sensory systems are involved in the perception of food. At a distance, vision and orthonasal olfaction (when odors are sniffed via the nares) play an important role. When taken into the mouth, food interacts with the somatosensory system to produce sensations of texture, temperature, pungency and spiciness. Interaction with the gustatory system produces sensations of sweet, salty, sour, bitter and savory or ‘umami’ (savory, the taste of monosodiumglutamate). Chewing and swallowing will produce sounds involving the auditory system, and also cause volatile molecules to travel up to nasal cavity to produce olfactory sensations (termed retronasal olfaction). Retronasal olfactory, oral somatosensory, and gustatory components

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fuse into a flavor object that perceptually cannot readily be reduced to its components (Auvray and Spence, 2008, Small, 2008, Small and Prescott, 2005) and that seems to originate from the mouth entirely (Heilmann and Hummel, 2004, Rozin, 1982, Small et al., 2005). For the perception of personal care products, vision, orthonasal olfaction, and tactile perception are important. Superficially, there seems to be a fair amount of overlap in the sensory systems involved in distal perception of food and the perception of personal care products (vision and orthonasal olfaction). However, distal perception of food often precedes intake and physiological effect after ingestion. As a result the distal perception of food will be involved in anticipatory and preparatory mechanisms, as well as being more intimately tied to reward learning compared to the perception of personal care products. The neural correlates of vision can be functionally divided into two networks; the ventral and dorsal stream. These two networks are thought to be responsible for the “what” (object recognition and form representation) and “where” (actions and locations of objects in space) aspects of vision respectively. Presumably the ventral stream is most important in the processing of the distal visual perception of food and personal care objects. The ventral stream describes the flow of information from primary visual cortex (in occipital areas) to inferotemporal areas and from there to amygdala, OFC, and perirhinal cortex (Peissig and Tarr, 2007, Riesenhuber and Poggio, 2000, Rolls and Deco, 2002, Van Essen and De Yoe, 1995). Several studies have shown that the OFC and insula respond preferentially to visual perception of food compared to nonfood objects (Beaver et al., 2006, Gordon et al., 2000, Morris and Dolan, 2001, Porubska et al., 2006, Simmons et al., 2005), and as such it has been suggested that the object category “food” may be represented in these regions (Simmons et al., 2005). Auditory information about food in the mouth flows from primary auditory cortex in the posterior superior temporal gyrus (where basic characteristics of sound such as pitch and rhythm are coded) to the adjacent auditory association area near the lateral cerebral sulcus in the temporal lobe of the brain (where sounds can be identified as speech, music, or noise) (Kaas et al., 1999). Parallel to the functional division in the visual system, a “what” (sound source) and “how” (sound pattern) segregation has been proposed for the auditory system (Belin and Zatorre, 2000, Kaas and Hackett, 1999). Currently not much is known about the specific neural correlates of intra-oral auditory stimuli, but a ventral auditory stream specialized for object recognition and emotional processing is likely to be involved (Kaas and Hackett, 1999). Recent work has also shown that there are multisensory interactions between the somatosensory and auditory systems when encoding the crispiness of a food (Zampini and Spence, 2004). Somatosensory stimulation activates the pre- and postcentral gyrus, with oral somatosensation specifically being represented towards the bottom of the central sulcus, near the parietal operculum. Oral somatosensation is

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further represented in insula, OFC, and ACC (Cerf-Ducastel et al., 2001, De Araujo and Rolls, 2004, Guest et al., 2007). Odors commonly activate the piriform cortex, an area involved in the encoding of perceptual aspects (such as odor quality), and learned aspects of odors (Gottfried, 2006, Gottfried and Zald, 2005, Savic, 2002, 2005). Higher-order aspects of odors are thought to be coded in the ventral insula, OFC, amygdala, hippocampus, and ACC (Gottfried, 2006, Gottfried and Zald, 2005, Savic, 2002, 2005). Presentation of taste activates several areas within the insula and overlying operculum, as well as the amygdala, ventral insula, OFC, hippocampus and ACC (Faurion et al., 2005, Small et al., 1999, Veldhuizen et al., 2010). The underlying organization of all of these sensory systems is hierarchical processing, where sensory aspects of stimuli are thought to be coded in primary areas and higher-order processing is thought to take place in less modality-specific areas. Pleasantness of a stimulus is thought to arise in areas where multimodal aspects can be integrated into an object and an overall perceived pleasantness can be computed. Of the areas mentioned previously, the insula, the OFC, the amygdala, and the ACC would qualify as areas capable of integrating multisensory information. Consistent with this, it has been shown that multimodal information about stimuli in the mouth converges in the insula and OFC (De Araujo et al., 2003b, Kringelbach, 2005, McCabe and Rolls, 2007, Small et al., 2004, Verhagen and Engelen, 2006). In the following section we will first discuss the neuroimaging of food pleasantness. Based on their work in rodents, Berridge and colleagues introduced the concept of two mechanisms in food pleasantness perception that are easily confused: ‘liking’ and ‘wanting’ (Berridge, 1996, Berridge, 2003, Berridge and Grill, 1984, Berridge and Robinson, 2003). ‘Wanting’ refers to the willingness to obtain a specific stimulus and the anticipation of the consumption of a food, while ‘liking’ reflects the purely hedonic evaluation of its sensory aspects during consumption independent of motivation. As such, a stimulus eaten to satiety during a meal may be unchanged in liking, but has become unwanted. We will discuss the neural correlates of these dissociable aspects of pleasantness in Sections 21.3.3 and 21.3.4. We will also discuss an additional dissociation of implicit (i.e. without awareness) and explicit (or conscious) pleasantness perception in Sections 21.3.6 and 21.3.7. Finally, we will discuss the neural correlates of the pleasantness of personal care products, which involves the pleasantness of orthonasal olfaction and touch (in Section 21.3.8).

21.3.3 Neural correlates of liking Several early neuroimaging studies looked at the differences between pleasant and unpleasant tastes and frequently observed activation of the amygdala and/or the OFC (Francis et al., 1999, O’Doherty et al., 2001, Zald et al., 2002, 1998). One problem with these studies is that usually the

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pleasant and unpleasant stimuli differed in subjective intensity, with the unpleasant stimulus being more intense. Small et al. directly addressed this problem by matching quinine sulfate and sucrose in subjective intensity, both at a strong subjective intensity and a weak subjective intensity (Small et al., 2003a). This design allowed for a dissociation between brain areas responding to changes in intensity or to changes in pleasantness. Responses to pleasantness independent of intensity were observed in the caudolateral OFC, thalamus, ventral striatum and ACC. The amygdala responded to intense taste compared to weak taste. Thus this study provided evidence of a functional dissociation between the amygdala and the OFC in pleasantness coding. The OFC seems more concerned with pleasantness coding per se, while the amygdala seems responsive to tastes that have a strong valence (whether negative or positive). In agreement with this, activity in the OFC has been shown to correlate with pleasantness ratings of odors, tastes, flavors, complex food stimuli, and (oral) tactile stimuli (De Araujo et al., 2003a, 2003b, Francis et al., 1999, Guest et al., 2007, Kringelbach et al., 2003, McClure et al., 2004, Royet et al., 2000), providing strong evidence that this area is the location of the coding of conscious pleasantness perception of a range of stimuli. This has been confirmed by recent studies which show that when subjects are asked to evaluate the pleasantness of taste solutions, the OFC is preferentially activated compared to other tasks (Bender et al., 2009, Grabenhorst and Rolls, 2008, Grabenhorst et al., 2008a). The pregenual ACC is frequently coactivated with the OFC, and shows, similar to OFC, a preferential activation to pleasantness judgments, as well as a graded response with increasing pleasantness of tastes, flavors and tactile stimuli (Grabenhorst and Rolls, 2008, Grabenhorst et al., 2008a, 2008b, Guest et al., 2007).

21.3.4 Neural correlates of wanting As mentioned before, one aspect of pleasantness refers to the willingness to obtain a specific stimulus. The neural correlates of wanting can be measured when motivation increases with hunger or decreases with satiation. In humans, fMRI has been used to track brain response following intake of a high glucose load, and in the hypothalamus the response decreases as satiety develops (Liu et al., 2000; Matsuda et al., 1999; Smeets et al., 2005a,b). Studies examining whole-brain response to tasting pleasant flavors or foods (Kringelbach et al., 2003; Small et al., 2001) showed that as the food was eaten to satiety, the response in mOFC, insula, and striatum decreased with reducing pleasantness ratings. In contrast, activity in the lOFC, extending into ventrolateral prefrontal cortex, increased with satiety and correlated inversely with pleasantness ratings (Small et al., 2001). Smeets et al. (2006) showed that responses to a palatable food eaten to satiety may differ for men and women, with preferential changes in OFC activity in men and in hypothalamus and amygdala for women. With PET it has been shown that

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the neural response to a liquid meal is greater during hunger compared to satiety in caudal OFC and insula, while activity in the dorsal and ventral prefrontal cortex, extending into the lateral most part of the OFC, is greater in satiety compared to hunger (Tataranni and DelParigi, 2003, Tataranni et al., 1999). When subjects are exposed to food cues (Arana et al., 2003, Gottfried et al., 2003, Hinton et al., 2004, LaBar et al., 2001, Mohanty et al., 2008, Morris and Dolan, 2001, O’Doherty et al., 2000) neural response also changes as a function of internal state. Activity in the OFC, amygdala, insula, anterior cingulate, and ventral striatum decreases in response to an odor or visual stimulus that is associated with a food eaten to satiety (Gottfried et al., 2003, O’Doherty et al., 2000). A recent study asked participants, when hungry or when satiated, to perform a detection task with motivationally relevant (food) and irrelevant (tool) targets (Mohanty et al., 2008). Dissociable responses were observed in the OFC, with increased medial OFC activity associated with faster reaction times for food-related targets while hungry and increased lateral OFC activity associated with slower reaction times while hungry and faster reaction times to food-related stimuli while full. This indicates that neural response in OFC is sensitive to internal state and the reward value of the target, and that this information is used to bias attention towards relevant targets (i.e. food). Similar dissociations in the functional role between medial and lateral prefrontal cortex in food reward were observed in other studies. For example, medial OFC activity was observed when subjects read a menu with preferred versus a menu with non-preferred food items (Arana et al., 2003, Hinton et al., 2004), and lateral OFC activity was observed when participants had to inhibit responses to various attractive menu items in choosing one of them (Arana et al., 2003). Similarly, it has been shown that when looking at appetizing food pictures, activity in mOFC is correlated with motivation to obtain reward as measured by a personality questionnaire (Beaver et al., 2006). Summarizing, these data suggest that in several areas like the OFC, insula, the amygdala, and the hypothalamus there are changes in activity related to internal state, and ensuing motivation to seek out food. More specifically, the activity in hypothalamus tracks satiety with internal state, and within OFC a medial/lateral segregation distinguishes between an increased or decreased motivation for consumption.

21.3.5 Incentive value; the amygdala and ventral striatum How then is the role of the amygdala different from the OFC? We know that response in the amygdala does not correlate with ratings of the perceived pleasantness of taste or flavor itself (De Araujo et al., 2003a, 2003b, Kringelbach et al., 2003, McClure et al., 2004). However, as we saw above, response in the amygdala decreases with devaluation of the cue associated with a food as a subject gets satiated with that food (Gottfried et al., 2003,

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O’Doherty et al., 2000), so the structure nevertheless likely plays an important role in affective coding. Similarly, it has been shown that response in ventral striatum is not sensitive to devaluation of food or drink (Kringelbach et al., 2003, Small et al., 2001), but appears to be selectively sensitive to devaluation of visual and olfactory food cues (Gottfried et al., 2003, O’Doherty et al., 2000). In agreement with the role of the amygdala in encoding negative stimuli and fear (LeDoux, 2000), early chemosensory studies suggested that the amygdala responded to aversive tastes, flavors, and odors (Small et al., 1997, Zald et al., 2002, 1998). However, a later study that matched pleasant and unpleasant tastes in intensity, showed that the amygdala responds to both strong sweet and bitter solutions (Small et al., 2003a). Winston and colleagues (2005) showed that during sensation of two concentrations of pleasant, unpleasant, and neutral odors, concentrationdependent responses are only observed for the pleasant and unpleasant stimuli, and not for the hedonically neutral stimulus. Thus it was proposed that the amygdala is driven by the interaction between valence and intensity, and is involved in the encoding of the salience of a stimulus. This was confirmed when a known characteristic of the olfactory system was employed to show the dissociation between odors-as-a-cue (predicting food) and odors-as-food (part of food intake). As highlighted in Section 21.3.2, food is sensed distally and proximally; that is, at a distance and in contact with the body. For example, perception of the sight and smell of a food often precedes and predicts its ingestion and perception of its flavors. There is now good evidence to indicate that dissociable networks encode distal and proximal sensation of food. When a food odor is presented orthonasally, it activates the amygdala more strongly than when the same odor is presented retronasally (Small et al., 2005). Similarly, O’Doherty and colleagues (O’Doherty et al., 2002) reported that the amygdala, midbrain, and ventral striatum respond preferentially to abstract visual stimuli that predicted the arrival of sugar water compared to neutral cues and compared to the receipt of the sugar water. In a subsequent study, it was shown that the amygdala, thalamus, midbrain, ventral striatum, and ventral pallidum also respond more to smell of a food aroma that predicted its taste compared to the actual receipt of that taste (Small et al., 2008). These studies highlight how the ventral striatum and amygdala respond to food cues and how this response is sensitive to devaluation, indicating that it encodes the incentive value of food cues, and that it is sensitive to changes in the incentive value related to internal state.

21.3.6 The amygdala and craving So far, we have focused primarily on understanding factors that determine perceived pleasantness of food. However, there are also nonconscious or implicit representations that may not be readily accessible to conscious introspection, but which nevertheless exert powerful influences over

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behavior (Berridge and Robinson, 2003). For example, it has been shown that the amygdala mediates the unconscious perception of desire or food craving (Pelchat et al., 2004). Pelchat and colleagues examined neural response to food cues that did or did not elicit subjective cravings. They found that although the amygdala responded to food cues, it was the insula and dorsal striatum that respond during time periods in which subjects reported experiencing cue-induced cravings. These findings suggest that neural representation of conscious pleasure experienced during eating and conscious desire experienced during food anticipation is at least partially segregated from the encoding of the predictive value of food cues, which may not be conscious.

21.3.7 Liking responses without consciousness There is evidence for other types of liking responses without consciousness. For example, affective responses to tastes can occur without conscious perception in anencephalic newborns. These newborns are born without a cortex, but with an intact brainstem, and display the same reactions as normal newborns to taste stimulation; they smile or smack after sweet and grimace and gape in response to bitter tastes (Steiner, 1973; Steiner et al., 2001). Similarly, the exceptional case of patient B who lost almost all of his paralimbic brain structures (including the insula, OFC, anterior cingulate gyrus, and amygdala) provides another indication that taste affective responses can be orchestrated without sensory cortex (Adolphs et al., 2005). When patient B was given a pleasant (sweet) and unpleasant (salty) drink one at a time, he would respond indiscriminately positive to either of the stimuli: he displayed a pleased facial expression, said both drinks tasted delicious and would drink both of them readily. Yet in a slightly different situation, when given a choice between the two presented at the same time, patient B would consistently prefer sugar water over saline. Moreover, when encouraged to try, he would refuse to drink the saline, even though he was unable to express why beyond stating that he just liked the other solution better. This indicates that neither conscious sensation nor sensory cortex is a necessary condition for affective responses or complex choice behavior to occur. This highlights the role that unconscious processes may have in the coding of pleasantness of food.

21.3.8 Development of liking We are currently unaware of neuroimaging studies investigating how preference for stimuli develops in children. Experience and exposure are known crucial factors in the development of liking for flavors (Cardello, 1996, Laing et al., 1994, Mela, 2001, Prescott et al., 1996). In a study of perceptual learning, Li et al. observed that previously indistinguishable odors become perceptually distinct with exposure and distinctness is encoded in the OFC

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(Li et al., 2006). The development of liking may parallel the neural implementation of the development of distinguishability that occurs with exposure. It has been shown that positive post-ingestive effects (like the release of energy after a caloric load, or the effects of caffeine) are important in the development of preference for food odors and flavors (Birch et al., 1990, Johnson et al., 1991, Zellner et al., 1983, Richardson et al., 1996), however it as of yet unknown how postingestive effects contribute to the development of liking at a neural level. This will probably develop into an area of great interest in the field and will likely provide important insights for new product developers. In relation to this and the obesity epidemic it will also be important to study and understand food perception in children, which will be challenging in the face of ethical concerns over burdening children with a neuroimaging context that is frequently uncomfortable.

21.3.9 Encoding of the pleasantness of personal care products As reviewed above, the object pathway of the visual system, the orthonasal olfactory system and tactile system are involved in encoding of the perceived pleasantness of personal care products. FMRI studies of the pleasantness of touch have focused primarily on unpleasant touch and pain. To discuss the perception of pain is beyond the scope of this chapter. On the other end of the spectrum of the affective dimension of tactile sensation is pleasant touch. It has recently been suggested that there are specialized tactile afferents that mediate the sensation of pleasant touch (McGlone et al., 2007), and these afferents are thought to be present in hairy skin only. Accordingly, touch aimed specifically at these pleasant touch fibers preferentially activates medial OFC (McCabe et al., 2008) compared to pleasant touch on the palm of the hand. Tactile stimuli that differ in pleasantness have been shown to elicit activation in the OFC and ACC. For example, the pleasant touch of velvet on skin leads to stronger activation than the touch of wood (Francis et al., 1999, Rolls et al., 2003). Response in OFC and ACC also correlates with the pleasantness of a skin cream being rubbed on the arm (McCabe et al., 2008). For a review on fMRI studies on pleasant touch, see Rolls (2010). The OFC and amygdala are involved in encoding emotionally valenced vs emotionally neutral olfactory stimuli (Royet et al., 2000, Winston et al., 2005) and response in the OFC and pregenual ACC (prACC) correlates with odor pleasantness (Rolls et al., 2009b). Some personal care products use food odors (vanilla or orange, for example). Presumably, these products may activate part of the food reward networks. We are currently unaware if these personal care products evoke response in different neural areas from personal care products that have odors that have never been experienced retronasally. This means that the pleasantness of personal care products is reflected in a similar but somewhat simplified architecture as in food perception, with

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the OFC and prACC being involved in the computation of the stimulus pleasantness and the amygdala in encoding a stimulus’ saliency.

21.3.10 Summary of key neural substrates of pleasantness Several brain areas are involved in the coding of multiple aspects of pleasantness. For example, the OFC is important in the coding of stimuli that are pleasant because they are salient as a result of internal state, e.g. when a subject is hungry. The neural response in OFC and prACC correlates with the pleasantness of stimuli that are hedonically pleasant, regardless of internal state. The amygdala is strongly implicated in neural encoding of cues that have come to predict food reward, but the amygdala also responds to craving a food. This section underwrites exactly how multifaceted food reward and the neural networks encoding food pleasantness is. We hope then that this also underwrites the difficulty to infer from activity in a specific area (OFC, amygdala, or striatum) the pleasantness that a specific stimulus has evoked in a subject. The encoding of the pleasantness of personal care products is presumably less multifaceted as it is not intricately involved in ingestion and guarding the internal milieu, and the encoding of pleasantness of tactile, olfactory and visual components is proposed to occur in OFC and prACC.

21.4 Product choice and neuroeconomics Even though there are multiple networks encoding food reward that are not yet well understood, this does not mean we cannot study the decision processes surrounding food choice. In one of the early studies in this field subjects made a virtual tour through a supermarket and were required to select, from a visual scene, among products of different brands within a category (such as beers, cereals and body care products) while neural response was measured by MEG (Ambler et al., 2004). From 0 to 1000 ms after stimulus onset, several different stages of processing could be distinguished; during the first 500 ms stronger effects for the product choice task were observed compared to a control task in occipital and temporal areas. The authors concluded that during this stage there was sensory encoding in visual cortex, and then object encoding in visual association and semantic areas. After about 500 ms familiarity effects started taking place in frontal and parietal areas, indicative of higher order cognitive processing. These results suggest that brand choice is a multistage process. The study of this type of decision making has been part of “neuroeconomics” or “neuromarketing”, a field that studies (among others) the neural processes behind consumer perception and behavior (Lee et al., 2007). Initially this field was accused of revolving around the “buy”-button in the brain only, and was the subject of ethical discussion, however, now this field has made

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major advances towards understanding human behavior in an economical environment. Decision making is generally thought to involve multiple components: 1) sensory representation of the alternatives to choose from; 2) a comparison between options; 3) a prediction of the probability of the outcomes of each option; 4) a representation of the reward value of each of the options; and 5) action selection (Heekeren et al., 2008, Montague et al., 2006, Rangel et al., 2008). These processes are thought to take place in different parts of the brain. A sensory representation is first encoded in modality-specific sensory cortex (see Section 21.3.2). A comparison between options is thought to be made in the dorsolateral prefrontal cortex (DLPFC; for its location in the brain, see Fig. 21.5) (Heekeren et al., 2004, 2008). This is exemplified in an fMRI study where subjects looked at pictures of faces and houses and had to decide whether they were perceiving a house or a face. Critically, in half of the presentations this task was made more difficult by blurring the pictures. Lower level sensory areas responded preferentially to houses (the parahippocampal place area) and to faces (the fusiform face area), regardless of the difficulty of identifying them. The DLPFC showed a greater response to difficult than to easy items, reflecting its recruitment during decision making. Moreover, the absolute difference in neural response between the two sensory areas and percent correct responses (both indicative of sensory discriminability) on each trial was positively related to response in DLPFC. These results indicate that in the DLPFC signals from lower-level sensory areas are integrated and that a computation towards a decision is made here (Heekeren et al., 2004). In order to choose from a set of products, a predictive representation of each of them has to be computed. Tracking of the predictive value of stimuli has been proposed to occur for example in the ventral striatum and ventral midbrain (Hare et al., 2008, O’Doherty et al., 2004, 2006, 2003, Pagnoni et al., 2002), areas strongly involved in reward learning. This was illustrated in a study in which subjects sampled four differently flavored juices and ranked them in order of pleasantness (O’Doherty et al., 2006). Next they participated in a conditioning experiment, during which an arbitrary visual cue was paired with one of the juices. Meanwhile their brain response was measured with fMRI. In both the ventral midbrain and ventral striatum the authors observed a neural response that, over the course of the experiment, shifted in time from the presentation of the juice to the time of presentation of the cue (which slowly became predictive of the juice). Critically, this pattern of phasic shifts in neural response was observed in the ventral striatum for the least pleasant and most pleasant of the four juices, and in the ventral midbrain this pattern increased as the preference of the juice increased. This suggests that the ventral midbrain and ventral striatum are involved in encoding the predictive value of stimuli, but that the ventral midbrain tracks the predictive pleasantness value and the ventral striatum

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tracks the predictive value of strongly valenced stimuli (irrespective of whether the stimulus is pleasant or unpleasant) (O’Doherty et al., 2006). We discussed the multitude of neural correlates of representation of pleasantness in Sections 21.3.3–21.3.8; however, a less complicated picture of the representation of value appears when tasks focus on subjects making a choice among products or food items based on, for example, the pleasantness or another attribute like healthiness. Tracking of the value of a product has been shown to involve the ventromedial prefrontal cortex/medial OFC; for example, neural response here correlates with pleasantness scores of wines (Plassmann et al., 2008) and soft drinks (McClure et al., 2004), with healthiness ratings of pictures of food items that subjects would receive after the experiment (Hare et al., 2009). This is in agreement with the decrease in neural response that can be observed in this area after devaluation of stimuli during satiation (Kringelbach et al., 2003, O’Doherty et al., 2000, Small et al., 2001). Based on all these computations, in the action selection phase an action will be chosen and executed to obtain the preferred stimulus. Depending on the type of movement that is needed for the actual behavioral response certain neural subtrates will be recruited for the execution of the movement, e.g. motor cortex along the central sulcus for hand movements for example (Heekeren et al., 2006). However, there is also a phase before the execution of the action that is not dependent on the specific motor system, and during this phase activity in the DLPFC has been observed (Heekeren et al., 2006). Summarizing, when making a decision from a set of products, a network of brain areas will be recruited. Firstly, the sensory systems are involved in the encoding of the perceptual aspect of each of the alternatives. The DLPFC is concerned with the integration of sensory signals (from lower level sensory areas), the comparison of sensory signals, the computation of a decision, and the execution of that decision. Additionally, in the ventromedial prefrontal cortex/medial OFC the value of each alternative is represented, while the ventral striatum and midbrain track the predictive value of the alternatives.

21.5 Pitfalls of neuroimaging of sensory perception and food reward 21.5.1 Required instruments and skills What do new product developers need to start using neuroimaging techniques to study sensory perception? First, one needs the expertise to decide which technique is most appropriate for the research question (i.e., an education and training in neuroscience and neuroimaging techniques). If the appropriate technique is fMRI, MEG, or PET, then one needs access to a medical/academic environment where there is supportive personnel for operating the equipment. This is because this type of equipment is very

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expensive to acquire and keep operational (rough estimates of building costs for fMRI and PET are $3 M each, and operational costs are estimated at $2 M anually for PET and $1 M anually for fMRI). If EEG or fNIRS is the appropriate technique, then it is more feasible to make an in-house neuroimaging lab, which would involve purchasing the equipment and building an electrically and/or magnetically shielded room. For all of the neuroimaging techniques, there is a lot of post-data-acquisition computing. For this, extensive training and education, specialized software, and powerful computers are needed. 21.5.2 Validity of neuroimaging environment As touched upon in the section about the different neuroimaging techniques that are available, one of the biggest challenges is in creating an environment that allows for the experience of pleasantness. The situation in a laboratory or scanner is often sufficiently artificial and unpleasant that it provides at the very least a strong distractor and may even overshadow the pleasantness a subject would be able to experience from a stimulus. We know that pleasantness ratings that are given in a behavioral research setting are frequently unpredictive of subsequent consumption or market success (De Castro and Plunkett, 2001, Rudolph, 1995). We can only speculate about the (in)validity of pleasantness ratings that are collected in an environment where the subject is lying on his/her back, (which is known to influence sensitivity to odors (Lundstrom et al., 2006, 2008), but was also recently shown to be of relatively little influence on retronasal olfaction and flavor perception (Hort et al., 2008)), where the subject is potentially slightly anxious, and is not allowed to move (as is the case in a fMRI scanner). As far as we are aware, no work has been done on validation of pleasantness ratings in an fMRI scanner and subsequent behavior in a naturalistic setting. However, a product developer should assume that validity is low and consider the laboratory situation as a major pitfall for imaging of pleasantness of food reward or personal care products. By choosing neuroimaging methods like fNIRS, EEG, and MEG, this concern may be somewhat reduced. 21.5.3 Cognitive influences on neural encoding In 1978, a documentary about the chemical senses ended with the broadcasting of a tone, which, as viewers were told, would evoke vibrations resulting in the experience of a smell. Viewers were encouraged to call in with information about the identity of the odor. Reports from viewers covered a wide range of odors, sometimes even accompanied by physiological reactions like hay fever symptoms and sleepiness or dizziness (O’Mahony, 1978). It is hard to imagine a similar richness of experiences if it had been suggested that the tone would evoke a sight. This example demonstrates that chemosensory perception may be particularly susceptible to

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suggestion. It is not clear why this may be the case, but part of it may stem from the fact that chemosensory information is for a large part coded in heteromodal cortex that is also thought to be involved in general cognitive functions like attention (Kincade et al., 2005) and awareness (Craig, 2009). Indeed, we have shown that attention to taste in the absence of a taste stimulus leads to a greater increase in neuronal activity in insula and overlying operculum than sensory stimulation (Veldhuizen et al., 2007). Likewise, patients unable to perceive taste will show stronger responses in this area than healthy controls, presumably as a result of trying hard to taste anything (Hummel et al., 2007). A potential result of chemosensory information being coded in the same cortical areas that are involved in general cognitive tasks is that the signal from the senses may easily be overshadowed by beliefs, expectations, and information acquired via other sensory modalities. Indeed, a white wine will be identified as having red wine notes if a red food coloring is added, even by experts (Morrot et al., 2001). Several neuroimaging studies have shown how expectations and beliefs shape the encoding of incoming chemosensory signals. For example, in a study by McClure and colleagues (McClure et al., 2004), in which different brands of soda were presented, response in medial OFC reflected preference based on blind sensory ratings. However, when brand information was provided, the stated favored brand was now preferred and brain response changed such that response in the hippocampus, dorsolateral prefrontal cortex, and midbrain now reflected preference. Later work showed that neural response to chemosensory stimuli could also be altered by labels that cause an increase in perceived pleasantness of odors (De Araujo et al., 2005) or decreased aversiveness of taste (Nitschke et al., 2006, Sarinopoulos et al., 2006). Plassman and colleagues assigned differing prices to the same wine and showed that increasing the price of the wine increased pleasantness ratings and as well as response in the medial OFC/ventromedial prefrontal cortex (Plassmann et al., 2008). These studies demonstrate that separable networks are involved in preference judgments when product information is provided versus when sensory information guides judgements. Expertise is another important factor that influences neural response to food stimuli. Castriota-Scanderberg et al. (2005) delivered wine in an fMRI study to two groups of subjects. The first group consisted of sommeliers, who taste wine on a daily basis, are experts in identifying notes in wines and have a large vocabulary pertaining to the flavor component of wine. The other group of subjects consisted of normal (naïve) controls. Remarkably, the hippocampus and amygdala were activated in response to the wine, in the naïve subjects, but not in the sommeliers. This is suggestive of the potentially higher salience of the stimulus to the inexperienced subjects, and illustrates how expertise influences the encoding of stimuli. Similarly, the sight of skin cream, which precedes the tactile sensation of cream on the skin of the arm, elicits activation in OFC and ACC, areas involved in the encoding of pleasantness of touch (McCabe et al., 2008).

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Also, providing labels for skin creams, such as “rich moisturising” compared to “basic cream” activates these same areas (McCabe et al., 2008). This study convincingly shows that areas encoding the tactile pleasantness of personal care products are susceptible to cognitive influences. These studies underwrite the influence of attention, labels, and expertise on response in those areas involved in the encoding of sensory and hedonic aspects of food and personal care products. This indicates that a sensory scientist interested in moving into the field of neuroimaging or someone who is considering comissioning a neuroimaging study should consider carefully how to present products (for example with packaging and brandname, or without any other cues than chemosensory or tactile) and how to select subjects (e.g., from a target population, trained panelists, university students, or randomly selected from a representative sample of the population).

21.5.4 Reverse inference problem As mentioned before, the encoding of food pleasantness is multifaceted; multiple areas are involved and each one of those areas may be involved in one or more aspects of plesantness. To observe an area in the brain that responds stronger to a highly pleasant food odor than to a neutral food odor, does not mean that this single area computes and encodes the pleasantness of a stimulus. There are many other factors than just pleasantness that may contribute to activation of this brain area, like attention, labels, expertise, hunger, etc. This then illustrates the difficulty of inferring from the presence of activity in a specific area how pleasant a specific stimulus is, as the activity might as well have resulted from any of the other factors mentioned above. Hypothetically, if one single brain area can be identified that shows a graded response with perceived pleasantness, then one might present a range of stimuli with an unknown pleasantness and infer the pleasantness of the stimulus from neural response. However, as reviewed in Section 21.3, evidence so far shows that pleasantness encoding is a process that occurs in a distributed network of brain areas, in which some nodes are specialized (OFC encodes conscious pleasantness, amygdala is responsible for cues predicting reward), but that is also redundant (prACC is frequently coactivated with OFC, and shares some of its functional properties). This means that currently it is not possibe to make reverse inferences about the pleasantness an individual subject experienced. Unfortunately, one can regularly come across examples of this mistake in scientific journals and in the popular media. For example, in an fMRI study cited in a New York Times article, activity in the ventral striatum in response to health-warnings on cigarettes is used to infer that health-warnings invoke craving for cigarettes (Lindstrom, 2008). This result is then used to suggest that these warnings have the unintended effect of increasing smoking and that government policy should be

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changed. However, we have also observed that the ventral striatum responds to attention to taste and odor (Veldhuizen and Small, 2008), illustrating the wide variety of tasks that the striatum may be involved in. Thus to conclude that activity in the ventral striatum means that cravings are induced by health-warnings is extremely speculative and not warranted by the data. Similarly, in a recent fMRI study subjects had to choose between sets of two or three products, with the latter including a non-relevant alternative or decoy. It was concluded that less activity in the amygdala in the condition with a choice out of three versus a choice out of two indicated that the choice from three options was less negative to the subjects (Hedgcock and Rao, 2009). As reviewed in Section 21.3.5, the amygdala does not just respond to negative stimuli, but also to positive stimuli and particularly to salient stimuli that are predictive of reward. As a result, less activity in the amygdala may also have been the result of a decreased saliency of the three-choice-situation compared to a choice from two alternatives. These two examples clearly illustrate that one cannot simply infer experienced pleasantness from activity in brain areas involved in encoding positive and negative stimuli. However, this does not mean that neuroimaging holds no promises for the development of new products, which we will discuss in the next section.

21.6 Promises of neuroimaging for new product developers 21.6.1 Contribution of neuroimaging As reviewed in Section 21.3, neuroimaging techniques have helped advance understanding of the dissociable neural encoding of many aspects of food reward, such as the different neural substrates associated with wanting versus liking, with anticipatory and consummatory responses to food, and orthonasal vs. retronasal olfactory perception. Moreover, these studies have shown that a change in perception does not just result from a change in activity in one area, but often a completely different neural network may be behind these changes. Similarly, it was shown that the encoding of the pleasantness of food is not just a unidimensional process computing a stimulus’s sensory properties, but a multitude of neural networks encoding the multiple facets of food reward.

21.7 Future trends The focus of neuroscience is not just on explaining behavior, but also on predicting behavior by studying neural response. Recently some promising advances have been made in predicting a thermal stimulus’ pleasantness from neural response in prACC and OFC (Rolls et al., 2009a). In this study, neural response was measured during a delay period after a thermal

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stimulus was applied to the hand, but before a subject actually makes a pleasantness rating (on a 4-point scale). This neural response was then used to predict the pleasantness rating, which was accurate for neural response in prACC and OFC in 60–80% of single trials. This achievement is in keeping with the somewhat more simple configuration of the proposed network encoding tactile pleasantness. Note, however, that predicting a stimulus’ pleasantness does not necessarily relate to consumer behavior. To address this, subjects were asked to make a binary choice on some trials. On these trials subjects were asked if they would like to receive the same thermal stimulus again after the fMRI scanner session, which presumably relates to approach and avoidance to the stimulus (Grabenhorst et al., 2008b). Neural response in the medial prefrontal cortex could be used to accurately predict these decisions (Rolls et al., 2009a). Note, however, that this decision was always followed by another presentation of a thermal stimulus regardless of the answer because the decision referred to a presentation of the stimulus after the scanning session. As such, it is unclear how validly these “decisions” reflect true decision behavior, as subjects had no real immediate control over the stimulus received next. In the field of chemosensory perception, neural response in piriform has been used to predict the quality of odors (Howard et al., 2009), but we are unaware of any attempts at predicting the pleasantness of odors from neural response. However, the first steps towards dissociating neural response to pleasantness ratings and binary choices in odor perception have recently been made (Rolls et al., 2009b). Being able to predict the pleasantness of a thermal stimulus from neural activity measured with fMRI is a big step forward, but as mentioned before, pleasantness ratings do not necessarily equate to ensuing consummatory behavior. It will be an entirely different challenge to predict the pleasantness of food stimuli, as many different processes and neural networks are involved. We are currently unaware of any attempts to predict the appreciation of more abstract aesthetical objects (such as label design). A well known property of neural signal may be explored for the study of product perception. It is known that the fMRI response is measurably smaller following repeated presentations of the same stimulus (Buckner et al., 1998, Tootell et al., 1998). This property can be exploited by first adapting a neuronal population to repeated presentations of a single stimulus. Second, a new stimulus can be applied, and, if this stimulus is perceptually different, recovery from adaptation can be observed. This technique is called fMRI-adaptation (Grill-Spector et al., 1999, Grill-Spector and Malach, 2001), and was designed to detect overlapping but selective neural events thought to occur within a small region of cortex. This technique may be used to, for example, present a product of interest after repeated presentations of another product. If then a rebound of habituation of neural response is observed, this may mean that the product of interest is sufficiently different from the other product. Presumably one can use other neuroimaging techniques like MEG, fNIRS, EEG for this paradigm as well. This technique

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has been used to demonstrate how initially indistinguishable odors can be become perceptually different over time, and that neural response in piriform and OFC shows a rebound in habituation following perceptual learning (Li et al., 2006). New powerful techniques like this have yet to be applied to the study of food reward and personal product perception, but hold promise.

21.8 Conclusion This chapter illustrated that neuroimaging has been able to dissociate different processes within product perception, and that this is characterized by a complex set of interactions between networks. It is important for food product developers to realize that one cannot simply infer the pleasantness of a product from activity in a single brain area, that networks of areas are responsible for the neural encoding of pleasantness, that there are many aspects to pleasantness including ones that subjects are not necessarily aware of, and that the environment in which neuroimaging data are collected will pose limits on validity. This means that, at the present time, neuroimaging cannot readily be applied to new product development. However, as discussed in the last section, despite these complexities, new techniques within neuroimaging studies show preliminary evidence that neural response in piriform, OFC, ACC, and medial prefrontal cortex can be used to predict odor quality, pleasantness ratings of and approachavoidance responses to thermal stimuli.This means that we are advancing towards letting the brain speak for us and reveal our preferences.

21.9 Sources of further information and advice For an introduction to neuroimaging techniques we recommend “The Student’s Guide to Cognitive Neuroscience” by Jamie Ward (Psychology Press). A great book that deals with fMRI in detail: “Functional Magnetic Resonance Imaging, Second Edition” (Eds Huettel, S. A, Song, A. W., and McCarthy, G., Sinauer Associates, Inc). For the analysis of PET, EEG and fMRI, the most widely used software package is SPM (www.fil.ion.ucl. ac.uk), and the principles behind analysing these data is described in “Statistical Parametric Mapping : The Analysis of Functional Brain Images” (Eds, Karl J. Friston, John T. Ashburner, Stefan J. Kiebel, and Thomas E. Nichols, Academic Press). To read more about the chemical senses the reader is referred to “Taste and Smell, an update” (Eds Hummel, T. and WelgeLüssen, A., Karger, Basel) and “Handbook of Olfaction and Gustation, Second Edition” (Ed Doty, R. L., Marcel Dekker, New York). “Pleasures of the brain” (Eds Kringelbach, M. L., and Berridge, K., Oxford University Press) deals with the neural correlates of pleasantness perception in many

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sensory modalities, and includes a chapter on the pleasantness of taste, flavor and food. This chapter did not go into great detail about the neural process of decision making. The interested reader is referred to the following reviews (Heekeren et al., 2008, Montague et al., 2006, Rangel et al., 2008) and the books “Why Choose This Book?: How We Make Decisions” by Read Montague (Dutton Adult, Penguin Group USA Inc) and “Neuroeconomics: Decision making and the brain” (Eds Glimcher, P. W., Camerer, C., Poldrack, R., and Fehr, E., Academic Press, Elsevier).

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22 Molecular gastronomy, chefs and food innovation: an interview with Michael Bom Frøst M. Bom Frøst, University of Copenhagen, Denmark and S. R. Jaeger, The New Zealand Institute for Plant and Food Research Limited, New Zealand

Abstract: Michael Bom Frøst speaks about molecular gastronomy, and emerging science that integrates technological advances in food science with gastronomes’ visions for new dishes that go beyond tasting fantastic, to surprising and challenging our senses and food experiences. The conversation progresses to explore how collaboration between chefs and food scientists can be a route to new product development by industry. Michael also speaks about the MAYA principle – Most Advanced, Yet Acceptable – as a framework for guiding innovation. Key words: food science, gastronomy, molecular gastronomy, food experiences, working with chefs in innovation.

22.1

Interview with Michael Bom Frøst

Interviewer1 Molecular gastronomy is a field of research that has only recently gained attention outside a small select audience. My intention for our conversation today is to tap into your knowledge of this area and make it more widely accessible. For that purpose, I’d like to begin with some basics: What is molecular gastronomy? Who were the seminal contributors to this field? What is its history? Michael The definition of what molecular gastronomy is continues to evolve. When we started using the term here at our university, molecular gastronomy was 1

Interview with Sara Jaeger in August and November 2009.

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a fashionable term. So naturally we included the term in a large research application, only to discover a few months after the project was funded in 2006 that some of the those people who are considered icons of molecular gastronomy now denounced the use of the term molecular gastronomy for their type of cooking and preferred saying that they were doing experimental cooking or technoemotional cuisine. Interviewer And who were these icons of molecular gastronomy? Michael They were chefs, of course, not scholars. There was Ferran Adria from El Bulli in Spain and Heston Blumenthal from the Fat Duck in England and Thomas Keller from Per Se in California and the fantastic food writer Harold McGee, who on his website ‘The Curious Cook’ reveals the history behind the start of the term molecular gastronomy and its use. The first public use of the term molecular gastronomy was to announce an international workshop in Sicily in 1992 that was to be held at an old monastery, Erice. Apparently the working title for the workshop was ‘science and gastronomy’, but the people who were in charge of the workshop, among them Professor of Physics Nicholas Kurti from University of Oxford, thought it was too frivolous to use the term ‘science and gastronomy’ because the hosts at Erice wanted something that sounded more pompous, and in line with the other scientific meetings at the monastery. So they used the term molecular and physical gastronomy to announce the workshop. Interviewer What does research in molecular gastronomy entail? What are the research questions? Michael I’m a sensory scientist and a food scientist who works on issues related to how we perceive food when we eat meals, and the relationship between the chemical/physical background and the sensory properties of the food. It’s interdisciplinary; that’s important too. For my research interests, the main focus is on the experience of food. Our food chemist colleagues work on the molecular and physical aspects of culinary processes. I’d say that the first question that I try to answer taps the understanding of the experience when you eat a meal and especially when you eat a meal at a high-end restaurant. There it’s not just about getting full, it’s about the experience you have and how the way that this food is made becomes an experience for you. For example, some of Brian Wansink’s work on canteen foods has shown that if you give a hedonically positive description of foods to be eaten, they’re perceived as being more tasty. An interesting question is whether that extends to high-end gastronomy, because at that level you

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expect it to taste fabulous. In molecular gastronomy, or highly experimental cooking, the chef may sometimes have an agenda to surprise, arouse or challenge the eater. However, when you do that, the way you present the food may affect whether it comes out positive or negative for the guest. So, the research question in our first restaurant experiment was: Is it enough just to talk nice about the food, give it a hedonically positive description or do you need to understand the dish? By that I mean, do you need to have the dish declared in a way that links the raw materials with the end result on your plate? Does that make it a better experience for you? Or is the declaration only needed to let you know what to expect from the sensory properties of the dish? My colleague Line Mielby and I examined the issues about different types of verbal presentation of the food when served. How much does it affect how people like the food, are they surprised by what they perceive and are they challenged by what they perceive? Interviewer So, you conducted the research in a restaurant and participants enjoyed high-end cuisine created using approaches of molecular gastronomy? Michael Yes, that’s exactly what we did, although it was only a restaurant setting, in a well-known Copenhagen gastronomic venue – Meyer’s Madhus. We do not only use the term molecular gastronomy. We are located in Copenhagen and are very much in the sphere of New Nordic Cuisine, so even though the dishes were indeed highly experimental cooking, they also had their inspiration from our region’s raw materials and traditional dishes. The chef, Torsten Vildgaard, who was in charge of developing the menu works at noma which is a recognised world class restaurant that emphasises New Nordic Cuisine (www.noma.dk). The intention of the dishes was to surprise and challenge the eater. In different ways the dishes pushed the idea of what can be eaten, or what constitutes a good restaurant dish. Interviewer In order to create the dishes that surprise, molecular gastronomy relies on science-based cooking – is that how it works? Michael Yes. This is where the field draws on the science that provides knowledge about the relationship between the chemical/physical background and the sensory properties of the food. Through work on the molecular and physical aspects of culinary processes it becomes possible to create some totally and hitherto unseen dishes – dishes that surprise, dishes that provide novel sensory experiences.

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Interviewer What would be an example of a completely different food experience that did not exist 10–15 years ago? Michael There are many examples, and new ones keep coming. The development in modern cuisine is very rapid. Foams are probably a good example. From a sensory point of view foams can be used to give lots of different types of sensory properties, not only texture properties but also flavour properties. And the use of foams has recently been extended into more solid types, like microwaved sponge cake, that provide new opportunities for the chefs. Just recently at the Fat Duck restaurant I ate filled chocolate, where the melted chocolate was infused with a gas – nitrous oxide and solidified under vacuum, to create a wonderfully light aerated texture, but still with the rich chocolate flavour. It is a technique that I believe is already scaled-up for products to the mass market. Creative use of technology on new raw materials is a characteristic of molecular gastronomy, or experimental cooking. Interviewer Can you share an example of a molecular gastronomy enabled dish from your own research? Michael Torsten Vildgaard, our chef, once came to us with a vision of a milk skin that he thought could contribute to making a wonderful dish. His inspiration came from several sources. Hot chocolate milk forms a thin skin on the surface, and Torsten had always wanted to use a skin of this type in a dish. Further, his inspiration was from Japanese cuisine, a product called Yuba made from soy milk. When you heat soy milk then you can peel off a thin skin from the top. The milk skin had been used at El Bulli in Spain and in one of their cookbooks they supply a recipe for it, but we were unsuccessful in making a milk skin from it using cow’s milk. Interviewer And the reason you wanted to use cow’s milk was to stay with Nordic ingredients? Michael Correct. With the help of food chemists and dairy technologists we solved the technological challenges to make the milk skin with cow’s milk. The problem is that there’s not as much protein in cow’s milk as in soy milk and the fraction of small globular proteins is quite low. When you add micro particulated whey protein you increase the fraction of small globular proteins. They unfold during heating and become hydrophobic. They try to escape from the water phase and rise to the surface, creating a skin that is

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strong enough to be used in other food preparations. We used the milk skin in a dish in our first restaurant experiment. Among many chefs who practise experimental cooking, there is openness about techniques and new ingredients. So already when we used the milk skin preparation in our experimental work it was established as a dish in several restaurants in Copenhagen, based on our instructions. It was fun to see this innovation ending up in the real world. Interviewer How did you use the milk skin to surprise? Michael It was used in a dish called Milk in Textures (http://www.moleculargastronomy. life.ku.dk/billeder.aspx). There are several elements to the dish. There was a skye2-based mousse wrapped in a milk skin and then it was rolled in powdered mustard seeds and decorated with some mustard flowers. It was served on top of a polystyrene box that you could open. Inside the box was a wood sorrel sorbet. The purpose was to have a creamy soft texture from the mousse and the milk skin and then you could switch and rinse your mouth with the very icy and sour wood sorrel sorbet. This gives the guest a rapid change in texture and temperature. Additionally, there was a time dimension in the mustard flavour. The active ingredient in mustard, allyl isothiocyanate is produced when the enzyme myrosinase reacts with its substrate sinigrin. The compounds are soluble in water, and as the different compartments of the seed have been destroyed the enzymes and substrate are brought together. It is not strong immediately. It takes a while for it to build up because the activity of the enzyme has to take place and this happens over time. So the initial bites are not strong but then it builds up in your mouth and you receive quite a strong mustard heat towards the end of the mousse. So there’s a time dimension in the mustard experience, and there’s the texture and temperature contrasts and then there’s the surprise of having the milk skin which is a little bit rubbery in texture. There were a number of surprises in this dish, and the combination of them was completely novel. Interviewer You describe how molecular gastronomy is enhanced by the exchange between chefs and food scientists. Does anyone beyond those involved benefit from such collaboration? Michael There has been an increase in interaction between food scientists and chefs, from which I think we can both benefit. Food scientists have a technical 2

Skye is a traditional Icelandic fermented dairy product, characterized by a high protein content, and a low fat content.

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background. They know what to do to achieve certain visions that the chefs may have. We can assist them, and chefs inspire the food industry to make food that is more palatable. I expect the quality of the processed food will increase, with inspiration from the chefs. They have more focus on sensory properties and the composition of a dish. If you consider some of the leading chefs like Ferran Adrià and Heston Blumenthal they work together with food companies and inspire them to new foods. For instance, at El Bulli they have quite an extensive collaboration with different food companies to develop new concepts and new foods. We haven’t seen them in Denmark yet. But they already exist in Spain and other markets. Interviewer How far down the chain from high-end cuisine to daily foods and beverages do collaborations between chefs and food scientists extend? Michael All the way. I see that the increased collaboration between gastronomes and the processed food industry can lead to new products. If a food manufacturer gets the impression that it can be used and that there is a market for developing high-end mass products, then it will definitely happen, but it won’t happen if they don’t collaborate. An example is the dairy cooperation Arla, among the largest dairy co-operations in the world. Based on input from chefs and their visions for good cheese the company developed what they call gourmet cheeses. They market them exclusively for restaurants. Although they probably will not make any of the products in this range in large quantities, nor make large sums of money on these cheeses, it still benefits the company. Those that work with high-end products inspire those that develop and manufacture other types of cheese. The ideas that they develop feed into the line of innovation in the mass market products. Interviewer Are you aware of other examples of how chefs work with industry, and how the focus on high-end can give inspiration for new mass market products? Michael There are some really good and recent examples of chef-led successes for the mass market products of high quality. In 2008 the Danish supermarket chain Irma launched a series of healthy porridges dry mixture, where the consumer would cook the porridge themselves, by adding water or milk. The series was a joint development project between the chef Rasmus Koefoed, and the manufacturer Aurion. Apparently the initial success was so large that the production had difficulties keeping up with demand. In Sweden, there is Gooh – a joint venture between Läntmannen, a

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large food manufacturer and Stefano Catenacci, chef at Stockholm top restaurant Operakällaren. They produce microwavable ready meals of an unprecedented high quality and good nutritional value. Since their beginning in 2005, they have expanded continuously and now have more than 90 outlets in Stockholm and its larger suburban area. The meals have captured significant market share in the category of convenience food. Interviewer When talking about foods and innovation, it is hard not to consider the health angle and the focus on chronic health problems that may be linked to lifestyle. Does molecular gastronomy have a contribution to make in that regard? Michael You can see the chefs and scientists working in this field as role models for other chefs and other scientists to develop tasty delicious food with good health properties. The big question to me right now is: how do we teach more Danes to appreciate fine dining and culinary arts? The answer to that requires collaboration between many fields. I’m quite certain that if more people learn to appreciate fine dining and culinary arts, the health benefits will trickle down to more mundane cooking and home cooking. Recently in our university we started a new education in gastronomy and health. Here we teach students how to develop and produce healthy food with high palatability, based on knowledge of both food science and nutrition, as well as practical knowledge about culinary processes. Our students will contribute to the reinvention of Danish and Nordic cuisine. They will access the technology and know-how that we have now to reinvent classical dishes in a modern way – and to use gastronomy and consumer insight to make them irresistible. I think that’s quite powerful! Interviewer You seem to intertwine molecular gastronomy, Nordic cuisine and health. I sense a specific purpose in that combination. Michael Danes have been very good at going abroad and spending money on memorable experiences, but in my opinion there’s a lot of benefit for Danish society if we try to keep some of that money in Denmark for local foods. Many people have an interest in experiencing special locations to eat food in, food from special places that have a reputation in terms of sensory properties, have a history and have a in a way become icons for a region. New Nordic cuisine is on the rise, and the chefs are searching high and low for new Nordic raw materials that can be put to good gastronomic use. A non-profit organisation Nordic Food Lab explores relevant raw materials and techniques. This started a few years ago, when the Nordic

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Council3 initiated a program to promote Nordic food. Some of the funds have been used for research into the identity of Nordic food. Interviewer Is the tie to local identity something that can be harnessed for product development? Perhaps in combination with molecular gastronomy? Michael Starting with New Nordic cuisine and food in general it has already initiated a whole cascade of new products. Especially in the high end category, and certainly there is much more to come. However, it is a challenge to develop new products from traditions. In a research project called Quality Brew, that we just started, we will work with this. It is a consortium of microbrewers, technologists and scientists working together. The part I will do is to increase the understanding how to develop new innovative beers. The question is – how can you develop a new beer that is true to its roots but is still new? When you want to design a completely new food you have to make a link to known foods. Consumers are interested in novelty, but not too extreme novelties. First of all we have to find out which situations we can create to make people more prone to try new things. That goes both for healthy food and regional foods, but also for other types of foods and food-related behaviour. When the consumer tries new foods or experiences – how do we make sure that they are perceived as new, but they’re not so advanced that people are not capable of appreciating them? There’s an ambiguity that you want to try new experiences, but if it’s completely out of your understanding of that type of food then it doesn’t appeal to you. There is a familiarity aspect that’s important to consider. The challenge of New Nordic cuisine is to make new foods and dishes that are perceived as novel, but maintain a link to the tradition they came from, and to do so in a way that makes consumers understand the link. Interviewer Can you expand on the part about innovations that should be novel, but not too novel? Michael The industrial designer Raymond Loewy coined the acronym MAYA for this phenomena in 1951. MAYA stands for Most Advanced, Yet Acceptable. For all new products, there is a balance between novelty and the typicality of the product category. But it has never been explored in relation to foods, and it may be particularly important for foods and beverages with a regional origin. 3

The formal organization for co-operation in the Nordic region. Its members consist: Denmark, Finland, Iceland, Norway, Sweden and the autonomous territories of the Faroe Islands, Greenland and Åland.

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Interviewer We’re straying a bit from molecular gastronomy. Clearly this is an emerging approach. Is it gaining momentum? Michael Hopefully. I think it’s difficult to say directly for molecular gastronomy but definitely I see that there is an increased interest from scientists in working with gastronomy and culinary processes rather than food that runs in steel pipes. So there is increasing research in small-scale food preparation, and that’s happening in different spheres, for example with food for hospitals, food for children and so on. It is becoming more legitimate to work on high quality food. Ten years ago in Denmark people would say: we can never sell that, it’s too expensive. That does not happen so much anymore. As I said briefly before, we are seeing increased collaboration, not just in Denmark but also in places like Italy and Spain, between scientists and gastronomes. In several places they are building up education where gastronomy is part of the curriculum and, like us, they train their students in the kitchen to do basic gastronomic preparations and that’s completely new. Ten years ago, you would never have a food science student do kitchen work. Well, they could do that in their spare time if they wanted to. Now it becomes part of the curriculum and I see that as part of the interest in high quality food. Interviewer What has it been like to work with chefs? Michael Having not done so before I began research in molecular gastronomy, there has been a learning curve. One of the first issues that we discovered as a cultural difference, was time perception. Chef’s deadlines are extremely short, on a normal workday almost all preparations have to be finished before service starts. So compared to scientists, they appear very impatient. Further, chefs often need a solution to an immediate problem, where scientists may sometimes just explore the possibilities. I don’t think we still have found the best way to work together to our mutual benefit. We may have to develop new interaction types or platforms to achieve successful long-term collaborations. Interviewer Do you know if they are some of the same challenges for collaboration between chefs and the food industry? Michael It appears that they have more of a similar end goal, especially in connection with NPD. But still, there may be cultural differences. I suggest that

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food manufacturers who wish to start collaboration make it clear to themselves that as the chefs’ ideas of high quality often outdo those of the industry, so changes stemming from collaboration may require quite some investment before it is profitable. Nevertheless, clearly they have a mutual goal in developing highly palatable foods and food products – but it may require substantial changes in the processes and the logistics of the supply chain. I see this type of collaboration as different from the celebrity chef name coupled to mass market products. I know of some collaborations where chefs have contributed as consultants, both with and without being linked by name to the final product. Interviewer My final question is slightly different. One of the aims I have for these interviews is to add a human touch, and I’m interested in hearing more about how you connect with your work. Michael I think one of the things that comes to mind for me is that through this research interest, I have increasingly sought experiences to challenge my eating. Once, together with some fellow sensory scientists, I visited a restaurant called Unsichtbar in Hamburg. The name means invisible in German. Here you sit in complete darkness while you eat the food. The waiters are blind so they don’t mind the dark. They serve the food in complete darkness. Your attention is brought to the other sensory properties of the food, and to what it means to experience food in a different context than usual. We were sitting four friends together at a small table and no-one could see anything – it was pitch black. You could not see your plate, you could see absolutely nothing. You could feel the table edges and this was sort of the only holding point you had. For me, one of the interesting observations of it was the way the waiters use theirs and the guests’ other senses during the meal. For instance, when the waiter came with our wine glasses, he used sound cues to guide our attention to where the glass was. He used his ring to tap the glass and by that you could reach for it. There was the plate in front of you and a place mat which in a way set the boundaries for the nonfood impressions from the meal. It wasn’t much more difficult to eat. To me, it was more about which impressions do I get now when I cannot see my food. The first course contained smoked salmon, and even before it arrived at the table it was recognized from its smell alone. In contrast, there were other ingredients where you had to really search your sensory library for their identity. In the main course there was ostrich and I totally missed it as something unusual. I just had the impression that it was veal or some other kind of more common meat. Under normal circumstances, you have your manners. But, you don’t have to behave in a certain way when it’s pitch dark. The dessert was fabulous so I licked my plate clean, just because I could. The whole dining situation to me was about experiencing food in an

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unusual way – to explore my perception of food. How is my food and dining experience affected by different factors? We actually wrote a chapter for a book about it – You have to see it to believe it (Mielby and Frøst, expected 2011). Interviewer Definitely sounds like an interesting experience, and very much like one of those moments where you go: I’ve got a great job! Michael Most people with us were excited when we left, but some said: ‘This was fun but I don’t think I’ll do it again’. Some people were quite uncomfortable with being completely in the dark. To me it was just great fun and at the same time it challenged my senses and altered the food experience. But it is not only the foods that did that, but the total experience, and another venue that does this excellently is Madeleine’s in Copenhagen, a food theatre that produces a dining show. They use the meal as a stage for a whole narrative. It has existed for three years now. I’ve only attended one show, called ‘Under the Surface’, which was about things and events that are not what they appear to be. In my opinion exploring your own experiences in this manner provides a lot of inspiration for my research. I’m certain that it goes for product developers too. By challenging themselves they can get inspiration for NPD. Interviewer That’s a good point to finish on – thinking outside the box for inspiration for NPD. Thank you for your time.

22.2 Sources of further information and advice Adria F, Blumenthal H, Keller, T, & McGee, H (2006). Statement on new cookery. Guardian, December 10, London. Barham, P, Skibsted, L H, Bredie, W L P, Frøst, M B, Møller, P, Risbo, J, Snitkjær, P, & Mortensen, L M (2010). Molecular Gastronomy - a new emerging scientific discipline. Chemical Review, 110, 2313–2365. Hamilton, R & Todoli, V (2009) Food for Thought. Thought for Food: A Reflection on the Creative Universe of Ferran Adrià: The Creative Universe of el Bulli’s Ferran Adrià – A Reflection on the Worlds of Avant-Garde Cooking and Art. Barcelona: Actar. Mielby, L M & Frøst, M B (2010) Expectation and surprise in a molecular gastronomic meal. Food Quality and Preference, 21, 213–224. Mielby, L M & Frøst, M B (expected 2011) Eating is believing. In C. Vega, J. Ubbink, & E. van der Linden (Eds.), The kitchen as a laboratory:

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Reflections in science inspired by the kitchen. New York: Columbia University Press. Svejenova, S, Mazza, C, & Planellas, M (2007) Cooking up change in haute cuisine: Ferran Adrià as an institutional entrepreneur. Journal of Organizational Behavior, 28, 539–561. van der Linden, E, McClements, D J, & Ubbink, J (2008) Molecular gastronomy: A food fad or an interface for science-based cooking? Food Biophysics, 3, 246–254. Vega, C & Ubbink, J (2008) Molecular gastronomy: a food fad or science supporting innovative cuisine? Trends in Food Science & Technology, 19, 372–382. Wansink, B, van Ittersum, K, & Painter, J E (2005) How descriptive food names bias sensory perceptions in restaurants. Food Quality and Preference, 16, 393–400. Web resources www.gooh.se www.nordicfoodlab.org www.madeleines.dk (in Danish only) www.khymos.org (Extensive website with much information on molecular gastronomy) Good reading for non-scientists on foods and their preparation Barham, P (2000) The science of cooking. Heidelberg: Springer. McGee, H (2004) On food and cooking – the science and lore of the kitchen. New York, NY: Simon and Schuster.

22.3 Short biography for Michael Bom Frøst Michael Bom Frøst is an associate Professor at the Sensory Science group at University of Copenhagen. Since the beginning of 2007 Michael has worked in the research project on Molecular Gastronomy at the Food Science Department. The project is funded by the Danish Ministry of Science, Technology and Innovation – Danish Research Council for Technology and Production Sciences, and University of Copenhagen, Faculty of Life Sciences. Michael also directs the MSc education in Gastronomy and Health. Michael is a well-recognized all-round sensory scientist with solid expertise in performing research that combines basic science with industrially relevant applications. He is keen on dissemination of sensory science; its relevance and application to other researchers, industry and the general public. Michael has lectured on four different continents, with the goal of adding at least two more. He performs sensory research and publishes in relevant scientific journals, as well as teaching and supervising students at all levels.

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Index

AA1000, 521 absolute thresholds, 137 abstract conceptualisations, 224–6 A.C. Nielsen, 74 Academia, 63 acquisition, 282 ACTIVATE Vitamin water, 94 affective sensory analyses, 16 AIC criteria, 350 aliasing, 440–3 alliesthesia, 597 ambience, 211 AMOS, 476 amygdala and craving, 614–15 and ventral striatum, 613–14 analysis of covariance, 367 analysis of variance, 377, 437 anchor bias, 287 ANCOVA see analysis of covariance androstenone, 580 ANOVA see analysis of variance anti-consumption, 539–63 definition, 540 factors, 541–51 innovation resistance, 541–4 risk aversion, 544–6 undesired self, 546–8 voluntary simplicity, 548–51 personal care products and innovation in food, 551–62 bottled water, 551–6 genetically modified food, 556–62 antioxidant properties, 98 Aqua Bimini, 94

Arla, 639 aromatherapy, 97 ASDA, 38 assimilation effect, 182–3 attitude, 181–2 attitude survey, 471 auction prices, 312 Aurion, 639 availability bias, 287 avatars, 239 β-ionone, 577 backward elimination technique, 450, 453 balanced incomplete block designs, 464 BASES, 74, 109 Bayesian network theory, 488–510 advantages/disadvantages, 508–10 discretisation of continuous variables, 509 end-user friendly communicator, 508 handle complex problems, 508 handle incomplete datasets, 509 potential applications, 509–10 use of prior knowledge, 508 concepts, 490–3 snack consumption among teenagers, 491 inference in complex models, 500–6 dependence and conditional independence, 504–5 ‘eating with friends’ variable, 503 independence and conditional dependence, 502–4 joint probability distribution, 505–6 ‘purchase intention’ variable, 502

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Index sample problem, 501–2 ‘snack type,’ ‘liking,’ and ‘purchase intention’ variables, 504 inference in simple models, 498–500 conditional probabilities of interest, 500 joint probabilities calculation, 499 joint probability calculation, 501 ‘liking’ in relation to ‘snack type,’ 498 marginal probability distribution, 499 overall marginal probabilities, 500 learning Bayesian networks, 506–8 definition, 506 known structure, complete data, 507 known structure, incomplete data, 507–8 prior knowledge and data, 507 qualitative aspect, 490 quantitative aspect, 490 snack consumption data, 513 uses, 493–7 combined influence of variables, 496–7 initial probability distribution, 493–4 reasoning from cause to effect, 494–6 reasoning from effect to cause, 497 snack type influence, 495 BDM elicitation mechanism see Becker, DeGroot, and Marschak elicitation mechanism Becker, DeGroot, and Marschak elicitation mechanism, 336 behaviour, 282 behavioural learning theory, 388 benefit screening, 70 best-minus-worst, 147 best-worst scaling, 136, 166, 233–4, 247, 248 advantages/disadvantages, 161–2 application to hedonics analysis of best-worst data, 146–7 introduction and methodology, 146 results, 147–50 future applications in sensory and consumer science, 162 introductory example, 144–6 vs other hedonic scaling methods, 160–1 bias, 287–8 BIBD see balanced incomplete block designs BIC criteria, 350 binary rating, 79, 80 biplots, 251–4 bitterness perception, 574–5 Blackberry, 62 blind sensory ratings, 621 blood oxygenized level dependent, 604 bottled water, 94, 551–6 risk aversion, 553–4 undesired self, 554–5 voluntary simplicity, 555–6 Box-Behnken design, 448, 452

647

brain, 608–17 amygdala and ventral striatum, 613–14 craving and amygdala, 614–15 development of liking, 615–16 encoding of pleasantness of personal care products, 616–17 neural correlates of food and personal care product perception, 609–11 neural correlates of liking, 611–12 neural correlates of wanting, 612–13 organisation, 608–9 overview of neural substrates, 609 summary of neural substrates of pleasantness, 617 unconscious liking responses, 615 brainstorming, 66, 67–8 brand extensions, 306–7 Brand Sense, 230 built marketplace contexts, 364 bulleted concepts, 79 business of concepts, 63 buyer behaviour, 5 Cabernet, 306 CACM see calibrated auction-conjoint method calibrated auction-conjoint method, 353 car marques, 246–57 Carrefour, 30, 35 Casino, 30 categorical responding, 159–60 categorical scale, 396 category appraisal, 13 CD36, 576 central composite design, 447–8 central location test, 177–80 external validity into question, 194 implementation, 195–8 choice of controlled or naturalistic testing conditions, 197–8 vs home use tests, 193–8 advantages and limitations, 196 budgetary and logistic considerations, 195 product consumption mode characteristics, 197 CGC Japan, 35 channel control, 35 Chardonnay, 306 chefs, 634–45 chemosensory perception, 620–1 chi-square model, 374 choice-based scaling, 136–40 cis-3-hexenol, 580–2 claim compliance table, 293–4 building in a worldwide view local, 294 master, 294 consumer verbatim, 293–4 credibility and validation, 294

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Index

claim understanding test, 290–8 future trends, 299–300 method and implementation, 291–8 applications, 296–8 consumer recruitment, 291 exposure to stimuli, 292 protocol focused on open questions, 292 verbatim coding and qualification, 292–4 results analysis, 294–8 budget, 295 practical points, 295 rate of misinterpretations, 294–5 timing, 295 classical conditioning, 388 classification and regression trees, 375 CLT see central location test Co-Evolution Quarterly, 548 cognitive bias, 287–8 cognitive learning theory, 388 collaboration concept, 67–8 collaborative filtering, 68 commitment, 397 communication technologies, 27 comparative scaling, 396 compensatory choice model, 324 concept-creation, 69 concept performance, 69–74 full concepts screening, 70–4 complete concept, 72 ConScreen results with three concepts, 73 food concept where elements are bulletized, 71 simple concept with a picture, 71 modelling potential concept performance, 74 qualitative screening, 70 concept research recent advances for product development, 53–84 academia vs industry, 63–4 collaboration and the “wisdom of the many”, 67–8 companies survival or sustainability, 55 concept screening, 69–70 concept writing, 64–5, 68–9 concepts from observation, 67 creating the product and marketing, 83–4 design and sales messaging, 82–3 design introduction, 75–82 experimental designs, 74–5 ideation and big ideas entry points, 56–7 ideation tools, 66–7 opportunities and the use of deep knowledge, 57–62 qualitative screening, 70

R&D as innovators in food and beverages, 53–5 R&D role in food companies, 62–3 screening processes and full concepts, 70–4 simulated market test, 74 tapping the consumer mind, 65–6 concept testing, 415 conceptual lexicon, 247 conceptual maps, 251–4 conceptual profiling, 246–72 car marques emotional profiling, 246–57 dark chocolate, 258–72 brand vs unbranded product, 266–7 functional profiling, 267–72 methodology, 259–60 sensory overlay, 262–6 unbranded chocolate, 260–2 emotional profiling of car marques biplot for car marques and conceptual terms (D1 vs D2), 251 biplot for car marques and conceptual terms (D1 vs D3), 252 biplot for Citroen, 258 conceptual maps, 251–4 conceptual profiles, 249–51 implications for new product development, 257 individual differences, 254–7 methodology, 247–9 scale values for Audi and Volvo car marques, 249 share of profile data, 250 conceptualisation, 220, 221 measurement, 232–46 capturing emotionality, 234–5 capturing functionality, 232–4 emotion checklists, 235–7 faces and figures, 237–9 how objects influence behaviour, 235 imagery, 239–43 words and language, 243–6 words and thoughts interrelationships, 246 vs perception, 220–6 concurrent design, 19 conditional dependent, 504 conditional independence, 505 conditional marginal probability, 494 conditional probabilities, 493 conditional probabilities of interest, 500 conditional probability table, 490 configural invariance, 475 confirmation bias, 287 confirmatory factor analysis model, 476 conflicting desiderata, 180 conjoint analysis, 14 conjoint designs, 439 conjoint measurement, 75 ConScreen, 71–3

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Index consent form, 379, 380 construct, 392 consumer attitudes, 558 consumer behaviour studies, 522–9 literature review, 524–7 consumer brand competence, 391 consumer choice understanding processes, 220–32 3 × 3 matrix, 228–31 liking vs wanting, 226–8 perception vs consumer choice processes, 220–6 consumer culture, 404 consumer field research, 358–82 considerations, 365–77 clustered bar chart, 373 field data analysis, 373–7 field questionnaire for observational study, 370 food questionnaire for hybrid study, 371 moderation or mediation, 365–8 number of occasions, 368–9 questionnaire design, 369–73 sample classification and regression tree, 375 considerations for different field study types, 366 field mistakes, 377–81 local environment of field study, 378 place, 377–8 product, 377 promotion, 378–81 importance, 358–9 nature of field, 361–4 hybrid fields, 364 internet fields, 362 marketplace fields, 363–4 observational fields, 362–3 realism-control matrix, 361 planting, irrigating and harvesting, 359–61 consumer heterogeneity, 315 consumer involvement implications for consumer-driven innovation, 413–16 innovative products challenge, 414–15 proposed strategies to involve consumers in new product, 415–16 importance and implications for new product development, 386–416 consumer involvement theory premise, 388–9 defining the involvement construct, 390–1 measurement methods, 392–7 theoretical background of involvement construct, 387–91 measurement methods, 392–7 Likert scale example, 397 measurement development process, 394

649

methodological issues, 393–7 psychometric scale development, 394 scale properties, 394–5 types of scale, 395–7 reflective vs formative scale measurement perspective, 392–3 reflective or formative measurement, 393 theoretical considerations, 392–3 role in consumer purchase and consumption behaviours, 404–13 case of food products, 405–10 case of non food products, 410–13 involvement as segmenting variable, 404–5 scales, 397–404 antecedents of involvement, 402–4 involvement measurement scales and dimensions, 399 involvement types, 400 low and high involvement products, 401–2 semantic differential scale, 398 consumer involvement profile, 398, 401, 402–3, 409 consumer involvement theory, 388–9 consumer learning theory, 388 consumer orientation, 49 consumer-oriented innovation, 3–20 consumer preferences in food markets, 5–16 analysis of quality perception and preference formation, 11–16 consumers quality perception, 6–11 context and situations, 10–11 quality perception, 9–10 definition, 4, 5 innovation management and market orientation, 16–19 cross-functional cooperation and user knowledge, 18–19 pros and cons, 16–18 consumer packaged goods, 219 consumer product difference, 391 consumer product testing, 427–67 consumer quality expectations, 6 consumer space, 349–51 consumer test, 121 evaluation of consumer understanding of health claims, 288–90 action standard, 290 consumer approach, 289–90 validation of information, 290 which consumer, 290 content validity, 395 context central location test vs home use tests, 193–8 budgetary and logistic considerations, 195

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650

Index

external validity of CLTs into question, 194 HUT limitations, 194–5 implementation, 195–8 contextual variables influencing food choices and liking, 187–93 consumption time, 191 contextual variables influencing hedonic response, 189 food eaten during tasting, 187, 189–90 scope for choosing the food tasted, 192–3 social environment, 191–2 test food preparation and food environment, 190 current practice of hedonic tests, 177–80 conflicting desiderata, 180 tests under controlled conditions, 177–8 tests under natural conditions, 178–9 effect on preferences, 180–93 CLT vs HUT results, 188 contextual factors, 181 definition, 180–1 hedonic data from standardised vs naturalistic tasting conditions, 183–7 mean overall hedonic scores, 186 psychological constructs and attitudes on judgement, 181–3 effects on liking and implications for NPD hedonic measurements, 175–212 future trends, 211–12 evocation with a written scenario, 202–3 food testing improvement, 198–211 bringing together field and lab hedonic studies, 207–11 hedonic response, 201–7 mean intent to eat scores, 204, 206 mean situation frequency profiles, 205 more pertinent physical conditions for consumption, 199–201 repartition of choices, 209 mechanisms that influence judgement, 182–3 simulation with audio scenarios, 203–7 context effect, 184, 187 continuous responding, 159–60 contrasts effects, 183 convenience stores, 35, 36 convergent validity, 395 Corporate Accountability International, 551 corporate social responsibility, 517–32 components, 518–21 marketing and management perspectives, 518–20 perspectives on a fuzzy concept, 520–1 future trends, 530–2

mapping the field of consumers’ response, 521–9 consumer behaviour studies, 522–9 literature review, 524–7 new product development, 529–30 correlated observer data, 374 CPG see consumer packaged goods CPT see conditional probability table craving, 614–15 Cronbach’s alpha, 395 cross-cultural research data handling, 470–87 assessing measurement invariance, 474–8 bias correction, 480–1 numerical example, 478–80 structural equation modelling software, 483–7 cross-pollination, 49 C&RT see classification and regression trees cues, 7, 8 Cup-a-Soup, 57, 59–61 customer-focused method, 387 customer self-service, 26 CUT see claim understanding test dark chocolate, 258–72 DCE see discrete choice experiments decision making, 618 deep drive, 56 descriptive sensory analysis, 16 Design Expert, 463 Design Express, 466 design of experiments see statistical design of experiments Dietary Supplement Health and Education Act, 280 difference thresholds, 137 differential item functioning, 474 dimensionality, 395 direct ratio scale, 142–3 direct scaling, 136, 140–1 new methods, 135–67 directed acyclic graph, 491 discrete choice analysis, 14 discrete choice experiments, 313–28 description, 313–15 typical process of discrete choice research project, 314 example DCE to new to the market attributes, 315–27 attribute importance for aggregated analysis, 324 attribute importance for consumer segments, 326 choice task for prawn study, 320 data analysis, 321–3 data collection, 320–1 design the choice task, 319 experimental design, 317–19

© Woodhead Publishing Limited, 2010

Index list of attributes and levels, 318 part worth estimates for segmented model, 325 part worth utility estimates for aggregated multinomial logit model, 323 preference share simulator for prawn experiment, 327 research design, 315–17 results and simulation interpretation, 323–7 value in new product development, 327–8 limitations, 328 strengths, 327–8 discriminal dispersions, 138 discriminant validity, 395 disease risk claim, 281 dissemination, 18 dorsal stream, 610 dorsolateral prefrontal cortex, 618 DSHEA see Dietary Supplement Health and Education Act dummy variable, 80 dummy-variable modelling, 80 ecological awareness, 549 EFQM see European Foundation for Quality Management EFSA see European Food Safety Authority elaboration, 282 electroencephalography, 599–602 overview, 601 emergent segments, 82 emotion checklists, 235–7 emotional conceptualisation, 223–4, 241, 242 emotional consequence, 241, 242 emotional lexicon, 247 emotionality, 225, 234–5 end anchor labels, 156–8 endpoint anchor, 151 enduring involvement, 390 English auction, 336 epithelial sodium channel, 576 error variance invariance, 475 EsSense Profile, 236 ethnography, 12 EU regulation no. 1924/2006, 281, 288 Europe food retailers, 26 European Food Safety Authority, 281 European Foundation for Quality Management, 529 expectation-maximisation algorithm, 508, 509 experimental auction markets frontier research, 352–5 data combination strategies, 353 field experiments, 352 hybrid elicitation methods, 352–3 on-line auctions, 354 psychology and bidding behaviour, 354

651

studying consumer preferences, 332–55 application description, 335–7 characterising consumer space, 349–51 characterising product space, 346–9 determinants of auction bids, 345 determinants of willingness-to-pay, 343–6 market share predictions, demand elasticities, and optimal pricing, 339–43 summary statistics and relative importance of product attributes, 337–9 summary statistics, 338 experimental design, 74–83 design concept and sales messaging, 82–3 instructions for creating concept and selling messages set, 83 water concepts, 75–82 four silos in water project, 76–7 orientation page for water study, 78 test stimulus, 79 expertise, 403, 621 exploratory factor analysis, 346, 348–9 external validity, 194 extrinsic cues, 7, 8, 10, 15 facial coding, 237, 238 facial scaling, 238 factor analysis, 474 factor covariance invariance, 475 factor-screening experiments, 428 factor variance invariance, 475 factorial experiments, 428, 432–8 distribution of experimental samples, 433 main effects and interactions, 433–6 a-b-c and +/− notations for threevariable factorial experiment, 434 interaction charts, 435 numerical example, 436–8 ANOVA table, 437, 454 contour plot of overall liking, 455 data analysis, 437 design of the panel, 437 design of the samples, 436 objective, 436 overall liking rating of the fifteen samples, 453 perturbation chart of overall liking, 454 recommendations, 438 results interpretation, 437–8 samples and overall liking for fourvariable factorial experiment, 436 silicone-level-by-polymer-level interaction, 438 questions they answer, 433 structure, 432–3 factorial invariance, 474

© Woodhead Publishing Limited, 2010

652

Index

familiarity, 403 fashion clothing involvement scale, 411 clothing involvement as multidimensional construct, 411–12 effect on behaviours related to consumption and purchase, 412–13 fast moving consumer goods, 540 global innovation strategies and trends, 87–104 concept-based innovation, 95–6 convenience, 99 current trends, 100–1 global trends, 98 information source, 88–9 ingredient-based innovation, 96–8 innovation based market position, 94–5 innovation cycles, 101 Mintel, 103–4 NPD barriers, 101–2 other pillars of global innovation, 99–100 premium or indulgence as pillars of innovation, 98–9 product development vs innovation, 89 retail power growth, 102–3 simple approach to successful innovation, 90–91 simplifying innovation, 89–90 stealing with pride implementation, 91–2 stealing with pride platforms, 92–3 scale, 87–8 global new production introductions and percentage change, 88 FDA see Food and Drug Administration feedback, 54 field data analysis, 373–7 clustered bar chart, 373 describing, 373–4 predicting, 374 sample classification and regression tree, 375 targeting, 374–7 first-order designs, 447 FIS see food involvement scale FMCG see fast moving consumer goods foams, 637 focus groups, 12 Food and Drug Administration, 279 food and personal care products sector anti-consumption, 539–63 definition, 540 factors, 541–51 future trends, 562–3 personal care products and innovation in food, 551–62 food choice, 10 food innovation, 89, 634–45

food involvement scale, 405–6, 408 applications, 407–10 implications for new food product development, 410 product class involvement in foodpurchasing behaviour, 406–7 Food Neophobia Scale, 406 food preference/consumption, 582–4 food-related lifestyle instrument, 478–9 food retailing changes and implications for new product development, 25–51 directions of change, 28–33 changed relationship with consumer, 31, 33 concentration ratios in European grocery sector, 31 food distribution channel changes, 32 increased market concentration, 30 increased scale of firms, 28–30 leading food retailers in major world regions, 29 retailer control of channel, 31 technology transfer, 33 widening scope of firms, 30 fundamental innovations, 26–8 adoption of marketing by retailers, 26–7 convergent information and communication technologies, 27–8 customer self-service innovation, 26 growth model, 33–5 retail growth model based on innovation, 34 innovation key areas for retailers, 35–49 brand innovation, 38–41 considerations in NPD, 46 expansion into new markets, 41–4 faster operation of processes, 46–7 innovation interactions, 47–50 retail formats, formulae and items, 36–8 scale exploitation and scope economies, 44–6 food safety, 545–6 food science Bayesian networks theory and applications, 488–510 backward reasoning, 497 combined evidences, 496 concepts, 490–3 inference in complex models, 500–6 inference in simple models, 498–500 learning Bayesian networks, 506–8 snack consumption data, 513 uses, 493–7 Foods for Specific Health Use, 279 format, 35 formative, 392 formula, 35 forward inclusion technique, 450 FOSHU see Foods for Specific Health Use

© Woodhead Publishing Limited, 2010

Index function claims, 280 functional barriers, 542–3 functional conceptualisations, 222–3, 232 functional foods, 278–82 definition, 278–9 health related statements or claims in US health claims, 280 nutrient content claims, 280 structure/function claims, 280 nutrition and health claims in Europe health claims, 281 nutrition claims, 281 reduction of disease risk claim, 281 question of claim understanding, 281–2 scientific base, 279–81 European Union, 281 Japan, 279 United States, 279–80 functional magnetic resonance imaging, 604–5 overview, 605 functional measurement, 75 functional near infrared spectroscopy, 606 functional resolution, 599 functionality, 225, 232–4 fundamental innovations three phases in food retailing, 26–8 customer self-service innovation, 26 information and communication technologies innovation (ICT), 27–8 marketing adoption by retailers, 26–7 General Foods, 111 general labelled magnitude scale, 151 generation, 18 Generation Y, 95 genetic modification, 541–2 genetic variation taste and odour perception as guide in new product development, 570–91 human genetics, 571–4 human taste perception, 574–6 impact on food preference and consumption, 582–4 industry opportunities and issues, 584–90 odour perception, 576–82 genetically modified food, 556–62 innovation resistance, 558–60 risk aversion, 560–1 undesired self, 561 voluntary simplification, 561–2 genome wide association, 580 genotype, 588 gLMS see general labelled magnitude scale Global Compact, 521 Global New Products Database, 88 GNPD see Global New Products Database goodness-of-fit, 477, 479–80 Gooh, 639–40

653

halo effect, 287–8 Harvard Business Review, 54 Health and Wellness, 98 health claims, 280, 281 claim understanding test, 290–8 claim compliance table, 293 code frame, 293 level of misunderstanding global view, 295 level of understanding, 297 method and implementation, 291–8 pack, 297 split of responses, 296 consumer understanding and reaction, 277–300 consumer understanding evaluation, 288–90 building consumer methodology, 289–90 focus on regulatory requirements, 288–9 consumer understanding of health benefit, 282–8 global process of understanding, 282–4 model of decision taken (Grunert), 283 model of decision taken (Moorman), 283 factors that can impact understanding, 284–8 cognitive biases, 287–8 knowledge and motivation, 284–5 socio-demographic factors, 285–6 wording of health claims, 286 functional foods, 278–82 claim understanding, 281–2 definition, 278–9 scientific base, 279–81 future trends, 298–300 consumers, 299 information, 299 other applications, 299–300 healthcare, 41–2 hedonic data standardised vs naturalistic tasting conditions, 183–7 context effects depend on product type, 187 level effect, 184–5 order effect, 186–7 sensitivity effect, 185–6 hedonic measurements best worst scaling, 144–9 application to hedonics, 146–9 data from evaluating four samples, 145 example using four samples, 145 introductory example, 144–6 meal choice factor importance, 148 relationship with multinomial logit regression, 150

© Woodhead Publishing Limited, 2010

654

Index

comparisons among hedonic scaling methods, 160–5 best-worst scaling vs other methods, 160–2 LAM scaling vs other methods, 162–5 effects of context on liking central location test vs home use tests, 193–8 context effect on preferences, 180–3 current practice of hedonic tests, 177–80 food testing improvement, 198–211 future trends, 211–12 historical developments in hedonic scaling, 136–44 9-point hedonic scale, 141–4 direct scaling methods, 140–1 indirect and choice-based scaling, 136–40 labelled magnitude scales, 150–60 9-point hedonic scale and LAM scale, 154 continuous vs categorical responding, 159–60 early research, 150–1 LAM scale application and testing, 154–6 LAM scale development, 151–3 mean ratings, 157 mean responses to verbal descriptions of sensory experiences, 158 recent developments, 156–8 word label phrases and geometric mean magnitude estimates, 153 liking, and implications in NPD, 175–212 new methods for direct and indirect scaling in product development, 135–67 see also hedonic scaling hedonic price analysis, 308–12 advantages and disadvantages, 311–12 wine in Australia, 309–11 hedonic price equation for Australian wine by variety, 310 hedonic response measured in various evoked consumption situations, 201–7 context evocation with a written scenario, 202–3 context simulation with audio scenarios, 203–7 hedonic reward neuroimaging of sensory perception, 597–626 future trends, 623–25 neural substrates of pleasantness, 608–17 neuroimaging techniques, 599–608 new product development, 623 pitfalls of neuroimaging, 619–23 product choice and neuroeconomics, 617–19

hedonic scaling, 124, 130, 141–4, 154 9-point hedonic scale, 141–4 impetus for more recent developments, 143–4 S.S. Stevens and direct ratio scale methods, 142–3 direct scaling methods early origins, 140–1 indirect and choice-based, 136–40 applications to food and food-related stimuli, 139 early origins, 136–8 Thurstone’s choice-based methods, 138 turning point in the use of indirect methods, 139–40 hedonic studies, 207–11 hedonic tests central location test and home use test, 177–80 conflicting desiderata, 180 tests under controlled conditions, 177–8 tests under natural conditions, 178–9 hemispheral lateralisation, 389 heuristic availability, 182 hierarchical approach, 6 Hierarchical Bayes algorithm, 254 home-grown values, 334 home use test, 177–80 implementation, 195–8 choice of controlled or naturalistic testing conditions, 197–8 limitations, 194–5 vs central location tests, 193–8 advantages and limitations, 196 budgetary and logistic considerations, 195 product consumption mode characteristics, 197 Hugin, 500 Human Genetic Diversity Project, 585 human genetics, 571–4 gene cluster location, 572 single nucleotide polymorphism, 573 human genome, 571 human participant ethics approval, 379 human scale, 549 HUT see home use test hybrid fields, 364 hypermarkets, 35, 36 IdeaMap.Net, 77 ideation, 55, 110 classes ideas borne of collaboration, 67 ideas borne of next generation ideation, 67 ideas borne of observation, 67 methods, 68

© Woodhead Publishing Limited, 2010

Index identity, 476 IHUT see in-home use test imagery, 239–43 immediate liking, 228, 229, 232 importance-performance analysis (IPA), 12 strategic prioritisation of product improvement, 13 in-depth interview, 11 in-home use test, 437 incremental innovation trap, 17, 18 indirect scaling, 136 new methods, 135–67 induced value experiments, 334 industry opportunities/issues, 584–90 assumptions, issues and ethics, 588–90 genetic basis of sensory perception, 584–7 status, 587–8 inference, 493 information processing, 388 Information Resources Inc., 74 information search, 522, 523 information technologies (IT), 27 initial probability distribution, 493–4 description of snack consumption among teenagers, 494 Innocent Juice, 98 innovation, 35–49, 89 brand, 38–41 2006 retail brand shares, 40 three tier branding strategy, 39 expansion into new markets, 41–4 marketing and merchandising approaches in product development, 43 new categories development, 42 Tesco international store activity, 44 faster operation of processes, 46–7 numbers of new hypermarkets opened by Carrefour, 48 interactions, 47–50 retail formats, formulae and items 2009 TESCO trading formulae, 36 scale exploitation and scope economies, 44–6 innovation cycles, 101 Shades of Darkness and Piz Buin product, 102 innovation management and market orientation, 16–19 cross-functional cooperation and user knowledge, 18–19 market orientation pros and cons, 16–18 innovation process foods and personal care products, 106–18 consumer-driven innovation, 106–7 factors to lunched the innovation, 114 ideation as business problem, 109–10 NPD central location tests, 114–15

655

NPD process of food/beverage vs personal/home care products, 113–14 NPD research barriers to business decisions, 109 NPD research into insights, 108–9 observational approaches, 107 product knowledge for NPD, 111 products sensory properties and performance benefits, 113 research for company’s growth, 111–12 sensory attributes, 113 sensory people in innovative research, 111 steps of making ideas operational, 110 innovation resistance, 541–4 innovative products, 121–2 innovative thinking, 127 Institutional Review Board, 360, 379 instrumental conditioning, 388 integrated product development, 19 Intel processors, 58 internal audience, 64 International HapMap Consortium, 585 international retailing, 30 Internet Age, 68 internet fields, 362 interval, 143 intrinsic cues, 7, 9, 15 involvement construct definition, 390–1 categorisations, 390–1 different perspectives, 390 dimensions, 391 methodological issues, 393–7 psychometric scale development, 394 scale properties, 394–5 types of scale, 395–7 role in consumer purchase and consumption behaviour, 404–13 involvement theory, 388 iPOD-Nano, 58 Irma, 639 ISO 14001, 521 isovaleric acid, 579 item by use appropriateness method, 203 Japan, 98 JMP, 463 JND see just noticeable differences joint probabilities, 499 Journal of Product Innovation Management, 54 judgement biases, 287–8 just noticeable differences, 137 k-means cluster, 349 Kansei design, 225 Kellogg’s, 100

© Woodhead Publishing Limited, 2010

656

Index

labelled affective magnitude scale, 126, 136, 166 advantages/disadvantages, 164–5 application and testing, 154–6 development, 151–3 9-point hedonic scale and LAM scale, 154 word label phrases and geometric mean magnitude estimates, 153 vs other hedonic scaling methods, 162–4 labelled magnitude scale, 150–60, 166 continuous vs categorical responding, 159–60 early research, 150–1 LAM scale application and testing, 154–6 LAM scale development, 151–3 recent developments, 156–8 end anchor label effects, 156–8 laddering interview, 11–12 laddering techniques, 415 LAM scale see labelled affective magnitude scale latent class analysis, 256 latent needs, 5, 7 lavender, 97 law of comparative judgement, 138 law of total probability, 500 Learning Company, 54 learning orientation, 17 least squares estimation technique, 347 Leclerc, 35 legislation, 101 level effect, 184–5 likelihood ratio test, 478 Likert scale, 396 Likert type scales, 308 liking development, 615–16 hedonic measurements in NPD, 175–212 neural correlates, 611–12 unconscious responses, 615 vs wanting, 226–8 liking segmentation, 254 Linux, 4 LISREL, 476, 479 LMS see labelled magnitude scale local claim compliance table, 294 logistic processes, 34 low-order learning, 17 loyalty cards, 33 magnetoencephalography, 599–600, 601 overview, 600 magnitude of bias, 481 Mark & Spencer, 103 market orientation, 16, 519 and innovation management, 16–19 cross-functional cooperation and user knowledge, 18–19 market orientation pros and cons, 16–18

market share equation, 340 marketplace fields, 363–4 master claim compliance table, 294 material simplicity, 549 materialism, 412 3 × 3 matrix, 228–32 MaxDiff Designer, 162 MaxDiff/Web, 162 maximum likelihood, 476, 479 MAYA see Most Advanced, Yet Acceptable means-end chain model, 6, 7 measurement invariance, 470–87 assessment, 474–8 identification, estimation and testing, 476–7 levels of measurement invariance, 475–6 model comparison, 477–8 multi-group confirmatory factor analysis, 474–5 correcting for bias, 480–1 numerical estimation, 481 partially constrained multi-group models, 481 removal of offending items, 480–1 existing practice, 472–3 general framework, 473–4 numerical example of data handling, 478–80 FRL items, 478 goodness-of-fit and model comparison statistics, 479 observed means and covariances, 479 Merriam-Webster, 89 mesolimbic dopamine pathway, 603–4 messy environments, 364 metaphor elicitation, 66 metric invariance, 475 metric multidimensional scaling, 347 Meyer’s Madhus, 636 Milk in Textures, 638 milk skin, 637–8 Minitab, 463 Mintel, 88, 103 Mittal’s questionnaire, 407 mixture experiments, 428–9, 455–9 numerical example, 458–9 contour plot of sweet taste intensity, 460 data analysis, 459 design of the panel, 458 design of the samples, 458 nine experimental samples, 459 objective, 458 recommendations, 459 results interpretation, 459 predictive models, 457–8 questions they answer, 456–7

© Woodhead Publishing Limited, 2010

Index structure, 455–6 constrained three-component mixture experiment, 457 unconstrained three-component mixture experiment, 456 MNL see multinomial logit regression model-selection regression technique, 450 moderating variables, 365 molecular gastronomy, 634–45 monosodium glutamate, 575 Monte Carlo integration, 344 Morrison branded superstore, 38 Most Advanced, Yet Acceptable, 641 motivation, 285 Mplus, 476 multi-attribute attitude models, 6 multi-day consumer tests, 466 multi-format/formula strategies, 45 multi-group confirmatory factor analysis, 474–5 multidimensional approach, 6 multidimensional construct, 393 multidimensional scaling, 13 multinomial logit regression, 146–7, 150 multinominal logit model, 321 National Centre for Biotechnology Information, 574 naturalistic tasting vs standardised tasting conditions, 183–7 context effects depend on product type, 187 level effect, 184–5 order effect, 186–7 sensitivity effect, 185–6 need recognition, 522, 523 negative emotions, 547 negative utility, 81 negativity bias, 287 neophobia, 414 neuroeconomics, 617–18 neuroimaging sensory perception and hedonic reward, 597–626 application in new product development, 623 future trends, 623–25 neural substrates of pleasantness, 608–17 pitfalls, 619–23 product choice and neuroeconomics, 617–19 techniques, 599–608 neuroimaging techniques, 599–608 functional magnetic resonance imaging, 604–5 functional near infrared spectroscopy, 606 limitations, 606 magneto- and electroencephalography, 599–602

657

positron emission tomography, 602–4 summary, 606–8 advantages/disadvantages, 607 New Nordic Cuisine, 636, 641 new product development, 16, 28, 33, 36, 40, 42, 108, 121, 123, 529–30 application of neuroimaging, 623 biplot Audi, 255 car marques and conceptual terms (D1 vs D2), 251 car marques and conceptual terms (D1 vs D3), 252 Citroen, 257 functional profiles of dark chocolate, 271 unbranded chocolates and conceptual terms, 263 unbranded dark chocolates and conceptual terms, 262 conceptual profiling case studies, 246–72 best/worst scalings, 248 car marques emotional profiling, 246–57 combined conceptual biplots for unbranded products and branding (D1 vs D2), 268 combined conceptual biplots for unbranded products and branding (D1 vs D3), 268 dark chocolate, 258–72 individual functional profiles for dark chocolates, 271 overlay of sensory data on biplot, 265 scale values for Audi and Volvo car marques, 249 sensory biplot of unbranded dark chocolates, 264 sensory/conceptual associations, 266 unbranded Cadbury’s Bournville Deeply Dark, 261 genetic variation in taste and odour perception, 570–91 hedonic measurements of liking, 175–212 implications for hedonic measurements central location tests vs home use tests, 193–8 context effect on preferences, 180–93 current practice of hedonic tests, 177–80 food testing improvement, 198–211 future trends, 211–12 implications of food retailing changes, 25–51 directions of change, 28–33 food retail growth model, 33–5 fundamental innovations, 26–8 key areas of retail innovation, 35–50 measuring conceptualisations, 232–46 adventurousness representation, 240 capturing emotionality, 234–5 capturing functionality, 232–4

© Woodhead Publishing Limited, 2010

658

Index

emotion checklists, 235–7 faces and figures, 237–9 how objects influence behaviour, 235 imagery, 239–43 interrelationships of words and thoughts, 246 words and language, 243–6 measuring emotional and conceptual profiles, 219–72 pricing, 303–29 basic discrete choice experiments, 313–28 changes and updates to existing products and categories, 304 hedonic price analysis, 308–12 new to the world products or features, 304–5, 307–8 rules of thumb for new flavours, styles, and brand extensions, 305–7 summary of three methods, 329 share of conceptual profile Audi segments, 256 car marques, 250 Citroen, 258 dark chocolate brands, 267 unbranded dark chocolates, 263 understanding consumer choice processes, 220–32 3 × 3 matrix, 228–31 liking vs wanting, 226–8 matrix, 229 perception vs conceptualisation, 220–6 NLEA see Nutrition Labelling and Education Act nominal, 143 nominal scale, 396 nomological validity, 395 non-comparative scaling, 396 Nordic food Lab, 640–1 NPD see new product development nutrient content claims, 280 nutrition claims, 281 nutrition expertise, 284 Nutrition Labelling and Education Act, 280 objective sensory measurements, 16 observation method, 12 observational concept, 67 observational fields, 362–3 odorant receptors, 579 odour perception, 576–82 background, 577 lock and key model of odour binding, 578 genetic basis of cis-3-hexenol perception, 580–2 association plot for SNPs, 581 detection thresholds, 582 genetic determinants, 577–80

Olay, 95 on-line auctions, 354 on-the-go concept, 96 Open Innovation, 54 optimisation experiments, 446–55 numerical example, 452–5 data analysis, 453 design of the panel, 453 design of the samples, 452 objective, 452 recommendations, 455 results interpretation, 453–4 predictive models, 450–2 contour plot of overall liking on sweetener and acid levels, 451 contour plot with multiple action standards, 452 response surface plot of overall liking on sweetener and acid levels, 451 questions they answer, 449 structure, 446–9 three-variable Box-Behnken RSM design, 449 three-variable central composite RSM design, 448 order effect, 186–7 ordinal, 143 ordinal scale, 396 ordinary least-squares regression, 79, 80 OR11H7P, 579 orthogonal arrays, 439 over the counter, 42 overall marginal probability, 493, 500 paired eating method, 139 parameters, 493, 507 part worth utilities, 313 part worth values, 313 partial least squares regression, 15 path analysis, 376 PDI see purchase decision involvement pea-soup, 59 Penfolds brand wine, 306 Perceived Dietary Variety, 406 perceived quality, 6 perceived risk, 542, 559 perception, 221 vs conceptualisation, 220–6 perceptual mapping, 13, 16, 346, 347–8 Persil Power, 358–9 personal care products, 616–17 personal growth, 549 personal importance, 402 personal interest, 402 personal interview, 11 personal involvement, 390 Personal Involvement Inventory, 398, 406, 407 perturbation chart, 453–4 phantom brand names, 39

© Woodhead Publishing Limited, 2010

Index pharmacy, 41 phenylthiocarbamide, 573 Piz Buin, 101 platform innovations, 58–62 Cup-a-Soup case study, 58–61 how it works, 58 key success factors, 61–2 pleasantness key neural substrates, 617 personal care products, 616–17 pleasure, 402 portable computer, 62 positron emission tomography, 602–4 overview, 603 post-purchase intentions, 522, 523 pour-and-store concept, 92 predictive validity see external validity preference, 6, 8 preference mapping methods, 467 premium brands, 99 PrEmo, 239 pricing see product pricing principal component analysis, 13 proactive market orientation, 18 probabilistic approach, 489 product attributes, 6, 7 product choice, 617–19 product class involvement, 390 product delivery system, 59 product development, 4, 9, 89 product knowledge, 403 product optimisation experiments, 428 product pricing, 303–29 basic discrete choice experiments, 313–28 description, 313–15 example DCE to new to the market attributes, 315–27 value in new product development, 327–8 changes and updates to existing products and categories, 304 hedonic price analysis, 308–12 advantages and disadvantages, 311–12 hedonic price equation for Australian wine by variety, 310 wine in Australia, 309–11 new to the world products or features, 304–5, 307–8 rules of thumb for new flavours, styles, and brand extensions, 305–7 additions to existing product lines, 305–6 brand extensions, 306–7 summary of three methods, 329 product satisfaction, 8 product space, 346–9 Promax/oblique rotation, 349 promise testing see benefit screening PROP see 6-n-propylthiouracil

659

6-n-propylthiouracil, 573 protocepts, 62 pseudogenes, 579 psychological theory, 5 psychometric theory, 474 PTC see phenylthiocarbamide purchase decision involvement, 390, 401 purchase intentions, 9, 522, 523 quadratic regression model, 453 qualitative approach, 289 qualitative methods interviews, 11, 12 observation, 11, 12 Quality Brew, 641 quality cues, 7–8 quality expectations, 6 quality perception analysis, 11–16 cookies perceptual map, 14 importance-performance analysis, 13 consumers, 6–11 product development model, 9 total food quality model, 8 quality preferences, 10 quantitative approach, 289 quantitative research, 69, 70 questionnaire design, 369–73 confusion, 369–71 data transfer, 372–3 field questionnaire for observational study, 370 food questionnaire for hybrid study, 371 speed of completion, 371 radioactive tracers, 602 random nth price auction, 336 ratio, 143 ratio scale, 396 RC matrix see realism-control matrix realism-control matrix, 361 recommendation system, 68 regression analysis, 80, 367, 377 relevance, 388, 389, 391, 397 reliability, 395 representative bias, 287 research and development food and beverage industry, 53–5 food companies, 62–3 research gatekeepers, 378–9 research leveraging experimental methodologies, 374 response involvement, 390 response surface methodology, 447 responsive market orientation, 18 retailer’s formula, 35, 36 reverse inference, 622–3 risk, 391, 403 risk attitude, 544

© Woodhead Publishing Limited, 2010

660

Index

risk aversion, 544–6, 553–4 risk averters, 545 risk perception, 544 risk seekers, 545 risotto, 97 ritual, 403 RMSEA see root mean squared error of approximation robust maximum likelihood, 476 root mean squared error of approximation, 477, 480 RSM see response surface methodology rules of thumb pricing new flavours, styles, and brand extensions, 305–7 additions to existing product lines, 305–6 brand extensions, 306–7 SA 8000, 521 Sainsburys, 35, 38 sales growth, 44 sales revenue, 49 saltiness/sourness perception, 576 SAS, 463 Sauvignon blanc wines, 409 scalar invariance, 475 scale economies, 44 screening experiments, 438–45 aliasing, 440–3 +/− notation for full factorial experiment, 441 1/2–fraction of 23 factorial experiment, 441 numerical example, 443–5 data analysis, 444–5 design of the panel, 443 design of the samples, 443 objective, 443 probability plots for data analysis, 445 recommendations, 445 responses measured in six-variable screening experiment, 444 results interpretation, 445 samples for six-variable screening experiment, 443 variables that have significant effects on each of the responses, 446 questions they answer, 439–40 structure, 438–9 second-order designs, 447 second-order polynomial equations, 450 self-determination, 549 self expression, 412 self-service, 26 semantic differential scale, 396 sensation magnitude, 137 sensitivity effect, 185–6 sensory analysis, 16

sensory objective descriptive methods, 15 sensory overlay, 262–6 sensory perception, 584–7 neuroimaging, 597–626 see also odour perception; taste perception sensory properties, 113 sensory science, 15, 108, 112 innovation of practice and education, 120–30 9-point hedonic scale, 124 changes in past decades, 127–8 determining product failure or success, 122–3 emerging methods, 128–9 future, 128 importance in innovation, 121–2 innovative thinking, 127 new product development barriers, 123 role in innovation, 121 role in new product development, 121 sensory education, 129 simplicity, 122 situational appropriateness in innovation activities, 125 Sensory Spectrum, 109 sensory systems, 608, 610 sensory testing, 121 7–11 convenience store, 35 shelf ready packaging, 37 Shiraz, 306 simple innovation, 93 simultaneous tobit regressions, 345 single construct, 393 single nucleotide polymorphisms, 573 situational involvement, 390 SNP see single nucleotide polymorphisms social facilitation, 191 Social Judgement Theory, 388 social marketing, 519 social norms, 10 sodium chloride, 576 soft innovation, 62 Sony Walkman, 56 Spar, 35 spatial resolution, 599, 604 split-brain theory, 389 Stage Gate Method, 107 Stage Gate process, 386 Stage Gate Zero, 107 standard regression model, 374 standardised regression coefficients, 473 statistical design of experiments advantages, 429–32 ease and depth of interpretation, 429–30 efficiency, 430 robustness, 432

© Woodhead Publishing Limited, 2010

Index sensitivity, 431–2 traditional one-at-a-time vs DOE approaches, 431 factorial experiments, 432–8 main effects and interactions, 433–6 numerical example, 436–8 questions they answer, 433 structure, 432–3 and implication for consumer product testing, 427–67 brief description and alternative approaches, 427–9 design of the panel vs design of the samples, 429 implications of product testing with consumers, 463–7 incomplete serving designs, 464–6 incorporating instrumental and sensory information, 466–7 multi-day consumer tests, 466 serving orders balanced for position and carry-over effects, 465 three-of-four BIBD, 464 keys to successful DOE, 460–1 mixture experiments, 455–9 numerical example, 458–9 predictive models, 457–8 questions they answer, 456–7 structure, 455–6 optimal designs, 461–3 candidate set, 462 model for the design, 462 number of experimental samples, 463 optimisation experiments, 446–55 numerical example, 452–5 predictive models, 450–2 questions they answer, 449 structure, 446–9 screening experiments, 438–45 aliasing, 440–3 numerical example, 443–5 questions they answer, 439–40 structure, 438–9 selecting experimental variables and their ranges, 460–1 traditional designs and computer-aided optimal designs, 461–3 statistical theory, 473–4 steak preference experiment, 335 arc elasticities of demand for baseline case, 343 cluster analysis results applied to auction bids, 350 determinants of auction bids, 343 market share simulations, 342 matrix of bid differences, 347 mean bid for each steak type, 351 perceptual map from multidimensional scaling, 348 procedures, 335–6

661

standardised regression coefficients, 349 summary statistics, 338 unit demand curves for five steaks, 339 stealing with pride platform innovation concept innovation, 95–6 on-the-go concept in food and nonfood products, 96 innovation-based ingredients, 96–7 chocolate cross-over as skin care domain, 97 laundry care anti-aging concept, 95 market positioning innovation, 94–5 vitamin fortification via beverage, 94 packaging innovation, 92–3 Pour and Store concept by SunRice and Sugar Australia, 93 stealing with pride strategy, 90–2 stepwise selection, 450 stream of targeted, 98 structural equation modelling, 376, 411 structure claims, 280 subjective product knowledge, 403 Sugar Australia, 92 SunRice, 92 sweetness perception, 575 symbolic value, 412 synthesis-oriented approaches, 19 TAS1R, 575 TAS1R1, 575 TAS1R1, 575 TAS1R2, 575 TAS1R3, 575 TAS2R, 574 TAS1R3, 575 TAS2R38, 573, 575, 583, 585 TAS2R44, 575 taste perception, 574–6 genetic determinants bitterness perception, 574–5 other sensory modalities, 576 saltiness and sourness perception, 576 sweetness perception, 575 umami perception, 575–6 technology transfer, 33 temporal resolution, 599, 602, 604 Tesco, 30, 33, 35 Tesco Express, 37 Tesco Extra hypermarket, 35 Tetra Pak box, 304 Theory of Signal Detection, 138 thermal tasting sensations, 576 Thurstone’s choice-based methods, 138 Total Food Quality Model, 6, 7 illustration, 7 Total Quality Management, 529 TQM see Total Quality Management TRPV1, 576 two-way interactions, 442

© Woodhead Publishing Limited, 2010

662

Index

umami perception, 575–6 understanding, 282 undesired self, 546–8, 554–5 Unilever, 59 Unsichtbar, 643 user-driven innovation, 4 validity, 620 value scaling, 415 VARSEEK Scale, 406 vending machine, 35 ventral stream, 610 ventral striatum, 613–14

viral marketing, 416 voluntary simplicity, 548–51, 555–6 Wal-Mart, 35, 38, 40 Wald statistic, 324 wanting, 226–8 neural correlates, 612–13 water, 94 weak signals, 68, 75 web page, 36 William’s Square Design, 443, 453, 464 wine, 408 Wishdom of Crowds, 68

© Woodhead Publishing Limited, 2010

Consumer-driven innovation in food and personal care products (Woodhead Publishing Series in Food Science, Technology and Nutrition - Volume 195)

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