Acceptance of Remote Services: Perception, Adoption, and Continued Usage in Organizational Settings

Nancy V. Wünderlich Acceptance of Remote Services

GABLER RESEARCH Applied Marketing Science / Angewandte Marketingforschung Editorial Board: Prof. Dr. Dieter Ahlert, Universität Münster Prof. Dr. Heiner Evanschitzky, University of Strathclyde/UK Dr. Josef Hesse, Schäper Sportgerätebau GmbH Prof. Dr. Gopalkrishnan R. Iyer, Florida Atlantic University/USA Prof. Dr. Hartmut H. Holzmüller, Universität Dortmund Prof. Dr. Gustavo Möller-Hergt, Technische Universität Berlin Prof. Dr. Lou Pelton, University of North Texas/USA Prof. Dr. Arun Sharma, University of Miami/USA Prof. Dr. Florian von Wangenheim, Technische Universität München Prof. Dr. David Woisetschläger, Universität Dortmund

The book series ”Applied Marketing Science / Angewandte Marketingforschung“ is designated to the transfer of top-end scientific knowledge to interested practitioners. Books from this series are focused – but not limited – to the field of Marketing Channels, Retailing, Network Relationships, Sales Management, Brand Management, Consumer Marketing and Relationship Marketing / Management. The industrial focus lies primarily on the service industry, consumer goods industry and the textile / apparel industry. The issues in this series are either edited books or monographs. Books are either in German or English language; other languages are possible upon request. Book volumes published in the series ”Applied Marketing Science / Angewandte Marketingforschung“ will primarily be aimed at interested managers, academics and students of marketing. The works will not be written especially for teaching purposes. However, individual volumes may serve as material for marketing courses, upper-level MBA- or Ph.D.-courses in particular.

Nancy V. Wünderlich

Acceptance of Remote Services Perception, Adoption, and Continued Usage in Organizational Settings

With a foreword by Prof. Dr. Florian von Wangenheim

RESEARCH

Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de.

Dissertation Technische Universität München, 2009

1st Edition 2009 All rights reserved © Gabler | GWV Fachverlage GmbH, Wiesbaden 2009 Editorial Office: Claudia Jeske | Sabine Schöller Gabler is part of the specialist publishing group Springer Science+Business Media. www.gabler.de No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright holder. Registered and/or industrial names, trade names, trade descriptions etc. cited in this publication are part of the law for trade-mark protection and may not be used free in any form or by any means even if this is not specifically marked. Cover design: KünkelLopka Medienentwicklung, Heidelberg Printed on acid-free paper Printed in Germany ISBN 978-3-8349-1957-1

Foreword The way services are conceived, developed, and delivered has changed considerable in view of the recent advances in information and communication technologies. New "intelligent products" contain IT in the form of microchips, software, and sensors and are able to collect, process, and produce information. The continuous data flow from embedded IT-applications enables seamless services delivered in real-time and directed at connected objects. In this environment, remote services are quickly emerging as a new class of fascinating technology-mediated services. The number of remote service offerings has grown enormously in recent years and is expected to be the fastest growing technology-driven service type over the next decade. Remote services are highly complex, depend on mediating technologies, and require human-to-human interaction. One of the greatest challenge in this realm has proven to be the interplay between the drivers and barriers of customer adoption and acceptance, especially since remote services are predominantly found in heterogeneous B2B-environments. Theories of technology adoption that are well established in literature tend to focus mostly on the technology itself as the primary determinant of adoption and usage. This was sufficient for more basic types of services such as self-, or e-services but falls short with more advanced services in organizational settings. Thus, this dissertation is very important from a theoretical perspective. Nancy Wünderlich developed and validated a new model – the ITSUM – that forms a sound theoretical base for explaining intended and actual use of interactive remote services and can be used to predict actual service usage. The ITSUM introduces additional constructs including trust in and control of the ’service counterpart’, and aspects of customer co-production behavior like role clarity, ability and intrinsic motivation. By incorporating the ’human element’ into the model, Nancy Wünderlich contributes to the underlying theory and increases overall understanding of the phenomenon. She also shows that the predictors of remote service usage vary across groups, depending on whether the respondent’s company is in the early stages (pre-adopter) or already a user of remote services (continued usage). A major strength of the dissertation is its conceptual, theoretical, and qualitative work that precedes the rigorous quantitative testing of the ITSUM model. The model is very well supported by the data, but equally important it is also strongly supported by an extensive, inter-disciplinary

VI

Foreword

literature review and a careful, detailed, and deep qualitative interview study conducted in Germany, USA and China. This work is also critically important from a practical perspective. Helping organizations to understand the underlying drivers of customer acceptance and adoption of new types of services is of paramount interest not only in competitive dynamic markets but also to advance the organization itself. Nancy Wünderlich derives clear and concise managerial implications for remote service providers on how to increase remote service acceptance among their customers and facilitate the export of remote services. In sum, this is a remarkable thesis that substantially enhances the theoretical understanding of remote services as well as serving as a guide for managerial practice. Nancy Wünderlich has already been honored with several national and international awards – e.g., IMS & AMA SERVSIG Dissertation Proposal Award 2009, Doctoral Proposal Award of the Society for Marketing Advances 2008, Young Career in Service Science Award of the BMBF 2008, ASU/Liam Glynn Scholarship Award 2007 – for her dissertation proposal. I highly recommend this book to academics and practitioners who are interested in the management and marketing of innovative, technology-based services. Florian v. Wangenheim

Acknowledgements Foremost, my gratitude goes to my advisors, Professor Florian v. Wangenheim and Professor Mary Jo Bitner, for their endless support, enthusiasm, guidance, and inspiration. Their knowledge and insight were paramount to the success of this dissertation. Florian v. Wangenheim has provided the ideal environment for my work. He not only allowed me great freedom to pursue independent work, but from the beginning he encouraged me to participate in the international research community. Florian’s rigor, intelligence, and kindness have been invaluable not only to my development as a researcher, but also to my path as a human being. I am deeply grateful to Mary Jo Bitner for the long discussions that helped shape the direction of this work, and of my career. Mary Jo has always been there to listen and to offer indispensable advice. She has shown faith in my work from the start and has been a ceaseless advocate for me throughout the project and beyond. My truly memorable time with her, as a visiting PhD scholar at the marketing department of Arizona State University, will have a lasting impact on me. I want to thank the members of the service science community for creating a stimulating and friendly atmosphere that widened my scientific understanding in many respects. In particular, I thank Professors Ruth Bolton, Stephen Brown, Michael Ghoul, Hartmut Holzmüller, Amy Ostrom, Kay Lemon, and Ralf Reichwald for the lively discussions and for their insightful and encouraging comments. I gratefully acknowledge the institutional support that I have received while working on my dissertation. My study was conducted in the context of the project "EXFED - Export ferngelenkter Dienstleistungen" (FKZ: 01HQ0553), which was funded by the German Federal Ministry of Education (BMBF) and Research, and was supported by the German Aerospace Center (DLR). My gratitude also extends to my colleagues for their tremendous support during the time of my dissertation. In particular, I wish to thank Jan H. Schumann and Markus Wübben, whose sense of humor propelled me through the ups and downs of life as a PhD candidate. Jan and I undertook some of the most fun-filled and adventurous field trips one can ever hope to make.

VIII

Acknowledgements

Also, I want to thank my colleagues and friends at the marketing department of Technische Universität München who supported me in countless ways: Sebastian Ackermann, Armin "Raj" Arnold, Marcus Demmelmair, Christian Heumann, Clemens Hiraoka, Michael Lödding, Sabine Mayser, Anne Scherer, and Marcus Zimmer. I also received valuable input from practitioners. In particular, I acknowledge the assistance of Florian Bornemann, Veselin Panshef, Michael Pfeffer, and Weiwei Wang. Not only did their expert knowledge provide a continuous stream of insights, but they also put me in touch with the printing companies in Germany, USA, and China, which were central to my work. My deepest thanks go to my husband, Robin Wünderlich, for his unfailing love, encouragement, support, and kind indulgence with my mood and temper – especially as the deadline loomed. Without him this dissertation would not have been possible. Last but not least, I credit my cat Peppers for amazing me every day with his valiant efforts to distract me from typing. Nancy V. Wünderlich

Short Table of Contents Foreword

V

Acknowledgements List of Figures

VII XVIII

List of Tables

XIX

List of Abbreviations

XXI

1

Introduction

1

2

Conceptual Framework: Remote Services in Context of Technology-Mediated Services

7

3

Theoretical Framework for Remote Service Adoption and Continued Usage

31

4

Methodological Superstructure and Empirical Setting

85

5

Qualitative Exploratory Interview Study

93

6

Hypotheses Development

131

7

Quantitative Studies

149

8

Summary and Conclusions

201

References

209

A Additional Tables and Figures

255

Table of Contents Foreword

V

Acknowledgements List of Figures

VII XVIII

List of Tables

XIX

List of Abbreviations

XXI

1

2

Introduction

1

1.1

Motivation and Goals of the Thesis . . . . . . . . . . . . . . . . . . . . . . . .

1

1.2

Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

1.3

Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

Conceptual Framework: Remote Services in Context of Technology-Mediated Services

7

2.1

Emerging Technology-Mediated Service Types . . . . . . . . . . . . . . . . .

7

2.1.1

E-Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

2.1.2

Self-Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

2.1.3

Mobile Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

2.1.4

Industry Specific Technology-Mediated Services . . . . . . . . . . . .

13

2.2

2.3

2.1.4.1

Teleservices in Engineering and Manufacturing Industries . .

13

2.1.4.2

Telematics in the Automotive Industry . . . . . . . . . . . .

15

2.1.4.3

Telemedicine in Health Care . . . . . . . . . . . . . . . . .

16

2.1.4.4

Services in the IT-Sector . . . . . . . . . . . . . . . . . . . .

18

Classification of Remote Services . . . . . . . . . . . . . . . . . . . . . . . .

19

2.2.1

Definition of Remote Services . . . . . . . . . . . . . . . . . . . . . .

19

2.2.2

Characteristics of Remote Services . . . . . . . . . . . . . . . . . . .

20

2.2.3

Benefits of Remote Services . . . . . . . . . . . . . . . . . . . . . . .

23

Classification of Interactive Remote Services . . . . . . . . . . . . . . . . . .

24

2.3.1

24

Definition of Interactive Remote Services . . . . . . . . . . . . . . . .

XII

TABLE OF CONTENTS

2.4 3

2.3.2

Characterization and Demarcation of Interactive Remote Services . . .

2.3.3

Positioning of Interactive Remote Services . . . . . . . . . . . . . . .

26

Conclusions and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . .

29

Theoretical Framework for Remote Service Adoption and Continued Usage 3.1

31

3.1.1

Behavioral Theories from Social Psychology and Sociology . . . . . .

32

3.1.1.1

Innovation Diffusion Theory and Variants . . . . . . . . . .

32

3.1.1.2

The Theory of Reasoned Action and Variants . . . . . . . . .

34

3.1.1.3

The Theory of Planned Behavior and Variants . . . . . . . .

36

3.1.1.4

3.1.3

The Decomposed Theory of Planned Behavior . . . . . . . .

37

Models in IT-Adoption Based on Behavioral Theories . . . . . . . . .

39

3.1.2.1

The Technology Acceptance Model and Variants . . . . . . .

39

3.1.2.2

The Motivational Model and Variants . . . . . . . . . . . . .

41

3.1.2.3

The Unified Theory of Acceptance and Use of Technology .

42

3.1.2.4

Compeau and Higgins’ Model based on Social Cognitive Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

44

Theoretical Foundations of Continued Use of Technology . . . . . . .

45

3.1.3.1

Importance of Prior Experience . . . . . . . . . . . . . . . .

45

3.1.3.2

Studies On Continued Usage . . . . . . . . . . . . . . . . .

46

3.1.3.3

Comparison of Adoption and Continuance Drivers . . . . . .

48

Summary and Overview of Models in Technology Adoption . . . . . .

50

Theoretical Foundations of Interaction in the Service Encounter . . . . . . . .

58

3.2.1

Perceptions of Service Providers’ Employee Behavior . . . . . . . . .

58

3.2.1.1

Importance of Employee Behavior in the Service Encounter .

58

3.2.1.2

Customer Orientation of Employees . . . . . . . . . . . . .

60

3.2.1.3

Role of Employee Behavior in Service Quality Assessments .

60

3.2.1.4

Employee Behavior in Technology-Mediated Service Encoun-

3.1.4

ters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2

3.2.3

62

Customer Integration in the Service Process . . . . . . . . . . . . . . .

65

3.2.2.1

Research on Customer Co-Production . . . . . . . . . . . .

65

3.2.2.2

Drivers of Customer Co-Production . . . . . . . . . . . . . .

66

Customer Beliefs Regarding the Interaction with Service Technology .

69

3.2.3.1

Consumer Readiness as Driver of Technology-Mediated CoProduction . . . . . . . . . . . . . . . . . . . . . . . . . . .

69

Technology Readiness as a Driver of Technology Usage in Services . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69

Transcending Concepts of Trust and Control across Disciplines . . . . . . . . .

71

3.2.3.2 3.3

31

Theoretical Foundations of Technology Adoption . . . . . . . . . . . . . . . .

3.1.2

3.2

25

3.3.1

Importance of Trust and Trustworthiness . . . . . . . . . . . . . . . .

71

3.3.2

Importance of Control Beliefs . . . . . . . . . . . . . . . . . . . . . .

73

TABLE OF CONTENTS 3.3.3 3.4

3.5 4

5

XIII

The Trust-Control Nexus . . . . . . . . . . . . . . . . . . . . . . . . .

75

Technology-Intensive Service Adoption in B2B contexts . . . . . . . . . . . .

77

3.4.1

. . . . . . . . . . . . . . . . . . . . .

77

3.4.2

Decision Making and the Adoption Process in Organizations . . . . . .

78

3.4.3

Organizational Adoption Drivers . . . . . . . . . . . . . . . . . . . . .

81

Summary of the Theoretical Foundations of Remote Services . . . . . . . . . .

82

Business Service Relationships

Methodological Superstructure and Empirical Setting

85

4.1

Methodological Superstructure . . . . . . . . . . . . . . . . . . . . . . . . . .

85

4.2

Empirical Setting of the Employed Studies . . . . . . . . . . . . . . . . . . . .

87

4.2.1

Selection of the Printing Industry . . . . . . . . . . . . . . . . . . . .

87

4.2.2

Printing Machine Manufacturing . . . . . . . . . . . . . . . . . . . . .

88

4.2.3

The Printing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . .

89

Qualitative Exploratory Interview Study

93

5.1

Motivation and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

93

5.2

Qualitative Research Methodology . . . . . . . . . . . . . . . . . . . . . . . .

94

5.2.1

Semi-Structured Interviews as Means of Data Collection . . . . . . . .

94

5.2.2

Qualitative Content Analysis as Means of Data Analysis . . . . . . . .

94

5.2.3 5.3

5.4

Validity and Reliability . . . . . . . . . . . . . . . . . . . . . . . . . .

95

Field Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97

5.3.1

Sample Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97

5.3.2

Interview Situation and Questionnaire Design . . . . . . . . . . . . . . 102

5.3.3

Category Development and Coding . . . . . . . . . . . . . . . . . . . 105

Results of the Qualitative Interview Study . . . . . . . . . . . . . . . . . . . . 105 5.4.1

Assessment of Intercoder Reliability . . . . . . . . . . . . . . . . . . . 105

5.4.2

Structure of Results Presentation . . . . . . . . . . . . . . . . . . . . . 106

5.4.3

Technology Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

5.4.4

Relational Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5.4.4.1

Trust in the Remote Service Technician . . . . . . . . . . . . 110

5.4.4.2

Trust in the Remote Service Provider Company . . . . . . . 113

5.4.5

Process Control Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . 114

5.4.6

Economic Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5.4.7

Participation Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

5.4.8

Cultural Differences in the Customer’s Willingness to Collaborate . . . 122

5.4.9

Prior Experiences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

5.4.10 Organizational Factors . . . . . . . . . . . . . . . . . . . . . . . . . . 124 5.4.11 Contextual Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 5.4.12 Discussion of the Results . . . . . . . . . . . . . . . . . . . . . . . . . 127

XIV 6

TABLE OF CONTENTS

Hypotheses Development 6.1

6.1.1

6.1.2

Counterpart Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 6.1.1.1

Controllability of the Counterpart’s Actions . . . . . . . . . 132

6.1.1.2

Trustworthiness of the Counterpart . . . . . . . . . . . . . . 134

Technology Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 6.1.2.1

Trust in Technology . . . . . . . . . . . . . . . . . . . . . . 136

6.1.2.2

Ease of Use . . . . . . . . . . . . . . . . . . . . . . . . . . 136

6.1.3

Perceived Usefulness . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

6.1.4

Participation Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

6.1.5

7

131

Development of the ITSUM . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

6.1.4.1

Role Clarity . . . . . . . . . . . . . . . . . . . . . . . . . . 139

6.1.4.2

Role Ability . . . . . . . . . . . . . . . . . . . . . . . . . . 140

6.1.4.3

Intrinsic Motivation . . . . . . . . . . . . . . . . . . . . . . 140

Organizational Characteristics . . . . . . . . . . . . . . . . . . . . . . 141 6.1.5.1

Subjective Norms . . . . . . . . . . . . . . . . . . . . . . . 141

6.1.5.2

Company Size and Respondent’s Function . . . . . . . . . . 142

6.2

Link Between Usage Intention and Actual Usage Behavior . . . . . . . . . . . 143

6.3

Hypotheses Development for Group Comparisons . . . . . . . . . . . . . . . . 143

Quantitative Studies

149

7.1

Motivation and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

7.2

Methods and Techniques Employed . . . . . . . . . . . . . . . . . . . . . . . 150 7.2.1 7.2.2

Survey Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Structural Equation Modeling . . . . . . . . . . . . . . . . . . . . . . 151 7.2.2.1

Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 151

7.2.2.2

Assessment of Reliability and Validity . . . . . . . . . . . . 153

7.2.2.3

Assessment of Model Fit and Data Quality . . . . . . . . . . 154

7.2.2.4

Dependent Categorical Variables . . . . . . . . . . . . . . . 156

7.2.2.5

Multi-Group Comparison . . . . . . . . . . . . . . . . . . . 156

7.3

Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

7.4

General Outline of the Questionnaires . . . . . . . . . . . . . . . . . . . . . . 160

7.5

Operationalization of the Constructs . . . . . . . . . . . . . . . . . . . . . . . 161

7.6

Quality of the Questionnaire and Pre-Test . . . . . . . . . . . . . . . . . . . . 166

7.7

7.8

t1 -Study: Results of ITSUM Validation . . . . . . . . . . . . . . . . . . . . . . 167 7.7.1

Sample Structure and Description . . . . . . . . . . . . . . . . . . . . 167

7.7.2

Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

7.7.3

Measurement Validity . . . . . . . . . . . . . . . . . . . . . . . . . . 171

7.7.4

Assessing Common Method Variance . . . . . . . . . . . . . . . . . . 174

7.7.5

Validation of the ITSUM (n=717) . . . . . . . . . . . . . . . . . . . . 175

Multi-Group Comparison: Adoption vs. Continued Usage . . . . . . . . . . . 178

TABLE OF CONTENTS

7.9

XV

7.8.1

Description of the Groups . . . . . . . . . . . . . . . . . . . . . . . . 178

7.8.2 7.8.3

Assessing Measurement Invariance . . . . . . . . . . . . . . . . . . . 179 Results for Organizations in the Pre-Adoption Phase . . . . . . . . . . 184

7.8.4 Results for Organizations in the Continued Usage Phase 7.8.5 Comparison of Group Parameters . . . . . . . . . . . . t2 -Study: Intention - Behavior Link . . . . . . . . . . . . . . . . 7.9.1 Sample Description . . . . . . . . . . . . . . . . . . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

186 189 192 192

7.9.2 Logistic Regression Results . . . . . . . . . . . . . . . . . . . . . . . 194 7.10 Discussion of the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 8

Summary and Conclusions 201 8.1 Summary of the Central Results . . . . . . . . . . . . . . . . . . . . . . . . . 201 8.2 Managerial Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 8.3 Implications for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . 206

References

209

A Additional Tables and Figures

255

A.1 Interview Guideline of the Exploratory Qualitative Study . . . . . . . . . . . . 255 A.2 First Pages of the Online Survey t1 and t2 -study . . . . . . . . . . . . . . . . . 257 A.3 Exploratory Factor Analysis Results . . . . . . . . . . . . . . . . . . . . . . . 259 A.4 Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 A.5 Calculation of Moderating Effects . . . . . . . . . . . . . . . . . . . . . . . . 261

List of Figures 1.1

Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

2.1

The Continuum from eService to eCommerce . . . . . . . . . . . . . . . . . .

9

2.2

Categories and Examples of SST . . . . . . . . . . . . . . . . . . . . . . . . .

11

2.3

Application Fields of Teleservices . . . . . . . . . . . . . . . . . . . . . . . .

15

2.4

Categories of Telematic Services . . . . . . . . . . . . . . . . . . . . . . . . .

17

2.5

Activity Portfolio of Customer and Provider Actions . . . . . . . . . . . . . .

22

2.6

Features of Remote Services . . . . . . . . . . . . . . . . . . . . . . . . . . .

23

2.7

Benefits and Challenges of Remote Services . . . . . . . . . . . . . . . . . . .

24

2.8

Features of Interactive Remote Services Compared to Remote Services . . . . .

27

2.9

Technology-Interaction-Service Matrix . . . . . . . . . . . . . . . . . . . . . .

28

3.1

The Innovation Decision Process . . . . . . . . . . . . . . . . . . . . . . . . .

33

3.2

The Theory of Reasoned Action . . . . . . . . . . . . . . . . . . . . . . . . .

34

3.3

The Theory of Planned Behavior . . . . . . . . . . . . . . . . . . . . . . . . .

37

3.4

The Decomposed Theory of Planned Behavior . . . . . . . . . . . . . . . . . .

38

3.5

The Technology Acceptance Model 1 and 2 . . . . . . . . . . . . . . . . . . .

40

3.6

The Unified Theory of Acceptance and Use of Technology . . . . . . . . . . .

43

3.7

Compeau and Higgins’ (1995) Model Based on SCT . . . . . . . . . . . . . .

45

3.8

IT Continuance Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47

3.9

Adoption and Continuance Model of Karahanna, Straub, and Chervany (1999) .

49

3.10 The Customer Oriented-Skills of an Employee (COSE) . . . . . . . . . . . . .

61

3.11 Generic Dimensions to Evaluate Service Quality . . . . . . . . . . . . . . . . .

61

3.12 B-A-I Framework of Technology-Mediated Customer Service . . . . . . . . .

63

3.13 CSR Characteristics and their Effects on Customer Satisfaction . . . . . . . . .

64

3.14 Levels of Customer Participation across Different Services . . . . . . . . . . .

66

3.15 Key Predictors of Consumer Trial of Self-Service Technologies . . . . . . . . .

70

3.16 The Integrated Framework of Trust, Control, and Risk in Strategic Alliances . .

76

3.17 Key Differences between B2C and B2B Marketing . . . . . . . . . . . . . . .

77

3.18 Triangle Model and Square Model . . . . . . . . . . . . . . . . . . . . . . . .

79

3.19 The Innovation Process in Organizations . . . . . . . . . . . . . . . . . . . . .

82

XVIII

LIST OF FIGURES

4.1

Major Differences between Qualitative and Quantitative Research . . . . . . .

85

4.2

The Print Production Process . . . . . . . . . . . . . . . . . . . . . . . . . . .

91

5.1 5.2 5.3

Inductive Approach of Qualitative Content Analysis . . . . . . . . . . . . . . . 95 Coding Categories of the Qualitative Interview Study – Part 1 . . . . . . . . . . 103 Coding Categories of the Qualitative Interview Study – Part 2 . . . . . . . . . . 104

5.4 5.5

Conceptual Framework Resulting From Qualitative Study . . . . . . . . . . . . 107 Factors Influencing Remote Service Perception . . . . . . . . . . . . . . . . . 129

6.1

The Extended Interactive Technology-Mediated Service Usage Model . . . . . 132

7.1 7.2 7.3

Proposed Procedure for Assessing Measurement Invariance . . . . . . . . . . . 157 Company Size in the Printing Industry vs. Overall Sample . . . . . . . . . . . 168 Distribution of Respondent’s Age in the Overall Sample (n=717) . . . . . . . . 168

7.4

Self-Reported Classification of Business Segments . . . . . . . . . . . . . . . 169

7.5 7.6 7.7

Distribution of Respondent’s Function in the Sample . . . . . . . . . . . . . . 169 Results of the ITSUM (n=717) . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Distribution of Respondent’s Age Across Groups . . . . . . . . . . . . . . . . 180

7.8 Distribution of Respondent’s Gender Across Groups . . . . . . . . . . . . . . 180 7.9 Distribution of Company Size Across Groups . . . . . . . . . . . . . . . . . . 181 7.10 Distribution of Respondent’s Function Across Groups . . . . . . . . . . . . . . 181 7.11 Results of the ITSUM: Pre-Adopter Group (n=364) . . . . . . . . . . . 7.12 Results of the ITSUM: Continued User Group (n=353) . . . . . . . . . 7.13 Distribution of Respondent’s Age Across Samples (t1 and t2 -Study) . . 7.14 Distribution of Respondent’s Gender Across Samples (t1 and t2 -Study) . 7.15 Distribution of Respondent’s Function Across Samples (t1 and t2 -Study)

. . . . .

. . . . .

. . . . .

. . . . .

185 187 193 193 194

A.1 First Page of the t1 -Study Questionnaire . . . . . . . . . . . . . . . . . . . . . 257 A.2 First Page of the t2 -Study Questionnaire . . . . . . . . . . . . . . . . . . . . . 258

List of Tables 3.1

Relevant Empirical Studies on Technology-Intensive Services and IT Adoption

51

5.1

List of Interviewees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

99

5.2

Inter-Coder Judgement Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . 106

6.1

Summary of Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

7.1

Operationalization of the Constructs . . . . . . . . . . . . . . . . . . . . . . . 161

7.2

Crosstable: Respondent’s Function / Number of Employees . . . . . . . . . . . 170

7.3

Fit Statistics for the Measurement Model (n=717) . . . . . . . . . . . . . . . . 172

7.4

Statistics of the Measurement Model . . . . . . . . . . . . . . . . . . . . . . . 173

7.5

Correlation of ITSUM Variables (n=717) . . . . . . . . . . . . . . . . . . . . 175

7.6

Results of the ITSUM (n=717) . . . . . . . . . . . . . . . . . . . . . . . . . . 176

7.7

ITSUM (n=717): Mediating Effects . . . . . . . . . . . . . . . . . . . . . . . 177

7.8

ITSUM (n=717): Moderating Effects . . . . . . . . . . . . . . . . . . . . . . . 178

7.9

Model Fit Statistics for Pre-Adopter Group and Continued User Group . . . . . 182

7.10 Assessing Measurement Invariance: Model Fits . . . . . . . . . . . . . . . . . 182 7.11 Pre-Adopter Group (n=364): Direct Effects . . . . . . . . . . . . . . . . . . . 185 7.12 Pre-Adopter Group (n=364): Mediating Effects . . . . . . . . . . . . . . . . . 186 7.13 Pre-Adopter Group (n=364): Moderating Effects . . . . . . . . . . . . . . . . 187 7.14 Continued User Group (n=353): Direct Effects . . . . . . . . . . . . . . . . . 188 7.15 Continued User Group (n=353): Mediating Effects . . . . . . . . . . . . . . . 188 7.16 Continued User Group (n=353): Moderating Effects . . . . . . . . . . . . . . . 189 7.17 Comparison of Path Coefficients Across Groups . . . . . . . . . . . . . . . . . 190 7.18 Comparison of Factor Means Across Groups . . . . . . . . . . . . . . . . . . . 191 7.19 Results of Hosmer-Lemeshow-Test . . . . . . . . . . . . . . . . . . . . . . . . 195 7.20 Classification Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 7.21 Quantitative Results: Hypotheses Summary . . . . . . . . . . . . . . . . . . . 197 A.1 Structure Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 A.2 AVE and Squared Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . 260 A.3 Correlation Matrix without CMV . . . . . . . . . . . . . . . . . . . . . . . . . 260

XX

LIST OF TABLES

A.4 ITSUM (n=717): Calculation of Moderating Effects . . . . . . . . . . . . . . . 261 A.5 Pre-Adopter Group (n=364): Calculation of Moderating Effects . . . . . . . . 262 A.6 Continued User Group (n=353): Calculation of Moderating Effects . . . . . . . 262

List of Abbreviations ACN . . . . . . . . . . . . . . . . . . . . . . . . Automatic Collision Notification AIC . . . . . . . . . . . . . . . . . . . . . . . . . Akaike Information Criterion AOL . . . . . . . . . . . . . . . . . . . . . . . . America Online APS . . . . . . . . . . . . . . . . . . . . . . . . Automated Phone System ATM . . . . . . . . . . . . . . . . . . . . . . . . Automatic Teller Machine ATT . . . . . . . . . . . . . . . . . . . . . . . . Attitude AVE . . . . . . . . . . . . . . . . . . . . . . . . Average Variance Extracted B . . . . . . . . . . . . . . . . . . . . . . . . . . . Behavior / Usage Behavior B-A-I . . . . . . . . . . . . . . . . . . . . . . . Belief Attitude Intention B2B . . . . . . . . . . . . . . . . . . . . . . . . Business-to-Business B2C . . . . . . . . . . . . . . . . . . . . . . . . Business-to-Consumer BD . . . . . . . . . . . . . . . . . . . . . . . . . Business Development BIC . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian Information Criterion BVDM . . . . . . . . . . . . . . . . . . . . . . Bundesverband der Druck- und Medienunternehmen (German Printing and Media Industries Federation) CFA . . . . . . . . . . . . . . . . . . . . . . . . Confirmatory Factor Analysis CFI . . . . . . . . . . . . . . . . . . . . . . . . . Comparative Fit Index CI . . . . . . . . . . . . . . . . . . . . . . . . . . CMV . . . . . . . . . . . . . . . . . . . . . . . CONT . . . . . . . . . . . . . . . . . . . . . . COSE . . . . . . . . . . . . . . . . . . . . . . . CR . . . . . . . . . . . . . . . . . . . . . . . . . CRM . . . . . . . . . . . . . . . . . . . . . . .

Causal Inference Common Method Variance Control / Controllability Customer Oriented Skills of an Employee Composite / Construct Reliability Customer Relationship Management

CSR . . . . . . . . . . . . . . . . . . . . . . . . CTC . . . . . . . . . . . . . . . . . . . . . . . . df . . . . . . . . . . . . . . . . . . . . . . . . . . . DTPB . . . . . . . . . . . . . . . . . . . . . . . e-brokerage . . . . . . . . . . . . . . . . . . e-commerce . . . . . . . . . . . . . . . . . e-coupons . . . . . . . . . . . . . . . . . . .

Customer Service Representative Customer-Technology Contact Degrees of Freedom Decomposed Theory of Planned Behavior Electronic Brokerage Electronic Commerce Electronic Coupons

XXII

LIST OF ABBREVIATIONS

e-government . . . . . . . . . . . . . . . . Electronic Government e-health . . . . . . . . . . . . . . . . . . . . . Electronic Health e-learning . . . . . . . . . . . . . . . . . . . Electronic Learning e-payment . . . . . . . . . . . . . . . . . . . Electronic Payment e-tax . . . . . . . . . . . . . . . . . . . . . . . . Electronic Tax e-vendor . . . . . . . . . . . . . . . . . . . . Electronic Vendor e.g. . . . . . . . . . . . . . . . . . . . . . . . . . exempli gratia ECT . . . . . . . . . . . . . . . . . . . . . . . . Expectation Confirmation Theory EFA . . . . . . . . . . . . . . . . . . . . . . . . Exploratory Factor Analysis EOU . . . . . . . . . . . . . . . . . . . . . . . . et al. . . . . . . . . . . . . . . . . . . . . . . . . ETC . . . . . . . . . . . . . . . . . . . . . . . . etc. . . . . . . . . . . . . . . . . . . . . . . . . . ExFeD . . . . . . . . . . . . . . . . . . . . . .

Ease of Use et alii Electronic Toll Collection et cetera Export Ferngelenkter Dienstleistungen (Export of Remote Services)

EXP . . . . . . . . . . . . . . . . . . . . . . . . Expertise exp. . . . . . . . . . . . . . . . . . . . . . . . . . Expected f.e. . . . . . . . . . . . . . . . . . . . . . . . . . For Example FD . . . . . . . . . . . . . . . . . . . . . . . . . . FMS . . . . . . . . . . . . . . . . . . . . . . . . FTU . . . . . . . . . . . . . . . . . . . . . . . . GATF . . . . . . . . . . . . . . . . . . . . . . .

Factor Determinacy Flexible Manufacturing System Facilities Transformation Usage Framework Graphic Arts Technical Foundation

GM . . . . . . . . . . . . . . . . . . . . . . . . . General Manager I. . . . . . . . . . . . . . . . . . . . . . . . . . . . Interview Number i.e. . . . . . . . . . . . . . . . . . . . . . . . . . . id est ICT . . . . . . . . . . . . . . . . . . . . . . . . . IT Continuance Model IDT . . . . . . . . . . . . . . . . . . . . . . . . . Innovative Diffusion Theory INT . . . . . . . . . . . . . . . . . . . . . . . . . Intention IRCAD . . . . . . . . . . . . . . . . . . . . . Institute for Research of the Cancer and Digestive System IS . . . . . . . . . . . . . . . . . . . . . . . . . . Information System IT . . . . . . . . . . . . . . . . . . . . . . . . . . Information Technology ITSUM . . . . . . . . . . . . . . . . . . . . . Interactive Technology-Mediated Service Usage Model Jr. . . . . . . . . . . . . . . . . . . . . . . . . . . Junior KBA . . . . . . . . . . . . . . . . . . . . . . . . Koenig & Bauer AG LMS . . . . . . . . . . . . . . . . . . . . . . . . loc . . . . . . . . . . . . . . . . . . . . . . . . . . M.I. . . . . . . . . . . . . . . . . . . . . . . . . . MAN . . . . . . . . . . . . . . . . . . . . . . .

Latent Moderated Structural Equations Locus of Causality Modification Indices Maschinenfabrik Augsburg Nürnberg AG

LIST OF ABBREVIATIONS

XXIII

MIS . . . . . . . . . . . . . . . . . . . . . . . . Management Information System ML . . . . . . . . . . . . . . . . . . . . . . . . . Maximum Likelihood MLM . . . . . . . . . . . . . . . . . . . . . . . Maximum Likelihood Parameter Estimate M MLR . . . . . . . . . . . . . . . . . . . . . . . . MOTIV . . . . . . . . . . . . . . . . . . . . . n.s. . . . . . . . . . . . . . . . . . . . . . . . . . OLS . . . . . . . . . . . . . . . . . . . . . . . . P. . . . . . . . . . . . . . . . . . . . . . . . . . . .

Maximum Likelihood Parameter Estimate R Motivation Not Supported Ordinary Least Squares Participant Number

p. . . . . . . . . . . . . . . . . . . . . . . . . . . . Page p.s. . . . . . . . . . . . . . . . . . . . . . . . . . PA . . . . . . . . . . . . . . . . . . . . . . . . . . pb . . . . . . . . . . . . . . . . . . . . . . . . . . PC . . . . . . . . . . . . . . . . . . . . . . . . . . PDA . . . . . . . . . . . . . . . . . . . . . . . .

Partially Supported Proportional Agreement Prior Behavior Personal Computer Personal Digital Assistant

PL . . . . . . . . . . . . . . . . . . . . . . . . . . Perreault and Leigh Measure PRL . . . . . . . . . . . . . . . . . . . . . . . . Proportional Reduction in Loss PU . . . . . . . . . . . . . . . . . . . . . . . . . . Perceived Usefulness RA . . . . . . . . . . . . . . . . . . . . . . . . . Role Ability RC . . . . . . . . . . . . . . . . . . . . . . . . . Role Clarity RMSEA . . . . . . . . . . . . . . . . . . . . . Root Mean Squared Error of Approximation RRDM . . . . . . . . . . . . . . . . . . . . . . RS . . . . . . . . . . . . . . . . . . . . . . . . . . RSPC . . . . . . . . . . . . . . . . . . . . . . . RST . . . . . . . . . . . . . . . . . . . . . . . .

Remote Repair, Diagnosis and Maintenance Remote Service Remote Service Provider Company Remote Service Technician

s.d. . . . . . . . . . . . . . . . . . . . . . . . . . Standard Deviation s.e. . . . . . . . . . . . . . . . . . . . . . . . . . Standard Error SAT . . . . . . . . . . . . . . . . . . . . . . . . Satisfaction SCT . . . . . . . . . . . . . . . . . . . . . . . . Social Cognitive Theory SEM . . . . . . . . . . . . . . . . . . . . . . . . Structural Equation Modeling SME . . . . . . . . . . . . . . . . . . . . . . . . Small and Medium-Sized Enterprises SN . . . . . . . . . . . . . . . . . . . . . . . . . . Subjective Norms SRMR . . . . . . . . . . . . . . . . . . . . . . Standardized Root Mean Squared Residual SSTs . . . . . . . . . . . . . . . . . . . . . . . . Self-Service-Technologies T . . . . . . . . . . . . . . . . . . . . . . . . . . . Trust t-commerce . . . . . . . . . . . . . . . . . . Tele Commerce TAM . . . . . . . . . . . . . . . . . . . . . . . . TLI . . . . . . . . . . . . . . . . . . . . . . . . . TPB . . . . . . . . . . . . . . . . . . . . . . . . TR . . . . . . . . . . . . . . . . . . . . . . . . . .

Technology Acceptance Model Tucker-Lewis-Index Theory of Planned Behavior Technology Readiness

XXIV

LIST OF ABBREVIATIONS

TRA . . . . . . . . . . . . . . . . . . . . . . . . Theory of Reasoned Action TRAM . . . . . . . . . . . . . . . . . . . . . . Technology Readiness into Technology Acceptance Model TRI . . . . . . . . . . . . . . . . . . . . . . . . . Technology Readiness Index TT . . . . . . . . . . . . . . . . . . . . . . . . . . Trust in Technology TW . . . . . . . . . . . . . . . . . . . . . . . . . Trustworthiness URL . . . . . . . . . . . . . . . . . . . . . . . . Uniform Resource Locator USA . . . . . . . . . . . . . . . . . . . . . . . . United States of America UTAUT . . . . . . . . . . . . . . . . . . . . . Unified Theory of Acceptance and Use of Technology VDMA . . . . . . . . . . . . . . . . . . . . . . Verband Deutscher Maschinen- und Anlagenbauer (German Engineering Association) VIF . . . . . . . . . . . . . . . . . . . . . . . . . Variance Inflation Factor

Chapter 1 Introduction 1.1

Motivation and Goals of the Thesis "Service encounters are critical moments of truth in which customers often develop indelible impressions of a firm. ... Yet, across industries, technology is dramatically altering interpersonal encounter relationships." (Bitner, Brown, and Meuter 2000, pp. 139)

The increasing employment of information and communication technologies in companies and households has not only led to considerable changes in the way services are conceived, developed, and delivered, it has altered the nature of services themselves (Bitner, Brown, and Meuter 2000; Meuter et al. 2000). The convergence of technologies such as e-commerce, ubiquitous computing, and mobile communication is emerging as a promising new paradigm with the goal to provide services anytime, everywhere, and transparently to the user via devices embedded in the physical environment. New "intelligent products" contain IT in the form of microchips, software, and sensors and are able to collect, process, and produce information (Rijsdijk, Hultink, and Diamantopoulos 2007). Network technology embedded into such devices allows for connecting these objects to producers and customers enabling automatic identification, localization and remote sensor technologies (Jonsson, Westergren, and Holmström 2008; Lyytinen and Yoo 2002). This poses not only technical, social, and organizational challenges for product producers. It also has a strong impact on possibilities for service provision as the continuous data flow from embedded IT-applications enables seamless services delivered in real time, and directed at connected objects (Fano and Gershman 2002). In this environment, remote services are quickly emerging as a new class of fascinating interactive services. Remote services are predominantly applied as remote system administration, remote diagnosis, and remote repair of machines in organizational environments and high technology industries such as IT, automotive, and engineering (Biehl, Prater, and McIntyre 2004).

2

1. Introduction

An illustrative example in the field of mechanical engineering is the remote repair of a high volume printing machine. A service provider engineer located in Germany remotely accesses a printing machine in China to diagnose and solve a complex machine failure. He then interacts with a customer employee located at the machine in China to repair it remotely, jointly, and interactively while being thousands of kilometers apart. During the whole process, the service provider’s and customer’s employees are interacting and collaborating in a completely technology-mediated contact situation. The application fields of remote services are predicted to expand in scope and scale and become the fastest growing technology-driven IT-services within the next few years (Stiel 2004). The increasing application of remote services in business-to-business (B2B) settings foreshadows the tremendous impact this new technology will have on consumers as well. For example, remote surgeries have already been successfully conducted (Sila 2001). Just recently, Intel and General Electric announced a joint venture on telemedicine to market and develop applications to track the daily activities of patients in need of remote monitoring (The Wall Street Journal 2009). Remote control, repair, and diagnosis of car electronics are offered in the high-end luxury car segment (Chatterjee et al. 2001). Remote control of household amenities such as heating and water (Baker 2008) will change the way provider companies access our lives and raise new challenges in establishing security, trust, and protection of consumers’ privacy (Jonsson 2006). The implementation of remote services is expected to result in substantial efficiency gains on both the provider’s and the customer’s side, due to cost reductions, increased flexibility and time savings. For example, remote services in mechanical engineering help to substantially reduce travel and personnel cost up to 20–30% and the time of troubleshooting up to 10% (Borgmeier 2002). To maximize the benefits for the organization, it is crucial for remote service providers to increase the usage rates by attracting new customers and retaining users. Even though the opportunities are attractive for service providers and customers alike, remote services are associated with substantial challenges and barriers (Biehl, Prater, and McIntyre 2004; Wünderlich and Pfeffer 2007), for which neither practitioners nor academics have found an ideal solution. Even in lead industries like mechanical engineering, the acceptance rate is fairly low. Only about 28% of all companies used remote services in 1997; since then the acceptance rate has only marginally improved (Borgmeier 2002; Stolz 2006). In view of the increasing importance of remote services across industries, it is remarkable that there is no available research that goes beyond descriptive case studies of individual remote service applications. It is also significant that there is a lack of systematic research on the perception and acceptance of remote services. Further, studies on related services only capture a fraction of the relevant characteristics for understanding remote services. For example, studies on the effect of customer provider interaction and co-production on service perception are currently limited to services delivered via face-to-face encounters (e.g., Bendapudi and Leone

1.1 Motivation and Goals of the Thesis

3

2003; Bettencourt et al. 2002). Research on less complex and less demanding services like e-services and self-services mostly focuses on technology features as antecedents of (service) technology acceptance (e.g., Lin and Hsieh 2006; Featherman and Pavlou 2003). These partial approaches might be sufficient for their respective domains, but they fall short for interactive remote services where both the co-production with a service provider employee and the interaction through a mediating technology are essential for the service experience. Hence, to comprehensively analyze the remote service concept, this thesis links separate streams of literature from the fields of service marketing, inter-organizational relationship management, and information systems (IS) research.

The dissertation will contribute to theory by providing a holistic classification of remote services and interactive remote services. Moreover, this thesis explores the customer’s perception of these services and identifies drivers of organizational usage. A model to explain adoption and continuance, the ITSUM, is developed. Ultimately, this thesis aims to derive managerial implications for remote service providers on how to increase remote service acceptance among their customers.

Customers’ acceptance of remote services in a B2B setting does not only manifest in a first-time remote service usage decision instead it is embedded in business processes of repeated practice. Researchers criticize the extreme emphasis of acceptance (initial use) over continued usage in technology and technology-intensive service acceptance studies (Baron, Patterson, and Harris 2006; Bhattacherjee 2001). Baron, Patterson, and Harris (2006, p.111) call it "the inadequacy of a concentration on simple acceptance ... where technology is embedded in a ... community of practice." Support for this view comes from findings in relationship marketing, which also stress the need to retain existing customers (Grönroos 1990; 1996). Researchers claim that the importance of continuance is evident from the fact that acquiring new customers may cost as much as five times more than retaining existing ones given the costs of advertising, searching for new customers, setting up new accounts, and initiating new customers (Parthasarathy and Bhattacherjee 1998). Therefore, this thesis strives to provide a comprehensive approach of explaining both initial acceptance (adoption) and repeated, continued usage (continuance) of remote services in organizations.

From a methodological perspective this thesis aims at explaining organizational intention by linking it to perceptions of individual employees as a proxy. In addition, this doctoral research seeks evidence for the predictive power of organizational intention on actual usage behavior of organizations. Therefore, an empirical setting is chosen where intra-firm group decision making processes are minimal and individual personal attitudes and intentions can be related to organizational behavior.

4

1. Introduction

1.2

Research Questions

This doctoral research comprises different empirical studies that use both an exploratory and a confirmatory approach. The exploratory qualitative study is guided by the following fundamental research questions:

1

1. What benefits and obstacles of remote service stand out from a customer’s point of view? 2. How do customers perceive a remote service situation? 3. What factors determine the general acceptance of remote services? Within this thesis, I identify major belief groups that influence the intention of an organization to use interactive remote services. Building on these findings, my quantitative studies further explore the following research questions: 1. Do the identified beliefs affect an organization’s intention to use interactive remote services and, if they do, to which extent? 2. Do the identified beliefs affect an organization’s intention to continue to use interactive remote services and, if they do, to which extent? 3. How do the determinants of behavioral intention differ between organizations with few and organizations with more experience with interactive remote services? 4. Does the intention to use interactive remote services predict actual usage behavior of organizations and, if it does, to which extent?

1.3

Structure of the Thesis

This thesis employs a multi-methodological approach: it links conceptual, qualitative and quantitative research studies and aims at getting profound and accurate insights through triangulation. Therefore, the structure of this thesis follows the analytic procedure of the studies as shown in figure 1.1. Eight chapters comprise the dissertation. C HAPTER 2 is dedicated to the conceptualization and classification of remote services. Extant literature on new emerging technology-mediated services across industries is reviewed, remote services are characterized, and a demarcation between remote services and interactive remote services is given. Interactive remote services are discussed in comparison to self-services and face-to-face services. The reason why interactive remote services form a unique and distinct service type from both a theoretical and practical standpoint is outlined. 1

The research questions will be refined in the context of the individual empirical studies presented in chapter 5 and 7.

1.3 Structure of the Thesis

5

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Figure 1.1: Structure of the Thesis: Contents of the Chapters 1–8

Because literature on remote services is sparse, I approach the theoretical substantiation of the study by combining different research streams. In CHAPTER 3, I present the theoretical foundations of this thesis drawing from the fields of information systems, especially studies on ITand innovation adoption, concepts of personal customer-provider interaction and co-production from the service marketing field, and management literature on relationship management in strategic alliances. In addition, concepts of B2B-marketing and organizational innovation adoption are discussed. In CHAPTER 4 the methodological research structure and the triangulation through qualitative and quantitative studies are outlined. These are the building blocks of the analytical framework and guide the proceedings of this thesis. The rationale for choosing the empirical setting, which is the printing industry, is explained and background information on its structure in Germany, USA and China is given. The international qualitative exploratory study is described in CHAPTER 5 explaining the motivation, study design, and method in detail. The results of the qualitative study identify the relevant categories of beliefs that affect remote service attitudes. These are synthesized to develop a new framework for remote service perception.

6

1. Introduction

Based on the findings of the qualitative study and my conceptual work, I develop the Interactive Technology-Mediated Service Usage Model (ITSUM) for measuring the interactive remote service acceptance in CHAPTER 6. Hypotheses are derived for the ITSUM, the relationship between organizational usage intention and actual behavior, as well as for the comparison of organizations in the pre-adoption phase and in the continuance phase. The quantitative studies are described in CHAPTER 7. The first study measures remote service acceptance in the German printing industry and tests the formal hypotheses of the ITSUM derived in the previous chapter. Organizations in the pre-adoption phase are compared with organizations in the continued usage phase. A second quantitative study with the same subjects at a later point in time is conducted to validate the link between organizational usage intention and actual usage behavior. The chapter finishes with a synthesis of the quantitative results. C HAPTER 8 combines all individual results from the studies. It connects the insights gained and presents a comprehensive summary. Managerial implications for the provision of remote services are derived. This thesis concludes with an outlook towards future research with respect to the limitations of this work.

Chapter 2 Conceptual Framework: Remote Services in Context of Technology-Mediated Services New types of technology-mediated services such as e-services, mobile services, self-services, and recently remote services have become reality. In this chapter, these new service types will be defined, and their application in practice and recognition in scientific research will be discussed. This discussion will set the frame of reference for the main objects of this thesis: remote services and interactive remote services. A classification and demarcation will be given, and the unique service experience from a customer’s point of view will be examined.

2.1 2.1.1

Emerging Technology-Mediated Service Types E-Services

In e-services, the production, consumption, or provision of services takes place through electronic networks or the Internet. These characteristics form the core of most e-service definitions given in literature that see e-services as web-based services (Reynolds 2000), as interactive services that are delivered on the Internet (Boyer, Hallowell, and Roth 2002), or generally as information services because the primary value exchanged between the two parties is information (Rust and Lemon 2001). Despite the various attempts at defining e-services, no universal agreement has been reached. This thesis follows Colby and Parasuraman’s (2003, p.28) definition of e-services, because it reflects an understanding common to a majority of researchers: E - SERVICES are "all services delivered via an electronic medium (usually the Internet) and comprising transactions initiated and largely controlled by the customer."

8

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

The application of e-services is multifaceted. Familiar e-services include but are not limited to: online banking (Bradley and Stewart 2003; Vatanasombut et al. 2008); online auctions such as www.ebay.com (Chan, Kadiyali, and Park 2007; Reynolds, Gilkeson, and Niedrich 2009; Clark and Ward 2008); online retailing (Griffith 2005; Haynes and Taylor 2006); e-learning such as classes being videostreamed online (Santhanam, Sasidharan, and Webster 2008; Rossiter 2007); e-health such as online medical advice (Burchert 2003; Lewis Jr. 2008); e-government such as e-taxes (Hsu and Chiu 2004; Hung, Chang, and Yu 2006; Fu, Farn, and Chao 2006); e-libraries providing electronic access to journal articles or book chapters (Padgett 2004; Kajikawa, Abe, and Noda 2006); and information and location-based services (Yee and Korba 2005). The usage of e-services grows continuously. For example, from 2005 to 2008 the usage rate of internet banking in the USA increased from 27% up to 39%, and nearly all American Internet users (93%) have at one time or another conducted e-commerce (Horrigan 2008). E-services are not limited to the domain of new economy companies. Established organizations are also augmenting their traditional offerings with e-services and approach their customers via multi-channel-strategies (Cassab 2009; Müller-Lankenau, Wehmeyer, and Klein 2005). For example, airlines offer ticket ordering via their websites, retrieval of flight information via call centers, automated phone systems (APS), or check-in via self-check-in kiosks (Lufthhansa AG 2007). E-service applications are often tied to e-commerce business models that sell goods, for example the purchasing of physical goods that are then delivered by traditional means. A prominent example is Amazon.com, where a book is purchased online, but delivered by mail to the buyers. Voss (2000) proposes that e-commerce and e-service are two ends of a continuum ranging from pure sales on the web, with little or no service content, to pure service, delivered free of service contracts, or as a part of a service contract (see figure 2.1). In between, a variety of business models are found. These include: services that sell information (e.g., newspapers selling their content online); value-added services (e.g., online travel agency offering travel insurances); or bundles of services and goods (e.g., online selling of personal computers combined with support services). Key themes in the e-services literature are e-service quality and its associated dimensions and measures (Parasuraman, Zeithaml, and Berry 1985). Other frequently addressed topics include: the elements of the web experience (Lin, Wu, and Tsai 2005; Novak, Hoffman, and Yung 2000); customer satisfaction (Ha and Janda 2008; Zhang, Prybutok, and Huang 2006); customer’s buying intention and loyalty (Chellappa and Kumar 2003; Herington and Weaven 2007); and service operations (Roth and Menor 2003). Service quality has been recognized as the key to reach additional strategic and operational objectives such as improved customer satisfaction, increased retention rates, enhanced operational efficiency, and profitability (Al-Hawari and Ward 2006; Cronin Jr. 2003; Rust, Zahorik, and Keiningham 1995). In comparison to traditional shopping channels, researchers have found that customers perceive information availability and content quality as more important when shopping online (e.g., Zei-

2.1 Emerging Technology-Mediated Service Types

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Figure 2.1: The Continuum from eService to eCommerce Source: Own Illustration, based on Voss (2000, p. 21) thaml, Parasuraman, and Malhotra 2002). The shaping of these characteristics is an effective way to reduce the perception of uncertainty and risk (Featherman and Pavlou 2003; Rowley 2006). Many approaches stemming from traditional service quality measurement tools to measure e-service quality have been applied in various forms. Some of these approaches take the SERVQUAL measurement scale as a basis and extend it by web site and usability-specific quality dimensions (e.g., Parasuraman, Zeithaml, and Malhotra 2005; Zeithaml, Parasuraman, and Malhotra 2002). Researchers developed a number of web-specific scales such as WebQual (Loiacono, Watson, and Goodhue 2002), E-Qual (Kaynama and Black 2000), SITEQUAL (Yoo and Donthu 2001), e-SQ (Zeithaml, Parasuraman, and Malhotra 2002), ETailQ (Wolfinbarger and Gilly 2003), and E-S-QUAL (Parasuraman, Zeithaml, and Malhotra 2005) to capture eservice quality. Research on exploring e-service adoption has identified numerous antecedents. Service design and usability (Surjadjaja, Ghosh, and Antony 2003; Massey, Khatri, and Montoya-Weiss 2007), service delivery features (Dabholkar, Bobbitt, and Lee 2003; Iqbal, Verma, and Baran 2003) as well as relational factors such as trust (Dutton and Shepherd 2006), social presence (Gefen and Straub 2004), and internal communication (Surjadjaja, Ghosh, and Antony 2003; Walker and Johnson 2006) are relevant in explaining the adoption of e-services.

2.1.2

Self-Services

Advances in technology and high labor costs led to a surge in the provision of self-service and self-service technologies (Dabholkar, Bobbitt, and Lee 2003). These services feature a stronger active participation by customers in the service process (Bendapudi and Leone 2003; Zhu et al. 2007). According to Meuter et al. (2005, p.61), S ELF - SERVICE TECHNOLOGIES (SST) are technological interfaces that enable "customers to produce services for themselves without assistance from firm employees."

10

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

There is no agreement in literature on the conceptual relationship between e-services and selfservices. Surjadjaja, Ghosh, and Antony (2003) argue that to use a self-service, a customer has to go to the technology (such as an ATM) to receive the service whereas in e-service, a customer can receive the service through the Internet at home or in other places of his choosing. They consider a self-service to be less flexible than e-service due to constraints of location. Other authors do not follow this distinction and do not explicitly distinguish between self-services and e-services, e.g., they consider online banking to be a self-service (Curran and Meuter 2007; Meuter et al. 2005). In this sense Rowley (2006), takes the position that all e-services are essentially self-services, whether they are delivered through a web page on a PC, a mobile device, or a kiosk. Dabholkar (1994) follows this view, but includes in her typology of self-services a different distinction based on the location of the technology where customers can access the self-services technology. She distinguishes between "provider-based self-services" and "customer-based selfservices." In provider-based self-services, the access technology is provided by the service provider who sets up certain machines such as check-in kiosks or package pick-up centers. Customer-based self-services on the other hand can be accessed using technological devices that are available at the customers’ homes via a PC connected to the internet. In practice, self-services are found in a wide variety of business scenarios: monetary transactions (e.g., using ATM or online banking), shopping (e.g., online booking of a trip), self-help (e.g., distance learning), fully automated phone systems (e.g., telephone banking), and customer services (e.g., hotel checkout). The provision and usage of technology-based self-services is growing at exponential rates all over the world (Shamdasani, Mukherjee, and Malhotra 2008). Beatson, Coote, and Rudd (2006) predict prolific advances in technology and expect that SST facilities will continue to evolve and will play an even more important role in service delivery than they currently do. For example, in 2007, consumers in the United States conducted 14.9 billion ATM transactions at 415,000 ATM terminals compared to 10.5 billion ATM transactions at 396,000 ATM terminals in 2005 (Mohsberg 2008). The same trend can be seen in the airline industry when in 2007 when 20% of all passengers of the German airline Lufthansa used online check-in options or self check-in kiosks. Lufthansa expects the quota of passengers using self check-in solutions to rise up to 65% in 2010 and even up to 90% in 2015 (Lufthansa AG 2007). Extensive research has been conducted on self-services in recent years. Meuter et al. (2000) identify four different forms of interfaces: telephone/interactive voice response; online/internet; interactive kiosks; and video/cd. They also distinguish three main purposes of SST that are perceived as beneficial by customers (see figure 2.2): service surrogates (such as order status viewing); transactions (such as hotel checkout); and self-help (such as distance learning).

2.1 Emerging Technology-Mediated Service Types

  



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Figure 2.2: Categories and Examples of Self-Service Technologies Source: Own Illustration, based on Meuter et al. (2000, p. 52) The benefits derived for the self-service provider are manifold. They include: productivity and cost savings (Lovelock and Young 1979); improved process efficiencies and competitiveness (Gallagher 2002); increase in market share and in customer accessibility (Kauffman and Lally 1994); time savings (Pujari 2004); increase in customer satisfaction and loyalty (Shamdasani, Mukherjee, and Malhotra 2008); possibility to reach new customer segments (Parasuraman and Grewal 2000); differentiation through technological reputation (Meuter and Bitner 1998); extension of business hours (Dabholkar 1996); customization (Meuter et al. 2000); and integration of customer resources to produce the service (Dabholkar and Bagozzi 2002). Due to the uncertainties about the acceptance of SST and because self-services literature has mainly concentrated on exploring the factors that influence the customer’s decision to use selfservices (Childers et al. 2001; Dabholkar 1996; Dabholkar and Bagozzi 2002; Dabholkar, Bobbitt, and Lee 2003; Lin and Hsieh 2006; Meuter et al. 2000; 2005; Oyedele and Simpson 2007; Weijters et al. 2007), these studies have generally focused on explaining the circumstances under which consumers are willing to adopt self-services. Cost savings, reduced waiting time, convenient access, relative advantage, ease of use, compatibility, speed of delivery, and customization are just a few of the factors that drive consumers’ intentions, satisfaction, and perceived quality (Eriksson and Nilsson 2007; Dabholkar, Bobbitt, and Lee 2003; Meuter et al. 2005; Shamdasani, Mukherjee, and Malhotra 2008). The design, functionality, and perceived innovativeness of technology-based customer interfaces can enhance perceptions of quality, encourage repeat patronage, and result in increased loyalty to the firm (Kirca, Jayachandran, and Bearden 2005; Seybold 2001). Personal characteristics of the user such as technological readiness, motivation, ability, role clarity, inherent novelty seeking, need for interaction, trust in technology, and self-consciousness have also been demonstrated to influence adoption behavior (Dabholkar 1996; Dabholkar and Bagozzi 2002; Meuter et al. 2005; Parasuraman 2000; Johnson, Bardhi, and Dunn 2008). Furthermore, studies indicate that controllability (Bateson 1985; Langeard et al. 1981) or the loss of social

12

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

interaction (Ledingham 1984; Walker and Johnson 2006) are barriers for individual self-service users.

2.1.3

Mobile Services

Mobile services is a term that not so much demarcs an unique service type, but instead describes a distinctive delivery technology that is used to mediate e-services. Scupola (2008, p.i) interprets mobile services as e-services that are produced, provided, or consumed through mobile solutions. Nysveen, Pedersen, and Thorbjørnsen (2005, p. 331) forgo a concrete definition of mobile services but describe the range of services they consider as mobile services: M OBILE SERVICES comprise "text messaging, gaming, contact and payment" services. Mobile solutions refer to the delivery of information via wireless technology, by means of mobile devices such as a mobile phone or a PDA. As a result, mobile services can be consumed anywhere and at any time (Balasubramanian, Peterson, and Jarvenpaa 2002). Further, Balasubramanian, Peterson, and Jarvenpaa (2002) define the key elements of mobile information services as location-sensivity, time-criticality, and initiation by the user. Consumers increasingly use mobile devices to access wireless web sites where they can conduct transactions such as redeeming mobile coupons (Dickinger and Kleijnen 2008), conducting mobile banking (Laukkanen and Pasanen 2008), or accessing news and other information like mobile e-learning (Wu and Chao 2008). They interact with database services to acquire map guides (Pfendler and Schlick 2007) and entertainment downloads such as mobile games (Ha, Yoon, and Choi 2007) or television (Bauer, Ha, and Saugstrup 2007). E-service providers offer mobile services for e-health patient education (Mackert, Love, and Whitten 2009), and use mobile services as a medium for advertising (Kalakota and Robinson 2002; Sanayei and Mirzaei 2008; Choi, Hwang, and McMillan 2008). Despite the fact that the technology evolves quickly, and phones offer an increasingly smoother and richer access to mobile services, the worldwide success of mobile services has been relatively modest (Koivumäki, Ristola, and Kesti 2008). The slow acceptance of mobile services has sparked increasing curiosity among researchers during the past years. Key drivers for mobile services usage have been identified as social norms, network externalities, fun, and perceived enjoyment (Nysveen, Pedersen, and Thorbjørnsen 2005). In regard to usage intention, determinants such as service quality and satisfaction have been reexamined (Dickinger and Kleijnen 2008; Seth, Momaya, and Gupta 2008; Vatanparast and Asil 2007).

2.1 Emerging Technology-Mediated Service Types

2.1.4

13

Industry Specific Technology-Mediated Services

Whereas the aforementioned service types differentiate themselves either through the mediation of technology, the location of the access technology, or the degree of customer participation, other technology-mediated services described in literature do not form a general services type. Instead, in mechanical engineering, health care, the IT-sector, or the automotive industry, specific services are introduced and their application is discussed in specialist literature of the respective disciplines.

2.1.4.1

Teleservices in Engineering and Manufacturing Industries

In mechanical engineering, the advent of complicated and expensive capital equipment such as flexible manufacturing systems (FMS) made it necessary to run machines at high utilization rates. To improve performance, data links were set up between machines and manufacturer so the latter could quickly troubleshoot malfunctions (Biehl, Prater, and McIntyre 2004; Panshef 2009). The services the manufacturer can provide to the customer via this channel to the machine are commonly called "teleservices" (Hudetz and Harnischfeger 1997; Borgmeier 2002; Biehl, Prater, and McIntyre 2004). Stolz (2006, p. 42) defines teleservices as follows: T ELESERVICE is a collective term for services that aims at "supporting and performing actions and tasks at machines via information and communication technologies from a distance." Teleservices are often applied in industries such as microsystems engineering, mechanical engineering, and component manufacturing as well as in the printing, extraction and the textile industry (Borgmeier 2002; Stolz 2006). Based on these applications, some authors define teleservices as industrial services, meaning that they are applied only in B2B environments (Spiess 2003; Massberg et al. 1998). According to Stolz (2006), the distinct characteristics of teleservices can be described as: 1. provided via modern information and communication technology; 2. provided independently of the service partners’ location; and 3. tied to hybrid products. In literature on teleservices, there is no common understanding about whether the integration of a customer employee in the service production process is a characteristic of this service type. Whereas most authors consider machine-to-machine interactions without any contact between employees at the provider’s and customer’s place as teleservices (Stolz 2006; Hudetz and Harnischfeger 1997; Massberg et al. 1998; VDMA 2006; Kreidler 2004; Heine 2003; Stolz 2006), only two authors assess the participation of the customer employee in the service process as vital for the service provision (Borgmeier 2002; Spiess 2003). In this context, Borgmeier

14

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

(2002, p. 29) defines teleservices as "industrial technical after-sales services remotely provided at a machine at the customer’s location after its purchase via embedded IT devices and with synchronous and interactive communication."2 Teleservices are most frequently applied as remote control of production processes, remote diagnosis of machines, regular remote maintenance and remote repair of machines, which Biehl, Prater, and McIntyre (2004) summarize under the term remote repair, diagnosis and maintenance (RRDM) services. The various application fields of teleservices in mechanical engineering are described in figure 2.3 and include assembly, customer support, software development, sales, and training (Borgmeier 2002; Hudetz and Harnischfeger 1997). Research on managerial issues related to teleservices mainly stems from literature interfacing the fields of IS and mechanical engineering. Various guide-books on teleservices implementation for manufacturers who want to provide teleservices have been published (e.g., Hudetz and Harnischfeger 1997; VDMA 2004; 2006). Scientific research focuses on practical issues of teleservices such as exploring value creation through teleservice provision (Spiess 2003), potential for teleservices applications in micro-system engineering (Stolz 2006), employment of multi-media in teleservices (Massberg et al. 1998), and technological design and application possibilities (Borgmeier 2002; Kreidler 2004). Benefits of teleservice provisions are predominantly discussed from a provider’s perspective. They enable efficient resource and service management, global reach, customer loyalty, new service offerings, and additional service features leading to time and cost savings. Teleservices also support sales and marketing, increase the speed in troubleshooting, and increase employee satisfaction (Spiess 2003; Borgmeier 2002; Hudetz and Harnischfeger 1997; Biehl, Prater, and McIntyre 2004). Literature discusses the benefits and problems of teleservices from a customer’s point of view to a far lesser extent. Some authors name time and cost savings as well as increased productivity through higher availability, optimized process control, and decreased machine downtimes (e.g., Spiess 2003; Borgmeier 2002). Researchers see the technical environment, missing technical standards, and the compatibility between customer and provider technology as the main challenges to teleservices provision (Biehl, Prater, and McIntyre 2004). Borgmeier (2002) mentions potential barriers to adoption at the customer’s side such as concerns about data privacy and security, legal liability in case of damages, high communication costs, incompatibility effects, and fear of dependency on the provider.

2

This definition of teleservices comes closest to remote services as understood in this thesis, but interactive remote services are not limited to industrial technical services, emphasize collaboration in addition to communication, and are not restricted to after-sales services.

2.1 Emerging Technology-Mediated Service Types

15

Figure 2.3: Application Fields of Teleservices Source: Own Illustration, based on Borgmeier (2002, p. 34) 2.1.4.2

Telematics in the Automotive Industry

Car manufacturers offer their customers a variety of services aimed at facilitating the car’s purchase including financial services, regular maintenance services and breakdown assistance services (Lenfle and Midler 2009). These services are referred to as "telematics" or "telematic services." The term "telematics" stems from the combination of the German words "Telekommunikation" (telecommunications) and "Informatik" (computer science). According to Chatterjee et al. (2001, p. 21) the following definition of these services is presented: T ELEMATICS refer to "two-way voice and data communication between the vehicle and the information provider using wireless technology." Telematics combine in-vehicle devices, wireless connections, and service content to deliver four major benefits to consumers: safety, security, hands-free connectivity, and convenient access to mobile information and entertainment (Garretson, Howe, and Shuman 2001). Telematic services comprise, for example, navigation and traffic information services, collision avoidance

16

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

systems, mobile communications gear, remote diagnostics services, and emergency calls. Figure 2.4 shows the service spectrum of telematics currently available. Telematics are offered from automotive producers such as BMW ("Connected Drive" system) and General Motors ("OnStar" system) (Lenfle and Midler 2009). Bouvard, Cornet, and Rowland (2001) identify three distinct submarkets of telematics in the automotive industry. The "front-seat" market revolves around safety, security, and features that make driving easier. The "rear-seat" market includes interactive games, music, and video on demand, which are tailored for the non-drivers of the car. The third market, for the engine and other mechanical applications, uses data collected by on-board computers to provide tools such as remote diagnostics, remote engine tuning, and the intelligent ordering of replacement parts (Bouvard, Cornet, and Rowland 2001). While telematics experienced a period of inflated expectations, the technology still has tremendous potential for changing the landscape of the automotive industry in terms of technology content, vehicle design, customer relationship management (CRM), and the provisioning of new services. Koudal et al. (2004a) forcast that telematics are the next wave in automotive industry. The three submarkets could generate up to $100 billion in the United States, Japan, and Western Europe by 2010 (Bouvard, Cornet, and Rowland 2001). Research on car telematics mostly focuses on market data, revenue potential, and forecasts (Garretson, Howe, and Shuman 2001; Koudal et al. 2004a). While there is substantial revenue potential in offering value-added services on the basis of a "connected car" (Chatterjee et al. 2001) studies exploring the adoption and usage of telematics from customer perspectives are sparse. Only a few studies deal with managerial issues related to the provision of services based on car electronics (Bouvard, Cornet, and Rowland 2001; Chatterjee et al. 2001; Hiraoka 2009; Koudal et al. 2004b).

2.1.4.3

Telemedicine in Health Care

In medicine, new technology-mediated services pertaining to patient care are often summarized under the term telemedicine. The American Telemedicine Association (2009, p. 1) defines telemedicine as follows: T ELEMEDICINE is the "use of medical information exchanged from one site to another via electronic communications to improve patients’ health status." Burchert (2003) explicitly distinguishes between telemedicine and "telematics." He describes telematics in healthcare as the technology used for mediation, e.g., as a tool to provide telemedicine services or administrative services such as storing patient data. He views telemedicine as medical treatment services. In line with that is the description of "telehealth" by the American Telemedicine Association (2009) as a broader definition of all remotely provided services in

2.1 Emerging Technology-Mediated Service Types

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healthcare beyond clinical services. This includes patient portals, transmissions of medical images, and nursery call centers. Telemedicine offers a variety of application scenarios in healthcare such as prevention, diagnosis, therapy, and rehab ranging from remote diagnosis and consultation through videoconferencing up to remote surgery (Burchert 2003). The physical distance between a physician and a patient is overcome via technology. The physician can offer his health services over long distances to the patient without face-to-face contact. Examples from practice are the screening of the iris via Internet in ophthalmology (Zahlmann and Mann 1999) or electrocardiography (ECG) monitoring via phone (Marketing Health Services 2004). Advances in robotics technology are completely transforming today’s hospital operating rooms (Allan 2008). With robot control and assistance, surgery for any kind of injury or ailment is faster, more accurate, and less invasive than ever before. Meanwhile, medical robots are assisting in urological, neurological, gynecological, cardiac, orthopedic, gastrointestinal, pediatric, and radio-surgical procedures (Marketing Health Services 2004; Allan 2008). In telesurgery, the surgeon manipulates the robot’s hand from a distance using real-time imaging and haptic feedback (Allan 2008).

18

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

One prominent example for a successfully completed remote surgery is the gall bladder removal by physicians in New York on a patient 4,000 miles away. On September 7, 2001, a 68-year-old woman at the Hospital Civil/Institute for Research into Cancer of the Digestive System (IRCAD) in Strasbourg, France, was the first human patient to undergo a telesurgical procedure. She was released about 48 hours after surgery and resumed normal activities the following week (Sila 2001). In general, telemedicine is slowly starting to be used to service patients in rural areas in the U.S. that are understaffed by physicians (Biehl, Prater, and McIntyre 2004). In contrast, legal and financial issues hinder some countries from taking full advantage of telemedicine (Burchert 2003). On the patient’s side, barriers such as lack of personal contact, safety, and security concerns are mentioned (Burchert 2003; Hirzinger 2004).

2.1.4.4

Services in the IT-Sector

Technology-mediated services in the IT- and IT-consulting sector range from remote system administration, remote diagnosis, and remote support up to the complete remote realization of IT-projects from a service provider in a remote location (e.g., Fussell et al. 2004). It is still common in IT-consulting projects that service provider employees visit the customer onsite, even if much of the work (software developing, testing, and deploying) can be provided remotely (Chandrasekaran and Ensing 2004). These remotely provided services are considered as the "state-of-the-art"-approach in implementing software projects (Frenzel 2005). Cost savings and the flexibility of human resources have led to an increased application of technology-mediated services in the IT-sector, especially in long-distance and international settings. Although IT-services are often delivered "remotely", they should not be confused with "remote services", which are not limited to services in the IT-industry or to the virtual collaboration of project teams (see chapter 2.2). Stiel (2004) forecasted that remote IT-services will be the fastest growing services in the IT-sector up to 2010. Global connectivity enables a remote collaboration of geographically separated team members. To benefit from low wages, strategies like global delivery and global sourcing have become relevant for IT-service providers. Located at on-shore, off-shore, and near-shore locations, the virtual team members are able to remotely access their customers’ IT-systems via the Internet (Hammes et al. 2007). Management literature on virtual teamwork and collaboration (Fuller, Hardin, and Davison 2007; Jarvenpaa and Leidner 1999; Martins, Gilson, and Maynard 2004) is indirectly related to remotely provided services. It addresses virtual team collaboration, e.g., an IT-consulting project team that remotely works together in different locations and partially delivers services remotely to the customer (Ohr et al. 2007). Recent literature in this field centers on the challenges posed by collaborating in technologymediated contexts such as the organization of virtual knowledge, the implementation of workflow management, and the shaping of the working environment of virtual expatriates (Oshri,

2.2 Classification of Remote Services

19

van Fenema, and Kotlarsky 2008; Barcus and Montibeller 2008; Holtbrügge and Schillo 2007). Unfortunately, those studies solely focus on intra-organizational collaboration and leave out the difficulties of a technology-mediated provider-customer interaction.

2.2 2.2.1

Classification of Remote Services Definition of Remote Services

Wünderlich et al. (2007, p. 7) introduce the concept of remote services to describe a newly emerging class of technology-mediated services from a marketing perspective and independent of industry: R EMOTE SERVICES are "provided in a technology-mediated production process independent of the physical separation of customer and provider. Hereby, the service object is remotely modified via control and feedback devices." Although this service type derives its conceptual basis from industry specifics, it refers to a wide range of services including: remote system integration in the IT-sector (Frenzel 2005; Betzler, Schumann, and v. Wangenheim 2007); remote maintenance of machines in mechanical engineering (Borgmeier 2002; Biehl, Prater, and McIntyre 2004); remote diagnosis and surgery in healthcare (Burchert 2003; Allan 2008; Marketing Health Services 2004); remote update of traffic signs in traffic control (Rothenberg and King 2004); and telematics such as remote repair of car electronics in automotive (Chatterjee et al. 2001; Hiraoka 2009). Available research that focuses on remote services from a marketing perspective is very limited. In the context of the EXFED3 research project, a book edited by Holtbrügge, Holzmüller, and v. Wangenheim (2007) on remote services was published. It describes challenges and benefits of remote services and includes several case studies that show how remote services enhance the business model of service providers in consulting and mechanical engineering (Betzler, Schumann, and v. Wangenheim 2007; Grube, Malkusch, and Woisetschläger 2007; Hammes et al. 2007; Ohr et al. 2007; Wünderlich and Pfeffer 2007; Wünderlich, Zoll, and Holzmüller 2007). Future trends in remote service provision for customer and personnel management have been examined in Wünderlich et al. (2007); Wünderlich and v. Wangenheim (2007). 3

This doctoral research is embedded in a joint research project of three German universities on remote services "ExFeD - Export ferngelenkter Dienstleistungen", which is sponsored by the German Ministry of Education and Research (http://www.exfed.de).

20

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

2.2.2

Characteristics of Remote Services

Services are usually defined by a number of constitutive characteristics. To describe remote services, a classification will be conducted in reference to the key distinct service characteristics. The following attributes are viewed as the central criteria describing a service (Zeithaml, Parasuraman, and Berry 1985; Zeithaml et al. 2006; Hennig-Thurau, Walsh, and Wruck 2001; Fitzsimmons and Fitzsimmons 2008): 1. Intangibility 2. Simultaneity of consumption and production (so-called uno-acto principle) 3. Integration of an external factor in the service creation 4. Heterogeneity 5. Perishability Intangibility Intangibility refers to the characteristic that services cannot be perceived by the senses but can still lead to a physically result, e.g., modification of an object (e.g., Shostack 1977; Corsten 2001, p. 22). Remote services are intangible because they cannot be seen, felt, tasted, or touched, but they can comprise service outcomes that are of both, a tangible and intangible nature. For example, the remote repair reconfiguration of a machine can result in electronically triggered mechanical movements like changing some switches or the alignment parts, which is very tangible. Also, remote services can be directed at intangible assets like car electronics in the remote diagnosis of car parameters, resulting in an intangible outcome. Simultaneity of Consumption and Production The simultaneity of consumption and production usually requires that in services customer and provider have to share one location at the same time and that the service cannot be stored (Zeithaml et al. 2006, p. 23). This is often called the condition of inseparability. This characteristic does not apply to remote services because they are mediated via technology, which on the one hand leads to a reduced observability of the service process, but at the same time, it softens the requirement of inseparability as a key characteristic of services. Service provider and customer no longer need to occupy the same physical space to complete the transaction. As a consequence, this enables a world wide provision of remote services. The potential to automate the remote service determines how rigid the necessity of time synchronicity holds for remote services. As remote services can vary in their degree of complexity, they differ in their potential for automation and standardization. For example, very complex remote services like a remote repair of a machine are highly customized and individualized services and therefore not easy to automate and standardize. In contrast, remote services can

2.2 Classification of Remote Services

21

also consist of basic software updates that can be pre-produced, stored, and scheduled to be automatically sent to and installed at an IT-system. For instance, an automated pre-produced software update does not require temporal synchronicity of the provider and the customer during the programming of the software update itself. It is solely required at the moment the update is transferred. Software updates can be automatically transferred from the provider’s IT-system to the customer’s IT-system requiring no human interaction and therefore no temporal simultaneity of the provider’s and customer’s employees actions. Here, service delivery can be reduced to a pre-scheduled machine-to-machine interaction. This situation is in contrast to the scenario, where an emergency call leads to the remote diagnosis of faulty car electronics, which is usually conducted by a service provider employee, who has to be present at that point in time. Integration of an External Factor The integration of an external factor in the creation of a service refers to the provision of information or an object from the service customer. Further, the customer is often part of the production and delivery process by being either a recipient of the service or by actively participating in the service itself (Corsten 2001, p. 28; Lovelock and Wirtz 2007, pp. 33). Recent literature on co-production between customer and provider has focused on the merits of increasing the extent of active customer involvement in service production and delivery (Auh et al. 2007; Bendapudi and Leone 2003).4 Corsten (2001) developed an activity portfolio of service actions to demarc different types of customer participation in services (see figure 2.5). Services can be positioned based on the activity level of the customer and the provider. Corsten identifies nine fields of possible customer-provider interactivity combinations ranging from 1 – low provider activity level and high customer activity level – to 9 – high provider activity level and low customer activity level. In remote services, an external factor must be integrated in the production process as a recipient of the service. The recipient of a remote service can be a connected object such as a remotely controlled heating device. It can also be a patient in a tele-consultation situation. Moreover, the integration of an external factor can include customer participation ranging from low to high level participation. Low participation may only include providing access to the service object, whereas high participation might involve more activity from the customer. For example, in an interactive remote diagnosis of the eye’s iris via video-conferencing, a patient has to adopt different postures according to the instruction of the physician. Remote services show different levels of activity between provider and customer and can be positioned on Corsten’s (2001, p. 153) activity portfolio as a "seven" in a remote monitoring setting or as a "six" in a teleconsultation scenario (see figure 2.5). 4

Customer participation is similar to customer co-production, which has become a central tenet of a servicecentered logic for marketing (Vargo and Lusch 2004). A review on customer co-production and participation studies is presented in chapter 3.2.2.1.

22

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services





 
 












 











 











 


 









 



Figure 2.5: Activity Portfolio of Customer and Provider Actions Source: Own Illustration, based on Corsten (2001, p. 153) Heterogeneity Heterogeneity concerns the potential for high variability in the performance of services (Zeithaml et al. 2006, p. 23). When services are provided by people, variations are going to occur, and mistakes will happen in real time. The quality and essence of a service can vary from producer to producer, from customer to customer, and from day to day (Zeithaml, Parasuraman, and Berry 1985; Corsten 2001, p. 22). The assumption of heterogeneity does not hold for all remote services. In general, remote services are produced by humans, therefore no two services will be precisely alike. For example, a technician may fix a bug easier and quicker in the morning than in the afternoon due to fatigue. Remote services vary to the degree in which they can be standardized. The level of standardization in turn affects the heterogeneity: pre-produced services such as remote check-ups on machines can be programmed once and provided multiple times to different customers. As a result, remote services with a high level of standardization enable the remote service provider to deliver services with consistent service quality while other more individual or interactive remote services will show more heterogeneity in the service outcome. Perishability In general, a service is a perishable commodity (Fitzsimmons and Fitzsimmons 2008, p. 19). Perishability refers to the fact that services cannot be stored, saved, resold, or returned. Services that are not consumed at their appointed time cease to exist. Because of this, service businesses

2.2 Classification of Remote Services

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Figure 2.6: Features of Remote Service frequently find it difficult to synchronize supply and demand (Zeithaml et al. 2006, p. 24). To describe the characteristics of remote services regarding the perishability assumption, one has to differentiate between complex individualized remote services and standardized remote services. Whereas the first type cannot be stored or inventoried, the last type, which includes once programmed software updates, can be digitally inventoried and stored on the service providers’ server awaiting the scheduled processing date. The key characteristics of remote services that are shown in figure 2.6 and distinguish them from other service types. Remote services are related to e-services and self-services. Although they are also provided via an electronic medium, they are fundamentally different in their use cases: remote services are actively delivered by a remote service provider to a customer. In a remote service, the service provider is usually in control of the delivery time and facilitating conditions. In self-services, however, the customer interacts with technology in order to provide the service himself. The customer controls the service delivery independently from the selfservice provider.

2.2.3

Benefits of Remote Services

Remote services describe a class of services that are independent of industry, and therefore, comprise services from fields such as teleservices, telemedicine, telematics and IT-services. This suggests that the respective benefits, barriers, and challenges of the industry-specific services hold true for remote services.

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services


 

 
 
 
 
 


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Figure 2.7: Benefits and Challenges of Remote Service Remote services enable service providers to offer their services anytime and anywhere in the world where appropriate technology is available. At the same time, the service production process can be split into various parts that are performed at different locations. Service personnel can also be deployed much more effectively because the amount of travel time is reduced, employees are able to work for several customers concurrently and expertise is used much more efficiently. The provision of remote services does not only help service provider companies to strengthen their offerings through new types of service delivery, it also allows B2B producers to differentiate themselves from increasing competition by offering "customer solutions" (Tuli, Kohli, and Bharadwaj 2007). A comprehensive overview of the challenges and benefits of remote services derived from the related technology-mediated service types is shown in figure 2.7.

2.3 2.3.1

Classification of Interactive Remote Services Definition of Interactive Remote Services

In addition to remote services, this thesis examines a more complex subtype of remote services that is increasingly found in mechanical engineering and manufacturing, but is not limited to these industries. I term this subtype as "interactive remote services" and define it as follows: I NTERACTIVE R EMOTE S ERVICES are services that are provided via technologymediation to a connected service object in a collaborative production process based on a high level of human-to-human interaction between an active provider employee and and an active customer employee.

2.3 Classification of Interactive Remote Services

25

An example for an interactive remote service offering is a machine manufacturer who provides remote repair services for his machines located at the customer’s facility. In case of a malfunction or failure at the machine, the contact is initiated between a manufacturer’s remote service technician and the customer’s local machine operator either by automatic monitoring or by a traditional call. Then the technician asks for permission to remotely access the machine. The customer gives permission to the service technician who then remotely accesses the machine to gain information about its status. He communicates with the customer employee to further diagnose the problem. Finally, the service technician remotely re-configures the machine and gives repair instructions to the machine operator at the customer company’s place, who participates in the service by performing mechanical and diagnostic tasks (Wünderlich and Pfeffer 2007).

2.3.2

Characterization and Demarcation of Interactive Remote Services

Interactive remote services can be considered as a subtype of remote services, which is based on a high level of human-to-human interaction between an active provider employee and an active customer employee. Interactive remote services and remote services share the fact that they are intangible but differ in other service characteristics. To distinguish interactive remote services from remote services, the characteristics that differentiate them from remote services are discussed in the following section: Simultaneity of Consumption and Production The assumption of inseparability does not hold for interactive remote services due to their technology mediation. Providers and customers do not have to be in the same location during the service process, but the necessity of temporal synchronicity still holds. In interactive remote services, a real-time interaction between provider employee and customer employee is a constitutive characteristic of this service type. This differs from general remote services that can also be delivered without interaction. Integration of an External Factor The integration of an external factor in interactive remote services is crucial for the demarcation of this unique service type. In an interactive remote service, the integration of an external factor takes place in two ways. First, a customer’s actual collaborative behavior is necessary and vital for the service production. This can either be an information exchange, e.g., exchange on incidents and details of the failure, or physically performed tasks in the service production process, e.g., the opening a machine cabinet and exchanging spare-parts. Second, a connected object is the direct recipient of the service. This view comprises interactive remote services directed at humans being the service object such as in a remote surgery where an off-site surgeon interacts and collaborates with his col-

26

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

leagues while remotely accessing the medical equipment that is used to perform the surgery. In an interactive remote service situation, the level of customer participation and co-production behavior is high, and it demands more customer knowledge and ability. In Corsten’s (2001) activity portfolio, interactive remote services would correspond to sector three, because both the providers and the customers activity level is high (see figure 2.5). Heterogeneity Because humans produce interactive remote services interactively, no two services will be precisely alike. They are heterogeneous, because the service provider’s employees may differ in their performance from day to day. Also, the customer’s performance in the co-production may depend on his individual performance at the time of the service. In addition, these services are complex and observability of the service quality is reduced due to the technology mediation. As a consequence, the customers’ quality perceptions are influenced by high levels of uncertainty. Perishability Interactive remote services cannot be stored or inventoried because they are delivered by interactive collaboration between humans, e.g., a remote repair of a machine which is not "consumed" at their appointed time "ceases" to exist. Interactive remote services are very individualized services that have a very low potential to be standardized and automated. Therefore, it is difficult to synchronize supply and demand. Interactive remote services involve rather complex problem solving expertise of both the provider’s and customer’s employees. This raises high demands to the human resources management of both interaction partners especially in emergency cases. The defining characteristics of interactive remote services compared to characteristics of remote services are shown in figure 2.8.

2.3.3

Positioning of Interactive Remote Services

The inductive approach of this section will further explore the relation of interactive remote service to other technology-intensive services such as self-services and e-services from the customer’s perspective. To distinguish interactive remote services in the services universe in regard to the customer’s service experience, I develop the Technology-Interaction-Service matrix shown in figure 2.9. The horizontal axis describes the "intensity of human-to-human interaction" during the service from a customer’s perspective. The horizontal axis starts from a low intensity level on the left to a high intensity level on the right. High-contact, or in this study, high human-to-human interactivity refers to services that require high customer involvement and interaction between customers and service providers, either face-to-face or technology-mediated. Services in this

2.3 Classification of Interactive Remote Services

27

category can be found in healthcare, education, and psychiatry. They are often personalized to suit the specific needs of the customer. In contrast, "low contact" or, in this study, low humanto-human interactivity, refers to services that do not necessarily require customer-provider interaction. Moreover, the level of interaction is greatly reduced in low-contact situations such as discrete, routine, and mundane services, e.g., public transportation services (Ganesan-Lim, RussellBennett, and Dagger 2008). Schmenner (1986, p.22) defines a service with a high level of interaction as "one where the consumer can actively intervene in the service process at will, often to demand additional service of a particular kind or to request that some aspects of the service be deleted." The intensity of human-to-human interaction scale relates to Mersha’s (1990) contact framework and distinguishes between high-contact and low-contact services. The vertical axis describes the "intensity of high-end technology" and marks the degree of the perceived dominance of complex technology in the service experience from a customer’s viewpoint starting from low at the bottom to high at the top of the axis. This scale relates to the service contact types identified by Froehle and Roth (2004). They differentiate between: technology-free customer contact, a situation where information technology is not employed at all; technology-assisted customer contact, where technology is used by the service provider employee but inaccessible to the service customer; and technology-facilitated customer contact, where technology is simultaneously used by both the customer and the service personnel to enhance the service experience. Whereas the first three cases illustrate face-to-face service encounters, Froehle and Roth (2004) further distinguish technology-mediated customer contact as "face-to-screen", because the cus-




    


 





    
 





 


 



   


   


 




  
 
  

  



  

  

 








  
 

   

 
 


 



  

 

   


  












 










 



  










 





 

 



 

 


 

 






   


 
 
   


 
  
 



 


 










  












Figure 2.8: Features of Interactive Remote Services Compared to Remote Services

2. Conceptual Framework: Remote Services in Context of Technology-Mediated Services

intensity of high-end technology

high

28

SELF-SERVICES E-SERVICES

INTERACTIVE REMOTE SERVICES

VOICE-TO-VOICE SERVICES

low

FACE-TO-FACE SERVICES

low

intensity of human-to-human interaction

high

Figure 2.9: The Technology-Interaction-Service Matrix tomer is generally using some sort of visual display (and/or audible interface) to interact with the service provider. These are contact situations in which the service provider employee and the customer are interacting exclusively through some technology-based medium or a customer obtains a fully automated self-service (e.g., no human service provider employee is involved). Based on this scenario, I propose that the degree of technological intensity in a service encounter increases with the degree of technological integration during the face-to-face-contact and further increases if the service encounter is technology-mediated such as in a remote repair of a machine. Using this matrix, three major services groups can be identified: 1) self- and e-services, 2) interactive remote services; and 3) traditional face-to-face and voice-to-voice services. Low technology intensity and high human-to-human interaction are often found in traditional services like at a hair dresser or law services. Still belonging to this group but impacted slightly more by technology are technology-assisted services, like the check-in at an airline counter, and voice-to-voice services, e.g. call center information services. In this group, the services rely on personal contact or dialogue between the customer and the service provider employee, who acts as a service counterpart5 , whereas the technology is only applied as an assisting or communicating technology (Froehle and Roth 2004). 5

In this thesis, the term counterpart refers to the provider companies employee, usually the remote service technician (RST), who interacts with the customer employee during the service. From the point of view of the customer the RST acts as his counterpart.

2.4 Conclusions and Implications

29

Self-services and e-services, e.g. online banking or ATM services, comprise the next group. These services are mostly automated and consist of pre-stored procedures where there is no interaction between a customer employee and a service provider employee. The technology is a dominant factor in the perception of e- and self-services, and in this case, technology represents both the service-interface and the interaction partner to the customer. I argue that in interactive remote services where a) technology is the only mediator between customer and provider and b) the provider employee and the customer employee interact in realtime, both components - the perception of the technology and the perception of the interaction with the interactive remote service provider - play substantial roles in the interactive remote services experience. This distinguishes interactive remote services from other services such as e-, self- and face-to-face services and underscores the uniqueness of this service type.

2.4

Conclusions and Implications

Whereas other service types such as self-services and e-services have sparked a lot of research activity lately (see sections 2.1.1-2.1.3), there is only a very limited amount of research available on remote services (see section 2.2.1), and no research specifically exploring interactive remote services as defined in this research. Interactive remote services form a unique and distinct service type from both a theoretical and practical standpoint. A detailed characterization of interactive remote services and a demarcation of this new service type was presented. The technology-interaction-service matrix was developed to position interactive remote services compared to face-to-face, voice-to-voice, eservices, and self-services. The interactive remote service experience from a customer’s point of view is unique in that it involves "high-tech" and "high-touch" elements. The interactive remote service experience, the general service perception as well as the acceptance of potential and actual customers, however, have not yet been studied sufficiently. There is no clear understanding of the benefits, barriers, and challenges in managing these services and no empirical data in regard to the customers’ perception and to the drivers and obstacles for adoption and continued usage. This thesis aims at filling this gap.

Chapter 3 Theoretical Framework for Remote Service Adoption and Continued Usage In this research, I use an integrative approach to establish a theoretical framework for remote services and interactive remote services. Within this framework, I relate remote services to the fields of information systems, especially to studies on IT-adoption; to services marketing, in particular to concepts of customer-provider interaction and co-production; to management literature on organizational cooperations; and to concepts stemming from B2B marketing. From a methodological perspective this thesis aims at explaining organizational intention by linking it to perceptions of individual employees as a proxy. Therefore, remote service are related to literature on both individual and organizational behavior. This thesis adds and combines findings from different literature streams to form the theoretical basis for understanding (interactive) remote service perception, acceptance, and continued use.

3.1

Theoretical Foundations of Technology Adoption

In this section, the most important theories and studies concerning acceptance and adoption of technology are presented and discussed. These seminal theories were reflected in research within the last 30 years in numerous applications and various adaptations. First, theories stemming from behavioral theories in social psychology and sociology are presented, then models positioned in IT-adoption based on behavioral theories are discussed, and finally the theoretical foundations of the continued use of technology are examined. An overview of the studies and their salient findings is presented at the end of this section in table 3.1.

32

3. Theoretical Framework for Remote Service Adoption and Continued Usage

3.1.1

Behavioral Theories from Social Psychology and Sociology

3.1.1.1

Innovation Diffusion Theory and Variants

The Innovation Diffusion Theory (IDT) by Rogers (1962; 2003) is grounded in sociology and describes the social process by which an innovation is communicated through certain channels over time among the members of a social system. Mass media channels such as radio, television or newspapers, interpersonal channels such as face-to-face communication and interactive channels such as communication via the Internet are the most rapid and efficient means of informing individuals about the existence of an innovation. According to Rogers (2003), each member of a social system passes through an innovation decision process starting with first knowledge of an innovation and ending with the confirmation of his adoption/rejection decision. The decision process comprises five stages (see figure 3.1) and can be applied to individuals as well as to decision-making units such as a buying center or an organization as a whole (Rogers 2003, p. 20).6 In the first stage, knowledge is gained when an individual or a decision-making unit learns of the innovation’s existence. In the second stage, persuasion takes place when an individual forms a favorable or unfavorable attitude towards the innovation. Then at the decision stage, the individual chooses to adopt or reject the innovation. When an individual decides to put an innovation into use, the implementation stage follows. Confirmation occurs when an individual seeks reinforcement of an information-decision that has already been made, but he or she may also reverse this previous decision (Rogers 2003, p. 15). People’s attitude toward a new technology is a key element in the IDT, because consumers develop their beliefs about and attitudes toward the innovation based on their knowledge built in earlier stages. They directly base their decisions on their attitudes. Rogers (2003) identified five factors influencing a consumer’s adoption decisions of an innovation within the persuasionstage: relative advantage, complexity, compatibility, trialability, and observability. Relative advantage is the degree to which an innovation is perceived as better than the idea it supersedes. It may incorporate factors such as economic benefits, image enhancement, convenience and satisfaction. Complexity is the degree to which an innovation is perceived as difficult to understand and use. Some innovations are rapidly understood by most members of a social system; others are more complicated and will be adopted more slowly. New ideas that are simpler to understand will be adopted more rapidly than innovations that require the adopter to develop new skills and understandings. Compatibility is defined as the degree to which an innovation is perceived as being consistent with existing values, past experiences, and needs of potential adopters. An idea that is incompat6

The relation of the IDT to decision making units, the innovation process in organizations and various organizational factors will be discussed in detail within chapter 3.4.3 on the theoretical foundations of organizational decision making and adoption.

3.1 Theoretical Foundations of Technology Adoption

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Figure 3.1: The Innovation Decision Process Source: Own Illustration, based on Rogers (2003, p. 170) ible with the values and norms of a social system will not be adopted as rapidly as an innovation that is compatible. Trialability is the degree to which an innovation may be experimented with on a limited basis. New ideas that can be tried with only a small commitment - be it financially or effort wise - will generally be adopted more quickly than innovations that require a full commitment to the technology before it’s benefits can be assessed first hand. Observability is the degree to which the results of an innovation are visible to others. Obviously, the visibility of innovations and their success have a great influence on how they are perceived by potential and actual consumers. The easier it is for individuals to see the results of an innovation, the more likely they are to adopt it. In sum, innovations that are perceived by individuals as having greater relative advantage, compatibility, trialability, observability, and less complexity will be adopted more rapidly than other innovations. In terms of organizational adoption processes, Rogers (2003) notes that the diffusion of an innovation within a social system, such as an organization, is influenced by the structure of the system, the norms of the organization, the role of opinion leaders, and by the consequences of the innovation. Rogers’ (1962) work on the diffusion of innovation has stimulated various researchers to study a variety of innovations. In a review and meta-analysis of 75 studies, Tornatzky and Klein (1982) assess and support the effect of three of Rogers’ innovation characteristics (relative advantage, compatibility and complexity) on adoption behavior. Moore and Benbasat (1991) adapt Roger’s characteristics. They select and refine a set of constructs by combining research on diffusion on innovations with the Theory of Reasoned Action so that it could be used to study individual technology acceptance. Moore and Benbasat (1996) enrich the Rogers’ innovation characteristics by adding result demonstrability, image, and voluntariness as important adoption drivers and find support for the predictive validity of this new set of innovation characteristics.

34

3. Theoretical Framework for Remote Service Adoption and Continued Usage

Within the technology adoption literature, several constructs of Roger’s diffusion theory have received widespread attention. Davis (1989) derives some of his main technology adoption drivers — perceived ease of use and perceived usefulness — in the Technology Acceptance Model from the Diffusion of Innovation Theory (see chapter 3.1.2.1). Venkatesh et al. (2003) build on the innovation characteristics for developing the Unified Theory of Acceptance and Use of Technology (see chapter 3.1.2.3). In the services field, the innovation characteristics defined by Rogers have been successfully shown to be usage antecedents in self-service contexts. Meuter et al. (2005) empirically support the influence of Rogers’ innovation characteristics on customers readiness variables as well as on first-time-usage of self-services (see chapter 3.2.3.1).

3.1.1.2

The Theory of Reasoned Action and Variants

The Theory of Reasoned Action (TRA, Fishbein and Ajzen 1975; Ajzen and Fishbein 1980) is one of the most fundamental and influential theories of human behavior (Venkatesh et al. 2003). Drawn from social psychology, the TRA has its origin in Fishbein’s work on the psychological processes by which attitudes cause behavior (Fishbein 1967). According to the TRA, behavior is determined by the individual’s intention to perform the behavior, which in turn is determined by his attitude towards performing the behavior and by his subjective norms as displayed in figure 3.2. The TRA suggests that the proximal determinant of behavior is one’s intention to engage in that behavior. Behavioral intentions represent a person’s motivation in the sense of her or his conscious plan or decision to exert effort to enact the behavior (Conner and Armitage 1998). Also, intentions and behavior are considered to be strongly related when measured at the same level of specificity (Fishbein and Ajzen 1975). The TRA further posits that intention is defined

  
 
  


 


  



Figure 3.2: The Theory of Reasoned Action Source: Own Illustration, based on Ajzen and Fishbein (1980)

  


3.1 Theoretical Foundations of Technology Adoption

35

by factors that are personal in nature (attitude) and socially influenced (subjective norm). Ajzen and Fishbein (1975, p. 216) describe behavioral attitude relying on an expectancy-value model as "an individual’s positive or negative feelings (evaluative affect) about performing the target behavior." Hence, attitude toward a behavior comprises beliefs that performing the behavior will lead to positive or negative outcomes and the evaluation of these outcomes. These beliefs are unique to each special situation and a new set of beliefs must be elicited for each new situation (Ajzen and Fishbein 1980). Beliefs comprised under the term subjective norms relate to an individual’s perceived organizational or social pressure to perform a certain behavior. Fishbein and Ajzen define subjective norm as "the person’s perception that most people who are important to him think he should or should not perform the behavior in question" (Fishbein and Ajzen 1975, p. 302). As noted by Fishbein and Ajzen (1975), variables external to the model are assumed to influence intentions only to the extent that they affect either attitudes or subjective norms. In suggesting that behavior is solely under the control of intention, the TRA restricts itself to volitional behaviors (Conner and Armitage 1998). Therefore, behavior, which requires skills, resources, or opportunities not freely available, is not best predicted by the TRA (Fishbein 1993). The Theory of Reasoned Action has been widely used in social psychology for the last 30 years as a model for the prediction of behavioral intentions and actual behavior in application fields that include: participation in mentoring programs (Kim et al. 2001); usage of complementary medicine (Furnham and Lovett 2001); decision of choosing major (Jackling and Keneley 2009); condom use (Albarracin et al. 2001; Beadnell et al. 2008); substance abuse (Morrison et al. 2002; Morrison, Gillmore, and Baker 1995; Morrison et al. 1996); and organ donation (Weber, Martin, and Corrigan 2007). In marketing and the IS field, the TRA has been studied only in few contexts such as online advertising (Dinev, Qing, and Yayla 2008), online recommendation agents (Komiak and Benbasat 2006), or online buying (Hansen, Jensen, and Solgaard 2004; Yoh et al. 2003). Due to the high utilization of this theory in social psychology, several meta-analytic studies have been conducted to address the performance of the TRA, e.g., Sheppard, Hartwick, and Warshaw (1988), Van den Putte (1993), and Albarracin et al. (2001). Sheppard, Hartwick, and Warshaw (1988) reported a mean multiple correlation of 0.66 for predicting intention from attitude and subjective norm, and a mean correlation of 0.53 for predicting behavior from intention in their meta-analysis on 87 studies. Van den Putte (1993) provided a meta-analysis based on 113 studies and reported a mean multiple correlation of 0.68 for predicting intention from attitude and subjective norm and a mean correlation of 0.62 for predicting behavior from intention.

36 3.1.1.3

3. Theoretical Framework for Remote Service Adoption and Continued Usage The Theory of Planned Behavior and Variants

The TRA proceeds on the assumption that when someone forms an intention to act, he will be free to act without limitation. In practice, however, constraints such as limited ability, time and environmental, facilitating or organizational conditions limit this freedom to act. The Theory of Planned Behavior (TPB) (Ajzen 1985; 1988; 1991; 2002) is an extension of the TRA and attempts to resolve this limitation. It has emerged as one of the most influential and popular conceptual frameworks for human action (Ajzen 2002). The TPB is shown in figure 3.3. The TPB attempts to predict volitional and non-volitional behaviors by incorporating perceptions of control over performance of the behavior as an additional predictor (Ajzen 1991; 1988). Consideration of perceptions of control are important because they extend the applicability of the theory beyond easily performed, volitional behaviors to goals and outcomes that are dependent upon performance of a complex series of other behaviors (e.g. quit smoking). According to the TRA, and also in line with the TPB, a person’s actual behavior in performing a certain action is directly influenced by his behavioral intention and in turn, jointly determined by attitudes and subjective norms. The TPB extends the TRA model by adding the construct of perceived behavioral control as an antecedent of behavioral intention and behavior itself. Perceived behavioral control is aggregated by beliefs about the presence of factors that might further or hinder performance of the behavior. It reflects a person’s perception of ease or difficulty of implementing the behavior in interest (Ajzen 2002; 1991). Given a sufficient degree of actual control over the behavior, people are expected to carry out their intentions when the opportunity arises. Because some behaviors pose difficulties of execution that might limit volitional control, the TPB considers perceived behavioral control in addition to actual control. In its original meaning, perceived control is similar to Bandura’s 1986 concept of selfefficacy, which describes people’s judgments of their capabilities to perform certain activities (see chapter 3.1.2.4). In the context of IS-research, perceived behavioral control is understood in a broader definition as "perceptions of internal and external constraints on behavior" (Taylor and Todd 1995b, p.149). The TPB is a well-researched model that has been shown to predict behavior across a variety of settings. Ajzen argues that the TPB is "...open to the inclusion of additional predictors if it can be shown that they capture a significant proportion of the variance in intention or behavior after the theory’s current variables have been taken into account" (Ajzen 1991, p.199). Therefore, not only has it been applied in its original form, but many researchers extend it by incorporating context-specific factors such as prior behavior (Christian, Armitage, and Abrams 2003; Komiak and Benbasat 2006; Smith et al. 2008), moral norm (Lee, Lee, and Kim 2007; O’Connor and Armitage 2003), or denial of responsibility (Lee, Lee, and Kim 2007). The TPB is predominately applied in social psychology to predict human behaviors that might involve: healthy eating habits (Conner and Armitage 1998; Sparks, Guthrie, and Shepherd 1997), doing regular

3.1 Theoretical Foundations of Technology Adoption

37


  
  


  



 


  


  
   

Figure 3.3: The Theory of Planned Behavior Source: Own Illustration, based on Ajzen (1985) exercises (Terry and O’Leary 1995), performing in school (Manstead and van Eekelen 1998), breaking speed limits (Conner, Smith, and McMillan 2003), obeying rules (Broadhead-Fearn and White 2006), or commiting parasuicide (O’Connor and Armitage 2003). Validity of this theory has been tested in various meta-analyses where the predictive validity of the TPB was supported (e.g., Armitage and Conner 2000; Conner and Sparks 1996; Godin and Kok 1996; Rivis and Sheeran 2003; Sutton 1998). Empirical evidence suggests the explanatory power of TPB extends to IT adoption (Harrison, Mykytyn, and Riemenschneider 1997) and personal web usage (Lee, Lee, and Kim 2007). For explaining usage of technology-intensive services, the TPB has successfully predicted the use of telemedicine (Chau and Hu 2002), online banking (Liao and Shao 1999), e-commerce (Pavlou and Fygenson 2006), e-brokerage services (Bhattacherjee 2000), e-coupons (Kang et al. 2006), computing services (Taylor and Todd 1995b;a), and technology-free services such as housing services (Christian, Armitage, and Abrams 2003). Several studies compare the predictive power of the TPB with that of TRA, where often the TPB has performed as the better model to predict behavior (e.g., Furnham and Lovett 2001; Hansen, Jensen, and Solgaard 2004; Hagger, Chatzisarantis, and Biddle 2002; Madden, Scholder Ellen, and Ajzen 1992).

3.1.1.4

The Decomposed Theory of Planned Behavior

The Decomposed Theory of Planned Behavior (DTPB; Taylor and Todd (1995b) extends the TPB by "decomposing" its drivers of intention, which are attitude, subjective norm, and perceived behavioral control, into their underlying belief structure within a technology adoption

38

3. Theoretical Framework for Remote Service Adoption and Continued Usage

contexts. One major goal of the decomposition was to provide a stable set of beliefs that could be applied across a variety of settings. The DTPB is illustrated in figure 3.4. Researchers question the conceptualization of perceived behavioral control within the TPB (Pavlou and Fygenson 2006; Taylor and Todd 1995b). They perceive the behavioral control measurement model to contain items that pertain to two different factors. Items concerning the ease or difficulty to perform a behavior or the confidence in one’s ability to perform it, are more likely to measure perceived self-efficacy. Contrarily, items that address control over the behavior, or the extent to which a performance is up to the actor, address more general controllability beliefs. Therefore, in the DTPB, perceived behavioral control is conceptualized as a higher order factor formed by self-efficacy and controllability (Taylor and Todd 1995b). Pavlou and Fygenson (2006) follow this approach in their study on e-commerce adoption. The decomposition leads to the formation of higher-order constructs as functions of secondorder constructs (Taylor and Todd 1995b). Based on Rogers’ (2003) innovation characteristics and the Technology Acceptance Model (TAM, see chapter 3.1.2.1), Taylor and Todd (1995b) propose that attitude can be decomposed into the second-order constructs of compatibility, ease of use, and perceived usefulness. Subjective norm is decomposed into peer influence and superior’s influence. Perceived behavioral control is decomposed into self-efficacy and facilitating






 








 



 


 







 



  


 
  



 
 
 


 
 
 


Figure 3.4: The Decomposed Theory of Planned Behavior Source: Own Illustration, based on Taylor and Todd (1995b, p. 146)

 
 


3.1 Theoretical Foundations of Technology Adoption

39

conditions. With respect to IT usage, the facilitating conditions construct provides two dimensions for control beliefs: one relating to resource factors such as time and money and the other relating to technology compatibility issues that may constrain or enable usage (Taylor and Todd 1995b). The facilitating control aspect is somewhat related to the locus of control concept, which refers to the extent to which individuals believe that they can control events that affect them. Locus of control has been studied in technology adoption studies to a minor degree, e.g., in study on self-service technologies by Oyedele and Simpson (2007). Although Taylor and Todd (1995b) provide evidence for the relatively high explanatory power of the DTPB in the context of computing services. In comparison to the TPB and the TAM (Davis 1989; Davis, Bagozzi, and Warshaw 1989), this theory is not as popular as its predecessors. Bhattacherjee (2000) applies a modified TPB, which is very similar to the DTPB, to e-brokerage services. Hsu and Chiu (2004) apply the DTPB to predict e-service continuance.

3.1.2

Models in IT-Adoption Based on Behavioral Theories

3.1.2.1

The Technology Acceptance Model and Variants

The TAM originally formulated by Davis (1989) is one of the most widely tested models specifically geared to technology acceptance. The TAM, as shown in figure 3.5, has emerged as a powerful and parsimonious way to represent the antecedents of IT system usage. The TAM is an adaptation of the TRA. The TAM specifies that the perceived ease of use and perceived usefulness are determinants of attitude towards usage intentions and IT usage. Perceived usefulness is related to "the degree to which a person believes that using a particular system would enhance his or her job performance" (Davis 1989, p.320). Perceived ease of use is defined as "the degree to which a person believes that using a particular system would be free of effort" (Davis 1989, p.320). In the TAM, subjective norms are not included as antecedents for behavioral intention. According to the TRA, usage intentions are the sole direct determinant of usage. The TAM, however, departs from TRA in one significant way of proposing the effects. The direct path from perceived usefulness to intention violates the TRA model, which claims that attitude completely mediates the relationship between these types of beliefs and intention as shown in figure 3.5. According to Davis, Bagozzi, and Warshaw (1989), the reason for this deviation is that in work settings, intentions to use IT may be based on anticipated job performance consequences of using the system regardless of overall attitude. In other words, an employee may dislike a system (i.e., have a negative attitude toward it), but still use the system because it is perceived to be advantageous in terms of job performance. All other factors not explicitly included in the TAM are expected to impact behavioral intentions and usage through the antecedents ease of use and

40

3. Theoretical Framework for Remote Service Adoption and Continued Usage

perceived usefulness. The practical utility of the model stems from the fact that ease of use and usefulness are factors over which a system designer has some degree of control. They are determinants of usage and provide direction to designers as to where efforts should be focused (Taylor and Todd 1995b). The TAM has successfully predicted and explained individual’s intention to adopt as well as adoption behavior in a variety of studies. It received empirical support in research on information system usage such as communication systems (Karahanna, Straub, and Chervany 1999; Malhotra and Galletta 2008; Gefen and Straub 1997), office systems (Mathieson 1991), specialized business systems (Dishaw and Strong 1999), or general purpose systems (Gefen and Straub 2000). The TAM has also been used to explain technology-intensive service adoption including mobile commerce services (Pedersen 2003), e-payment services (Chen, Gillenson, and Sherrell 2002; Featherman and Pavlou 2003), computing services (Taylor and Todd 1995b), and telemedicine technology (Hu et al. 1999; Chau and Hu 2002). A number of researchers (Chau and Hu 2002; Taylor and Todd 1995a) has extended and modified the TAM. Single factors and belief groups from other models such as TRA, TPB, IDT, or new factors were frequently added and empirically validated within the TAM model. Some examples of these modifications include: social influence (Karahanna, Straub, and Chervany 1999; Hu et al. 1999), gender (Gefen and Straub 1997; Venkatesh et al. 2003; Venkatesh and Morris 2000), age (Venkatesh and Morris 2000), compatibility (Chen, Gillenson, and Sherrell 2002), subjective norms Yu et al. (2005), motivation (Venkatesh 2000; Malhotra and Galletta 2008),

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3.1 Theoretical Foundations of Technology Adoption

41

anxiety (Venkatesh 2000), playfulness (Venkatesh 2000), enjoyment (Venkatesh 2000; Hwang and Yi 2002), self-efficacy (Hwang and Yi 2002), usability (Venkatesh 2000), task-technologyfit (Dishaw and Strong 1999), locus of causality (Malhotra and Galletta 2008), desire for personal back up (Walker and Johnson 2006), perceived threat (Bhappu and Schultze 2006), desire for personal contact (Walker and Johnson 2006), or technology readiness (Lin, Shih, and Sher 2007). Especially trust (Keat and Mohan 2004; Wang and Benbasat 2005; Pavlou 2003; Gefen, Karahanna, and Straub 2003b), risk (Pavlou 2003; Featherman and Pavlou 2003), and control (Venkatesh 2000; Shin 2009) are constructs that have been well explored as direct and indirect antecedents of usage in recent research. Various meta-studies have been conducted on the TAM such as Gefen and Straub (2000), Ma and Liu (2004), and Lee, Kozar, and Larsen (2003). Yousafzai, Foxall, and Pallister (2007a; 2007b) published a comprehensive review of 145 papers on the TAM. They found the TAM to be valid and reliable. They also found that the model’s predictions are consistent across different populations and software choices (Ma and Liu 2004; Lee, Kozar, and Larsen 2003). Taylor and Todd (1995b) compare the TAM, the TPB, and the DTPBT and found that all three models performed well in terms of fit and were roughly equivalent in terms of their ability to explain behavior. Mathieson (1991) compares the TAM’s and TPB’s ability to predict intention to use an IS and concludes that TAM has a slight empirical advantage. Recently, Chin, Johnson, and Schwarz (2008) developed a fast form approach to measure technology acceptance by using sematic differential scales instead of the original 12-item Likert-scale of the TAM. In addition to the auxiliary research, the original authors of the TAM continually tested, modified, and revised their model over time. Davis, Bagozzi, and Warshaw (1989) tested the original TAM in a longitudinal study and suggested an improvement towards parsimony of the model by removing the attitudinal construct. Davis (1993) reports a modified version of the TAM, which omits behavioral intention and measures the direct effect of perceived ease of use and perceived usefulness on behavior by collecting actual behavior measures. Venkatesh and Davis (2000) propose the TAM 2, which is an extension of the TAM as shown in figure 3.5. Within the TAM 2, social influence such as subjective norm, voluntariness, and image as well as cognitive instrumental processes (job relevance, output quality, and result demonstrability) are added to the original TAM as antecedents of perceived usefulness. These antecedents are found to significantly influence user acceptance. Venkatesh and Bala (2008) developed TAM 3 as a combination of TAM 2 and antecedents of ease of use such as computer anxiety, perceived external control, computer playfulness, and self-efficacy.

3.1.2.2

The Motivational Model and Variants

Research in psychology has supported general motivation theory as an explanation for behavior, which was adapted for specific contexts (see Vallerand 1997). Within the IS domain, Davis,

42

3. Theoretical Framework for Remote Service Adoption and Continued Usage

Bagozzi, and Warshaw (1992) apply motivational theory to understand new technology adoption and use and develop the Motivational Model. They show that extrinsic and intrinsic motivation are key drivers of an individual’s intention to use technology. Motivation theorists often distinguish between these two broad classes of motivation to perform an activity (Davis, Bagozzi, and Warshaw 1992, see chapter 3.2.2.2 for a discussion of an application of this construct in marketing literature). Intrinsic motivation refers to the pleasure and inherent satisfaction derived from a specific activity (Vallerand 1997). Extrinsic motivation emphasizes performing a behavior because it is perceived to be instrumental in achieving valued outcomes. These outcomes are distinct from the activity such as increased pay and improved job performance (Vroom 1964). Davis, Bagozzi, and Warshaw (1992) conceptualize and operationalize intrinsic motivation as perceived enjoyment. Perceived enjoyment refers to the extent to which using a computer is perceived to be enjoyable, which is distinct from any performance outcomes that might be obtained. In addition, the motivational model includes extrinsic motivation, which is nearly identical to the perceived usefulness construct in the TAM (Davis 1989). Venkatesh and Speier (1999) use the motivational model to study the influence of pre-training mood on user acceptance of technology over time. Intrinsic and extrinsic motivation are discussed in this thesis in more detail in section 3.2.2.2. Venkatesh, Speier, and Morris (2002) form the Integrated Model by adding perceived ease of use to the Motivational Model. Venkatesh, Speier and Morris’ (2002) Integrated Model specifically examines the influence of pre-training and training environment interventions to understand how user perceptions are formed prior to system implementation.

3.1.2.3

The Unified Theory of Acceptance and Use of Technology

Venkatesh et al. (2003) developed the Unified Theory of Acceptance and Use of Technology (UTAUT). The UTAUT model draws upon and combines constructs of eight popular models and theories that relate to technology acceptance: the TRA; the TPB; the TAM; the "C-TAMTPB", a combination of TAM and TPB developed by (Taylor and Todd 1995a); the Motivational Model; the model of PC Utilization developed from Thompson, Higgins, and Howell (1991); the IDT; and Compeau and Higgins’ model based on Social Cognitive Theory (Compeau and Higgins 1995a;b) Figure 3.6 shows the UTAUT model. Performance expectancy, effort expectancy, and social influence are direct antecedents of behavioral intention, which impact usage behavior. Facilitating conditions, on the other hand, directly affect usage behavior. Gender, age, experience, and voluntariness of use partially moderate the relationships. The four core constructs condense mostly all behavioral antecedents of the eight models the UTAUT draws upon. 1. Performance expectancy is defined as "the degree to which an individual believes that

3.1 Theoretical Foundations of Technology Adoption

 
 


43







 


 
 


 

  













 




Figure 3.6: The Unified Theory of Acceptance and Use of Technology Source: Own Illustration, based on Venkatesh et al. (2003, p. 447) using the system will help him or her to attain gains in job performance" (Venkatesh et al. 2003, p. 447). The root constructs for performance expectancy are perceived usefulness (Davis 1989; Davis, Bagozzi, and Warshaw 1989), extrinsic motivation (Davis, Bagozzi, and Warshaw 1992), relative advantage (Rogers 2003), outcome expectations (Compeau and Higgins 1995a;b), and job-fit. The construct job-fit is derived from the Model of PC Utilization (Thompson, Higgins, and Howell 1991) and describes "how the capabilities of a system enhance an individual’s job performance" (Venkatesh et al. 2003, p. 448). 2. Effort expectancy is defined as the "degree of ease associated with the use of the system" (Venkatesh et al. 2003, p. 450). Effort expectancy stems from perceived ease of use (TAM) and complexity. Complexity is derived from Rogers’ innovation characteristics as well as from the Model of PC Utilization (Thompson, Higgins, and Howell 1991) and relates to "the degree to which an innovation is perceived as relatively difficult to understand and use" (Venkatesh et al. 2003, p. 451). 3. Social influence is defined as "the degree to which an individual perceives that important others believe he or she should use the new system" (Venkatesh et al. 2003, p.451). Its root constructs basically include subjective norm (from models such as TRA and CTAM-TPB and social factors. Social factors are drawn from the Model of PC Utilization (Thompson, Higgins, and Howell 1991), and are defined as "the individual’s internalization of the reference group’s subjective culture, and specific interpersonal agreements that the individual has made with others, in specific social situations" (Venkatesh et al. 2003, p. 452). 4. Facilitating conditions are defined as "the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system" (Venkatesh

44

3. Theoretical Framework for Remote Service Adoption and Continued Usage et al. 2003, p. 453). Facilitating conditions include perceived behavioral control (TPB), compatibility, and facilitating conditions, which are derived from the Model of PC Utilization (Thompson, Higgins, and Howell 1991). They are "objective factors in the environment that observers agree make an act easy to do, including the provision of computer support" (Venkatesh et al. 2003, p. 454).

Venkatesh et al. (2003) compared those eight original models with the UTAUT. They validated the UTAUT in a longitudinal study with data from four organizations over a six-month period. Within this study, the UTAUT outperforms the eight individual models in prediction of usage intention and usage (Venkatesh et al. 2003). Recently, Venkatesh et al. (2008) focus on the facilitating conditions construct of the UTAUT and develop a new model that employs behavioral intention, facilitating conditions, and behavioral expectation as predictors of system use. They measure system use with the dimensions duration, frequency, and intensity.

3.1.2.4

Compeau and Higgins’ Model based on Social Cognitive Theory

A powerful theory of human behavior is the Social Cognitive Theory (SCT, Bandura 1986). It posits that people are neither only driven by inner forces, or simply by external stimuli. Instead, it is based on the premise that environmental influences, cognitive and personal factors, as well as behavior are reciprocally determined. In the SCT, all of those variables operate interactively as determinants of each other. A key regulatory mechanism in this dynamic relationship that affects human behavior is self-efficacy. Bandura (1986). defines self-efficacy as "people’s judgments of their capabilities to organize and execute courses of action required to attain designated types of performances. It is not concerned with the skills one has but with judgments of what one can do with whatever skills one possesses" (Bandura 1986, p.391). Based on the SCT, Compeau and Higgins (1995b) develop a model of the role of individuals’ beliefs about their abilities to use a computer (see figure 3.7). The model proposes an effect of emotional reactions such as affect and anxiety of computer usage as well as an effect of outcome expectations and computer self-efficacy on computer use. Self-efficacy is a central construct of Compeau and Higgins’ (1995b). In their study, they find that computer self-efficacy exerts a significant influence on individuals’ expectations of the outcomes of using computers, their emotional reactions to computers (affect and anxiety), as well as on their actual computer use. Further, an individual’s self-efficacy and outcome expectations are found to be positively influenced by the encouragement of others in their work group, as well as of others’ use of computers (1995a). Various studies have integrated the concept of self-efficacy into models of user acceptance of technology (Compeau and Higgins 1995a; Compeau, Higgins, and Huff 1999; Hwang and Yi 2002; Venkatesh 2000; Agarwal, Sambamurthy, and Stair 2000).

3.1 Theoretical Foundations of Technology Adoption

  






    









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Figure 3.7: Compeau and Higgins’ (1995b) Model Based on SCT Source: Own Illustration, based on Compeau and Higgins (1995b, p. 194)

3.1.3

Theoretical Foundations of Continued Use of Technology

3.1.3.1

Importance of Prior Experience

In contrast to initial IT acceptance which focuses on users’ initial or first-time decision to use IT, IT continuance is defined as users’ decisions to continue using an IT over the long run (Bhattacherjee, Perols, and Sanford 2008). Most studies on technology adoption do not explicitly and strictly differentiate between experienced and non-experienced users at the adoption stage (e.g. Davis 1989; Venkatesh and Davis 2000; Compeau and Higgins 1995b; Gefen and Straub 1997; Taylor and Todd 1995b). In their meta-analysis on inclusion of prior use in IT adoption and use-research, Jasperson, Carter, and Zmund (2005) conclude that the majority of previous studies tend to either examine IT application use immediately after adoption or do not account explicitly for a user’s history in using a focal IT application. Some studies view continuance as an extension of acceptance behaviors (i.e., they employ the same set of pre-acceptance variables to explain both acceptance and continuance decisions). Those studies implicitly assume that continuance co-varies with acceptance. Therefore, in their studies on acceptance, some researchers include users with prior experience as well as users without prior experience (e.g., Davis, Bagozzi, and Warshaw 1989; Davis 1989; Venkatesh and Bala 2008). Also, IDT suggests that adopters re-evaluate their earlier acceptance decision during a final "confirmation" stage and decide whether to continue or discontinue using an innovation (DeLone and McLean 1992; Rogers 2003). Some researchers such as Taylor and Todd (1995a); Venkatesh and Morris (2000); Venkatesh (2000); Venkatesh et al. (2003); and Xia and Lee (2000) observe a change in predictors of intention over time. As expected, in studies that have considered the direct impact of prior use on post-adoptive behaviors, researchers find that prior use is a significant antecedent of

46

3. Theoretical Framework for Remote Service Adoption and Continued Usage

post-adoptive behavior (Bajaj and Nidumolu 1998; Jackson, Chow, and Leitch 1997; Jasperson, Carter, and Zmund 2005; Venkatesh, Speier, and Morris 2002). Also, the effect of experience on user behavior has been studied through the concept of habit (Cheung and Limayem 2005; Hong, Kim, and Lee 2008; Ouellette and Wood 1998). Especially in the context of the TPB, prior behavior has been discovered as a salient predictor of behavior (e.g., Sutton 1994). Several researchers have integrated self-reported past behavior into the TPB for measuring behavior intentions and predicting behavior across a range of behavioral domains (e.g., Bamberg, Ajzen, and Schmidt 2003; Conner et al. 1999; Hagger, Chatzisarantis, and Biddle 2002; Smith et al. 2008). Prior behavior has been shown to be a strong predictor. For example, Conner and Armitage (1998) report that the inclusion of a measure of past behavior in the TPB explained an additional 7% of variance in intentions and 13% of variance in behavior. Kim and Malhotra (2005) show that prior use, perceived ease of use, and perceived usefulness influence intention to use a system. Additionally, their results show that user evaluations such as perceived usefulness and perceived ease of use are updated sequentially with target system experience. In contrast, Agarwal and Prasad (1997) support the effect of Rogers’ (2003) innovation characteristics on usage intention and behavior but showed that future use intention is not determined by current usage.

3.1.3.2

Studies On Continued Usage

Some researchers criticizes the extreme emphasis of technology acceptance (initial use) over continued usage of technology in technology acceptance studies. Baron, Patterson, and Harris (2006, p.111) call it "the inadequacy of a concentration on simple acceptance of technology where technology is embedded in a consumer community of practice." Support for this view comes from findings in relationship marketing, which also stress the need to retain existing customers (Grönroos 1990; 1996). Reseachers claim that the importance of continuance is evident from the fact that acquiring new customers may cost as much as five times more than retaining existing ones, given the costs of advertising, searching for new customers, setting up new accounts, and initiating new customers (Parthasarathy and Bhattacherjee 1998). Furthermore, companies can increase their profits by almost 100% by retaining just 5% more of their customers (Reichheld and Sasser Jr. 1990). Studies that focus on continued usage, however, often refer to the same constructs that are used in pure acceptance studies, e.g. Davis (1989); Thompson, Higgins, and Howell (1991). Some studies explore usage behavior with a sample consisting of experienced and inexperienced users, where a construct termed prior behavior is integrated in the model to control for different levels of experience (Kang et al. 2006, Dickinger and Kleijnen 2008, Yoh et al. 2003). Hu et al. (2009) explore the subsequent continuance intention of e-services (e-taxes) customers, combining perceived usefulness, ease of use, and service quality as main factors for predicting

3.1 Theoretical Foundations of Technology Adoption

47

intention. They confirm the positive effect of perceived usefulness and of two service quality dimensions — assurance and reliability — on continued use. In a two-wave panel study design, they also show that prior evaluations and past use affect subsequent evaluations and past use affects future use. Naidoo and Leonard (2007) identify perceived usefulness as the strongest predictor of continued usage next to service quality and the perception of loyalty incentives. Jasperson, Carter, and Zmund (2005) integrate prior use with determinants such as ease of use, perceived usefulness, subjective norms, and perceived behavior control in a theoretical model on intention to continue usage of work systems. Other approaches to explore IT continuance and technology-intensive services have their origins in the expectation confirmation theory (ECT) developed in marketing literature (Oliver 1977; 1980; 1993; Anderson and Sullivan 1993). The ECT was designed to explain the determinants and outcomes of consumer satisfaction and dissatisfaction in product repurchase and service retention contexts. The theory proposes that consumers match the extent to which their actual product experience follows their initial expectations. The positive or negative disconfirmation and customers’ initial product expectations are postulated to jointly determine users’ extent of satisfaction or dissatisfaction with that product. Bhattacherjee (2001) adapted the ECT to information technology acceptance by combining the TAM with the ECT and postulating an IT continuance model (ICT). The ICT suggests that the satisfaction with IS use and perceived usefulness determine a user’s continuance intention. User satisfaction, in turn, is influenced by confirmation of expectation from prior IS use and perceived usefulness. The model introduced the satisfaction construct instead of attitude, which had been traditionally used by TRA-based IS research (Hong, Kim, and Lee 2008). Some studies have added Internet self-efficacy (Hsu and Chiu 2004), perceived playfulness (Lin, Wu, and Tsai 2005), perceived ease of use and perceived enjoyment (Thong,




  



 


  



  



  
 


 



 


Figure 3.8: IT Continuance Model Source: Own Illustration, based on Bhattacherjee, Perols, and Sanford (2008, p. 20)

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3. Theoretical Framework for Remote Service Adoption and Continued Usage

Hong, and Tam 2006) to the original IT continuance model. Hong, Kim, and Lee (2008) extended the ICT with current habit as a driver of switching costs and in turn perceived switching cost as a driver of intention. Bhattacherjee, Perols, and Sanford (2008) modify the ICT by linking continuance intention to behavior and elaborating on the contingent factors that shape IT continuance intention and behavior (e.g., IT self-efficacy and facilitating conditions as shown in figure 3.8).

3.1.3.3

Comparison of Adoption and Continuance Drivers

Studies in which user acceptance drivers are directly compared with continuance drivers are rare. In the IS field, Davis, Bagozzi, and Warshaw (1989) measure IT system acceptance at two different points in time with the TAM. Although a comparison of the drivers for adoption vs. continuance is not in the focus of their research, they show evidence that perceived usefulness has a stronger effect on usage intention for users with a 14 week experience background with the IT system than for non-users. In some studies on IT-adoption, experience of a user is modeled on the relationships between usage antecedents and intention to use and use. It is shown that with increasing experience, the effect of some antecedents including ease of use (Venkatesh and Bala 2008; Venkatesh and Davis 2000), subjective norms (Venkatesh et al. 2003; Venkatesh and Davis 2000), and control (Venkatesh et al. 2003) decrease , whereas the effect of perceived usefulness (Gefen, Karahanna, and Straub 2003a; Venkatesh and Davis 2000) and trust (Gefen, Karahanna, and Straub 2003a) increase. Taylor and Todd (1995a) extend the TAM with subjective norms and perceived behavior control from TPB. They use this model to measure computing services of students with and without prior experience with the services. Results of a multi-group comparison show that the model was suitable to measure intention to use and usage behavior for both groups. Significant difference were only supported for the relationship between control and behavioral intention (was stronger for experienced users) and the relationship between control and usage (stronger for inexperienced users) (Taylor and Todd 1995a). Venkatesh (2000) measures the TAM at three different points in time. The TAM was strongly supported, finding an increasing effect of perceived usefulness and a decreasing effect of ease of use on intention over time within the data pooled across studies. Venkatesh et al. (2003) conduct a longitudinal study to validate the UTAUT. They demonstrate that the influence of performance expectancy increases over time, where as the influence of facilitating conditions, social influence, and effort expectancy decreases over time (Venkatesh et al. 2003). In an organizational adoption context, Premkumar and Roberts (1999) showed that relative advantage is the only significant variable to discriminate adopters from non-adopters. Karahanna, Straub, and Chervany (1999) compare the effects of different belief groups on be-

3.1 Theoretical Foundations of Technology Adoption

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Figure 3.9: Adoption and Continuance Model of Karahanna, Straub, and Chervany (1999) Source: Own Illustration, based on Karahanna, Straub, and Chervany (1999, p. 187)

havioral intention when adopting the IT (acceptance) vs. using the IT (continuance). They compare potential adopters and actual adopters of a Window’s operating system with a nearly identical model as depicted in figures 3.9. Their models conceptualize an individual’s intention to adopt (or continue to use) IT, which is determined both by a personal factor, the attitude towards adopting (or continuing to use) the IT, and by a social influence factor represented by subjective norms. Attitude comprises behavioral beliefs such as relative advantage (or perceived usefulness), image, compatibility, complexity (or ease of use), trialability, visibility, and result demonstrability (Moore and Benbasat 1996; Rogers 2003). Salient referents for the social normative component are top management, supervisors, peers, the organization’s MIS department, local computer technology experts, and friends.

The results indicate that the relationship between attitude and behavioral intention is stronger for users than for potential adopters and that the relationship between subjective norm and behavioral intention will be stronger for potential adopters than for users. According to Karahanna, Straub, and Chervany (1999), potential adopter intention to adopt is solely determined by normative pressures, whereas user intention is solely determined by attitude. In addition, potential adopters base their attitude on a richer set of innovation characteristics than users. In detail, the study shows that perceived usefulness is the only belief affecting both attitude towards adopting and attitude towards continuing to use. In addition, image is significant for users while visibility, result demonstrability, ease of use, and trialability are significant for potential adopters (Karahanna, Straub, and Chervany 1999).

50

3.1.4

3. Theoretical Framework for Remote Service Adoption and Continued Usage

Summary and Overview of Models in Technology Adoption

The most important theories and studies concerning acceptance of technology and technologyintensive services have been presented and discussed. In particular, the following research models were examined: the Innovation Diffusion Theory (Rogers 1962); the Theory of Reasoned Action (Fishbein and Ajzen 1975); the Theory of Planned Behavior (Ajzen 1985; 1991); the Technology Acceptance Model (Davis 1989); the Motivational Model (Davis, Bagozzi, and Warshaw 1992); the Unified Theory of Acceptance and Use of Technology (Venkatesh et al. 2003); and Social Cognitive Theory (Bandura 1986). These seminal theories, their numerous applications, and various adaptations were reflected upon in view of remote service adoption and continuance. The most important empirical studies in the service and IS adoption context are summarized in table 3.1, including the studies’ central findings, main constructs, and applications field.

e-brokerage services

document management system

telemedicine technology

e-commerce, e-payment services

housing services

Bhattacherjee (2000)

Bhattacherjee, Perols, and Sanford (2008)

Chau and Hu (2002)

Chen, Gillenson, and Sherrell (2002)

Christian, Armitage, and Abrams (2003)

to be continued on the next page. . .

website usage

B2C

B2C

intra-firm

intra-firm

B2C

B2C

Study Context

Bart et al. (2005)

Authors

TPB

TAM IDT

TPB TAM

ECT

→ INT

→ INT ; INT → B

→ INT ; INT → B

CONT

ATT

PU , CONT , ATT

SAT , PU , SELF - EFFICACY → INT ; INT , CONT → B

→ INT

TAM , TPB ,

→ INT

ATT , CONT , SN

T

Antecedents on INT or B

DTPB

TRA

Base Theory

cross-sectional combined with actual usage data over a period

cross-sectional combined with self-reported usage data

cross-sectional

longitudinal

cross-sectional

cross-sectional

Method

Table 3.1: Relevant Empirical Studies on Technology-Intensive Services and IT Adoption

experienced users

experienced users were asked to recall back to time prior to first usage

mixed sample of experienced and non-experienced users

Subject’s Experience Level

consumers

consumers

mixed sample of experienced and non-experienced users

experienced users

physicians prac- mostly unexperienced users ticing in hospitals

employees

customers

consumer

Subjects

3.1 Theoretical Foundations of Technology Adoption 51

B2C

B2C

B2C

intra-firm

IS usage

health care services

Davis, Bagozzi, and Warshaw (1992)

Dellande, Gilly, and Graham (2004)

Dickinger and Kleijnen m-coupons (2008)

Fagan, Neill, and Wooldridge (2008)

B2C

B2C

e-services / online shopping

Gefen and Straub (2004)

Gefen, Karahanna, and online shopping Straub (2003b)

to be continued on the next page. . .

B2C

Featherman and Pavlou e-payment (2003) services

IS usage

B2C

Study Context

Compeau and Higgins IS usage (1995a)

Authors

TAM

TRA

TAM

TAM

TBP

—

TAM

SCT

Base Theory

→B

→ INT

→B

→ INT → INT

INTEGRITY T , PU , EOU

PREDICTABILITY,

→ INT

→ INT

RISK , PU

EOU

EXTRINSIC MOTIVATION ,

ATT , CONT , PB

RC , RA , MOTIVATION

PU , ENJOYMENT, QUALITY → INT ; PU , ENJOYMENT, INT → B

TIONS

SONAL OUTCOME EXPECTA -

COME EXPECTATIONS , PER -

EFFICACY, PERFORMANCE OUT-

BEHAVOR MODELING , SELF -

Antecedents on INT or B

cross-sectional

cross-sectional

cross-sectional

cross-sectional

cross-sectional

cross-sectional survey study & actual behavioral data

cross-sectional (2 studies)

experimental

Method

students

students

students

employees

consumers

patients

students

consumers

Subjects

experienced users

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

experienced users

inexperienced users

experienced users

Subject’s Experience Level

52 3. Theoretical Framework for Remote Service Adoption and Continued Usage

online banking

e-taxes

telemedicine technology

e-taxes

web-based class management system

e-coupons

e-mail usage

Ho and Ko (2008)

Hsu and Chiu (2004)

Hu et al. (1999)

Hu et al. (2009)

Hwang and Yi (2002)

Kang et al. (2006)

Karahanna and Straub (1999)

to be continued on the next page. . .

B2C

online shopping

Hansen, Jensen, and Solgaard (2004)

intra-firm

B2C

B2C

B2C

inter-firm

B2C

B2C

B2C

Study Context

Gefen, Karahanna, and online shopping Straub (2003a)

Authors

TAM

TPB

TAM

TAM

TAM

DTPB

TAM

TRA

TAM

Base Theory

→ INT

→ INT ; INT , →B

PU

→B

ATT , SN , C , PB

→ INT

→ INT

SELF - EFFICACY

U , EOU

→ INT

→ INT

QUALITY, PU

ATT , PU

SAT , SELF - EFFICACY

→ INT

→ INT

CUSTOMER READINESS

VALUE ,

ATT , SN , CONT

FAMILIARITY, PU , T

Antecedents on INT or B

comparison experienced vs. inexperienced users

cross-sectional

cross-sectional

longitudinal

cross-sectional

cross-sectional

cross-sectional

cross-sectional

experimental

Method

experienced users

experienced users

mixed sample of experienced and non-experienced users

comparison experienced vs. inexperienced users

Subject’s Experience Level

students

students

taxpayers

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

experienced users

physicians prac- mixed sample of experienced ticing in hospitals and non-experienced users

taxpayers

consumers

consumer

students

Subjects

3.1 Theoretical Foundations of Technology Adoption 53

online (shopping) services

personal web usage

online banking

e-trading services

web-based class management system

Lee and Allaway (2002)

Lee, Lee, and Kim (2007)

Liao and Shao (1999)

Lin, Shih, and Sher (2007)

Malhotra and Galletta (2008)

to be continued on the next page. . .

B2C

e-recommendations

Komiak and Benbasat (2006)

B2C

B2C

B2C

intra-firm

B2C

intra-firm

Study Context

Karahanna, Straub, and IS usage Chervany (1999)

Authors

TAM

TAM

TPB

TPB

TPB

TRA

TAM , TRA

Base Theory →

→ INT

→B

ATT , LOC

→ INT

→ INT

→ INT

PU , EOU , TR

ATT , CONT

INT , C

ATT , SN , CONT , DENIAL

→ INT ;

CONT , OUTCOME DESIRABLITY → INT

TRUST

EMOTIONAL AND COGNITIVE

INT

ATT , SN , VOLUNTARINESS

Antecedents on INT or B

cross-sectional

cross-sectional

cross-sectional

cross-sectional

experimental

experimental

cross-sectional combined with actual usage data over a period

Method

students

consumers

consumer

employees

students

consumers

employees

Subjects

mixed sample of experienced and non-experienced users

experienced users

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

inexperienced users

non-experienced users

mixed sample of experienced and non-experienced users

Subject’s Experience Level

54 3. Theoretical Framework for Remote Service Adoption and Continued Usage

online shopping

computer based tutorials

Pavlou and Fygenson (2006)

Premkumar and Bhattacherjee (2008)

to be continued on the next page. . .

online shopping

Pavlou (2003)

B2C

B2C

B2C

B2B

IS usage

Palvia, Means, and Jackson (1994)

B2C

B2C

e-health services

Naidoo and Leonard (2007)

B2C

Nysveen, Pedersen, and mobile services Thorbjørnsen (2005)

self-services

Study Context

Meuter et al. (2005)

Authors

TAM

TPB

TAM

—

TAM , TPB

TAM

IDT

Base Theory

→ INT

→B

→ INT ;

→B cross-sectional in two studies with self-reported usage

cross-sectional

cross-sectional

cross-sectional

cross-sectional (2 studies)

Method

→ INT

longitudinal

→ INT ; INT , CONT → longitudinal

PU , EOU , SAT

B

ATT , CONT

INT

T , RISK , PU , EOU

COMPANY, PROFIT

COMPANY SIZE , SKILLS , AGE OF

PU , EOU , CONT , SN

ATT , PERCEIVED ENJOYMENT,

PERCEIVED EXPRESSIVEDNESS ,

Q UALITY, PU , LOYALTY INCEN TIVES → INT

FERENCES

MOTIVATION , INDIVIDUAL DIF -

SIC MOTIVATION , EXTRINSIC

COMPLEXITY, RA , RC , INTRIN -

COMPATIBILITY, TRIALABILITY,

RISK , RELATIVE ADVANTAGE ,

Antecedents on INT or B

students

students

students / consumer

organisations

students

consumers

customers

Subjects

inexperienced users

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

mixed sample of experienced and non-experienced users

comparison experienced vs. inexperienced users

Subject’s Experience Level

3.1 Theoretical Foundations of Technology Adoption 55

IPTV services

online banking

computing services

computing services

IS-systems

IS-systems

Shin (2009)

Suh and Han (2003)

Taylor and Todd (1995b)

Taylor and Todd (1995a)

Venkatesh (2000)

Venkatesh and Bala (2008)

to be continued on the next page. . .

IS usage

intra-firm

intra-firm

B2C

B2C

B2C

B2C

B2B

Study Context

Premkumar and Roberts (1999)

Authors

→ INT

→B

TAM

TAM

TPB , TAM

→B

→ INT ;

cross-sectional combined with actual usage data

cross-sectional

cross-sectional

cross-sectional

Method

PU , EOU

PU , EOU

longitudinal

longitudinal

→ INT

→ INT ; INT → B

PU , SN , CONT → INT ; INT , CONT cross-sectional combined with →B actual usage data over a period

INT , CONT

DTPB : ATT , SN , CONT

TRA / TPB SUPPORTED ,

TAM , TPB , DTPB

ATT , T

→ INT ; INT → B

PLAYFULNESS

ATT , SECURITY, PU ,

VERTIKAL LINKAGE

PRESSURE ,

COMPANY SIZE , COMPETITIVE

MENT SUPPORT, IT EXPERTISE ,

COMPATABILITY, TOP MANAGE -

RELATIVE ADVANTAGE , COST,

Antecedents on INT or B

TRA

TAM

—

Base Theory

employees

employees

students

students

consumer

consumers

organisations

Subjects

comparison experienced vs. inexperienced users

comparison unexperienced and experienced users (after 1 and 3 months)

comparison experienced vs. inexperienced users

mixed sample of experienced and non-experienced users

experienced users

early adopters

comparison adopter vs. nonadopter

Subject’s Experience Level

56 3. Theoretical Framework for Remote Service Adoption and Continued Usage

intra-firm

intra-firm

B2C

Venkatesh et al. (2003) IS usage

Venkatesh, Speier, and IS usage Morris (2002)

e-recommendation agents

online shopping

t-commerce

Wang and Benbasat (2005)

Yoh et al. (2003)

Yu et al. (2005) TRA

TRA

TAM

TAM

→ INT ;

T

→ INT

ATT , SN , ENJOYMENT, PU ,

→ INT

→ INT

→ INT ; INT → B

ATT , SN , PB

PU , T

EOU , PU

INT , FACILITATING CONDITIONS →B

EFFORT EXPECTANCY

PERFORMANCE EXPECTANCY,

...

TAM , TPB , IDT, SCT

SN , PU , EOU

→ INT ; INT → B

Antecedents on INT or B

TAM

Base Theory

cross-sectional

cross-sectional

experimental

longitudinal

longitudinal (multiple studies)

longitudinal

Method

consumer

consumer

students

employees

employees

employees

Subjects

PU :

comparison experienced vs. inexperienced users

mixed sample of experienced and non-experienced users

inexperienced users

comparison experienced vs. inexperienced users

mixed sample of experienced and non-experienced users

comparison experienced vs. inexperienced users

Subject’s Experience Level

usefulness/perceived usefulness; SAT: satisfaction; TR: technology readiness; RA: role ability; RC: role clarity; PB: prior behavior; LOC: locus of causality.

Legend: ATT: attitude; CONT: control; EOU: ease of use; B: behavior; INT: intention; T: trust/trustworthiness; SN: subjective norms;

B2C

B2C

intra-firm

Study Context

IS usage

Venkatesh and Davis (2000)

Authors

3.1 Theoretical Foundations of Technology Adoption 57

58

3. Theoretical Framework for Remote Service Adoption and Continued Usage

3.2

Theoretical Foundations of Interaction in the Service Encounter

In this section, the most important concepts concerning customer-provider interaction in a service encounter, service provider employee characteristics, and the customer’s view of coproduction of the service are discussed.

3.2.1

Perceptions of Service Providers’ Employee Behavior

3.2.1.1

Importance of Employee Behavior in the Service Encounter

Service marketing literature has argued that the service process, or service encounter, may be the most important antecedent in customer evaluation of service performance (Brown and Swartz 1989; Lehtinen and Lehtinen 1982). Bitner, Brown, and Meuter (2000) describe service encounters as critical moments of truth in which customers develop lasting impressions of a firm. Often, a service encounter even resembles the service from the customer’s point of view (Bitner, Booms, and Tetreault 1990). During the service encounter, the customer receives an idea of the organization’s level of service provision (Bitner et al. 1997). Because of the intangible and interactive nature of services, customers often rely on the behavior of service provider employees when judging a service (Lehtinen and Lehtinen 1982). Consequently, the employees’ behavior is an important lever for service firms’ economic success as it is the main driver of the customer’s evaluation of the service encounter (Bettencourt and Gwinner 1997; Gremler and Gwinner 2000; Schneider and Bowen 1985), perception of service recovery (Liao 2007), loyalty (Castro, Armario, and Ruiz 2004), satisfaction (Adsit et al. 1996; Bitner, Booms, and Tetreault 1990; de Ruyter and Wetzels 2000; Solomon et al. 1985; van Dolen et al. 2002), service quality (Zeithaml 1988; Lehtinen and Lehtinen 1982), and well being (Ma and Dubé 2006). Using a critical incident method, Bitner, Booms, and Tetreault (1990) explore customer-employee interactions in restaurants, hotels, and airlines. They analyze 700 incidents within these settings and find that the factors affecting the evaluation of the service encounter can be classified into three main categories. 1. Employee response to service delivery failure: The content or the form of the employee response to events such as unavailable service, unreasonably slow service, or other core service failures determines the customer’s perceived satisfaction or dissatisfaction. 2. Employee response to customer needs and requests: This is the way the employees respond to customer needs and requests such as special needs of the customer, voiced preferences, admitted customer error, or to potentially disruptive others.

3.2 Theoretical Foundations of Interaction in the Service Encounter

59

3. Unprompted and unsolicited actions by employees: These are events and employee behaviors that are truly unexpected from the customer’s point of view such as attention paid to customers. These "out-of-the-ordinary" employee behaviors can either be pleasant and satisfactory for the customer or negative and unacceptable. Beyond this categorization, there is no comprehensive framework on distinctive employee behavior characteristics that are relevant for the customer’s perception in a service encounter. Some studies, however, do examine selected characteristics: rapport (Gremler and Gwinner 2000); attentiveness, common grounding, courteous behavior, connecting, and information sharing (Gremler and Gwinner 2008); effort (Mohr and Bitner 1995); active listening (Agrawal and Schmidt 2003); professional appearance (Gatewood and Feild 1998; Nguyen and Leblanc 2002); competence, helpfulness, and sociability (Surprenant and Solomon 1987); intimacy and humanity (Kellogg and Chase 1995); and the authenticity of employee’s emotional labor display (Henning-Thurau et al. 2006). Crosby, Evans, and Cowles (1990) look at the effect of perceived similarity between customer and provider, the provider employee’s service domain expertise, and his relational selling behavior such as interaction intensity, mutual disclosure, and cooperative intentions index on the buyer-seller bond. They conclude that the ability to convert sales opportunities into sales hinges on the customer’s perception of similarity and expertise. Further, relational selling behaviors such as cooperative intentions, mutual disclosure, and intensive followup contact generally produce a strong buyer-seller bond. Zolkiewski et al. (2007) give a good overview of service provider employee’s characteristics, which have been explored in a B2B context including responsiveness, complaint handling, experience, and efficiency. Wang and Davis (2008) identify extra-role behavior of the service provider employee and job tenure of a provider employee as a cause for perceived relationship quality inbalance of the customer, meaning that the customer thinks the contact employees of the provider are more important in delivering positive service experience than the firm (Wang and Davis 2008). Sundaram and Webster (2000) focus their conceptual work on how non-verbal behavior of employees might affect customer perceptions of the service provider employee’s characteristics such as friendliness, courtesy, empathy, competence, and credibility. They present a model that highlights the role of non-verbal communication in service interactions. Non-verbal communication cues have been grouped into four major categories: kinetics, paralanguage, proxemics, and physical appearance. Kinesics refer to body movements such as eye contact or nodding. Paralanguage refers to noncontent or nonverbal aspects of a message such as vocal pitch, loudness, pauses. Proxemics refer to the distance and relative postures of the interactants. The model further suggests that both verbal and non-verbal elements of communication between the service provider employee and the customer influence customers’ subjective feelings, which in turn influence their evaluation of the service encounter (Sundaram and Webster 2000).

60 3.2.1.2

3. Theoretical Framework for Remote Service Adoption and Continued Usage Customer Orientation of Employees

The concept of customer orientation emphasizes the importance of employee behavior and has gathered some momentum in the research community over the last decade (Brown et al. 2002; Dean 2007; Donavan, Brown, and Mowen 2004; Hennig-Thurau and Thurau 2003; HennigThurau 2004; Rafaeli, Ziklik, and Doucet 2008; Schneider, White, and Paul 1998; Voon 2006). There is no common understanding of how an employee shows customer orientation; most researchers do describe customer orientation more indirectly as general tendency of behavior or actions towards the customer. Brown et al. (2002, p.111) define customer orientation as "an employee’s tendency or predisposition to meet customer needs in an on-the-job context." Rafaeli, Ziklik, and Doucet (2008) relate customer orientation to the concept of organizational citizenship behaviors and refers to it as a predisposition to behave in ways that promote customer goals, and hence is expected to promote organizational effectiveness. Brady and Cronin Jr. (2001) state that customer orientation of a service provider is expressed through the behavior of service provider employees such as a willingness to go "above and beyond" or to "go the extra mile." Dean (2007) defines perceived customer orientation as the extent to which customers believe that the service provider employee is committed to understanding and meeting their needs and makes an effort to seek their opinions and monitor their feelings. Voon (2006) proposes a "proservice" construct that comprises perceptions of the employee’s customer focus, customer feedback, and evaluations of quality and after-sales service for perceived customer orientation. Hennig-Thurau and Thurau (2003) and Hennig-Thurau (2004) more explicitly describe and define customer orientation as the employee’s behavior in person-to-person interactions and identify three dimensions of customer orientation: the employee’s customer-oriented skills, the motivation to serve customers, and the self-perceived decision-making authority. According to Hennig-Thurau (2004), customer-oriented skills of an employee (COSE) encompass technical skills and social skills. Technical skills can be understood as the knowledge and motor skills a service provider employee must possess in order to fulfill the customer’s needs during a personal interaction process (Argyle and Kendon 1967). The concept of social skills refers to empathy, the ability of a service provider employee to take the customer’s perspective during interactions (JinJuan Feng, Lazar, and Preece 2004). Research shows that COSE positively affects customer satisfaction, commitment and customer retention (Hennig-Thurau 2004). The COSE concept is shown in figure 3.10.

3.2.1.3

Role of Employee Behavior in Service Quality Assessments

Service quality has been discussed in many studies and most of these indirectly address the behavior of the provider employees (e.g. Grönroos 1982; 2001). For example, in their comprehensive framework on service quality in traditional face-to-face service encounters, Dabholkar,

3.2 Theoretical Foundations of Interaction in the Service Encounter

61

 
 


 



 

 
  


 

 



 


Figure 3.10: The Customer Oriented-Skills of an Employee (COSE) Source: Own Illustration, based on Hennig-Thurau (2004, p. 464) Shepherd, and Thorpe (2000) include personal attention and comfort provided by a service provider employee as components of service quality. Parasuraman, Zeithaml, and Berry (1985) and Zeithaml, Berry, and Parasuraman (1988) developed the SERVQUAL scale. It is one of the most prominent examples of service quality measurement that includes employee behavior. Originally derived from focus group research, they identified ten criteria used by consumers in assessing service quality, which are shown in figure 3.11. Seven from those ten criteria pertain to employee behavior such as credibility, communication, understanding of the customer, reliability, responsiveness, competence, and courtesy (Lovelock and Wirtz 2007).

 


  


 







 


 



 


 


 


  
 


 
 






 


 


  





 


 

 




 


  
 
 
  


 




 

 





 


   


  


   


  


 


 


  

 
  


 
  

 
   


 

 


 
  

  


 

 
 



 


 




 
 

 

 


  
  
  

   

 
  


Figure 3.11: Generic Dimensions to Evaluate Service Quality Source: Own Illustration, based on Parasuraman, Zeithaml, and Berry (1985); Lovelock and Wirtz (2007, p. 421)

62

3. Theoretical Framework for Remote Service Adoption and Continued Usage

These criteria are consolidated into five broad dimensions that form the basis for the SERVQUAL scale (Parasuraman, Zeithaml, and Berry 1985; Zeithaml, Berry, and Parasuraman 1988): 1. Tangibles: physical facilities, equipment, and appearance of personnel 2. Reliability: ability to perform the promised service dependably and accurately 3. Responsiveness: willingness to help customers and provide prompt service 4. Assurance: knowledge and courtesy of employees and their ability to inspire trust and confidence 5. Empathy: caring, individualized attention the firm provides its customers Empirical support for SERVQUAL-based service quality measures applied to technology-based contexts has been uneven (Van Dyke, Prybutok, and Kappelman 1999; Carr 2002; Kettinger and Lee 2005). Although new scales are constantly developed, refined, and often specifically adapted to an online context (e.g., Kettinger and Lee 2005; Parasuraman, Zeithaml, and Malhotra 2005; Wolfinbarger and Gilly 2003), these scales merely focus on e-commerce or website services where hardly any service provider employee interaction with a customer takes place. For example, the E-S-QUAL scale (Parasuraman, Zeithaml, and Malhotra 2005) focuses on web site features. It refers to the dimension of reliability merely in terms of technical parameters consisting of correct technical functioning of the site. It also refers to the responsiveness dimension, e.g., the quickness of response is measured. On the other hand, the SERVQUAL dimension "ability" refers to human interaction partners in face-to-face encounters and assesses the customer’s experience with the service provider employee’s behavior, the ability dimension of E-S-QUAL focuses on ability to get on the site quickly and to reach the company when needed.

3.2.1.4

Employee Behavior in Technology-Mediated Service Encounters

Recently, in operations strategy a small number of studies have addressed the customer’s perception of the service provider employee in technology-mediated service encounters. Froehle and Roth (2004) offer a conceptual framework for customers’ technology-mediated service experience including the attitude towards the contact with the service representative, towards the contact medium, and towards the service provider as antecedents for service usage and medium choice. The Belief-Attitude-Intention (B-A-I) Framework, which is shown in figure 3.12, is only validated with respect to the existence of the factors in a call center scenario. The relationships between the factors are not yet empirically validated. Nevertheless, in this framework, attitude towards a service representative contact is affected by five belief groups: information richness belief; learning belief; usefulness belief; duration; and

3.2 Theoretical Foundations of Interaction in the Service Encounter

 
 
 
    
   


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Figure 3.12: B-A-I Framework of Technology-Mediated Customer Service Source: Own Illustration, based on Froehle and Roth (2004, p. 4) intimacy appropriateness beliefs. According to Froehle and Roth (2004), the information richness belief construct taps the customer’s cognitive assessment of the complexity and vividness of the communication between the provider and the customer. This is in line with the original description of media richness offered by Daft and Lengel (1983; 1986). A customer’s learning belief is defined as the idea that he increases his own knowledge, capacity for understanding, or perspective-taken during the contact episode (Boland Jr. and Tenkasi 1995). The usefulness belief refers to the degree to which the contact with the service representative fulfills the customer’s perceived needs and desires. Whereas duration appropriateness beliefs represent the customer’s belief about the duration of the contact episode, the intimacy appropriateness belief relates to the level of "mutual confiding and trust" established during the customer employee encounter. Based on Frohele and Roth’s (2004) framework Theotokis, Vlachos, and Pramatari (2008) identified three dimensions of perception of a customer-technology contact (CTC): time of interaction with the technology; information richness; and IT/media sophistication. Theotokis, Vlachos, and Pramatari (2008) showed a moderating effect of a CTC construct on the relationship between technology readiness (Parasuraman 2000)7 and attitude towards technology-based services. Froehle (2006) proposes an empirically validated model of the influence of customer service representative (CSR) behavior on customer satisfaction as shown in figure 3.13. Based on a qualitative study with employees of an international Internet service provider, he identifies six customer service representative characteristics. These characteristics are associated with communication, task-oriented behavior, and relationship building: 1. Courtesy refers to being well-mannered, polite, and considerate to customers; 2. Professionalism refers to the employees’ professional behavior and appearance; 3. Attentiveness refers to paying careful attention to individual customers such as active 7

The technology readiness concept will be presented and discussed in chapter 3.2.3.2.

64

3. Theoretical Framework for Remote Service Adoption and Continued Usage listening;

4. Knowledgeableness refers to an employee’s state as trained, up-to-date, and educated with respect to the details of their functions and their firms’ products and services;

5. Preparedness refers to an employee’s solid preparation for the task at hand;

6. Thoroughness represents the desire and effort associated with ensuring that the customer’s issue is addressed completely and systematically, and that the support task is fully executed.

The customer representative characteristics mirror some of the critical dimensions of service quality underlying the SERVQUAL instrument (Parasuraman, Zeithaml, and Berry 1985). In his emipirical study, Froehle (2006) supports the effects of knowledgeableness, preparedness, and thoroughness on satisfaction.





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Figure 3.13: CSR Characteristics and their Effects on Customer Satisfaction Source: Own Illustration, based on Froehle (2006, p. 20)




3.2 Theoretical Foundations of Interaction in the Service Encounter

3.2.2

Customer Integration in the Service Process

3.2.2.1

Research on Customer Co-Production

65

The concept of customer integration has recently become a major topic of discussion in services marketing thought and practice worldwide. Customer integration is an essential part in the service production process (e.g., Bendapudi and Leone 2003; Bettencourt et al. 2002; Corsten 2001; Lovelock and Wirtz 2007; Lovelock and Young 1979; Prahalad and Ramaswamy 2004b; Wikstrom 1996). Research has examined the importance of active customers for productivity gains (e.g., Fitzsimmons 1985; Lovelock and Young 1979; Mills, Chase, and Marguiles 1983) and for competitive effectiveness (e.g., Bendapudi and Leone 2003; Prahalad and Ramaswamy 2004b;a). Various studies focus on explaining antecedents and consequences of co-productive behavior and examine service perception loyalty, customer satisfaction, quality and recovery (e.g., Auh et al. 2007; Bendapudi and Leone 2003; Bettencourt 1997; Dabholkar 1990; Kelley, Donnelly, and Skinner 1990; Kellogg, Youngdahl, and Bowen 1997; Lengnick-Hall 1996). Prahalad and Ramaswamy (2004b) advocate co-opting customer competence as a competitive strategy. Customer participation in service co-production has been shown to be a very sensitive issue because participation can lead to a self-serving bias and affect customer satisfaction (Bendapudi and Leone 2003). In the past, many scholars studied the process through which customers participate in the service delivery process as "partial employees" and have discussed ways of managing such consumers (Bitner et al. 1997; Kelley, Donnelly, and Skinner 1990; Mills, Chase, and Marguiles 1983). Sometimes the term customer participation is used instead of customer integration. Dabholkar (1990, p.484) defines customer participation as "the degree to which the customer is involved in producing and delivering the service." Groth (2005) defines co-production as "those behaviors that customers need to perform in order to complete the service delivery." According to the service dominant logic (Vargo and Lusch 2004), the customer is always a co-creator. Lusch and Vargo (2006a) further distinguish the terms co-creation of value and co-production. The co-creation of value takes place in consumption acts (Lusch and Vargo 2006a), whereas coproduction occurs in "the core offering itself" (Lusch and Vargo 2006b, p.284). Though Lusch and Vargo define them as two separate constructs, they acknowledge that the two terms are linked as "nested concepts" with the co-production being a subordinate concept to that of cocreation of value. Within the Facilities Transformation Usage framework (FTU), Moeller (2008) identifies three stages of service provision: facilities, transformation, and usage. The stages are based on the distinctions between direct and indirect service provision (Vargo and Lusch 2004), and between co-production and co-creation (Lusch and Vargo 2006b). Bitner et al. (1997) propose a framework that examines three levels of participation required

66

3. Theoretical Framework for Remote Service Adoption and Continued Usage

of customers and providers across a variety of service contexts. They illustrate the different levels with several examples for both end consumers and business-to-business customers. The framework is shown in figure 3.14. According to this framework, the level of customer participation required in a service experience varies across services and ranges from a low to a high level of participation. In services with a low level customer participation, only the customer’s physical presence is required while the provider employees are doing all of the service production work. If the service requires a moderate level of participation, customer input aids the service organization in creating the service with information or physical-possessions sharing. In a situation with a high level of customer participation, customers can be involved in co-creating the service by performing tasks, compliance, and training. Characteristic for such services is that unless the customer does something, the service provider cannot effectively deliver the service outcome. The service outcome is highly dependent on customer participation (Bitner et al. 1997).

3.2.2.2

Drivers of Customer Co-Production

There are only a few comprehensive studies on the customer’s motivation or willingness to coproduce a service. Most studies only address the customer’s perception of co-production situations and focus on single beliefs and emotions (e.g., Auh et al. 2007; Bendapudi and Berry 1997; Dabholkar 1990). One exception is Etgar (2008) who proposes a five stage dynamic model of consumer involvement in co-production. Within this phase model, he comprehensively discusses drivers and barriers of customer’s willingness to co-produce. According to Etgar (2008), the co-production process includes five distinct stages: development of antecedent conditions;

  
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Figure 3.14: Levels of Customer Participation across Different Services Source: Own Illustration, based on Bitner et al. (1997, p. 194)

3.2 Theoretical Foundations of Interaction in the Service Encounter

67

development of motivations which prompt consumers to engage in co-production; calculation of the co-production costs versus benefits; activation when consumers become engaged in the actual performance of the co-producing activities; and generation of outputs and evaluation of the results of the process. One of the most prominent approaches to explaining customer participation behavior in service marketing literature is based on Vroom’s (1964) model of determinants of employee behavior. This theory is rooted in the human resources research and industrial psychology and identifies a set of determinants comprising role clarity, ability, and motivation as drivers of employee behavior. Bowen (1986) and Schneider and Bowen (1985) first adapt his model and identify it as a framework for explaining customer’s co-production behavior. According to Bowen (1986), customer behavior can be viewed as being shaped by three considerations: 1. whether customers understand how they are expected to perform; 2. whether they able to perform as expected; and 3. whether there are valued rewards for performing as expected. The drivers of customer co-production behavior are empirically validated in studies on service settings such as YMCA participation (Lengnick-Hall, Claycomb, and Inks 2000), health care services (Dellande, Gilly, and Graham 2004), and self-services (Meuter et al. 2005). Meuter et al. (2005) refer to this set of drivers as "consumer readiness" in form of a condition or state in which a consumer is prepared and likely to use an innovation for the first time" (see chapter 3.2.3.1). Role clarity reflects the customer’s knowledge and understanding of what kind of participation needs to take place. The rationale is that if customers know what to do and how they are expected to perform, they are more likely to do what is needed (Mills, Chase, and Marguiles 1983). Indirectly, this expresses a need to inform customers about the activities and behaviors that are needed for an effective service encounter (Schneider and Reichers 1983; Kelley, Donnelly, and Skinner 1990). A customer’s ability to perform needed tasks is similar to the self-efficacy concept (see chapter 3.1.2.4). Role ability relates to possessing the required skills and confidence to complete the tasks necessary during the participation (Meuter et al. 2005). Effective co-production requires customers who are capable of making useful and timely contributions to an organization’s activities (Schneider and Bowen 1985). Customer ability refers in particular to expertise capacity (Lusch et al. 1992), customer efficiency (Xue and Harker 2002), or dialogical capability. Ballantyne and Varey (2006) deem dialogical capabilities as a process of learning together rather than just an exchange of information. Prahalad and Ramaswamy (2004b) propose that customer ability improves through a process of repeated usage.

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3. Theoretical Framework for Remote Service Adoption and Continued Usage

Because co-production is an explicit result of decision making by customers reflecting their own preferences (Etgar 2008), co-producers must not only know what to do and be able to perfom useful tasks, they must also be willing to make direct contributions to various organizational activities (Kelley, Donnelly, and Skinner 1990; Lovelock and Young 1979; Schneider and Bowen 1985). In this context, motivation refers to a desire to receive the rewards associated with participation. It is closely tied to the concept of value the customers sees in participation. Holbrook (2006) suggests that customers’ values could be divided into two types: intrinsic and extrinsic. Intrinsic values imply that an experience is appreciated for its own sake, while extrinsic values serve as means to an end. In addition to contributing to their own satisfaction by improving the quality of service delivered to them, intrinsic motivation can refer to the fact that some customers simply enjoy participating in the service delivery process (Bateson 1985; Dabholkar 1996; Schneider and Bowen 1985). Feelings of accomplishment, prestige, convenience, greater control over the service outcome, personal growth, or mere pleasure from engaging in the activity are intrinsic motivational factors (Meuter et al. 2005). In addition, intrinsic motivation for co-production may simply be that consumers learn and master new skills and techniques. This in turn offers new challenges to customers to express themselves in a unique way (Hirschman 1980; Tian, Bearden, and Hunter 2001), to exercise and use personal capabilities not involved in their daily routines, and to realize hidden fantasies (Hirschman and Holbrook 1982). Extrinsic motivation refers to drivers of co-production behavior such as own self-interests (Schneider and Bowen 1985) and economic rewards (Lusch, Brown, and Brunswick 1992). Extrinsic motivation can be facilitated by a price discount, time savings, and the timing of delivery (Dabholkar 1996). In addition, Etgar (2008) suggests that cost reduction of the performance of a given activity can be a major motivator. In addition to the "consumer readiness" drivers, relational aspects influence customer’s willingnessto-coproduce. Social interaction and behavior are determinants of the relationship between the consumer and the service provider. They can facilitate cooperation and lead to more participation of consumers in the service process (Etgar 2008). Research by Kelley, Donnelly, and Skinner (1990) on customer participation in service provision shows the importance of empathy. Geyskens, Steenkamp, and Scheer (1996) and Lusch et al. (1992) point out the importance of trust for customer participation behavior. At the same time, however, co-production can create risks for service providers. The dangers of misperformance of relevant tasks by the consumer due to lack of the required skills, the threats of potential conflicts with the performance partners on the customer side, or dangers of legal entanglements and complications are some of these risks (Etgar 2008). Consumers also associate risks with the co-production of a service. These hazards can be physical, financial, psychological, performance, social, and time-related (Stone and Gronhaug 1993).

3.2 Theoretical Foundations of Interaction in the Service Encounter

3.2.3

69

Customer Beliefs Regarding the Interaction with Service Technology

3.2.3.1

Consumer Readiness as Driver of Technology-Mediated Co-Production

Building a bridge between research on innovation diffusion and technology-intensive services, Meuter et al. (2005) explored the key factors that influence the initial trial decision of consumers when adopting self-service technologies. Core constructs of the model are customer readiness variables, which are a set of drivers that comprise the beliefs role clarity, role ability, and motivation. These variables are based on Bowen’s (1986) model of determinants of employee behavior and adapted to the customer’s motivation to use a self-service (see section 3.2.2.2). As shown in figure 3.15, these variables directly affect trial behavior as a step in the adoption process as proposed by Rogers (2003). They show that customer readiness variables also mediate the effect of antecedent predictors such as Rogers’ innovation characteristics, perceived risk, and individual differences of customers such as inertia, technology anxiety, need for interaction, previous experience, and demographics. Meuter et al. (2005) provide empirical support for the overall effectiveness and relative strength of the so-called consumer readiness variables as a group. In detail, role clarity and extrinsic motivation are the two strongest consumer readiness predictors. Based on the set of consumer readiness variables, Ho and Ko (2008) develop a single consumer readiness construct and empirically validate the effect of the customer readiness construct on perceived value and continued usage intention of self-services.

3.2.3.2

Technology Readiness as a Driver of Technology Usage in Services

Not only does the willingness-to-participate influence a customer’s decision to use technologymediated services, individual personality traits about the technology can also hinder him to use a service. In this context, Parasuraman (2000) developed the technology readiness index (TRI). This concept is based on the assumption that although new technology-intensive products and services are proliferating fast, there is also evidence of increasing customer frustration in dealing with technology-based systems. Technology readiness (TR) conceptualizes consumers’ general beliefs about technology associated with their use of technology-based products and services. The construct refers to "people’s propensity to embrace and use new technologies for accomplishing goals in home life and at work" (Parasuraman 2000, p.308). It can be viewed as a person’s predisposition to use new technologies in products and services.

70

3. Theoretical Framework for Remote Service Adoption and Continued Usage

  
 


  
  


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Figure 3.15: Key Predictors of Consumer Trial of Self-Service Technologies Source: Own Illustration, based on Meuter et al. (2005, p. 63) The concept has been developed to explore customers’ individual readiness levels. The measurement of technology readiness is designed as an index that comprises four sub-dimensions: 1. Optimism relates to a positive view of technology and a belief that technology offers people increased control, flexibility, and efficiency; 2. Innovativeness refers to a tendency to be a technology pioneer and thought leader; 3. Discomfort consists of a perception of lack of control over technology and a feeling of being overwhelmed by it; 4. Insecurity involves distrust of technology and skepticism about its ability to work properly. The basic idea of the TRI is that people can be placed along a hypothetical technology beliefs continuum anchored by strong positive attitudes towards technology at one end and strong negative at the other. Moreover, people’s positions on this continuum can be expected to correlate with their propensity to embrace and employ technology, i.e., their technology readiness (Parasuraman 2000). Consumers’ TR has been shown to have a positive impact on online service quality perceptions, online behaviors and perceived risk (Lam, Chiang, and Parasuraman 2008; Zeithaml, Parasuraman, and Malhotra 2002), or to be a moderator on the relationship of technology design features on perceived control and interface evaluation (Zhu et al. 2007). Massey, Khatri, and MontoyaWeiss (2007) use the technology readiness construct to differentiate online customers based upon underlying positive and negative technology beliefs. They show that different customer segments vary in usability requirements and usability evaluations of specific online service in-

3.3 Transcending Concepts of Trust and Control across Disciplines

71

terfaces. Lin, Shih, and Sher (2007) integrate technology readiness and TAM in the context of consumer adoption of e-service systems and formed a new technology adoption model called TRAM (technology readiness and acceptance model). The results indicate that TRAM substantially broadens the applicability and the explanatory power of either of the prior models.

3.3

Transcending Concepts of Trust and Control across Disciplines

3.3.1

Importance of Trust and Trustworthiness

Trust has been a central construct in many fields of behavioral and economic sciences. This led to a conceptual diversity that is reflected in research on trust in a number of disciplines including the marketing field (e.g., Morgan and Hunt 1994; Crosby, Evans, and Cowles 1990), IT (Gefen 2002b; Pavlou 2003), and management literature (e.g., Dirks and Ferrin 2001; Rousseau et al. 1998). Despite different interdisciplinary view points on trust, most scholars agree that "trust is essentially a psychological state that manifests itself in the behavior toward others" (Costa and Bijlsma-Frankema 2007, p. 395). Moorman, Deshpande, and Zaltman (1993) define trust as a willingness to rely on a partner in whom one has confidence. People trust others based on assumptions that these others will behave in a certain way and that this will provide them with an expected desirable outcome (Mayer, Davis, and Schoorman 1995). Studies show that a higher degree of trust in an interaction partner leads to higher willingness to take risks (Mayer, Davis, and Schoorman 1995) and a higher tolerance for vulnerability to the actions of others (Rousseau et al. 1998). The growing importance of relationship marketing has heightened interest in trust as a means to foster strong relationships (Garbarino and Johnson 1999; Gundlach and Murphy 1993; Morgan and Hunt 1994; Sirdeshmukh, Singh, and Sabol 2002). In line with this paradigm, research shows that trust determines the nature of many interpersonal buyer-seller and business relationships (Doney and Cannon 1997; Ganesan 1994; Kumar, Scheer, and Steenkamp 1995). Sirdeshmukh, Singh, and Sabol (2002) explore trust in a service context and developed a framework for understanding the behaviors and practices of service providers that build or diminish consumer trust and trustworthiness. In line with mainstream research, they examine trustworthiness beliefs in interpersonal relationships in regard to face-to-face contacts (Mayer and Davis 1999; Rempel, Holmes, and Zanna 1985) and support a link between trustworthiness and value creation. The reliable, knowledgeable, and benevolent behavior of a service provider employee has been shown to be a key determinant of relationship building and service perception in service marketing literature (e.g., Crosby, Evans, and Cowles 1990; Doney and Cannon 1997). Trust in adoption of technology-intensive services has mostly been researched as "trust" in a

72

3. Theoretical Framework for Remote Service Adoption and Continued Usage

website or "trust" in the service provider and their effect on a customers’ service usage intention (e.g., Gefen, Karahanna, and Straub 2003b; Wang and Benbasat 2005). In contrast to the relational view on trust or trust building behavior, these studies mostly explore the consequences of a set of specific beliefs about the trustee, called trustworthiness (Gefen 2002b; Gefen and Straub 2004; Mayer, Davis, and Schoorman 1995). Trustworthiness of a person is examined as a set of specific beliefs about the other party and deals with beliefs about relevant combinations of its integrity, benevolence, and ability (Doney and Cannon 1997; Gefen 2002b; Gefen and Silver 1999; Greenberg, Greenberg, and Antonucci 2007; Hwang and Kim 2007; McKnight, Choudhury, and Kacmar 2003; Larzelere and Huston 1980). Integrity beliefs refer to a customer’s perception that the trustee adheres to a set of principles that the customer finds acceptable (Greenberg, Greenberg, and Antonucci 2007). Mayer, Davis, and Schoorman (1995) describe ability beliefs as a group of skills, competencies, and characteristics that enable a person to have influence within some specific domain. Benevolence beliefs refer to the extent to which one party or his proxy is believed to do good, show sensitivity to the needs of the other party, and not take economic advantage of the other party (Atuahene-Gima and Li 2002). There is extensive research on the trustworthiness of a service provider in an online context, e.g., trustworthiness has been shown as a driver of usage intention and usage in mobile services (Dahlberg, Mallat, and Öörni 2003), e-services (Bart et al. 2005; Gefen, Karahanna, and Straub 2003b; Gefen and Straub 2004; Pavlou 2003), and online recommendation systems (Wang and Benbasat 2005). These studies focus on trust towards an organizational unit, e.g., to an evendor. Only a few articles address the importance of interpersonal trust in technology-mediated services (Friedman, Kan Jr., and Howe 2000; JinJuan Feng, Lazar, and Preece 2004). Friedman, Kan Jr., and Howe (2000) look specifically at trust in online interactions. They explore the nature of online trust and offered the following nine characteristics of online interaction that affect cultivating trust and that service providers or interaction partners should be aware of when interacting online: 1. Reliability and security of the technology: Insecurity stems e.g., from the fact that is it not possible to know for certain that some third party is not impersonating a web site. 2. Knowing what people tend to do online: Users fear viruses, hackers, and other users in disguise as they have only limited accurate information about the likelihood, the magnitude, and the frequency of potential harm. This means, that it is important for customers to know what the interaction partners do and this knowledge helps to cultivate trust. 3. Misleading languages and images: Terms like "secure connection" are misleading for customers as they do not know what level of security they can expect. Trust depends not only on assessing harm and good will but what to reasonably expect of the technology. 4. Disagreement about what counts as harm: Customers have difficulties of assessing the

3.3 Transcending Concepts of Trust and Control across Disciplines

73

harms that may occur from an online transaction/event and even define harm in different ways. For example, some people consider accessing another’s computer file without reading the contents a privacy violation, others do not. 5. Informed consent: Informed consent involves telling users of the potential harm or benefit of an online interaction and giving them the explicit opportunity to consent or decline to participate in the interaction. 6. Anonymity: Anonymity refers to the absence of identifying information associated with an interaction. On the one hand, anonymity can erode a climate of trust by making assessments of potential harm and good will of others more difficult. On the other hand, anonymity can help cultivate a climate of trust by putting in place greater safeguards. 7. Accountability: High degrees of anonymity provide significant challenges for accountability. By increasing accountability, providers often decrease anonymity and therefore increase violations of privacy and undermine personal autonomy. 8. Insurance: Insurance refers to social arrangements which "promise" to compensate individuals for future harm if it occurs. In e-commerce, insurance is often offered in terms of financial compensation such as by fully covering the cost of a credit card purchase that goes awry. 9. Performance history and reputation: in order to judge one’s vulnerability online, people assess performance history, including direct past experiences with the party in question, along with the reported experiences of others. Over time, as performance histories develop, users are better positioned to assess the magnitude and likelihood of potential harm.

3.3.2

Importance of Control Beliefs

Parallel to trust research, studies on the concept of control have been conducted in various disciplines such as social and environmental psychology, IS, marketing, and management. Control is widely accepted as a human driving force and has often been defined as the need to demonstrate one’s competence, superiority, and mastery over the environment (Faranda 2001; White 1959). Averill (1973) distinguishes between three different types of control: behavioral control; decisional control; and cognitive control. Behavioral control refers to the "availability of a response that may directly influence or modify the objective characteristics of an event" (Averill 1973, p. 293). Cognitive control is defined as "the way in which an event is interpreted, appraised, or incorporated into a cognitive plan" (Averill 1973, p. 287) and has been broken down into predictable and cognitive reinterpretation of a threatening situation or information. De-

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3. Theoretical Framework for Remote Service Adoption and Continued Usage

cisional control refers to "having a choice among alternative courses of action" (Averill 1973, p.286). Skinner (1996, p. 31) suggests that three sets of beliefs determine action: control beliefs that relate to generalized expectancies of whether or not one can prevent or produce desired events; strategy beliefs that refer to the generalized expectancies about whether certain causes or means are "sufficient conditions for the production of ends or outcomes"; and capacity beliefs that refer to the expectancies about the extent to which "he self possesses or has access to certain causes." Lusch, Brown, and Brunswick (1992) define consumers’ desire for control according to Skinner (1996) as the inherent feeling of being able to dominate one’s own environment as well as the need of being able to determine what will be the final outcome of the services one is about to use. In a service context, control often relates to Skinner’s definition of strategy beliefs that customers want to influence, or have control over, the specification of the service they desire (Van Raaij and Pruyn 1998). Hui and Bateson (1991) demonstrate the contribution of the perceived control concept by identifying consumer choice (whether it is a person’s own decision to enter into and stay in a service encounter) and consumer density (the number of consumers that are present in a service setting) as variables affecting consumers’ satisfaction via perceived control. Research has shown a positive effect of perceived control on consumer feeling and consumer satisfaction (Hui and Bateson 1991; Namasivayam 2004) and identified perceived non control as the reason for many negative service encounters and the triggering of negative thoughts (Bateson 1985; Hui and Bateson 1991; Hui and Toffoli 2002). According to the TPB (Ajzen 1991), one customer’s perceived behavioral control refers his own behaviors. The focus is on the behavior and not on the outcomes of such behavior (see chapter 3.1.1.3). Adaptions of the TPB to IT-acceptance context (e.g., the DTPB) break down perceived control of a customer within self-efficacy beliefs and the customers beliefs as to whether the facilitating conditions of his intended behavior will hinder or support him (see chapter 3.1.1.4). According to the latter interpretation, in IT-adoption literature, perceived control is often interpreted as a process control over the outcomes and procedure of the use of a technology or a technology-intensive service (Baron, Patterson, and Harris 2006; Lee and Allaway 2002; Dickinger and Kleijnen 2008). The technology-based services and self-services literature points to perceived control as a key aspect of SST effectiveness. The term perceived control describes a subjective assessment of control over a task in an environment. In an SST setting, it refers to a customer’s sense of mastery over the processes and outcomes of the service interface, which is often influenced by environmental conditions and consumer traits, such as self-efficacy in handling a specific technology (Zhu et al. 2007). According to Namasivayam (2004), control does not need to be actually exerted, the possibility

3.3 Transcending Concepts of Trust and Control across Disciplines

75

to control the provider’s representative is often perceived as sufficient. Perceived control is generally regarded as representative of actual control. Komiak, Wang, and Benbasat (2005) state that the feeling of being in control is a real perception. Further, this perception involves bysituational factors in the relationship such as the number of choices provided by the trustee, the tendency of the trustee to influence customers’ decision making, and the customers’ opportunity to express their needs. In organizational science and management literature, control has been viewed as a process that regulates behaviors of organizational members in favor of the achievement of organizational goals (Bradach and Eccles 1989; Cardinal, Sitkin, and Long 2004; Das and Teng 2001). The control literature suggests that there are two main approaches to control. One approach focuses on the establishment and utilization of formal rules, procedures, and policies to monitor and reward desirable performance, that is, formal control. The other approach focuses on informal or social control and emphasizes the regulatory power of organizational norms, values, culture, and the internalization of goals to encourage desirable outcomes (Das and Teng 2001).

3.3.3

The Trust-Control Nexus

Trust and control refer to highly complex forms of social relationships, and their interrelation only increases this complexity. In organizational science and management literature, the interrelation between trust and control is the subject of some research and has given rise to various, sometimes seemingly contradictory, interpretations on how trust and control relate (e.g., Anderson and Narus 1990; Bradach and Eccles 1989; Das and Teng 1998; Zaheer and Venkatraman 1995). Three main views dominate the understanding of the trust-control interrelation: a strict substitution, a strict complementary view, and a differentiated view. From a substitution point of view, trust and control are inversely related, that is, low trust requires formal control and high trust allows for limited formal control (e.g., Costa and BijlsmaFrankema 2007; Dekker 2004; Inkpen and Currall 1997; Williamson 1975). Conceptualizing trust and control as opposing alternatives is common in management sciences (Costa and Bijlsma-Frankema 2007; Knights et al. 2001). In addition, control is often believed to be detrimental to trust because every regulation implies a sense of mistrust (Argyris 1952). This duality of trust and control also means that in any given situation, the trading parties can either trust each other or they rely on functionally equivalent control mechanisms, procedures, and protocols that monitor and control the performance of a transaction (Bons, Lee, and Wagenaar 1999). The complementary point of view, on the other hand, argues that trust and control can be mutually reinforcing and contribute to the level of cooperation needed in a relationship (Sitkin 1995; Zucker 1986). Some researchers are of the opinion that objective rules and measures actually increase trust as these control mechanisms function as a "track record" for companies and em-

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3. Theoretical Framework for Remote Service Adoption and Continued Usage



 


  




  

   



 



  




  





 



Figure 3.16: The Integrated Framework of Trust, Control, and Risk in Strategic Alliances Source: Own Illustration, based on Das and Teng (2001, p. 257) ployees (Goold and Campbell 1987; Sitkin 1995). For example, Beamish (1988) suggests that to reach confidence in a co-operation, partners can use trust and control to complement each other. It has been noted that a minimum level of trust is needed for any economic transaction (Das and Teng 1998). Tan and Thoen (2000) claim that independent of the trust in the cooperating party, the trust control mechanism, which reflects the trust in procedures and mechanisms implemented by the provider to control the service counterpart’s behavior, has an influence on the perception of a transaction. Bons, Lee, and Wagenaar (1999) define a trustworthy trade procedure as a procedure that governs a transaction in which there is a risk of opportunistic behavior by one or more parties but that provides sufficient interorganizational control to limit the risk. In his research on trust building in virtual salesperson Komiak, Wang, and Benbasat (2005) conclude that when customers feel that they have more control over a trustee, this feeling builds trust, while the feeling of less control may build distrust. From a contextual-based approach, Das and Teng (1998) state that control and trust beliefs are parallel concepts that are not necessarily restricted to complement each other. They see trust and control both as interacting determinants of risk in strategic alliances as shown in figure 3.16. Das and Teng (2001) suggest that the relationship between trust and control can be either complementary or substitutive in nature depending on the type of control. Das and Teng (1998) distinguish between formal and social control and argue that the use of formal control in terms of strict rules and formal objectives restricts the autonomy of employees and might lead to an atmosphere of mistrust. In contrast, they postulate that informal control through social norms and shared objectives increases understanding between group members and leads to more trust. They also examine the effect that trust has on control and conclude that in the context of business

3.4 Technology-Intensive Service Adoption in B2B contexts

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alliances, trust is a necessary prerequisite to have affective control over a partner (Das and Teng 1998). Trust is effective to overcome the resistance in the controller and controllee relationship and prevents the partners from questioning the motives and competences of each other. Sengün and Wasti (2007) support these propositions while testing Das and Teng’s (2001) model in longterm supply agreements.

3.4 3.4.1

Technology-Intensive Service Adoption in B2B contexts Business Service Relationships

There are fundamental differences between an organization marketing to another organization, referred to as B2B marketing, and an organization marketing to consumers, referred to B2Cmarketing. In B2B marketing, theory suggests that specific and unique characteristics of organizational markets distinguish them from consumer markets (see also figure 3.17). Thee characteristics include: derived demand; reciprocity; number of buyers; size and volume per buyer; negotiating time; expertise; relative importance; number of decision makers; relationship development; and negotiating power (Gounaris and Venetis 2002; Malhotra and Birks 2007, p. 773). In B2B services, relationships are often more complex compared to service relations in B2Csettings. This is because not only organizations as such participate in service delivery, but also different employees of the service provider’s organization interact with the customer’s organization and their employees. A commonly used model to illustrate service relationships is the Triangle Model. Kotler (1994) developed the Triangle Model with B2C situations in mind. The model shows the different forms of relationships that take place between a customer, a service provider company, and its employees. The model is used frequently in the marketing literature

 


  







 
 


 





 
 





 

 



 
 
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Figure 3.17: Key Differences between B2C and B2B Marketing Source: Own Illustration, based on Malhotra and Birks (2007, p. 773)

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3. Theoretical Framework for Remote Service Adoption and Continued Usage

(Bhappu and Schultze 2006; Wang and Davis 2008) and involves three pairs of relationships: customer-to-provider employee relationships; customer-to-provider organization relationships; and provider employee-to-provider organization relationships. Iacobucci and Ostrom (1996) identify the structure and characteristics of three commercial dyads within B2B contexts on a company-to-company level: "individual-to-individual;" "individual-to-firm;" or "firm-to-firm." Wang and Davis (2008) examine the relations within the triangle model further and distinguish the exchange, service delivery, and employment (see figure 3.18a). According to the Triangle Model, the "service provider" is decomposed in the two independent service units employee and organization. The "service customer", however, is seen as one unit. I argue that in B2B service settings, it should be distinguished between both the customer’s and provider’s employees and their respective organizations. This rationale leads to six dyads instead of three. Therefore, in this thesis, a new model – the Square Model of B2B marketing – is presented to illustrate the different relationships within B2B-services, which comprises six dyads (see figure 3.18b): 1. customer employee-to-provider employee relationship 2. customer employee-to-provider company relationship 3. customer employee-to-customer company relationship 4. customer company-to-provider employee relationship 5. customer company-to-provider company relationship 6. provider employee-to-provider company relationship A service can be delivered on four different relationships between customer company, customer employee, provider company and provider employee. The relationship of the customer employee and the provider employee is especially important for understanding interactive remote services in an B2B environment, because the direct service encounter interaction involves the personal relationship between both employees. The internal link between the customer organization and its employees facilitates the flow of organizational norms and company culture as well as the influences from individual to organizational decision making.

3.4.2

Decision Making and the Adoption Process in Organizations

Premkumar and Roberts (1999) and Rogers (2003, p. 403) see the decision making process to adopt or reject innovations within an organization governed by authoritative or collective mechanisms. The decision must be made by a consensus among members of the organization or handed down from a few individuals with authority. Most often this will be the top management

3.4 Technology-Intensive Service Adoption in B2B contexts






 









  


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(a) The Triangle Model

  


 
 


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Figure 3.18: The Triangle and Square Model a) Own Illustration, based on Wang and Davis (2008, p. 534); b) Own Illustration (Premkumar and Roberts 1999). Rogers (2003, p. 403) adds optional decision making, which refers to the choices to adopt or reject an innovation, that made is by the individual. In line with these mechanisms, Frambach and Schillewaert (2002) identify two types of organizational adoption decisions. These are the decision made by an organization and the decision made by an individual within an organization. They propose that the adoption decision on a organizational level preceeds the individual acceptance in an organizational context. But also, the individual adoption decisions of members of an organization, e.g., the adoption of a new IT-system, are of great importance to the organizational adoption behavior. This is because if there is no acceptance among the personnel, the desired consequences cannot be realized, possibly leading the organization to eventually discontinue the intended adoption. Therefore, both adoption decisions are closely connected to each other. Adoption behavior in a B2B context has predominantly focused on intra-organizational adoption behavior such as computer usage (Agarwal and Prasad 1998; Davis, Bagozzi, and Warshaw 1989). The most influential model of intra-organizational acceptance is the TAM of (Davis, Bagozzi, and Warshaw (1989), see chapter 3.1.2.1). The influence of individual role players in an organization is reflected in classical models of organizational buying behavior for larger companies (Choffray and Lilien 1980; Sheth 1973; Webster Jr. and Wind 1972) and in research on decision-making in small and medium-sized firms (SME).8 The processes of decision making are generally not very formalized within SME (Salles 2006). Small companies have a flat organizational structure and therefore have fewer decision making levels (Metts 2008). Also, day-to-day interactions between employee and management may encourage a more informal dimension to participation that opens a door for employees to directly influence owner’s or management decision (Wilkinson, Dundon, and Grugulis 2007). Nevertheless, in contrast to large companies the entrepreneur (who acts as a general manager) has central role in decision-making in SME. Entrepreneurs have been shown to be the main locus 8

There is no common understanding on the definition of SEM. This thesis follows the definition of the IfM Bonn, that define SME as organizations having between 1 and 500 employees or less than Euro 50 Mill. annual turnover (Wallau 2006). Similar, Anderson and Schwager (2004) define SME within the US market as organizations with less than 500 employees.

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and driver of innovation in SME (Marcati, Guido, and Peluso 2008). Research on innovation adoption identified owners’ personality traits such as innovativeness, conscientiousness and poenness as well as individual perceptions such as ease of use, perceived usefulness, trust, and relationship quality as ultimately influencing organizational adoption behavior (Marcati, Guido, and Peluso 2008; Nguyen and Barrett 2006; Rauyruen and Miller 2007; Zhao and Seibert 2006). Because of their centralistic roles in organizational attitude forming and decision making, the owner or senior management with their individual belief sets and attitudes are often used in survey research to approximate organizational intention and adoption (e.g., Anderson and Schwager 2004; Grover and Goslar 1993; Marcati, Guido, and Peluso 2008; Palvia, Means, and Jackson 1994; Premkumar and Roberts 1999; Teo, Lin, and Lai 2008). In contrast to decison making in SME, B2B buying in larger companies is a complex sequence of activities carried out by a number of people within a firm (Marshall et al. 2007). The group of individuals making these purchasing decisions is called the "buying center" (Webster Jr. and Wind 1972). Webster Jr. and Wind (1972, p. 17) define a buying center as a "subset of the organizational actors" who operate as part of the total organization. This concept is supported by numerous empirical studies (e.g., Farrell and Schroder 1999; Gronhaug and Bonoma 1980; Lewin and Donthu 2005; Venkatesh, Kohli, and Zaltman 1995; Wind 1978). The behavior of members of the buying center reflects the influence of others as well as the buying task, the organizational structure, and the technology. This interaction leads to unique buying behavior in each customer organization. According to Webster Jr. and Wind (1972), a buying center includes five roles: users, buyers, influencers, deciders, and gatekeepers - members who control the flow of information (and materials) into the buying center. These roles are not strictly attributable to one individual, because several individuals may occupy the same role. According to the buying center concept, all organizational buying behavior can be traced to individual behavior as only an individual member of a group can define, analyze, decide, and act in buying situations(Webster Jr. and Wind 1972). The individual is motivated by a complex combination of personal and organizational objectives. Because his decisions are constrained by policies and information filtered through the formal organization and influenced by other members of the buying center, he can be viewed as a constrained decision maker (Webster Jr. and Wind 1972). Building on this structure of organizational decision making, Rogers (2003, p. 421) proposed an innovative process that consists of a sequence of five steps as shown in figure 3.19. Before the implementation phase, an organization often has a long phase of initiation, where it gathers information, conceptualizes, performs tests with the innovation, and plans for the adoption, leading up to an adoption decision. The implementation phase after the adoption decision comprises all of the events, actions, and decisions involved in putting the innovation into use. In this organizational process, it is notable that Rogers sees prior experience with the innovation through testing or trial not as adoption, but as part of a matching process (Rogers 2003, p. 423).

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He sees the adoption as being reached later, when the innovation "begins to loose its foreign character" (Rogers 2003, p. 424). Redefinition then occurs when the innovation is re-invented so as to accommodate the organization’s needs and structure more closely, and when the organization’s structure is modified to fit with the innovation (Rogers 2003, p. 424).

3.4.3

Organizational Adoption Drivers

In search of a unifying theory of innovation adoption, research has recently focused on the integration of organizational adoption theory in IT-adoption studies in settings such as health care (Chang et al. 2007; Lee and Shim 2007), small and medium-sized enterprises (Caldeira and Ward 2003; Premkumar and Roberts 1999; Teo, Lin, and Lai 2008), and voluntary organizations (MacKay, Parent, and Gemino 2004). These studies identify internal structural characteristics, external environmental characteristics, and individual perceptions that are significant determinants of organizational adoption decisions in B2B-contexts (e.g., Iacovou, Benbasat, and Dexter 1995; Kimberly and Evanisko 1981; Kwon and Zmud 1987; Rogers 2003; Tornatzky and Fleischer 1990). In detail, the following organizational factors have been found to influence the propensity to adopt in B2B contexts: centralization, complexity, formalization, interconnectedness, and organizational slack (Rogers 2003); company size (Frambach and Schillewaert 2002; Premkumar and Roberts 1999; Rogers 2003; Teo, Lin, and Lai 2008); user involvement of the personnel, adequate resources, and internal need (Chang et al. 2007); financial resources (Caldeira and Ward 2003; Chwelos, Benbasat, and Dexter 2001; Iacovou, Benbasat, and Dexter 1995; Kuan and Chau 2001; Lee and Shim 2007; MacKay, Parent, and Gemino 2004); technical resources / IT sophistication (Chwelos, Benbasat, and Dexter 2001; Iacovou, Benbasat, and Dexter 1995; Lee, Lee, and Kim 2007; Kuan and Chau 2001); strategic resources (MacKay, Parent, and Gemino 2004); human resources (Caldeira and Ward 2003); organizational structure (Caldeira and Ward 2003; Frambach and Schillewaert 2002); trading partner readiness (Chwelos, Benbasat, and Dexter 2001); interconnectedness, organizational support, training, social persuasion, and organizational innovativeness (Frambach and Schillewaert 2002); skills of owner and age of company (Palvia, Means, and Jackson 1994); product champion (Lee, Lee, and Kim 2007); and information sharing culture (Teo, Lin, and Lai 2008). Environmental influences have also been determined to influence the adoption decision in B2Bscenarios, e.g., system openness (Rogers 2003); vendor support and government policy (Caldeira and Ward 2003; Chang et al. 2007; Teo and Tan 1998); business partner influence (Teo, Lin, and Lai 2008); consultant effectiveness and client and supplier pressure (Caldeira and Ward 2003); supplier marketing activities (Frambach and Schillewaert 2002); and competitive pressure and vertical linkages (Premkumar and Roberts 1999). A couple of studies explore the perceptions of managers and key actors in an organization as

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Figure 3.19: The Innovation Process in Organizations Source: Own Illustration, based on Rogers (2003, p. 421) proxies of organizational adoption decisions and intention. They include perceptions of performance expectancy, effort expectancy, social influence, and facilitating conditions (Anderson and Schwager 2004); usefulness and ease of use (Basoglu, Daim, and Kerimoglu 2007; Nguyen and Barrett 2006; Kerimoglu, Basoglu, and Daim 2008); and innovation characteristics, benefits and top management support (Premkumar and Roberts 1999; Teo and Ranganathan 2004; Teo, Lin, and Lai 2008). In addition, constructs such as learning orientation (Nguyen and Barrett 2006; Kerimoglu, Basoglu, and Daim 2008), IT expertise (Premkumar and Roberts 1999), satisfaction (Basoglu, Daim, and Kerimoglu 2007; Kerimoglu, Basoglu, and Daim 2008), as well as general attitude, self-efficacy, and innovativeness (Marcati, Guido, and Peluso 2008; Woon and Kankanhalli 2007) have attracted researchers’ attention.

3.5

Summary of the Theoretical Foundations of Remote Services

The goal of the analysis and theoretical foundations for remote service acceptance was to identify concepts, theories, and models that would be relevant to the measurement of remote service perception, acceptance and continued use by pertaining to the unique facets of remote services. The following conclusions in respect to explaining remote service acceptance are derived from the analysis of the theoretical foundations and the literature review: 1. It has been shown that behavioral models based on the TRA such as the TPB and TAM have been successfully employed to explain and predict e- and self-service usage intention and usage. Because there are no models measuring the acceptance of remote services yet, it seems to be reasonable to use behavioral models such as TRA, TPB or TAM as a basis

3.5 Summary of the Theoretical Foundations of Remote Services

83

for remote service acceptance measurement. 2. Adaptations of the TRA, TPB or TAM are used in the services contexts with additional constructs to capture the specific features of the service or to improve predictive and explanatory power. These constructs comprise technology features, risk perceptions, and individual traits of the customer such as self-efficacy and motivation. In terms of applicability to a remote service context, the variations of TAM, TPB and TRA seem to incorporate important technology beliefs such as ease of use, perceived usefulness, but fall short of integrating customers’ perceptions in regard to the interaction and collaboration. 3. A recent topic in IS-literature is the exploration of drivers of technology continuance. Many of the concepts have also been applied to service continuance. Research shows that the same belief groups, which measure intention to initial adoption have been successfully been used for measuring continued usages. Also, the measurement of prior experience is an important addition to models, which are applied across customers that are in the pre-adoption or continued usage phase. 4. Literature in the marketing and operations field underlines the importance of the service provider employee and his characteristics such as empathy, reliability, knowledgeableness and customer orientation. As a high degree of human-to-human interaction is a major factor influencing the interactive remote service experience, it seems reasonable to include the remote service provider employee’s characteristics in the overall measurement of attitudes towards interactive remote services. 5. Literature on customer co-production has identified a set of variables, which comprise a customer’s attitude towards co-production with three different belief groups such as role clarity, ability, and motivation. As this set of variables has been successfully applied to a self-service context, it would be suitable to measure the customer’s attitude towards co-production in an interactive remote service setting. 6. Trust and control are shown to be concepts of great importance in relational exchanges across disciplines such as psychology, marketing, management, and IS. The trustworthiness of a remote service provider employee as well as the existence of control mechanisms and the perceived process control especially seem to be important in the reduced observability of a interactive remote service, especially when considering the complex interplay between trust and control. 7. The review of the main concepts in B2B-Marketing showed that it is necessary to carefully examine the effect of individual experience on organizational decision. The complexities of organizational decision making can be reduced by targeting individuals, who ideally are responsible for the decision to use remote services and at the same time have experience with these services themselves. Further, the studies on adoption in a B2B con-

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3. Theoretical Framework for Remote Service Adoption and Continued Usage text showed, that factors such as management support, subjective norms, environmental influences, and company size affect organizational decisions in regard to technologies and technology-intensive services.

Chapter 4 Methodological Superstructure and Empirical Setting 4.1

Methodological Superstructure

This thesis employs a mixed-methodological approach that combines qualitative with quantitative research. Both methods are used to complement each other and bridge the gap between exploration, description, and causal inference. Naturally, the methodological superstructure must take the specifics of each research method into account, because they differ in research goals, focus, procedure, data sampling, and analysis. The major differences between qualitative and quantitative research are shown in figure 4.1.

   
 




  
 



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86

4. Methodological Superstructure and Empirical Setting

Qualitative research offers an insight into questions that address the way people think about a certain subject and how they feel rather than applying numerical data to their reports and reactions (Bellenger, Bernhardt, and Goldstucker 1976). Qualitative research is often of an exploratory nature. It provides representative results only in accordance with the subject of investigation or a carefully selected target group. It does not yield towards a representative picture of the research population; nor does it allow confirmatory generalization of the results (Hair Jr., Bush, and Ortinau 2009, p. 153). A classified sample is needed to make sure all possible views and opinions of the subjects can be expressed (de Ruyter and Scholl 1998). The goal of qualitative research is to describe processes as accurately as possible in verbal terms; therefore, the type of analyses usually employed for qualitative research are subjective and interpretative, such as Mayring’s (2003) content analysis. Further, qualitative research uses an analytic-inductive process rather than a hypothetic-deductive process. It does not begin with hypotheses. Instead, analytic-inductive researchers begin by analyzing their data and only after the data is analyzed do they develop theoretical concepts and propositions that derive from the analysis (Kidder 1981; Patton 1987). Quantitative research, however, aims at a validation of facts, estimates, relationships, and predictions (Hair Jr., Bush, and Ortinau 2009, p. 153). Quantitative research uses a hypotheticdeductive approach that aims to test hypotheses rather than their formulation. Researchers begin with theoretical premises, predict a pattern of results, and examine their data to test their theory driven hypotheses. Statistical methods are used on a sample to infer facts and causal relations about the population (Nevid and Maria 1999; Patton 1987). This understanding of qualitative and quantitative research lends itself to a sequential approach to combine both methods. Accordingly, a complementary study consists of a preliminary qualitative study to formulate hypotheses and a subsequent quantitative study for validation (Mayring 2001). This is the approach I take in this thesis. The design of the empirical studies is based on the conceptual framework for remote services and interactive remote services developed in chapter 2 and the extensive literature research presented in chapter 3. Thus, the goals of the qualitative study have been set as follows: 1. To explore (potential) customer employee’s perceptions of remote services in general and interactive remote services in particular; 2. To develop a framework for factors that influence an individual’s attitude towards remote services / interactive remote services; and 3. To derive hypotheses and specify the interactive technology service usage model (ITSUM) to explain intention to use. The design, methods, validity checks and the results of the qualitative study are presented in chapter 5. The methods and validity checks used in the qualitative study include interviewing

4.2 Empirical Setting of the Employed Studies

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and sampling methods, interview guideline design, content analysis, assessment of intercoderreliability. The hypotheses for the ITSUM combine the findings from the qualitative study and the results from previous chapters. They are proposed in chapter 6 and constitute the model to be tested in the ensuing quantitative studies. The quantitative studies aim to: 1. Validate the hypotheses of the ITSUM for organizations in the pre-adoption and continued usage phases; 2. Validate the relationship between organizational usage intention and usage behavior; 3. Measure the acceptance of interactive remote services in the German printing industry; and 4. Examine differences between drivers of adoption and continued usage of interactive remote services. The quantitative studies are imbedded in a two-phase longitudinal design to reduce biases inherent with most cross-sectional studies (Premkumar and Bhattacherjee 2008). In this thesis the measurement of intentions and behavior is clearly delineated by conducting two surveys at two different points in time. This design avoids relying on respondents’ to recall and assess pre-usage perceptions and pre-usage intentions. The design and the results of the quantitative studies are described in chapter 7. The methods and validity checks used in the quantitative study include: covariance-based structural equation modeling (SEM), multi-group SEM, assessment of common method bias, external validity, discriminant validity, and reliability. They are described in detail in chapter 7.2.2. Results of the quantitative studies are discussed comprehensively in chapter 7.10. The empirical setting for the qualitative and quantitative studies is the topic of the following section.

4.2 4.2.1

Empirical Setting of the Employed Studies Selection of the Printing Industry

The empirical setting of this thesis is grounded in mechanical engineering (printing machines) and in the printing industry9 (printed goods). Remote services in the printing industry are provided from press manufacturers to their clients who are usually printing companies. The printing industry is selected as an empirical setting to explore customer’s acceptance of remote service mainly because of two reasons: 9

The anglo-saxon languages refer to the printing industry commonly as the graphic communication industry due to the broadening of scope of services provided by many of the companies in this sector (PIA / GATF 2008). For practical reasons in this thesis the term printing industry is used.

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4. Methodological Superstructure and Empirical Setting

First, in the printing industry interactive remote services are offered as remote maintenance, remote repair, and remote diagnosis services. These services have been introduced within the last ten years as an alternative and addition to on-site visits of service technicians. As they have been widely promoted by some machine manufacturers, the level of awareness is quite high, but the acceptance of these services on the customers’ side is not on par. The printing industry has not only been a forerunner in the implementation and usage of remote services but also comprises a wide variety of companies having none, some, or extensive remote service experience. Also, customers are free in their decision to use a remote services in favor of traditional service delivery. Remote services co-exist with the alternative of personal services delivered by on-site technicians. Furthermore, first studies in the printing industry indicate massive barriers to their adoption (Wünderlich and Pfeffer 2007). Second, the industry structure of the printing industry is dominated by SMEs and, moreover, with over 97% by small business (<100 employees). This makes it possible to target the survey to a decision maker, who is also the direct recipient of interactive remote services (Panshef 2009). For example, in many small enterprises the owner is the decision maker and often also the one who supervises the production and machines. In organizational research on SME’s adoption behavior, this approach10 is commonly used based on the assumption that the managers are generally expected to be more knowledgeable about their firm’s activities than their employees from lower levels. In these studies, adoption is examined as the dependent variable and linked to attributes of the organization, the individual respondent, and the innovation itself, as perceived by the respondent. Based on senior management and owners’ responses, organizational IT and innovation adoption have already been successfully explained (e.g., Nguyen and Barrett 2006; Premkumar and Roberts 1999; Teo and Ranganathan 2004; Teo, Lin, and Lai 2008). This rationale is followed in this thesis, and it was additionally opted for a longitudinal study design to relate individual perceptions to actual organizational behavior. The qualitative studies are conducted with remote service customer employees from the printing industry and with engineers and managers from press manufacturers in Germany, USA, and China. The quantitative studies are conducted in the German printing industry.

4.2.2

Printing Machine Manufacturing

The market for printing machinery is characterized by an oligopoly structure in which few press manufacturers supply many smaller printing companies with presses and machinery (Panshef 2009, p. 44). Germany is the most important supplier in the world market with a market share of 38% before Japan (12%), Great Britain (7%), and Switzerland with a market share of 7% 10

This so-called "key informant concept" has been discussed controversial with respect to the benefits of multiinformants in large companies. Relying on key informants in a SME environment is appropriate when the content of inquiry is such that complete or in-depth information cannot be expected from randomly selected representative survey respondents (Kumar, Stern, and Anderson 1993; Gallivan 2001)

4.2 Empirical Setting of the Employed Studies

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(Otto 2008). Overall, the export rate of the German machinery industry for print- and paper machinery has decreased about 3% from 2006 to Euro 7 billion in 2007 (Overhoff 2008). Nevertheless, with an export rate of over 80% printing technology is one of the strongest export segment in the German mechanical engineering industry. German press manufacturers primarily direct their exports toward European markets (52%), and to a lesser extent, to important foreign markets such as the USA (market share: 15%), Great Britain (11%), and China (10,5%) (Otto 2008). The sheet-fed offset sector, the most important sector in the printing industry, is dominated by a few German and Japanese companies. Heidelberger Druckmaschinen AG (Heidelberg) is the world market leader with a share of 40%, followed by KBA 14%, Komori 13%, MAN Roland 12%, and Mitsubishi 6% (Heidelberg 2008b; Panshef 2009). Heidelberg generated an annual revenue of Euro 3,670 million in 2007/2008 (Heidelberg 2008a). Press manufacturers not only offer equipment for different steps in the print production workflow, they also offer a bundle of services to their customers. Although press manufacturers offered add-on-services for a long time, they only recently discovered that services can substantially augment product sales (Panshef 2009, p. 44). For example, in 2008 Heidelberg’s business segment "services and service materials" generated Euro 700 million and plans to increase its turnover up to 1 billion Euro in 2011 (Fichtl et al. 2009). According to Panshef (2009, p. 44), the services provided by press manufacturers to their customers can be divided into three categories: 1. Basic services that include delivery, repair, maintenance, support, and spare parts services, which are mostly included in the warranty phase; 2. Product support services that extend the basic services through regularly maintenance services beyond warranty; and 3. Business consulting services pertaining to productivity, efficiency, and rentability assessment of the plant, process analysis and optimization as well as services not specially centered on production such as financial services or project management. Press manufacturers traditionally deliver services on site via local service technicians. Recently there has been a transition toward offering more and more services remotely beginning with simple remote monitoring services up to the current offering of complex remote repair services.

4.2.3

The Printing Industry

The printing industry is a key part of the communications industry. If one considers the worldwide market volume of Euro 1,335 billion, print is number one with annual sales of Euro 500

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4. Methodological Superstructure and Empirical Setting

billion (Fichtl et al. 2009). This represents 38% of the worldwide communication market volume. In Germany, the revenues of the printing industry in 2007 were Euro 18,223 million, with 11,000 companies in the market and 173,000 employees (BVDM 2008). The market for printed goods in Germany is saturated and forecasted to have a low growth rate of 1.5% (Otto 2008). The German printing industry has a large number of small and mid-sized enterprises, which represent over 97% of all companies in the market (BVDM 2008). The degree of concentration in the market is very high. About 40% of the industry’s turnover is generated by only 2% of the companies (Otto 2008; Panshef 2009, p. 23). In 2008, there were 100,000 printing enterprises in China reflecting an increase of 10,000 enterprises from 2006 (Chinese Press and Publication Bureau 2008; Yang 2008). Additionally, there are 80,000 small copy shops located in china (Yang 2008). The total number of employees employed in the Chinese printing industry in 2008 was 35 million, and the total production value (2006) was Euro 42 billion (Chinese Press and Publication Bureau 2008; Yang 2008). In 2003, the 58 largest Chinese press manufacturers generated a turnover of Euro 634 million (European Commision Enterprise and Industry Directorate 2007). The Chinese printing industry still highly relies on imported modern machines especially for large newspaper presses. Overall, 2% of the printing machinery, which is mostly imported, produces 60% of the printed production (European Commision Enterprise and Industry Directorate 2007). In 2003, the ownership structure of Chinese printing establishments was as follows: 40% were privately owned, 26% belonged to collective enterprises, and 18% were joint-stock enterprises. The number of small companies exceeded that of larger companies (European Commision Enterprise and Industry Directorate 2007), with an industry average of 18 employees per company. With 38,900 printing companies, printing is the second largest manufacturing industry (printed goods) in the United States with regard to number of establishments. In 2007/2008, over one million people were employed in the printing industry. Almost 80 percent of printing companies employ less than 20 people (PIA/GATF 2008). Annual shipments of the American printing industry are about Dollar 174.5 billion (PIA / GATF 2008). Printing is America’s most geographically dispersed manufacturing industry, with a presence in every state and sizable metropolitan area (PIA/GATF 2008). The purchase of a press/printing machine is a high investment with financial consequences for 10 or more years on average. The purchase decision is essential for the operational and business processes of a company and also affects the capabilities and quality of core business processes (Panshef 2009, p. 26). The printing industry produces a vast mix of printed products which applies to a variety of processes and segments: general commercial printing; quick printing; digital imaging; magazine, newspaper, and book printing; financial and legal printing; screen printing; thermography; business forms printing; label and tag printing; packaging; greeting cards; and trade and finishing services (PIA/GATF 2008). Due to rapid technological changes,

4.2 Empirical Setting of the Employed Studies

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Figure 4.2: The Print Production Process Source: Own Illustration, based on Kipphan (2000, p. 41) printing companies have expanded their printing services to include additional services: creative design; e-commerce; web page design and hosting; mailing; fulfillment; and a host of services that provide horizontal marketing well beyond the core printing model (PIA/GATF 2008). In the core printing model, printed goods are created within a print production process. Conventionally, the print production workflow is broken down into pre-press, press, and post-press operations (Kipphan 2000, p. 42). This workflow, beginning with the information sources up to the end consumer, is illustrated in figure 4.2. The first step, pre-press, comprises all processes and procedures from the procurement of print media up to the press step, e.g., text editing, designing of graphic elements, layout of a page, the manufacture of a printing plate in conventional printing, or desktop publishing in digital printing (Kipphan 2000, p. 25). Press describes the printing step, where the process of transferring ink onto paper or another substrate takes place. Print finishing or the post-press step includes all steps that are carried out after printing such as cutting, folding, gathering, and binding (Kipphan 2000, p. 33).

Chapter 5 Qualitative Exploratory Interview Study 5.1

Motivation and Goals

The qualitative approach taken in this thesis emphasizes discovery over confirmation (Holliday 2008, p. 6) and explores the perception of remote services from a customer’s perspective. This study aims to provide insight into relevant individual and situational circumstances that affect the remote service perception. Based on these insights, a comprehensive conceptual framework of factors that influence the perception of remote services is developed. This forms the foundation for the subsequent steps in the research process and allows the relevant constructs to be operationalized in the quantitative study. The qualitative interview study addresses the following research questions: 1. How do customers perceive remote services and interactive remote services? In particular, (a) How do customers perceive the interaction with the remote service technician? (b) How do customers perceive the usability of the technology? (c) How do customers perceive the collaboration with the remote service technician? 2. What benefits and obstacles of remote services and interactive remote services stand out from a customer’s point of view? 3. What factors determine the customer’s perceptions towards remote services and interactive remote services?

94

5.2 5.2.1

5. Qualitative Exploratory Interview Study

Qualitative Research Methodology Semi-Structured Interviews as Means of Data Collection

This research study uses interviews as a method to capture the underlying dimensions of how customers perceive the remote service situation. Kvale (1984, pp. 173 f.) defines the qualitative research interview as "an interview, whose purpose is to gather descriptions of the lifeworld of the interviewee with respect to interpretation of the meaning of the described phenomena." According to Kvale (1984), a qualitative research interview is characterized by the interviewee’s life world, the meaning of phenomena for his life-world and an interpersonal interaction. It is qualitative, descriptive, specific, pre-suppositionless, focused on certain themes and dependent upon the sensitivity of the interviewer (Kvale 1984). Technically, a qualitative research-interview is "semi-structured", it is neither a free conversation nor a highly structured questionnaire. It follows an interview-guide, which rather than containing exact questions focuses on certain themes (Kvale 1984; Carson et al. 2001). Throughout the interview, the researcher follows the rules of good interviewing such as using small encouragers like murmurs of understanding, maintaining eye contact, smiling expectantly during pauses as if expecting the interview to continue, using active listening technique of feeding back dialogue in the researcher‘s own words to check her understanding, and asking non-directive questions (Carson et al. 2001). In this study, face-to-face interviews with synchronous communication in time and place were conducted as part of the interviewee’s business life-world. This is the most common form of interview for integrating social cues in the interview analysis (Opdenakker 2006). The interviews are aimed at gaining specific nuanced descriptions from different qualitative aspects of attitudes toward remote service and interactive remote services, at obtaining uninterpreted descriptions on how the participants experience and imagine these services, and at recording how they feel and act. As such, the interviews are classified as theme-oriented rather than person-oriented, and data is collected based on what interviewees directly express as well as to what is "said between the lines."

5.2.2

Qualitative Content Analysis as Means of Data Analysis

To analyze the interview material, this study uses the qualitative content analysis method (Mayring 2003). Content analysis consists of a bundle of techniques for systematic text analysis. Mayring (2000, p. 5) states that "qualitative content analysis defines itself ... as an approach of empirical, methodological controlled analysis of texts within their context of communication, following content analytical rules and step by step models, without rash quantification." The material is

5.2 Qualitative Research Methodology

95

to be analyzed step by step, following rules of procedure, devising and summarizing the material into content analytical units (Mayring 2003, p. 61). Following the research questions, emerging aspects of text interpretation are put into categories, which are carefully chosen and revised within the process of analysis. As with most qualitative oriented procedures of text interpretation, the inductive approach of category development is central to an exploratory study design. The main idea of the inductive category development, as shown in Figure 5.1 is, to formulate a coding definition for each category derived from the theoretical background and guided by the research questions. Following these coding guidelines and the tentative categories, a step by step process is started. Within the feedback loop, the categories are revised, eventually reduced to main categories, and then checked with respect to their reliability (Mayring 2000).

5.2.3

Validity and Reliability

Whereas validity and reliability are often regarded as the cornerstone of any research (de Ruyter and Scholl 1998), there is a controversial discussion whether those concepts apply to qualitative research in general. For example, Healy and Perry (2000) assert that the quality of a qualitative study should be judged by its own paradigm’s terms. In line with this approach, researchers


 

  


   
  

  

 

 

 
 





  

 
 


 
 
 

   



 
 

 
 

  

 
  

 

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Figure 5.1: Inductive Approach of Qualitative Content Analysis Source: Own Illustration, based on Mayring (2000, (11))

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5. Qualitative Exploratory Interview Study

recommend new criteria such as credibility, neutrality, confirmability, consistency, dependability, applicability or transferability as essential for quality in qualitative research (Lincoln and Guba 1985). Other researchers develop a re-definition of validity and reliability in qualitative research (de Ruyter and Scholl 1998; Rao and Perry 2003) and claim that validity and reliability in qualitative research can be achieved through forms of cross-checking (Rao and Perry 2003). In qualitative research, validity is primarily related to the fact that constructs or emerging topics are closely aligned to their real-life context (de Ruyter and Scholl 1998). Therefore, results become meaningful if they are in relation to the respondent’s everyday reality. In this sense, de Ruyter and Scholl (1998) argue that qualitative research offers the possibility of "ecological validity" instead of construct validity or internal validity. The ecological validity of data collection depends on the purpose of the study. External validity in view of quantitative research addresses the question whether the resulting conclusions can be generalized to a population beyond the immediate study context (Emory and Cooper 1991). Qualitative research does not aim to validate generalizations from a sample for a population but instead aims at a discovery of typical patterns or themes (Helfferich 2004, p. 153). Yin (1989) transfers the concept of generalizability into qualitative research by requiring that results have to be general in respect to theory. The analytical generalization can be achieved by lifting the empirical material to a general level (Stenbacka 2001). Reliability refers to how consistently a technique measures the concepts it is supposed to measure, enabling other researchers to repeat the study and obtain similar findings (Emory and Cooper 1991). The reliability of qualitative research, in the sense of comparability of reproducibility, is often questioned. Stenbacka (2001) argues that because reliability issues concerns measurement, it has no relevance in qualitative research. Nevertheless, de Ruyter and Scholl (1998) claim that in-depth insight into the motivations and perceptions of respondents does not primarily require reproducibility, but instead requires operating in a systematic way. It is important to note that the "quality" of a "qualitative" interview study is crucially dependent on the preparatory design of the study. To ensure the maximum validity and reliability in the terms that were discussed above, the ten best-practices in qualitative study design recommended in literature were employed in this thesis (de Ruyter and Scholl 1998; Mayring 2003; Rao and Perry 2003; Stenbacka 2001). These can be broken down into 3 main groups. First, to ensure meaningful consistent respondent perceptions tied to real life contexts, the following recommendations were adhered to: • Face-to-face-interviews were conducted in the respondent’s own environment as recommended by de Ruyter and Scholl (1998); • Interaction between interviewer and interviewee was allowed for questions, verification, and to scrutinize statements as recommended by Stenbacka (2001); and • During the interviews, carefully worded questions were raised that looked at remote ser-

5.3 Field Phase

97

vice experiences from different angles as recommended by Rao and Perry (2003). Because this study’s goal is to learn what factors influence perception of remote services on a general level and in respect to theory, external validity was assured by three complementary approaches: • Interviewees were selected based on their strategic relevance to the study’s general focus as recommended as by Stenbacka (2001); • Interviewees with different perspectives on remote services were selected to ensure that a cross-section of opinions was obtained, thus achieving theoretical replication as recommended by Rao and Perry (2003); and • Categorization of units for embracing remote service perceptions on an abstract level were repeatedly developed during the analysis as recommended by Stenbacka (2001). In qualitative research the requirement of reliability does not yield to reproducibility but should ensure inferability of the research findings. In this study, these demands were secured by the means of four tactics: • The interviews were conducted in a flexible manner, e.g., it was possible to change research questions during the interview to look behind the facts and follow interesting emerging themes, as recommended by de Ruyter and Scholl (1998); • Questions were built on existing literature and concepts as recommended by de Ruyter and Scholl (1998); • Repeated listenings to recorded interviews and multiple coding to foster intra-personal consistency as recommended by Mayring (2003); and • Independent coding checked by another person and evaluation of inter-coder reliability as recommended by Mayring (2003).

5.3 5.3.1

Field Phase Sample Selection

As discussed above, qualitative research does not aim to validate generalizations from a sample for a population. It aims at discovering typical patterns or themes (Helfferich 2004, p. 153). Therefore, a theoretical sampling procedure (Glaser and Strauss 1999) was used to recruit the interview partners. The goal of the sample selection was to develop a maximal diversity of knowledgeable people such as participants from different countries (Germany, USA, China), from different perspectives on remote services (printing machine manufacturer/remote service provider, printing company/remote service customer), from different functions and hierarchical

98

5. Qualitative Exploratory Interview Study

levels (e.g., machine operator, foreman, production manager, general manager, owner, service manager, sales manager, remote service technician, or service manager), with different experience levels in remote service usage (with frequent experience, with few experiences, or without experience), and with experiences of different types of remote services (e.g., remote monitoring services, remote diagnosis, or remote repair). Participants were identified with the help of associations in the printing industry and through scientific collaboration with a German printing machine manufacturer. The use of recommended interviewees was necessary for two reasons: first, to establish a trustful relationship to printing companies, which are suited for participation as (potential) remote service customers, and second, to foster their readiness to participate in the study. Remote service provider employees were contacted through the collaboration partner and were recruited from different branches and sub-companies in diverse countries and cities. The remote service customer (or potential customer) companies were selected independent of their relationship to the collaboration partner, if possible. To assure results that are valid beyond provider-specific scenarios, the sample selection aimed at including (potential) customer companies that had business relationships to different machine manufacturers and (potential) remote service providers beyond the collaboration partner. All interview partners participated voluntarily in this study and were not paid for participation. The exploratory study was conducted from August 2006 to January 2008 with participants from Germany, the US, and China. In sum, 30 individuals were interviewed in person representing 16 different companies. Seventeen of the interviewees are working for 13 different companies that purchase and use remote services. Thirteen of interviewees represent 3 different companies providing and selling remote services. Geographically, 8 participants are from Germany, 6 from the USA, and 16 from China. The descriptive characteristics of all participants are shown in table 5.1. After interviewing the thirtieth subject, the need for further interviews ceased when it was apparent that a point of information saturation had been reached and that unique findings were no longer expected to be obtained under the current interviewing procedure (Glaser and Strauss 1999; Strauss and Corbin 1990). Therefore no further interviews were conducted.

RS provider

RS provider

RS provider

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5

6

7

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Manager Technical Support

Director BD

Sales Manager

Production Manager

Sales Manager

Foreman

Production Manager

Owner

General Manager

Machine Operator

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Function

Press Manufacturer & RS provider

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Printing Company & Book Bindery

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—

—

—

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—

high

high

low

no

high

low

low

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male

male

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male

male

male

male

male

male

male

male

male

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USA

USA

USA

USA

Germany

Germany

Germany

Germany

Germany

Germany

Germany

Germany

Place

English

English

English

English

German

German

German

German

German

German

German

German

Language

Interview

5.3 Field Phase 99

9

9

10

11

11

11

12

12

12

13

14

14

15

13

14

15

16

17

18

19

20

21

22

23

24

25

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RS customer

RS customer

RS customer

RS provider

RS provider

RS provider

RS provider

RS provider

RS provider

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to be continued on the next page. . .

Interview

Participant

Number

General Manager

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Machine Operator

Production Manager

Remote Service Technician

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Branch Manager

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Remote Service Technician

Branch Manager

Remote Service Technician

BD Specialist

Manager Technical Support

Function

Printing Company

Printing Company

Printing Company

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Press Manufacturer & RS provider

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Press Manufacturer & RS provider

Press Manufacturer & RS provider

Press Manufacturer & RS provider

Press Manufacturer & RS provider

Press Manufacturer & RS provider

Press Manufacturer & RS provider

Press Manufacturer & RS provider

Business Field

Interviewee

low

high

high

—

—

—

—

—

—

—

—

—

—

Exp. Levelc

male

male

male

male

male

male

male

male

male

male

male

female

male

Gender

China

China

China

China

China

China

China

China

China

China

China

USA

USA

Place

Cantonese

Cantonese

Mandarin

Mandarin

English

English

English

English

English

English

English

English

English

Language

Interview

100 5. Qualitative Exploratory Interview Study

16

17

18

19

19

26

27

28

29

30

RS customer

RS customer

RS customer

RS customer

RS customer

Typ

Production Manager

IT Manager

Production Manager

Production Manager

Machine Operator

Function

Legend: c : customer’s remote service experience.

Interview

Participant

Number

Printing Company

Printing Company

Printing Company

Printing Company

Printing Company

Business Field

Interviewee

no

no

low

no

high

Exp. Levelc

male

male

male

male

male

Gender

China

China

China

China

China

Place

Wu

Wu

Wu

Wu

Cantonese

Language

Interview

5.3 Field Phase 101

102

5.3.2

5. Qualitative Exploratory Interview Study

Interview Situation and Questionnaire Design

All interviews lasted between 60 and 90 minutes. Each interview was conducted at the subject’s place of business. The interview situation depended on the on-site actualities. Some interviews were conducted with multiple participants based on the explicit wish of the participants. The reason given by these interviewees were related to time constraints as they were interviewed during their working hours. The interviews at companies providing remote services were conducted by myself and a colleague, whereas the customer company interviews were conducted solely by myself. The interviews in Germany were conducted in German. The interviews in the USA and some of interviews in China were conducted in English. The remaining interviews with remote service customers in China were conducted in Mandarin, Wu, or Cantonese with English translation. For translation and communication support during the interviews, the interviewer was accompanied by a translator and a remote service technician to assure accurate translation of technical terms. In each face-to-face interview situation, the interviewer aimed at creating a positive atmosphere to enable a forthright conversation about remote services between experts. All participants were assured of confidentiality and anonymity. According to the principle of presuppositionlessness (Kvale 1984) of qualitative research, most of the questions that arose during the course of the interviews were context-specific. To guide the participants’ flow of ideas, however, a small set of structured questions was used in the interviews. The questions were carefully worded to cause participant responses in a non-directive manner in order to avoid active listening (McCracken 1988). For example, interviewees were asked to think of a remote service they are familiar with and to describe how they, themselves, and how their counterpart might have experienced it. The interview guideline comprises a small set of questions based on the findings from the literature review and aims at exploring the general perceptions of remote services and interactive remote services as well as challenges and barriers to their usage. Deeper themes like coproduction and interaction with the remote service technician were meant to be explored, if possible, when the interviewee brought them up himself during the course of the interview. Two variants of the guideline were used to capture the attitudes toward remote services (see Appendix A.1). Both comprise the same central themes and questions but were adjusted to the different perspectives of remote service customer companies (version A) and remote service provider companies (version B). Also, the questions slightly varied for customers with or without remote service experience. While the questions provided a general framework, they were followed up with additional questions requesting clarifications, examples, and more details into potentially interesting ideas if the situation gave chance to it.

5.3 Field Phase

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5.4 Results of the Qualitative Interview Study

5.3.3

105

Category Development and Coding

Out of a total of 19 interview sessions, some of which involved more than one employee, 17 were audio taped and transcribed verbatim in English or German. In the case of the Mandarin, Wu, and Cantonese interviews, the English translation by the on-site translator was transcribed. Three participants declined the request to record interviews due to the sensitive nature of the subject, but detailed notes were taken during the interviews. The transcribed versions of the interviews, notes, and tapes including both the original language audio and the comments by the on-site translators constitute the material for the subsequent interpretation of meaning via qualitative content analysis (Mayring 2003). The source material was coded using an inductive approach of category development according to the qualitative content analysis (Mayring 2003). The categories were formed in an iterative process with constant revision of the coding and the tentative categories. The final coding scheme consists of nine main categories and 49 subcategories. In sum, 357 statements – text units that can include one or more sentences – from the source material were related to the subcategories. I used a qualitative data analysis software package, NViVo7, to support the text interpretation (Mayring 2000). This can, in turn, improve the rigor of the analysis by validating some of the researcher’s impressions (Welsh 2002). The hierarchical structure of the coding categories together with the number of statements attached to those categories as well as a description of those categories are shown in figures 5.2 and 5.3.

5.4 5.4.1

Results of the Qualitative Interview Study Assessment of Intercoder Reliability

Researchers generally agree that an estimate of intercoder reliability must correct for chance agreement among coders, register systematic coding errors, and determine the stability and quality of the data obtained (Hughes and Garrett 1990; Rust and Cooil 1994). Assigning statements during the coding process to mutually exclusive categories is sometimes not clear cut and to a certain degree "fuzzy" involving subjective judgement. In contrast, a categorization is reliable if coders can be shown to agree on the categories assigned to statements to a certain extent (Artstein and Poesio 2008). This study employs four intercoder reliability assessments metrics based on multiple judges assigning codes to the statements derived within the coding process such as proportional agreement (PA), Cohens κ (κ), Perreault and Leigh measure (PL), and the proportional reduction in loss (PRL) (Hughes and Garrett 1990; Rust and Cooil 1994). To confirm the inter-coder reliability of the content analysis, two judges (J=2) were used. The 357 statements (n=357) and 9 categories (K=9) were given to both judges, individuals with expertise in remote service themes, to independently assign each statement to one category.

106

5. Qualitative Exploratory Interview Study

The resulting Matrix Ω is shown in table 5.2. Every element on the main diagonal holds the frequency of statements, which both judges assigned to the same category. The sum of all diagonal elements, therefore, is the total number of agreements. The entries not on the main diagonal represent disagreements between the two judges. For example, the entry for judge A/category 6 and judge B/category 3 holds the number of statements assigned by judge A to category 6 and by judge B to category 3, in this case 1 statement. Using the matrix Ω and the total number of pairwise judgements11 , four intercoder reliability measures can be derived. The resulting proportional agreement (PA) of 0.882 is well beyond the recommended cutoff point of 80% proposed by Neuendorf (2002). Cohens κ, the most pessimistic measure, equals 0.863 and indicates a good reliability. This metric is generally considered adequate if it is above 0.4 (Artstein and Poesio 2008; Landis and Koch 1977). The theoretically most accurate measure are PL and PRL. In a two-judge case PL and PRL are equivalent12 , their value in this study is 0.931. Following Rust and Cooil (1994), this value can be considered good because it is higher than the suggested minimum of 0.8. In conclusion, intercoder reliability can be assumed.

5.4.2

Structure of Results Presentation

The major themes that emerged from the interviews with customers and suppliers form a conceptual framework for understanding the factors that influence the customer’s attitude toward remote services. The framework is shown in figure 5.4. It depicts the grouping and relationship between the relevant variables influencing remote service perception: relational beliefs; economic values; technology beliefs; former experiences; beliefs about customers’ participation Table 5.2: Inter-Coder Judgement Matrix Ω C ODER A: C ATEGORIES

1 2 3 4 5 6 7 8 9 ∑

11

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24 57 4 31 51 51 14 68 57 357

1

In a two judge scenario the total number of pairwise judgements T: T = N, generally the total number of pairwise judgements is: T = J(J−1) 2 N For a prove see Rust and Cooil (1994).

5.4 Results of the Qualitative Interview Study

107

in a remote service; organizational factors; process control beliefs; and contextual factors such as the geographical distance. These themes represent the major results discovered through the process of inductive category development. The following sections explain in the individual results and the derivation of the conceptual framework. This chapter is structured by following each of the major themes that form the framework.

5.4.3

Technology Beliefs

Due to the high technology intensity during a remote service encounter, perceived attributes of the technology play a major role in the customers’ perception, e.g., the attitudes the customers have about the necessary network equipment. The findings from the qualitative interview study show that a high risk perception is a major issue in offering remote service to customers. One theme that arises in most of the interviews is the customers’ fear of third party attacks, e.g., that the remote service connection could be used by hackers to access the network and/or the machine. The following statements exemplify the range of potential dangers customers see: "[The threat] is reasonable. We must avoid that the hackers can access our network. If we would get some viruses due to attack from hackers, it would influence our operation on the machine. Therefore, it must be avoided. ... Could [the remote service provider] 100% guarantee that no virus can access my machine?" (P.28, I.18, customer, China) 13

 




  


   


 
  



 


 

 


 

  
 
  


 
 


Figure 5.4: Conceptual Framework Resulting From Qualitative Study 13

In the following the references to interviews are written in short-form: P. = participant no.; I. = interview no.; customer = remote service (potential) customer company; provider = remote service provider company; China/Germany/USA = country of interview conduction.

108

5. Qualitative Exploratory Interview Study

The importance of these perceived technology risks should not be underestimated. A remote service encounter is perceived as risky and one customer even explicitly stated that these risks are a major barrier to buy remote service offerings: "If so, we would like to pay the 2000 Yuan. Because if there is no virus, which can access this machine, we would like to connect to the remote service permanently, otherwise we are not interested." (P.28, I.18, customer, China) Another customer of remote services even explains, that he rejects using remote services because of his security concerns: "Secure or not? Because we are no experts in this area, we have worries about it. For example, we have a [company name] machine and they have remote services, too, but we never used it because we are worried about the security." (P.30, I.19, customer, China) Some customers did not have any serious security concerns and did not see any real issues with network security when using remote services. A certain technological affinity or trust in technology helps to reduce the fears. Also, customers’ own security measures like firewalls impact the customer in having a more positive attitude towards remote services: "From the aspect of technology, I like to use new technologies. I can also give some suggestions to our company or technical department." (P.27, I.17, customer, China) "[The remote service provider company] has done something to protect the data transmission, like data encryption. But internet is a public network, if we do it via a public network, it may be a little bit unsafe, however, normally it is acceptable." (P.29, I.19, customer, China) "I just have superficial knowledge regarding information technology, but I am quite sure [the remote service provider] can protect against it [hacker attacks]. I don’t think it is a real threat that somebody can access private data." (P.4, I.3, customer, Germany) "It is normal and natural that others worry about the security. But for us, we have a lot of servers in our company; we do not think this is a problem for us." (P.27, I.17, customer, China) Apart from unauthorized third party access such as hacker attacks, remote service customers are also concerned about the remote service provider company’s unauthorized access to their system. Some customers seem to not know exactly whether the remote service technician can access private information beyond their agreement and to what extent the provider can access confidential information. Other customers fear espionage and imagine that the remote service technician (RST) accesses sensitive information out of curiosity or in order to check up on them. Additionally, a number of customers handle sensitive information themselves, e.g., print jobs for

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their respective customers. Beyond the concerns about their own business data, customers fear that their customers’ information might get stolen, especially in pre-press and digital printing businesses: "The service provider promises to just take agreed-on actions, but maybe he does something else instead. [A remote service] is really risky and dangerous." (P.4, I.3, customer, Germany) "If I connect to the remote service, what I am doing on the machine, the content of my printjob could perhaps be watched by them [RSTs], to my mind it could be a reason [for not using remote services]." (P.30, I.19, customer, China) "Because there is a lot of information that you can put on that machine that you do not want people to know about. ... The thing is, as if I had something on there I didn’t want them to see, I will delete it. ... And then I can put it on my machine later. For me, it would just take the temptation away and there is no problem." (P.9, I.7, customer, USA) "Through the connecting, the engineers of [the remote service provider] can look at our processes. I think, they do this because they want to know something about other companies." (P.24, I.14, customer, China) It is fair to say, that especially in interviews with unexperienced users (P.4, P.5, P.28, P.29, P.30), potential risks were a resurfacing theme, which usually were perceived as a major draw-back for remote service usage. Experienced users such as P.24 and P.9, however, were aware of the risks, but assessed them to be no hinderance to use the service. In contrast to a self-service system where the system response is rather predictable and a worstcase scenario is that the service is simply unavailable, in a remote service encounter, the RST could actually damage the service object (printing machine) resulting in substantial downtime and costs. Customers expressed the potential danger as follows: "I am a little bit afraid of somebody remotely shutting down my machine." (P.1, I.1, customer, Germany) "I don’t worry about our work being spied on, I am afraid of that the machine is destroyed." (P.28, I.18, customer, China) "Such a remote service is not without danger. Just recently our printing configuration was erased in a flawed repair attempt [by the counterpart]. That cost us a huge amount of time." (P.7, I.5, customer, Germany) "Often something gets broken right one day after an on-site visit of an engineer. So, they [the technician] can really misconfigure something with harmful consequences. I guess the same could happen during a remote service." (P.5, I.4, customer, Germany)

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In addition to risk, technology characteristics such as the ease of use, usability and convenience of using the technology necessary in the remote service provision seem to be relevant for the customers’ general evaluation of remote services. An owner of a German printing company emphasized the effect of inconvenience in using remote services: "I think remote services are pretty useful, but just recently I wanted to start the remote service procedure and I had problems with the technology and it was very difficult for me to handle this." (P.7, I.5, customer, Germany) Most of the customers, however, seemed very well aware of how to request/initiate a remote service and how to cooperate during the remote service process. The process of connecting the machine was perceived as very easy and the customers seemed to be well trained. "As they trained us last time, it was easy. It took ten and a few minutes to explain how to connect to the server, that’s all!" (P.28, I.18, customer, China) "It is very easy to operate." (P.22, I.13, customer, China) "Of course, it is easy, because what we do is just pressing buttons or connecting cables." (P.23, I.14, customer, China)

5.4.4

Relational Beliefs

The exploratory interviews highlight the importance for relational beliefs affecting the remote perception. Trust and trustworthiness related themes frequently emerged and were emphasized from both service provider and customer as important for the customer’s attitude toward remote services. It is striking that the object of trust beliefs are both the RST as the social interaction partner during a remote service and the remote service provider company (RSPC) on an organizational level. In the following sections I will distinguish between those two trust objects.

5.4.4.1

Trust in the Remote Service Technician

The statements show that personal relational factors to the RST have strong positive effects on the customer’s perception of the remote service. In addition, the majority of the customers seem to build a relationship with the company through individual relationships with the RSTs. The following statements underline the importance of the customer’s trust in the individual RST: "It really ... matters which engineer or technician is send to us. There are huge differences. It depends strongly on the individual person." (P.1, I.1, customer, Germany) "Of course the persons we know before are more trustworthy. Because we knew him, he has worked with us before, therefore, we trust him more." (P.28, I.18.

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customer, China) "Because we have had a good relationship with them [the RSTs] for a long time, we know each other and trust each other." (P.30, I.19, customer, China) "My trust towards [company name] is based on the relationships I have with the people, the technicians." (P.9, I.7, customer, USA) "We trust the technicians more than [company name] in general." (P.7, I.5, customer, Germany) The trustworthiness of the RST seems to be very important for the customers, especially if they are evaluating an interactive remote service with an active RST accessing their machine. The interviews indicate many aspects of trustworthiness comprising a number of slightly different accentuated beliefs such as benevolence or knowledge of the RST. Due to the high risk perception of remote services, the customers’ perception that the RST adhered to a set of agreed-on principles seemed to be important because this aspect was repeatedly emphasized in the interviews. This belief in the integrity of the RST was implicated by the following statements from managers in Germany and in the USA: "Because we have a good relationship I believe that they only take agreed-on actions, e.g., just checking the fixing unit .... and if I tell them that we don’t want them to check anything else then they wouldn’t do that." (P.3, I.2, customer, Germany) "This is my experience with [company name], how the work has well trained, very well mannered technicians, and if I ask them not to do something I’m sure that they will just not do it." (P.9, I.7, customer, USA) In general, the RSTs were attributed with high skills and competencies. Some customers ventured the opinion that the skills of an RST are higher than the skills of on-site technicians, because remote services enable them to get in contact with experts compared to locally available on-site technicians. The competence of the RSTs was seen as one of the major and essential reasons for a successful service outcome: "...It is similar in remote services, meaning that the guy who remotely logs into our machine is most likely the absolute specialist for this model. Not like a typical mechanic, who has to do 20 different machine models and just happens to be the guy located in the branch nearby." (P.1, I.1, customer, Germany) "... web remote service is only as good as the technician is. If you don’t have good people it becomes like calling AOL – America Online – they’ve got these guys that read from a script." (P.9, I.7, customer, USA) In only a few cases was the general trustworthiness of a RST seen negatively. One customer in Germany, who was dissatisfied in general with the remote service, attributed his feeling of disappointment to a lack of benevolent behavior of the RST. This underlines the importance of

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trust-assuring behavior in gestalt of doing good, showing sensitivity to the needs of the other party, and not taking economic advantage of the other party. The customer stated his opinion of technicians with these words: "Sometimes I get the impression that they [the technicians] don’t have a clue what to do. They do not really help me. At least they don’t do everything in their power to help me and they do not think proactively and foresighted." (P.5, I.4, customer, Germany) Customers favored the personal contact of an remote service technician and liked to work with acquainted personnel. The interviews conveyed the need for social contact and interaction with the RST, e.g., via telephone during a repair of a machine. ".... Well, yes, I miss the personal contact, because I’ve liked everyone of the guys, they’ve sent in here. But, we got a business to run." (P.9, I.7, customer, USA) Customers tended to require a remote service technician with whom they were acquainted with, especially when there was an emergency case. Based on prior encounters and service experiences, customer employees and provider employees were able to build up a stable relationship. The concrete relationship between an RST and the customer is a key risk-levering issue, which nearly every interviewee mentioned. If the customer knew the remote service engineer personally, he felt reassured about the know-how and integrity of this person: "Especially the new recruited engineers might sometimes do something wrong." (P.28, I.18, customer, China) "If we know the roles and skills of the technician, we can require a suitable remote technician for the task. It is good to get a qualified technician, it improves our communication and it can help speed up fixing the problem." (P.30, I.19, customer, China) Nonetheless, the latent wish for personal contact can negatively affect the perception of a remote service when the remote service is perceived as not sufficiently providing social contact. The customers complained about the limited means of communication with the RST during the remote service process. A production manager from a small print shop stated: "I hope we could have more communication with the service technicians." (P.28, I.18, customer, China) Also, to improve the cooperation between customer and engineer, a personal dialogue is helpful as a customer employee from China stated: "There are many things which can only be understood after much communication with [the remote service provider]. Without face-to-face communication or phone calls many things are difficult to understand, because we are not in the same level, maybe sometime we can understand what he told us. Therefore, we

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need more [face-to-face or telephone] communication." (P.29, I.19, customer, China) Moreover, some customers chose local services with direct personal contact to a technician over a remote service based on their need for social interaction but demanded immediate remote service when the problem was urgent and severe. It seems likely that the incentives to use a remote service vs. a local service are not enough to convince customers to increase their usage of remote services, except in emergency situations as a machine operator from a Chinese printing company stated: "It depends on if production is urgent. If it is not urgent, then we let them come to us. If it is urgent, then we can use it to solve problems at once and then continue production." (P.23, I.14, customer, China)

5.4.4.2

Trust in the Remote Service Provider Company

The interviews show that both the remote service provider employees and the remote service customer employees consider trust itself as fundamental when a customer adopts remote services. In addition to the RST, who acts as a service counterpart for the customer employee during the service delivery, the trust the customers have in the remote service provider company [RSPC] is important in forming their attitudes towards remote services. A manager of a US service provider company expressed it this way: "Trust is one of the most important factors for our customers. If they do not trust our company we could not sell the remote services to them." (P.12, I.9, provider, USA) A Chinese Manager from a remote service provider company located in Shanghai assesses the role that trust in the RSPC plays for his customers: "90% of the customers trust the brand, they are very trusting." (P.20, I.12, provider, China) Customers referred to the RST as their trustee more frequently than they referred to the RSPC. The brand image and reputation of the provider company also seemed to influence their evaluation of the remote services those companies offer. Customers’ statements confirmed these views. Further, when it comes to security concerns towards remote services it is especially obvious that a trusting relationship between the RSPC and the customers helps to overcome barriers: "So long as [the RSPC] guarantees that, I could certainly throw away my worry." (P.28, I.18, customer, China)

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5. Qualitative Exploratory Interview Study "We trust [company name] very much because we only use [company name] press machines up to now. ... The most important is we trust them and they can fix our problem." (P.27, I.17, customer, China) "No, there we do not have any concerns, because we have a good relation [to the RSPC] and trust him. He [the RSPC] must also trust us, especially in regard to "full-test" machines, as these are machines the customer is not allowed to touch. They [the RSPC] must trust us and we must trust them - mutually." (P.3, I.2, customer, Germany)

This understanding of mutual trust was mirrored by an RST, who also saw a need to establish trust in the customer employee: "For example if there is a new customer, a new operator, we worry about if we told them to check something, maybe he gets the wrong places, the wrong action, then we get a big problem. Still, we have to trust him." (P.15, I.10, provider, China) The trust the customers have in the RSPC strongly relates to the level of security a reputable company can guarantee and the experience it has with security issues. The brand name and a good reputation of an RSPC was evaluated by some customers as an additional security guarantee for the trustful behavior of the company as the customers assume that providers do not want to risk reputation loss. "With regard to security, we don’t worry about it. [The RSPC] is a big company, they would not set any bad options in my machine." (P.28, I.18, customer, China) "We do not think [hacker attacks] are a problem for us. .... Our system has installed a firewall. I think [company name] should have thought about this problem if they supply such a service." (P.27, I.17, customer, China) "In so far, that if a vendor like [company name x] or [company name y] .... would do something like that, it would make rounds in the business community. I believe the negative consequences would be catastrophic for the vendor." (P.1, I.1, customer, Germany)

5.4.5

Process Control Beliefs

Based on the interviews, I obtained evidence for the importance of process factors for the customers’ perceptions of remote services. Reoccurring statements touched upon themes such as the imagination of the process, the perception of control, transparency, and social presence. The customers mentioned the importance of control regarding the remote service process itself and also with regard to the actions of the RST. The importance of control beliefs also became

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clearer as the interviewees remarked on the need for control mechanisms to stop, abort, change, or direct the RST’s action; or that they should require such mechanisms. "Control plays a very important role. I want to decide what exactly is done with my machine." (P.4, I.3, customer, Germany) In an interactive remote service encounter, the importance of the employee who delivers the company’s services, usually the RST, is reinforced. An RST actively accesses the machine and sometimes asks the customer employee for support in the repair. As the RST’s actions are invisible and hard to control due to physical dislocation of customer and provider, customers lack sufficient evidence for service and may have difficulties evaluating the effort, quality, and security of the service process even while collaborating. The reduced observability of remote services triggers a mental simulation of the service production process in the customers’ minds, as one customer, a production manager from Germany, indicated: "Of course I’m thinking about what the technician is doing right now [during the remote service]. I can afterwards check the login-protocols but during the service process I have to essentially trust him." (P.7, I.5, customer, Germany) In the remote service situation where the stakes are high, control beliefs are likely to play an additional and substantial role in the remote service perception. Most customers view remote services as risky and search for tangible clues about the interactive collaboration process with the service provider, e.g., observable login-protocols or a real-time representation of the RST’s actions. "From the point of view of an operator like me, [the displayed information] certainly is useful." (P.23, I.14, customer, China) "One point, we must know what he is doing and make sure that his doings are allowed and does not disturb our production." (P.22, I.13, customer, China) Regarding the high risk perceptions of some customers, it is understandable that customers have a need to monitor the actions of the RST to prevent him from making (in their mind) mistakes and damaging the machine. Based on the customers’ statements it is fair to say that a high level of transparency and the availability of control mechanisms is definitely desirable during the complete remote service process. For example, an abort button displayed on screen and the possibility to terminate the remote service at any time is a feature that most customers said they appreciate: "I have complete control. If they’re doing something and I think that I don’t want them to do that, I can disconnect the service and they loose control over the machine." (P.9, I.7, customer, USA) "Yes, it’s important that there is some kind of "Power Off" button. .... Because first with the help of these two buttons we can check the status of the remote service; second, if some made a misoperation, we can correct in time, without the two

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5. Qualitative Exploratory Interview Study buttons we cannot. " (P.30, I.19, customer, China)

Also, the communication with the RST is not only seen as possible social interaction but also as a tool to control the RST as a general manager of a German printing company stated: "I would like it better ... if there is always a simultaneous telephone dialog taking place to check on his actions." (P.4., I.3., customer, Germany) The fear of loosing control even seems to hinder customers to permanently connect their machines to the remote service provider to allow constant remote monitoring. Some did not like to imagine being remotely monitored: "There are software companies, which hide some clauses in the contractual fine print, so that they theoretically have free access to all your data at any time; something I find partly illegal." (P.1, I.1, customer, Germany) "[Constant remote monitoring] would probably be going to far and I find it suspect, I would rather expect the machine to first alert the machine operator here and he decides whether to allow the machine "phoning" home or not. This is all going too far... I want to be in control or our operator or somebody like his foreman, the manager of operations or the head of printing. They should decide, if its ok to use this service or if its not ok." (P.4, I.3., customer, Germany) In addition to tools that provide transparency and control, the interviews show that trust in the RST can decrease control beliefs. Therefore, control beliefs will be strongly affected by the degree of confidence the customer has in the remote service provider company or the RST in person. This can be seen in the following statements: "With [company name] I don’t worry, I trust their people and only afterwards I look if anything changed. But with another provider it’s different. I am on alert during the whole process and always think about what he [the remote service technician] might do just now." (P.7, I.15, customer, Germany) "It is necessary that we have the control during all operations because the engineer especially the new recruited engineer might sometimes do something wrong, too." (P.28, I.18, customer, China) Closely tied to trust and the way personal contact is presented in a remote service process is the degree of social presence of an RST. From the interviews, it can be derived that a higher degree of social presence helps to build trust and decrease mistrust and the longing for control of the customer. Verbal communication, video-conferencing or the provision of information about the engineer like a photo seemed to be appreciated and wished for by the customers: "We think telephone call is easier to communicate with them. We feel that they are more closely with us." (P.22, I.13, customer, China) "Sure, it is very useful [video conferencing]. That will be the best if you can talk

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face-to-face for this kind of service. But costs must be considered." (P.23, I.14, customer, China) "[Things we would like to know are:] Name, telephone number. It is very important if we know him [the RST]. But if we do not know him, we want to know his specialty, for which kind of problems he is good at. .... In the new version we can look the photos of engineers. We feel it is good." (P.22, I.13, customer, China) "If I know you and the next time you call or I call you, I have a face to put with the voice and it just makes it more .... Even if I knew some of the man I haven’t met or ladies, it makes it a little bit easier for me to make me feel comfortable with them." (P.9, I.7, customer, USA) The customers wished to get more information about the emergency cases and the reasons for the problems. They also wanted to have further documentation covering the status of the machine parts, reports on the health-status of tools, repairs, damage and how the issue was solved. Some customers emphasized their wish to learn how to conduct the repair steps from the RST. In the interviews, a need for process transparency became apparent. Transparency can be established by communication on the process or log-in mechanisms and protocols: "I wish the service has a documentation function. That means, it can record detailed information of each time, for example, when, where, which machine, the reason, how to fix, and so on. We record the information by ourselves, too. The function is just like case history of a patient. It is very important. " (P.27, I.17, customer, China) "We can only see who is connecting to the remote service. Sometimes a photo of a German engineer is shown on the screen. I can only see these two things. And I can also see on which pages are they operating, but I cannot see his conclusion." (P.30, I.19, customer, China) "I hope, the screen can show us both, components that are working well and components that have problem. It can also show us what the problem is or which potential problem some components have. In case of problem it can give us a result description." (P.27, I.17, customer, China)

5.4.6

Economic Values

In a business setting, the benefits of remote services and their relative advantage compared to face-to-face delivered services expectedly influence the customer employees’ perception of remote services. In general, the customers were very well aware of the advantages that come along with using remote services compared to using on-site services. The most prominent advantage in the view of the customer was the increased availability of the machine due to time

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savings in emergency cases, especially when compared to the time needed for a technician to physically go to the customer: " And it’s much faster and easier for both sides, it’s a win-win-situation ... for both sides of the remote service. They can solve the problem faster and easier and less costly for both parties .... If I had the option to give up remote service and call for somebody, I wouldn’t. With the remote service they are going to answer the phone today. And they going to call me today. And we are going to work on that press." (P.9, I.7, customer, USA) "The biggest advantage [of remote services] is simply: time. The distances to the printing machines vendors are often very large, especially with [company name x] it’s over 600 km. And also you immediately have a competent counterpart that also has access to the machines controls. He can say where the problem is, they even can locate mechanical parts." (P.2, I.1, customer, Germany) "That’s the main aspect of the whole thing [remote service]. I find that we save costs and - foremost - time through this remote service, especially machine downtime can be reduced, as in many cases the problem can immediately be solved." (P.3, I.2, customer, Germany) Additionally, using remote services for scheduled/planned maintenance purposes is also seen as an advantage. Flexibility in scheduling maintenance has a positive effect on maintaining machines and can reduce downtime. "It can shorten the maintenance time. If they can fix the problem through remote service, the maintenance time will be reduced." (P.25, I.15, customer, China) As to be expected, customers regarded price as an important factor for the decision to buy or continue using remote services. Some of the interviewees, who had already used remote services, were still in the guarantee period, so that they hadn’t had to pay for these services yet or were preferred customers who got them for free. Considering the importance of the price criterion, it is striking that some potential customers had only a vague idea regarding the costs of remote services. When the customers who were uncertain about the price of remote services were asked to estimate they imagined the price as being "high" and as a barrier to using the remote service: "It is difficult to say. I think it depends on how much we must pay for it. If the price is high, then perhaps we will stop it." (P.25, I.15, customer, China) "My question is, it is free of charge in the guarantee period, after this period it will be charged, but how much it will be charged?" (P.28, I.18, customer, China) "Another reason [for not using remote services] are maybe the costs." (P.29, I.19, customer, China) "A lot of customers are worried about the costs in future." (P.15, I.10, provider,

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China) In addition to the full amount of costs, the pricing model influenced the customer’s rating of usefulness of remote services. The pricing models seem to vary within the printing industry, e.g., remote services can be bought in a contractual setting or just purchased at an hourly rate, as the following statements show: "With [company name] I think it [remote service] should be standard, included with the machine. In post press some providers offer it as an option. With others it is standard. There are different models. Some charge a hefty surcharge when buying [the machine] with a modem-connection, but this then includes a lifetime of remote support service. Others include the modem but charge for the usage and their effort in the remote service." (P.1, I.1, customer, Germany) "In press side, we have a maintenance contract. This is, we buy service hours for either on-site or remote services."(P.22, I.13, customer, China) Customers further stated that apart from the maintenance contracts with the RSPC, they saw additional costs associated with the remote service such as internet fees, network equipment, and cabling. The customers evaluated these costs as relevant and consider them in their evaluation on whether to buy remote services. "The cost includes Internet fee and the service fee [the RSPC] charged." (P.29, I.19, customer, China) The customer interviews indicate that often an on-site visit from technician has to follow a remote service because the problem could not be fully resolved. But until then, the remote diagnosis bears the advantage to gain information about the problem in advance to prepare the right tools and spare parts for the on-site technician. Customers interpreted this fact in different ways. Some customers felt safer if they got an accurate description of the situation through a quick remote service check. This allows them to assess the seriousness of the problem as well as the extent of the necessary repairs. Other customers saw the difference but did not value it as much: "Sometimes, we do not understand the showed problem, so we can ask for a remote service or call. Through remote service, they can direct check the errors showed in computer. It is better than telephone call because they can know our problem accurately." (P.23, I.14, customer, China) "If we really use the remote service, for example, they know what happened, such as some parts are broken, and then they can bring it along." (P.24, I.14., customer, China) "Only sometimes does the remote service allow to pinpoint mechanical problems. Only in rare cases the problems are due to software flaws, that could be fixed remotely. In so far the benefit of remote service should not be overestimated, even

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5. Qualitative Exploratory Interview Study though I would not want to do without [remote services]." (P.1, I.1, customer, Germany) "The biggest advantage first: I can get help with any problem I have within a half hour. If I had to wait, if we did the phone based supporting and I could not resolve the issue with their help, I had to wait for a technician. I still may have to wait for a technician with the remote service. But when that technician shows up he has all what he needs to fix the machine." (P.9, I.7, customer, USA)

Customers also consider the likelihood of an emergency event in their overall evaluation of remote services, because they perceive the remote service as being useful only when there is a problem with the machine leading to down-time. Depending on the quality perception of the machine manufacturer, they judge the likelihood of these incidents and base their decision to use remote services on that evaluation. Because the customers only rarely had problems during the warranty period, they did not experience the benefits of remote services first hand. This leads to customers forecasting the actual usage too low, judging the benefits as too small, or to the customers feeling that the price is too high: "Yes. I think, in the future two or three years the possibility is very high that the condition of the machine is similar to now. That means, we just use it once per year. Then the price should be relative high. As a result we will not consider it." (P.25, I.15, customer, China)

5.4.7

Participation Beliefs

Interactive remote services are intensively co-produced by the business customer employee together with the remote service engineer. Therefore, the customer’s own attitude becomes an important driver of his service perception. This is supported by the interviews, which indicates that the motivation of the customer and his mental image of the collaboration with the RST affected his attitude towards interactive remote services. This factor is not so important in remote monitoring services but becomes more so in interactive remote services such as in a remote repair where the customer might support the RST by performing mechanical tasks. Nearly all interviewees were very positive regarding the co-production activities of the customers during a remote service, i.e., performing mechanical tasks on the machine, opening and checking the water cabinet, or changing replacement parts as a customer explained: "Yes, we know. For example, some problem occurs, like problem about water cabinet, we do not know how to solve the problem, after the engineer has checked they tell us, the reason is some switch is turned off. Then we will turn it on. It is just some easy operations like this." (P.24, I.14, customer, China) In general, customers were not too worried about the collaboration. They have had good expe-

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riences and appreciated the guidance they got from the RST, which provided them with a clear image about what was expected from them during the interactive remote service: "It’s very easy. And I’m very comfortable with the equipment. I have been running the equipment for a while, so I’m comfortable doing that. It’s easy. They take me through it step by step. They are not asking me to take the machine down into a lot of little pieces. But they are asking me to do the things that I should be able to do on this end." (P.9, I.7, customer, USA) "This is implemented in a very good manner, as if for example a technician or a software engineer logs into the machine - and I stand next to the machine’s screen - they can guide me through all the menus. With the mouse he directs me to click on item a or item b; he guides me exactly ... But without this guidance, a normal machine operator would not find it." (P.3, I.2, customer, Germany) "I think it is no problem, because only in case that we understand what they told us through internet, then we do some actions; if we do not understand, we do nothing." (P.27, I.17, customer, China) In contrast to the customers’, the remote service provider employees frequently mentioned the existence of customers with doubts about their own ability to perform in an interactive remote service. Managers of remote service provider companies in Germany and China emphasized the existence of self-efficacy doubts of the customer: "I believe that especially inexperienced employees of our customers have fears of using a remote service technology." (P.8, I.6., provider, Germany) "... we talk to the operator on the phone and tell him to do some job in the machine. This is why the operator worries about touching the machine, because he worries about he is not a electrician or a mechanician. .... I think, it is true, that the worries are acceptable because he is just an operator, he needs to take care of this engineering job on behalf of our company at this moment." (P.16, I.11, provider, China) It is striking that the participation of a customer in a remote service is potentially dangerous if he fails in performing his task. Remote service managers in China classified such failures as a disaster with respect to legal issues and consequences for the production process: "There is another safety issue, about which we also worry. If we are telling them [the remote service customer employees], I mean, to do the wrong step, unluckily or accidentally; if they really do the wrong thing then that would be really a disaster." (P.18, I.11, provider, China)

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5. Qualitative Exploratory Interview Study

Cultural Differences in the Customer’s Willingness to Collaborate

Although outside the scope of this study, the point of cultural differences emerged during the coding and analysis process of the study. In my qualitative interview study, I could identify two extreme views of customer’s on their attitudes towards co-production in an interactive remote service, that were expressed by customers from China and Germany. The difference in their attitude towards collaboration is backed-up by the opinions of remote service provider employees from the United States. Customers from Germany and China seem to have opposing views on their own participation in an interactive remote service. A statement of a German machine operator represents a skeptical attitude: "Sometimes I think it is not fair that I should help the remote service engineer. It is his task - not mine - to repair the machine. He should come over to repair it." (P.5, I.4, customer, Germany) "I, for example, do not think that the exchange of spare parts is the task of a printer [printing machine operator]." (P.7, I.5, customer, Germany) Although most of the customers I interviewed had a professional and positive view on their support function in a joint remote repair, it is striking that in some Chinese customer interviews the collaboration process was tightly connected to an experience of knowledge sharing and worthwhile accomplishment. This is in contrast to statements of refusal from two German customers. In my interviews, most Chinese customers saw more than just the practical benefits in supporting the remote service engineer. The customers felt honored to help in the remote service repair process and thought that they could speed up the repair process by sharing their knowledge. In contrast to statements of customers in USA and in Germany, most customers in China mostly trusted the analysis of the RST and were willing to follow his instructions. "I like that I have to help the [remote service] engineer. I feel appreciated and I am happy that he values my support and knowledge. I think that without me, the remote repair would not be effective." (P.26, I.16, customer, China) "It is very important that we can share some knowledge through remote services." (P.22, I.13, customer, China) Just once in the interviews, the remote service provider employees mentioned a refusal of a Chinese machine operator to support the RST, but this was because of an unclear understanding of hiararchy: "You know, sometimes they [machine operators] refuse to do [support]. They only accept orders from their management, they have to do it. Because the Chinese, they still, you know, do follow the rules." (P.18, I.11, provider, China) In addition, three remote service provider employees from the USA saw cultural difference in

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terms of a willingness to co-produce between the customers in the USA and Germany. They especially referred to German customers as more unwilling to co-produce than customers from the US, which they considered to be more experienced with remote collaboration: "So, they [the American customer] will go much further than the German customers in order to try to resolve the press over the phone. .... We have multiple people like machine operators, electricians, and mechanics basically look at the screen and fuel their knowledge, resolve a mechanical lock-up of a protector device, which is a complicated device, and resolve it over the phone. I don’t think anybody has done this outside of the US. So this is the typical US-stuff, they always go further than everybody else. ..... it’s just their willingness to go the extra mile and they rely on their own knowledge or whatever." (P.11, I.9, provider, USA) "The customers in the US are a lot more used to working, actually working, with people over the phone to help troubleshoot their problems." (P.14, I.9, provider, USA) "It is integration of the customer because you have to have a relationship with him and he has to be willing to do stuff. And that’s I think the big difference between Germany and the US. The customers in the US are much more used to, for years have always done that." (P.12, I.9, provider, USA) The study of cultural differences was not a primary objective of this research, but the statements hint at differences in the attitude towards co-production between customers in different countries. Thus, in the interviews with Chinese customers, the role of appreciation and knowledge sharing stands out, whereas in the interviews with German customers, some customers emphasized their disapproval of providing support during a service for which they pay. American remote service providers see a difference in the willingness to collaborate between German and American customers mostly due to the fact that the latter have more experience with this type of collaboration. These findings raise interesting questions on cultural differences and could be a fruitful avenue for future research.

5.4.9

Prior Experiences

Experience seems to influence how customers evaluate the perceived usefulness of remote services. Customers who have had positive experiences tended to be more loyal and give a more positive evaluation of remote services. They also based their decision to continue using remote services on these positive experiences: "And we [note: the RSPC and the customer] have been successful .... And as long as that continues, we’ll be in good shape and we will stick to [RSPC]." (P.9, I.7, customer, USA)

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5. Qualitative Exploratory Interview Study "We have been a "full-test" customer of [company name] for 10 to 15 years.... through that we have gained a lot of experience with how good remote service is. Based on that we decided to buy or rent remote services for all our machines." (P.3, I.2, customer, Germany)

If the customer had bad experiences, e.g., the remote service did not fully solve the problem, then he was skeptical about the general value of remote services or did not see the value at all: "This is a very practical problem. Maybe remote services has not let us experience its full benefit. .... this service can not let us get the feeling that our problem will be solved if we use it. Normally, we must connect with them for several times. At the same time a telephone call is also absolutely necessary. And at last, they have to come to us. I think, maybe because the limitation of their knowledge, they cannot always solve all problems if we have trouble." (P.22, I.13, customer, China) "From a remote diagnosis I expect that I get a definite statement if they can solve the problem or not, that they log in the control and remove the blockade, that the machine works again. But most of the time they cannot do that." (P5., I.4, customer, Germany) "The downside is that if somebody doesn’t have any problems, which is a good thing, then he doesn’t see the value. He thinks "All this year I didn’t have to use my remote service." It is just like anything else, if you don’t utilize it, you find the value lower." (P.10, I.8, provider, USA)

5.4.10

Organizational Factors

The interviews suggest that the tasks and responsibilities in remote services are performed by a small set of functional roles within the customer companies. These vary slightly in their terminology from company to company but commonly include: production managers; owners/managers; and machine operators. In the smaller companies multiple roles are taken by one individual, e.g. the owner/manager. Most interviewees had participated in a remote service and were included in the decision to use or buy a remote service contract: "I am involved in making the decision to use remote services." (P.3, I.2, customer, machine operator, Germany) "I have a little bit of, my opinion weighs pretty heavy for the owner. I am the production manager and he hired me specifically for my talents to work with the people and the equipment. And if I’m madly against it, he is going to pay a lot of attention to that. " (P.9, I.7, customer, production manager, USA)

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"Typically I think the guy who would call in is the supervisor, something like that, the production manager, not as often the press man." (P.11, I.9., provider, USA) "Normally they have a production manager, a guy to handle the operation. And then the two operators at the machine." (P.17, I.11, provider, China) "The foremen and department managers are the ones who are responsible for the remote service." (P.1, I.1, customer, Germany) Employees, especially from lower ranks, do not form their attitudes solely on their own perceptions. Instead, there seems to be a general understanding of attitudes towards remote services that is influenced by superiors. These attitudes towards remote services seems to be tied to a general openness on an organizational level towards new technologies itself. For instance, bosses saw colloborative behavior as a duty of their employees: "We are open to improvements and new technologies in our company. My boss always supported us in using remote services." (P.3, I.2, customer, Germany) "Of course they will, they must be willing to do that. If they don’t like it, it means they are irresponsible for their job ... ."(P.28, I.18, customer, China) In interviews with some Chinese companies, it was striking that some customer companies wanted to learn from remote services to make their own repair attempts and that the management advised them to observe and learn from a remote service. The management of customer companies sometimes not only wanted their employees to learn but also wanted to monitor their staff through the remote monitoring feature. They wished to find out who might be responsible for problems and exert higher pressure onto their personnel to become more efficient: "We can learn something through this [remote service] process. Then perhaps we can solve it by ourselves if the same problem occurs." (P.22, I.13, customer, China) "If there are some problems with the machines, feedback can be shown on the screen and they can tell us, for example, first, what went wrong; second, what we have done wrong; and what we should improve. Then, it should be better." (P.30, I.19, customer, China) "We hope that the machine can permanently connect with the remote service, like just said, and the engineer can periodically, let’s say once a month connect with our machine to check if our operators work with the machine regularly. If his work isn’t reasonable, he should tell him and let him improve, if he won’t change because of laze, he should tell his supervisor about that. Then we will let him change, so that the machine can work normally." (P.28, I.18. customer, China) The influence of a company’s general attitude toward technological advances on the decision to favor remote services instead of face-to-face services was mentioned briefly by a remote service

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provider employee: "No, it’s just that in the press world they were slow to transitions into the computer age. Yes, it is, because it is very analog system that hasn’t really changed or revolved over the time and people get into a pattern of being used to one thing and having a service tech come in." (P.10, I.8, provider, USA)

5.4.11

Contextual Factors

From the results of the interview study, it is apparent that the infrastructure of the remote service customer companies influences the availability of these services. Although most customers had no problems with accessing the internet, two customers in China had issues with their internet connection and extended these negative associations to remote services: "In general, we feel they [remote services] are very good. But sometimes because of network problems, for example, the network is very slow, the efficiency will be affected." (P.22, I.13, customer, China) "I think another problem is hardware, in Beijing especially, hardware and the network situation here. Some customers, small customers, rent a house very simple in the countryside. Even for the phone line the telephone is a problem." (P.15, I.10, provider, China) When examining the customers’ internal network situation, one aspect, which might have had an effect on remote service usage, became obvious from visiting customer’s facilities and some off-the-record discussions. Usually, the printing machines need to be connected via a physical cable to the network. A number of facilities were not prepared to have the network access near the machine. The cable then was just lying across the workspace. This led to disconnects when somebody accidentally got caught in the cable and pulled it out of its socket, breaking the network connection. A Chinese customer put it into words: "It is ok when it is connected all the time. But we may take it away if the machine doesn’t work wrong because the cable is a hindrance to us if we are working. Therefore, we take it away." (P.28, I.18., customer, China) The same inconvenience factor also applies to a German customer, who complained about insufficient technological compatibility of the machines with standard network configuration. "What bothers me, ... and what is a huge problem with the remote access is that they [the providers] requires an analogue modem connection. This led to the addition of analogue ports to our telephone system, this meant a huge effort to do the cabling, instead of just using a wireless-lan card inside the machine. We already have wireless-lan everywhere in-house. I would imagine that to be

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much simpler, but here the machine vendors and providers are very reluctant and still work with analog modems." (P.1, I.1, customer, Germany) From the interviews, it can be derived that one of the reasons for not using a remote service is the availability of a traditional local service in the customer’s vicinity, especially in China and the USA. A customer from Hong Kong, who operates near a remote service provider company’s office in Hong Kong, put it like this: "Maybe, because we are very close to [company name’s] office, we do not need this kind of service. In case that we were very far way from them, if they can [remotely] identify the problem, for example, and if some parts are broken, they can sent them to us. Therefore we can save time. At this point it is helpful ... But we are nearby, if I suggest it [remote service] to him [contact at company x], he will refuse and will say "we can ask them to come quickly."" (P.24, I.14, customer, China)

5.4.12

Discussion of the Results

The qualitative interview study delivers unique insights, because it is the first study focusing on remote service perception. It identifies not only single beliefs groups, but also enables a holistic view of various remote service perceptions. Based on the interview analysis, I developed a comprehensive conceptual framework of factors that are relevant for remote service attitudes and perception. The framework summarizes the findings of the qualitative study and is presented in figure 5.5. The conceptual framework of factors influencing the perception of remote services comprises 44 different beliefs nested in eight main belief categories. The identified belief groups encompass the beliefs remote service customers directly or indirectly mention in this study. The relevant main influencing factors are discussed below: 1. Relational Beliefs: Relational factors such as trust towards both the remote service provider company and the remote service technician positively relates to the customers’ perception of a remote service. Customers’ trust a remote service provider company because of its good brand image and well-known reputation. In turn, this is taken as an assurance for not letting them down. The trustworthiness of the remote service technician seems to be very important for the customers, especially if they are evaluating an interactive remote service with an active RST accessing their machine. The behavior of an RST is perceived as trustworthy when he acts with integrity, has the interest of the customer in mind, and shows competence and effort. For customers who value personal contact, the perception of a remote service can be negatively evaluated if the remote service is perceived as not sufficiently providing social contact.

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2. Process Factors: Customers want to have control over the remote service process. The interaction with a non-observable remote service technician becomes the object of control wishes, especially in interactive remote services. The customer’s imagination of the remote service process can be influenced by fears over misbehavior of the RST. Further, a high level of transparency and the availability of control mechanisms are positively remarked on because they enable the customer to stop, abort, change or direct the RST’s actions. In addition to perceived controllability, a high level of social presence of the remote service technician enabled via advanced communication technology such as video streaming or live chat enhances customer’s attitudes towards remote services. As much as customers wish to control the RST, there is also an equally strong fear of being controlled by the remote monitoring of their machines. 3. Participation Beliefs: Customers showed different views on their collaborative part in interactive remote services. Their perceptions ranged from seeing collaboration as an appreciation of their work and the sharing of knowledge, to uncertainties about whether the remote service technician had the power to direct the customer, to the outright refusal to take responsibility during the repair. The efforts of the remote service technician to guide the customer during the collaboration process were evaluated positively by a majority. Self-efficacy and ability beliefs were also important when the customer formed his attitude towards interactive remote services. 4. Technology Beliefs: During a remote service encounter, perceived attributes of the technology play a major role in the customers’ perceptions. A high risk perception of the customer regarding security issues negatively affects customer’s attitude towards remote services. It is important to note that especially interview partners with none or just few remote service experiences perceive the risk as a major drawback for remote services usage. Trust in the technology, the ease of use, and the convenience in using the technology, as well as a certain level of technology affinity foster a positive evaluation of remote services. 5. Economic Value: In a business setting the benefits of remote services and their relative advantage compared to face-to-face delivered services strongly influence the general perception of customer employees of remote services. Time savings, costs, pricing, and contractual models are factors that are perceived as important attributes of remote services. 6. Experience: Prior experience seems to influence how well the customers evaluate the usefulness of remote services. Customers who have had positive experiences tend to show loyal behavior, satisfaction, and a more positive evaluation of remote services. Customer’s who did not have any experience rely on their imagination of remote service scenarios. In contrast, customers with bad experiences are more reluctant to use remote services.

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7. Organizational Factors: Employees, especially from lower ranks, do not form their attitudes solely on their own perceptions. Instead, there are influenced by norms set by immediate superiors, management, and the organization as a whole. The attitude of the individual employee, the attitudes of superiors, management support, and the organizational openness for technology and services all influence the organizational decision towards remote services. 8. Contextual Factors: The technical infrastructure of the remote service customer companies, the distance to the service provider, and the size of the company impacts the availability, past experiences, and attitudes towards remote services. The findings from this qualitative study contribute to marketing literature by discovering the substantial role of the RST’s behavior on the customers’ perception of remote services. Also, it sheds new light on the interplay between trust and control, the relationship between interaction and technology, and the customer provider nexus in B2B service settings. Managerial implica-

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Figure 5.5: Factors Influencing Remote Service Perception

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tions will be discussed in the end of this dissertation together with insights from the quantitative studies. The above results, foremost the conceptual framework, are the foundation for the hypotheses derivation and the model development discussed in the next chapter. There, the identified factors influencing remote service adoption and continuance will be put in the context of literature and the interdependencies are discussed. The resulting model will then be validated within the quantitative studies presented in chapter 7.

Chapter 6 Hypotheses Development In this chapter, I propose three major groups of hypotheses. First, I outline the hypotheses of the Interactive Technology-Mediated Service Usage Model (ITSUM) based on the results of the qualitative interview study (see chapter 5.4.12) and on the findings of prior research in marketing, IS, and management literature (see chapter 3). The ITSUM aims to clarify the causal relationships of the variables and to explain the customer companies’ intention to use remote services. Next, I will extend the ITSUM by proposing a link between a company’s intention to use interactive remote services and actual usage behavior. In a third step, I present my hypotheses regarding group differences between antecedents of adoption and continued usage of interactive remote services.

6.1

Development of the ITSUM

The ITSUM hypotheses are based on behavioral models such as the TRA (Fishbein and Ajzen 1975) and the TPB (Ajzen and Fishbein 1980). These models aim at explaining usage behavior14 via intention to use, which in turn is determined by individual beliefs and attitudes towards performing the behavior. This reference frame is adapted to the most salient interactive remote services belief groups that were identified in the qualitative study: 1. C OUNTERPART BELIEFS comprise trustworthiness and controllability beliefs directed at the remote service counterpart and his actions. They refer to the process control beliefs and relational beliefs identified in the qualitative study. 2. T ECHNOLOGY BELIEFS consist of ease of use and trust in technology. 3. U SEFULNESS BELIEFS comprise perceived usefulness as identified in the qualitative study as a part of economic value. 14

The link between the individual perception of intention, organizational intention, and actual organizational behavior is addressed by a follow-up study at a second point in time (see chapter 6.2 and chapter 4.1)

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Figure 6.1: The Extended Interactive Technology-Mediated Service Usage Model 4. PARTICIPATION BELIEFS comprise role clarity, role ability, and motivation of the remote service customer. 5. O RGANIZATIONAL CHARACTERISTICS comprise subjective norms, function, and company size as identified in the qualitative study. An overview of the final result in the form of a graphical representation of the ITSUM model is given in figure 6.1. The derivation of each hypothesis is described in the next sections.

6.1.1

Counterpart Beliefs

6.1.1.1

Controllability of the Counterpart’s Actions

The findings of the exploratory study show that customers wish for transparency of the RST’s actions and the possibility to control the actions of the RST. The RST’s actions are mediated through technology and hard to control due to the physical dislocation of customer and provider companies. In particular, the customers’ need to be able to control the actions of the service counterpart that directly affect the service object. Or as a customer put it: "Control plays a very important role. I want to decide what exactly is done with my machine." (P.4, I.3, customer, Germany).

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Especially interview partners with few or no remote service experiences perceive potential risks as major barriers for remote services usage, e.g., they fear that the RST could actually damage the printing machine. In this context the absence of control has been described as undesirable, and the perception of controllability of a customer is closely tied to his risk evaluation and will likely influence his decision to use remote services: "One point, we must know what he is doing and make sure that his doings are allowed and does not disturb our production." (P.22, I.13, customer, China). These findings are supported by literature in the context of e-services adoption, as researchers found that service perception and intended service usage are adversely affected by risk perceptions (e.g., Featherman and Pavlou 2003; Hsu and Chiu 2004; Pavlou 2003). In this study, controllability beliefs refer to the degree to which the service customer believes that he is able to exert control over the RST’s behavior during the service process. Control over a (human) counterpart’s actions has not yet been measured in an adoption or continuation context. Even though this construct is new in the context of technology mediated services, controllability has been studied in other contexts such as control over one’s own behavior, control over service processes, and control over another organization in strategic alliances. In the TBP the perceived control over one’s own action is a usage antecedent (Ajzen 1985; 1991) and reflects a person’s perception of ease or difficulty toward implementing the behavior of interest (Ajzen 2002; 1991). Because some behaviors pose difficulties of execution that might limit volitional control, the TPB considers perceived behavioral control in addition to actual control. Control over one’s own behavior has been shown to positively affect consumer feeling and consumer satisfaction (Hui and Bateson 1991; Hui and Toffoli 2002; Namasivayam 2004). In the context of technology or technology-intensive service adoption, the perception of control is interpreted as the perceived control over facilitating conditions (e.g., the functionality of a website or an ATM) or as the perceived procedural control over the outcomes and processes when using a technology or technology-intensive service. For explaining usage of services, the effect of control on usage intention has been supported in studies on telemedicine (Chau and Hu 2002), online-banking (Lee and Allaway 2002; Liao and Shao 1999), e-commerce (Pavlou and Fygenson 2006), e-brokerage services (Bhattacherjee 2000), e- and mobile coupons (Dickinger and Kleijnen 2008; Kang et al. 2006), computing services (Taylor and Todd 1995b;a), and technology free services such as housing services (Christian, Armitage, and Abrams 2003). The view of control in organizational science and management literature which defines control as a process that regulates behaviors of organizational partners in alliances and cooperations (Bradach and Eccles 1989; Cardinal, Sitkin, and Long 2004; Das and Teng 2001) is closest to the form of control proposed in this thesis. Control increases the predictability of the partner’s future behaviors (Nooteboom 2002; Vlaar, Van den Bosch, and Volberda 2007). Therefore it reduces uncertainty and encourages risk taking (Mayer, Davis, and Schoorman 1995).

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The more control over the RST the customer perceives, the more likely he will use interactive remote services despite potential uncertainties and risks. For example, if a customer is able to terminate the remote service engineer’s access to the service object or is able to make decisions regarding the service process at any time, he will have a higher perceived controllability and, thus, would be more likely to accept services being performed remotely. The control does not need to actually be exerted. The possibility to control the provider’s proxy is often perceived as sufficient (Namasivayam 2004). Assuming that an individual customer employee in a B2B remote service situation participates in the decision to use remote services, the following is proposed: H1: The individual customer employee’s perception of the controllability of the service counterpart’s actions is related positively to the organizational intention to use the service.

6.1.1.2

Trustworthiness of the Counterpart

In the qualitative interview study, not only the control over the service counterpart emerged as a dominant topic, trust towards the service counterpart was also a major factor in forming customers’ attitudes about remote services. The customers lack sufficient "evidence for service" (Bitner 1993) because of the reduced observability and the perceived risks of interactive remote services. This triggers the imagination to run a "mental simulation" of the service production process in the customers’ minds (Taylor et al. 1998). For example, a production manager stated: "Of course I’m thinking about what the technician is doing right now [during the remote service]. I can afterwards check the login-protocols but during the service process I have to essentially trust him." (P.7, I.5, customer, Germany). Interview partners with few or no remote service experiences perceived the risk as a major barrier for remote services usage, e.g., they fear that the RST could actually damage the printing machine and worry about data espionage. These findings are supported by literature in the context of e-services adoption, as researchers found that service perception and intended service usage is adversely affected by risk perceptions (e.g., Featherman and Pavlou 2003; Hsu and Chiu 2004; Pavlou 2003). This is recognized by providers as well as their customers, or as one provider put it: "Trust is one of the most important factors for our customers. If they do not trust our company we could not sell the remote services to them." (P.12, I.9, provider, USA) The trustworthiness of the remote service engineer itself was often named by the customers as one of the fundamental considerations when adopting a remote service: "My trust towards [company name] is based on the relationships I have with the people, the technicians." (P.9, I.7, customer, USA) Customers trust remote service technicians if their service counterpart acts with integrity, has the interest of the customer in mind, and shows competence and effort. Due to the reduced

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observability during an interactive remote service, customers must to a certain degree, simply have faith that the counterpart will act in a trustworthy and authorized way: "Sometimes I get the impression that they [the technicians] don’t have a clue what to do. They do not really help me. At least they don’t do everything in their power to help me and they do not think proactively and foresighted." (P.5, I.4, customer, Germany). The trustworthiness of the RST seems to be important for the customers, especially if they are evaluating an interactive remote service with an active RST accessing their machine. Trustworthiness of a service interaction partner has frequently been researched in marketing. A number of characteristics of trustworthiness have been identified including competence, customer orientation, helpfulness, sociability, friendliness, courtesy, empathy, credibility, and attentiveness (e.g., Gremler and Gwinner 2008; Hennig-Thurau 2004; Sundaram and Webster 2000; Surprenant and Solomon 1987). Service quality perceptions have been shown to be influenced by the service counterpart’s responsiveness, attentiveness, and empathy (Parasuraman, Zeithaml, and Berry 1985; Zeithaml, Berry, and Parasuraman 1988) as well as satisfaction (Froehle 2006). Trustworthiness beliefs are drivers of interpersonal relationship building (Crosby, Evans, and Cowles 1990; Hwang and Kim 2007) and encourage risk taking (Mayer, Davis, and Schoorman 1995). In inter-organizational relationships, trustworthiness of the partner organization has been shown to positively influence the engagement success (Costa and Bijlsma-Frankema 2007). The influence of interpersonal trust on technology-mediated service usage intention has not been researched explicitly before. In contexts where the object of trust is not a human service counterpart, trust has been researched, e.g., as "online-trust" towards e-vendors, e-service providers, or to websites. In this context, empirical evidence for the positive effect of trust on customer’s service usage intention and usage has been shown in studies on online banking (Suh and Han 2003), online recommendation systems (Wang and Benbasat 2005), web site usage (Bart et al. 2005), online shopping (Gefen, Karahanna, and Straub 2003b;a; Gefen and Straub 2004; Pavlou 2003) and e-services (Gefen and Straub 2004). In this study, the trustworthiness of a customer’s interactive remote service counterpart is understood as the customer’s beliefs in regard to his integrity, benevolence, and ability as defined by Gefen (2002b); Mayer, Davis, and Schoorman (1995). If the customer thinks that the RST acts in a competent and benevolent way and shows only agreed-on behavior, he is more likely to use remote services (Mayer, Davis, and Schoorman 1995). Therefore, assuming that an individual customer employee in a B2B remote service situation participates in the decision to use remote services, the following is proposed: H2: The individual customer employee’s perception of the trustworthiness of the service counterpart is related positively to the organizational intention to use the service.

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6.1.2

Technology Beliefs

6.1.2.1

Trust in Technology

One finding of the qualitative study is that customers may not perceive the remote service technology as trustworthy for a number of reasons. They fear data espionage by third parties, are afraid of hacker attacks, or might believe that use of the technology is more in the interest of the service provider rather than their own. A customer put it like this:"[The threat] is reasonable. We must avoid that the hackers can access our network. If we would get some viruses due to attack from hackers, it would influence our operation on the machine. Therefore, it [remote services] must be avoided. ... Could [the remote service provider] 100% guarantee that no virus can access my machine?" (P.28, I.18, customer, China) Trust in the technology counteracts a high risk perception and fosters the customer’s positive evaluation of remote services. Thus, customer employees may believe that benefits of the technology will outweigh its risks. A customer from China put it bluntly: "If so, we would like to pay the 2000 Yuan. Because if there is no virus, which can access this machine, we would like to connect to the remote service permanently, otherwise we are not interested." (P.28, I.18, customer, China) These findings are in line with trust and control theories that state that if a customer looses his trust in the technology he will be unlikely to take risks (Mayer, Davis, and Schoorman 1995). Bart et al. (2005) show that website privacy and security characteristics affect trust in a website. Johnson, Bardhi, and Dunn (2008) emphasize the influence of trust in technology on service satisfaction in self-service settings. Gefen, Karahanna, and Straub (2003b) and Pavlou (2003) identify trust in an e-vendor as a strong driver of the perceived usefulness of a website and a suppressor for perceived risk (Pavlou 2003). Trust in the recommendation systems influences a customer’s perceived usefulness of the system (Wang and Benbasat 2005). In this study, trust in technology refers to the trust the interactive remote service customer has in the technology necessary to deliver a remote service. The technology can be network and communication technologies or a remote service technology within the service object. Therefore, the following is proposed: H3: The individual customer employee’s perception of general trust in the service technology is related positively to his perceived usefulness of the service.

6.1.2.2

Ease of Use

Perceived ease of use (EOU) is one of the core factors of the TAM (Davis, Bagozzi, and Warshaw 1989). EOU was found to be a significant antecedent of perceived usefulness in several meta-analyses of the TAM (Lee, Kozar, and Larsen 2003; Ma and Liu 2004; Yousafzai, Foxall,

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and Pallister 2007a;b). This effect has been supported by numerous studies on IS-acceptance and technology-intensive service acceptance including: computing services (Taylor and Todd 1995b;b); online shopping (Pavlou 2003); online recommendation systems (Wang and Benbasat 2005); and IS system usage (Davis, Bagozzi, and Warshaw 1989; Venkatesh and Bala 2008; Venkatesh and Davis 2000). The interview study supports the major role technology attributes play in the customers’ perceptions. In the course of the interviews, employees from remote service customer organizations explicitly stated that factors such as the perceived EOU of the technology and the convenience in using the technology influence their evaluation of general usefulness of the technology: "I think remote services are pretty useful, but just recently I wanted to start the remote service procedure and I had problems with the technology and it was very difficult for me to handle this." (P.7, I.5, customer, Germany). In an interactive remote service situation, EOU is the extent to which the interactive remote service customer believes that using technology to request and receive a remote service is free of effort. The less effort the remote service technology requires, the more it increases the perceived usefulness. Therefore, the following is proposed: H4: The individual customer employee’s perception of the ease of use of the service technology is related positively to his perceived usefulness of the service.

6.1.3

Perceived Usefulness

The exploratory study shows that the benefits of remote services and their relative advantage compared to face-to-face delivered services strongly influence the general perception of customer employees and the organizational decision to use these services. Reasons why customers perceive remote services as useful compared to on-site visits of service technicians are for example, time savings and increased flexibility. A remote service customer stated: "And it’s much faster and easier for both sides, it’s a win-win-situation I think for both sides of the remote service. They can solve the problem faster and easier and less costly for both parties .... If I had the option to give up remote service and call for somebody, I wouldn’t do. With the remote service they gonna answer the phone today. And they going to call me today. And we are going to work on that press." (P.9, I.7, customer, USA). Perceived usefulness is a core construct of TAM research, which proposes that employees form their intentions toward behaviors they consider useful (Davis, Bagozzi, and Warshaw 1989). Meta-studies validating the TAM show, that perceived usefulness is the strongest determinant of behavioral intention (Ma and Liu 2004; Lee, Kozar, and Larsen 2003; Yousafzai, Foxall, and Pallister 2007a;b). Perceived usefulness is closely related to achieving extrinsic rewards, for example through increases in time savings and efficiency increases (Davis, Bagozzi, and Warshaw 1992; Fagan, Neill, and Wooldridge 2008; Vroom 1964). Extrinsic motivation has been shown to influence usage behavior in IS usage (Fagan, Neill, and Wooldridge 2008; Venkatesh,

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Speier, and Morris 2002) and in self-service technologies trial (Meuter et al. 2005). Next to extrinsic motivation, perceived usefulness also strongly relates to the relative advantage construct from Rogers’ (2003) innovation characteristics. In a review and meta-analysis of 75 studies, Tornatzky and Klein (1982) assess and support the effect of relative advantage on adoption behavior. Perceived usefulness has been shown to positively influence IS usage (Davis 1989; Davis, Bagozzi, and Warshaw 1989; Venkatesh and Davis 2000; Venkatesh and Bala 2008; Venkatesh 2000); online shopping (Gefen, Karahanna, and Straub 2003b;a; Pavlou 2003); and online recommendations systems (Wang and Benbasat 2005). Empirical evidence suggests that perceived usefulness is a main driver of technology-intensive service usage including: e-services (Kang et al. 2006; Lin, Shih, and Sher 2007; Pavlou 2003); mobile commerce services (Nysveen, Pedersen, and Thorbjørnsen 2005); e-payment services (Featherman and Pavlou 2003); computing services (Taylor and Todd 1995b;a); and telemedicine technology (Hu et al. 1999; Chau and Hu 2002). Perceived usefulness has been shown to predict usage intention in both adoption and continuance scenarios (Gefen, Karahanna, and Straub 2003a; Hu et al. 2009; Venkatesh and Bala 2008; Venkatesh and Davis 2000). For example, Naidoo and Leonard (2007) identify perceived usefulness as the strongest predictor of continued usage. In the context of interactive remote services, perceived usefulness is the degree to which the customer believes that using an interactive remote service would be helpful to the organization. If the customer thinks that an interactive remote service would be useful, he is more likely to intend to use it. Therefore, assuming that an individual customer employee in a B2B remote service situation participates in the decision to use remote services, the following is proposed: H5: The individual customer employee’s perception of the usefulness of the service is related positively to the organizational intention to use the service. Perceived usefulness is not only proposed to have a direct effect on intention to use interactive remote services, but also to mediate the effects of trust in technology and ease of use on intention to use remote services. This is based on the findings of the meta-analysis by Lee, Kozar, and Larsen (2003), who show that perceived ease of use is an unstable measure for predicting behavioral intention. Featherman and Pavlou (2003); Kang et al. (2006); Pavlou (2003); and (Wang and Benbasat 2005) identify a mediating effect of perceived ease of use on intention. Pavlou (2003) found that the effect of trust in web retailing on behavioral intention was mediated by perceived usefulness. Therefore, assuming that an individual customer employee in a B2B remote service situation participates in the decision to use remote services, the following is proposed: H5a: Perceived usefulness mediates the relationship between trust in technology and intention to use the service.

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H5b: Perceived usefulness mediates the relationship between ease of use and intention to use the service.

6.1.4

Participation Beliefs

The customer’s attitude towards the collaboration with an RST is an important driver of his intention to request a interactive remote service. The interviews indicate that the motivation of the customer and his mental image of the collaboration with the RST, his role clarity, and his self-efficacy affect his attitude towards interactive remote services. These attitudes towards the co-production in an interactive remote service are in line with findings on customer participation in human resources research and industrial psychology (Bowen 1986; Schneider and Bowen 1985; Vroom 1964). Bowen (1986) identifies a set of determinants comprising role clarity, ability, and motivation as drivers of customers’ co-producing behavior that is frequently used in explaining customer participation (Lengnick-Hall, Claycomb, and Inks 2000; Dellande, Gilly, and Graham 2004; Meuter et al. 2005).

6.1.4.1

Role Clarity

According to Bowen (1986) customers’ behavior is shaped by how they understand what is expected from them. A clear understanding results in high role clarity. In interactive remote service settings, role clarity reflects the customer’s knowledge and understanding of how and when he needs to participate in order to support the RST. The rationale is that if customers know what to do and how they are expected to perform, they are more likely to collaborate with the remote service technician and to use an interactive remote service. For example, a customer employee, who does not have a clear vision of how to support the RST in an interactive remote service may fear this uncertainty and decide to favor an on-site visit of an technician. The efforts of the remote service technician to guide the customer during the collaboration process were evaluated positively by customers in the interview study, emphasizing the importance of role clarity perceived by the customer. Research on customer compliance in health care supports the effect of role clarity on behavior (Dellande, Gilly, and Graham 2004) and on self-service trial (Meuter et al. 2005). Meuter et al. (2005) developed the term "customer readiness" for a combination of constructs including role clarity, role ability, and motivation. Ho and Ko (2008) developed a single construct called "customer readiness" (with items pertaining to role clarity) and proved the positive effect of the customer readiness construct on intention to continue using online banking. Assuming that an individual customer employee in a B2B remote service situation participates in the decision to use remote services, the following is proposed:

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6. Hypotheses Development H6: The individual customer employee’s role clarity is related positively to the organizational intention to use the service.

6.1.4.2

Role Ability

In addition to role clarity, the existence of customers’ doubts about their own ability to perform in an interactive remote service is frequently mentioned in the qualitative study. Role ability refers to the customer employee’s perception of whether or not he possesses the required skills and confidence to complete the tasks necessary during an interactive remote service. Effective co-production requires customers who are capable of making useful and timely contributions to support the remote service technician. It is reasonable to assume that if a customer considers himself as able to collaborate, he will less likely perceive uncertainties and is more motivated to show his abilities by using an interactive remote service. Research on health care services, self-services, and online banking (Dellande, Gilly, and Graham 2004; Meuter et al. 2005) supports the effect of role ability on behavioral intention and actual behavior, just as it did with role clarity. A customer’s perception of his ability to perform is similar to the self-efficacy concept (see chapter 3.1.2.4). Self-efficacy has been shown to be a driver of information system usage (Agarwal, Sambamurthy, and Stair 2000; Compeau and Higgins 1995a; Hwang and Yi 2002; Venkatesh 2000) and e-service acceptance (Hsu and Chiu 2004). Assuming that an individual customer employee in a B2B remote service situation participates in the decision to use remote services, the following is proposed: H7: The individual customer employee’s role ability is related positively to the organizational intention to use the service.

6.1.4.3

Intrinsic Motivation

The decision to participate in an interactive remote service is dependent on the customers’ willingness to co-produce. They must not only know what to do and be able to perform these tasks, they must also be willing to make direct contributions to various organizational activities (Etgar 2008). This is reflected in the qualitative interview study as remote service customers hold different views on their collaborative part. Their motivational beliefs range from understanding collaboration as an appreciation of their work, a sharing of knowledge, to the outright refusal to take responsibility during the repair. One customer likes to use remote services and feels appreciated: "I like that I have to help the [remote service] engineer. I feel appreciated and I am happy that he values my support and knowledge. I think that without me the remote repair would not be effective." (P.26, I.16, customer, China). While another outright rejects his participation in a remote service collaboration: "Sometimes I think it is not fair that I should help the

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remote service engineer. It is his task - not mine - to repair the machine. He should come over to repair it." (P.5, I.4, customer, Germany). Therefore, this study proposes the rationale that the more motivated a customer is to collaborate the more likely he is to seek a situation, e.g. use an interactive remote service, which requires his collaboration. Intrinsic motivation15 refers to the pleasure and inherent satisfaction derived from a specific activity (Vallerand 1997), e.g., a customer’s feeling of appreciation when he can share his knowledge during an interactive remote service. Davis, Bagozzi, and Warshaw (1992) show that extrinsic and intrinsic motivation are key drivers of an individual’s intention to use technology. Meuter et al. (2005) provide empirical support for the overall effectiveness and relative strength of intrinsic motivation beliefs on self-services usage. Ho and Ko (2008) demonstrate evidence for intrinsic motivation beliefs (within the customer readiness construct) on online banking usage and Dellande, Gilly, and Graham (2004) support the effect of motivation on compliance behavior in health care. It is proposed that the higher the customer is motivated by intrinsic beliefs, the more likely he is to use an interactive remote services. Or in other words, if the customer does not feel appreciated, maybe even utilized, this will negatively affect his intention to use an interactive remote service. Assuming that an individual customer employee in a B2B remote service situation participates in the decision to use remote services, the following is proposed: H8: The individual customer employee’s intrinsic motivation to co-produce is related positively to the organizational intention to use the service.

6.1.5

Organizational Characteristics

6.1.5.1

Subjective Norms

The results of the qualitative interview study suggest that organizational norms towards interactive remote service usage are very likely to influence the companies intention to use remote services. Employees do not form their attitudes solely on their own perceptions, instead there seems to be a general understanding of attitudes towards remote services inside a customer company, which is influenced by peers, colleagues, competitors and superiors. In literature this general understanding is summarized under the term subjective norms (Mathieson 1991) and has been identified as a direct determinant of intention in behavioral theories such as TRA (Fishbein and Ajzen 1975), TPB (Ajzen 1991) as well as in adoption models such as TAM2 (Venkatesh and Davis 2000), TAM3 (Venkatesh and Bala 2008), and UTAUT (Venkatesh et al. 2003). Subjective norms have been found to be an antecedent of intention in e-brokerage services (Bhattacherjee 2000), e-coupons (Kang et al. 2006), computing services 15

In comparison extrinsic motivation refers to valued outcomes such as improved job or organizational performances and is included in the ITSUM within the perceived usefulness construct (see 6.1.3).

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(Taylor and Todd 1995b;a), mobile services (Nysveen, Pedersen, and Thorbjørnsen 2005), online shopping (Hansen, Jensen, and Solgaard 2004; Yoh et al. 2003), t-commerce (Yu et al. 2005), and general technology usage (Karahanna, Straub, and Chervany 1999; Venkatesh and Davis 2000). Assuming that an individual customer employee in a B2B remote service situation is part of a remote service customer organization the following is proposed: H9: The individual customer employee’s perception of subjective norms is related positively to the organizational intention to use the service.

6.1.5.2

Company Size and Respondent’s Function

The impact of an individual’s personal beliefs on the company’s behavioral intention is dependent on characteristics of the organization. Premkumar and Roberts (1999) and Palvia, Means, and Jackson (1994) show the effect of company size on organizational adoption of IT technology in small and middle-sized companies. Furthermore, studies show that in smaller companies, informal working environments enable stronger employee involvement in management decisions (Wilkinson, Dundon, and Grugulis 2007). In this study, it is assumed that with increasing size of an organization, the impact of an individual’s opinion on a decision at an organizational level decreases (Premkumar and Roberts 1999). This leads to the proposition of a moderating effect of company size: H10: The company size has a moderating effect on the structural relationships between the antecedents of intention to use the service and the intention to use the service. The function of the respondent, whether he is the ultimate decision maker or an employee having to get approval of his superiors, will affect how strongly personal beliefs influence organizational decision. The owner may tend to base his strategies on personal desires and backgrounds as opposed to selecting the best-fit strategy based on rational analysis (Brouthers, Andriessen, and Nicolaes 1998). Compared to this, his employees are often forced to convince a number of different peers and colleagues across functional units and hierarchical levels to influence future organizational behavior. Therefore, it is hypothesized that the effect of the different beliefs on intention to use interactive remote services proposed in H1-H9 will be stronger, if the respondent has a decision making role in the organization: H11: The function of an individual customer employee has a moderating effect on the structural relationships between the antecedents of intention to use the service and intention to use the service.

6.2 Link Between Usage Intention and Actual Usage Behavior

6.2

143

Link Between Usage Intention and Actual Usage Behavior

The role of intention as a predictor of individual behavior is critical and has been well-established in social psychology within the TRA and TPB (Fishbein and Ajzen 1975; Ajzen and Fishbein 1980). Both suggest that the proximal determinant of behavior is one’s intention to engage in that behavior. Behavioral intentions represent a person’s motivation in the sense of his conscious plan or decision to exert effort to enact the behavior (Conner and Armitage 1998). Also, intentions and behavior are considered to be strongly related when measured at the same level of specificity (Fishbein and Ajzen 1975). In IS adoption literature, which is based on behavior models, the link between intention to use and usage behavior is supported by several studies such as Hwang and Yi (2002), Venkatesh and Davis (2000), Sheppard, Hartwick, and Warshaw (1988); and Taylor and Todd (1995b). Sheppard, Hartwick, and Warshaw (1988) report a mean correlation of 0.53 for predicting behavior from intention in their meta-analysis on 87 studies. Van den Putte (1993) provides a meta-analysis based on 113 studies and reports a mean multiple correlation of 0.62 for predicting behavior from intention. In this thesis, the assumption is made that the individual perceptions ultimately affect the decision taken on the company level. Also, it is assumed that if the organizational intention, as perceived by an individual, to use remote services increases it becomes more likely that the organization actually uses a remote service, instead of an on-site service. Drawing upon TRA’s theoretical rationale and previous empirical evidence, the following thesis proposes an extension to the ITSUM regarding actual behavior: H12: The organizational intention to use the service will positively affect actual usage behavior of the organization. This hypothesis is validated through a longitudinal study design that includes a follow-up survey at a second point in time (t2 -study).

6.3

Hypotheses Development for Group Comparisons

The results of the qualitative interview study suggest that the more experienced a company and its employees are with remote services technology and the service itself, the more trustful and self-confident the employees feel and the more they have an optimistic view of remote services. For example, a long time user of remote services stated: "We have been a "full-test" customer of [company name] for 10 to 15 years.... through that we have gained a lot of experience with how good remote service is. Based on that we decided to buy or rent remote services for all our machines." (P.3, I.2, customer, Germany).

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Especially inexperienced customers perceive a remote services situation as risky (see chapter 5.4.3). The customer employee’s fear triggers a negative mental simulation of remote service processes, which they haven’t truly experienced yet: "This is a very practical problem. Maybe remote services has not let us experience its full benefit. .... this service can not let us get the feeling that our problem will be solved if we use it. Normally, we must connect with them for several times. At the same time telephone call is also absolutely necessary. And at last, they have to come to us. I think, maybe because the limitation of their knowledge, they cannot always solve all problems if we have trouble." (P.22, I.13, customer, China) Attitude is formed based on three general classes of information: information concerning past behavior; affective information; and cognitive information (Zanna and Rempel 1988). The past expertise of an organization was found to be an influence factor for explaining technology adoption (Palvia, Means, and Jackson 1994; Premkumar and Roberts 1999). In case of the continued usage of remote services it seems to be plausible that the earlier evaluations will affect later evaluations, because knowledge gained from experience is certainly a critical piece of information for decision making (Bolton 1998; Hogarth and Einhorn 1992; Hu et al. 2009). According to Karahanna, Straub, and Chervany (1999), beliefs in an organization’s adoption phase are formed primarily based on indirect experience while post-adoption usage beliefs are formed based on past experience. Prior experience and past behavior have been shown to influence service usage and service usage intention (Dickinger and Kleijnen 2008; Kang et al. 2006; Yoh et al. 2003) or moderate the effect of drivers of usage intention (Venkatesh and Davis 2000; Venkatesh et al. 2008). Nevertheless only few studies explicitly compared experienced and inexperienced users to derive insights in how far adoption drivers differ from continuance drivers (see Karahanna, Straub, and Chervany (1999)). In order to explore the effect of past experiences on the formation of attitudes and intentions about remote services, I compare organizations with little or no experience to organizations with considerable remote service experience. I also distinguish between pre-adopter organizations (pre-adopter group) and organizations that already show continued usage of remote services (continued user group). This distinction is in line with Rogers’ (2003) two main stages of adoption: an initiation phase, where organizations have not yet adopted interactive remote services but may have tested them; and an implementation phase, where organizations have already implemented the usage of interactive remote services in their business processes on a regular basis. The employees in pre-adoption organizations may feel higher uncertainty surrounding the adoption decision compared to a continued usage decision in a more experienced organization. One would expect the former to have a richer, more complex set of behavioral beliefs. Karahanna, Straub, and Chervany (1999) state that it is likely that customers focus on a wider set of beliefs when uncertainty is high. Karahanna, Straub, and Chervany (1999) test attitudes towards using services for non-users compared to users and found that only two beliefs significantly form the

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users attitude, whereas five beliefs underlie the attitudes of pre-adopters. Klonglan and Coward Jr. (1970) found that sociological variables may be more important in explaining mental acceptance of innovations, whereas economic variables may be more important in explaining use. In an IS adoption context, this is supported in the comparison of usage antecedents over time with the UTAUT framework. The influence of performance expectancy (including perceived usefulness) increased over time, whereas the influence of facilitating conditions (including control), social influence (including subjective norms), and effort expectancy (including ease of use) decreased over time (Klonglan and Coward Jr. 1970). Nooteboom (2002) and Vlaar, Van den Bosch, and Volberda (2007) show that with a lower risk perception, trustworthiness and control perceptions decline in importance. This coincides with results from the qualitative study that suggests that after companies routinely use interactive remote services, uncertainties become less relevant, because the fears are reduced and more rational considerations such as the relative advantages of remote services become more important. Further, inexperienced customers seem to have vague ideas about the security of a remote service and feel uncertainty when they think about letting an RST access their machine remotely. Based on the assumption that prior evaluations and past use affects subsequent evaluation, the importance of beliefs regarding the service counterpart, such as perceived trustworthiness and controllability, will decrease since the level of uncertainty declines as organizations move from the pre-adoption phase to continued usage of remote services: H13: The individual customer employee’s perception of controllability of the remote service technician will have a stronger impact on intention to use the service for organizations in the pre-adoption phase than for organizations in the continued usage phase. H14: The individual customer employee’s perception of trustworthiness of the remote service technician will have a stronger impact on intention to use the service for organizations in the pre-adoption phase than for organizations in the continued usage phase. With regard to the customer employees’ participation beliefs, much of the same reasoning can be applied. The qualitative study shows that a customer employee feels even more assured about his own ability when he frequently experiences a remote service. It is reasonable to assume that in organizations that have already implemented interactive remote services in their business processes, employees have already formed a clearer understanding of their participating role in an interactive remote service. Therefore, it can be assumed that the impact of the customer participation variables on the organizational intention to use interactive remote services will decrease within an increasing experience level. This view is backed by research on habitual individual behavior (Bargh 1989; 1994; Logan 1989; Ouellette and Wood 1998; Triandis 1971; 1980), which states that over time new behav-

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iors become routine and an individual returns to a state of habitual behavior without requiring conscious processing (Bargh et al. 2001; Jasperson, Carter, and Zmund 2005; Kim, Malhotra, and Narasimhan 2005). As a result, interactive remote service usage may occur automatically without the process of establishing associated goals. Therefore, it is assumed that the influence of the employee’s participation beliefs on usage intention will decrease as organizations become more experienced: H15: The individual customer employee’s perception of his role clarity will have a stronger impact on intention to use the services for organizations in the pre-adoption phase than for organizations in the continued usage phase. H16: The individual customer employee’s perception of his role ability will have a stronger impact on intention to use the service for organizations in the pre-adoption phase than for organizations in the continued usage phase. H17: The individual customer employee’s motivation to co-produce with the remote service technician will have a stronger impact on intention to use the service for organizations in the pre-adoption phase than for organizations in the continued usage phase. Subjective norms in terms of normative pressure from supervisors and peers to adopt the innovation reduces the individual risk of adoption and uncertainty because it provides strong reassurance of the legitimacy and appropriateness of the adoption decision (Karahanna, Straub, and Chervany 1999). Since the level of uncertainty declines as the organization gets more experienced with remote services, analogous to the trustworthiness and control assumption, I assume that the perceptions of subjective norms will have a stronger impact on usage intention in organizations in the pre-adoption phase than in organizations that already use remote services: H18: The individual customer employee’s perception of subjective norms will have a stronger impact on intention to use the service for organizations in the preadoption phase than for organizations in the continued usage phase. In line with the findings of the qualitative study are the findings of Klonglan and Coward Jr. (1970) that emphasize that economic variables may be more important in explaining continued usage than adoption. Also, findings from Hu et al. (2009) show that in a group of users of website services, the effect of perceived usefulness on usage intention increases over time. These results are commonly supported by numerous studies in the IS and technology-intensive services field. For example, Davis, Bagozzi, and Warshaw (1989); Karahanna, Straub, and Chervany (1999); Venkatesh et al. (2003); Venkatesh and Davis (2000); Gefen, Karahanna, and Straub (2003b); and Venkatesh (2000) show that the influence of perceived usefulness on intention increases with growing experience. In line with these findings, I propose the following: H19: The individual customer employee’s perception of the usefulness of remote

6.3 Hypotheses Development for Group Comparisons services will have a weaker impact on intention to use the service for organizations in the pre-adoption phase than for organizations in the continued usage phase. All hypotheses are summarized in table 6.1.

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Table 6.1: Summary of Hypotheses Direct Effects H

Independent Variable

Dependent Variable

Expected Effect

1 2 3 4 5 6 7 8 9 12

Controllability Trustworthiness Trust in Technology Ease of Use Perceived Usefulness Role Clarity Role Ability Motivation Subjective Norms Intention to use

Intention to Use Intention to Use Perceived Usefulness Perceived Usefulness Intention to use Intention to Use Intention to Use Intention to Use Intention to Use Actual Usage

Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive

Mediating Effect H

Mediator

Path

Expected Effect

5a 5b

Perceived Usefulness Perceived Usefulness

Trust in Technology → Intention Ease of Use → Intention

Mediation Mediation

Moderating Effect H

Mediator

Path

Expected Effect

10a 10b 10c 10d 10e 10f 10g 11a 11b 11c 11d 11e 11f 11g

Company Size Company Size Company Size Company Size Company Size Company Size Company Size Function Function Function Function Function Function Function

Perceived Usefulness → Intention Controllability → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention Perceived Usefulness → Intention Controllability → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention

Attenuated Attenuated Attenuated Attenuated Attenuated Attenuated Attenuated Strenghened Strenghened Strenghened Strenghened Strenghened Strenghened Strenghened

Group Comparison H

Path

Expected Effect

13 14 15 16 17 18 19

Controllabilty → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention Perceived Usefulness → Intention

Stronger for pre-adopters Stronger for pre-adopters Stronger for pre-adopters Stronger for pre-adopters Stronger for pre-adopters Stronger for pre-adopters Weaker for pre-adopters

Legend: H: Hypothesis.

Chapter 7 Quantitative Studies 7.1

Motivation and Goals

In this chapter, I discuss the validation of the ITSUM and present a multi-group comparison to understand organizational intention to use interactive remote services in the pre-adoption phase and in the continuance phase. Moreover, I analyze the link between behavioral intention and actual behavior. The quantitative data offers insights into the relative strength of remote service adoption and continuance drivers as well as their interactions. The research is guided by the following research questions: 1. Do the identified perceptions of a customer’s individual employee affect the organization’s intention to use interactive remote services and, if they do, to what extent? In particular, (a) Do controllability and trustworthiness beliefs regarding the service counterpart influence the organization’s intention to use interactive remote services, and if they do, to what extent? (b) Do usefulness beliefs influence the organization’s intention to use interactive remote services and, if they do, to what extent? (c) Does the perception of technology characteristics, including ease of use and trustworthiness of the technology, influence the organization’s intention to use interactive remote services and, if they do, to what extent? (d) Do participation beliefs including role clarity, role ability, and intrinsic motivation influence the organization’s intention to use interactive remote services and, if they do, to what extent? (e) Do subjective norms influence the organization’s intention to use interactive remote

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2. Does the strength of the proposed antecedents for usage intention differ for organizations in the pre-adoption phase compared to organizations in the continuance phase and, if they do, to what extent? 3. Do organizational characteristics such as company size and the respondent’s function interact with the identified antecedents of usage and, if they do, to what extent? 4. Does the individual perception of organizational intention lead to actual organizational behavior and, if it does, to what extent? The survey methodology, structural equation modeling techniques, the multi-group analysis approach, and ordered logistic regressions will be described in the first part of this chapter. The second part lays out the questionnaire design, operationalization of constructs, and the data collection. The final part presents and discusses the obtained results.

7.2 7.2.1

Methods and Techniques Employed Survey Research

Over the last decades survey research has been one of the most used and vital techniques in social sciences (Malhotra and Birks 2007). Rindfleisch et al. (2008) show that of the 636 empirical articles published in the Journal of Marketing and the Journal of Marketing Research, between 1996 and 2005, approximately 30% use survey methods. Survey research techniques are based upon the use of structured questionnaires given to a sample of a population. They may be classified by mode of administration as telephone interviews, personal interviewing, and mail or electronic interviewing. Biases reduce the accuracy and the quality of the data gained from a survey study. To ensure the validity of survey research techniques and the ability to generalize the findings, researchers such as Podsakoff et al. (2003), Lindell and Whitney (2001), or Rindfleisch et al. (2008) urge colleagues to address potential biases early in the survey design. The following brief discussion of potential biases and guidelines serves as an overview. A detailed examination will be presented in latter sections. Common method variance (CMV) refers to the amount of spurious covariance shared among variables because of the common method used in collecting data (Buckley, Cote, and Comstock 1990). Common method biases are problematic because the actual phenomenon under investigation becomes hard to differentiate from measurement artifacts (Hufnagel and Conca 1994; Malhotra, Kim, and Patil 2006). CMV is caused by a systematic method error due to the use of a single rater or a single source (Rindfleisch et al. 2008).

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Cross-sectional survey design in general is prone to potential CMV (Podsakoff et al. 2003) even though Malhotra, Kim, and Patil (2006) find that common method biases in the IS domain are less serious than in other disciplines. Podsakoff et al. (2003, p. 882) identify potential sources of common method bias including common rater effects, item characteristics and context effects, and measurement context effects. The latter refer to the fact that measures of different constructs obtained at the same point in time, at the same location, or with the same medium might produce artificial covariance independent of the content of the constructs themselves. Rater effects, (or response bias) refers to the tendency of the respondent to answer a question in "a particular and unique systematic way that distorts their answers and true thoughts" (Hair Jr., Bush, and Ortinau 2009, p. 240). For example, respondents may give socially desirable responses. Respondent’s tendencies in replying to a survey (e.g., response styles) can also result in CMV. Not only the responses themselves can be biased, but also a systematic non-response bias can occur when the final sample differs from the planned sample (Hair Jr., Bush, and Ortinau 2009, p. 240). Causal inference (CI) describes the ability to infer causation from observed empirical relations. Especially cross-sectional research is widely viewed as being incapable of causal insights (Rindfleisch et al. 2008). In this thesis, I follow the guidelines set forth by Lindell and Whitney (2001), Podsakoff et al. (2003), and Rindfleisch et al. (2008) to minimize potential biases in all phases of the study design, operationalization, and data collection to data analysis. The strategies employed include aiming at a concise questionnaire, multiple waves or reminders, building credibility, offering incentives, assuring anonymity, multiple scale formats, minimal question ambiguity, multiple respondents, multiple periods, multiple data sources, coherence evaluation of causality, and the CMV proxy marker method. The implementation of these guidelines will be addressed in detail in the respective sections on study design (chapter 7.3), operationalization (chapter 7.5), questionnaire design (chapter 7.4), pre-test (chapter 7.6), sample characteristics (chapter 7.7.1), and within data analysis (chapter 7.7.4).

7.2.2

Structural Equation Modeling

7.2.2.1

Methodology

Structural equation modeling (SEM) is a statistical methodology with a confirmatory approach to the analysis of a structural theory and provides a flexible framework for testing complicated models involving latent and observed variables (Byrne 2001, p. 3). It comprises the concept of latent variables in psychometrics, path models in sociology, and structural models in econometrics (e.g., Bollen 1989; Cheung 2008; Jöreskog and Sörbom 1996; Muthen and Muthen 1998-2007). It is a collection of statistical techniques including factor analysis and multiple regression. SEM allows the researcher to examine relationships between several continuous or

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discrete independent variables and several continuous or discrete dependent variables (Malhotra and Birks 2007, p. 604). A full latent variable model comprises a measurement model and a structural model related by a set of linear equations whose unknown coefficients can then be estimated (Bollen 1989, p. 11 ; Malhotra and Birks 2007, p. 604). The measurement model relates to the measurement of latent variables and hypothetical constructs that are not directly observed. It relies on a multivariate regression model that describes the relationships between a set of observed dependent variables and a set of continuous latent variables. The observed variables serve as indicators of the underlying construct that they are presumed to represent (Byrne 2001, p. 3). The observed variables are referred to as factor indicators and the continuous latent variables are referred to as factors. It is important to note that observed variables are exposed to a certain measurement error, which may comprise random error and systematic error (Bagozzi, Yi, and Phillips 1991). The structural model encompasses the relations among the latent variables (Byrne 2001, p. 3) and are the "causal" relations between variables (Bollen 1989, p.11). The structural model describes three types of relationships in one set of multivariate regression equations: the relationship among factors; the relationships among observed variables; and the relationships between factors and observed variables that are not factor indicators. In this thesis, the Mplus notation of structural equation models is used (Muthen 1998-2004). Equation 7.1 describes the measurement model in regard to the p-dimensional latent response variable y∗ . y∗i = ν + Ληi + Kxi + εi

(7.1)

The latent variables (factors or constructs) are denoted by the m-dimensional vector η, x is a q-dimensional vector of independent variables, ε is a p-dimensional vector of residual errors. Means are represented by a p-dimensional vector ν, Λ is a p × m parameter matrix of factor loadings λ , and K is a p × q parameter matrix of regression coefficients. The residual errors are uncorrelated by default. The matrix K allows for direct effects of independent variables x on the latent response variables y. In other formulations this matrix is assumed to be zero. The structural part of the model, equation 7.2, is a regression of the latent variables on each other and the q-dimensional vector x of independent variables. B is an m × m parameter matrix of regression slopes β . Γ is a m × q slope parameter matrix for regression of the latent variables on the independent variables. ζ is the m-dimensional vector of residuals. α is an m-dimensional parameter vector. Additionally it is assumed that B has zero diagonal elements and that [I−B]−1 exists. ηi = α + Bηi + Γxi + ζi

(7.2)

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The hypothesized model can be tested statistically in a simultaneous analysis of the entire system of variables to determine the extent to which it is consistent with the data. In the covariance based SEM approach (as chosen in this study), this is achieved via the parameter estimation of the covariance matrix based on the observed measures and the comparison of this estimated covariance matrix with the covariance matrix predicted by the theoretical model. This approach attempts to minimize the difference between the sample’s actual covariances and the theoretical model’s predicted covariances (Chin and Newsted 1999). A model may be estimated by using different estimators. The most widely used fitting function for general structural equation modeling is the maximum likelihood (ML) function (Bollen 1989, p. 8), which is efficient with large samples. If the observed covariance matrix resembles the model’s matrix, the model’s goodness of fit is adequate and indicates plausibility of postulated relations among variables (Byrne 2001, p. 3). Estimation is computationally expensive and usually performed by dedicated software packages on workstations. In this thesis, the software Mplus 5.1 is used, which also allows for distribution-free estimators for continuous variables or weighted/unweighted least squares estimators for categorical variables. It accommodates for continuous, dichotomous, and categorical data; allows for confirmatory factor analysis (CFA), exploratory factor analysis (EFA), SEM, latent class, growth modeling, and Monte Carlo Simulation; and offers a wide range of estimators.

7.2.2.2

Assessment of Reliability and Validity

The quality of a full model is assessed by the reliability and validity of the measurement models as well as the overall model fit. For reflective measurement models, reliability is defined as "the degree to which measures are free from random error and thus reliability coefficients estimate the amount of systematic variance in a measure" (Peter and Churchill Jr. 1986, p. 4). Reliability is established when a good proportion of the variance of the observed variables is explained through the factor (Homburg and Giering 1998, p. 116). Validity is established "...when the differences in observed scores reflect true differences on the characteristics one is attempting to measure and nothing else..." (Churchill Jr. 1979, p. 65). Validity comprises content validity, convergent validity, discriminant validity, and nomological validity. Content validity reflects the degree to which the indicators of a measurement model reflect the semantic meaning of a construct. It can be assessed via qualitative expert opinions on the scales (Homburg and Giering 1998, p. 117). Convergent validity is the degree to "which multiple attempts to measure the same concept are in agreement" (Bagozzi and Phillips 1982, p. 468). The idea is that two or more measures of the same thing should highly covary if they are valid measures of the concept. Discriminant validity is the degree to which measures of different concepts are distinct. The notion is that if two or more concepts are unique, then valid measures

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of each should not correlate too highly (Bagozzi, Yi, and Phillips 1991, p. 425). Nomological validity represents the degree to which predictions based on a concept are confirmed within the context of larger theory, for example through grounding hypothesis development in general adoption and service theory (see chapter 6). The term "first-generation" tests of reliability refers to measures such as factor loadings of the observed variables, explained variance of the observed variables, item-to-total correlation, and Cronbach’s α (Homburg and Giering 1998, p. 119). These measures require interpretation which is often simplified by using cut-off values to assess the measure in question. There is considerable variance on the threshold values employed in literature. Because of this, I avoid extreme opinions and adhere to the most commonly given recommendations: Homburg and Giering (1996, p. 8) consider factors loadings ≥ 0.4 as acceptable; Bearden, Netemeyer, and Teel (1989, p. 475) regard an item-to-total correlation ≥ 0.5 as sufficient; according to Peter (1997, p. 180) the cut-off value for explained variance of an indicator ≥ 0.5 is to be used; Cronbach’s α should be equal to or exceed 0.7 according to Nunally and Bernstein (1978, p. 245). Usually the criteria for convergent validity are assessed within a confirmatory factor analysis. It includes tests for construct reliability (CR; or factor or composite reliability), average variance extracted (AVE), factor determinacy (FD), and significance test of the factor loadings. The thresholds used in this thesis to assume convergent validity are: construct reliability≥ 0.6 as stated by Bagozzi and Yi (1988, p. 82); factor determinacy should be near one (Muthen and Muthen 1998-2007, p. 586); and the AVE should be equal to or greater than 0.4 (Bagozzi and Baumgartner 1994, p. 402) to indicate that at least 40% of the variance in a construct is due to the hypothesized underlying items. The Fornell-Larcker-criterium can be used to judge discriminant validity (Fornell and Larcker 1981, p. 46). If the average variances extracted by the correlated latent variables is greater than the square of the correlation between the latent variables then discriminant validity requirement is satisfied.

7.2.2.3

Assessment of Model Fit and Data Quality

For assessing the overall model fit, several ways to measure goodness-of-fit measures are available (Hu and Bentler 1999; Marsh, Hau, and Wen 2004). The most popular ways to evaluate fit are those that involve the χ 2 -goodness-of-fit statistic. The χ 2 -statistic is sensitive to sample size (n > 200) and distributional misspecification; therefore, some researchers recommend supplementing the χ 2 -test statistic with alternative measures of absolute and incremental fit (Bentler and Bonett 1980). Absolute fit indices assess how well an a-priori model reproduces the sample data, e.g., the Root Mean Squared Error of Approximation (RMSEA) and the Standardized Root Mean Squared Residual (SRMR). Incremental fit indices measure the proportionate improvement in fit by

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155

comparing a target model with a more restricted, nested baseline model, e.g., the Tucker-LewisIndex (TLI), Comparative Fit Index (CFI) for the ML method. The CFI is one of the most reliable incremental fit indices and it is the most reported measure of fit in the literature (McDonald and Ho 2002). The CFI avoids TLI’s problems concerning underestimation of fit and considerable sampling variability in small samples (Yu 2002). For model comparison, the Akaike Information Criterion (AIC) serves as an indicator for a better fit. The absolute value of AIC has relatively little meaning, rather the focus is on its relative size. The rationale is that if two models are compared then the model with the smaller AIC should be preferred (Bühner 2006, p. 352). This thesis uses the following values recommended by literature to assess model fit: ratio χ 2 /d f ≤ 3.0 (Homburg and Giering 1996, p. 13); RMSEA ≤ 0.06 (Hu and Bentler 1999, p. 1); SRMR ≤ 0.08 (Hu and Bentler 1999, p. 1). The incremental fit thresholds to assume model fit are: TLI ≥ 0.9 (Janssens et al. 2008, p. 296) and CFI ≥ 0.9 (Janssens et al. 2008, p. 296). Multicollinearity describes a state of high inter-correlations among the latent exogenous constructs (Grewal, Cote, and Baumgartner 2004; Hair Jr., Bush, and Ortinau 2009). Multicollinearity can result in several problems such as SEM estimates far from the true parameters and large standard errors of the estimates (Jagpal 1982). Criteria for assessing multicollinearity are the variance inflation factor (VIF) and the tolerance value. Rule of thumbs recommend values under 4.0 for VIF and values above 0.10 for the tolerance statistic (Moosmüller 2004, p. 131; Myers 1993, p. 369). Grewal, Cote, and Baumgartner (2004, p. 526) show that SEM estimations should be doubted when multicollinearity is extremely high (correlation > 0.8). Even when multicollinearity is between 0.4 and 0.8, one should be cautious if composite reliability is low (<0.7), r2 is low (≈ 0.25), or sample size is relatively small (ratio of less than 3 observations per variable). In general, researchers strongly recommend a reasonable sample size, complete data, or appropriate handling of incomplete data. The required sample size depends on several aspects of the specified model, such as the model size and distributional characteristics. In general, studies have shown that a sample size of 200 or larger is desirable for medium-sized models that involve 15 observed variables (e.g., Hu and Bentler 1999; Marsh, Balla, and McDonald 1988). There is no common consensus on the minimum sample sizes necessary. Bentler and Chou (1987) use a rule of thumb that suggests a ratio of ten responses per parameter required to obtain trustworthy estimates. Recommendations for appropriate handling of incomplete data focus on choosing an appropriate imputation technique and defining acceptable levels of incomplete data. For example, Olinsky, Chen, and Harlow (2003) consider samples where a maximum of more than 16% of data is missing as not appropriate.

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7. Quantitative Studies Dependent Categorical Variables

When outcome variables are not measured on a continuous scale, special models and estimation procedures are needed to take the scale of the outcome variables into account. Mplus has special estimation procedures for binary, ordered categorical (ordinal), unordered categorical (nominal), and count dependent variables. For binary and ordered categorical outcome variables, both weighted least squares and ML estimators are available (Muthen and Muthen 1998-2007, p. 447). To predict the outcome of a non-continuous variable, the logit regression models regress a function of the probability that a case falls into a certain category on a linear combination of regressors (Agresti 1990). Mplus allows estimation of SEM that contains such variables as dependent variables with a ML estimator for logistic regressions. For binary outcomes, thresholds are estimated with a proportional odds specification. Yu (2002) recommends only a reduced set of fit indices as reliable for models with categorical outcome variables including: CFI, TLI, and RMSEA. In logistic regression there is no direct equivalent to the r2 as found in ordinary least square (OLS) regression, but few Pseudo r2 ’s such as Nagelkerke’s and McKelvey and Zavoina’s r2 that range between 0 and 1 provide an analogy to r2 in OLS multiple regression (McKelvey and Zavoina 1975; Veall and Zimmermann 1994; Backhaus et al. 2003, p. 440). Mplus provides McKelvey and Zavoina’s Pseudo r2 . Veall and Zimmermann (1994) showed that McKelvey and Zavoina’s Pseudo r2 outperforms other Pseudo r2 measures and performs best to mimic the OLS-r2 for binary probit models. Another test for the predictive power of the logistic model is the Hosmer-Lemeshow-Test that divides the data into up to 10 groups and compares the predicted result with the actual data (p. 446 Backhaus et al. 2003). A Hosmer-Lemeshow-Test statistic has two degrees of freedom less than the number of groups and is regarded to accept the model if ρ > 0.10. The logit coefficient for a binary dependent variable can be directly interpreted as the proportional change in the log-odds given a unit change in the independent variable.

7.2.2.5

Multi-Group Comparison

This thesis aims at understanding the heterogeneity between respondents and validating the ITSUM in a multi-group framework. An essential prerequisite to gain valid insights into group differences is to establish an invariant measurement model. Differences in observed measures can only be attributed to differences between groups, if the measures of the ITSUM are equivalent across the groups. The general hierarchical procedure to assess measurement invariance as proposed by Steenkamp and Baumgartner (1998a) is illustrated in figure 7.1. The level of invariance required depends on the goal of the study. For examining structural relationships with other constructs across groups, full or partial metric invariance has to be established. Scalar invariance is required if

7.2 Methods and Techniques Employed


  
 
 







 





 


 






   


   


 
 



 
 

 





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Figure 7.1: Proposed Procedure for Assessing Measurement Invariance Source: Own Illustration, based on Steenkamp and Baumgartner (1998a, p. 83)




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the comparison of absolute means across the groups is in the focus of a study. Otherwise this step can be omitted from the procedure (Steenkamp and Baumgartner 1998b, p. 414). If the researcher wants to compare standardized measures of association such as correlation coefficients and standardized regression coefficients across groups, factor variance invariance is required in addition to partial metric invariance. Lack of error variance invariance does not create a problem as long as differences in measurement error are explicitly taken into account, for example through latent variable modeling (Steenkamp and Baumgartner 1998a). The following forms of measurement invariance are necessary for comparing path coefficients: 1. Configural invariance is the most basic level of invariance. It is achieved if the pattern of salient (non-zero) and nonsalient (zero) factor loadings is equal across groups and the model of interest fits across the groups. Although the model is the same across groups, the unknown parameters of the model are assumed to be different across the groups. 2. Metric invariance requires a stronger test of invariance compared to configural invariance. Loadings are constrained to be the same across groups, which implies equal metrics or scale intervals across groups. If an item satisfies the requirement of metric invariance, different scores on the item can be meaningfully compared across groups, and these observed item differences are indicative of similar cross-group differences in the underlying construct. 3. Scalar invariance is required if a mean comparison across groups is meant to be meaningful. Even if metric invariance is satisfied, scores on the items can be biased. By constraining the vector of item intercepts across groups, equality of measurement intercepts is achieved. 4. Factor variance and covariance invariance is required in addition to metric invariance if one wants to compare standardized regression coefficients across groups. If both the factor variance and the covariances are invariant, the correlations between the latent constructs are invariant. In addition to full invariance tests, individual constraints on different parameters can be relaxed to explore for subtle group differences leading to partial invariance assumptions (Byrne, Shavelson, and Muthen 1989). The assessment of configural, metric, scalar, and factor (co)variance invariance as well as individual parameter tests are conducted based on χ 2 -difference tests that compares less restricted models to progressively more restricted models (see chapter 7.8.2). If the models are not significantly different, invariance of the models in the constrained parameters is assumed. In addition to the χ 2 -difference test, Steenkamp and Baumgartner (1998a) suggest to include the CFI, TLI, AIC, and the RMSEA in assessment of the nested models. Steenkamp and Baumgartner (1998a; 1998b), and van Birgelen et al. (2002) showed, that the assessment of the fit indices can outweight significant χ 2 -difference test results when large sample sizes are estimated(Steenkamp and Baumgartner 1998a) and form the basis for invariance

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159

assessment.

7.3

Study Design

The study aims to measure interactive remote service usage intention and actual usage within the German printing industry. The study is split into two parts, with surveys at two different points in time. The first part entails the survey conducted in May and June 2008 and contains the full measurement instrument to validate the ITSUM derived in chapter 6. It will be referred to as the t1 -study in the further course of this thesis. The second part is a follow-up survey and was conducted in March 2009. It comprises a shortened questionnaire with a focus on the respondent’s usage behavior displayed since June 2008 to explore the relationship between intention and behavior. This study will be called the t2 -study. Addresses and electronic contact details could be obtained for 7,139 companies from the printing industry in Germany. The 7,139 companies represent over 65% of all printing companies in Germany according to numbers published by the Bundesverband der Druck und Medienunternehmen (BVDM) – the German Printing and Media Industries Federation (BVDM 2008). The survey phase of the t1 -study started with a postal announcement sent out to 4,000 randomly chosen companies to increase response rate (May 2008). Also an electronic email invitation to the survey was sent to all 7,139 companies two weeks later (June 2008). In both cases the respondents could choose to participate electronically through an electronic survey system or through a traditional fill-in questionnaire to be sent in by traditional mail. A professional internet based online survey software (EFS Survey) was used for the electronic data collection. The electronic survey system’s URL was personalized for each respondent so he could be identified for the t2 -study. The questionnaire was accessible from May 15, 2008 – July 8, 2008 (through http://www.unipark.de/uc/Drucker/40ed/individualkey). The last written questionnaire sent by traditional mail came in on the August 4, 2008. The invitations were personalized and targeted at those individuals within the organizations who have the most influence on the decision to use remote services and, at the same time, are involved in operational processes. The printing industry in Germany is dominated by small companies, where the owner/general manager fulfills a multitude of functional roles simultaneously and often even operates the machines himself. The owner/general manager was targeted in small companies while in mid-sized or larger companies the production manager was addressed. The specific structure of the industry suggests a strong link between the targeted individual’s perceptions and organizational decision making.16 If no name was available, the survey was sent to the companies main address with the pledge to forward the mail to a person who is responsible for decisions with regard to the purchase, usage, or discontinuance of main16

For further details, see the discussion on the empirical setting in chapter 4.

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tenance services and other printing machine related services. Only one respondent was targeted per organization and identified across the t1 and t2 -studies. The t2 -study — conducted in March 2009 — was only sent to respondents, who participated in the first survey in June 2008 and whom completely filled-in the survey and agreed to be contacted for the second survey study. In total, an invitation to the follow-up survey was sent to 567 companies. The questionnaire was accessible from March 1, 2009 – April 1, 2009. The main goal of this follow-up survey was to explore the actual behavior of the organizations — the usage of interactive remote services — after a time lag and relate it to the perceptions and intention variables of the same respondent in the t1 -study.

7.4

General Outline of the Questionnaires

The questionnaires were developed to comprise a specific set of questions that operationalize and measure the constructs proposed. They ultimately serve to test the hypotheses of the ITSUM (see chapter 6) and allow the collection of demographical data and characteristics of the respondents and the company he presents. The questionnaires at t1 and t2 were developed according to Malhotra and Birks’ (2007, pp. 375) guidelines for questionnaire design. The opening questions are supposed to raise interest and be simple and non-threatening. A description of a typical interactive remote service is presented at the beginning of the questionnaire. The opening question refers to this example and enquires whether the respondent has ever had experience with this kind of service. Difficult, sensitive, or complex questions are placed late in the sequence. The final questions cover personal data. The first page (see appendix, figure A.1 and figure A.2) of both surveys provided information on the author and the institution conducting the survey, as well as the reason for approaching the potential respondent, the aim of the research, the approximated duration for completing the questionnaire, and an expression of gratitude in advance for completing the questionnaire. Also, the respondents were reassured that: • All answers, no matter if the respondents had extensive, little, or even no experiences with interactive remote services, were a valuable contribution to the research; • There were no right or wrong answers; and • All data would be analyzed anonymously. Response rates can be increased by offering incentives (Malhotra and Birks 2007, p. 446; Hair Jr., Bush, and Ortinau 2009, p. 265). Because of this, a lottery to win a portable electronic music device and shopping coupons were offered on the first page of the survey. If the

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respondent wished to participate in this lottery, he was asked to fill in his contact information at the end of the survey. In both cases anonymity was assured.

7.5

Operationalization of the Constructs

The questionnaire of the t1 -survey aims at capturing the constructs of the ITSUM. Seven-point scales were used for all of the constructs’ measurements, with 1 being the positive end of the scale and 7 being the negative end of the scale. Controllability was measured with seven categories within a semantic differential; the items of the intention scale were measured with sevenpoint-Likert-scales with the end points of 1 indicating "very likely" and 7 indicating "very unlikely." The other constructs were measured with seven-point-Likert scales verbally anchored with 1 equaling "I strongly agree" and 7 meaning "I strongly disagree." The items for the latent variables and their origins are shown in table 7.1 and will be discussed below. Table 7.1: Operationalization of the Constructs Perceived Controllability Please express how you, as a customer, would feel about the behavior of the RST. That is, the behaviors of the RST make you feel: Item No.

Items

adapted from

CONT1

controlling – controlled dominant – submissive influential – influenced staying on top of things – kept in the dark confident – helpless

Poon et al. (2004) Poon et al. (2004) Poon et al. (2004) Qualitative Study Poon et al. (2004)

CONT2 CONT3 CONT4

—a

Trustworthiness of the RST How would you evaluate the following statements regarding the RST? Item No.

Items

adapted from

TW1

An RST is experienced in performing necessary tasks during a remote service. I expect our needs and wishes to be important to an RST. The intentions of an RST are benevolent. An RST is competent. I do not doubt the honesty of an RST.

Gefen (2002)

TW2 TW3 TW4 TW5

to be continued on the next page. . .

Gefen (2002) Gefen (2002) Gefen (2002) Gefen (2002)

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Perceived Ease Of Use How strongly do you agree with the following statements about remote technology? Item No.

Items

adapted from

EOU1

Learning how to use and operate a remote service would be easy. I think the interaction with the remote service technology would be clear and understandable. I would find the remote service technology to be flexible to interact with the service provider company.

Davis (1989)

EOU2

—b

Davis (1989) Davis (1989)

Perceived Usefulness How do you evaluate the advantages and disadvantages of remote services compared to face-to-face delivered services? Item No.

Items

adapted from

PU1

Remote services provide greater convenience than face-toface delivered services. Remote services save time. Remote services make it easier for us to get the services we want. Remote services improve service quality. Using remote services enhances our effectivity. Remote services are useful for my individual job tasks.

Qualitative Study

PU2 PU3

PU4 PU5

—b

Davis (1989) Davis (1989) Davis et al. (1989) Davis (1989) Davis (1989)

Role Clarity Please focus on the collaboration with the RST. How strongly do you agree with the following statements? Item No.

Items

adapted from

RC1

I feel certain about how to effectively collaborate during a remote service. The steps in the process of using a remote service are clear to me. I know what is expected of me in a remote service situation. I believe there are clear directions regarding how to collaborate during a remote service.

Meuter et al. (2005)

RC2

RC3

—a

Meuter et al. (2005) Meuter et al. (2005) Meuter et al. (2005)

Role Ability Please focus on the collaboration with the RST. How strongly do you agree with the following statements? Item No.

Items

adapted from

RA1

I am fully capable of supporting the RST during a remote service. I am confident in my ability to cooperate during a remote service. I feel I am qualified for the task of collaborating with the RST.

Meuter et al. (2005)

RA2

RA3

to be continued on the next page. . .

Meuter et al. (2005) Meuter et al. (2005)

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163

Intrinsic Motivation The collaboration with the RST) during a remote service would . . . Item No.

Items

adapted from

MOTIV1

. . . provide me with personal feelings of appreciation of my knowledge. . . . allow me to have increased confidence in my skills. . . . allow me to feel innovative in how I interact with a service provider . . . provide me with feelings of worthwhile shared accomplishment.

Qualitative Study

MOTIV2 MOTIV3

MOTIV4

Meuter et al. (2005) Meuter et al. (2005) Meuter et al. (2005)

Intention to Use Remote Services Please rate the likeliness of your future activities below. Item No.

Items

adapted from

INT1

We intend to use remote services in the next 6 months. We plan to use remote services in case of the next emergency incident.

Venkatesh et al. (2003) Venkatesh et al. (2003)

INT2

Legend: a : deleted during pre-test; b : deleted during scale improvement.

In practice, the term "remote services" and "interactive remote services" are used rather freely. There are no clear cut definitions used throughout the industry, and the understanding of remote services even varies from individual to individual. For this reason and to reduce other sources of ambiguity and confusion to the respondents, I presented an example at the beginning of the questionnaire to illustrate interactive remote services and provide a common reference base. For the same reason, the term "remote services" was used, instead of the technically more accurate term "interactive remote services" throughout the survey. The following explanation of interactive remote services was given to the respondents: When performing a remote service an employee of the service provider (remote service technician) logs onto the customer’s printing machine remotely through the internet or a modem connection. During a remote diagnosis and repair of a printing machine, the remote service technician remotely monitors and might even change certain parameters. The customer has to collaborate with the remote service technician in order to perform a remote repair. This collaboration includes mechanical tasks, for example the opening and changing of parts, which cannot be performed remotely by the remote service technician. In the following, you will be asked about your personal experience as a customer of this kind of service regardless of specific provider companies. For clarification throughout the questionnaire, this example was available to participants who were unfamiliar with this type of remote service. The questions either directly referred to this

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example, stressing the interactive and collaborative characteristics of the scenario, or to comparable experiences that the customers might have had. The survey was mostly sent to business addresses where it was likely to be answered by employees at their place of work. To reach a maximum fill-in rate and to avoid aborters, especially due to respondents’ limited time at work, I aimed for a parsimonious, concise, and not too timeconsuming questionnaire. Accordingly, original scales with six or more reflective items were reduced to four or five item scales, if possible, and if the omission did not change the meaning of the latent variable. Bergkvist and Rossiter (2007) and Rossiter (2002) state that "when in the mind" of the survey respondents 1) the object of the construct is "concrete singular", meaning that it consists of one object that is easily and uniformly imagined, and 2) the attribute of the construct is concrete, again meaning that it is easily and uniformly imagined, a measurement with multiple reflective items is not necessary and even single items can suffice. Perceived controllability (CONT) of the RST’s actions was measured by adapting the seven seven-point semantic differential scales from Poon, Hui, and Au (2004) based on the "dominance" scale of Mehrabian and Russell (1974, appendix B) and the "helplessness" concept of Glass and Singer (1972). The items were adapted to measure the expected perceived control felt by customer employees with respect to the behavior and actions of the RST during a remote service. The semantic differential item "staying on top of things" to "kept in the dark" was newly added to the scale based on the findings of the qualitative study (see chapter 5.4.5). The trustworthiness perception (TW) of an RST scale was conceived as a single construct combining beliefs in ability, integrity, and benevolence. Trustworthiness was measured as a single scale according to the studies of Gefen (2002a) and Jarvenpaa, Tractinsky, and Vitale (1999). This thesis follows this approach and measures the perception of trustworthiness of the RST as a latent variable with five items stemming from Gefen’s (2002) seven-items scale. The items were adapted with regard to the context of the trustworthiness of an interaction partner in an interactive remote service. Ease of use (EOU) was measured using three items from Davis’ (1989) scale for perceived ease of use. The wording of all items was adapted to the remote service setting. Perceived usefulness (PU) was measured using four items from Davis’ (1989) scale for perceived usefulness. To adapt the scale to the special characteristics of remote services one item was added from Davis, Bagozzi, and Warshaw’s (1989) perceived usefulness scale. Also, one item was newly developed based on the results from the qualitative study: "Remote services provide greater convenience than face-to-face-services" (see chapter 5). The wording of all items was adapted to the remote service setting and contrasted face-to-face services. The construct of role clarity (RC) was measured by adapting Meuter et al.’ (2005) scale used to study a consumer’s trial behavior of self-service technology. All four items from the scale were applied and adapted to the remote services context. Also, role ability (RA) was measured with

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three items stemming from the Meuter et al.’ (2005) scale. The items were selected with respect to their applicability in the interactive remote service scenario and verbally adapted to capture the customer’s perceived ability to collaborate in a remote service situation. Intrinsic motivation was measured with three items also stemming from Meuter et al.’ (2005) instrumentality scale for intrinsic motivation. Based on the qualitative interviews, the scale was extended with one item referring to the "feelings of appreciation" of the customer’s knowledge (see chapter 5.4.8)

The organization’s intention to use interactive remote services (INT) was measured by using the individual employee’s perception of the organization’s intention as a proxy. This is done under the assumption that in the printing industry, the decision is made by few people, mostly the owner, general managers or production manager. Intention was measured with a two-item scale adapted from Venkatesh et al. (2003).

Trust in technology (TT), subjective norms (SN), and organizational remote service experience were measured by single questions. This was done to cut back the time needed for respondents to answer the questionnaire while at their place of business. Trust in technology was measured using a single-item — "In general, remote service technology is trustworthy." from the Johnson, Bardhi, and Dunn (2008) scale. Subjective norms were measured with the item — "My peers or superiors expect me to use remote services." — referring to the subjective norms scales used in studies on IT-adoption such as in Venkatesh and Davis (2000), but adapted for the B2Bsetting.

In addition, respondents were asked to specify the number of employees and their own function within the company. The number of employees is used as a categorical variable indicating company size as done in Premkumar and Roberts (1999). The categories of company sizes were chosen according to the BVDM as 1–9, 10–19, 20–49, 50–99, 100–499, 500–999, >1000 employees (BVDM 2008).

To measure the level of remote service implementation in the organization according to Rogers’ (2003) classification of innovation adoption in organizations the single-item "We have already strongly implemented remote services in our business processes." was used. In the t2 -study, the organizational usage behavior was measured with the question: "How often did your company use remote services since the last questionnaire in June 2008?" The respondents could choose between the following categories: "never", "once", "2–3 times", or "four and more times." For the t2 -study, the same respondents, who answered the t1 -study were contacted to capture the behavior of the organization since the completion of the t1 -survey.

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Quality of the Questionnaire and Pre-Test

A multistep procedure was employed to ensure the quality of the t1 and t2 questionnaires’. It includes the following steps: 1. Established and existing scales for most of the constructs were adapted for the survey instrument. They were extended, if necessary, based on the results of the qualitative study (see chapter 7.5). 2. The survey was reviewed by two employees of a remote service provider company and five experts in the field of remote service research to verify content validity, applicability, and linguistic clarity of all constructs and items. 3. The survey was pre-tested with a convenience sample of ten employees from printing companies to assess its clarity. The pre-test was conducted in early May 2008 to identify and eliminate problems with the questionnaire. Ten employees from printing companies in Germany were asked to fill-in the online questionnaire and to check for wording, comprehensibility, logical flow, and overall gestalt of the questionnaire. During this process, the wording and ambiguous questions were clarified to reach maximum comprehensibility of the target respondents, as required by Rossiter (2002). Adjustments were made to the controllability and role clarity constructs. One item in each construct was eliminated due to ambiguous interpretations of the pre-testers (see table 7.4). The survey design was chosen carefully to address and prevent potential biases. Several of the procedural remedies that Podsakoff and colleagues (2003, pp. 887) recommend were used: 1. The measurement of the behavioral intention and actual behavior was temporally separated by introducing a time lag to avoid measurement context effects and response bias; 2. Different scale formats were used such as semantic differential and Likert-scales to avoid common rater effects; 3. Respondents anonymity was protected and evaluation apprehension was avoided through the assurances that each survey would be submitted anonymously and that no identifying information would be collected in the survey (to address response bias); 4. Respondents were assured that no particular answer was encouraged or discouraged, i.e. there are no right or wrong answers to these questions (to adress response bias); 5. Careful wording was used and pre-tests of the questionnaires were conducted to reduce method bias; and 6. Scales were improved by eliminating ambiguous items (e.g., complicated syntax).

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To further control for common method bias, a marker variable (Lindell and Whitney 2001) was used in the analysis of the model.

7.7 7.7.1

t1-Study: Results of ITSUM Validation Sample Structure and Description

The t1 -survey was sent out to 7,139 companies in the German printing industry. The questionnaire was answered by 1,076 participants reflecting a response rate of 15.07%. From the sample with 1,076 cases, all responses were selected that met the following conditions: All intention items had to be fully filled-in, all organizational characteristics had to be fully filled-in, and the percentage of missing values per case had to be lower than 5%. Based on these constraints, 359 cases were deleted from the sample resulting in a sample of 717 questionnaires and an effective response rate of 10%. This is somewhat low overall, but not unusual for B2B-research of comparable industries (Rauyruen and Miller 2007). Responses from organizations who declined to participate revealed that a lack of time and business pressures were the main reason for not completing the survey. In the following text, the sample of 717 respondents is referred to as the "overall sample." The distribution of companies of different sizes within the sample and the distribution of companies of different sizes in the industry according to the official industry statistics (BVDM 2008) is illustrated in figure 7.2. The categories on the x-axis reflect the number of employees; the dark bars denote the percentages in the industry, and the light bars show the percentages in the sample. Companies with 19 employees and less form the majority of the sample with 67.09% — as expected from the industry structure. SME represent over 98% of all companies sampled. The overall sample distribution according the company size reflects the distribution of the whole industry fairly well, even though larger companies are slightly over represented. The gender distribution of the overall sample respondents includes 87% male and 12% female. Distribution of age groups indicates that majority of respondents are between 41–60 years old (see figure 7.3). The respondents classified their respective organization according to its business segment. They were able to choose between pre-press, press, and post-press. Multiple answers were possible. The self-reported classification is shown in figure 7.4. Within the sample, 86% of 717 companies reported themselves operating in the core segment — press — of the printing industry. The functions of the respondents are illustrated in figure 7.5. Within the overall sample, over 90% of all respondents are owners, general managers, or production managers. These are the key actors who decide whether a remote service is used in a company (Herdler 2006).

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Figure 7.2: Company Size (Number of Employees) in the Printing Industry vs. in the Overall Sample in %






  











 



 


 


Figure 7.3: Age Distribution in the Overall Sample in % (n=717)

 


7.7 t1 -Study: Results of ITSUM Validation

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Figure 7.4: Self-Reported Classification of Business Segments in % (multiple answers possible)

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Figure 7.5: Distribution of Respondents’ Function in the Sample in % (n=717))

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Crosstable 7.2 shows the distribution of the respondent’s function over company size. In the smaller companies, the general manager is more often the respondent when compared to larger companies. The sample is divided in to an early and a late group according to the point in time they answered the questionnaires. The sample is split at June 10, 2008 and a test was performed on the two groups to check for a potential non-response bias. The results of the χ 2 -tests17 indicated no response bias in terms of respondent’s function (χ 2 (2) = 4.341), respondent’s age (χ 2 (2) = 3.468), and gender of the respondent (χ 2 (1) = 1.141). A further examination of the validity across time will be conducted in the t2 -study (see chapter 7.9). Although the overall targeting was successful, further validation is needed to reduce sources of heterogeneity. Tests for the influence of company size, function of the respondent, and expertise of the respondent (EXP) will be employed further in data analysis.

7.7.2

Data Quality

The data was checked for sufficient sample size, missing values, and normality of data distribution. The overall sample of 717 respondents consists of 33 variables per case, resulting in a 22:1 (cases:variables) ratio that signals a sufficient sample size according to Bentler and Bonett (1980) and Hu and Bentler (1999). For the constructs used to estimate the ITSUM, the number of missing values for the 717 cases were very low (< 4% per variable). They were imputed with an expectation-maximizationbayesian (EMB) algorithm provided by AMELIA software (Honaker, King, and Blackwell 2007). This software allows users to appropriately impute incomplete data sets so that estimations, requirering complete observations can use all the information present in a dataset with missing cases. It also allows researchers to avoid biases and inefficiencies that can result from dropping all partially observed observations from the analysis. Multivariate normality is required by ML estimation, which is the predominant method in SEM for estimating structure (path) coefficients (Bollen 1989, p. 8). Multivariate normality is the assumption that all variables and all combinations of variables are normally distributed. In a first step, univariate normality was assessed by inspecting skewness and kurtosis values. SkewTable 7.2: Crosstable: Respondent’s Function / Number of Employees

Owner / GM Production Manager Machine Operator Others 17

1–9

10–19

20–49

50–99

100–499

500–999

>1000

263 41 0 21

121 27 1 7

73 21 2 17

48 12 0 8

15 18 0 11

3 1 0 3

0 3 0 1

Categories with less than 5 cases were not included in the tests.

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ness values for the ITSUM items range from -0.05 up to 1.62, kurtosis values range from -1.4 up to 2.23 and, thus, lay under the cut-off values of 2.0 for skewness and 7.0 for kurtosis recommended by Finch, West, and MacKinnon (1997). To further assess multivariate normality Mardia’s statistic (Mardia, Kent, and Bibby 1979, p. 21) was used to assess the joint multivariate normality of the collection of ITSUM items. The average multivariate kurtosis value across the sample is 837.697 (s.d. = 3.033), returning a Z-test value of 1124.407 (ρ < 0.0001). The average multivariate skewness value across the sample is 33.892 (s.d. = 0.87), returning a Z-test value of 114.271 (ρ < 0.0001). Because the null hypothesis of both tests is that the data arise from a joint multivariate normal population distribution, these results suggest non-normality of the ITSUM items’ distributions. Therefore, for the confirmatory factor analysis and the structural equation modeling, the Mplus MLM estimator was chosen. The MLM estimator delivers maximum likelihood parameter estimates with standard errors and a mean-adjusted χ 2 -test statistic that is robust to non-normality (Muthen and Muthen 1998-2007, p. 484). The MLM χ 2 -statistics in MPlus equals the Satorra-Bentler-χ 2 , which adjusts the ML χ 2 estimate for kurtosis (Satorra and Bentler 2001). In the Satorra-Bentler χ 2 -statistic, the usual normal-theory χ 2 -statistic is divided by a scaling correction to better approximate χ 2 under non-normality. The Satorra-Bentler χ 2 -statistic has been shown to outperform other asymptotic robust test statistics in nearly all conditions of sample size and distribution (Chou, Bentler, and Satorra 1991). Accordingly, model fit indices that depend on χ 2 , the statistic will be scaled.

7.7.3

Measurement Validity

To assess the general validity of the measurement model, an EFA, a CFA and reliability tests were conducted on the overall sample. The 30 variables that form the eight latent variables are measured on a seven-point Likert-scale, which, strictly speaking, have an ordinal measurement level. Yet, in practice they are often treated as being interval-scaled because of the assumption of equal appearing intervals (Janssens et al. 2008, p. 255). The Bartlett’s test of sphericity could not support the hypothesis that the variables in the ITSUM are uncorrelated (ρ < 0.000). Also, the "Kaiser-Meyer-Olkin measure of sampling adequacy" statistic equals 0.908 and indicates a sufficient correlation because it is over the recommended cut-off value of 0.5 (Janssens et al. 2008, p.255). Both criteria indicate that a factor analysis is meaningful. A principal component factor analysis was carried out to confirm the expected factor structure of the ITSUM (see structure matrix of the EFA in Appendix, figure A.3). Because of crossloadings (> 0.5) on different factors, two variables, one from the ease of use scale and one from the perceived usefulness scale, were omitted from the study leaving 28 dependent variables assigned to 8 latent variables in the ITSUM. As a result, all variables of the ITSUM show significant and strong loadings (> 0.7) on their respective factors. The measurement model with eight latent factors was confirmed in the exploratory factor analy-

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sis. It contains the factors trustworthiness, controllability, perceived usefulness, perceived ease of use, role clarity, role ability, intrinsic motivation, and intention to use remote services. In line with the work of Anderson and Gerbing (1988), the confirmatory factor analysis corroborates the convergent validity and reliability for the scale items of all constructs. The fit statistics for the eight-factor measurement model (χ 2 (322) = 812.158) indicate that the measurement model fits the data well with a χ 2 /d f < 3.0 and CFI, TLI, RMSEA, and SRMR values well above the recommended cut-off criteria as shown in table 7.3. The χ 2 -statistic is significant (ρ < 0.001), but this was expected because of the sensitivity of the χ 2 -statistic to large sample sizes (Marsh, Balla, and McDonald 1988). Complete results of the confirmatory factor analysis, including factor loadings, Cronbach’s α coefficients, composite reliability, factor determinacy and AVE values are provided for each variable (item) in table 7.4 and discussed in the following section. In addition, multicollinearity statistics are presented. All measures included in the analysis are reliable. All loadings on hypothesized factors are highly significant (p<0.001)18 and sufficiently large (23 of 28 items have loadings greater than 0.70), which establishes convergent validity. The reliability of all the individual scales are above recommended levels, ranging from 0.93 to 0.96 for factor determinacy (Muthen and Muthen 1998-2007, p. 586), from 0.83 to 0.89 for composite reliability (Baumgartner and Homburg 1996), and from 0.82 to 0.90 for Cronbach’s α (Bagozzi and Yi 1988). The correlations for all variables in the ITSUM are shown in the correlation matrix in table 7.5. All correlations are significant (ρ < 0.001). Most factor and variable correlations were under 0.6. Only the correlation between trustworthiness and motivation is about 0.60, and the correlation between role ability and role clarity is somewhat higher with 0.72, which might raise concerns about discriminant validity and multicollinearity. Both issues are addressed below. First, the Fornell-Larcker test of discriminant validity was employed (Fornell and Larcker 1981). Consistent with Fornell and Larcker’s (1981) test for discriminant validity, the AVE is greater than 0.5 for all constructs, and the AVE were greater than the squared correlations for all pairs of constructs (see Appendix, section A.4). Therefore, I assume that all scales measure distinct model constructs. Table 7.3: Fit Statistics for the Measurement Model (n=717) Model Fit Statistics (nn = 7 1 7 ) χ 2 (df) ratio

χ 2/df

SCALING 18

812.158 (322) 2.522 1.305

RMSEA SRMR

0.046 0.050

CFI TLI

0.947 0.937

For testing factor loadings estimates and path estimates Mplus calculated the estimates divided by their respective standard errors (est./s.e.). This tests the null hypothesis that the parameter estimate is zero. An unstandardized estimate divided by its standard error may be evaluated as a Z-statistic to which Mplus provides the ρ-values.

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Table 7.4: Statistics of the Measurement Model Description

Mean

Loadings

Controllability 4.020 4.570 4.170 3.740

0.688 0.779 0.838 0.661

2.620 2.850 2.580 2.650 2.870

0.770 0.802 0.819 0.840 0.688

2.230 2.500

0.839 0.897

Perceived Usefulness PU 1 2.920 PU 2 2.030 PU 3 3.410 PU 4 2.590 PU 5 2.590

0.671 0.789 0.675 0.774 0.839

CONT 1 CONT 2 CONT 3 CONT 4

Trustworthiness TW 1 TW 2 TW 3 TW 4 TW 5

Ease of Use EOU 1 EOU 2

Role Clarity 2.740 3.500 2.930

0.779 0.811 0.833

2.540 2.470 2.480

0.899 0.919 0.756

Intrinsic Motivation MOTIV 1 3.300 MOTIV 2 3.290 MOTIV 3 3.160 MOTIV 4 2.810

0.740 0.764 0.780 0.806

RC 1 RC 2 RC 3

Role Ability RA 1 RA 2 RA 3

Intention to Use INT 1 INT 2

3.800 3.640

AVE

CR

Crohnb.α

F. D.

Tolerance

VIF

0.555

0.832

0.821

0.925

0.833

1.200

0.617

0.889

0.885

0.952

0.575

1.739

0.750

0.857

0.859

0.936

0.724

1.380

0.566

0.866

0.862

0.942

0.673

1.486

0.653

0.849

0.847

0.941

0.478

2.093

0.741

0.892

0.89

0.961

0.541

1.848

0.597

0.856

0.857

0.934

0.674

1.483

0.805

0.892

0.897

0.950

0.890 0.904

Legend: AVE: Average Variance Extracted; CR: Composite Reliability; VIF: Variance Inflation Factor; F.D.: Factor Determinacy.

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Second, to detect multicollinearity, the VIF and the tolerance values for the latent variables were inspected (see table 7.4). All VIF of the ITSUM predictor variables including perceived usefulness are under the recommended cut-off value of 4.0 and have the highest VIF at 2.09 (Moosmüller 2004, p. 131; Myers 1993, p. 369). Also, all tolerance values have a minimum value of 0.48 and are above the recommended cut-off value of 0.10 (Moosmüller 2004, p. 131; Myers 1993, p. 369). Because the cut-off values are rules of thumb, the multicollinearity is also judged according to the criteria of Grewal, Cote, and Baumgartner (2004) who state that correlations between 0.6 and 0.8 may not induce problems in SEM calculation if composite reliability of each latent predictor variable is above 0.8, the r2 is high enough, and the sample ratio is relatively large (Grewal, Cote, and Baumgartner 2004). In this study, the composite reliability is well above 0.8 for all variables (see table 7.4); the r2 is 0.52 for intention to use and 0.36 for perceived usefulness (see chapter 7.7.5); and the sample size is large with a 22:1 ratio. Therefore, multicollinearity is assessed as not overly harmful to the model’s results.

7.7.4

Assessing Common Method Variance

The potential common method bias is addressed by the marker-variable technique proposed by Lindell and Whitney (2001). In this approach, a marker variable CMV is added to the model, which is theoretically unrelated to at least one variable in the model. CMV can then be assessed based on the correlation between the marker variable and the theoretically unrelated variable (Lindell and Whitney 2001). A scale for measuring the individual’s general desire for service failure handling included in the questionnaire is considered to be theoretically unrelated to at least one of the other variables in the survey.19 As shown in table 7.5, the smallest positive correlation of this variable with the other constructs is 0.024 (correlation with the latent variable role clarity), and the second smallest is 0.034 (correlation with role ability). According to the suggestions of Lindell and Whitney (2001), the second smallest positive correlation was chosen as a conservative estimate of the common method variance. The assumption is made that a method factor is assumed to have a constant correlation with all of the measured items. Therefore, the initial correlation matrix was adjusted by the CMV correlation (see Appendix, A.4). The adjusted correlations were tested for significance, and all significant correlations stayed significant after subtracting the assumed CMV (t value > 2.4, at least at ρ < 0.05). As Lindell and Whitney (2001, p. 18) note, "this suggests that the results cannot be accounted for by CMV." Similar to the approach of Agustin and Singh (2005), Grayson, Johnson, and Chen (2008), and Malhotra, Kim, and Patil (2006), the proxy for CMV was included in the structural equations analysis (CMV PROXY). Incorporating this factor in a structural equation model offers the advantage of accounting for measurement error and hypothesized structural relationships in 19

The variable was included for purposes outside this thesis.

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Table 7.5: Correlation of ITSUM Variables (n=717)

PU CONT RC RA TW INT MOTIV EOU SN TT EXP

PU

CONT

RC

RA

TW

INT

MOTIV

EOU

SN

1.000 0.242 0.283 0.255 0.325 0.410 0.249 0.373 0.209 0.584 0.123

1.000 0.442 0.287 0.356 0.317 0.371 0.223 0.221 0.402 0.208

1.000 0.720 0.572 0.483 0.485 0.499 0.365 0.400 0.438

1.000 0.433 0.261 0.459 0.521 0.206 0.340 0.300

1.000 0.445 0.604 0.377 0.342 0.516 0.220

1.000 0.249 0.308 0.578 0.408 0.397

1.000 0.261 0.288 0.404 0.123

1.000 0.227 0.427 0.229

1.000 0.336 0.402

TT

1.000 0.169

EXP

1.000

addition to CMV, a benefit that the matrix adjustment approach does not provide (Malhotra, Kim, and Patil 2006).

7.7.5

Validation of the ITSUM (n=717)

The general validity of the ITSUM is assessed using the overall sample of 717 cases. The SEM was estimated using the MLM estimator in Mplus 5.1, which yielded the results shown in table 7.6. The path diagram with all path estimates β is illustrated in figure 7.6. The CFI is at a satisfying level of 0.928. The TLI is at a level of 0.914. Both values indicate a good model fit. The RMSEA is fairly good at 0.051 and the SRMR is well under the cut-off value of 0.08 with 0.073 (Hu and Bentler 1999). The χ 2 /d f ratio is 2.877, indicative of a good fit. One can conclude that the data fits the research model ITSUM, judging by the fit indicators. All relationships in the ITSUM, except the effect of control on intention to use, are strong and significant. Six of nine hypotheses regarding the direct effects of the ITSUM are supported. Trustworthiness has a strong significant effect on intention to use, which supports H2. Also, the effects of ease of use and trust in technology on perceived usefulness are significant and strong (β = 0.152, ρ < 0.01; β = 0.349, ρ < 0.001) supporting H4 and H3. As postulated, perceived usefulness is a significant predictor of usage intention which supports hypothesis H5. In addition, role clarity has the strongest significant effect on intention (β = 0.259, ρ < 0.001), supporting H6. Role ability and motivation have significant negative effects on intention, therefore H7 and H8 could not be supported for the overall sample. Also, the effect of controllability on intention is not significant, meaning that H1 cannot be supported for the ITSUM model for the full sample of 717 cases. Path analysis reveals that subjective norms has a significant effect of β = 0.168 (ρ < 0.001) on intention, thus, H9 is supported. Overall, the antecedents explain 52% of the variance of intention to use interactive remote services. To test the mediated relationship between trust in technology and ease of use on perceived usefulness and perceived usefulness on intention (H5a and H5b), the product of coefficients

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Table 7.6: Results of the ITSUM (n=717) Direct Effects H

Independent Variable

Dependent Variable

Expected Effect

Standardized Path Coefficients

ρ-value

1 2 3

Controllability Trustworthiness Trust in Technology Ease of Use

Intention to use Intention to use Perceived Usefulness Perceived Usefulness Intention to use

Positive Positive Positive

0.060 (0.043) 0.180 (0.050) 0.349 (0.025)

0.160 0.000 0.000

n. s. supported supported

Positive

0.152 (0.044)

0.001

supported

Positive

0.237 (0.028)

0.000

supported

Intention to use Intention to use Intention to use Intention to use

Positive Positive Positive Positive

(0.064) (0.045) (0.045) (0.015)

0.000 0.007 0.005 0.000

supported n. s. n. s. supported

0.001 (0.021) 0.056 (0.019)

0.977 0.003

4 5 6 7 8 9 C C

Perceived Usefulness Role Clarity Role Ability Motivation Subjective Norms CMV Proxy

0.259 −0.131 −0.128 0.168

Intention to use Intention to use

EXP

Result

Model Fit Statistics (nn = 7 1 7 ) χ 2 (df) ratio

χ 2/df

SCALING

1185.385 (412) 2.877 1.197

RMSEA SRMR

r2 (INT)

0.051 0.073 0.517

CFI TLI

r2 (PU)

0.928 0.914 0.359

Legend: Values in parentheses are standard errors if not otherwise denoted; C: Control Variable; H: Hypothesis; n. s.: not supported.

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Figure 7.6: Results of the ITSUM (n=717)

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7.7 t1 -Study: Results of ITSUM Validation

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method was applied (MacKinnon et al. 2002). Indirect effects, their standard errors, and a test of significance are estimated in Mplus based on the Sobel test (Baron and Kenny 1986; MacKinnon et al. 2002; MacKinnon and Fairchild 2009; Sobel 1986).20 The indirect effect of trust in technology on intention to use over perceived usefulness is significant (β = 0.083, ρ < 0.001). Also, the indirect effect of ease of use on intention to use over perceived usefulness is significant (β = 0.036, ρ < 0.01). Therefore, it can be concluded that hypotheses 5a and 5b are supported (see table 7.7). According to the hypotheses 10a-g, the interaction effects of company size with the proposed direct antecedents of intention to use are tested. In line with Baron and Kenny’s (1986) definition of moderation, I used the latent moderated structural equations (LMS) estimation procedure, as implemented in Mplus 5.1, to test interaction effects between variables within a SEM (Klein and Moosbrugger 2000). This procedure is viewed as a reliable and valid method to explore moderating effects (Marsh, Hau, and Wen 2004). I compared adjacent nested models (model with a moderating effect / model without a moderating effect) with a χ 2 -difference test based on loglikelihood values and scaling correction factors obtained with the MLR estimator.21 It delivers scale-adjusted loglikelihood values. Scaled values cannot be used for χ 2 - difference testing of nested models because a difference between two scaled loglikelihood values for nested models is not distributed as χ 2 . Therefore, a χ 2 -difference test was computed based on loglikelihood values and scaling correction factors obtained with the MLR estimator according to Satorra (2000). Interaction effects are identified for two pairs of constructs: company size and perceived usefulness (path estimate = -0.181, ρ < 0.001), and company size and company norms (path estimate = -0.088, ρ < 0.001). This is indicated by significant effect sizes of the interactions, an inTable 7.7: ITSUM (n=717): Mediating Effects Mediating Effects H

Mediator

Path

exp. effect

Std. Path Coef.

ρ-value

5a

Perceived Usefulness Perceived Usefulness

Trust in Technology → Intention Ease of Use → Intention

mediation

0.083 (0.018)

0.000

supported

mediation

0.036 (0.015)

0.013

supported

5b

Result

Legend: Values in parentheses are standard errors if not otherwise denoted. 20

21

The more commonly used causal steps method by Baron and Kenny (1986) is not chosen for two reasons. The coefficient method of MacKinnon et al. (2002) is superior as it provides a direct estimate of the size of the indirect effect, whereas the causal steps method does not provide a statistical test for indirect effects. Also, the requirements for Baron and Kenny’s (1986) approach cancel out models in which the independent and dependent variables have no significant relation or effects with opposite signs. This is the case for the variables ease of use and trust in technology in this study. Furthermore, preliminary tests with both variables showed no significant effects on intention to use. The MLM estimator is not available for the LMS estimation within Mplus. The MLR estimator is chosen because it is robust to non-normality of observations and can be employed with continuous and categorical variables in the interaction effect calculation.

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Table 7.8: ITSUM (n=717): Moderating Effects Moderating Effects H

Moderator

Path

exp. effect

Path Coefficients

10a 10b 10c 10d 10e 10f 10g 11a 11b 11c 11d 11e 11f 11g

Company Size Company Size Company Size Company Size Company Size Company Size Company Size Function Function Function Function Function Function Function

Perceived Usefulness → Intention Controllability → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention Perceived Usefulness → Intention Controllability → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention

Attenuated Attenuated Attenuated Attenuated Attenuated Attenuated Attenuated Strenghened Strenghened Strenghened Strenghened Strenghened Strenghened Strenghened

−0.181*** not supported not supported not supported not supported not supported −0.088*** not supported not supported not supported not supported not supported not supported not supported

Moderating effects of function were tested on a reduced sample (n=649). H: Hypothesis; * : ρ < 0.05; ** : ρ < 0.01; *** : ρ < 0.001.

crease of the AIC value, and a significant χ 2 -difference test that show an improvement of the model fit with the interaction effect compared to the model without an interaction effect (see table A.4 in the Appendix). The results support the Hypotheses 10a and H10g and suggest that with increasing company size, the effect of perceived usefulness and subjective norms will be weaker as individual perceptions of an employee may be more unlikely to affect decisions of the organization. Furthermore, function as a categorical variable with the categories owner/general manager, production manager, and machine operator was tested. The category "others" was omitted and the sample size was reduced by the 68 respective respondents for this test. This left 649 individuals in the sample. Respondents’ function had no significant effect with any antecedent of intention to use interactive remote services. Therefore, H11a-g could not be supported (see table 7.8).

7.8

Multi-Group Comparison: Adoption vs. Continued Usage

7.8.1

Description of the Groups

It was hypothesized that the effects of an individual’s perceptions on organizational decisions to use interactive remote services are different for organizations that are mainly in a pre-adoption phase and organizations that are in a continued usage phase. To accurately detect differences in the effect of antecedents on usage intention between these two groups of organizations, a

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multi-group comparison was conducted. To yield groups big enough for the estimation and to get a conservative estimation of the pre-adopter group, a median split was performed on the measure "organization’s level of remote service implementation" according to Rogers’ (2003) classification of innovation adoption. The median of the measure is at 5, and the arithmetic middle at 4.52. Group 1 consists of organizations in the pre-adoption phase and includes cases with ratings from 1 to 4 on the implementation level scale (n=353). This group is referred to as the "pre-adopter group." Group 2 comprises organizations that are in a continued usage phase and includes cases with ratings from 5 to 7 on the implementation level scale (n=364). This group is referred to as the "continued user group." The split of the sample leads to two groups, which are similar in their demographic characteristics (see figure 7.7 and 7.8). Results of the χ 2 -test indicate no difference in the distribution of the respondent’s age (χ 2 (2) = 0.683).22 Figure 7.9 illustrates the distribution of company size in both groups. The pre-adopter group includes more small enterprises (62%) when compared to the continued user group (27%). The continued user group, however, contains more of the larger companies (> 20 employees). This finding suggests that remote services are more frequently used in middle sized and larger companies, whereas small enterprises still hesitate to adopt this new service type. This corresponds to the finding that in the pre-adopter group, the respondent is more often a general manager or managing owner (76%) than in the continued user group (69%) (see figure 7.10). A multi-group analysis and further tests for moderation of company size and function of the respondent on both groups are conducted below to gain insight into the interplay of these factors.

7.8.2

Assessing Measurement Invariance

To assess measurement invariance of the multi-group model, the ITSUM is separately fitted within each group (pre-adopter and continued user). The overall model fit indices for both groups are shown in table 7.9. The results of the ITSUM for organizations within the preadoption phase (n=364) exhibit a good fit. The CFI is at a satisfying level of 0.916. The TLI is at a level of 0.900. The RMSEA is at 0.056 and the SRMR is well under the cut-off value of 0.08 with 0.069 (Hu and Bentler 1999). The results of the structural test for organizations within the post-adoption phase (n=353) provide strong support for the proposed ITSUM. The CFI is at a satisfying level of 0.920, and the TLI is at a level of 0.905. The RMSEA is at 0.051. The SRMR is 0.079, which is under the cut-off value of 0.08. The variance of the dependent variable, intention to use interactive remote service, is explained on a good level of 43% in the continued user group. In the pre-adopter group, intention to use remote service is explained on an acceptable level (r2 = 0.360). 22

Categories with less than 5 cases were not included in the test.

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Figure 7.8: Distribution of Respondent’s Gender Across Groups (in %)

7.8 Multi-Group Comparison: Adoption vs. Continued Usage

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Figure 7.10: Distribution of Respondent’s Function Across Groups (in %)

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Table 7.9: Model Fit Statistics for Pre-Adopter Group and Continued User Group Pre-Adopter Group (nn = 3 6 4) χ 2 (df) ratio

χ 2/df

SCALING

883.078 (412) 2.140 1.114

RMSEA SRMR

r2 (INT)

0.056 0.069 0.360

0.916 0.900 r2 (PU) 0.312 CFI

TLI

Continued User Group (nn = 3 5 3 ) χ 2 (df) ratio

χ 2/df

SCALING

789.642 (412) 1.920 1.118

0.051 0.076 r2 (INT) 0.429 RMSEA

CFI

SRMR

TLI

r2 (PU)

0.920 0.905 0.296

Table 7.10: Assessing Measurement Invariance: Model Fits Invariance Configural regular χ 2 scaled χ 2 scaling corr. df CFI TLI RMSEA AIC

1485.482 1198.091 1.240 644 0.936 0.925 0.049 63516.017 Model 1

Legende:

p:

Metricp 1521.887 1230.159 1.237 663 0.935 0.926 0.049 63514.422 Model 2

Scalarp 1553.932 1259.147 1.234 674 0.933 0.925 0.049 63524.468 Model 3

Factor Variancep 1583.678 1283.139 1.234 671 0.931 0.922 0.050 63560.213 Model 4

Factor Co-Variancep 1585.363 1285.474 1.233 690 0.932 0.925 0.049 63523.899 Model 5

partial.

The comparison of the effects of antecedents on usage intention within the ITSUM across the pre-adopter and the continued user groups is only reliable and statistically justifiable if measurement invariance is assured across groups. This thesis follows the recommended procedure by Steenkamp and Baumgartner (1998a) and uses a hierarchical ordering of four nested models and a baseline model as introduced in chapter 7.2.2.5. In addition to the χ 2 -difference test, Steenkamp and Baumgartner (1998a) suggest that the CFI, TLI, AIC, and the RMSEA should be included in the assessment of the nested models. Steenkamp and Baumgartner (1998a; 1998b), and van Birgelen et al. (2002) also show that the assessment of the fit indices can outweight significant χ 2 -difference test results when large sample sizes are estimated. Within Mplus, the MLM estimator delivers scaled χ 2 -values. The scaled χ 2 cannot be used for χ 2 -difference testing of nested models because a difference between two scaled χ 2 -values for nested models is not χ 2 distributed. Therefore, a corrected χ 2 -difference test statistic is computed according to Satorra (2000) for the nested model comparison. The results were obtained by using Steenkamp and Baumgartner’s (1998a) procedure. All parameter estimates are shown in table 7.10 including the unscaled χ 2 -value, scaled χ 2 -value,

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183

scaling correction factor, degrees of freedom (df), CFI, TLI, RMSEA, and AIC. In sum, five models were estimated: 1. Model 1: Configural invariance is assessed by first testing a multi-group model without measurement invariance. The results indicate that the data fits well with the a priori hypothesized model (CFI = 0.936, TLI = 0.925, and RMSEA = 0.049). Configural variance can, therefore, be assumed. 2. Model 2: Metric invariance has to be satisfied to compare the scale intervals of items across groups. Because the factor loadings carry the information about how changes in latent scores relate to changes in observed scores, metric invariance can be tested by constraining the factor loadings to be equal across groups (Steenkamp and Baumgartner 1998a). A model with all factor loadings constrained did not pass the test for full metric invariance. Therefore, the procedure by Byrne, Shavelson, and Muthen (1989) for assessing partial invariance was followed. Byrne, Shavelson, and Muthen (1989) argue that full metric invariance is not necessary for further tests of invariance, provided that at least one item of a construct is metrically invariant. The modification indices (M.I.) indicated the item "role1" as being a cause for non-invariance (M.I. = 24.248). According to Steenkamp and Baumgartner (1998a), the invariance constraint for this loading was relaxed across groups for all of the subsequent tests. The model fit of this model (model 2) is very good (CFI = 0.935, TLI = 0.926, RMSEA = 0.049). All model fit values only differ very marginally if at all from the configural invariance model (model 1), the TLI and the AIC even improve. In terms of the χ 2 -difference test, the fit of this model is not highly significantly worse than the fit of the configural invariance model (corr. Δχ 2 (19) = 32.066, ρ = 0.031). Based on these results, partial metric invariance is assumed. 3. Model 3: To assess scalar invariance a third model was tested by additionally holding the measurement intercepts equal. This model did not pass the test for full scalar invariance. The modification indices indicated the items of the role clarity and motivation construct to be a cause for the non-invariance (M.I .> 18.248). According to Steenkamp and Baumgartner (1998a), the invariance constraints for these loadings and means were relaxed across groups, except for the marker items, to assess scalar invariance. The data fit is good with this hypothesized model (model 3, CFI = 0.933, TLI = 0.925, RMSEA = 0.049). Compared with model 2, the increase in the χ 2 -value is statistically significant — corrected Δχ 2 (11) = 30.427, ρ < 0.01 — but is very modest in magnitude. This might due to the sensitivity of the χ 2 statistic to large sample sizes. However, the inspection of the other fit indices such as AIC, CFI, TLI and RMSEA, which are less sensitive to sample-size, shows a less substantial decrease in fit - if any at all. Therefore, it is concluded that partial scalar invariance is supported. The means for all constructs except role clarity and motivation can be compared across groups. 4. Model 4: To assess factor variance invariance a forth model was tested while holding

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7. Quantitative Studies the factor variances equal. The data fit satisfactorily with the hypothesized model (CFI = 0.931, TLI = 0.922, RMSEA = 0.05). The increase in the χ 2 -value between model 2 and the this model is statistically significant — corrected Δχ 2 (8)= 62.708, ρ < 0.001 — but is modest in magnitude. This might be affected by the sensitivity of the χ 2 -statistic to large sample sizes. The inspection of the other fit indices shows a less substantial decrease in fit. For example the RMSEA-value is nearly unchanged. Therefore, it is concluded that partial factor variance invariance is supported.

5. Model 5: A fifth model was tested to assess if the factor covariances were invariant across groups compared to model 2. The increase in corrected χ 2 between model 2 and the fifth model is significant: corrected Δχ 2 (27)= 55.937 (ρ < 0.001), but modest in magnitude. The fit of the fifth model is good (CFI = 0.932, TLI = 0.925, RMSEA = 0.049) and most model-fit values are nearly the same as in model 2, the RMSEA value is the same as in model 2. Therefore, partial factor covariance invariance is supported and it can be concluded that most factor correlations are identical across groups. Overall, I found support for configural invariance, partial metric invariance, partial scalar invariance, partial factor variance invariance, and partial factor covariance invariance. Therefore, it is possible to compare path coefficients and factor means across groups. The comparison of the path coefficients across groups is based on model 2. The comparison of the factor means across groups is based on model 3. The means for the constructs role clarity and motivation cannot be compared in a meaningful way.

7.8.3

Results for Organizations in the Pre-Adoption Phase

The estimation of the pre-adopter group model shows that the ITSUM hypotheses are supported to a large extent. Seven of the nine hypotheses regarding the direct effects are supported. All path coefficients for the ITSUM (pre-adopters) are summarized in table 7.11, and illustrated in figure 7.11. The analysis reveals that the proposed antecedents of behavioral intention regarding the actions of the service counterpart have a significant positive effect on intention to use interactive remote services. Controllability beliefs (β = 0.132, ρ < 0.05) and trustworthiness beliefs (β = 0.215, ρ < 0.01) directly affect the organization’s intention to use remote service, which supports hypotheses H1 and H2. Ease of use and trust in technology have been proven to be antecedents of perceived usefulness with strong significant effects (β = 0.142 and β = 0.326) supporting H3 and H4. Perceived usefulness has a significant positive effect on behavioral intention (β = 0.141, ρ <0.01). Role clarity has the strongest effect (β = 0.252, ρ < 0.01) on intention to use interactive remote services, whereas motivation is insignificant in the behavioral intention equation, thereby resulting in negative results for H8. Hypothesis H7 is also not supported, because role ability had a significant negative effect on intention instead the expected positive

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Table 7.11: Pre-Adopter Group (n=364): Direct Effects Direct Effects H

Independent Variable

Dependent Variable

Expected Effect

Standardized Path Coefficients

ρ-value

1 2 3

Controllability Trustworthiness Trust in Technology Ease of Use

Intention to use Intention to use Perceived Usefulness Perceived Usefulness Intention to use

Positive Positive Positive

0.132 (0.067) 0.215 (0.071) 0.326 (0.033)

0.049 0.003 0.000

supported supported supported

Positive

0.142 (0.062)

0.022

supported

Positive

0.141 (0.043)

0.001

supported

Intention to use Intention to use Intention to use Intention to use

Positive Positive Positive Positive

(0.089) (0.069) (0.066) (0.025)

0.004 0.002 0.055 0.000

supported n. s. n. s. supported

−0.011 (0.031) 0.072 (0.027)

0.719 0.018

4 5 6 7 8 9 C C

Perceived Usefulness Role Clarity Role Ability Motivation Subjective Norms CMV Proxy EXP

Intention to use Intention to use

0.252 −0.214 −0.127 0.146

Legend: Values in parentheses are standard errors if not otherwise denoted C: Control Variable; H: Hypothesis; n. s.: not supported.

Result

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effect. Subjective norms have been shown to be a strong predictor of intention to use interactive remote service (β = 0.146, ρ <0.001), thus H9 is supported. The product of coefficients method was applied (MacKinnon et al. 2002) to test the mediated relationship between trust in technology and ease of use; perceived usefulness; and intention as stated in hypotheses H5a and H5b. The indirect effect of trust in technology on intention to use over perceived usefulness is significant (path estimate = 0.046, ρ <0.05), thus H5a is supported. H5b is not supported for the pre-adopter group. The results of the mediation tests are shown in table 7.12. According to the hypotheses 10a-g, the interaction effects of company size and of function of the respondent with the proposed direct antecedents of intention to use are tested (for calculation see Appendix, table A.5). The LMS estimation procedure revealed interaction effects for two pairs of constructs. Table 7.13 shows the results for the interaction effects of company size and perceived usefulness (path estimate = -0.118, ρ < 0.01), company size and subjective norms (path estimate = -0.059, ρ < 0.001). The results support the hypotheses 10a and H10g in that with increasing company size, the effect of perceived usefulness and subjective norms are weaker as individual perceptions of an employee are less likely to affect decisions of the organization. In all pairings of function and the direct antecedents of intention, no interaction effect could be detected. Therefore, all hypotheses relating to an interaction effect between function and the direct antecedents of intention are not supported (H11a-g) (see table 7.13).

7.8.4

Results for Organizations in the Continued Usage Phase

The estimation of the ITSUM for the continued user group leads to interesting findings. Five of nine hypotheses are supported in the continued user group. All path coefficients for the ITSUM for the continued user group are displayed in table 7.14. Figure 7.12 illustrates the results. The analysis reveals that in the group of continued users, three beliefs of nearly the same effect size determine the continued usage intention: perceived usefulness; role clarity; and subjective norms. Perceived usefulness is the strongest predictor for usage intention (β = 0.282, ρ < 0.001). Its antecedents, ease of use (β = 0.208, ρ < 0.01) and trust in technology (β = 0.317, ρ < Table 7.12: Pre-Adopter Group (n=364): Mediating Effects Mediating Effect H

Mediator

Path

exp. effect

Std. Path Coef.

ρ-value

5a

Perceived Usefulness Perceived Usefulness

Trust in Technology → Intention Ease of Use → Intention

mediation

0.046 (0.022)

0.038

supported

mediation

0.020 (0.014)

0.149

n. s.

5b

Result

Legend: Values in parentheses are standard errors if not otherwise denoted; n. s.: not supported.

7.8 Multi-Group Comparison: Adoption vs. Continued Usage

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Table 7.13: Pre-Adopter Group (n=364): Moderating Effects Moderating Effects H

Moderator

Path

exp. effect

Path Coefficients

10a 10b 10c 10d 10e 10f 10g 11a 11b 11c 11d 11e 11f 11g

Company Size Company Size Company Size Company Size Company Size Company Size Company Size Function Function Function Function Function Function Function

Perceived Usefulness → Intention Controllability → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention Perceived Usefulness → Intention Controllability → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention

Attenuated Attenuated Attenuated Attenuated Attenuated Attenuated Attenuated Strenghened Strenghened Strenghened Strenghened Strenghened Strenghened Strenghened

−0.118** not supported not supported not supported not supported not supported −0.059*** not supported not supported not supported not supported not supported not supported not supported

Moderating effects of function were tested on a reduced sample (n=333). H: Hypotheses; * : ρ < 0.05; ** : ρ < 0.01; *** : ρ < 0.001.

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Figure 7.12: Results of the ITSUM: Continued User Group (n=353)

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Table 7.14: Continued User Group (n=353): Direct Effects Direct Effects H

Independent Variable

Dependent Variable

Expected Effect

Standardized Path Coefficients

ρ-value

1 2 3

Controllability Trustworthiness Trust in Technology Ease of Use

Intention to use Intention to use Perceived Usefulness Perceived Usefulness Intention to use

Positive Positive Positive

−0.063 (0.060) 0.128 (0.086) 0.317 (0.047)

0.291 0.138 0.000

n. s. n. s. supported

Positive

0.208 (0.071)

0.004

supported

Positive

0.282 (0.047)

0.000

supported

Intention to use Intention to use Intention to use Intention to use

Positive Positive Positive Positive

(0.101) (0.081) (0.078) (0.024)

0.045 0.327 0.419 0.000

supported n. s. n. s. supported

−0.009 (0.032) −0.020 (0.033)

0.770 0.540

4 5 6 7 8 9 C C

Perceived Usefulness Role Clarity Role Ability Motivation Subjective Norms CMV Proxy EXP

Intention to use Intention to use

0.202 0.079 −0.063 0.181

Result

Legend: Values in parentheses are standard errors if not otherwise denoted C: Control Variable; H: Hypothesis; n. s.: not supported.

0.001), also have strong effects. Thus, hypotheses H5, H4, and H3 are supported. Hypothesis H6 is supported as the analysis shows that role clarity affects intention to use with β = 0.202 at a ρ-value smaller than 0.05. Subjective norms is a strong predictor of intention to use (β = 0.181, ρ < 0.001); thus, H9 is supported. The effects of controllability (H1), trustworthiness (H2), role ability (H7), and intrinsic motivation (H8) are not supported for the continued user group. These results will be addressed and discussed in chapter (see chapter 7.10). The test of mediation for the relationship between trust in technology and ease of use; perceived usefulness; and intention to use interactive remote services as stated in hypotheses H5a and H5b supports both mediating effects. The indirect effect of trust in technology on intention to use over perceived usefulness is significant (path estimate=0.090, ρ <0.05). In addition, the indirect effect of ease of use on intention over perceived usefulness (path estimate=0.059, ρ < 0.05) is significant. The results of the mediation tests are shown in table 7.15. In the continued user group, four interaction effects are identified (see calculation in Appendix, Table 7.15: Continued User Group (n=353): Mediating Effects Mediating Effect H

Mediator

Path

exp. effect

Std. Path Coef.

ρ-value

5a

Perceived Usefulness Perceived Usefulness

Trust in Technology → Intention Ease of Use → Intention

mediation

0.090 (0.038)

0.019

supported

mediation

0.059 (0.029)

0.042

supported

5b

Legend: Values in parentheses are standard errors if not otherwise denoted.

Result

7.8 Multi-Group Comparison: Adoption vs. Continued Usage

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Table 7.16: Continued User Group (n=353): Moderating Effects Moderating Effects H

Moderator

Path

exp. effect

Path Coefficients

10a 10b 10c 10d 10e 10f 10g 11a 11b 11c 11d 11e 11f 11g

Company Size Company Size Company Size Company Size Company Size Company Size Company Size Function Function Function Function Function Function Function

Perceived Usefulness → Intention Controllability → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention Perceived Usefulness → Intention Controllability → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention

Attenuated Attenuated Attenuated Attenuated Attenuated Attenuated Attenuated Strenghened Strenghened Strenghened Strenghened Strenghened Strenghened Strenghened

−0.277*** not supported not supported -0.143* -0.129* not supported −0.103*** not supported not supported not supported not supported not supported not supported not supported

Moderating Effects Of Function were tested on a reduced sample (n=316). H: Hypothesis; * : ρ < 0.05; ** : ρ < 0.01; *** : ρ < 0.001.

table A.6). Interaction effects of company size with perceived usefulness (path estimate= 0.277, ρ <0.001), subjective norms (path estimate= -0.103, ρ <0.001), role clarity (path estimate= -0.143, ρ <0.05), and role ability (path estimate= -0.129, ρ <0.05) indicate that the continued user group’s answers depend on the company size. With increasing company size the effect of personal beliefs on usage intention are weaker, maybe because employees’ perceptions are more unlikely to affect decisions the larger the organization is. The results support the hypotheses 10a, d, e, and g. As before, no interaction effects for the function of the respondent could be detected. In sum H10b,c,f and H11a-g is not supported (see table 7.16).

7.8.5

Comparison of Group Parameters

A meaningful comparison of path coefficients between the pre-adoption group and the continued user group is possible because partial metric invariance is sufficiently established. A two tailed t-test was conducted to test for significance of the differences based on raw path coefficients. All differences in effect size are significant, results are shown in table 7.17. The path from role clarity to intention is significantly stronger in the pre-adopter group (t=6.215), supporting H15. A significant effect of trustworthiness and controllability could only be detected for the pre-adopter group, whereas the paths are insignificant for the continued user group. In terms of effect size, the path coefficients of both effects are weaker in the continued user group (t=12.267 and t=41.387). Thus, H13 and H14 are partially supported. As postulated in H19, the effect of perceived usefulness on usage intention is decidedly stronger for the continued user group (t=40.350).

Intention to use Intention to use Intention to use Intention to use Intention to use Intention to use Intention to use

Controllability Trustworthiness Role Clarity Role Ability Motivation Subjective Norms Perceived Usefulness

Legend: Values in parentheses are standard errors if not otherwise denoted.

Dependent Variable

Independent Variable

Table 7.17: Comparison of Path Coefficients Across Groups

0.049 0.003 0.005 0.002 0.055 0.000 0.002

(0.094) (0.112) (0.106) (0.084) (0.084) (0.084) (0.063)

0.184 0.333 0.302 −0.260 −0.160 0.248 0.193

ρ-value

Pre-adopter (n = 364) Raw Path Coefficients −0.078 0.214 0.249 0.101 −0.098 0.268 0.392

(0.074) (0.146) (0.122) (0.103) (0.121) (0.040) (0.069) 0.291 0.143 0.042 0.329 0.421 0.000 0.000

ρ-value

Continued user (n = 353) Raw Path Coefficients 41.387 12.267 6.215 51.501 7.992 4.050 40.350

t-value (df = 715)

0.000 0.000 0.000 0.000 0.000 0.000 0.000

ρ-value

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H16 and H17 propose a stronger effect of role ability and intrinsic motivation on usage intention in the pre-adopter group than in the continued user group. The effects in both groups are indeed significantly different (t=51.501 and t=7.992). The effect of intrinsic motivation on intention to use is not significant in either group, and the sign of the path differs between groups. Therefore, H17 cannot be supported with any certainty and might warrant further analysis. The effect of role ability on intention to use is not significant in the continued usage group, but significant in the pre-adoption group. The effect of role ability, however, is stronger in the pre-adopter group, the sign of the effect in both groups, however, is negative. The hypotheses H16, therefore, is partially supported, but might also warrant further analysis. The hypothesis that subjective norms have a stronger effect on usage intention in the pre-adopter group cannot be supported. Instead, the effect is significantly stronger in the continued user group (t=4.05). A meaningful comparison of factor means between the pre-adoption group and the continued user group is possible as scalar invariance was sufficiently established for all constructs except role clarity and motivation (see chapter 7.8.2). The mean differences are tested and result in Z-values that indicate whether two means are significantly different from each other. Mean differences of the single items for subjective norms and trust in technology were tested with a two-tailed t-test. Results are shown in table 7.18. It can be seen, that in the pre-adopter group the perceptions of controllability (f.m. = 4.418), trustworthiness of the RST (f.m. = 3.023), usefulness (f.m. = 3.335), ease of use (f.m.= 2.555), role ability (f.m. = 2.704), subjective norms (mean = 5.32), and trust in technology (mean = 3.63) are significantly higher than in the continued user group (all ρ <0.001). The average perception of controllability, trustworthiness, role ability, ease of use, usefulness, trust in technology and subjection norms are lower in the pre-adopter group than in the continued user group. The results of the multi-group comparison will be discussed in detail together with a comprehensive interpretation of individual effects in chapter 7.10.

Table 7.18: Comparison of Factor Means Across Groups

INT PU CONT RC RA TW MOTIV EOU TT SN *:

Overall sample (n = 717)

Pre-adopter group (n = 364)

Cont. User Group (n = 353)

Z-statistic

ρ-value

3.716 2.789 4.125 3.050 2.500 2.713 3.134 2.372 3.200 4.280

4.950 3.340 4.420 3.490 2.700 3.020 3.360 2.560 3.630 5.320

2.450 2.230 3.820 2.610 2.290 2.390 2.900 2.190 2.760 3.200

17.480 11.040 6.320 — 4.330 7.360 — 3.840 156.045 266.415

0.000 0.000 0.000 — 0.000 0.000 — 0.000 0.000* 0.000*

Comparison of single-item mean with t-test.

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7.9 7.9.1

t2-Study: Intention - Behavior Link Sample Description

The link between the behavioral intention, as captured by the ITSUM, and actual behavior is studied in this section. In March 2009 behavioral data was collected in a follow up study (t2 ) to the initial t1 -study. This survey was sent to 567 respondents who participated in the first survey in June 2008 and gave their permission to be contacted again for a follow-up study. The questionnaire was filled-in completely by 249 participants, which reflects a response rate of 43.84%. The data from the t1 -study was matched with the data of the t2 -study. The distribution of respondents’ gender, age and function in the second study closely resembles that of the overall t1 sample and is shown in figure 7.13, 7.14, and 7.15. The results of the χ 2 -tests indicated no response bias in terms of the respondent’s function (χ 2 (2) = 0.266); the respondent’s age (χ 2 (2) = 2.998); and the respondent’s gender (χ 2 (1) = 0.868).23

23

Categories with less than 5 cases were not included in the tests.

7.9 t2 -Study: Intention - Behavior Link

193

 


 




  





 


 


 


















 












 

 










 









Figure 7.13: Distribution of Respondent’s Age Across Samples in % (t1 and t2 -Study)

 


 


 


 


 


 


 




















Figure 7.14: Distribution of Respondent’s Gender Across Samples in % (t1 and t2 Study)

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Figure 7.15: Distribution of Respondent’s Function Across Samples in % (t1 and t2 Study)

7.9.2

Logistic Regression Results

The central variable in the t2 questionnaire was the organization’s actual usage behavior of interactive remote services. It was measured by asking how often the respondents had used interactive remote services since the questionnaire in June 2008. The respondents could choose between four categories: never, once, two to three times, and more than four times. A logistic regression was conducted using MPlus’ MLR estimator to estimate the effect of intention to use in t1 (June 2008) on the actual behavior measured in t2 (March 2009). First, a model with a dependent ordinal categorical variable using all four categories was proposed. Estimation indicated that the first threshold is not significant (threshold = -0.221, s.e. = 0.185, threshold/s.e. = -1.197, ρ= 0.231). This was further investigated by an HosmerLemeshow-Test of the first category against all other categories in a binary regression. The resulting statistic for nine groups (chi2 (7) = 12.972, p = 0.073) corroborated the indiscernibility of the first two categories. Therefore the first two categories were combined. That left the third category with only 18.1% of all cases. To avoid problems due to sparsity, actual behavior was recoded into a binary variable combining the first two and the last two categories. The outcome variable can now be understood as being 0 for little usage and 1 for frequent usage. The binary outcome variable divides the sample into the respondents that did not use or had only once used interactive remote services since June 2008 (59%), and those respondents that had used remote services at least twice (41%). For the final model estimation to allow intuitively

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195

interpretable results, the intention to use variable was reverse coded, so that 7 equals the highest intention and 1 equals the lowest intention. A higher scale value now refers to higher behavioral intention. The proposed binary logistic regression model has no degree of freedom. Model fit indices indicate a perfect model fit, but as the model is just-identified, the assessment of goodness-of-fit looses its meaning. In this case the model can be judged on the ground of significants of path coefficients and thresholds, and foremost on it’s predictive power. The model was estimated by MPlus and yielded a significant path coefficient (logit) of 0.719 (s.e. = 0.109, ρ = 0.000) and a significant threshold of 0.643 (s.e. = 0.208, ρ = 0.002). To assess the predictive quality of the model MPlus computes McKelvey and Zavoina’s r2 that was found to outperform other Pseudo r2 measures in mimicing OLS-r2 behavior (Veall and Zimmermann 1994). McKelvey and Zavoina’s r2 was estimated at 0.439 suggesting that 44% of variance in the actual behavior is explained through the measured intention. Additionally, a Hosmer-Lemeshow-Test with nine categories indicates a significant predictive power of intention (chi2 (7) = 9.174, ρ = 0.240; see table 7.19). The classification table shows that overall 75.5% (79,4% = 1; 72.8%=0; cut-off value = 0.5) of cases are correctly predicted (see table 7.20) compared to 51.62% that would be obtained by chance. Hair Jr. et al. (1995, p. 105) suggest that if the classification accuracy is 25% greater than chance, the classification accuracy is acceptable. The classification accuracy of this study has clearly surpassed the desired accuracy rate of 64.5%. The logit of 0.719 indicates the change in log odds of actual interactive remote service usage behavior for a unit increase in intention to use. For the intention-behavior link, this means that for a one unit increase in intention to use, the log odds of being a frequent user vs. the odds of only rarely using remote services increases by 0.719. A log odd increase of 0.719 corresponds to an increase of the odds ratio by a factor of 2.053. Therefore, it can be concluded that H12 is supported. Table 7.19: Results of Hosmer-Lemeshow-Test Actual Usage = .00

Actual Usage = 1.00

Group

observed

expected

observed

expected

total

1 2 3 4 5 6 7 8 9

46 21 13 15 12 11 14 4 11

45.507 21.826 16.127 13.811 9.044 9.661 12.054 3.256 15.713

2 3 7 5 4 9 16 6 50

2.493 2.174 3.873 6.189 6.956 10.339 17.946 6.744 45.287

48 24 20 20 16 20 30 10 61

χ 2 (df): 9.17 (7); ρ: 0.240

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Table 7.20: Classification Accuracy Actual usage

Observed

Predicted

0 1 total

0

1

107 21

40 81

Correct Predictions

72.8 79.4 75.5

cut-off value: 0.5

The measurement of intention and behavior with two separate surveys with different questions at different points in time supports the reliability of ITSUM and makes a common method bias less likely. Furthermore, the measurement of behavior following the measurement of intention strengthens the assumption of causal inference of the direction from intention to usage behavior.

7.10

Discussion of the Results

The ITSUM has been validated in the German printing industry confirming the belief structures identified in the qualitative study. It is a valid model to jointly analyze customers’ beliefs about collaboration, the perceived usefulness of the service, the underlying technology as well as organizational characteristics of the customer organization in a technology-mediated B2B service context. Moreover, the t1 and t2 studies demonstrate that the individual customer employee’s perceptions influence the organizational intention to use interactive remote services and that the organizational intention (t1 -study) has a high predictive power to explain later actual usage (t2 -study). The comparison of the pre-adopter and continued user groups within the sample, revealed a number of key differences (see table 7.21). The groups not only differ in the number of relevant belief groups, but also in their relative importance, and, in some cases, in the measured level of the belief. An organization’s intention to use interactive remote services in the pre-adoption phase is formed by a wider range of belief groups than in the continued usage phase. Organizations in the pre-adoption phase primarily build their attitudes towards interactive remote services on indirect experience and therefore need to assess a broader belief system. Whereas post-adoption usage beliefs are based on past experience that strengthens the perceived usefulness and subjective norms beliefs. The direct effect of individual trustworthiness beliefs towards the RST on organizational intention has been confirmed in the overall sample and in the pre-adopter group. Trustworthiness comprises the customers’ belief about the benevolence, competence, and integrity of the RSTs behavior. It is the second strongest antecedent of intention in the pre-adopter group. Controllability beliefs also have a significant effect on usage intention in the pre-adopter sample. Both

7.10 Discussion of the Results

197

Table 7.21: Quantitative Results: Hypotheses Summary Direct Effects H

Independent Variable

Dependent Variable

Expected Effect

sample (n = 717)

pre-adopter (n = 364)

cont. user (n = 353)

1 2 3

Controllability Trustworthiness Trust in Technology Ease of Use

Intention to Use Intention to Use Perceived Usefulness Perceived Usefulness Intention to use

positive positive positive

n. s. supported supported

supported supported supported

n. s. n. s. supported

positive

supported

supported

supported

positive

supported

supported

supported

Intention to Use Intention to Use Intention to Use Intention to Use

positive positive positive positive

supported n. s. n. s. supported

supported n. s. n. s. supported

supported n. s. n. s. supported

Actual Usage

positive

supported

4 5

Perceived Usefulness Role Clarity Role Ability Motivation Subjective Norms

6 7 8 9

Effect of t1 on t2 12

Intention to Use

Mediating Effect H

Mediator

Path

Expected Effect

sample (n = 717)

pre-adopter (n = 364)

cont. user (n = 353)

5a

Perceived Usefulness Perceived Usefulness

Trust in Technology → Intention Ease of Use → Intention

mediation

supported

supported

supported

mediation

supported

n. s.

supported

5b

Group Comparison H

Path

Expected Effect

Results

13 14 15 16 17 18 19

Controllabilty → Intention Trustworthiness → Intention Role Clarity → Intention Role Ability → Intention Motivation → Intention Subjective Norms → Intention Perceived Usefulness → Intention

stronger for pre-adopters stronger for pre-adopters stronger for pre-adopters stronger for pre-adopters stronger for pre-adopters stronger for pre-adopters weaker for pre-adopters

p. s.* p. s.* supported n. s.*** n. s.** n. s. supported

Legend: H: Hypotheses; n. s.: not supported; p. s.: partially supported; * Path in continuance group insignificant; ** Path in both groups insignificant; *** Path in continuance group insignificant and negative in both groups.

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controllability and trustworthiness beliefs have significantly stronger effects in the pre-adopter group. This supports the assumption that especially inexperienced users and organization that are in the phase of testing interactive remote services value control and trustworthiness of the RST and form their intentions based on these beliefs. The higher the believed control and the more trustworthy RSTs seem, the more organizations will intend to use interactive remote services. Even in view of the decision making processes in B2B settings and the reduced observability in technology-mediated services, the human component and relational beliefs ultimately influence organizational decisions. The average level of trustworthiness beliefs is significantly lower in the pre-adopter group, compared to the continued user group. Also, the pre-adopters average level of controllability is significantly lower than that of organizations in the continued user group. This was to be expected as first hand collaborative experience with an RST fosters trustworthiness, reduces fears and eases the need for control. The qualitative study already suggested the strong role that perceived usefulness has in determining the usage intention in B2B-settings. This was confirmed by the quantitative study. The effect of perceived usefulness on organizational usage intention is strong and reliable in all tested samples: the overall sample; the pre-adopter group; and the continued user group. The effect of perceived usefulness has been shown to be the strongest predictor of usage intention for the continued user group and the second strongest predictor for the pre-adopter group. This construct captures core success factors for enterprises in competitive markets such as increased availability of machines or time and cost savings. The weaker effect of perceived usefulness on intention and the lower average level of perceived usefulness in the pre-adopter group indicates that in terms of adoption, the customers may not yet have a clear vision of the advantages of this new type of service. The attitude of customer’s towards technology-mediated services is closely tied to the characteristics of the required technology. Trust in technology and the ease of use of the technology have a strong effect on perceived usefulness in both groups (pre-adopter and continued user). This is in line with research on e- and online-services and confirms that these variables are universal and also affect attitudes in a B2B-context. They are stabile across groups and have an indirect effect on usage intention. This underlines the importance of the technology. It not only enables the request or delivery of the service, but in a remote service situation, it also provides a medium for communication and collaboration. The "customer readiness variables", role clarity, role ability, and motivation, which are strong determinants in some B2C studies, have ambiguous effects with regard to interactive remote service usage intention in this research. Role clarity has the strongest influence on usage intention in the full sample as well as in the pre-adopter group. This stresses the importance of

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199

collaboration in forming the customer’s attitudes. The expectations about the role and the tasks to support the RST directly influence the intention to use remote services. For one item that measures role clarity, no full measurement invariance could be established. While the influence of role clarity seems to be weaker than in the pre-adopter sample, a direct comparison between the groups cannot be made with the same degree of reliability. One explanation for this invariance could be that the experiences the customers have during remote services constantly update their understanding of role clarity in this specific context. This suggests that for customers of different experience levels the understanding of "role clarity" diverges, even though role clarity still strongly influences the organizational intention for both pre-adopters and continued users. Role ability has been shown to be a negative antecedent of intention in the overall sample and in the pre-adopter group. One explanation might be that an employee who attributes himself with high role ability, thinks that this enables him to do a repair on his own without actually requiring the remote service engineer. Therefore, to him, a remote service seems to be unnecessary. This is supported by the fact that role ability does not influence intention in the continued user group, where employees and organizations with experience have a clearer understanding of the tasks during a interactive remote service. Interestingly, the qualitative study yielded evidence that a majority of customers consider themselves fully able, whereas providers think they have selfefficacy doubts. This might support the assumption that some pre-adopters overestimate their role ability. Subjective norms are a reliable predictor of usage intention in the overall sample and in both sub groups. Company culture and norms exert social pressure through colleagues and superiors on the individual employee and affect his attitude towards interactive remote services. Interestingly the effect of subjective norms did not decrease with experience and implementation level in the organization. Instead it increases. A reason might be that with higher levels of usage, usage itself becomes a company norm, which might even manifest itself in a quasi or actual contractual setting. Personal intrinsic motivation for co-production is found to be insignificant in the behavioral intention equation for the pre-adopter and continued user group. This might be due to the B2Bsetting where organizational goals, captured through perceived usefulness, have precedent over the motivation of an individual to co-produce. In context of the model, this would implicate that employees do distinguish between personal motivation that is not in line with organizational goals and motivation that is. This may also support the validity of measuring organizational intention through individual perceptions. In the full sample, intrinsic motivation has a significant negative effect on intention to use. Because the effect is not significant in both sub-groups and intrinsic motivation and intention are positively correlated this anomaly could be due to heterogeneity among respondents or due to statistical chance. It deserves further exploration outside the scope of this study.

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Company size affects the link between perceived usefulness and intention to use, and the link between subjective norms and intention to use. This illustrates that in larger organizations, regardless of their experience level with remote services, the opinion of an individual employee does not impact organizational decision as much as in smaller companies where personal communication and decision-making is more informal and common. The measurement of intention and behavior with two separate surveys with different questions at different points in time made it possible to analyze the effect of perceived organizational intention on actual organizational usage behavior. The results demonstrate that the individual customer employee’s belief about organizational intention to use interactive remote services has a high predictive power to explain later actual usage. Further, no significant moderating effects of the respondent’s function on the antecedent of organizational intention could be identified. This confirms that the perception of organizational intention through an individual is an adequate proxy for organizational intention in this empirical setting - even when considering the heterogeneity of the respondent’s functions.

Chapter 8 Summary and Conclusions 8.1

Summary of the Central Results

This thesis contributes to contemporary research on the use of technology-mediated services by conceptualizing a new emerging service type and by providing insights into factors that determine its organizational usage. The perception and acceptance of remote services have been explored in a B2B-environment and drivers of interactive remote service adoption and continued usage have been identified. In detail, the following contributions were made: I established the conceptual foundations for the definition, classification, and positioning of remote services and interactive remote services. The relation of remote services to face-to-face services, e-services, self-services, mobile services, and more industry specific services including teleservices, telematics, telemedicine, and IT-services, was analyzed. Specifically, I outlined that interactive remote services form a unique and distinct service type from both a theoretical and practical standpoint. From a customer’s point of view, the interactive remote service experience is unique because it involves both "high-tech" and "high-touch" elements. Through the qualitative study conducted in Germany, USA, and China, single beliefs groups have been identified as factors that influence the remote service perception. The results of the qualitative study were synthesized within a newly developed framework of remote service that comprises 44 different beliefs nested in eight main belief groups including: relational beliefs; process control beliefs; participation beliefs; technology characteristics; economic value evaluation; organizational and contextual factors as well as prior experiences. Based on the remote service perceptual framework, I propose a new model, the ITSUM, to further explore the belief groups as antecedents of intention. The ITSUM allows researchers, for the first time, to jointly analyze customers’ beliefs about the collaboration within technology-mediated services, the usefulness of the service, the characteristics of the underlying technology, as well as the organizational characteristics of the customer

202

8. Summary and Conclusions

organization in a B2B service context. The ITSUM has been shown to be valid for explaining organizational intention to use interactive remote services in the German printing industry. It also holds for organizations with different experience levels, in particular for organizations in a pre-adoption phase and organizations in a continued usage phase. Furthermore, it is shown that organizational intention (t1 -study) has a high predictive power to explain later actual interactive remote services usage (t2 -study). An increase of one unit on the intention scale, leads to an increase of the odds of frequently using remote services vs. rarely using them by a factor of 2.053. The predictive power of intention on actual organizational usage behavior underlines the importance of individual’s perceptions in organizational adoption decisions. A multi-group analysis comparing the pre-adopter and continued user group within the overall sample yields a number of key differences. Relational beliefs of the individual customer employee and his expectations regarding the interaction with the remote service technician have a stronger effect on organizational intention to use interactive remote services for organizations in the pre-adoption phase than for organizations in the continued usage phase. The intention to continue remote service usage, however, is mainly driven by economic reasoning, the customer employee’s perceptions of role clarity, and by updated subjective norms. Major antecedents of interactive remote service usage intention are the individual customer employee’s beliefs of controllability and trustworthiness of the RST, the customer employee’s beliefs regarding the tasks and process of his collaboration with the RST and the perceived usefulness of the services. The substantial role of the relationship between a customer and his human service counterpart is reinforced as customers perceive interactive remote services as risky. If the behavior of the RST is believed to be trustworthy and the customer perceives a high level of control over the RST the organizational intention to use remote services in the pre-adoption phase increases. Furthermore, the customer employee’s role expectations and beliefs about his participation and collaboration during an interactive remote services has been shown to be a major factor in adoption and continuation behavior. This effect could be shown for both pre-adopters and continued users, even though their understanding of "role clarity" is different. This also has managerial ramifications as measures to increase adoption have to be tailored to the more fluid beliefs of pre-adopters, while measures to increase continued usage should address the perceived usefulness. In respect to technology characteristics such as ease of use and perceptions of security risks this study confirms research on technology acceptance, for example, the TAM and its applications. Interestingly, although data security according to a customer is "a theoretically solved problem", on a rational level many customers still have concerns about third parties accessing their machines or hacker attacks. In line with research on innovation adoption, especially in

8.2 Managerial Implications

203

organizational context, I found that the benefits of remote services and their relative advantage compared to face-to-face delivered services strongly influences the customers general perception of remote services. Time savings, costs, pricing and contractual models are factors that are perceived as important attributes of remote services. Because the theoretical framework for understanding interactive remote service was established through an integrative multi-research approach, this thesis contributes to multiple literature streams. It adds to service marketing literature by identifying and exploring a new technologymediated service, influence factors, and its perception and usage. It sheds new light on service relationships between customer and provider in a technology-mediated collaboration situation by stressing the importance of both the customer employee’s relational beliefs and the perceptions of technology for the service experience. Furthermore, the ITSUM combines the view of innovation research with research on relationship marketing and organizational management literature. The findings of this thesis further add to studies on technology and technology-intensive service adoption by showing statistical evidence to integrate customer beliefs on collaboration and interaction with a human service counterpart into adoption models. It adds to literature on control and trust by showing the complex interplay between both as complementary antecedents of intention. Finally, this thesis contributes to literature through a better understanding of the adoption-continued usage nexus in interactive remote service scenarios and provides guidelines that allow managers to actively shape the service-scape to gain acceptance for their service offerings.

8.2

Managerial Implications

The empirical results of this work imply many issues relevant for providers of interactive technology-mediated services that include human-to-human collaboration and interaction. Based on the findings of the studies employed in this thesis this section provides practical advice to remote service providers on how to overcome acceptance barriers, how to shape service production, and train their remote service technicians. Although remote services are provided with no direct face-to-face contact between the service provider and the customer, a sole focus on technology acceptance is not enough to increase the customers’ intention to use remote services. This contradicts strategies from some service providers who mainly enhance the design of technology interfaces and strive to increase the ease of use of the underlying technology. The process of service co-production by the service provider employee and the customer strongly affects the perception of such services. This interpersonal factor should be considered by interactive remote service providers at every stage in the service production process. Remote services should not be seen as a series of single transactions, but rather as a relational exchange between organizations, a remote service technician,

204

8. Summary and Conclusions

and a customer employee having ramifications for CRM. Even though the use of remote services is offered as an alternative to an on-site service, it is surprising that over 50% of the organizations in the full sample have not integrated remote services in their business processes. Perceived usefulness is a strong predictor of intention to use and actual behavior. It is notable that in the pre-adopter group, perceived usefulness has less influence compared to the continued usage group. This suggests that pre-adoption customers are not fully aware of the benefits or do not perceive them as beneficial enough compared to costs. This implies also that the current benefits of remote services are not clearly communicated to pre-adopters and that this deficit should specifically be addressed by service marketing practices. This study reveals that in contrast to the service providers, customer employees perceive remote services as risky, due to the non-observable nature of these services. Remote service customers have a need for trust in and control of the service technician’s behavior. Also, transparency of the process and the customer employee’s perception of his role during service delivery are key characteristics that can be shaped to enhance remote service acceptance. Based on the findings of this thesis, a number of strategies can be recommended: A high level of transparency is desirable during the complete remote service process from a customer’s point of view. It gives the customer employee the feeling of control over the remote service. For example, a monitoring system should display all objects that are accessed by the service counterpart in real time and logged with a time-stamps. Also, to strengthen interpersonal relations, an electronic portrait of the actual service technician providing service should be displayed. Proxy control should be assured to the customer employee by mechanisms where he has to confirm his service counterpart’s actions during the ongoing service. The study corroborates that personal relational factors have strong a positive effect on the customers’ risk perception of the remote service. Some customers miss the personal contact with a technician, show a strong preference for face-to-face delivered services, or like to work with acquainted personnel. Therefore, the RST should gain the trust of the customer through competence and benevolent behavior. It is also important to assure the trustful relationship during the remote service process by sticking only to the agreed-on actions. The remote service experience should be personalized as much as possible. To raise the customers’ confidence in the RST’s skills and to foster personal bonding it seems useful to provide the customer employee with additional background information about the RST, e.g., a photo, a CV or report on his repair history, or certificates available via the machine’s monitor. In addition, customers’ employees should have the opportunity to use additional communications channels, to reach the remote service technician during the interactive remote service, e.g. through chat functionality or maintaining a telephone call/video conference during the service. Also, the RSTs should receive training and additional guidance to improve their social interaction skills, as they are the face to the customer.

8.2 Managerial Implications

205

The lack of experience with interactive remote services is responsible for some customers’ unclear expectations. A remote service provider should stress the benefits of this new services type through clear communication. In addition, frequent remote services check-ups and frequent demonstrations and trials may lead to reduce fears and give a vivid impression of what the remote service technician actually does and what kind of collaboration is expected from a customer. Customers have reservations and perceive a high level of risk when they are required to connect their machines to the internet. Remote service provider companies should convey that they see security and integrity as top priorities and inform customers of data encryption protocols and technical security measures to improve security perceptions. The customers have to be assured and reassured that the provider has tight internal procedures in place to prevent employees from misusing data. The offering of guarantees/insurance, e.g., that the provider company pays for damages resulting from a proven security breaches through the remote service system might also lower the acceptance barrier. Customers also wish for auxiliary benefits from an interactive remote service. For example, they wish to get more information about the emergency cases and the reasons for their problems, status of the machine parts, reports on the health-status of tools, damage reports, and most importantly how the issue was solved. They also wish for follow-ups on service incidents. Especially, if a remote check wasn’t successful, the customer wishes to get unprompted reports addressing why the problem could not be solved and how the provider will handle this issue. Customers explicitly suggest a worldwide support network and for the pre-press area they specifically ask for 24/7 support. The study indicates that if a customer employee perceives his own role ability as high, this may lead to less usage-intention. This can be explained by the customer feeling that he can repair the machine on his own. Even if objectively, this is an overestimation, this leads to the thought that the service should not appear as too simple, but rather appear complex enough so that the customers perceive a need to use the remote service. Global provision of remote services should be sensitive to cultural and organizational differences in the customer employee’s participation and individual motivation to collaborate. Coproduction is not always seen as appropriate and reasonable. It is important to motivate customers to participate in a remote service in a way that they do not feel only instrumental to the service provider, but in a way to convey mutual benefits. The study indicates that some customer employees feel honored and valued to participate in a service, others want to learn from the RST, but some refuse to collaborate in a service they pay for. It is important to not solely try to improve the service on an operational level but to keep the requirements of higher management such as costs and economic value in mind. The strong influence of subjective norms and perceived usefulness on a customer’s intention to use interac-

206

8. Summary and Conclusions

tive remote services suggest that it is necessary to reach key-decision makers, gatekeepers and lead users. A formal feedback report could be directed to customer management, emphasizing the savings, speed and/or efficiency compared to a local service whenever a remote service was successfully performed. Remote service technology could be deployed to the management of customer companies as a monitoring tool to monitor capacity and load of individual machines in-house. Once the technology is in place, it could function as a "door opener" for the provision of remote services to the customer. The qualitative study shows that price is an important decision criterion. Therefore it is striking, that some customers are not fully aware of the exact price or that they think the price is higher than it actually is due to their low usage experience. In general, remote service providers should make their customers more aware of the price model for remote service and the pricing model itself should be transparent. The qualitative interview study also shows that customers favor a usage-based pricing concept – for example, costs per service hours or number of remote services. Finally, I want to emphasize the tremendous opportunities remote services offer to providers. Not only can they position themselves as solution providers and offer flexible, cost saving services, more importantly, they can use remote services as a tool for bonding and increasing customer retention. It does not replace classical face-to-face contacts, but offers a quasi permanent channel directly to the customer’s workplace and extends traditional CRM. This opens up new business fields and the ability to offer additional services such as preventive monitoring services, financial services, or consulting services for helping the customer to produce with the machine. Also, promotional information on new machine features or service features can easily be transferred to customer screens at the machine, reaching directly the targeted individuals.

8.3

Implications for Future Research

Interactive remote services do not only offer fascinating opportunities in B2B-settings, but are rapidly gaining importance in the consumer-sector as well. There are exciting developments in fields as diverse as telemedicine, automotive services, smart housing and intelligent clothing. The exploration of customer perceptions of remote collaboration with service providers and the risk associated with non-observable technology-mediated service production will be a future challenge to service practitioners and researchers alike. The ITSUM may serve as a canvas on which other researchers can envision and explore the exciting applications of interactive remote services or even radically new services not yet conceived. As it is the case for virtually all research efforts, this work is subject to several limitations that need to be taken into account when evaluating the results. Furthermore, some findings raise

8.3 Implications for Future Research

207

new interesting questions that are outside the scope of this thesis, but should be addressed by future research. The ITSUM has been validated in the German printing industry. This specific empirical setting might raise concerns about the generalizability of the inferred results. I believe that it extends to related industries and technology mediated services that rely on high investment machinery, medical equipment, tool machines, and manufacturing industries. The survey data is subject to some limitations because of their self-reported nature and potential heterogeneity typical for B2B context. Under the restrictions of B2B environments, the difficult access to data of this type this study design was the best conceivable, under the given budget and time constraints. The concept of organizational intention is complex. Ultimately all organizations consists of individuals who are the only source for the operationalization of intention on an organizational scale. The gap between individual perceptions of intention and the concept of organizational intention was narrowed by the specific empirical setting where decision making and actual remote service usage was often done by the same individuals. The bridge between organizational intention and actual behavior was additionally validated in a follow-up survey at a later point in time. I encourage future research to extend the scope of this survey design to other industries, multiple respondents per organization, and to a longitudinal design that extends the measurement beyond two points in time. It would be interesting to see how attitudes change over time with increasing or decreasing usage. Additional sources of heterogeneity can be explored such as the utilization rate, the age of the machine, unpredictable incidents and macroeconomic factors to gain further insights into organizational behavior and interactive remote services usage. A particularly interesting result of this study is that role clarity is a dominant predictor of interactive remote service usage across different experience levels of the organizations whereas there is no measurement invariance across the pre-adoption and continued usage group. This is explained by the permanent update of beliefs in regard to the customer employee’s role in a remote service among the continued usage group. Researchers should address questions such as: How do these role clarity beliefs evolve? What exactly are the differences in understanding? Does this extend to other tasks in organizational service adoption? Role ability has a negative effect on intention to use, because the customer employees who perceive themselves as highly able believe they can perform the service themselves without additional assistance from an RST. In the same context, it would be interesting to explore whether negative assessments of the customer’s task affect service outcome and customer acceptance. Who, from a customer’s perspective, is responsible for the service outcome? How much participation does the customer actually wish for? The qualitative exploratory interview study hints at cultural differences in the remote service acceptance, which should be addressed by future research. Different views on the motivation

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to co-produce within an interactive remote service might be rooted in global culture differences or in different company cultures. A further quantitative cross-cultural validation of the ITSUM would not only help to ensure the accuracy of the survey instrument but is also a fascinating opportunity to explore differences in belief structures, interdependencies, and gain an understanding of a hypothetical "service culture."

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Appendix A Additional Tables and Figures

A.1

Interview Guideline of the Exploratory Qualitative Study

VERSION A: Guideline for interviews with customers (main questions): 1. Could you please tell me about the Remote Services you used lately? 2. Could you please describe how that came about? 3. Could you please describe how you experience the delivery process of Remote Service? (a) How do you feel during the service? (b) What do you do during the service? 4. What do you think happens at the service provider side, while youÕre using the service? 5. What kind of experiences did you have with Remote Services? 6. To which extent does your experience with Remote Services differ from your experience with face-to-face services? 7. To what extent did Remote Services influence your working day? 8. What are the advantages for your work? 9. What are the disadvantages for you? 10. To which extent are you satisfied with Remote Services?

256

A. Additional Tables and Figures

VERSION B: Guideline for interviews with employees (main questions): 1. Could you please tell me about the Remote Services you offer? 2. What did you expect from your decision towards offering Remote Services? 3. Could you please describe how you experience the delivery process of Remote Services? 4. How do you feel during the service? 5. What do you do during the service? 6. What do you think happens at the customer, while youÕre delivering the service? 7. What kind of experiences did you have with Remote Services? 8. To which extent does your experience with Remote Services differ from your experience with face-to-face services? 9. To what extent do Remote Services influence your working day? 10. What are the advantages for your work? 11. What are the disadvantages for you? 12. How do you stage/design Remote Services for the customer? 13. To which extent do your customers accept Remote Services?

A.2 First Pages of the Online Survey t1 and t2 -study

A.2

First Pages of the Online Survey t1 and t2-study

Figure A.1: First Page of the t1 -Study Questionnaire

257

258

Figure A.2: First Page of the t2 -Study Questionnaire

A. Additional Tables and Figures

A.3 Exploratory Factor Analysis Results

A.3

259

Exploratory Factor Analysis Results

Table A.1: Structure Matrix Factor

Structure Matrix

1 PU 2 PU 3 PU 4 PU 5

PU

EOU EOU

1 2

1 2 CONT 3 CONT 4 RA 1 RA 2 RA 3 RC 1 RC 2 RC 3 MOTIV 1 MOTIV 2 MOTIV 3 MOTIV 4 TW 1 TW 2 TW 3 TW 4 TW 5 INT 1 INT 2

CONT CONT

1

2

3

4

5

6

7

8

0.339 0.467 0.396 0.510 0.496 0.299 0.348 0.216 0.217 0.298 0.418 0.421 0.393 0.311 0.449 0.463 0.486 0.441 0.368 0.434 0.635 0.781 0.813 0.817 0.837 0.675 0.420 0.445

0.684 0.804 0.676 0.756 0.833 0.277 0.353 0.190 0.174 0.258 0.318 0.309 0.289 0.210 0.321 0.381 0.364 0.302 0.296 0.435 0.573 0.450 0.452 0.433 0.545 0.407 0.462 0.514

0.162 0.255 0.220 0.312 0.312 0.448 0.467 0.180 0.171 0.222 0.328 0.885 0.939 0.750 0.635 0.533 0.583 0.363 0.338 0.323 0.439 0.322 0.344 0.358 0.394 0.342 0.260 0.243

0.162 0.221 0.230 0.243 0.276 0.167 0.216 0.691 0.799 0.836 0.644 0.257 0.279 0.186 0.343 0.398 0.340 0.338 0.250 0.234 0.348 0.217 0.287 0.319 0.283 0.306 0.290 0.276

0.257 0.303 0.361 0.421 0.405 0.156 0.215 0.250 0.227 0.255 0.378 0.405 0.392 0.324 0.417 0.336 0.368 0.763 0.829 0.782 0.750 0.397 0.476 0.414 0.467 0.370 0.227 0.227

0.318 0.406 0.310 0.472 0.486 0.167 0.179 0.210 0.177 0.259 0.265 0.271 0.195 0.186 0.294 0.435 0.425 0.192 0.137 0.177 0.319 0.345 0.306 0.408 0.396 0.351 0.936 0.872

0.192 0.273 0.258 0.264 0.229 0.997 0.766 0.124 0.127 0.148 0.213 0.441 0.436 0.373 0.426 0.344 0.360 0.135 0.125 0.128 0.258 0.266 0.269 0.280 0.257 0.238 0.156 0.192

0.211 0.306 0.276 0.367 0.385 0.388 0.414 0.298 0.279 0.371 0.441 0.631 0.578 0.485 0.756 0.826 0.839 0.448 0.305 0.227 0.419 0.361 0.415 0.481 0.436 0.460 0.462 0.430

Extraction Method: Maximum Likelihood. Rotation Method: Promax with Kaiser Normalization.

260

A.4

A. Additional Tables and Figures

Correlations

Table A.2: AVE and Squared Correlations

PU CONT RC RA TW INT MOTIV EOU

PU

CONT

RC

RA

TW

INT

MOTIV

EOU

0.57 0.06 0.08 0.07 0.11 0.17 0.06 0.14

0.55 0.20 0.08 0.13 0.10 0.14 0.05

0.65 0.52 0.33 0.23 0.24 0.25

0.74 0.19 0.07 0.21 0.27

0.62 0.20 0.36 0.14

0.80 0.06 0.09

0.60 0.07

0.75

Legend: Bold print is average variance extracted (AVE).

Table A.3: Correlation Matrix without CMV

PU CONT RC RA TW INT MOTIV EOU SN TT EXP

PU

CONT

RC

RA

TW

INT

MOTIV

EOU

SN

1.000 0.208 0.249 0.221 0.291 0.376 0.215 0.339 0.175 0.550 0.089

1.000 0.408 0.253 0.322 0.283 0.337 0.189 0.187 0.368 0.174

1.000 0.686 0.538 0.449 0.451 0.465 0.331 0.366 0.404

1.000 0.399 0.227 0.425 0.487 0.172 0.306 0.266

1.000 0.411 0.570 0.343 0.308 0.482 0.186

1.000 0.215 0.274 0.544 0.374 0.363

1.000 0.227 0.254 0.370 0.089

1.000 0.193 0.393 0.195

1.000 0.302 0.368

TT

1.000 0.135

EXP

1.000

A.5 Calculation of Moderating Effects

A.5

261

Calculation of Moderating Effects

Table A.4: ITSUM (n=717): Calculation of Moderating Effects without interaction χ 2 (df) Test of Model Fit CFI TLI Loglikelihood/H0 VALUE Loglikelihood/H1 VALUE

free parameters Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC scaling correction factor Δ df Δχ 2 Δ scaling correction interaction effect ρ-value (path estimate)

company size × PU

SN

1152.110 (412) 0.923 0.909 −37126.177 −33106.767 −33093.792 −36416.827 134 113 113 74520.355 66439.534 66413.585 75133.415 66956.518 66930.568 74707.929 66597.713 66571.763 1.484 1.478 1.476 21 21 5301.653 5281.283 1.516 1.527 −0.181 −0.088 0.000 0.000

262

A. Additional Tables and Figures

Table A.5: Pre-Adopter Group (n=364): Calculation of Moderating Effects company size ×

without interaction χ2

Test of Model Fit

PU

SN

837.438 (412) 0.912 0.896 −19336.555 −17193.325 −17189.573 −36416.827 134 113 113 38941.109 34612.649 34605.146 39463.328 35053.027 35045.525 39038.203 34694.527 34687.024 1.360 1.432 1.432 21 21 4407.350 4415.063 0.973 0.973 −0.118 −0.059 0.002 0.000

CFI TLI Loglikelihood/H0 VALUE Loglikelihood/H1 VALUE

free parameters Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC scaling correction factor Δ df Δχ 2 Δ scaling correction interaction effect ρ-value (path estimate)

Table A.6: Continued User Group (n=353): Calculation of Moderating Effects without interaction χ 2 (df) Test of Model Fit CFI TLI Loglikelihood/H0 VALUE Loglikelihood/H1 VALUE

free parameters Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC scaling correction factor Δ df Δχ 2 Δ scaling correction interaction effect ρ-value (path estimate)

company size × PU

SN

RA

RC

771.608 (412) 0.915 0.899 −17291.822 −15451.522 −15448.516 −15469.220 −15468.681 −16850.505 134 113 113 113 113 34851.643 31129.044 31123.032 31164.441 31163.362 35369.750 31565.955 31559.943 31601.351 31600.273 34944.648 31207.473 31201.461 31242.870 31241.791 1.144 1.527 1.528 1.525 1.536 21 21 21 21 4014.157 3997.256 4022.770 3777.226 −0.917 −0.922 −0.906 −0.965 −0.277 −0.103 −0.129 −0.143 0.000 0.000 0.025 0.022