European Retail Research: 2011 Volume 25 Issue I

Dirk Morschett, Thomas Foscht, Thomas Rudolph, Peter Schnedlitz, Hanna Schramm-Klein, Bernhard Swoboda (Eds.) European Retail Research

GABLER RESEARCH Editors Dirk Morschett, University of Fribourg, Switzerland, [email protected] Thomas Foscht, University of Graz, Austria, [email protected] Thomas Rudolph, University of St. Gallen, Switzerland, [email protected] Peter Schnedlitz, Vienna University of Economics and Business, Austria, [email protected] Hanna Schramm-Klein, Siegen University, Germany, [email protected] Bernhard Swoboda, University of Trier, Germany, [email protected] EDITORIAL ADVISORY BOARD In the editorial advisory board, a number of distinguished experts in retail research from different countries support the editors: – Steve Burt, University of Stirling, UK – Michael Cant, University of South Africa, South Africa – Gérard Cliquet, University of Rennes I, France – Enrico Colla, Negocia, France – Ulf Elg, Lund University, Sweden – Martin Fassnacht, WHU - Otto Beisheim School of Management, Germany – Marc Filser, University of Dijon, France – Juan Carlos Gázquez Abad, University of Almeria, Spain – Arieh Goldman, Hebrew University, Israel (†) – David Grant, University of Hull, UK – Andrea Gröppel-Klein, Saarland University, Germany – Herbert Kotzab, Copenhagen Business School, Denmark – Michael Levy, Babson College, USA – Cesar M. Maloles III, California State University, USA – Peter J. McGoldrick, Manchester Business School, Manchester University, UK – Richard Michon, Ryerson University, Canada – Dirk Möhlenbruch, University Halle-Wittenberg, Germany – Heli Paavola, University of Tampere, Finland – Luca Pellegrini, IULM University Milan, Italy – Barry Quinn, University of Ulster, Northern Ireland – Will Reijnders, Tilburg University, The Netherlands – Thomas Reutterer, Vienna University of Economics and Business, Austria – Jonathan Reynolds, Oxford, UK – Sharyn Rundle-Thiele, University of Southern Queensland, Australia – Brenda Sternquist, Michigan State University, USA – Gilbert Swinnen, Universiteit Hasselt, Belgium – Ikuo Takahashi, Keio University, Japan – Waldemar Toporowski, University of Goettingen, Germany – Volker Trommsdorff, Technical University Berlin, Germany – Gianfranco Walsh, Koblenz-Landau University, Germany – Barton Weitz, University of Florida, USA – Joachim Zentes, Saarland University, Germany

Dirk Morschett, Thomas Foscht, Thomas Rudolph, Peter Schnedlitz, Hanna Schramm-Klein, Bernhard Swoboda (Eds.)

European Retail Research 2011 I Volume 25 Issue I

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.

”Jahrbücher zur Handelsforschung“ were first published at: Physica-Verlag (1986-1988) Gabler Verlag (1989-1999/2000) BBE-Verlag (2000/01-2004) Kohlhammer Verlag (2005-2007) The 25th Volume Issue I is sponsored by

1st Edition 2011 All rights reserved © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011 Editorial Office: Stefanie Brich | Sabine Schöller Gabler Verlag is a brand of Springer Fachmedien. Springer Fachmedien is part of 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. Umschlaggestaltung: KünkelLopka Medienentwicklung, Heidelberg Printed on acid-free paper Printed in Germany ISBN 978-3-8349-3093-4

V

Preface EUROPEAN RETAIL RESEARCH is a new bi-annual that is in the tradition of the reputable book series “Handelsforschung” (Retail Research) which has been published by Prof. Dr. Volker Trommsdorff in Germany for more than two decades. Since 2008, this publication is edited by a team of retail researchers from Austria, Germany, and Switzerland. With this issue, the initial team is complemented by Thomas Foscht from Austria. The aim of this book series is to publish interesting and innovative manuscripts of high quality. The target audience consists of retail researchers, retail lecturers, retail students and retail executives. Retail executives are an important part of the target group and the knowledge transfer between retail research and retail management remains a crucial part of the publication’s concept. EUROPEAN RETAIL RESEARCH is published in two books per year, Issue I in spring and Issue II in fall. The publication is in English. All manuscripts are double-blind reviewed and the book invites manuscripts from a wide regional context but with a focus on Europe. We respect the fact that for many topics, non-English literature may be useful to be referred to and that retail phenomena from areas different from the US may be highly interesting. The review process supports the authors in enhancing the quality of their work and offers the authors a refereed book as a publication outlet. Part of the concept of EUROPEAN RETAIL RESEARCH is an only short delay between manuscript submission and final publication, so the book is – in the case of acceptance – a quick publication platform. EUROPEAN RETAIL RESEARCH welcomes manuscripts on original theoretical or conceptual contributions as well as empirical research – based either on large-scale empirical data or on case study analysis. Following the state of the art in retail research, articles on any major issue that concerns the general field of retailing and distribution are welcome, e.g. - different institutions in the value chain, like customers, retailers, wholesalers, service companies (e.g. logistics service providers), but also manufacturers’ distribution networks; - different value chain processes, esp. marketing-orientated retail processes, supply chain processes (e.g. purchasing, logistics), organisational processes, informational, or financial management processes; - different aspects of retail management and retail marketing, e.g. retail corporate and competitive strategies, incl. internationalisation, retail formats, e-commerce, customer behaviour, branding and store image, retail location, assortment, pricing, service, communication, in-store marketing, human resource management; - different aspects of distribution systems, e.g. strategies, sales management, key account management, vertical integration, channel conflicts, power, and multichannel strategies.

VI

Preface

Basically, we seek two types of papers for publication in the book: - Research articles should provide a relevant and significant contribution to theory and practice; they are theoretically well grounded and methodologically on a high level. Purely theoretical papers are invited as well as studies based on large-scale empirical data or on case-study research. - Manuscripts submitted as more practice-oriented articles show new concepts, questions, issues, solutions and contributions out of the retail practice. These papers are selected based on relevance and continuing importance to the future retail research community as well as originality. In addition, the editors will invite articles from specific authors, which will also be double blind reviewed, but address the retailing situation in a specific country. Manuscripts are reviewed with the understanding that they are substantially new, have not been previously published in English and in whole, have not been previously accepted for publication, are not under consideration by any other publisher, and will not be submitted elsewhere until a decision is reached regarding their publication in EUROPEAN RETAIL RESEARCH. An exception is given by papers in conference proceedings that we treat as work-in-progress. Contributions should be submitted in English language in Microsoft Word format by e-mail to the current EUROPEAN RETAIL RESEARCH managing editor or to [email protected]. Questions or comments regarding this publication are very welcome. They may be sent to anyone of the editors or to the above mentioned e-mail-address. Full information for prospective contributors is available at http://www.european-retailresearch.org. For ordering an issue please contact the German publisher “Gabler Research” (www.gabler.de) or a bookstore. We are very grateful for editorial assistance provided by Matthias Schu. Graz, St. Gallen, Siegen, Vienna, Trier and Fribourg, Spring 2011 Thomas Foscht, Thomas Rudolph, Hanna Schramm-Klein, Peter Schnedlitz, Bernhard Swoboda Dirk Morschett (managing editor for Volume 25 Issue I)

Contents Why Does Segmentation Matter? Using Mixed Methodology to Identify Market Segments .........................................................................................................................1 Jaime R.S. Fonseca RFID-Based Tracking of Shopping Behaviour at the Point of Sale – Possibilities and Limitations .....................................................................................................27 Günter Silberer and Stefan Friedemann Prospects for PoS Market Research with RFID Technology: Examination of Consumers’ In-Store Shopping Processes.................................................................................47 Thorsten Blecker, Carsten Rasch and Thorsten Teichert In-Store Logistics Processes in Austrian Retail Companies .....................................................63 Alexander Trautrims, David B. Grant and Peter Schnedlitz Ethical Sourcing – Choice of Sourcing Strategies and Impact on Performance of the Firm in German Retailing ...............................................................................................85 Jonas Bastian and Joachim Zentes Country Reports Retailing in India – Background, Challenges, Prospects ........................................................107 Doreén Pick and Daniel Müller Retail in Poland í New Challenges and New Strategies ........................................................141 Tomasz DomaĔski

EUROPEAN RETAIL RESEARCH Vol. 25, Issue I, 2011, pp. 1-180

Why does Segmentation Matter? Using Mixed Methodology to Identify Market Segments Jaime R.S. Fonseca

Abstract The purpose of this chapter is to describe how markets can be segmented. In other words, it studies ways of grouping customers for the most effective targeting by means of a new conceptual model which combines the use of latent segment models with a mixed research scheme (merging qualitative and quantitative research methods). A particular retail market segmentation solution depends on both market segmentation base variables and a specific segmentation procedure providing a better understanding of the market. Knowledge of segment structure is extremely important in marketing because of its managerial utility, particularly with regard to targeting and positioning. Companies that identify underserved segments can then outperform the competition by developing uniquely appealing products and services. This research begins with an overview of segmentation aspects and aims, and uses a mixed research scheme to present an application with a latent segment model (LSM) procedure for retail market segmentation and information criteria AIC3 and AICu for model selection, in order to uncover the segment structure underlying a dataset from retail chain customers.

Keywords Market Segmentation, Base Segmentation Variables, Segmentation Methods, Latent Segment Models, Mixed Research

Jaime R.S. Fonseca Chair for Data Analysis, School of Social and Political Sciences (ISCSP), Centre for Public Administration and Policies (CAPP), Technical University of Lisbon, Portugal (E-mail: [email protected]).

Received: September 28, 2010 Revised: February 7, 2011 Accepted: February 16, 2011

EUROPEAN RETAIL RESEARCH Vol. 25, Issue I, 2011, pp. 1-25

D. Morschett et al (eds), European Retail Research, DOI 10.1007/978-3-8349-6235-5_1, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

2

1.

European Retail Research Vol. 25, Issue I, pp. 1-25

Introduction and Objectives

Market segmentation is a theoretical marketing concept involving artificial groupings of consumers constructed to help managers design and target their strategies (Wedel/Kamakura 1998). Today, companies recognize that they cannot appeal to all customers in the market or at least not to all customers in the same way, because each customer is unique and they all come from different backgrounds, live in different areas and have different interests and goals. As a result, they are too varied in their needs and buying practices. Furthermore, companies themselves vary widely in their abilities to serve different segments of the market and rather than trying to compete in an entire market, each company must identify the parts of the market that it can serve best and most profitably (Sun 2009). Companies that identify segments efficiently can then outperform the competition by developing uniquely appealing products and services. By dividing the market into relatively homogenous subgroups or target markets, both strategy design and tactical decision-making can be more effective and robust for successfully bridging the gap between segmentation principles and successful application, which continues to be a major challenge for the marketing community. Segmentation technique – identifying homogenous sub-populations within larger heterogeneous populations – has emerged as an important marketing tool over the past half-century, as a response to the need to effectively communicate with and spur into action an increasingly diverse population of individuals, families and businesses who rely on a rapidly multiplying set of communication channels (Heuvel/Devasagayam 2004). It is well known that customer segmentation is most effective when a company tailors offerings to segments that are the most profitable and serves them with distinct competitive advantages. This prioritisation can help companies develop marketing campaigns and pricing strategies to extract maximum value from both low- and high-profit customers. By tailoring the product to different groups, companies are able to meet the needs of more customers more accurately and consequently to gain a higher overall share or profit from a market. This article develops an overall framework that describes how markets can be segmented. In other words, the focus of this study is the way customers are grouped together for the most effective targeting. It uses a new conceptual scheme that combines latent segment models in mixed research (merging qualitative and quantitative research methods) and is expected to result in market segments that satisfy homogeneity within and heterogeneity across segments. Regardless of the tool used to segment the population, each segment must contain homogeneous elements. The bases of these similarities should be easily interpretable and should provide useful guidelines for the promotion of products or services specific to each segment.

Fonseca, J.

3

It is planned to delve more deeply into the third part of the market segmentation scheme (Table 3), i.e. the best conceptual scheme for effective market segmentation. It is organised as follows. In section 2, we give an overview of the subject, while in section 3 we present our proposed market segmentation model and corresponding information criteria. In section 4 we report the results from a retail dataset and finally, in section 5, we make some concluding remarks.

2.

Why Segmenting?

Consumer diversity is increasing rapidly and companies have long sought to differentiate their products from those of competitors, and this is where market segmentation comes in. Why segmenting? Because identifying segments where competitors see an undifferentiated mass market creates several opportunities for new marketing strategies based on a better knowledge of specific customers’ needs and preferences. It is generally agreed that the foundation of strategic marketing is market segmentation, target marketing and product positioning. Nowadays, segmentation is a crucial marketing strategy, helping marketers to identify consumer needs and preferences and find new marketing opportunities. It also enables marketers to regulate marketing mixes to meet the needs of particular segments. Several marketing researchers have responded to management needs by conducting market segmentation studies, for instance Assael/Roscoe (1976), Calantone/Sawyer, (1978), Punj/ Stewart (1983), Beane/Ennis (1987), Kamakura/Kim/Lee (1996), Lockshin/Spawton/Macintosh (1997), Cohen/Ramaswamy (1998), Dibb (1999), Kim/Srinivasan/Wilcox (1999), Bock/Uncles (2002), Palmer/Miller (2004), Sun (2009). The marketing planning process flows from the selection of target markets to the formulation of a specific marketing mix and positioning, the objective for each retail chain product. Segmentation theory suggests that groups of customers with similar needs and purchasing behaviours are likely to demonstrate a more homogeneous response to marketing programmes and the constitution of segments is essential to target marketing (Fonseca/Cardoso 2007b). Segments are derived from the heterogeneity of customer wants. Smith (1956) defines market segmentation as a process that involves viewing a heterogeneous market as a number of smaller homogeneous markets, in response to differing preferences, attributable to the desires of consumers for more precise satisfaction of their varying wants. The definition of Kotler (1972) was conceptually consistent with Smith’s, and he defined it as the subdivision of a market into homogeneous subsets of customers, where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. For Dolnicar (2008) market segmentation is a strategic tool that accounts for heterogeneity among individuals by grouping them into market segments that include members similar to each other and dissimilar to members of other segments. According to Sun (2009), market segmentation is dividing

4

European Retail Research Vol. 25, Issue I, pp. 1-25

the whole market into meaningful, relatively small and identifiable market segments, which are groups of individuals or organisations with similar product needs. In other words, market segmentation is the science of dividing an overall market into segments whose members share similar characteristics and needs (member homogeneity). A market segmentation solution is a function of the market segmentation base variables and of a specific segmentation (clustering) procedure, and it provides a better understanding of the market and, consequently, the means to develop more successful business strategies (Fonseca/ Cardoso 2005) by addressing the specific needs of the selected segments. Because an organisation adopts either mass-market or market segmentation strategies, two essential questions must be addressed when a market segmentation decision is made: (1) which method is to be used to segment the market and (2) which segmentation base variables to use. Concerning methods, since the appearance of Smith’s now classic article (1956), market segmentation has become an important tool both in academic research and applied marketing (Punj/Stewart 1983), and the primary use of cluster analysis in marketing has been for market segmentation. Cluster analysis is a very weak analytical segmentation technique, but traditionally it is perhaps the one used most for segmentation. We have therefore selected several uses of this tool in marketing (see Table 1) from 1967 to 2007. Hierarchical cluster algorithms are among the most commonly used for clustering analysis in marketing research. However, users of these approaches tend to discard much of the detail found in the dendrogram (Arabie et al. 1981). Moreover, as is well known, the dendrogram does not constitute a unique solution, which is a disadvantage of hierarchical cluster analysis. Quantitative segmentation tools can range from simple categorisation analysis, such as CART and CHAID regression tree analyses (McCarty/Hastak 2007; Thomas/Sullivan 2005; Chen 2003; Levin/Zahavi 2001), to more sophisticated clustering techniques, such as hierarchical cluster analysis, two-step cluster analysis, K-means (Lee/Lee/Wicks 2004; Hruschka/Natter 1999; Jedidi/Jagpal/DeSarbo 1997), conjoint analysis (DeSarbo/Ramaswamy/Cohen 1995; Green/Srinivasan 1990; Green/Krieger 1991), multidimensional scaling (Carroll/Green 1997; Biggadike 1981; Wind,/Douglas/Perlmutter 1973), discriminant analysis (Tsai/Chiu 2004; Harvey 1990; Moore 1980), or latent segment models (Cohen/Ramaswamy 1994; Fonseca 2010).

Fonseca, J.

5

Table 1: Use of Cluster Analysis Authors

Goal

Green/Robinson 1967

To identify matched cities for test marketing

Green/Carmone 1968

To identify similar computers in the computer market

Bass/Pessemier/Tigert 1969

To identify market segments with respect to media exposure

Montgomery/Silk 1971

To identify opinion leadership and consumer interest segments

Morrison/Sherman 1972

To determine how some individuals interpret sex appeal in advertising

Greeno/Sommers/Kernan 1973

To identify market segments with respect to personality variables and implicit behaviour patterns

Sexton 1974

To identify homogeneous groups of families with product and brand usage data

Anderson/Cox/Fulcher 1976

To identify the determinant attributes in bank selection decisions and use them to segment commercial bank customers

Calantone/Sawyer 1978

To study the stability of market segments in the retail banking market

Schaninger/Lessig/Panton 1980

To identify segments of consumers on the basis of product usage attributes

Kiel/Layton 1981

To develop consumer taxonomies of search behaviour in Australian new car buyers

Becker et al. 1985

To divide consumer markets by looking at a consumer’s personality

Jain 1993

To analyse markets through social, economic and special segmentation variables such as brand loyalty and consumer attitude

Segal/Giacobbe 1994

To use cluster analysis to uncover four basic “naturals" demographic segments

DeSarbo et al. 1995

K-means cluster analysis for major packaged goods

Kotler 1997

Proposed that consumer markets should be divided according to geographic, demographic, psychographic (lifestyle and personality), and behavioural variables

Dibb 1998

Cluster analysis to identify segments in 270 pregnant women, by using demographic and satisfaction variables

Hruschka/Natter 1999a

K-means using demographic and attitude variables

Hofstede/Steenkamp 1999

To develop an integrated methodology based on consumer means-end chains to identify segments in international markets

Baker/Burnham, 2001

To identify market segments based on a cluster analysis of respondents' brand and price preferences

Lin 2002

To consider demographic and psychographic variables

Dibb/Stern/Wensley 2002

A cluster analysis for measuring the impact on organisational performance

Kau/Tang/Ghose 2003

A cluster analysis for seeking patterns, motivations and concerns for online shopping

Lee et al. 2004

To segment the festival market based on motivation factors

Jayawardhena/Wright/Dennis 2007

Cluster analysis and K-means for stability

6

European Retail Research Vol. 25, Issue I, pp. 1-25

In this issue of data analysis, the proposed latent segment model approach to clustering, part of our conceptual scheme, offers some advantages when compared to other, more traditional techniques. For example, (1) it identifies market segments (Dillon/Kumar 1994), (2) it provides the means to select the number of segments (McLachlan/Peel 2000), (3) it is able to deal with diverse types of data/different measurement levels (Vermunt/Magidson 2002), (4) it outperforms more traditional approaches (Vriens 2001), and (5) it is appropriate for dealing with covariates for a better understanding of customers (Fonseca/Cardoso 2007a). Basically it enables to simultaneously optimise a research function (LSM and information criteria) and efficiently find segments of cases within that framework. It is therefore useful for a better understanding of market structures. In order to be valuable to marketers, a market segmentation plan needs to be able to identify different segments of customers who have uniform, stable responses to a particular set of marketing variables, the segmentation base variables (see Table 2). This is the second question we have to address, and several authors have conducted research into it, such as Sharma/Lambert (1994); Wedel/Kamakura (1998); Kim et al. (1999); González-Benito/Greatorex/Muñoz-Galleg (2000); DeSarbo/Degeratu/Wedel/Saxton (2001); Vriens (2001); Heilman/Bowman (2002); Fennell et al (2003). The greatest opportunity for creating a competitive advantage often comes from new ways of segmenting, because a company can meet buyer needs better than competitors or improve its relative cost position (Porter 1985). The identification of segmentation variables is therefore one of the most creative parts of the segmentation process. Table 2: Some Segmentation Base Variables Segmentation base

Description

Demographics

Consumers can be grouped on the basis of characteristics such as age or household

Socioeconomic

Consumers can be grouped on the basis of characteristics such as income, occupation and education

Product usage

Potential to use the firm’s product is behaviourally based segmentation, with attributes such as awareness, used in the past, would consider using

Psychographics

Consumers can be grouped on the basis of personality, attitudes, opinions, and life styles

Generation

Generation, or cohort, refers to people born in the same period of time: similar age, similar economic, cultural, and political influences in formative years

Fonseca, J.

7

Generally, a combination of psychographics (for understanding) and demographics (for targeting) will give good results. For instance, concerning demographic variables, Sharp/Romaniuk/Cierpicki (1998) and Lin (2002) have suggested that they are useful in segmenting markets, though most of the evidence does not support this assertion (Fennell et al. 2003; Uncles/Lee 2006). Some studies have shown insignificant or no effect of demographics on consumer price responsiveness, such as Kim et al. (1999) and Scriven/Ehrenberg (2004). Granzin (1981) suggests a simple solution to the problem that links in with Simcock/Sudbury/Wright (2006) calling for more sophisticated segmentation: Choosing other variables to work alongside demographics. We argue that demographic variables are very important to a better understanding of segments, and can be used as covariates when estimating latent segment models and not as being part of segmentation base variables.

3.

Methodology

The process of identifying segments requires a thorough analysis of the entire market, not only focusing on customer’s needs and shopping habits but also providing knowledge of changing market conditions and competitive actions (Segal/Giacobbe 1994). From traditional market segmentation studies, including mixed research methods, we can enumerate six steps in the market segmentation process. They are summarised in Table 3. As for step 3, selecting market research tools, we can use data collecting tools - varying from qualitative to quantitative. Market research design and staged design can be sustained by a mixed or pragmatism methodology, which can be defined as research using both qualitative and quantitative methods and by mixing the two methods when beneficial (Onwuegbuzie/Leech 2005; Leech et al. 2010). In this methodology, both quantitative and qualitative approaches are about taking observations of the world (data) and presenting them within a framework (a model) (White 2002). In order to design a market research questionnaire, we often start step 3 with qualitative research to define ways in which customers view product or service categories and the differences between these views. We conduct preliminary focus groups or other qualitative methods, such as in-depth interviews, in order to achieve an insight into how consumers and business audiences feel about the product category and competitive brands, for instance, uncovering and refining our learning about customers to obtain a fuller picture and deeper understanding of the segments. Owing to its use of situation- and context-appropriate designs and methods, mixed method research seems particularly suited to action research (Vitale/Armenakis/Feild 2008). In questionnaire design, we can, for instance, use market segmentation dimensions such as behaviour, attitude or a combination of these to form psychographic segments, and another dimension, demographic for instance, as covariates, for a better characterisation of the

8

European Retail Research Vol. 25, Issue I, pp. 1-25

segments. In a developmental survey, we also use a questionnaire for collecting data, and then a quantitative method for analysing the obtained dataset. Table 3: Segmentation Steps Segmentation step

Description

Step 1 Determine the market boundaries

Select a market or product category to study

Step 2 Segmentation base variables

Marketers must use their knowledge of the market to select a few relevant variables in advance

Step 3 Selecting market research tools (mixed research process)

Select tools for collecting and analysing data. From the stages of social research, we notice that qualitative and quantitative can coexist in each researching process. (1) In the first phase we have research preparation, in which we determine the study subject (specification of problem, paper overview, research theory) and the research structure (test structure, measurement, sampling, ethics). (2) This is followed by research (direct observation, indirect interviews, life history, discussion group, content analysis, survey, secondary data, simulation). (3) Finally, an information analysis (data processing and analysis) is conducted. It would be very difficult to exclude one of the two methodologies in any of these three phases, but social scientists frequently do not manage the available information in statistical results, thus missing chances to present statistics that could result in a bigger clarification of research questions (King/Tomz/Wittenberg 2000).

Step 4 Profiling each market segment

Involves selecting those variables that are most closely related to consumers' actual buying behaviour

Step 5 Segment targeting

A marketer should look for opportunities that provide a good strategy. In step 3, selecting tools for collecting and analysing data, we introduce a mixed methodology, in order to test the solution, by using all the information obtained from the qualitative data collection tools, such as interviews, focus groups and participant observation, for exploring new topics, assisting theory building, providing context for quantitative data, and helping to explain or clarify quantitative findings (segments). We think that we are finding out more about the needs and preferences of customers by merging knowledge and using qualitative (quantitative) conclusions to update quantitative (qualitative) conclusions. In step 2, one of the most important steps in segmentation schemes, there is a large array of possible segmentation bases - set of variables or attributes used to assign potential customers to homogeneous segments. For a review we can see (Wilkie, 1990) and (Wedel & Kamakura, 1998), for instance. Following the latter authors, “The identification of market segments is highly dependent on the variables and methods used to define them.” This sentence stresses the great importance of segmentation base variables and methods for analysing data. Table 2 summarises some examples of possible segmentation bases.

Step 6 Product positioning

This involves developing a product and marketing plan that will appeal to the selected market segment

In this study we focus more on steps 3 and 4, especially tools for analysing data and profiling our segments. But the market segments identified should mostly satisfy the three criteria that we show in Table 4. These criteria are all met by using latent segment models, with the aforementioned advantages. It is a probabilistic/statistic clustering approach which assumes that observation of the vari-

Fonseca, J.

9

ables in a sample arises from different segments of unknown proportions. They are very good models for modelling complex phenomena, then synthesizing and extracting knowledge. The proposed conceptual segmentation scheme (1) provides internal homogeneity and external heterogeneity, (2) enables marketers to reach segments separately using observable characteristics of them and (3) because of sparing use of theoretical information criteria for model selection balances (fitting a model with a large number of components requires estimating a very large number of parameters and potential loss of accuracy in these estimates (Leroux/Puterman 1992) and complexity of models (which tends to improve the model fit to the data), the selected latent class model shows a trade-off between a good description of the data and the model number of parameters. Table 4: Segment Criteria Criterion

Meaning

Internal Homogeneity/External Heterogeneity

Customers within a segment should have similar responses to the marketing mix variable of interest but a different response to members of other segments

Parsimony

The degree to which the segmentation makes every customer a unique target. That is, segmentation should identify a small set of groupings of substantial size

Accessibility

The degree to which marketers can reach segments separately using their observable characteristics

The segmentation process is used to distinguish between customers and non-customers, where "customers" are extended to include buyers, payers, loyal customers, etc, and to understand their composition and characteristics  Who they are? What do they look like? What are their attributes? Where do they live? This analysis supports a whole array of decisions, ranging from targeting decisions to determining efficient and cost effective marketing strategies or even evaluating market competition, (Levin & Zahavi, 2001). The three most relevant criteria for segments (Table 4) are always reached by this conceptual scheme, when segment structure really exists, which is not the case with other tools, such as cluster analysis models. ‫ିݕ‬௜ ൌ ൫‫ݕ‬௜௣ ൯denotes the vector representing the scores of the ith case for the pth segmentation base variable (i = 1,…,n ; p = 1,…,P). We consider that the cases on which the attributes are measured arise from a population which we assume to be a mixture of S segments, in proportions Os (mixing proportions or relative segment sizes), s = 1,…,S. The statistical probability density function ‫ିݕ‬௜ of the vector, ‫ିݕ‬௜ given that comes from segment s, is represented by, ݂௦ ሺ‫ିݕ‬௜ ȁߠି௦ ሻ, with ߠି௦ representing the vector of unknown parameters associated with the specific chosen probability density function. Then the population density can be represented as a finite mixture of ݂௦ ሺ‫ିݕ‬௜ ȁߠି௦ ሻthe densities of S distinct segments, i.e.

10

European Retail Research Vol. 25, Issue I, pp. 1-25

[1]

where i = 1,…,n,.

f ( y |\ ) i

O s ! 0,

S ¦ Os s 1

1, \

S

P

s 1

p 1

¦ Os – f s ( yi | T s )

{O , 4 }, with

O

{ O1 ,  , O s  1 } , 4

{T 1 ,  , T s }

\

is the vector of all unknown parameters. The LSM estimation problem simultaneously addresses the estimation of distributional parameters and classification of cases into segments, yielding mixed probabilities. The estimation process is typically directed to maximum likelihood using the expectation-maximisation (EM) algorithm (Dempster/Laird/Rubin 1977; McLachlan/Peel 2000). LSM naturally provides means for constituting a partition by means of assigning each case to Max Wˆis the segment with the highest segment-membership probability, that is with s 1,..., S where (k )

[2]

Oˆ Wˆ is

s

f s ( y | Tˆ i

(k ) s

)

S (k ) (k ) ¦ Oˆ f j ( y i | Tˆ ) j j 1 j

In order to derive meaningful results from clustering, the mixture model must be identifiable, i.e. a single maximum likelihood solution should exist (Bozdogan 1994). One goal of traditional LSM estimation is to determine the smallest number of latent segments S sufficient to explain the relationships between the segmentation base variables. If the baseline model (S = 1) provides a good fit to the data, no LSM is needed since there is no relationship between the variables to be explained. Otherwise, a model with S = 2 segments is then fitted to the data. This process continues by fitting successive LSM to the data, adding another dimension each time by incrementing the number of segments by 1, until a parsimonious model is found that provides an adequate fit. They are very good models for modelling complex phenomena and then synthesizing and extracting knowledge. Concerning methods for the selection of the appropriate latent class model, we propose to use traditional information criteria Especially, because all the observed variables have similar measure, all of them categorical, we will use the AIC3 information criterion the best one for this situation (Fonseca 2010). We can now answer the questions on page 5, concerning segmentation tool and segmentation base variables. Thus, as the best segmentation tool we propose latent segment models and for segmentation base variables we consider, with marketers, some store attributes and some customer attributes that interact between them.

Fonseca, J.

11

These variables are considered as manifest variables or indicators (USAGE FREQUENCY, …, INTERNET , from which model parameters are estimated and some covariates used (SEX, ... , CLASS), which are only employed for a better understanding of segments and their members (see Table 5). Results from the estimation of these models are valid for all cases, products, branches, countries, services and all kinds of variables (categorical, continuous, or mixed), because they are probabilistic/statistic models.

USE)

Table 5: Variables and Covariates of the Dataset Variables used for a retail chain customers’ segmentation Usage frequency Visit pattern Coming from Travel time Why shopping Monthly spending on purchases for the home Monthly spending in store Quality of fresh produce Store treatment Efficiency of Store’s staff variety of products Product presentation Store environment Cleanliness Shop Prices Product quality Private Label Internet use Covariates Sex Age Family size Life cycle Income Education Occupation Class

4.

Segmentation base

Psychographic

Demographic

Socioeconomic

Results from a Retailing Dataset and Discussion

We used two types of data collection in the research: Qualitative and quantitative. Here, we only report the quantitative data analysis, based on a dataset obtained from a questionnaire given to a retail chain's customers. Table 6: AIC3 for Model Selection Model

LL

AIC3

1-Cluster 2-Cluster 3-Cluster

-45613.3 -44022.5 -43082.7

91484.666 88681.023 87179.382

12

European Retail Research Vol. 25, Issue I, pp. 1-25

After eliminating the questionnaires with non responses, we had a dataset with 1,449 customers characterised by the segmentation base variables shown in Table 5.

Table 7: Parameter Estimates of Two-Class Latent Model Cluster size Indicators Usage frequency Every day Two or three times a week Once a week Twice a month Once a month Occasionally Visit pattern During the week At the weekend Both Coming from Home Work Passing by Other Travel time Two minute walk Two to five minute walk Five to ten minute walk More than ten minutes' walk Five minutes or less by car Five to ten minutes by car Ten to fifteen minutes by car More than fifteen minutes by car Why shopping Near home Near work Passing by Low prices Variety of brands Variety of products in general Habit Quality products Quality of fresh produce Cleanliness / hygiene shop Fast service Friendly service Promotions Opening hours Other Monthly spending on purchases for the home Mean Monthly spending in store Mean

Cluster 1 (61 %)

Cluster 2 (39 %)

0.2280 0.3754 0.1842 0.0410 0.0700 0.1013

0.4265 0.2670 0.1523 0.0278 0.0586 0.0679

0.3511 0.1838 0.4652

0.1898 0.1246 0.6857

0.6468 0.2643 0.0598 0.0291

0.8012 0.1133 0.0622 0.0234

0.1587 0.1963 0.1584 0.0673 0.1467 0.0858 0.0982 0.0886

0.2085 0.2524 0.1521 0.0770 0.1582 0.0674 0.0425 0.0417

0.6015 0.0851 0.1291 0.0430 0.0066 0.0196 0.0346 0.0250 0.0060 0.0068 0.0119 0.0072 0.0034 0.0077 0.0011

0.6912 0.0436 0.0419 0.0424 0.0216 0.0278 0.0432 0.0353 0.0047 0.0070 0.0061 0.0243 0 0.0023 0.0018

266.1543

380.0798

89.3863

200.9802

By estimating these LSM from the baseline model (homogeneity model or non-structure segments) to a three-class latent model, we selected a two-class latent model by using AIC3 and AICu (Fonseca 2010a) for model selection, because we had a mixed-mode dataset (Monthly spending on purchases for the home and monthly spending in store are continuous, the others categorical). These models automatically select the number of segments, 2-segment in this case, because the graph for AIC3 shows an elbow (see Table 6), by using an information criterion, which is an advantage when compared with cluster analysis.

Fonseca, J.

13

Because we wanted to use a segmentation scheme, as we have explained, we estimated latent class models, in order to select effective segments and then target marketing and product positioning. By estimating the parameters of model (1) from the segmentation base variables used we reached model parameter estimates, which are shown in Table 7. Table 7: (Continued) Cluster size Quality of fresh produce Very good Good Fair Bad Very bad DK/na Treatment Very good Good Fair Bad Very bad DK/na Efficiency of staff Very good Good Fair Bad Very bad DK/na Variety of products Very good Good Fair Bad Very bad DK/na

Cluster 1 (61 %)

Cluster 2 (39 %)

0.0236 0.5042 0.3837 0.0498 0.0011 0.0400

0.2793 0.4427 0.1650 0.0474 0 0.0633

0.0506 0.7112 0.2190 0.0180 0.0011 0

0.6155 0.2995 0.0476 0.0053 0 0.0320

0.0207 0.6971 0.2572 0.0169 0.0023 0.0058

0.4654 0.3982 0.0692 0.0142 0 0.0531

0.0087 0.5063 0.4460 0.0364 0.0045 0.0023

0.1641 0.5212 0.2357 0.0322 0 0.0427

Table 8 summarises parameter estimates for this model, from the covariates used as inactive, i.e., they were not used for parameters estimates. Thus we used for a better understanding of segments, especially the segments’ customers. There are two kinds of probabilities in these tables’ parameters: (1) simple probabilities or mixed probabilities (relative segment size), in which we can see that we have 61 % of customers in segment 1 and 39 % in segment 2, and (2) conditional probabilities: probabilities of customers selecting some category for answering a question, knowing that they belong to a certain segment. For instance, 0.7033 and 0.7743 from Table 8 are the probabilities of the respondents’ answers being female, given that they belong to segment 1 and segment 2, re-

14

European Retail Research Vol. 25, Issue I, pp. 1-25

spectively. Thus it allows us to conclude that in segment 2 we have a majority of female respondents. The use of probabilities as parameters is another advantage in cluster analysis, which uses distance measures (there are several), and/or different clustering methods resulting in different solutions. Table 7: (Continued) Cluster size

Cluster 1

Cluster2

0,6119

0,3881

Product presentation Very good

0.0050

0.266

Good

0.6154

0.5839

Fair

0.3593

0.1039

Bad

0.0203

0

Dk/na

0

0.0462

Store environment Very good

0.0101

0.3700

Good

0.7391

0.5151

Fair

0.2388

0.0627

Bad

0.0120

0.0042

Dk/na

0

0.0480

Cleanliness of shop Very good

0.0414

0.4647

Good

0.7527

0.4083

Fair

0.1919

0.0690

Bad

0.0128

0.0082 0

Very bad

0.0011

Dk/na

0

0.0498

Prices

0.0414

0.4647

Very good

0.0082

0.0635

Good

0.2689

0.2482

Fair

0.5361

0.4582

Bad

0.1614

0.1741

Very bad

0.0110

0.0112

Dk/na

0.0144

0.0449

Very good

0.0144

0.1676

Good

0.4232

0.4726

Fair

0.3942

0.1733

Product quality Private Label

Bad

0.0231

0.0169

Very bad

0.0028

0.0028

Dk/na

0.1423

0.1668

Internet usage At home

0.1851

0.1544

At work

0.0998

0.0471

Both

0.1723

0.1320

No access

0.5428

0.6664

Fonseca, J.

15

The estimated probabilities allow us to name segments and show the segment profile, based on both segmentation base variables and covariates, for a better understanding of the clusters (see Table 9). Table 8: Parameter Estimates of Two-Class Latent Model by Covariates Cluster size Covariates Sex Female Male Age Under 25 25 to 34 35 to 44 45 to 54 Over 55 Occupation Independent Dependent Both DK/na Education Incomplete primary ed. Primary ed. th 6 grade th 9 grade th 12 grade Undergraduate student Foundation degree Honours degree DK/na Family size 1 person 2 people 3 people 4 people 5 people 6 or more people Life cycle Single pre family Couple pre family Young family Maturing family Established family Single post family Couple post family Older single Older couple Income Less than EUR 400 EUR 401 to EUR 798 EUR 799 to EUR 1,197 EUR 1,198 to EUR 1,596 EUR 1,597 to EUR 1,995 More than EUR 1,996 DK/na Class Class A Class B Class C1 Class C2 Class D

Cluster 1 (61 %)

Cluster 2 (39 %)

0.7033 0.2967

0.7743 0.2257

0.2274 0.2204 0.2119 0.1646 0.1757

0.1127 0.1717 0.1993 0.2189 0.2974

0.1814 0.7885 0.0241 0.006

0.1763 0.7929 0.0261 0.0048

0.0189 0.1664 0.0984 0.1721 0.1624 0.0991 0.0499 0.2198 0.0129

0.0502 0.1964 0.0903 0.1519 0.1636 0.0553 0.0404 0.2349 0.017

0.1701 0.2568 0.3141 0.2248 0.049 0.0179

0.1568 0.2240 0.2694 0.2128 0.0703 0.0339

0.2857

0.1648

0.0634 0.0861 0.0936 0.2468 0.0485 0.0793 0.0463 0.0504

0.0477 0.0509 0.0765 0.3507 0.0764 0.0741 0.0711 0.0877

0.0512 0.1475 0.1885 0.1284 0.0741 0.115 0.2841

0.0562 0.1551 0.1996 0.1461 0.0841 0.105 0.2651

0.0838 0.1515 0.2689 0.3384 0.1094

0.0766 0.1311 0.3087 0.3938 0.137

16

European Retail Research Vol. 25, Issue I, pp. 1-25

As a result, we can name the segments Occasional Customers (Segment 1), with 61 %, and Loyal Customers (Segment 2), with 39 %. We can see that the Occasional Customers live near the store and are more concerned with opening hours, fast service and promotions. Where store products are concerned, they think that all is down to good quality, cleanliness, environment, efficiency, variety and prices. They spend EUR 266 on purchases for the home and EUR 89 at the store (only 33 %) every month and they access the internet both at home and at work. Table 9: Profile of Retail Chain Customers Variable

Occasional customers (61 %)

Loyal customers (39 %)

Usage frequency

Occasionally to two or three times a week

Every day

Visit pattern

During the week; at the weekend

Both

Coming from

Work; other

Home; Passing by

Travel time

Five to ten minute walk; five or more minutes by car

Two to five minutes walk; More than ten minutes walk; five minutes or less by car

Why shopping

Near work; passing by; low prices; quality of fresh produce; fast service; promotions; opening hours

Near home; variety of brands; variety of products in general; habit; quality products; cleanliness / hygiene shop; friendly service; other

Monthly spending on purchases for the home

266.20

380.10

Monthly spending in store

89.40

201.00

Quality of fresh produce

Good; fair; bad; very bad

Very good

Treatment

Good; fair; bad; very bad

Very good

Efficiency of staff

Good; fair; bad; very bad

Very good

Variety of products Product presentation Store environment Cleanliness Shop Prices

fair; bad; very bad Good; fair; bad; very bad Good; fair; bad; very bad Good; fair; bad; very bad Good; fair; bad; very bad

Very good; Good Very good Very good Very good Very good

Product quality Private Label

fair; bad; very bad

Very good; Good

Internet Access

At home; at work; both

No access

Covariates Sex

Male

Female

Age

Up to 44

Over 44

Occupation

Independent

Dependent; both

Education

th th 6 and 9 grade; undergraduate; foundation degree

Incomplete and complete primary ed.; 12th grade ; graduate

Family size

Up to 4 people

5 or more people

Life cycle

Single pre family; couple pre family; young family; maturing family

Established Family; Single Post Family; Older Single; Older Couple

Income

More than EUR 1,996

Up to EUR 1,995

Class

Class A; Class B

Class C1; Class C2 and Class D

Fonseca, J.

17

In contrast to the Occasional Customers, Cluster 2 or the Loyal Customers do not live close to the store and are more concerned about variety of brands, variety of products in general, quality products, cleanliness/hygiene in the shop and friendly service. They think that almost all store products are very good for quality, cleanliness, environment, efficiency, variety and prices. After identifying the two segments, we evaluated their socio-demographic profiles taking account of the socioeconomic and demographic variables, here used as covariates. As we can see, the majority are male, aged under 44, independent, with a low level of education, family size up to 4 people. As for their life cycle, the majority are pre-family singles or couples, young or maturing families, with an income of over EUR 1,996 and they are in class A or B. Quality of service is a very important construct. By building on and extending earlier research (Aaker 1991; Anderson/Fornell/Lehmann 1994; Oliver 1997), we find a framework of service showing high quality that leads to satisfaction, which in turn affects loyalty (Harris/ Goode 2004; Fonseca 2009). Empirical research about customer satisfaction also concludes that service quality leads to customer’s satisfaction. Loyal customers spend more money every month (Harris & Goode, 2004), in our case EUR 380 on purchases for the home and, EUR 200 at the store (53 %), and they did not access the internet. Thus, loyal customers buy more, are willing to spend more, are easier to reach, and, more than that, they act as enthusiastic advocates for companies. Again, through socioeconomic and demographic profiles, we can understand this segment better, by learning that they are mainly female, older, dependent, have higher educational attainment, families with more than 4 people and established families, are post family singles and older singles, earn less than EUR 1,995 and are in class C1, C2 or D. Table 8 shows that there is a great difference in the socioeconomic and demographic profiles of occasional and loyal customers. Knowledge of segment structure is very important because of its managerial utility, particularly in targeting and positioning. Because customers in each segment must respond differently to variations in the marketing mix, it means that this classification into two segments is a true market segmentation scheme, as the segments exhibit differences in behavioural response. All of this is reinforced by the interviews we conducted at the beginning of this marketing segmentation plan. They confirm the existence of two segments, because all of them fall into the segment structure revealed. This is an important aspect of a mixed methodology, merging knowledge by using data from the qualitative research to complement the quantitative research findings. This mixed methodology is of great importance in guaranteeing that we have reached effective segments, because a poorly segmented market is often worse than making mass-market assumptions. Finally, we used binary logistic regression to ascertain which factors most influenced internet usage (after recoding internet access: 1, use; 2, non-use). Model estimation allows us to con-

18

European Retail Research Vol. 25, Issue I, pp. 1-25

clude that education (p = 0), age (p = 0), income (p = 0), sex (p = 0.005) and usage frequency (p = 0.022), in this order, are the variables that contribute most to explaining internet use. The model also states that the influence is the reverse for education and age. The influence for income is direct. This is in accordance with the segment structure found. Occasional customers access the internet at home and at work, the majority are male, younger, with lower educational attainment, but a higher income. Thus, we have good reasons to believe in the veracity of our segment structure, firstly reinforced by the interviews (qualitative validating quantitative – mixing), and now by quantitative data. As future research into internet use, this retail chain organisation would like to get more information about it, in order to consider future e-commerce.

5.

Results and Implications

Firstly, we gave an overview of the great importance of market (the retail market in particular) segmentation, because by learning more about market segments and tailoring product range to different groups, companies are able to meet the needs of more customers more precisely, and consequently to gain a higher overall share or profit from a market. Accurate measurement of preferences allows marketers to gain a deeper understanding of consumers’ wishes, desires, likes, and dislikes, and thus permits better implementation of the marketer's tools (Cohen/Neira, 2003), by concentrating marketing energy on segments to gain a competitive advantage within the segment. Secondly, we applied latent segment modelling to market segmentation for a dataset with mixed-mode data, and AIC3 and AICu for model selection. A two-class latent model fitted the data well, and the two segments are internal homogeneous/external heterogeneous. They constitute a very parsimonious solution and marketers can reach segments separately using the segments' observable characteristics. This is a good solution and the advantage of efficient clusters is that marketers can easily understand them and develop different, more successful business strategies. This allows these managers to focus limited resources on meeting or exceeding the needs of particular customers (Beynon et al. 2005). We tested this solution by using all the information obtained from the qualitative collection methods, such as interviews, focus groups and participant observation, in accordance with retail chain marketers at the beginning of the segmentation scheme, in order to demonstrate that the segments would respond differently to variations in the marketing mix. With the information obtained from the qualitative treatment, we learned that customers were likely to react to an offer, a price or a promotion on the basis of occasional customers and loyal customers. Thus, from a mixed scheme of market segmentation and AIC3 and AICu information criteria, we can support the idea that latent segment models are accurate in efficiently repre-

Fonseca, J.

19

senting heterogeneous customers, by identifying two homogeneous segments which accommodate customers' needs and preferences. The great strength of this pragmatic approach to social science research methodology in general, and market segmentation in particular, is its emphasis on the connection between epistemological concerns about the nature of the knowledge that we produce and technical concerns about the methods that we use to generate that knowledge. This moves beyond technical questions about mixing or combining methods and puts us in a position to argue for a properly integrated methodology for the social sciences (Morgan 2007). Our empirical findings indicate that customers’ perceptions are quite different for all the variables used, including internet use. Loyal customers indicate that quality is very important to them, varying from quality of fresh produce to prices. We find that all the covariates used are important in order to differentiate between retail chain customers. Finally, we conclude that education, age, income, sex and usage frequency, in this order, are the variables that contribute most to explaining internet use (use/don't use, after recoding), by using a binary logistic regression. The effectiveness of our structure is quite clear from these results – testing the solution. To sum up, we conclude that the scheme of latent segment models used for segmentation combined with a mixed methodology is an advantageous research scheme for uncovering the underlying market typology, when compared to more traditional methods. In future studies, this retail chain organisation must study the ecommerce situation carefully, given the importance of internet access, in order to find out what benefits customers seek and what risks they fear.

20

European Retail Research Vol. 25, Issue I, pp. 1-25

Acknowledgements The author wishes to thank the two anonymous referees for their many valuable suggestions, which led to significant improvements in this article.

References Aaker, D. (1991): Managing brand equity, Ontario. Anderson, E.W.; Fornell, C.; Lehmann, D.R. (1994): Customer satisfaction, market share, and profitability: Findings from Sweden, in: Journal of Marketing, Vol. 58, No. 3, pp. 53-66. Anderson, W.T.; Cox, E.P.; Fulcher, D. G. (1976): Bank selection decisions and market segments, in: Journal of Marketing, Vol. 40, pp. 40-45. Arabie, P.; Carroll, J.D.; DeSarbo, W.; Wind, J. (1981): Overlapping clustering: A new method for product positioning, in: Journal of Marketing Research, Vol. 18, pp. 310-317. Assael, H.; Roscoe, A.M. (1976): Approaches to market segmentation analysis, in: Journal of Marketing, Vol. 40, pp. 67-76. Baker, G.A.; Burnham, T.A. (2001): Consumer response to genetically modified foods: Market segment analysis and implications for producers and policy makers, in: Journal of Agricultural and Resource Economics, Vol. 26, pp. 387-403. Bass, F.M.; Pessemier, E.A.; Tigert, D.J. (1969): A taxonomy of magazine readership applied to problems in marketing strategy and media selection, in: Journal of Business, Vol. 42, pp. 337-363. Beane, T.P.; Ennis, D.M. (1987): Market-segmentation  a review, in: European Journal of Marketing, Vol. 21, No. 5, pp. 20-42. Becker, B.W.; Brewer, B.; Dickerson, B.; Magee, R. (1985): The influence of personal values on movie preferences, in: Austin, B.A. (ed.), Current Research in Film: Audiences, Economics, and the Law, Norwood, NJ., pp. 37-50. Beynon, M.; Goode, M.M.; Moutinho, L.; Snee, H. (2005): Modelling satisfaction with automated banking channels: Using variable precision rough set theory, in: Services Marketing Quarterly, Vol. 26, No. 4, pp. 77-94. Biggadike, E.R. (1981): The Contributions of marketing to strategic management, in: The Academy of Management Review, Vol. 6, No. 4, pp. 621-632. Bock, T.; Uncles, M. (2002): A taxonomy of differences between consumers for market segmentation, in: International Journal of Research in Marketing, Vol. 19, pp. 215-224. Bozdogan, H. (1994): Mixture-model cluster analysis using model selection criteria and a new informational measure of complexity, in: Bozdogan, H. (ed.): Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Approach, Vol. 2, pp. 69-113. Calantone, R.J.; Sawyer, A.G. (1978): The stability of benefit segments, in: Journal of Marketing Research, Vol. 15, pp. 395-404. Carroll, J.D.; Green, P.E. (1997): Psychometric methods in marketing research: Part I, conjoint analysis, in: Journal of Marketing Research, Vol. 32, pp. 385-391.

Fonseca, J.

21

Chen, J.S. (2003): Market segmentation by tourists’ sentiments, in: Annals of Tourism Research, Vol. 30, No. 1, pp. 178-193. Cohen, S.H.; Neira, L. (2003): Measuring preference for product benefits across countries: Overcoming scale usage bias with maximum difference scaling, Paper presented at the ESOMAR 2003, Latin America Conference Proceedings, Amsterdam. Cohen, S.H.; Ramaswamy, V. (1998): Latent segmentation models: New tools assist researchers in market segmentation, in: Marketing Research, Vol. 10, No. 2, pp. 14-21. Dempster, A.P.; Laird, N.M.; Rubin, D.B. (1977): Maximum likelihood from incomplete data via EM algorithm, in: Journal of the Royal Statistics Society, B, Vol. 39, pp. 1-38. DeSarbo, W.S.; Degeratu, A.M.: Wedel, M.; Saxton, M.K. (2001): The spatial representation of market information, in: Marketing Science, Vol. 20, No. 4, pp. 426-441. DeSarbo, W.S.; Ramaswamy, V.; Cohen, S.H. (1995): Market segmentation with choicebased conjoint analysis, in: Marketing Letters, pp. 137-147. Dibb, S. (1998): Market segmentation: Strategies for success, in: Marketing Intelligence & Planning, Vol. 16, pp. 394–406. Dibb, S. (1999): Criteria guiding segmentation implementation: Reviewing the evidence, in: Journal of Strategic Marketing, Vol. 7, pp. 107-129. Dibb, S.; Stern, P.; Wensley, R. (2002): Marketing knowledge and the value of segmentation, in: Marketing Intelligence & Planning, Vol. 20, pp. 113-119. Dillon, W.R.; Kumar, A. (1994): Latent structure and other mixture models in marketing: An integrative survey and overview, Chapter 9, in: Bagozi, R.P. (ed.): Advanced methods of Marketing Research, Cambridge, pp. 352-388. Dolnicar, S. (2008): Marketing segmentation in tourism, http://ro.uow.edu.au/commpapers/ 556.htm. Fennell, G.; Allenby, G.M.; Yang, S.; Edwards, Y. (2003): The effectiveness of demographic and psychographic variables for explaining brand and product category use, in: Quantitative Marketing and Economics, Vol. 1, pp. 233-244. Fonseca, J.R. (2009): Customer satisfaction study via a latent segment model, in: Journal of Retailing and Consumer Services, Vol. 16, pp. 352-359. Fonseca, J.R. (2010): On the performance of information criteria in latent segment models, Proceedings of World Academy of Science, Engineering and Technology, Vol. 63, p. 4572. Fonseca, J.R.; Cardoso, M.G. (2005): Retail clients latent segments, in: Bento, C.; Cardoso, A.; Dias, G. (eds.): Progress in Artificial Intelligence, Berlin et al., pp. 348-358. Fonseca, J.R.; Cardoso, M.G. (2007a): Mixture-model cluster analysis using information theoretical criteria, in: Intelligent Data Analysis, Vol. 11, No. 2, pp. 155-173. Fonseca, J.R.; Cardoso, M.G. (2007b): Supermarket customers segments stability, in: Journal of Targeting, Measurement and Analysis for Marketing, Vol. 15, No. 4, pp. 210-221. González-Benito, Ó.; Greatorex, M.; Muñoz-Gallego, P.A. (2000): Assessment of potential retail segmentation variables. An approach based on a subjective MCI resource allocation, in: Journal of Retailing and Consumer Services, Vol. 7, pp. 171-179.

22

European Retail Research Vol. 25, Issue I, pp. 1-25

Granzin, K.L. (1981): An investigation of the market for generic products, in: Journal of Retailing, Vol. 57, pp. 39-55. Green, P.E.; Carmone, F.J. (1968): The performance structure of the computer market: A multivariate approach, in: Economic and Business Bulletin, Vol. 20, pp. 1-11. Green, P.E., Frank, R.E.; Robinson, P.J. (1967): Cluster analysis in test market selection, in: Management Science, Vol. 13, pp. 387-400. Green, P.E., Krieger, A.M. (1991): Segmenting markets with conjoint analysis, in: Journal of Marketing, Vol. 55, pp. 20-31. Green, P.E.; Srinivasan, V. (1990): Conjoint analysis in marketing: New developments with implications for research and practice, in: Journal of Marketing, Vol. 54, No. 2, pp. 3-19. Greeno, D.W.; Sommers, M.S.; Kernan, J.B. (1973): Personality and implicit behavior patterns, in: Journal of Marketing Research, Vol. 10, pp. 63-69. Harris, L.C.; Goode, M.M. (2004): The four levels of loyalty and the pivotal role of trust: A study of online service dynamics, in: Journal of Retailing, Vol. 80, pp. 139-158. Harvey, J.W. (1990): Benefit segmentation for fund raisers, in: Journal of the Academy of Marketing Science, Vol. 18, pp. 77-86. Heilman, C.M.; Bowman, D. (2002): Segmenting consumers using multiple-category purchase data, in: International Journal of Research in Marketing, Vol. 19, pp. 225-252. Heuvel, D.A.; Devasagayam, P.R. (2004): Multivariate cluster analysis model of benefitbased market segmentation: A case study from the recreation and leisure industry, http://www.danavan.net/pdf/cluster.analysis.publication.pdf , accessed August 5, 2010. Hofstede, F.T.; Steenkamp, J.-B. E.M. (1999): International market segmentation based on consumer-product relations, in: Journal of Marketing Research, Vol. 36, pp. 1-17. Hruschka, H.; Natter, M. (1999): Comparing performance of feedforward neural nets and Kmeans for cluster-based market segmentation, in: European Journal of Operational Research, Vol. 114, pp. 346-353. Jain, S.C. (1993): Marketing Planning & Strategy, 4th ed., Cincinnati, OH. Jayawardhena, C.; Wright, L.T.; Dennis, C. (2007): Consumers online: Intentions, orientations and segmentation, in: International Journal of Retail and Distribution Management, Vol. 35, No. 6, pp. 515-599. Jedidi, K.; Jagpal, H.S.; DeSarbo, W.S. (1997): Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity, in: Marketing Science, Vol. 16, pp. 39-59. Kamakura, W.A.; Kim, B.D.; Lee, J. (1996): Modeling preference and structural heterogeneity in consumer choice, in: Marketing Science, Vol. 15, No. 2, pp. 152-172. Kau, A.K.; Tang, Y.E.; Ghose, S. (2003): Typology of online shoppers, in: Journal of Consumer Marketing, Vol. 20, No. 2, pp. 139-156. Kiel, G.C.; Layton, R.A. (1981): Dimensions of consumer information seeking behavior, in: Journal of Marketing Research, Vol. 18, pp. 233-239. Kim, B.-D.; Srinivasan, K.; Wilcox, R.T. (1999): Identifying price sensitive consumers: The relative merits of demographic vs. purchase pattern information, in: Journal of Retailing, Vol. 75, No. 2, pp. 173-193.

Fonseca, J.

23

King, G.; Tomz, M.; Wittenberg, J. (2000): Making the most of Statistical Analyses: Improving Interpretation and Presentation, in: American Journal of Political Science, Vol. 44, No. 2, pp. 341-355. Kotler, P. (1972): Marketing management analysis, planning, and control, 2nd ed., Englewood Cliffs, N.J. Kotler, P. (1997). Marketing management analysis, planning, implementation, and control, 9th ed., Englewood Cliffs, NJ. Lee, C.-K.; Lee, Y.-K.; Wicks, B.E. (2004): Segmentation of festival motivation by nationality and satisfaction, in: Tourism management, Vol. 25, pp. 61-70. Leech, N.L.; Dellinger, A.B., Brannagan, K.B.; Tanaka, H. (2010): Evaluating mixed research studies: A mixed methods approach, in: Journal of Mixed Methods Research, Vol. 4, No. 1, pp. 17-31. Leroux, B.G.; Puterman, M.L. (1992): Maximum-penalized-likelihood estimation for independent and Markov-dependent mixture models, in: Biometrics, Vol. 48, No. 2, pp. 545558. Levin, N.; Zahavi, J. (2001): Predictive modeling using segmentation, in: Journal of Interactive Marketing, Vol. 15, No. 2, pp. 2-22. Lin, C.-F. (2002): Segmenting customer brand preference: demographic or psychographic, in: Journal of Product & Brand Management, Vol. 11, No. 4, pp. 249-268. Lockshin, L.S.; Spawton, A.L.; Macintosh, G. (1997): Using product, brand and purchasing involvement for retail segmentation, in: Journal of Retailing and Consumer Services, Vol. 4, No. 3, pp. 171-183. McCarty, J.A.; Hastak, M. (2007): Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression, in: Journal of Business Research, Vol. 60, pp. 656662. McLachlan, G.F.; Peel, D. (2000): Finite mixture models, Hoboken, NJ. Montgomery, D.B.; Silk, A.J. (1971): Clusters of consumer interests and opinion leaders' sphere of influence, in: Journal of Marketing Research, Vol. 8, pp.317-321. Moore, W.L. (1980): Levels of aggregation in conjoint analysis: An empirical comparison, in: Journal of Marketing Research, Vol. 17, pp. 516-523. Morgan, D.L. (2007): Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods, in: Journal of Mixed Methods Research, Vol. 1, No. 1, pp. 48-76. Morrison, B.J.; Sherman, R.C. (1972): Who responds to sex in advertising, in: Journal of Advertising Research, Vol. 12, pp. 15-19. Oliver, R.L. (1997): Satisfaction a behavioural perspective on the consumer, New York. Onwuegbuzie, A.J.; Leech, N.L. (2005): Taking the "Q" out of research: Teaching research methodology courses without the divisive between quantitative and qualitative paradigms, in: Quality & Quantity, Vol. 39, pp. 267-296. Palmer, R.A.; Miller, P. (2004): Segmentation: Identification, intuition, and implementation, in: Industrial Marketing Management, Vol. 33, pp. 779-785.

24

European Retail Research Vol. 25, Issue I, pp. 1-25

Porter, M.E. (1985): Competitive advantage creating and sustaining superior performance, New York. Punj, G.; Stewart, D.W. (1983): Cluster Analysis in Marketing Research: Review and suggestions for application, in: Journal of Marketing Research, Vol. 10, pp. 134-148. Schaninger, C.M.; Lessig, V.P.; Panton, D.B. (1980): The complementary use of multivariate procedures to investigate nonlinear and interactive relationships between personality and product usage, in: Journal of Marketing Research, Vol. 17, pp. 119-124. Scriven, J.; Ehrenberg, A. (2004): Executive summaries  consistent consumer responses to price changes, in: Australasian Marketing Journal, Vol. 12, No. 3, pp. 19-20. Segal, M.N.; Giacobbe, R.W. (1994): Market segmentation and competitive analysis for supermarket retailing, in: International Journal of Retail & Distribution Management, Vol. 22, pp. 38-48. Sexton, D.E. (1974): A cluster analytic approach to market response functions, in: Journal of Marketing Research, Vol. 11, pp. 109-114. Sharma, A.; Lambert, D.M. (1994): Segmentation of markets based on customer service, in: International Journal of Physical Distribution & Logistics Management, Vol. 24, No. 4, pp. 50-58. Sharp, A.; Romaniuk, J.; Cierpicki, S. (1998): The performance of segmentation variables: A comparative study, unpublished manuscript, Marketing Science Centre, Adelaide. Simcock, P.; Sudbury, L.; Wright, G. (2006): Age, perceived risk, and satisfaction in consumer decision making: A review and extension, in: Journal of Marketing Management, Vol. 22, pp. 355–377. Smith, W.R. (1956): Product differentiation and market segmentation as alternative marketing strategies, in: Journal of Marketing, Vol. 21, pp. 3-8. Sun, S. (2009): An analysis on the conditions and methods of market segmentation, in: International Journal of Business and Management, Vol. 4, No. 2, pp. 63-70. Thomas, J.S.; Sullivan, U.Y. (2005): Managing marketing communications with multichannel customers, in: Journal of Marketing, Vol. 69, pp. 239-251. Tsai, C.-Y.; Chiu, C.-C. (2004): A purchase-based market segmentation methodology, in: Expert Systems with Applications, Vol. 27, 265-276. Uncles, M.; Lee, D. (2006): Brand purchasing by older consumers: An investigation using the Juster scale and the Dirichlet model, in: Market Letters, Vol. 17, pp. 17-29. Vermunt, J.K.; Magidson, J. (2002): Latent class cluster analysis, unpublished manuscript, Cambridge. Vitale, D.C.; Armenakis, A.A.; Feild, H.S. (2008): Integrating qualitative and quantitative methods for organizational diagnosis: Possible priming effects?, in: Journal of Mixed Methods Research, Vol. 2, No. 1, pp. 87-105. Vriens, M. (2001): Market segmentation: Analytical developments and applications guidelines, Millward Brown IntelliQuest, New York, NY. Wedel, M.; Kamakura, W.A. (1998): Market segmentation: Concepts and methodological foundations, Boston.

Fonseca, J.

25

White, H. (2002): Combining quantitative and qualitative approaches in poverty analysis, in: World Development, Vol. 30, No. 3, pp. 511-522. Wilkie, W.L. (1990): Consumer behavior, 2nd ed., Hoboken, NJ. Wind, Y.; Douglas, S.P.; Perlmutter, H.V. (1973): Guidelines for developing international marketing strategies, in: Journal of Marketing, Vol. 37, pp. 14-23.

RFID-Based Tracking of Shopping Behaviour at the Point of Sale – Possibilities and Limitations Günter Silberer and Stefan Friedemann

Abstract RFID can act as a data-collection method in the study of shopping behaviour (wayfinding and approach & attention behaviour) at the point of sale. This article focuses on the possibilities of automatic RFID-based shopper tracking. Apart from the technical aspects of this data collection, the data analysis is also described. The existing Data Protection Act, further regulations, and consumer and public acceptance are legal and social restrictions. Including these points in the analysis, some practicable and legally permitted methods of RFID-based shopper tracking are presented. If all products are equipped with RFID-Tags in the future, these methods can be extended further. It will be very important to keep the social acceptance and legal restrictions in mind. Under these circumstances, consumer research at the Point of Sale stands to gain greatly from these new methods – not only for retail marketing but also manufacturer marketing.

Keywords RFID, Behaviour Tracking, Shopping Behaviour, Wayfinding, Approach and Attention Behaviour

Prof. Dr. Günter Silberer (corresponding author) University of Göttingen, Germany (Tel: +49 551 397 328; E-mail: [email protected]). Stefan Friedemann Chair of Application Systems and E-Business, University of Göttingen.

Received: June 19, 2010 Revised: February 1, 2011 Accepted: February 10, 2011

EUROPEAN RETAIL RESEARCH Vol. 25, Issue I, 2010, pp. 27-45

D. Morschett et al (eds), European Retail Research, DOI 10.1007/978-3-8349-6235-5_2, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

28

1.

European Retail Research Vol. 25, Issue I, pp. 27-45

Introduction

The behaviour at the Point of Sale (POS) is of paramount importance for the success of the manufacturers and distributors offering the product as that is where many of a potential customer’s decisions are first made and many preferences concretised. Besides the buying behaviour, which is ultimately decided at the checkout counter and very often registered there by scanning the products, other behavioural actions are also of interest: wayfinding, which governs potential product contacts and determines the sequences in which items are taken; the viewing and handling of the product ranges that do not have to be taken; and the potential and actual contacts with POS media (e.g. displays, posters and advertising boards). The time a customer spends in front of shelves, at self-service counters and standing at the checkout may also be of interest. Such facts, which are not revealed by checkout scanners, not only highlight possible reasons for purchases or non-purchases but also customer contacts from which the perception and assessment of stores and product ranges ultimately result. They therefore help control the impact of the POS design, placement of the product range and use of POS media. The concrete wayfinding and attention & approach behaviour at the POS can be recorded using various methods (Wells/SoSciuto 1966; Granbois 1968; Payne/Ragsdale 1978; Otnes/ McGrath/Lowrey 1995; Lowrey/Otnes/McGrath 2005; Belk/Kozinets 2005; Silberer 2008; Silberer 2009; Silberer/Büttner 2008). The classical data collection methods include observations and surveys, which require the use of personnel and for which selectivity and reactivity effects are to be expected. This explains the interest in such methods and processes, with the help of which the behaviour at the POS can be recorded automatically. In searching for rationalisation possibilities in trade and industry, logisticians and inventory managers already encountered the possibility of locating and identifying products via radio frequency years ago. “Radio Frequency Identification” (RFID) is a technique for locating and identifying objects without having to touch or even see them. These advantages, coupled with the falling prices of the technology, should see it become increasingly widespread in inventory management. For stationary trade and the sales of manufacturers who have their own stores, RFID technology offers possibilities to register the wayfinding and attention & approach behaviour of their customers. This behaviour is of interest insofar as the wayfinding behaviour (sometimes also referred to as “shopping trip behaviour” or “consumer wayfinding”, e.g. by Titus/Everett 1996) decides the visual contacts with the product range and the attention & approach behaviour the sensory impressions. Such customer contacts, which are important success factors of the product presentation and POS marketing, do not figure in the sales data in the checkout scanners. This article is aimed at investigating the potential and, in the future, increasingly costeffective use of RFID technology in behaviour analysis at the POS more closely. After all,

Silberer, G.; Friedemann, S.

29

Larson/Bradlow/Fader (2005) already showed years ago with their study of shopper wayfinding behaviour that using RFID for the purpose of researching behaviour at the point of sale is technically feasible (similar statements were also made by Decker/Kubach/Beigl 2003; Kaapke 2004; Uotila/Skogster 2007; Skogster/Uotila/Ojala 2008). In contrast, however, consumerists have now raised their hands and pointed out that tracking shopper behaviour without the appropriate consent is not permissible – at least in Germany. In one German “future shop”, they even managed to expose a RFID-based fidelity card tracking project where the relevant permission had not been obtained from the cardholders (FoeBuD 2006). Consequently, an analysis of the possibilities of tracking shopping behaviour automatically should not merely examine the technical possibilities of using RFID, but also the boundaries that legislature – e.g. in the Federal Republic of Germany – has drawn and that have arisen as a result of the interests of the individual customers and the critical public. This article will primarily examine the possibilities of using RFID to track shopper behaviour in stationary retail – especially supermarkets – more closely. However, the same holds for other stores involved in stationary trade with a high degree of self-service, such as cash-andcarry markets and sports, clothes, shoe, toy, and hardware stores. Compared to other areas of stationary trade and sales there is often fierce competition between manufacturer brands and brand names and a large amount of customer traffic in supermarkets. Consequently, it comes as no surprise that contemplating the use of RFID technology for behaviour research makes the most sense here (Kaapke 2004; Larson/Bradlow/Fader 2005). Looking to the future, two development phases, and therefore two fundamentally different situations, should be distinguished: The first concerns the current situation, where only a handful of products are equipped with RFID transponders; the second relates to a future where many products are equipped with RFID transponders. The experts expect the latter to happen in about 10 to 15 years (e. g. according to Schulz 2008; Verdi 2007, p. 13). In any event, possibilities of the RFID-based tracking of customer behaviour at the POS emerge for both development phases. Hence, the RFID-assisted tracking of shopper behaviour also has to be analysed in a correspondingly differentiated manner. Section two below begins by highlighting tracking-relevant aspects of the purchasing behaviour at the POS. Section 3 outlines the RFID technology and technical possibilities of “movement tracking”. Following the basic and tracking-relevant distinction between wayfinding and attention & approach behaviour, the possibilities and limitations of the RFID-based, automatic recording of wayfinding behaviour in the store and recording the attention & approach behaviour at the shelves are examined in more detail in sections 4 and 5 respectively. This will require a distinction to be drawn between the behaviour at the presentation area and purchasing behaviour, which only becomes apparent at the checkout.

30

European Retail Research Vol. 25, Issue I, pp. 27-45

A systematic analysis of the customer and shopper behaviour in stationary trade that can be carried out automatically and anonymously will advance shopping behaviour research and help improve the “science of shopping” (Sörensen 2003). Furthermore, new kinds of practical applications for behaviour tracking at the POS, such as the just-in-time generation of purchase recommendations (as is common practice in e-commerce), are to be expected (Decker/Kubach/Beigl 2003). Such consequences of developed and advanced behaviour research at the POS can only be outlined roughly at the end of this article, however.

2.

Tracking-Relevant Categories of Shopping Behaviour at the POS

Shopping behaviour at the POS consists of numerous different behavioural actions. In the case of an automatic recording of this behaviour as an automatic registration of movements, first a distinction needs to be drawn as to where this behaviour occurs: In front of the entrance, behind the exit, in the store or presentation area and at the checkout, or if necessary still at the information stand or service point. In the following, however, only the behaviour in the presentation area, which is often a particularly crucial factor in the competition, should be of interest to us. In the case of the behaviour in the presentation area, a distinction then needs to be drawn between the wayfinding behaviour, the movement in the shopping area, and the attention & approach behaviour at the shelf where the customer stops. He can then do different things here: view products, pick them up and look at them, and replace or take the products handled (Decker/Kubach/Beigl 2003, p. 328). Taking a product does not exclude the possibility that it might be discarded somewhere else or given back at the checkout. In the case of such behaviour, which should be regarded and recorded as movement, a categorical distinction might also be made between approach behaviour and avoidance behaviour. As the behaviour in the store is very often equated with purchasing behaviour in both the literature and practice, a closer consideration of the behaviour at the POS quickly reveals the problem of the term. The truth is that the act of purchasing often only occurs as a definitive purchasing intent, a real commitment and conclusive behaviour at the checkout. The surreptitious consumption of (immediately edible) food and beverages, which is bound to happen at least sometimes, and much more frequently shoplifting and the replacement of goods where they came from or elsewhere also emphasise the fact that taking a product is far from means it will actually be purchased. At any rate, here the taking of a product should only be classified as a purchase if the product is presented and paid for at the checkout.

3.

Radio-Based Technique for Recording Movements

RFID systems record both the presence and movement of objects or subjects via radio-based waves. They consist of transponders, which are attached to the mobile objects or subjects, readers and an IT system operating in the background (Informationsforum RFID 2008, p. 2;

Silberer, G.; Friedemann, S.

31

Kummer/Einbock/Westerheide 2005, p. 15). The readers generate electromagnetic fields that reach the transponders and supply them with power for a short period of time. The transponders activated as a result receive signals in the process and subsequently send the data stored on them back to the readers. Consequently, the transponders are both transmitters and responders, as the word suggests. Figure 1 illustrates the structure of an RFID transponder attached to an adhesive smart label. Figure 1: Structure of an RFID transponder

Transponders can also be constructed as active units. Unlike passive ones, active transponders have their own power supply and thus afford higher reading ranges, can process more data and perform additional functions (e.g. sensory tasks). By way of contrast, not only are the passive transponders, which draw the power they need from the reader’s field, smaller; they are also considerably cheaper than their active counterparts. There are two kinds of readers: Stationary and mobile. They can be differentiated further according to the frequency of the radio waves (see Table 1). Three frequency ranges are currently used: low frequency, high frequency and ultrahigh frequency (Lampe/Flörkemeier 2005, p. 73). The higher the frequency, the bigger the reading range and rate (Kern 2006; Informationsforum RFID 2008, p. 4). Low-frequency transponders have a lower range and reading rate, but are less sensitive to inference from metallic packaging materials and liquid product components, such as in canned goods and drinks (Lampe/Flörkemeier 2005, pp. 7981; Jones et al. 2005; SToP 2007, p. 17). The high-frequency transponders’ sensitivity to interference from metal is due to the reflection of the waves on the metallic surfaces (BITKOM 2006, p. 29; Kern 2006, p. 43). By using special attachment techniques and placing the transponders on areas of the product that can easily be read, these problems can often be avoided. As many retail products have metallic packaging or are kept on metal shelves, however, the use of low-frequency transponders is at least recommended there. If transponders are located, their identification number can be redirected to the central computer via WLAN, for instance.

32

European Retail Research Vol. 25, Issue I, pp. 27-45

Apart from the aforementioned advantages of RFID systems over common barcodes, namely the non-contact, automatic recording and identification, there are other benefits: orientating the object or subject towards the reader is no longer being necessary, their insensitivity to contaminants, the greater possible distance from the reader to the object, and bulk processing. Bulk processing means the simultaneous recording of several objects, e.g. the complete registration of all the articles on a palette or in a shopping cart or basket (Kummer/Einbock/ Westerheide 2005, p. 139). Table 1: RFID Frequencies and their Fields of Application Low frequency

High frequency

Ultrahigh frequency

Frequency

125 - 135 kHz

13.56 MHz

860 - 960 MHz

Reading distance

up to 1.5 m

up to 1.5 m

up to 4 m

Reading speed

5 kbps

10 kbps

60 kbps

Reading rates

10 - 40 tags/s

10 - 50 tags/s

100 - 500 tags/s

Water

marginal

medium

strong

Metal

marginal

medium

strong

access control, immobilizers, animal identification, automatic production

asset management, ticketing, book loans, smart labels

palette logging, container tracking

Influence of

Typical fields of application

Source: Lampe/Flörkemeier (2005); Informationsforum RFID (2008); Kern (2006).

4.

Possibilities of the RFID-Based Recording of Wayfinding Behaviour

4.1.

Possible Starting Points for Recording Wayfinding Behaviour

Recording wayfinding behaviour can focus on three points: The movement of the shopping cart used by the shopper; the movement of the shopping basket used; and the movement of the shopper himself. Tracking Shopping Carts Larson/Bradlow/Fader (2005) and Sörensen (2008), who advocate the use of RFID systems to track shoppers, particularly with North-American supermarkets in mind, perceive the movement of shopping carts that have been fitted with transponders as a good approximation of the shopper’s movement and path. However – from a European perspective at least – two limitations should be noted in this respect: firstly, not all shoppers use a shopping cart; secondly, the carts are not always taken on every wayfinding route, such as if the shopper enters a narrow side aisle or goes along a shelf in search of a particular product.

Silberer, G.; Friedemann, S.

33

Tracking Shopping Baskets Transponders can be attached to shopping baskets provided by the individual stores. The baskets tend to be left much less frequently than shopping carts as the customer walks around a shop, making their movement a better indication of the wayfinding. However, the method also has its shortcomings in that there are shoppers who prefer to use their own baskets or bags rather than the shopping carts and baskets provided by the store, or even do without such shopping aids altogether. Tracking People If shoppers carry objects that include a transponder, their complete movements can be recorded. When transponders are incorporated into current fidelity cards (Bose/Lee/Yen 2008, p. 194; Uhrich et al. 2008, p. 225), for instance, they have a low range due to the small antennae. Consequently, somewhat larger visitor cards that can accommodate transponders with a larger range might be more conceivable. The cards could be handed out prior to the store visit and collected afterwards, all the while being used completely anonymously (Rauch 2005, p. 8; Silberer 2009, p. 30). A suitable incentive, such as small gifts to be presented upon returning the card, might increase participation.

4.2.

Technical Possibilities of Tracking Wayfinding

In RFID-based shopper tracking, the readers, transponders and central server perform the main tasks (Hinske/Langheinrich 2007, pp. 10-12). The antennae installed in the floor, the ceiling or on shelves each generate a reading field. If active or passive transponders in shopping carts, baskets and/or fidelity or visitor cards enter the respective area, they respond and identify themselves with their ID code. The antennae then transmit these responses on to the central server. The sequence of these signals and their mapping can then be used to determine the wayfinding. There are two methods of calculating the location of the shopper. In the overlapping method, the antenna network (also referred to as “cell-of-origin system”) is set up all over the store with several fixed installed antennae in the form of a structured network (Hinske/Langheinrich 2007, pp. 10-12; Jin/Lu/Park 2006). The position of the transponder can be extrapolated from the overlapping of the reception area of several antennae; the transponder has to be in the overlapping area, or its signal would not be received by all the antennae. In Figure 2, the transponder, the signal of which is received by three antennae, is located in the dark area in the middle. The closer the antennae and the smaller the ranges are, the more accurate the localisation because the network becomes increasingly close-meshed and the overlapping area increasingly smaller. This means several antennae can be managed by one reader to minimise investment costs. As the antennae emit the energy field, passive transponders are sufficient. In order to produce a reliable reading and an accurate recording, the ranges of these transponders

34

European Retail Research Vol. 25, Issue I, pp. 27-45

have to be taken into consideration so as to produce field radii with exact precision in the range of several meters. Trilateration is a method that is also used in land surveying and satellite navigation. An active transponder sends out a signal, which has to be received by at least three readers; the position of the transmitter, i.e. the transponder, is determined from the signal’s time difference of arrival at the receiving readers using trigonometric calculations (Hinske/Langheinrich 2007, pp. 10-12). It would also be possible to use the method in reverse, the position being determined in a transponder from the delay time of the signals emitted from at least three readers. However, the current transponders lack the computing capacity that such a calculation would require. Figure 2: How the Antenna Network Works

As far as the accuracy of the localization and movement detection is concerned, the following should be noted: the more closely meshed the network is and the smaller the necessary ranges are, the more accurate the localization. For smaller ranges, the transmission signal of passive transponders is sufficient. Measurement errors can also occur if the signals transmitted by antennae and sent back by transponders are reflected by metallic surfaces or containers for liquids (e.g. drink and soup cans) and the detour thus taken is not recognised. However, such errors can be minimised if the (greater) probability of the actual movement can be calculated using the previously recorded movements of a transponder (Hähnel et al. 2004, p. 1018-1020).

4.3.

Analysing Wayfinding Data

In analysing the data, the first consideration is to calculate transponder locations in the wayfinding areas. Their frequency alone is sufficient to produce so-called “heat maps”. These heat maps can be used to show which store areas or departments are hardly visited at all (“dead corners”). The customer wayfinding is deduced by recording the location sequences of indi-

Silberer, G.; Friedemann, S.

35

vidual store visits or shoppers. If the wayfinding sequences are analysed for similarities and classified within the context of a cluster analysis, different frequencies also emerge: frequent shopping paths or so-called “race tracks” on the one hand; infrequent shopping paths on the other. By recording wayfinding via clustering, so-called wayfinding patterns can also be identified (Larson/Bradlow/Fader 2005; Uotila/Skogster 2007; Skogster/Uotila/Ojala 2008). Beyond these analysis steps, the following issues can also be established with the data generated via RFID: firstly, the relationship between wayfinding patterns and the visiting time (day of the week and time of day); secondly, the relationship between wayfinding patterns and the duration of the visit; thirdly, the relationship between wayfinding patterns and the use of shopping carts and baskets; fourthly, the number of wayfinding areas visited by particular shoppers more than once; and finally, the number of sub-sequences that indicate toing and froing and might be interpreted as indicators of an unsuccessful search. If personal or household-related data, such as information on the household size and income, place of residence and profession, is available for certain customers and the people in question have granted their permission to use this data for the purposes of behaviour research at the POS, how the wayfinding differs for different socio-demographic groups can be determined. As in such cases a personal or household-related analysis of historical data is also possible, how the wayfinding of people or households remain similar or change over time can also be ascertained. If anonymous wayfinding data are merged with data containing personal information and recorded for entirely different purposes, the anonymous wayfinding data becomes personal data. Whilst data fusions of this nature improve the chances of obtaining better customer knowledge, however, the risk of breaking the law, annoying customers and alarming consumerists also increases. This is particularly the case if such activities are carried out without the informed consent of those involved.

4.4.

Legal Limitations and Social Acceptance

Under data protection law, which is based upon protecting privacy, in the EU personal data can only be recorded, stored and communicated if those involved authorize such activities and are informed about it fully in advance so that they can give their consent (see EU Directive 95/46/EG from 1995 and its implementation in national legislation, e.g. the provisions of the German Data Protection Act (BDSG); the law to amend the BDSG from May 2001; the BMJ 2006; GS1 2006; Polenz 2008. In the USA, on the other hand – in keeping with case law – numerous laws with sometimes extremely different regulations on diverse issues apply). To record the wayfinding behaviour at the POS, it is therefore important to verify when recordings involve personal data (Holznagel/Bonnekoh 2007, pp. 368-371). If the movements of shopping carts, shopping baskets and anonymous day and visitor cards are recorded and there

36

European Retail Research Vol. 25, Issue I, pp. 27-45

is no subsequent fusion of the data that connects it to a person, according to the BDSG this does not require the shopper’s clarification and approval; however, clarification and approval is always required if the tracking is linked to fidelity or payback cards (as recommended for instance by Ngai et al. 2008; GS1 2006, p. 6); if data fusion transforms the anonymous data into personal data; and if, even in the case of “anonymous shopper data”, the individual shoppers are known personally to the store or certain staff members (BMJ 2006; Holznagel/Bonnekoh 2007, pp. 368-371 and pp. 377-379; Holznagel/Schumacher 2009, p. 5-6; Polenz 2008, pp. 92-96). If tracking based on transponders in fidelity cards is planned and the cards are equipped accordingly beforehand, informed consent needs to be obtained when issuing the cards. If legal obligations are violated, shopper tracking cannot only reckon with sanctions as stipulated by law, but also opposition from consumer and data protectionists (Hüttl 2007; Data Protection Officer Conference 2006; Informationsforum RFID 2006; GS1 2006). Campaigns against the RFID adoption already initiated by consumerists like FoeBuD (2006) and CASPIAN (2007) highlight this. However, even if all the legal standards are regarded, opposition on the part of the customer still cannot be ruled out; more or less reasonable criticism in the media alone can harm the image and thus trigger a decrease in sales. Consequently, in recording shopper behaviour based on RFID not only should the statutory requirements and regulations be considered, but also social acceptance (Smith 2005; Uhrich et al. 2008, p. 225). Capgemini (2005) already pointed out a number of years ago in a representative study that 18 % of European and 23 % of American consumers had heard of RFID; 8 % of the respondents indicated that they only had negative expectations of RFID; however, few were able to substantiate their expectations and misgivings. This should change all the more quickly and radically the more often problematic shopper tracking is practised and the more often it is criticised in the media. The fact that the attitude towards RFID is not only characterised by convictions but also emotions was established in a study by Boslau/Lietke (2006, p. 36). It revealed that consumers tend more to have a positive attitude towards new technologies “in general”, including RFID “per se” (p. 37). By contrast, in their representative survey of German consumers Günther/Spiekermann (2005, pp. 73-76) concluded that the expected advantages of RFID technology could not compensate for the feared encroachment into the private sphere and the associated loss of control. As insightful as such findings are, we are still a far cry from a specific analysis of the social acceptance of RFID-based shopper tracking on the part of informed shoppers.

Silberer, G.; Friedemann, S.

37

5.

Possibilities of the RFID-Based Recording of Attention & Approach Behaviour

5.1.

Relevant Attention and Approach Behaviour at the Shelf and its Recordability

If shoppers approach a shelf or any other product carrier, such as a freezer or table, this might be deduced from the wayfinding behaviour registered. This primarily holds true if it becomes apparent that a person remains in the immediate vicinity for an extended period of time. However, this kind of attention does not reveal anything about the product-specific attention in itself. The same is true for the attention to media at the POS, such as information terminals and boards. Even so, at least a so-called “opportunity to see” can be extrapolated. The concrete, product-specific attention at the shelf and other product carriers chiefly includes the following: -

Viewing (without touching; in short: viewing) Viewing, touching and putting back (in short: touching) Viewing, touching and taking (in short: taking) Taking before putting back at the same or a different place (in short: putting back)

If the attention & approach behaviour is to be recorded using RFID, the question once again arises as to the starting points. For an approach and thus a so-called “opportunity to see” (OTS), once again the approximation of shopping carts and baskets fitted with transponders comes into question, as does the approximation of people who, for instance, carry visitor cards equipped with transponders. However, products fitted with transponders are also possible reading points because the movement of a product off the shelf can also be an indicator of it being “touched” or “taken” and a return of it being “put back” (Decker/Kubach/Beigl 2003). This does, however, raise the question as to when the attention & approach behaviour of a particular shopper can be extrapolated from such product movements. This would be the case if the wayfinding behaviour of particular people was tracked and the person and the attention & approach behaviour at the shelf could be correlated using precise time designations and the localization of these people. However, in such cases attribution problems can also materialize if, for example, several people simultaneously turn their attention to the product range at a particular shelf.

5.2.

Technical Possibilities of Attention & Approach Tracking at the Shelf

As long as only few or no products are fitted with transponders, transponders can be attached to product carriers, such as particular shelf areas. If shopping carts or baskets fitted with readers draw near, this can be registered as approach behaviour. This also applies if shelf areas are

38

European Retail Research Vol. 25, Issue I, pp. 27-45

fitted with readers and the approaching carts or baskets with transponders (Jannasch/Spiekermann 2004, p. 37). A well-differentiated recording of diverse forms of productspecific attention is not possible here. If in future – as is to be expected – a large number of products are fitted with transponders, different and product-related attentions can be recorded far more effectively via the movements of these products. Incidentally, this is already the case today if, for example, only selected products or all the products on a certain shelf have been fitted with transponders for reasons of cost. If readers are oriented towards particular shelf areas, it is critical they actually achieve the necessary localization accuracy. The same applies where readers are attached to shopping carts and baskets to record the arrival and removal of goods correctly. Based on a future in which all products are fitted with transponders and test stores already equipped accordingly today, Table 2 provides an overview of the possibilities of using RFID to record the product-related attention at the shelf automatically. Table 2: Possibilities of the RFID-based Recording of Product-related Attention at the Shelf Attention

Recording via RFID

Possible explanation

Taking

Product with a transponder leaves the reading range without returning

Customer is interested in the product and wants to buy it

Picking up & putting back a product at the same location

Product with a transponder leaves the reading range and returns shortly afterwards

Customer shows interest in the product and/or the product information, but decides against taking it

Picking up & putting back several products at the same location

Several products with transponders leave the reading range and return

Customer shows interest and compares several products, but decides against taking any of them

Picking up & putting back a product at a different location

Product with a transponder leaves the reading range and returns at another location or is not registered at the checkout counter

Customer shows interest and initially decides to take and purchase a product, only to change his mind later

The Metro Group’s pilot project demonstrates what attention & approach tracking at a shelf might look like with the “smart shelf” (Metro Group 2007, p. 30; Schneider 2004, p. 2; Ver.di 2007, p. 27). Similar experiments have also been conducted in science (Decker/Kubach/Beigl 2003). Here, the registration system inside the shelf records any movement of the products it contains, and therefore what is picked up and taken or replaced. And if products are put back in the wrong place, the smart shelf alerts the members of staff responsible so that they can return them to where they belong (Vogell 2004, p. 12). Even if the central computer has to perform many calculations and storage functions for attention & approach tracking on account of product movements in future, this should not founder on the provision of the necessary storage and computing capacities; however, the readers might prove to be an obstacle if the recording capacities reach their limits with a high number of products and customers. Should this be the case, a denser use of readers might be an option.

Silberer, G.; Friedemann, S.

39

If the “realised or actual purchases” are also to be registered with the aid of RFID, conducting bulk processing at the checkout would be an option so long as all products were fitted with transponders. If the goods located in the shopping cart or basket have already been registered, it would only remain for the return of the products at the checkout to be registered and matched to the basket of goods. As the purchases realized are mostly already recorded via scanner checkouts, at least in food retailers, this RFID-aided bulk processing at the checkout might be unnecessary. However, a decision would still have to be made as to whether the product-taking sequence is also of interest. If this is the case, the basket of goods in the RFID system would have to be matched with the corresponding basket of goods in the scanner system.

5.3.

Analysing Attention & Approach Data

The analysis of attention & approach-related data can be used to answer very different questions. If we now single out some of these here, we start on the presumption that only the shoppers’ attention & approach behaviour was recorded; in a second step, we assume that both the wayfinding behaviour and the attention & approach behaviour were recorded. Based on a particular shopper’s recorded attention & approach behaviour, it seems reasonable to count how often he exhibited which behaviour patterns. Such data and its analysis in the context of a cluster analysis raise the question as to whether fundamental behaviour patterns can be detected. Based on all shoppers, it would make sense to correlate the frequency of attention & approach types to concrete shelves, particular shelf areas and concrete product types. In doing so, a distinction could also be drawn between the time of day and days of the week. If data on the attention to product ranges and in-store media are available, the issue as to how the possible advertising contacts or “opportunities to see” correlate with the attention to the brands advertised can be pursued. In the case of announcements, it would equally be interesting to verify whether they change the attention & approach behaviour and, if so, how and for how long. Furthermore, the attention that leads to the product being taken and purchased can also be investigated. With regard to the competition between different product brands, how often and in what sequence competing brands were removed from the shelf, taken and replaced can also be calculated. If data is available that provides information on both the wayfinding behaviour and the attention & approach behaviour of a shopper, the following questions can be pursued via the aforementioned analytical steps: how often was a section or shelf visited before a product was taken? How long did the shopper stay near a shelf before taking one or several products? Which attentions took place when a shelf or freezer was repeatedly visited? How does the number of particular attentions correlate with the wayfinding pattern and the duration of the store visit? If particular wayfinding patterns have been identified in analysing the wayfinding data, how the wayfinding patterns resembles or differs from the wayfinding patterns regarding

40

European Retail Research Vol. 25, Issue I, pp. 27-45

the attention & approach behaviour at the product carriers and, if necessary, the in-store media could also be verified. If personal and household-related data is also available for customers and can be accessed with their consent, it could help explain their behaviour at the POS, e.g. via the correlation of POS behaviour and needs that result from the household size, place of residence and profession. It could also suggest a targeted approach in advertising and on site, e.g. information on new products, changes in position and current sales promotions.

5.4.

Legal Limitations and Social Acceptance

For the recording of the shelf- and product-specific attention & approach behaviour and its statutory regulation, the same observations already mentioned above for recording the wayfinding behaviour apply. The crucial factor – at least for the EU – is not only the direct personal nature of the data, but also the transformation of initially anonymous data into personal data. One particularity in the matter of data fusion should be noted, however: if an attention & approach analysis yields data from which the products that were taken can readily be detected, it can easily be compared with the scanner checkout data; if the individual can be identified on the basis of this comparison, such as on the basis of a fidelity card for instance, the initially anonymous attention & approach data has to be regarded as personal. The attention & approach data involved and the remaining personal data may only be stored, used and passed on with the informed consent of the person concerned (EU Directive 1995; BDSG 2001; GS1 2006, p. 5-6). As regards social acceptance, it can be assumed that shoppers, customers and the public perceive the analysis of the attention & approach behaviour as a greater intrusion into the privacy of shoppers than the analysis of the wayfinding behaviour; the customers thus having become one step more “transparent” (Uhrich et al. 2008, p. 225). The danger that customers might react with indignation and migrate, the media with critical reports, and third parties with protests is certainly greater for attention & approach tracking than wayfinding tracking; however, it should be considered that the scope of shopper tracking is scalable, i.e. variable, and that the social rejection can hence be reduced. Shopper tracking can namely be conducted on a random basis: on the one hand, as a random test in particular store areas; on the other hand, as a random test in particular time phases (the relevant amount of data to be analysed is therefore also scalable). This means attention & approach tracking can also be restricted extremely clearly to particular sections and shelves, as well as particular time phases, such as a few weeks a year for instance, in the interests of the shoppers.

Silberer, G.; Friedemann, S.

6.

41

Summary and Prospects

To sum up, it can be said that RFID systems not only open up new possibilities in the field of inventory management, especially rationalisation opportunities, but also for behaviour research at the point of sale, especially for the automatic recording of shoppers’ wayfinding and attention & approach behaviour. This is especially true if the products in a store are fitted with transponders. From a technical viewpoint, the attachment, functions and effectiveness of the readers and transponders are of particular importance. However, potential sources of error and the possibilities of reducing localization errors should also be taken into consideration. Anyone who is looking to discover all the aspects of shopper behaviour, such as the visual attention and cognitive and emotional reactions, cannot content himself with automatic behaviour registration; he then has to fall back on other methods like observation, interviews, thinking-aloud and videography (see Silberer 2009 for the differentiated representation of such methods). In many cases, it stands to reason that one might combine various methods if not only the observable, external behaviour is to be recorded, but also the concealed, internal behaviour of the shoppers. Compared to the outlined technical limitations of using RFID for behaviour research at the point of sale, the limitations set by legislature and the limitations arising from social acceptance are more significant and restrictive. This does not only concern using these systems in such a way that they are not even noticed by the shopper, thus violating the legal norms; it also concerns the refusal of shoppers, and maybe even staff, to consent to the collection and analysis of personal data. Moreover, it also involves public and private fears regarding an intrusion into the private sphere and data abuse, which might well be unfounded in individual cases but, doubtlessly, will also prove justified through instances of abuse in the future. At any rate, users of RFID systems in behaviour research must not content themselves with observing the legal norms; anticipating opposition and encouraging acceptance by informing the consumer and allowing him to decide for himself must follow. This not only applies to “pure behaviour analysis” at the POS, but also to the use of the relevant data. In this, one has to consider the personalization of advertising messages and purchase recommendations that are used extremely often in e-commerce and can equally be produced and communicated automatically, just-in-time on site and in an RFID-based fashion (Decker/Kubach/Beigl 2003, p. 328). If the shopper behaviour is recorded automatically at the POS, well-nigh avoiding the selectivity and reactivity effects both within the framework of the law and the acceptance of critical customers and critical sections of the public, the results thus obtained can be used in designing the stores, product ranges, product presentation, communication at the POS, and thus also in sales promotions.

42

European Retail Research Vol. 25, Issue I, pp. 27-45

References BDSG (2001): Bundesministerium des Inneren: Gesetz zur Änderung des Bundesdatenschutzgesetzes (BDSG) und anderer Gesetze vom 22. Mai 2001, Referat V 7, Berlin. Belk, R. W.; Kozinets, R. V. (2005): Videography in marketing and consumer research, in: Qualitative Market Research: An International Journal, Vol. 8, No. 2, pp. 128-141. BITKOM (2006): BITKOM RFID Guide 2006, Berlin. BMJ (2006): Bundesministerium der Justiz: Bundesdatenschutzgesetz (BDSG), Berlin. Bose, I.; Lee, J.-N.; Yen, P.-C.B. (2008): Editorial: RFID in retailing and customer relationship management, in: Communications of the Association for Information Systems, Vol. 23, No. 1, pp. 193-196. Boslau, M.; Lietke, B. (2006): RFID aus Konsumentensicht: Umfrageergebnisse und Implikationen, Göttinger Handelswissenschaftliche Schriften, Göttingen. Capgemini (2005): RFID and Consumers, http://www.de.capgemini.com/m/de/tl/ RFID_and _Consumers.pdf, accessed January 26, 2011. CASPIAN (2007): spychips.com  how RFID will compromise privacy, security, freedom, online: http://www.spychips.com/, accessed January 26, 2011. Data Protection Officer Conference (2006): Regelungen der für den Einsatz von RFIDTechnologien. Entschließung der 72. Konferenz der Datenschutzbeauftragten des Bundes und der Länder vom 26.-27. Oktober 2006 in Naumburg, http://www.sachsenanhalt.de/LPSA/index.php?id=20563, accessed January 26, 2011. Decker, C.; Kubach, U.; Beigl, M. (2003): Revealing the retail black box by interaction sensing, in: International Conference on Distributed Computing System Workshop, Providence, Rhode Island, pp. 328-333. EU Directive (1995): Richtlinie 95/46/EG des Europäischen Parlaments und des Rates vom 24. Oktober 1995 zum Schutz natürlicher Personen bei der Verarbeitung personenbezogener Daten und zum freien Datenverkehr, online: http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri=CELEX:31995L0046:DE:NOT, accessed January 26, 2011. FoeBuD e.V. (2006): RFID – Das Problem, http://www.foebud.org/rfid/das-problem, accessed January 26, 2011. Granbois, D. H. (1968): Improving study of customer in-store behavior, in: Journal of Marketing, Vol. 32, No. 4, pp. 28-33. GS1 (2006): RFID Daten- und Verbraucherschutz - Positionspapier der deutschen Wirtschaft, Köln. Günther, O.; Spiekermann, S. (2005): RFID and the perception of control: The consumer's view, in: Communication of the ACM, Vol. 48, No. 9, pp. 73-76. Hähnel, D.; Burgard, W.; Fox, D.; Fishkin, K.; Philipose, M. (2004): Mapping and localization with RFID technology, in: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Vol. 1, pp. 1015-1020. Hinske, S.; Langheinrich, M. (2007): An RFID-based infrastructure for automatically determining the position and orientation of game objects in tabletop games, in: Magerkurth, C.;

Silberer, G.; Friedemann, S.

43

Röcker, C. (eds.): Concepts and technologies for pervasive games – a reader for pervasive gaming research, pp. 311-336. Holznagel, B.; Bonnekoh, M. (2007): Rechtliche Dimensionen der RadiofrequenzIdentifikation, in: Bullinger, H.-J.; ten Hompel, M. (eds.): Internet der Dinge, pp. 365-420. Holznagel, B.; Schumacher, P. (2009): Auswirkungen des Grundrechts auf Vertraulichkeit und Integrität informationstechnischer Systeme auf RFID-Chips, in: MultiMedia und Recht, Vol. 12, No. 1, pp. 3-8. Hüttl, M. (2007): Wahrnehmungen im Spannungsfeld neuer Technologien – Welchen Einfluss haben weiche Faktoren auf die Entwicklung von RFID? In: Bullinger, H.-J.; ten Hompel, M. (eds.): Internet der Dinge, pp. 421-440. Informationsforum RFID (2006): Gemeinsame Stellungnahme zu der Entschließung der 72. Konferenz der Datenschutzbeauftragten des Bundes und der Länder „Verbindliche Regelungen für den Einsatz von RFID-Technologien“, http://www.inforfid.de/content/publikationen/broschueren/index_ger.html, accessed June 20, 2009. Informationsforum RFID (2008): Basiswissen RFID, Berlin. Jannasch, U.; Spiekermann, S. (2004): RFID Technologie im Einzelhandel der Zukunft: Datenentstehung, Marketing Potentiale und Auswirkungen auf die Privatheit des Kunden, Lehrstuhl für Wirtschaftsinformatik, Humboldt Universität zu Berlin, Berlin. Jin, G.-Y.; Lu, X.-Y.; Park, M.-S. (2006): An indoor localization mechanism using active RFID tag, in: Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Taichung, Taiwan. Jones, P.; Clarke-Hill, C.; Hillier, D.; Comfort, D. (2005): The benefits, challenges and impacts of radio frequency identification technology (RFID) for retailers in the UK, in: Marketing Intelligence & Planning, Vol. 23, No. 4, pp. 395-402. Kaapke, A. (2004): Big metro is watching you – RFID und Datenschutz – ein Kurzkommentar, in: Handel im Fokus – Mitteilungen des Institut für Handelsforschung, Vol. 56., No. 2, pp. 105-107. Kern, C. (2006): Anwendung von RFID-Systemen, Berlin et al. Kummer, S.; Einbock, M.; Westerheide, C. (2005): RFID in der Logistik: Handbuch für die Praxis, Wien. Lampe, M.; Flörkemeier, C. (2005): Einführung in die RFID-Technologie, in: Fleisch, E.; Mattern, F. (eds.): Das Internet der Dinge – Ubiquitous Computing und RFID in der Praxis, Berlin et al., pp. 69-86. Larson, J.S.; Bradlow, E.T.; Fader, P.S. (2005): An exploratory look at supermarket shopping paths, in: International Journal of Research in Marketing, Vol. 22, No. 4, pp. 395-414. Lowrey, T.M.; Otnes, C.C.; McGrath, M.A. (2005): Shopping with consumers: Reflections and innovations, in: Qualitative Market Research, Vol. 8, No. 2, pp. 176-188. Metro Group (2007): METRO Group RFID Innovation Center: Informations- und Entwicklungsplattform für die Zukunft des Handels, Düsseldorf.

44

European Retail Research Vol. 25, Issue I, pp. 27-45

Ngai, E.W.; Moon, K.K.; Liuz, J.N.; Tsang, K.F.; Law, R.; Suk, F.F.; Wong, I.C. (2008): Extending CRM in the retail industry: An RFID-based personal shopping assistant system, in: Communications of the Association for Information Systems, Vol. 23, No. 1, pp. 277294. Otnes, C.C.; McGrath, M.A.; Lowrey, T.M. (1995): Shopping with consumers: Usage as past, present and future research technique, in: Journal of Retailing and Consumer Service, Vol. 2, No. 2, pp. 97-110. Payne, J.W.; Ragsdale, E.K. (1978): Verbal protocols and direct observation of supermarket shopping behavior: Some findings and a discussion of methods, in: Hunt, K.; Abor, A. (eds.): Advances in consumer research, Vol. 5, pp. 571-577. Polenz, S. (2008): RFID-Techniken und Datenschutzrecht  Perspektiven der Regulierung, Chemnitz. Rauch, M.(2005): Kundenlauf und Zuwendungsverhalten am Point of Sale: State-of-the-Art, No. 13 of the Contributions to Tracking-Research, Institut für Marketing und Handel, Göttingen. Schneider, M. (2004): A smart shopping assistant utilizing adaptive plan recognition, in Hoto, A.; Stumme, G. (eds.): Lehren  Lernen  Wissen  Adaptivität, pp. 331-334. Schulz, G. (2008): One-to-one interview with Gregor Schulz (Metro Group, Real Future Store) and guided tour of the Real Future Store, Tönisvorst, December 12, 2008. Silberer, G. (2008): Behavior at the POS – classical and newer methods of recording it, in: Lowrey, T. M. (ed.): Brick & Mortar Shopping in the 21st Century, pp. 257-280. Silberer, G. (2009): Verhaltensforschung am POS - Ansatzpunkte und Methodik, Göttingen. Silberer, G.; Büttner, O. (2008): Marktforschung am Point-of-Sale, in: Herrmann, A.; Homburg, C.; Klarmann, M. (eds.): Handbuch der Marktforschung, pp. 1097-1123. Skogster, P.; Uotila, V.; Ojala, L. (2008): From mornings to evenings: is there variation in shopping behaviour between different hours of the day?, in: International Journal of Consumer Studies, Vol. 32, No. 1, pp. 65-74. Smith, A.D. (2005): Exploring radio frequency identification technology and its impact on business systems, in: Information Management & Computer Security, Vol. 13, No. 1, pp. 16-28. Sörensen, H. (2008): Now We See! A Study of Shopper Traffic http://www.sorensen-associates.com/documents/6.3WHTPR_NowWeSee.pdf, January 26, 2011.

Patterns, accessed

SToP (2007): Report and Analysis on State-of-the-Art Tagging Technologies Specific to the SToP Project Requirements, online: http://www.stop-project.eu/Portals/1/deliverables/ 061107_SToP_D4.1_V2_MS.pdf, accessed January 26, 2011. Titus, P.A.; Everett, P.B. (1996): Consumer wayfinding tasks, strategies and errors: An exploratory field study, in: Psychogy & Marketing, Vol. 13, No 3, pp. 265-290. Uhrich, F.; Sandner, U.; Resatsch, F.; Leimeister, J.M.; Krcmar, H. (2008): RFID in retailing and customer relationship management, in: Communications of the Association for Information Systems, Vol. 23, No. 1, pp. 219-234.

Silberer, G.; Friedemann, S.

45

Uotila, V.; Skogster, P. (2007): Space management in a DIY store analysing consumer shopping paths with data-tracking devices, in: Facilities, Vol. 25, No. 9/10, pp. 363-374. Ver.di (2007): RFID Basisinformation  Was Betriebsräte über den Einsatz von Funkchips wissen sollten, Berlin. Vogell, K. (2004): RFID und Verbraucherschutz. Szenarien für die Anwendung von RFID/EPC am Point of Sale, online: http://www.gs1-germany.de/content/e39/e466/e468/ datei/ epc_rfid/prozessszenarien_eicar_taskforce_040923.pdf, accessed January 5, 2009. Wells, W.D.; LoSciuto, L.A. (1966): Direct observation of purchasing behavior, in: Journal of Marketing Research, Vol. 3, pp. 227-233.

Prospects for PoS Market Research with RFID Technology: Examination of Consumers’ In-Store Shopping Processes Thorsten Blecker, Carsten Rasch and Thorsten Teichert

Abstract RFID (Radio Frequency Identification) technology yields the chance to examine consumer behaviour at the point-of-sale in detail. RFID provides accurate information about the interaction process of consumers and products in real-world settings. Important aspects of consumer behaviour can be validated in “real life”, using thorough empirical process data. Hereby, the advantages of online metrics, customer-specific PoS marketing, can be transferred to an offline context. We identify three layers of potential impacts of consumer research with RFID technology: validation and refinement of consumer models in real-life settings, improvement of consumer models by integration of behavioural metrics, and the identification of influential contingent factors on consumer behaviour. Potentially valuable practical implications are drawn from the prospected research issues.

Keywords RFID Technology, Consumer Behaviour, Point of Sale, Market Research, Behavioural Process Metrics, Model-Validation

Thorsten Blecker Chair for Logistics and Management, Technical University Hamburg-Harburg, Germany. Carsten Rasch Chair of Marketing and Innovation, University of Hamburg, Germany. Thorsten Teichert (corresponding author) Chair of Marketing and Innovation, University of Hamburg, Germany (Tel: +49 40 42 838 8282; E-mail: [email protected]).

Received: November 3, 2010 Revised: January 18, 2011 Accepted: January 30, 2011

EUROPEAN RETAIL RESEARCH Vol. 25, Issue I, 2011, pp. 47-62

D. Morschett et al (eds), European Retail Research, DOI 10.1007/978-3-8349-6235-5_3, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

48

1.

European Retail Research Vol. 25, Issue I, pp. 47-62

Introduction

Extending PoS marketing research by applying automated data capturing technologies may lead to a new and enriched understanding of consumer behaviour and overall in-depth empirical perspectives for behavioural consumer sciences. Hitherto applied methods for consumer research do not meet the requirements to achieve this objective. Scanner panel data, observations, and experimental examinations are not detailed enough, not objective, or not externally valid. The utilisation of RFID (Radio Frequency Identification) technology yields the chance to examine the process of buying at the point-of-sale in detail. RFID provides accurate information about the interaction process of consumers and products in real-world settings. Critical elements of consumer behaviour can be validated in “real life”, using thorough empirical process data provided by RFID technology. Hereby, the advantages of online metrics, customer-specific PoS marketing, can be transferred to an offline context. Consumer models, validated and refined on basis of behavioural process data, yield the potential to optimise product positioning and presentation. RFID-enabled behavioural metrics allow retailers to optimally design store layouts according to customer needs. Contingent factors that accompany every purchase, as decoy-products and self-imposed time constraints, could be identified on product-level and therefore utilised to increase customer satisfaction and re-purchase probability. Although the social acceptance of RFID for examination of consumer behaviour is not scope of this paper, it is worth mentioning that all participants should be informed about the measurements and granted full information security (anonymity) before entering a store. This is especially important as it was found that perceived convenience and information security override privacy concerns of customers confronted with RFID (Hossain/Prybutok 2008). An eventual behavioural bias due to prior information will not neutralize the advantages of RFID based PoS market research compared to conventional methods (scanner panel data, observations, laboratory experiments). In the following, we provide a short outline on the potential of RFID in PoS market research, before taking a closer look at previous RFID applications and the detailed prospects for the behaviour-based examination of consumer processes.

2.

Conceptual Outline

2.1.

Applying RFID in Behavioural Consumer Research

The first layer of potential impact is concerned with the application of RFID technology as means to test existing theories related to consumers’ purchase processes. RFID technology enables to re-examine consumer behaviour models in a real-world setting, closer to the “real” purchase process than before, thus contributing to the uncompleted task of building behav-

Blecker, T.; Rasch, C.; Teichert, T.

49

iourally realistic structural models. As products (for example Jeans and T-Shirts) are holistically perceived (e.g. touched) during the purchasing process, retrieval of consideration sets without any influence on or obtrusion of test persons is feasible. The second layer of potential impact relates to the examination of online purchase models with regard to the question, if those findings are restricted to an online environment or also apply to a physical shopping situation. RFID tagged products enable the measurement of an equivalent to click-stream data. It is possible to record every physical movement of a product. Therefore, transferring and testing models of online purchase behaviour that used click-stream analysis, is assumed to result in invaluable insights. However, due to different characteristics of offline and online shopping, it is expected that findings in online behaviour cannot be exactly transferred to physical purchase environments; physical products are available for handling, which might alter consumers’ decision making processes (cf. Peck/Childers 2003; Westerman et al. 2007). Model-testing with quasi-real-world RFID-based data can disclose validity-gaps of current consumer behaviour models. RFID-based process data constitutes a potential basis for formulation of improved models within the existent paradigms. It is assumed that process data gained with RFID allows for refining knowledge on process contingencies in consumer behaviour, for example how time-based and process-based indicators impact purchase. Furthermore, contextual contingencies, as decoy products, loom large in explaining consumer behaviour. The third layer of potential impact is concerned with these contingent factors, whereas RFID data enables the examination of causal structures between context, personspecific factors and purchase of a product. White spaces in consumer behaviour can be addressed, due to technological advances in RFID measurement.

2.2.

RFID-Technology for Process Integrated Measurement

As part of the 6th framework program of the EU, new technologies for process integrated measurement are prototypically developed by applying Radio Frequency Identification (RFID) technology in the textile supply chain (BRIDGE 2007). RFID is an Automatic Identification and Data Capture (AIDC) technology using radio waves to transmit information over larger distances, at great speeds and a high storage capacity (Finkenzeller 2006). Specific problems of RFID, for example disturbances of radio waves due to reflection and absorption were largely solved in the BRIDGE project. Nevertheless, products containing fluids and tightly stored items still pose challenges for interference free product location but constitute only a small constriction compared to the remaining potential of RFID technology. A simple RFID tag provides a tagged item with a unique ID; each tagged item is able to announce its presence, location and combined with backend system, a chain of custodies. Track and trace performance components provide three major performance dimensions (Kele-

50

European Retail Research Vol. 25, Issue I, pp. 47-62

pouris/McFarlane 2006): accuracy (identification granularity, location granularity, time), completeness (location, condition, custodian) and timeliness (real time data capture). So far, research with RFID has been conducted in several fields as (1) development of the technology itself, electrical engineering and computer sciences (e.g. Glidden et al. 2004), (2) systems and software engineering including, and (3) applications and potential impacts in several industries as health management (e.g. Chen et al. 2009), pharmaceuticals (e.g. Wyld 2008), aviation (e.g. Kelepouris/McFarlane 2006), public transportation, and libraries (e.g. Yu 2008). Whereas the primary utility of RFID in supply chains is obvious  optimised product management  the vast opportunities for retail research are yet undiscovered. The potential of AIDC (Automatic Identification and Data Capture) technologies to gather process information of consumers has scarcely been studied in a consumer research context (e.g. Uotila/Skogster 2007). Specifically, it has been mentioned that RFID technology has a yet unexploited potential to enlighten consumer’s purchase behaviour (e.g. Silberer et al. 2007). For example, Larson/Bradlow/Fader (2005) came up with 14 descriptive RFID-enabled “canonical path types” of grocery store customers, but do not take explanation of purchase behaviour and its prediction into consideration. Consumers’ buying behaviour has been studied traditionally with in-store videos, manual observations and interviews (Underhill 1999). However, due to the relatively small datasets of customers’ shopping paths, results of such studies are just some general recommendations to increase the in-store convenience of customers (Larson et al. 2005). RFID has been used only recently as a data collecting technology of the shopping paths of customers in a store in a research work by Sorensen (2003). In Sorenson’s examination customers’ shopping trolleys or baskets were equipped with RFID tags and customers were tracked while they were travelling in the store. By using RFID, Sorenson was able to analyse 200,000 shopping paths in a supermarket, which increased the data base by a factor of 1000 as compared to earlier studies. The study concluded that this method creates more objective results than traditional consumer tracking methods. However, RFID was not used to gather explanatory data, in order to predict purchase of a target group customer for example. Due to the exhibited potential for objective and large-scale data collection processes, there is vast potential of RFID as a data capturing device in purchase situations for PoS marketing research purposes. The core functionalities of RFID, namely temporal and spatial accurate and immediate automatic data gathering, may trigger the integration of quantitative and qualitative consumer research: RFID allows retrieving externally highly valid, detailed behavioural measures of large samples. Therefore, RFID may provide unique possibilities for measuring consumer behaviour in real life conditions.

Blecker, T.; Rasch, C.; Teichert, T.

3.

Prospects for PoS Market Research with RFID Technology

3.1.

RFID for Validating and Refining Models of Consumer Behaviour

51

PoS marketing research is driven by the desire to understand consumers’ decision-making processes and accurately predict purchase. Neither economic nor behavioural decision research has so far fully accomplished this task, even though current approaches in decision theory provide an extensive conceptual foundation. An integrated approach that combines benefits of economic and behavioural consumer research methods needs further conceptual and empirical substantiation (Kivetz et al. 2008; Simonson 2008). The traditional focus on outcome predictions (economic perspective) has to be overcome and richer descriptions of processes should be provided to enhance behavioural consumer research (e.g. Johnson et al. 2008). Researchers are recently exploring various process-tracing methods, which gather data during the decision-making process of individuals to depict their decision processes (e.g. Glockner/ Betsch 2008; Russo/Leclerc 1994). Process-tracing methods overcome some gaps of traditional consumer research, since they do not require participants to be conscious of their cognitive processes, e.g. to articulate and evaluate their behaviour (e.g. Riedl et al. 2008b). However, they have several shortcomings of which the most serious is the artificial and very influential experimental setting in which consumer behaviour is examined. Several authors provide proof for the influence of option-sequence (e.g. Huber et al. 1982), framing of options (e.g. Levin et al. 1987; Yoon/Simonson 2008) and elicitation method (e.g. Tversky et al. 1988) on decision behaviour. Peck/Childers (2003) point out that haptic information processing is more reliable than just visual attention, which stipulates the necessity for real life consumer research. The deeper insight into the importance of a holistic and therefore externally valid consideration of sensory processes is growing (Krishna 2010). External validity is essential in studies of decision making, because real world conditions (e.g. opportunity to touch) under which preferences are formed or retrieved tend to have significant interactions with consumer behaviour (e.g. Bettman et al. 1998). Due to these deficits, current process-tracing methods will not satisfactorily bridge the modelling-empiricism gap. This may explain why processtracing methods remain a prominent research topic even after two decades. Solutions can be found with integrated behavioural process measurements in unobtrusive real-life settings. The first step of bridging the gap between behavioural decision theory and PoS marketing research is the combined consideration of process and outcome. Therefore, existing choice models and theories related to consumer’s decision processes should be validated in a real world context. The re-examination of consumer models with RFID technology in a real-world setting contributes to the uncompleted task of building behaviourally realistic structural models. Consumer’s decision-making would be addressed in an especially unbiased approach, for instance testing two-stage choice models based on Payne (1976) or Bettman/Whan Park

52

European Retail Research Vol. 25, Issue I, pp. 47-62

(1980). RFID-tagged products and consumers allow for the measurement of consideration sets and the foregoing processes with minimal obtrusion of test persons. Recent choice models aim to incorporate dynamics and complexities of a real-world purchase situation. Specifically, consumer’s screening, as the first process-stage, has been critically investigated. Gilbride/Allenby (2004) estimate a choice model that reflects the inherently discontinuous decision process and addresses the problematic issue of thresholds and discontinuities in experimental empirical research. Their model enables the analysis of disjunctive, conjunctive and compensatory screening rules and allows attribute-level inference for individuals. Similarly, Jedidi/Kohli (2005) focus on the incorporation of screening rules in choice models: By introducing a generalisation of disjunctive and conjunctive screening rules and accommodating uncertainty in consumers´ use of screening rules, a flexible screening process is captured. RIFD-based data, which features timeliness and completeness, can be employed to validate these models. Attention sets, represented by visited product zones, considerationsets, indicated by moved (touched) products and choice sets, products taken to the dressing room for example, allow for reconstruction of applied screening rules. Once a causal correlation has been empirically confirmed between specific screening rules for target group customers (identified by user provided information, e.g. bonus card, or latent class analysis) and the final purchase event, the PoS marketing could take accordant steps of action. For example, young women interested in shoes might screen disjunctive with a focus on brands. Products for this target group should be marketed with clearly visible, spatially detached brand signs in order to meet the information needs and to increase purchase probability. In contrast, middle aged women might screen compensatory. A separate shopping zone with similar products but differing product information (compensatory) could therefore boost sales volume. Corollary 1:

In-depth knowledge about consumer screening behaviour obtained by RFID consumer research will allow retailers to optimise product positioning and presentation.

The RFID features accuracy and completeness of captured data allow to detect mixed decision strategies by providing large scale process data of consumer’s behavioural measures. Consideration sequences of RFID-tagged products (move out of the shelf, take to dressing room, put back on the shelf, and so on) reveal what attributes and what time periods are used to make a decision. For example, individuals who initially consider only products regarding a few attributes in a short time period (non-compensatory decision making) might re-consider products on other attributes in a longer time period in the following (compensatory decision making) resulting in a purchase of the initially considered product category (e.g. Ariely 2000; Ariely/Levav 2000). With that knowledge at hand, the retailer could position products that are utilised for purchase decisions and group them together, in order to increase the observed impact on purchase in the new optimised shopping environment.

Blecker, T.; Rasch, C.; Teichert, T.

Corollary 2:

53

RFID consumer research will allow retailers to match physical product locations based on expected decision sequences.

Generally, established models of consumer choice processes can be validated by RFIDcontrolled purchase experiments, e.g. the dynamic Bayes two-stage choice model, the dynamic heuristic choice model (Siddarth et al. 1995) and Andrews/Manrai´s (1998) consideration by aspect choice model, Gilbride/Allenby’s (2004) conjunctive, disjunctive and compensatory screening rules choice models and Jedidi/Kohli’s (2005) probabilistic subset conjunctive model. Consequently, the development of more valid consumer decision models would be initiated on basis of the gathered evidence for mixed decision strategies and screening rules that impact consumer behaviour. By this means, retailers will be enabled for an efficient and effective information presentation and positioning of their product offerings.

3.2.

RFID-enabled Behavioural Metrics – Transferring Online to Offline

Process-based metrics aim to improve the quality of existing consumer research, as opposed to outcome-based metrics. Online behaviour research implemented this objective by the means of various click-stream data (e.g. Moe 2006; Senecal et al. 2005). General click-stream behaviour (average consideration time/total number of products considered), as well as detailed click-stream information (time per consideration lower than average/recency of consideration/number of bundled products considered) contributes to the prediction of onlinepurchasing behaviour (Van den Poel/Buckinx 2005). There are several indicators that describe the information processing in an experimental online environment. For example the “variability of search measure”, based on work of Payne (1976) and Billings and Marcus (1983). It denotes if attributes are considered balanced or dominated by one or two attributes, whereas compensatory choices are assumed to be of higher quality. Further indicators, such as “thoroughness of search” (proportion of attributes-levels examined by all attribute levels; Ford et al. 1989; Payne 1976), are positively tested in experimental decision paradigms (e.g. "mouselab", see Harte/Koele 1995) but not generalised to behavioural decision making. Only recently, research in business informatics introduced process-based metrics to offline environments that allow for identification of up to 13 decision strategies. Riedl et al. (2008a) propose the metrics: Ratio of option-wise transitions to attribute-wise and mixed transitions, and the correlation between attribute rank and number of products considered for each attribute. These measures can be utilised to describe the purchase process in a sequence of action steps which ultimately lead to the final purchase. Prior research suggests that a separation of information handling in pre- and post-screening is needed as people who screen will make different choices than people who don’t screen information, which indicates the potential impact of the sequence of actions taken by a customer (e.g. Chakravarti et al. 2006). RFID data enables the retailer to precisely follow the process of purchase. For example, the purchaseimpact on product A of a transition from product (category) B to product (category) C could

54

European Retail Research Vol. 25, Issue I, pp. 47-62

be validly examined. Accordingly, product positioning could be optimised by grouping causally influencing products. Corollary 3:

New process metrics provided by RFID consumer research will enable retailers to optimise their store layout based on process models of consumers´ information search activities.

Another example of sequence-dependent behaviour is the classic mug experiment: Wolf/Arkes/Muhanna (2008) test the effect of physical contact with products during test persons´ decision process. Findings show that the strength of pre-ownership attachment depends on the duration of exposure in the trial period. In respect to choice-modelling, Haaijer/Kamakura/ Wedel (2000) and Otter,/Allenby/Van Zandt (2008) could show that integration of time taken improves the accuracy of the choice model in an experimental setting. This is not yet shown in real life settings. The RFID features accuracy and completeness of data provide an opportunity to model this up to date fractioned consumer insights. For instance, precise data of touch duration and its impact on purchase could yield invaluable information of how to design shop interiors. For example, a product that shows increased purchase probability with increased consumer contact could be actively mediated by shop assistants. Corollary 4:

RFID enabled knowledge about the impact of contact time will allow retailers to proactively design for tactile product interactions within stores.

A neglected white space in behavioural decision theory is the research on causal relations of joint purchase decisions or sequential choices. Larson et al. (2005), who observed in-store shopping paths, suggest that joint purchases may be determined to a great extent by stochastic elements and should be modelled as “series of blink-to-blink choices”. This highlights the potential contribution of process data retrieved by RFID to an understanding of sequential choice, specifically the state dependence of choices. Since distinct patterns in sequential choices, such as reinforcement or balance, have already been revealed (Huber et al. 2008), it is expected that the sequence of a fixed set of purchases influences product choices ("forwardlooking tendencies", Hui et al. 2009). Above and beyond scanner data, RFID identifies which products are considered jointly before the actual purchase. We argue that such findings can improve the positioning of these products, in order to increase purchase probability. Corollary 5:

RFID will provide in-depth knowledge about joint purchase processes and thus will allow retailers to optimise the in-store positioning of complementary products.

Actions of consumers can be presented as sequence of events, which is here defined as an array of controllable events leading to defined events of consumption by consumers (cf. Abbott/Blum 1996). In this sequence, all transactions of a consumer can be located according to

Blecker, T.; Rasch, C.; Teichert, T.

55

the time of transaction in ascending order. The method of sequence analysis however is only rarely transferred to consumer research (Silberer et al. 2007). Most existent works on sequential choices focus on the analysis of (long-term) portfolio effects on the household level. Even recent research limits itself to abstract models which cannot be transferred to in-store purchase situations (e.g. Otter et al. 2008; Song/Chintagunta 2007). Qualitative research work, however, provide initial insights about the relevance of this topic (Baumeister et al. 2008; Dhar et al. 2007; Wang et al. 2007). Reflecting those works, Huber/Goldsmith/Mogilner (2008) concluded the need for both conceptual as well as empirical research: Identified patterns of behaviour reveal to be fragile in short-term subsequent choices, as there exist a variety of internal and external effects which influence them. Complex models need to be generated and their causal effects estimated (e.g. by hidden Markov Chains) in order to gain relevant insights for the retail industry. In this regard, RFID-based data hosts the potential to transfer qualitative insights into choice models. The three characterising features of RFID-based data: accuracy, completeness, and timeliness, may allow integrating quantitative and qualitative research approaches and closing white spaces of behavioural decision theory.

3.3.

Exploring White Spaces – Contingent Factors in Consumer Behaviour

Up to now, methodological constraints led to considerable white spaces in the modelling of consumer behaviour. We expect that novel aspects of consumer behaviour can be modelled on basis of RFID-enabled detailed process information. Its possibilities for obtaining detailed process data on a very large scale should provide the needed data required for complex modelling issues. It is now known that contextual factors to a great extent contribute to the heterogeneity of consumers´ decision-making processes. Differences occur across individuals in the same decision situation and within individuals across decision situations (Adamowicz et al. 2008). For instance, the composition and framing of a consideration set influence the outcome of a decision-making process (Scarpi 2008; Yoon/Simonson 2008). As an example, the effect and impact of decoys on consumer’s decision-making process may be refined with the detailed process information provided by RFID, specifically by tagging decoys themselves. This allows to track, at which stage of the purchase decision process decoys have the greatest influence on test person’s decision-making. It can be expected that decoys regularly belong to consumers’ awareness set due to their favourable product features. Nevertheless, it is unlikely that they frequently belong to consumers’ consideration set and final choice. Thus, traditional scanner data analysis can hardly reveal effects of decoys on consumer decision making. However, it is already shown in experimental settings, that decoys do influence choice behaviour (e.g. Pettibone/Wedell 2000). These findings can only be scrutinised in a complex behavioural choice modelling paradigm. By providing an in-depth analysis of interaction processes between consumers and products at the PoS, RFID technology

56

European Retail Research Vol. 25, Issue I, pp. 47-62

allows to reveal the effects of non-purchased items on the final purchase decision. Information about interaction sequences can be used to relocate decoys to the most effective locations. Corollary 6:

PoS market research with RFID technology will enable retailers to effectively utilise decoys, especially to optimise its location within the store.

Time constraints in either the screening or evaluation stage in consumer behaviour are shown to provoke time-dependent decision heuristics. Underlying assumption is that individuals minimise the amount of considered information and mental effort due to limited mental and time capacities (cf. Manrai/Andrews 1998). Individuals tend to shift from complex to simple decision strategies, when they are faced with time constraints (e.g. Payne et al. 1988). Findings so far base primarily on research limited to experimental conditions. RFID may provide further insights into the interdependencies between temporal characteristics and applied decision strategies based on its potential to generate precise time stamps during observations. On a more general level, Glöckner/Betsch (2008) question empirical findings from experimental settings. They show that commonly applied experimental designs provoke simplified decision strategies. Their findings suggest that individuals are in fact able to apply complex decision strategies even in a narrow time frame. The authors’ obtained this finding with a new unobtrusive experimental method of process tracing considering multiple correlates of internal processes (e.g. decision time). RFID technology could be used to expand this approach: The features completeness and timeliness of RFID-based data make comprehensive and precise behavioural measures available. By this means, detailed patterns of the decision-making process can be identified with respect to time. It is hypothesized that less time taken for the screening phase will result in small, homogenous consideration sets leading to just a few chosen items, and an overall worse decision quality, compared with a purchase situation without self-imposed time-constraint (see Haaijer et al. 2000; Otter et al. 2008 as examples in a laboratory setup). Accordingly, PoS marketing can use the gained insights (products that are bought in short time) and improve their positioning and presentation according to the decision strategy in use. For example, a product category that is being decided upon within very short time (eventually non-compensatory) should be positioned close to the shop exit together with similar products in order to serve customers needs, improve decision quality, and increase customer satisfaction and therefore repeated purchase. Corollary 7:

PoS market research with RFID technology will enable retailers to effectively deal with time constraints for optimising product positioning and presentation.

As RFID technology enables capturing real-world complexity of purchase situations, the interdependencies of contextual factors during the decision process pose a promising research issue for future examinations of consumer behaviour.

Blecker, T.; Rasch, C.; Teichert, T.

4.

57

Research Outlook

In a ubiquitous in-store shopping process, consumers regularly screen displayed products, establish physical contact to those products that raise their awareness, retrieve further information by testing some products and take the final choice(s) to the cash desk. RFID technology can be used to capture all of these individuals’ interactions with products at the PoS by recording visited product zones, contact instances and physical movements of products. What cannot be captured in RFID-equipped stores is the “out-of-store process”, i.e. the interrelation of repeated store-visits. For example, a customer might take a look at a product several times while shopping, but not buying it. In a repeated visit the customer makes a swift purchase of this product on grounds of the foregoing inspections. RFID-based process measurement is limited to the in-store process, unless a longitudinal study, in which a subject carries a personspecific RFID tag for a longer time period, is advised. Despite this limitation to in-store processes, RFID based process measurement adds value by its unobtrusiveness and detailed information about the process of decision making at the PoS, which ensures a high external validity of observed consumer-behaviour. Within each of the above proposed research-layers, RFID may help to address and solve open research issues. To fill existing research gaps, retrieval of empirical data from a series of experiments in a laboratory quasi-real-life setting is crucial. RFID-experiments provide a multi-level approach in which basic premises can be tested and further refined in due course. Tested and modified models of consumer behaviour will provide improved validity. Consequently, PoS marketing directly benefits from this kind of experimental approach by consumer models with more predictive power, and a tool for the externally valid examination of consumer behaviour. Gained insights will have implications for a broader scope of behavioural sciences as well and will provide a sound foundation for future behavioural process research. To implement RFID research an equipped setting is needed to trace customers in interaction with physical objects in order to retrieve representations of customers’ behavioural process in a real-world setting. Products in the test environment can be tagged and test persons asked to carry an anonymised RFID-enabled wristband (dominant, pointing hand). A high density of RFID measurement instruments should be located within those points where customers are likely to show more actions, which hence provide additional data about customer behaviour, such as mirrors in dressing room, product shelves and product zone transitions. The fix costs for a temporarily equipped setting are largely dependant on store characteristics. The bigger the store, i.e. the more RFID tags on products and the more RFID antennas in the store the higher the financial effort. The stated prospects of this article and the already shown interest of many retailers in consumer research with RFID (e.g. Galeria Kaufhof, see Hodel/Jacobs

58

European Retail Research Vol. 25, Issue I, pp. 47-62

(2008) for detailed cost issues regarding RFID equipment) make clear that this invest might pay off. Overall, market researchers will be able to record individual customer’s actions without disturbing her/his shopping process, because RFID enables real-time monitoring of tagged products and of people. With the increased demand to data processing in RFID-experiments, specialized software is needed to cope with RFID induced time- and location based datacomplexities. Despite heightened effort for data analysis, RFID-experimental validation of consumer behaviour at the point of sale is considered as invaluable progress for retail research.

Blecker, T.; Rasch, C.; Teichert, T.

59

References Abbott, L.F.; Blum, K.I. (1996): Functional significance of long-term potentiation for sequence learning and prediction, in: Cerebral Cortex, Vol. 6, No. 3, pp. 406-416. Adamowicz, W.; Bunch, D.; Cameron, T.A.; Dellaert, B.G.; Hanneman, M.; Keane, M.; Louviere, J.; Meyer, R.; Steenburgh, T.; Swait, J. (2008): Behavioral frontiers in choice modeling, in: Marketing Letters, Vol. 19, No. 3-4, pp. 215-228. Andrews, R.L.; Manrai, A.K. (1998): Feature-based elimination: Model and empirical comparison, in: European Journal of Operational Research, Vol. 111, No. 2, pp. 248-267. Ariely, D. (2000): Controlling the information flow: Effects on consumers' decision making and preferences, in: Journal of Consumer Research, Vol. 27, No. 2, pp. 233-248. Ariely, D.; Levav, J. (2000): Sequential choice in group settings: Taking the road less traveled and less enjoyed, in: Journal of Consumer Research, Vol. 27, No. 3, pp. 279-290. Baumeister, R.F., Sparks, E.A.; Stillman, T.F.; Vohs, K.D. (2008): Free will in consumer behavior: Self-control, ego depletion, and choice, in: Journal of Consumer Psychology, Vol. 18, No. 1, pp. 4-13. Bettman, J.R.; Luce, M.F.; Payne, J.W. (1998): Constructive consumer choice processes, in: Journal of Consumer Research, Vol. 25, No. 3, pp. 187-217. Bettman, J.R.; Whan Park, C. (1980): Effects of prior knowledge and experience and phase of the choice process on consumer decision processes: A protocol analysis, in: Journal of Consumer Research, Vol. 7, No. 3, pp. 234-248. Billings, R.S.; Marcus, S.A. (1983): Measures of compensatory and noncompensatory models of decision behavior  Process tracing versus policy capturing, in: Organizational Behavior and Human Performance, Vol. 31, No. 3, pp. 331-352. BRIDGE (2007): Retrieved from BRIDGE: http://www.bridge-project.eu, accessed October 15, 2010. Chakravarti, A.; Janiszewski, C.; Ulkumen, G. (2006): The neglect of prescreening information, in: Journal of Marketing Research, Vol. 43, No. 4, pp. 642-653. Chen, C.C.; Wu, J.; Su, Y.S.; Yang, S.C. (2009): Key drivers for the continued use of RFID technology in the emergency room, in: Management Research News, Vol. 31, No. 4, pp. 273-288. Dhar, R.; Huber, J.; Khan, U. (2007): The shopping momentum effect, in: Journal of Marketing Research, Vol. 44, No. 3, pp. 370-378. Finkenzeller, K. (2006): RFID Handbook: Fundamentals and applications in contactless smart cards and identification, Munich. Ford, J.K., Schmitt, N.; Schechtman, S.L.; Hults, B.M.; Doherty, M.L. (1989): Process tracing methods  Contributions, problems, and neglected research questions, in: Organizational Behavior and Human Decision Processes, Vol. 43, No. 1, pp. 75-117. Gilbride, T.J.; Allenby, G.M. (2004). A choice model with conjunctive, disjunctive, and compensatory screening rules, in: Marketing Science, Vol. 23, No. 3, pp. 391-406.

60

European Retail Research Vol. 25, Issue I, pp. 47-62

Glidden, R.; Bockorick, C.; Cooper, S.; Diorio, C.; Dressler, D.; Gutnik, V.; Hagen, C.; Hara, D.; Hass, T.; Humes, T.; Hyde, J.; Oliver, R.; Onen, O.; Pesavento, A.; Sundstrom, K.; Thomas, M. (2004): Design of ultra-low-cost UHF RFID tags for supply chain applications, in: Communications Magazine, IEEE, Vol. 42, No. 8, pp. 140-151. Glöckner, A.; Betsch, T. (2008): Multiple-reason decision making based on automatic processing, in: Journal of Experimental Psychology-Learning Memory and Cognition, Vol. 34, No. 5, pp. 1055-1075. Glöckner, A.; Betsch, T. (2008): Modelling option and strategy choices with connectionist networks: Towards an integrative model of automatic and deliberate decision making, in: Judgment and Decision Making, Vol. 3, No. 3, pp. 215-228. Haaijer, R.; Kamakura, W.; Wedel, M. (2000): Response latencies in the analysis of conjoint choice experiments, in: Journal of Marketing Research, Vol. 37, No. 3, pp. 376-382. Harte, J.M.; Koele, P. (1995): A comparison of different methods for the elicitation of attribute weights  structural modeling, process tracing, and self-reports, in: Organizational Behavior and Human Decision Processes, Vol. 64, No. 1, pp. 49-64. Hodel, D.; Jacobs, S. (2008): RFID in der Modebranche  Anwendungsmöglichkeiten, Realisierung, Praxisbeispiele, Köln. Hossain, M.M.; Prybutok, V.R. (2008): Consumer acceptance of RFID technology: An exploratory study, in: IEEE Transactions On Engineering Management, Vol. 55, No. 2, pp. 316-328. Huber, J.; Goldsmith, K.; Mogilner, C. (2008): Reinforcement versus balance response in sequential choice, in: Marketing Letters, Vol. 19, No. 3-4, pp. 229-239. Huber, J.; Payne, J.W.; Puto, C. (1982): Adding asymmetrically dominated alternatives  violations of regularity and the similarity hypothesis, in: Journal of Consumer Research, Vol. 9, No, 1, pp. 90-98. Hui, S.K., Fader, P.S.; Bradlow, E.T. (2009): Path data in marketing: An integrative framework and prospectus for model building, in: Marketing Science, Vol. 28, No. 2, pp. 320335. Jedidi, K.; Kohli, R. (2005): Probabilistic subset-conjunctive models for heterogeneous consumers, in: Journal of Marketing Research, Vol. 42, No. 4, pp. 483-494. Johnson, E.J., Schulte-Mecklenbeck, M.; Willemsen, M.C. (2008): Process models deserve process data: Comment on Brandstatter, Gigerenzer, and Hertwig (2006), in: Psychological Review, Vol. 115, No. 1, pp. 263-272. Kelepouris, T.; McFarlane, D. (2006): Track and trace performance measurement, Auto-ID Lab University of Cambridge, Cambridge. Kivetz, R.; Netzer, O.; Schrift, R. (2008): The synthesis of preference: Bridging behavioral decision research and marketing science, in: Journal of Consumer Psychology, Vol. 18, No. 3, pp. 179-186. Krishna, A. (2010): Sensory marketing  research on the sensuality of products, Ann Harbour, MI. Larson, J.S.; Bradlow, E.T.; Fader, P.S. (2005): An exploratory look at supermarket shopping paths, in: International Journal of Research in Marketing, Vol. 22, No. 4, pp. 395-414.

Blecker, T.; Rasch, C.; Teichert, T.

61

Levin, I.P.; Gaeth, G.J.; Johnson, R.D.; Theeuwes, J. (1987): Information framing effects  their robustness and interpretation, in: Bulletin of the Psychonomic Society, Vol. 25, No. 5, pp. 353-53. Manrai, A.K.; Andrews, R.L. (1998): Two-stage discrete choice models for scanner panel data: An assessment of process and assumptions, in: European Journal of Operational Research, Vol. 111, No. 2, pp. 193-215. Moe, W.W. (2006): An empirical two-stage choice model with varying decision rules applied to Internet clickstream data, in: Journal of Marketing Research, Vol. 43, No. 4, pp. 680692. Otter, T.; Allenby, G.M.; van Zandt, T. (2008): An integrated model of discrete choice and response time, in: Journal of Marketing Research, Vol. 45, No. 5, pp. 593-607. Payne, J.W. (1976): Task complexity and contingent processing in decision making: An information search and protocol analysis, in: Organizational Behavior and Human Performance, Vol. 16, No. 2, pp. 366-387. Payne, J.W.; Bettman, J.R.; Johnson, E.J. (1988): Adaptive strategy selection in decision making, in: Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 14, No. 3, pp. 534-552. Peck, J.; Childers, T.L. (2003): Individual differences in haptic information processing: The "need for touch" scale, in: Journal of Consumer Research, Vol. 30, No. 3, pp. 430-442. Pettibone, J.C.; Wedell, D.H. (2000): Examining models of nondominated decoy effects across judgment and choice, in: Organizational Behavior and Human Decision Processes, Vol. 81, No. 2, pp. 300-328. Riedl, R., Brandstatter, E. and Roithmayr, F. (2008): Identifying decision strategies: A process- and outcome-based classification method, in: Behavior Research Methods, Vol. 40, No. 3, pp. 795-807. Russo, J.E.; Leclerc, F. (1994): An Eye-Fixation Analysis of Choice Processes for Consumer Nondurables, in: Journal of Consumer Research, Vol. 21, No. 2, pp. 274-290. Scarpi, D. (2008): The impact of decoys and background information on consumers' preferences and decision making, in: The International Review of Retail, Distribution and Consumer Research, Vol. 18, No. 1, pp. 1-15. Senecal, S.; Kalczynski, P.J.; Nantel, J. (2005): Consumers' decision-making process and their online shopping behavior: A clickstream analysis, in: Journal of Business Research, Vol. 58, No. 11, pp. 1599-1608. Siddarth, S.; Bucklin, R.E.; Morrison, D.G. (1995): Making the cut  modeling and analyzing choice set restriction in scanner panel-data, in: Journal of Marketing Research, Vol. 32, No. 3, pp. 255-266. Silberer, G., Büttner, O.B.; Gorbach, A. (2007): Exploring shopping paths and attention behavior at the point of sale, in: DeMoranville, C.W. (ed.): Marketing theory and practice in an inter-functional world, Vol. 8, Verona. Simonson, I. (2008): Will I like a "medium" pillow? Another look at constructed and inherent preferences, in: Journal of Consumer Psychology, Vol. 18, No. 3, pp. 155-169. Song, I.; Chintagunta, P.K. (2007): A discrete-continuous model for multicategory purchase behavior of households, in: Journal of Marketing Research, Vol. 44, No. 4, pp. 595-612.

62

European Retail Research Vol. 25, Issue I, pp. 47-62

Sorensen, H. (2003): Inside the mind of the shopper: The science of retailing. Upper Saddle River, NJ: Pearson. Tversky, A.; Sattath, S.; Slovic, P. (1988): Contingent weighting in judgment and choice, in: Psychological Review, Vol. 95, No. 3, pp. 371-384. Underhill, P. (1999): Why we buy: The science of shopping. New York, NY. Uotila, V.; Skogster, P. (2007): Space management in a DIY store analysing consumer shopping paths with data-tracking devices, in: Facilities, Vol. 25, No. 9/10, pp. 363-374. Van den Poel, D.; Buckinx, W. (2005): Predicting online-purchasing behaviour, in: European Journal of Operational Research, Vol. 166, No. 2, pp. 557-575. Wang, J.; Novemsky, N.; Dhar; R.; Baumeister, R.F. (2007): Effects of depletion in sequential choices, New Haven. Westerman, S.J.; Tuck, G.C.; Booth, S.A.; Khakzar, K. (2007): Consumer decision support systems: Internet versus in-store application, in: Computers in Human Behavior, Vol. 23, No. 6, pp. 2928-2944. Wolf, J.R.; Arkes, H.R.; Muhanna, W.A. (2008): The power of touch: An examination of the effect of duration of physical contact on the valuation of objects, in: Judgment and Decision Making, Vol. 3, No. 6, pp. 476-482. Wyld, D.C. (2008): Genuine medicine?: Why safeguarding the pharmaceutical supply chain from counterfeit drugs with RFID is vital for protecting public health and the health of the pharmaceutical industry, in: Journal of Global Competitiveness, Vol. 18, No. 3, pp. 206216. Yoon, S.-O.; Simonson, I. (2008): Choice set configuration as a determinant of preference attribution and strength, in: Journal of Consumer Research, Vol. 35, No. 2, pp. 324-336. Yu, S.C. (2008): Implementation of an innovative RFID application in libraries, in: Library Hi Tech, Vol. 26, No. 3, pp. 398-410.

In-Store Logistics Processes in Austrian Retail Companies Alexander Trautrims, David B. Grant and Peter Schnedlitz

Abstract This paper reports on a study of in-store logistics processes conducted at two market-leading Austrian retailers. The paper’s aim is to investigate how retailers manage their replenishment operations and which factors they consider important in the design of their replenishment processes. The study was exploratory and used a qualitative case study approach. The data collection method comprised interviews from several employees at different hierarchical levels in each retailer and in-store researcher observation. Interview data were analysed using content analysis. Replenishment operations differ between the two retailers and are determined by their individual strategic and operational requirements. This paper shows how components from the demand and the supply side – such as shopping patterns and product characteristics – are incorporated in the retailers’ considerations.

Keywords Retailing, Non-grocery, In-store Replenishment Processes, On-shelf Availability, Austria

Alexander Trautrims (corresponding author) Logistics Institute, The University of Hull, Kingston-upon-Hull, United Kingdom (E-mail: [email protected]). David B. Grant Logistics Research Centre, Heriot-Watt University, Edinburgh, United Kingdom Peter Schnedlitz Chair of Retailing and Marketing, Institute for Retailing and Marketing, WU Wien - Vienna University of Economics and Business, Vienna, Austria.

Received: August 25, 2010 Revised: February 2, 2011 Accepted: March 2, 2011

EUROPEAN RETAIL RESEARCH Vol. 25, Issue I, 2010, pp. 63-84

D. Morschett et al (eds), European Retail Research, DOI 10.1007/978-3-8349-6235-5_4, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

64

1.

European Retail Research Vol. 25, Issue I, pp. 63-84

Introduction

Improving product availability is a constant task for retail firms, particularly in saturated retail markets with relatively little growth such as Western Europe. The availability of products is essential for market success since every out-of-stock (OOS) may increase customer dissatisfaction. Consequently, retailers have tried to increase on-shelf availability (OSA) performance and set-up collaborative strategies with suppliers to ensure customers can shop for their favourite choices. Due to this ongoing process upstream retail supply chains have become highly optimised and have achieved high product availability figures. However, the area of in-store processes remains widely underresearched, despite recent research showing that many OOS situations were actually caused within the store itself. This study contributes to the area of in-store logistics by looking at the store operations of two Austrian retailers. Most retail logistics research so far has been conducted in the grocery sector due to the focus provided in that sector by the efficient consumer response (ECR) movement. In order to widen this research area, this exploratory qualitative study involves one grocery and one do-it-yourself (DIY) retailer. ECR aims to improve supply chain performance by enhancing collaboration and integration of supply chain partners. It takes a holistic view of the supply chain to embed a customer-oriented design (Kotzab/Hartig/Ludvig/Steinbrecher 2009). A further investigation of in-store logistics processes increases the understanding of replenishment systems and therefore fits into the ECR research concept (Meiȕl/Steinbrecher/ Hartig/Ludvig 2009). This paper first introduces the problem and consequences of OOS and then outlines existing research into the area of store logistics and store operations processes. OOS and OSA are often used interchangeably however their meanings are different. OSA is the availability of products on the shelf for customers to buy; while OOS can occur anywhere in the supply chain and is thus not unique to the consumer setting. In the following sections the paper portrays the Austrian retail market environment, where the study was conducted, and the two investigated retailer case studies. The information gained from interviews at retail stores is presented and finally discussed and integrated into the existing knowledge base of in-store logistics processes.

2.

Research Background

During the last two decades of the twentieth century time the retail sector started to consolidate and retailer power began to become concentrated (Fernie/Pfab/Marchant 2000). This situation had two consequences. Firstly, retailers took the control of the supply chain from manufacturers and could therefore force suppliers to deliver according to demand rather than

Trautrims, A.; Grant, D.; Schnedlitz, P.

65

to production schedules. With the rise of large and often stock exchange listed retail firms, retailers started to optimise their operations. Secondly, as many retailers across several sectors achieved a significant market share there was little opportunity to benefit from more than competitors through buying power. This led to even more attention being paid towards the optimisation of operations and the awareness of costs for inventory and handling increased (Seth/Randall 2001; Grant et al. 2006). Logistics activities represent a significant share of total costs for a retailer, between 10 to 30 % of total costs, which is higher than for most manufacturing firms. Thus, better performing retailers can achieve a competitive cost advantage by improving OSA and reducing OOS (Kotzab 2005). Research into OSA started in the US in 1968 with an article in Progressive Grocer, which was the first to measure stockouts and consequent financial impacts of them. Further studies by Schary/Becker (1978) and Schary/Christopher (1979) investigated this problem from the perspective of the impact of OOS on the customer service quality and the retailer’s performance. Despite originating from a customer perspective, OSA research can be portrayed in two distinct streams: the demand side, which investigates the impact of OOS on consumer behaviour; and the supply side, which searches for the causes of OOS and potential improvements in the retail supply chain (Grant/Fernie 2009). A widely used study about consumer response to an OOS situation is a global meta-study by Corsten/Gruen (2003). It argues that 31 % of customers buy at another store, 45 % substitute the product and 24 % either delay or omit the purchase. However, further studies show that responses to OOS vary enormously depending on the characteristics of the consumer, product and buying situation (Campo/Gijsbrechts/Nisol 2000; van Woensel et al. 2007; Campo/Gijsbrechts/Nisol 2003; Fernie/Grant/Trautrims 2009; Magnus 2007). Nevertheless, the impact of OOS is necessary to calculate the trade-off between expenses for additional resources and higher sales caused by improved OSA. However, sometimes such a calculation might reveal that is not in the retailer’s financial interest to increase OSA (Fernie/Grant/Trautrims 2009). Within a single retail firm there might be opposite targets between functions, stores and headquarters. One example from McKinnon/Mendes/Nababteh’s (2007) root cause analysis is the range density of a retail store. Also, the store’s shelf is not only the point of purchase decision making for the consumer, but from a retail logistics perspective it is storage space within the store. Retailers therefore try utilising storage space as much as they can whilst also presenting a tidy display to their shoppers (Hulbert 2009). However, retailers cannot base the decision of shelf-space allocation only on the proportionate sales of a product. Consumers desire choice and variety in their shopping regardless that they may be loyal to one product. Retailers will thus try to adapt a strategy that suits their customers; but there are also constraints within retailers that prevent a proportionate allocation of

66

European Retail Research Vol. 25, Issue I, pp. 63-84

shelf space. These include physically available shelf and store space; the strategic positioning of a retailer within the market; and the width of private labels within categories. As available shelf display influences the rate of sales of a product, marketing and sales decisions along with supplier interference affect shelf space allocations. Even if a retailer establishes an optimal allocation of shelf space; this decision is only valid at the point in time it is made; the retail market, economic conditions and consumer preferences are characterised by constant change (Mantrala et al. 2009). Consequently, a retailer’s decision making is not only based on product availability; and even though high OSA is a major aim for a retailer it must be considered along with other tasks within the firm that have an opposite effect. On the supply side much research has been conducted to investigate at which point in the supply chain OOS is caused. A study by the Coca-Cola Research Council (1996) revealed that less than one-third of OOS is actually caused in the upstream supply chain before the store. Further, ECR Europe (2003) recognised a drop in product availability from distribution centres to the store shelf. McKinnon/Mendes/Nababteh (2007) identified the root causes by interviewing store managers who located various reasons at different stages in the supply chain. Even though store managers preferred to note reasons out of their own control, many of them were lying within the store or at a mismatch of integration between store and centrally-made decisions. The importance of in-store processes for OSA was confirmed by Helm/Hegenbart/ Stölzle/Hofer (2009) who found that more than 90 % of OOS resulted from root causes within a store’s direct influence and one third of OOS caused by the fulfilment process in a store. According to Ton (2002) the way store execution is designed clearly has an impact on OSA performance. Store operations therefore need to be considered as a part of the retail supply chain and taken into consideration when optimising it. One aspect of the retail store supply chain is the involvement of people. With increasingly sophisticated and more efficient logistics systems store employees need sufficient qualifications to handle their day-to-day tasks (Kotzab/Teller 2005; Baxter 2007; Esbjerg/Buck/Grunert 2010). Further, the store systems need to be designed in a way that makes the use of them easy and straightforward (Thonemann et al. 2005). So far the literature agrees that most OOS situations are caused within stores. In addition, the store handling costs for the ‘last 50 metres’ of the retail supply chain can represent 50% of total retail operations costs (Broekmeulen et al. 2004). Better management of in-store operations will therefore have a double impact on a retailer’s performance, as it affects both the input (costs) and output (OSA) sides. A study by McKinsey and the University of Cologne shows that improved in-store processes can lead to a significant cost advantage: store staff at average grocery retailers spend 43 % of their time on in-store replenishment whereas at the best practice retailers they spend only 22 % of time on that activity. At the same time the best practice retailers show a 61 % lower OOS rate (Thonemann et al. 2005).

Trautrims, A.; Grant, D.; Schnedlitz, P.

67

Figure 1: The Kotzab/Teller In-store Logistics Model

Retailer/Manufacturer Delivery Loading/Unloading Bay G. Reorder

A. Receipt B. Transport 1

D. Transport 2

H. Disposal

C. Storage 1

SALES FLOOR

STORE

Shelf

E. Handling/Storage 2 Customer F. Processing/Transaction

Till product flow information flow

Despite the impact of in-store logistics processes there has been little research done in this area so far. Kotzab/Teller (2005) mapped the path of goods through a store and described a general model for dairy products in an Austrian supermarket chain. The model í shown in Figure 1 í considers physical transportation and the flow of information, but provides more

68

European Retail Research Vol. 25, Issue I, pp. 63-84

detail of the former activity. The model was derived from interviews with 202 store managers of different store formats at one retail chain. The model illustrates the flow of goods and information at the store as well as the boundaries between the store and the sales floor regarding physical products. Products come in through delivery operations and then move forward to the shelf and eventually pass the cash till with the customer. Reverse operations are limited to waste products being disposed. Since the Kotzab/Teller (2005) study was limited to one specific product category at one retail chain, we consider there needs to be more study into other categories of product and retail. In contrast to warehouses there is actually very little technical equipment to support picking and replenishment inside stores. This increases the reliance on employees to provide effective replenishment in store operations. Comparing the performance of in-store logistics is difficult as requirements and characteristics differ between stores; additionally in-store performance relies on previous stages in the logistics system which are outside a store’s influence (Kotzab/ Reiner/Teller 2007). The Kotzab/Teller (2005) model was based on the investigation of a single product category in a supermarket environment: Chilled dairy products. This limitation suggests further study should consider other product categories with different characteristics. Further, Grant/Fernie (2008) called for an investigation of OSA in non-grocery sectors, as most research to date has been conducted in grocery. Hence, this study aims to extend the Kotzab/Teller model into non-grocery retailing and also consider wider product categories in grocery. Further, it adds to the research stream of OSA by investigating ‘the last 50 metres’ of the retail supply chain.

3.

Methodology

Due to the exploratory nature of the research objective this study uses an inductive and qualitative approach (Ellram 1996). A suggested way for empirical research in an exploratory setting is the application of the case study method. According to Ellram (1996) and Yin (2003), conducting case studies is a suitable approach for investigating a contemporary phenomenon in a real-life context, where the boundaries between the phenomenon and the context are unclear. In-store processes involving people can be best analysed whilst they are actually executed. The real-life setting and the investigated phenomenon store processes provide the surrounding context at the same time and thus this method was chosen to cover a wider range and expand knowledge into new phenomena (Stake 1994; Nohl 2006). As noted above, Grant/Fernie (2008) highlighted a gap in investigating OSA issues in the non-food sector. Thus, this study combines a case from the grocery sector, which is considered as being a driver of product availability research, with a case from the non-grocery sector.

Trautrims, A.; Grant, D.; Schnedlitz, P.

69

The study considers two cases: The grocery segment of a hypermarket retailer and a DIY retailer, which show sufficiently different characteristics in terms of product characteristics and supply patterns. In order to widen the knowledge base about in-store logistics processes, the methodology required the use of case studies to determine such different characteristics. By choosing a grocery chain and a DIY retailer, the knowledge base is broadened in two directions: It should increase the depth of the investigation into grocery store operations and at the same time widen the research to another retail sector. Additionally, the selected companies needed to operate a significant number of stores in order to satisfy an assumption regarding the existence of complex logistics and store operations. With this aim of widening the knowledge base, the study does not necessarily provide generalisation (i.e. external validity) outside the two cases, but aims to discover much more about store logistics processes than current knowledge. The first case undertaken was a hypermarket grocery retailer. For the second case, a DIY retailer was selected. In this order the grocery arena could be investigated and compared with processes at the non-grocery retailer. Data at both companies was gathered using semi-structured interviews, which allows participants to contribute new insights into the topic, whilst the interview framework provides structure for responses. Two stores at each retailer were chosen to spot variations and consistencies between the company’s internal operations and procedures. Due to the exploratory nature of the study, interviews were conducted in an open and loose pattern. Adapted from previous research, three main areas were identified for the interview guide: The replenishment process; product availability; and human resources. These three areas set the main structure for the interview guide. Probes were prepared for each area; their selection was again based on the current stand of knowledge in the field. However, within the main areas the interview flow was directed by the interviewee, and the prepared probes were only applied if the suggested issue was not mentioned by them. Also, emerging aspects not covered by extant literature and the suggested framework were followed up by probes and further inquiries. Five employees from different hierarchical levels at each company were interviewed during their regular working time: One central functions manager, two store managers and two customer service employees. The interviews took between one-half and one hour, with the store manager and central function interviews generally taking longer than shop floor replenishment workers. It should also be noted that the hierarchically higher levels generally found it easier to get into a story telling mode. Data analysis was based on the interview transcripts and information gathered from the participants during the store visits. There was no structured observation of the stores during the visit, but in every case the researcher was given a tour by employees to follow the product flow around the store to illustrate their statements and to provide context. The study thus takes

70

European Retail Research Vol. 25, Issue I, pp. 63-84

an objective perspective on the gathered data; considering all statements of the participants as ‘true’. The study is looking at the logistics systems within the retail stores, which strongly rely on the human beings that are applying and using them. Therefore it is appropriate for the researcher to take the retail workers’ perception of these systems as an expression of their ‘reality’ and consider their perception of their environment as ‘true’. Thus, the study relies on interviewees’ comments and expressions. The interview transcripts were investigated for themes along the process flow within the store. The initial question at each main topic area was always the same to all participants. The interview flow then followed the participants’ responses and inquired deeper into issues brought up by the interviewees to find hidden topics that were so far not considered in store logistics research; but also to confirm existing knowledge of store operations. The extracted themes were compared between the interviews; firstly within its own case; and afterwards between the two cases.

3.

The Austrian Retail Environment

Sector structure, shopping patterns and other factors shape the way retailers position their organisations and manage their operations. This section presents the Austrian retail environment, in which both case studies were conducted. Due to the availability of data, much of it refers to the grocery sector. Nevertheless, it can be considered that general patterns are valid across all sectors. With 84,000 km² and 8.3 million inhabitants, Austria is one of the smaller countries in the European Union. The population structure within the country is quite diverse due to strong geographical contrasts. In the capital Vienna there are about 1.7 million people. It is the major metropolitan area in Austria and it is densely populated with an average of 4,000 inhabitants per km²; the national average being 99 inhabitants per km². Economically the gross domestic product (GDP) per inhabitant is significantly higher in Vienna with EUR 41,500 compared to the national average of EUR 30,078. Accordingly, the number of retail stores is higher in Vienna than in other regions (Statistik Austria 2008). Overall, these regional differences mean different challenges for retail companies on the demand and the supply side. To ensure comparability of store processes, the investigated stores of both retailers are located in Vienna. Retailing is the largest economic sector in Austria in sales terms. It is also the second largest private employment sector with about 561,000 employees (Handelsverband 2009). According to Nielsen (2008) the number of stores has been in constant decline by approximately 2 % over each of the last four years, whilst sales were developing positively with growth rates of around 4 % in 2007 and 2008. Retail concentration in Austria increased to the point where the two largest players in the grocery sector, the German REWE group and the Austrian SPAR, hold together 58.6 % of the entire market. Together with the third largest grocery retailer, the

Trautrims, A.; Grant, D.; Schnedlitz, P.

71

hard discount store Hofer which is a part of the German ALDI Süd, they have 78.5 % market share. The remaining retailers have 5 % or less market share each, most of them being characterised by a declining share of the grocery market. On average, Austrian consumers spend EUR 5,952 per year on consumer goods and retailers achieve an average 2.2 % sales profitability (Metro Group 2009; KMU Austria 2008). Retail firms’ own-label product sales are increasing and represent a share of 28 %. Due to planning restrictions the hypermarket format enjoys only a relatively small share of 9 % of all sales, the strongest format being supermarkets between 400 and 999 m2 with 64 % of all grocery sales (Hoffmann/Schnedlitz 2008). Compared to other European countries Austria is much more restrictive on opening times and stores have to close at night time during the work week; from late evening on Saturdays, and on Sundays shops are generally closed. Night replenishment is not well-established; this may be caused by strong labour unions but there is also much larger retail space in Austria that makes replenishment throughout the day easier and less interfering for customers (Metro Group 2009). Generally, the Austrian retail sector appears to be similar to other Western retail markets. On the supply side oligopolistic structures emerge whilst on the demand side retailers face market saturation; this requires them to differentiate themselves against their competitors. Austrian consumers appreciate quality and are less price-driven than other European nations. The foregoing factors impact operations at the two case study retailers. Their approaches to in-store replenishment operations are presented and analysed in the following sections.

4.

Case Study 1: Grocery Retailer

The company runs several hundred stores of different shop formats from neighbourhood convenience type stores to large hypermarkets. However, this study only focuses on the hypermarkets of which there are around one hundred spread all over the country. Store 1 is located in a residential area and consists of 4,000 m2 sales space and 400 m2 backroom storage space. Store 2 is part of a mall complex. It is larger in size with 5,600 m2 sales area and 1,800 m2 backroom storage, spread over two floors. Most the deliveries come from the own central distribution centre (DC). Only local fresh products such as bread are delivered directly by suppliers. Deliveries come in everyday from Monday through Friday. The stores close over night and on Sundays. Products at the stores arrives at different times throughout the day and are delivered from two DCs. One DC dispatches fast moving goods every working day; the other dispatches slow moving goods every second working day. Perishables are brought in via the retailer’s own delivery network and by suppliers every day at store opening in the morning. Regular deliver-

72

European Retail Research Vol. 25, Issue I, pp. 63-84

ies arriving go straight to the shelves; only chilled products are stored in temperature controlled warehouses. Products are expected to fit onto the shelves, only very well selling products have to be put into the backstore and replenished throughout the day. Shelf space is usually the only storage place for most dry goods. The way promotional goods deliveries are handled differs between stores depending on available storage space. Store 1 exchanges old promotional goods with new ones once they are delivered. Promotional goods for further replenishment are kept in the backstore. Depending on the volume regularly listed, left-over products from promotions are either put on-shelf or in the back storeroom. Store 2 usually receives promotional goods a few days before they start as it has a considerably large back storeroom. Also, store 2 orders much larger quantities of regularly listed products when they are on promotion. After the promotion is over the products are replenished onto the regular shelf; but due to the lower promotional buying price the profit margin increases. Promotional products are also often not deliverable and the causes include incorrect forecasting and supplier problems. Deliveries are supposed to arrive at certain time slots during the day. For those times more workers are scheduled in the work plan to cope with the arrival and replenishment activities. These time slots are often not matched and only about 90 % of deliveries arrive at the dedicated time slot. This has led to inefficiencies in the work agenda and a delay in replenishment. The re-ordering of regular products is based on a computer system that uses point of sale (POS) data to calculate the need for new deliveries. The system also takes past sales data and patterns into consideration. The order lead time is day 1 for day 3, i.e. a product sold on Monday is delivered on Wednesday. This Wednesday delivery can be adjusted until Tuesday store closing time; only for perishable products does ordering still mainly rely on shop employees. The ordering for promotions happens differently from regular ordering. A store has to order promotional items one month in advance of the promotion. The system or the head office then suggests an amount to be taken by the store. This amount can be increased or reduced by the store, but eventually central decisions have priority over the store’s request. The way products are returned depends on the supplier and the reason for the return. Products that pass the expiry date are disposed of at the store. Generally, products that come close to crossing the expiry date are reduced in price to be sold before they expire. Broken or damaged products are sent back through the forward supply chain. However, some suppliers prefer the retailer to dispose of faulty products instead of reversing them. OOS is not routinely searched for or measured at the retailer. The way of detecting OOS is optically, i.e. staff examine the shelves, during replenishment or if an employee notices a shelf gap. When employees noticed an empty shelf they needed to check the system on the handheld scanner. There were three potential responses: The scanner indicated that a product

Trautrims, A.; Grant, D.; Schnedlitz, P.

73

was not deliverable, the shelf plan was changed, or the stock record was incorrect. In case there was stock on the system, the employee would have to search at the backstore for it. If it could not be found, the stock record had to be changed. It was experience and training by the department leaders that made employees know how to respond to an empty shelf. Inexperienced workers were more likely to get it wrong. In the recent past the company ran a pilot OSA study in which an external agency recorded shelf gaps. However, that pilot had finished and so far no OSA measurement system has been installed. As a follow up for the pilot, for a certain time period one department was chosen to get OSA measured by store employees; the OSA numbers usually improve due to the increased awareness. Then another department was chosen, and so on. The performance figures were sent back to the local office of the retailer for monitoring. A major prevention against OOS was the maintenance of correct inventory records. With 60,000 SKUs a frequent check of all products was impossible. Some lines were priority products that were constantly checked, other products that needed checking were selected by the computer system when it spotted irregularities. Also, the stores needed to interfere manually with the automated ordering system, as it did not consider non-linear demand fluctuations, for example due to a price reduction. Store execution faced challenges from the fact that every store is different and even the shelf systems are different in stores. Therefore common shelf plans are impossible. The large range of products makes it impossible to stock all products deep enough to ensure availability at all times. Store employees are usually allocated to a certain department. Each department is examined from a turnover per working hour perspective and a successful department may thus justify having extra employees that other stores do not have. The stores can employ people full-time, part-time or chose external agencies. It is entirely the store managers’ decision within the negotiated budget. Generally, there is a tendency to reduce the number of full-time employees due to cost considerations. Only leading positions remain full-time positions and the rest are filled with part-time workers and external labourers. Overall the number of workers has been reduced and therefore the labour output and quality of each individual worker needed to be increased. Workers are clearly separated between the logistics and sales functions. There are central suggestions and plans of what logistics processes should look like, but whether they are applied depends on the store manager.

74

5.

European Retail Research Vol. 25, Issue I, pp. 63-84

Case Study 2: DIY Retailer

The second case is a major DIY retailer that operates more than sixty stores in Austria. It employs a workforce of several thousand people. Store 1 is considered too small by the company but due to its setting in an urban residential area expansion is not possible. It consists of 3,200 m2 sales area and 500 m2 backroom storage. The second store however is in a commercial area of the city and the building hosts the largest store format of the company. The store has 5,000 m2 sales space and 2,000 m2 backroom storage. The supply chain works very differently from that of the grocery retailer. As items tend to be huge and heavy many need technical equipment such as forklifts to be handled. Also, inventory turnover is much lower than the grocery sector and replenishment lead times are much longer. A DIY store also sells many high value items, store operations therefore have to take theft into consideration as an important factor. Between three to five trucks arrive at the smaller store from the DC every day, plus direct deliveries from suppliers. The larger store is delivered to less frequent from the DC, as it does not face the same traffic restrictions as store one and the retailer can therefore use larger vehicles. The delivery frequency also depends on the season and varies with demand. Depending on the source and method of delivery they are managed in different logistics streams. Deliveries from the DC are generally not double-checked. However, if a product has been only crossdocked at the DC and not stored there, it is treated like a direct delivery at the store and needs detailed checking. Back of store workers receive deliveries, check them according to their logistics stream, and prepare them for in-store replenishment. The check at the back of store and the system entry of the delivery are organisationally separated. Since the two stores are of significantly different size the available storage space differs, too. The smaller store brings products to the shop floor as quickly as possible; whilst the larger store can take until the next morning after the preparation and separation of the delivery to start shelf replenishment. The storage system in the back of store is colour coded, it categorises products in three groups: ready to be used for replenishment; products waiting for being checked and approved by the backstore logisticians; and products allocated to a customer order. The replenishment operations itself are organised differently at the two stores. The larger store 2 operates a system with specialised employees where external agency workers take care of most of the early morning replenishment. Replenishment throughout the day is done by sales employees and back office staff. Internal employees are split into pure customer consultants, who do not replenish at all; back-office workers, who are mainly responsible for the processes surrounding the goods; and regular sellers, who mainly help customers but also have some responsibility for the shelves.

Trautrims, A.; Grant, D.; Schnedlitz, P.

75

Store 1 does not use any external workers. The store manager considers their employment as a threat to customer service quality, as the use of external workers in the past had made the regular employees unaware of the availability and location of products. The ordering of products is almost entirely automated through the central distribution. The only way to interfere with this system is to either change stock records via the store office or to communicate with the distribution by using an electronic form on the computer system. The employees can access the computer system and find out about stock levels but they are not authorised to make adjustments on the system. When communicating through the electronic form with distribution they need to provide a reason for their request. Employees can also place special orders for customers either for listed products or directly from suppliers for non-listed products. Very rarely are products returned; they are usually sold off at a reduced price rather than sending them back. However, some returns are agreed with suppliers in advance because the retailer consciously overstocks the stores for some promotions. The shelves are checked every Monday morning for gaps by the back of store workers. On a Monday morning the availability is usually particularly poor as Saturdays are the busiest day of the week, and there is usually no delivery on a Saturday and consequently no replenishment early Monday morning. The back-of-store employees walk through the entire store and scan every shelf gap. This process results in a list of shelf gaps, which is then worked through: Gaps are replenished with stock; wrong stock records get corrected, so that automated ordering starts again; or items needed to be reallocated from second placements in the store. Every regular sales employee in a department gets a zone of responsibility for a certain stretch of shelf metres. The size of the responsibility zone depends on the products that are offered. A weekly task list comes with the zone responsibility. As most products require explanations for customers, the first priority is always on helping customers followed by replenishment, shelf maintenance and shelf optimisation. The level of shelf presentation is not as high as in grocery retailing. Nevertheless, the shelves are standardised modules and every store of the same size within the company looks the same. The store has to ask for permission to diverse from any standardised process. When a product is OOS the sales adviser can respond in several ways. He/she can lead the customer to a substitute, look for stock in buffer storage, or place an order for the customer. Most the inventory is stocked on the shelf itself and stock keeping levels and ordering thresholds are set by central distribution. If a product is OOS too often the store can communicate this through the electronic form to distribution which then looks into the problem. The required qualifications of employees depend entirely on their position. As the main task of sales employees is to help customers with their specific questions they need to have a pro-

76

European Retail Research Vol. 25, Issue I, pp. 63-84

fessional qualification in a trade combined with general retailing skills such as friendliness and stress resistance. Having learnt a trade is an essential qualification for all team leaders. For every position there is a job description given by central organisation. Staff is allocated differently at the two stores as they have different approaches to the specialisation of workers. Back of store logisticians are always allocated to their area. Shop floor employees need to be allocated so that there is someone available for customers all the time. The company’s central organisation defines processes for the store. For example, the concept of organising incoming goods in logistics streams and colour coding them is given by the central organisation as are the shelf modules and the zone of responsibility concept. The central organisation checks every store two to four times a year for compliance with processes. Exceptions from standard processes have to be applied for by the store manager and can only be allowed if shrinkage or other monetary losses do not increase from it.

7.

Discussion

The interviews in both cases were based on a store process framework proposed by Kotzab/Teller (2005). As their framework was constructed on the flow of chilled dairy products in a supermarket, they explicitly called for further exploratory case studies into in-store logistics processes. By looking at a hypermarket and a DIY retailer this study contributes to that knowledge of in-store logistics processes by extending the Kotzab/Teller model. It will further present why extensions to the initial model need to be made for the two retailers studied; and how their individual supply and demand characteristics influence their store operations. The extended and amended Kotzab/Teller model is shown in figure 2. As the original model uses letters for the steps in the replenishment process, extensions are made by adding a number to this letter to highlight them. In terms of forward product flow, the DIY retailer and one store of the grocery retailer use store shelving with an on-top storage capacity. This “Überlager” (storage space on top of the shelf which is not accessible to the customer) permits keeping a small amount of a product within the sales area without the customers actually being able to access it (C2). Also, deliveries were not always dragged to the shelf in full trolleys; often the store employees would select and arrange the required products in the backstore first before they transport them to the shelf. This is not generally happening at all retailers as they may pack delivered roll cages in sequence. The arrangement of products can either happen at the store or at the DC. From a supply chain perspective, a total cost view should be taken on this to reduce overall logistics costs. It might also explain the huge variance in the amount of time that store employees spend on replenishment as described by Thonemann et al. (2005). It might be beneficial to pre-sort the deliveries for retailers that require skilled store workforces.

Trautrims, A.; Grant, D.; Schnedlitz, P.

77

Figure 2: The Extended and Amended Kotzab/Teller Model

Retailer/Manufacturer Delivery

G. Reorder

Loading/Unloading Bay A. Receipt B. Transport 1 C. Storage 1 [backstore]

C2. Storage 2

H. Disposal

D+E. Transport 2/Handling

SALES FLOOR

STORE

E2. Handling [unwrapping, placing] Shelf E3. Handling [shelf maintenance] Customer F. Processing/Transaction Till forward product flow reverse product flow information flow

There is also a potential handling step after the customer takes the product from the shelf. If the customer reconsiders the purchase of a non-temperature controlled item and leaves it at a

78

European Retail Research Vol. 25, Issue I, pp. 63-84

random place in the store the product can be put back on the according shelf by the store employees. One could argue that a further handling step E4 is needed later than the customer. However, the customer’s action of taking the product from his trolley can be considered as a reverse flow, with the item then being returned to its shelf place as a part of regular store and shelf maintenance. The reverse flow in the Kotzab/Teller model only considers the disposal of products along the supply chain and the only reverse flow arrows go straight to H. This can be confirmed from the two case studies in this project for temperature controlled products with a short shelf-life. However, the reverse supply chain for ambient products mostly uses the same path as the forward supply chain, just in a reverse order. Recalled products are taken off the shelf and then prepared for the return journey by the back of store employees so that the return can be processed efficiently at the receiving DC. Therefore, reverse flow arrows needed to be added between most the process steps. Depending on the way they were initially delivered into the store the products would then either been sent back to the retailer’s DC or the manufacturer on one of the delivery trucks. Generally, both retailers try to avoid reverse flows of products. One manager at the grocery retailer described the effort that is needed for reverse flows as a ‘nightmare’. However, the DIY retailer could return certain promotional items to the manufacturer; which due to shelf life issues was impossible for most products at the grocery retailer. Seasonal and promotional stock would be sold off, stocked at the store, or returned; with the chosen option strongly depending on the available storage space at the stores. For some promotional and seasonal items the DIY retailer would have already agreed the return with the supplier, which was particularly used for promotions that were consciously overstocked to enhance sales. The reverse flow was rather individualised to the specific case than the highly standardised forward flow. In the altered model the reorder box is stretched over sales floor, store, and outside the store, whereas the original model considers reordering only happening at the store zone. The reason for this is that the DIY stores in particular heavily rely on information that comes directly from the sales floor. The departments can place customer orders and communicate directly with the central purchasing and merchandising departments through electronic forms. Also, the grocery retailer uses information gathered and transmitted through handhelds for its reordering. Nevertheless, the reordering also uses input from the store management level, which at the grocery retailer has the final say in placing store orders. The reordering is further extended outside the store area as at both retailers the central office has the final decision on product orders. Regarding promotions and the DIY retailer, even regular orders could be pushed into the store. Thus, the reordering takes information input from the store, but depending on the occasion the reordering itself can happen at all levels of operations. As reordering also relies strongly on correct stock records, the reordering decision makers always rely on the

Trautrims, A.; Grant, D.; Schnedlitz, P.

79

interaction with frontline employees. Designing easily usable communication channels between those who order and those who physically see what is on the shelf is therefore essential for this part of store processes. Collaboration and reordering is often seen as a matter between suppliers and retailers. However, the internal supply chain needs to overcome barriers as well. This is illustrated by case one, where the store orders from the central organisation like a customer; and in case two, where the central purchasing organisation relies on the frontline staff to provide information maintenance. As a retail store is the meeting point between the supply side and the demand side of a retail shop, the store processes of the two case studies are looked at from these two perspectives. Product characteristics initially determine the supply into the store. The sometimes heavy and bulky products at the DIY retailer are not particularly suitable for cross-docking or reprocessing operations in DCs; hence the stores still receive a large amount of deliveries directly from the suppliers. Further, with the volume of product many more deliveries arrive at a store than at the grocery retailer. As expiry dates are no concern in DIY retailing, the lower inventory turnover is less of an issue. Nevertheless, inventory holding costs were an important issue to the DIY retailer, which are determined by the high value of each individual product and the time it sits on a shelf until it is sold. The lower inventory turnover also allows the stores to keep most of the inventory in the sales area, which keeps inventory more visual and therefore stock records easier accurately than having stock in the sales area and the backroom as it is typical in grocery retailing. Also, keeping stock records accurate may be much more difficult for the 60,000 SKUs at the grocery retailer, whose product turnover is more rapid. However, DIY retailing also includes small items, such as little packs of screws, which are accordingly more difficult to be kept accurate than larger items. As stock records would have to be adjusted more often at the grocery retailer the sales floor staff there could easily access the stock systems and correct stock numbers; whereas the DIY staff needed to inform the store administration to get stock records changed. The point of ordering new products differs between the cases and therefore one must look critically at the transferability of previous research into root-causes for OOS such as McKinnon/Mendes/Nababteh (2007) since retailers’ store operations may differ and exclude certain store logistics issues from the store’s influence. Whilst grocery products are generally mainly self-explanatory, most customers at DIY stores need advice. As home improvement activities happen less frequently than grocery shopping and the average item price is much higher, the consumer might also show a higher involvement in the purchasing decision. Also, the consequence of a wrong purchase decision can be higher for the DIY retailer than for a fast-moving consumer good. Consequently, consumers expect advice from the workforce, which at this retailer was accordingly skilled to satisfy this demand.

80

European Retail Research Vol. 25, Issue I, pp. 63-84

According to Campo/Gijsbrechts/Nisol (2000) product, consumer and situation characteristics influence customer responses towards OOS. In the same way these categories also impact the level of product availability provided by the retailers. From the retailers’ perspective product characteristics, shopping patterns and shopping situation determine the OSA that can be provided. Product characteristics determine the supply patterns in combination with customers’ shopping habits and expectations. Additionally, situation characteristics can occur, such as special promotions or the seasonal provision of goods. As a consequence of the advisory role that shop floor staff at the DIY retailer possess the company attempted to reduce logistics processes from working time so that sales floor staff could actually spend more time helping customers rather than having to take care of product replenishment. The fact that customers were usually seeking advice from staff gave sales employees the chance to lead customers to a product which they knew was available. In terms of OSA it posits the notion that personal sales advice may lead to a reduction of perceived OOS. The sales floor workforce at the grocery retailer however was mostly addressed when a customer was unable find a product. The higher skilled workforce needed for advising the customer at the DIY retailer is also more expensive than at the grocery retailer. Thus, the higher costs associated with the workforce have encouraged a system that embeds a high specialisation of the workforce on the logistics side, which can be seen at the dedicated back of store operations and the streamlined logistics operations at the DIY stores. This specialisation stands in contrast to a general assumption that retail staff need more skills, as suggested by the Kotzab/Teller (2005) study and literature such as Baxter (2007) and Esbjerg/Buck/Grunert (2010). Upskilling might happen in specialised areas, but not necessarily at basic logistics functions. In general, we conclude that both demand and supply side at the two case studies from different retail sectors had an impact on the design of store processes. On the customer side the need to explain DIY products and the expectation of being advised by the retailer shaped the replenishment process in a way that tried to cut off the logistical activities from the sales advisors. On the supply side, these qualified sales advisors are more expensive than unskilled replenishment workers. The shift of replenishment activities from the more costly workers therefore seems to be a logical consequence. The lower sales frequency in DIY compared to groceries on the demand side resulted in longer order lead times on the supply side, as delivery speed was less important. The main factors on the supply side however were the dimensions of products and their perishability.

Trautrims, A.; Grant, D.; Schnedlitz, P.

81

Grocery items are usually boxed and can all be handled without any technical devices, whilst the heavier items in DIY need forklifts and also make direct deliveries more applicable. Perishability added extra processes in grocery retailing, as products have to be checked for expiry dates and also need to be sold off or disposed for this reason. Overall, both retail sectors follow similar logistical consideration in their replenishment operations. But due to the characteristics of their businesses and retail sectors the adjustments they made resulted in very different approaches towards their individual store operations.

8.

Limitations and Future Research

The limitations of this study arise mostly from the general characteristics of exploratory qualitative case studies. Although the case studies have the advantage of in-depth insight into the respective cases, the number of two investigated cases with two stores each is too small to make major generalisations. The area of non-grocery retailing was covered by a company from the DIY sector, but that leaves plenty of other retail sectors that are not yet covered. We suggest that future research investigate other retail sectors, particularly those that show different product and shopping characteristics from grocery retailing. Also, the concentration in one retail market is a limitation to the study; retailers in countries with different market environments might arrange their in-store processes differently. Finally, both retailers investigated in this study belong to larger retail chains with a strong central organisation. Retail organisations with other structures such as cooperatively organised independent shops might even have different processes within themselves with the result of different supply chain structures.

82

European Retail Research Vol. 25, Issue I, pp. 63-84

References Baxter, I. (2007): Workforce management: The next greatest contributor to increasing retail performance, in: European retail digest, Oxford Institute of Retail Management, Oxford, pp. 30-32. Broekmeulen, R.; van Donselaar, K.; Fransoo, J.; van Woensel, T. (2004): Excess shelf space in retail stores: An analytical model and empirical assessment, BETA Working paper series, Technische Universiteit Eindhoven, Eindhoven. Campo, K.; Gijsbrechts, E.; Nisol, P. (2000): Towards understanding consumer response to Stock-outs, in: Journal of Retailing, Vol. 76, No. 2, pp. 219-242. Campo, K.; Gijsbrechts, E.; Nisol, P. (2003): The impact of retailer stockouts on whether, how much, and what to buy, in: International Journal of Research in Marketing, Vol. 20, No. 3, pp. 273-286. Coca-Cola Retailing Research Council (1996): Where to look for incremental sales gains: The retail problem of out-of-stock merchandise, research report, Arthur Andersen Consulting. Corsten, D.; Gruen, T. (2003): Desperately seeking shelf availability: An examination of the extent, the causes, and the efforts to address retail out-of-stocks, in: International Journal of Retail and Distribution Management, Vol. 31, No. 12, pp. 605-617. ECR Europe (2003): Optimal shelf availability í increasing shopper satisfaction at the moment of truth, research report. Ellram, L.M. (1996): The use of case study method in logistics research, Journal of Business Logistics, Vol. 17, No. 2, pp. 93-138. Esbjerg, L.; Buck, N.; Grunert, K.G. (2010): Making working in retailing interesting: A study of human resource management practices in Danish grocery retail chains, in: Journal of Retailing and Consumer Services, Vol. 17, No. 2, pp. 97-108. Fernie, J.; Pfab, F.; Marchant, C. (2000): Retail grocery logistics in the UK, in: International Journal of Logistics Management, Vol. 11, No. 2, pp. 83-90. Fernie, J.; Grant, D. B.; Trautrims, A. (2009): On shelf availability in UK retailing, British Academy of Management (BAM) Conference, Harrogate. Grant, D.B.; Lambert, D.M.; Stock, J.R.; Ellram, L.M. (2006): Fundamentals of logistics management, Maidenhead. Grant, D.B.; Fernie, J. (2008): Research note: Exploring out-of-stock and on-shelf availability in non-grocery, high street retailing, in: International Journal of Retail and Distribution Management, Vol. 36, No. 8, pp. 661-672. Grant, D.B.; Fernie, J. (2009): On-shelf availability and out-of-stocks in UK retailing, in: Schnedlitz, P.; Morschett, D.; Rudolph, T.; Schramm-Klein, H.; Swoboda, B. (eds.): European retail research, Wiesbaden, pp. 51-76. Handelsverband, (2009): Die Bedeutung des Handels http://www.handelsverband.at, accessed November 16, 2009.

in

Österreich,

Trautrims, A.; Grant, D.; Schnedlitz, P.

83

Helm, R.; Hegenbart, T.; Stölzle, W.; Hofer, F. (2009): Advanced optimal shelf availability – Regallücken, Ursachen und Kundenreaktionen: Neueste Erkenntnisse aus der Praxis, in: Helm, R.; Stölzle, W. (eds.): Optimal shelf availability – Effiziente Managementkonzepte zur Optimierung der Regalverfügbarkeit, Frankfurt a.M., pp. 9-30. Hoffmann, N.A.; Schnedlitz, P. (2008): The Austrian retail market: A profile, in: Schnedlitz, P.; Morschett, D.; Rudolph, T.; Schramm-Klein, H.; Swoboda, B. (eds.): European Retail Research, Vol. 22, pp. 207-227. Hulbert, L. (2009): Innovation in replenishment solutions to improve on-shelf availability, IGD availability and demand planning conference 2009, London. KMU Forschung Austria í Austrian Institute for SME Research (2008): Der österreichische Handel 2008 í Daten-Fakten-Analysen, Vienna. Kotzab, H. (2005): Retail logistics and supply chain management, in: Kotzab, H.; Bjerre, M. (eds.): Retailing in a SCM-perspective, Copenhagen. Kotzab, H.; Hartig, N.; Ludvig, S.; Steinbrecher, A.C. (2009): Efficient consumer response – Hintergrund, in: Kotzab, H.; Steinbrecher, A.C. (eds.): ECR efficient consumer response – Standards, Prozesse, Umsetzungen, Vienna, pp. 11-33. Kotzab, H.; Teller, C. (2005): Development and empirical test of a grocery retail instore logistics model, in: British Food Journal, Vol. 107, No. 8, pp. 594-605. Kotzab, H.; Reiner, G.; Teller, C. (2007): Beschreibung, Analyse und Bewertung von InstoreLogistikprozessen, in: Zeitschrift für Betriebswirtschaft, Vol. 77, No. 11, pp. 1-24. Mantrala, M.K.; Levy, M.; Kahn, B.E.; Fox, E.J.; Gaidarev, P.; Dankworth, B.; Shah, D. (2009): Why is assortment planning so difficult for retailers? A framework and research agenda, in: Journal of Retailing, Vol. 85, No. 1, pp. 71-83. Magnus, K.-H. (2007): Erfolgreiche Supply Chain Kooperationen zwischen Einzelhandel und Konsumgüterherstellern. Eine empirische Untersuchung der Händlerperspektive, Muenster. McKinnon, A.C.; Mendes, D.; Nababteh, M. (2007): In-store logistics: an analysis of on-shelf availability and stockout responses for three products groups, in: International Journal of Logistics: Research and Applications, Vol. 10, No. 3, pp. 251-268. Meiȕl, A.; Steinbrecher, N.; Hartig, S.; Ludvig, S. (2009): Spezifische ECRProblemstellungen und Empfehlungen, in: Kotzab, H.; Steinbrecher, A. C. (eds.): ECR Efficient Consumer Response – Standards, Prozesse, Umsetzungen, GS1 Austria/ECRAustria, Vienna, pp. 197-243. Metro Group (2009): Handelslexikon 2009/2010, Düsseldorf. Nielsen (2008): Handel in Österreich: Basisdaten 2008 í Konsumententrends 2008, Vienna. Nohl, A. (2006): Interview und dokumentarische Methode í Anleitungen für die Forschungspraxis, Wiesbaden. Schary, P.B.; Becker, B.W. (1978): The impact of stock-out on market share: Temporal effects, in: Journal of Business Logistics, Vol. 1, No. 1, pp. 31-44. Schary, P.B.; Christopher, M. (1979): The anatomy of a stock-out, in: Journal of Retailing, Vol. 55, No. 2, pp. 59-70. Seth, A.; Randall, G. (2001): The grocers: The rise and rise of the supermarket chains, London.

84

European Retail Research Vol. 25, Issue I, pp. 63-84

Stake, R.E. (1994): Case studies, in: Denzin, N.K.; Lincoln, Y.S. (eds.): Handbook of qualitative research, London, pp. 236-247. Statistik Austria (2008): Wirtschaftsatlas Österreich, http://www.statistik.at, accessed November 16, 2009. Thonemann, U.; Behrenbeck, K.; Kuepper, J.; Magnus, K. (2005): Supply chain excellence im Handel: Trends, Erfolgsfaktoren und Best-Practice-Beispiele, Wiesbaden. Ton, Z. (2002): The role of store execution in managing product availability, Harvard University. Trautrims, A.; Grant, D.B.; Fernie, J.; Harrison, T. (2009): Optimizing on-shelf availability for customer service and profit, in: Journal of Business Logistics, Vol. 30, No. 2, pp. 231247. Van Woensel, T.; van Donselaar, K.; Broekmeulen, R.; Fransoo, J. (2007): Consumer responses to shelf out-of-stocks of perishable products, in: International Journal of Physical Distribution & Logistics Management, Vol. 37, No. 9, pp. 704-718. Yin, R. K. (2003): Case study research: Design and methods, London.

Ethical Sourcing – Choice of Sourcing Strategies and Impact on Performance of the Firm in German Retailing Jonas Bastian and Joachim Zentes

Abstract The paper discusses the influence of corporate social responsibility (CSR) on sourcing strategies of retailers and wholesalers in Germany. Furthermore it is investigated how different sourcing strategies influence the ethical sourcing performance (ESP) and how ESP influences a company’s success. An empirical study with 47 German retailers, using PLS regression, supports the conclusions that ethical sourcing intentions and ESP are both positively correlated to the use of vertical cooperation and third party controlled acceptance standards in sourcing activities. Whereas an ethical sourcing intention also leads to a higher direct influence of retailers on their suppliers, no correlation between a high influence on suppliers and ESP was found. Finally, a higher ESP is correlated with a lower monetary success in the short run, but it can significantly increase non-monetary performance indicators like customer satisfaction. Implications for supply chain participants and opportunities for researchers are also discussed.

Keywords Ethical Sourcing, Corporate Social Responsibility, Sourcing Strategies, Strategic Sourcing, Ethical Sourcing Performance, Performance of the Firm

Jonas Bastian (corresponding author) Institute for Commerce & International Marketing, Saarland University, Saarbruecken, Germany (Tel: +49 681 302 4471; E-mail: [email protected]). Joachim Zentes Institute for Commerce & International Marketing, Saarland University, Saarbruecken, Germany

Received: November 15, 2010 Revised: February 1, 2011 Accepted: March 1, 2011

EUROPEAN RETAIL RESEARCH Vol. 25, Issue I, 2011, pp. 85-105

D. Morschett et al (eds), European Retail Research, DOI 10.1007/978-3-8349-6235-5_5, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

86

1.

European Retail Research Vol. 25, Issue I, pp. 85-105

Introduction

Since the 1980s, the rising strategic importance of the purchasing function has been highlighted in numerous studies (e.g. cf. Kraljic 1983; Dumond 1994; Gadde/Håkansson 1994; Carr/Smeltzer 1997; Pressey/Tzokas/Winklhofer 2007). At the same time, a change from a passive, short-term-oriented economic procurement to a competitive or strategic behaviour took place in retailing practice (Hong/McGoldrick 1996, p. 18; Moser 2007; Svahn/Westerlund 2009, p. 173). Today, the development of purchasing strategies “as a set of rules that guides the configuration of the firm’s purchasing effort over time” (cf. Koplin 2006, p. 69), is more and more common in retail companies. On this account, purchasing is not only the result of adapting to market conditions anymore but rather a deliberately planned behaviour integrated in a strategic view. From this we argue that retailers, at least to a certain extent, actively shape the configuration of their sourcing activities and choose sourcing channels, suppliers, as well as the relationship to their suppliers strategically. When we contrast this development with the increasing strategic importance of environmental and social aspects in the supply chain (Murphy/Poist 2002; Ciliberti et al. 2009, p. 117), the relevance of the question of how the aspiration for a high corporate social performance (CSP) influences and changes purchasing behaviour becomes obvious. Gold/Seuring/Beske (2010, p. 239) emphasise the importance of this topic while describing the development of supply and distribution capabilities as corporate core competencies that are essential if supply chains aim at incorporating social and environmental goals. Although a number of studies have addressed ethical issues in supply chains, empirical research, especially for retail companies, is very scarce. In contrast, retailers often take the position of a gatekeeper to the customer in consumer goods supply chains. On this account their sourcing behaviour is highly relevant for the implementation of sustainability in supply chains. Therefore we try to give some answers to the questions of if and how ethical sourcing intentions of retailers influence their choice of coordinating mechanisms within the supply chain as well as how the use of different coordinating mechanisms influences the ethical sourcing performance of retail companies. Before these relationships are further examined, an introduction into the concepts of CSR, ethical sourcing as well as an overview of the current state of research is given in the next section.

2.

Corporate Social Responsibility and Ethical Sourcing

Today, in Europe, the definition by the European Commission can be seen as the common and widely accepted understanding of CSR, according to which it is “a concept whereby companies integrate social and environmental concerns in their business operations and in

Bastian, J.; Zentes, J.

87

their interaction with their stakeholders on a voluntary basis” (Commission of the European Communities 2001, p. 6). Hence, activities which are part of the CSR commitment of a company consider social and environmental concerns to a greater extent than is mandatory by law. So far, many researchers have focused on the identification of different aspects of CSR. At corporate level, substantial research categories include gender and racial diversity in the workplace (Ibarra 1993), the impact of business on the ecosystem and natural environment (Shrivastava 1995), philanthropic contributions and community involvement (Fry/Keim/Meiners 1982), working and living conditions of employees (Jennings/Entine 1998), and workplace safety (McLain 1995). The concept of ethical sourcing or purchasing social responsibility (PSR) applies these deliberations to sourced products and processes in the supply chain. Carter/Jennings (2004, p. 151) give an appropriate definition of PSR: “Purchasing activities that meet the ethical and discretionary responsibilities expected by society.” In purchasing and supply management literature, several studies have examined stand-alone ethical sourcing activities. Table 1 gives an overview. Table 1: Selected Contributions to Ethical Sourcing Research Focused issue

Studies

Sourcing from minority business enter-

Dollinger/Enz/Daily 1991; Carter/Auskalnis/Ketchum 1999;

prises

Krause/Handfield/Scannell 1998 Drumwright 1994; Min/Galle 1997; Carter/Ellram 1998; Carter/Carter 1998; Quinn

Environmental purchasing and logistics

Human rights issues like labour practices at supplier plants

1999; de Buck/Hendrix/Schoorlemmer 1999; Montabon et al. 2000; Qiu/Prato/McCamley 2001; Carter/Dresner 2001; Carter 2005 Emmelhainz/Adams 1999; Rivoli 2003; Roberts 2003

Affirmative action purchasing

Carter/Auskalnis/Ketchum 1999

Ethical purchasing behaviour

Haynes/Helms 1992; Razzaque/Hwee 2002

Carter/Jennings (2002; 2004) empirically established the primary supply chain CSR categories of “environment”, “diversity”, “human rights”, “philanthropy”, and “safety”. The elements of ethical sourcing are diverse and may differ considerably, for example between industries, sourcing region and products (Loew 2006). Therefore, a comparison between companies’ ethical sourcing performance (ESP), as the evaluated sum of social and environmental achievements and harms caused by a company’s sourcing and logistic activities, with regard to concrete issues is not appropriate in many cases. For this reason, especially in cross industry studies, the accumulation of concrete issues under the social and the environmental dimension as generic terms seems to be promising.

88

European Retail Research Vol. 25, Issue I, pp. 85-105

As many of the ethical issues in retailing concern upstream supply chain tiers, logistic processes, or refer to the way how the company interacts with its suppliers, ethical sourcing is certainly one of the most important fields of CSR in retailing. Thus, it is not surprising that retailers are continuously expanding their responsibility for the products in their assortment (cf. Bloemhof-Ruwaard/van Beek/van Wassenhove 1995) and start managing the CSR of their partners in the supply chain (Emmelhainz/Adams 1999; Kolk/van Tulder 2002). Although several studies exist which have dealt with ethical sourcing, most of them focus only on single CSR issues, or discuss the possibilities to improve ESP on the basis of single case studies. While the number of empirical studies dealing with retail sourcing is already scarce (Janz 2004, p. 23), the question of how CSR affects the sourcing strategies of retailers as a whole is insufficiently studied (Cramer 2008, p. 395).

3.

Theoretical Background

3.1

Characteristics of Ethical Sourcing

Supply chains have to meet final customers’ demands and hence it is necessary to coordinate the processes carried out by chain members. The four sourcing strategies discussed in this paper are coordinating mechanisms within the continuum between market, in the case of a series of independent firms interacting, and hierarchy, where all processes are carried out within one vertically integrated company (Ciliberti et al. 2010). While searching for the optimal coordination of ethical procurement it seems to be worthwhile to characterise ethical value added. Möhlenbruch/Wolf (2009) state that in hindsight ethical surplus can only be proven under enormous efforts, if at all, and therefore can be characterised as a credence quality (Trommsdorff/Götze/Herm 2006). Taking this into consideration, at least two challenges for retailers arise: they have to take measures to build customer trust and to solve the agency problems arising at upstream supply chain tiers. In today’s business environment ethical surplus is becoming more important as a source of differentiation to the competition, e.g. for higher positioned and premium private labels of retailers (Möhlenbruch/Wolf 2009). Creating ethical surplus is currently one of the most important sources for innovation in fast moving consumer goods (cf. Zentes/Bastian/Lehnert 2010). In retailing practice ethical sourcing is closely related to the use of codes of conducts and strict directives which lead to a number of exclusion criteria, such as the use of child labour, genetic engineering, exhaustive cultivation or the use of former nature reserves for cultivation (Loew 2006). This restricts the number of potential suppliers and should lead to a scarce offer. As explanatory approach for retailers’ behaviour in this situation we suggest primarily the principal agent theory as well as the resource dependency theory which are discussed in the following section.

Bastian, J.; Zentes, J.

3.2

89

Principal Agent Theory

The principal agency theory discusses the problems of adverse selection, hidden intentions and moral hazard in contractual relationships between a principal and an agent with information asymmetries (Eisenhardt 1989). Applications of the agency theory to supply chains are rather scarce, but nonetheless an appropriate approach while examining CSR issues (Ciliberti/de Haan/de Groot/Pontrandolfo 2010). Especially the theories’ explanatory contribution to handling goal incongruities in supply chains (Halldorsson et al. 2007) is beneficial in the context of this paper. In our situation the buyer/retailer would be the principal whereas the companies on upstream supply chain tiers are considered as agents. In this constellation the retailer can try to measure the actual outcome in terms of e.g. cost and quality (that alone is hard to do for credence qualities), but the individual effort and the real production conditions are normally known only to the agent involved (asymmetric information). Some potential partners may promise more than they are able to deliver and in doing so systematically underprice responsible competitors (adverse selection). Also, there is a risk that suppliers may underperform or break conditions once they are accepted as chain members (moral hazard) to enhance their own profit at the retailer’s expense (Ciliberti et al. 2010, p. 2). Following the principal agent theory the choice among the two extreme coordination mechanisms market and hierarchy and other intermediate mechanisms depends on the risk of opportunistic behaviour. In situations where this risk is low, market would be preferable due to its transparency; otherwise hierarchy should be preferred as it is the strongest safeguard against opportunism (de Haan et al. 2003; Ciliberti et al. 2009). Alternatively, the principal could reduce information asymmetries, e.g. concerning production conditions, by installing mechanisms to monitor the actions of the supplier, or search for suppliers with similar goals regarding ethical sourcing.

3.3

Resource Dependency Theory

A second explanatory approach for CSR-induced sourcing strategies is provided by the resource dependency theory. It implies that a company’s success is dependent on “the ability of the organisation (…) to exploit its environment in the acquisition of scarce and valued resources” (Yuchtman/Seashore 1967, p. 898). Following the theory, companies try to reduce the dependency on important and scarce resources (Pfeffer/Salancik 1978, p. 45). Important strategies for reducing such dependencies are the integration of the owner of the scarce resource (Pfeffer/Salancik 1978, p. 113) as well as cooperative arrangements (Boyd 1990, p. 420). The application of the resource dependency theory on input factors, like sourced products, is common in research (cf. Werner 1996, p. 23). As shown above, ethical sourcing encompasses several criteria of exclusion regarding possible products and suppliers, which reduce the number of potential sources. Following the resource dependency theory, a smaller supplier pool will lead to higher dependencies. Especially for companies differentiating them-

90

European Retail Research Vol. 25, Issue I, pp. 85-105

selves from competitors through sustainable added value, ethically sourced products have higher turnover shares and are critical for the build-up of their intended corporate image, which leads to a higher importance of these products.

4.

Conceptual Model

As endogenous variables we chose “environmentally responsible sourcing intention” as well as “socially responsible sourcing intention”. They signal in which degree a company aims at integrating the respective issues into its sourcing decisions, whereas these two dimensions can be understood as an aggregation of the various issues identified in literature. Corresponding items are the aspiration level concerning social/environmental responsibility and the importance of a high social/environmental performance as a strategic goal in purchasing decisions. We decided to abstract from sector-specific fields of CSR in order to receive constructs which can be used in cross-industry studies. The principal agent theory as well as the resource dependency theory indicates that the choice of coordinating mechanisms is of high importance in ethical purchasing. Furthermore, Zsidisin/Siferd 2001 postulate that certification and specification can support environmental goals in purchasing. Therefore, we chose four different coordination mechanisms and degrees of collaboration with suppliers as our constructs regarding sourcing strategies (cf. Kampstra/Ashayeri/Gattorna 2006, p. 326). “Third party standards” symbolises the use of respective standards and certifications, defined and audited by third parties like NGOs or special certifiers . This method for securing quality and CSR standards can be used in transactional supplier relations as well and does not automatically signal a close retailer-supplier relationship. Recent years have seen an increase in the use of labels in retailing, what has led to more labels and more labelled products (Zentes/Bastian/Lehnert 2010) and there is some evidence that these development runs parallel to the increasing importance of social and environmental issues. The second sourcing strategy under research is “direct influence on suppliers’ processes”. This construct indicates in which degree a company directly influences or controls the production processes and/or products of a supplier, without the inclusion of third parties. One of the items is e.g. “we strongly influence suppliers’ processes at final production level”. This is certainly more extensive than the use of third party certifications and requires a minimum of power over the supplier, as well as a closer or, at least, a longer lasting relationship. The construct “vertical cooperation” describes a retailer’s propensity for long lasting, partnerlike relationships and also contains the willingness to build up network structures in the supply chain. Several studies describe the build-up of cooperation as well as collaborative relationships to suppliers as important strategies in order to improve social and environmental issues in the supply chain (Gold/Seuring/Beske 2010; Vachon/Klassen 2006). While third

Bastian, J.; Zentes, J.

91

party standards and direct influence on suppliers’ processes are primarily concerned with monitoring and controlling suppliers, cooperative arrangements involve “a pro-active stance toward other supply chain actors aiming for substantial engagement in two-way” (Gold/Seuring/Beske 2010, 237). The closest form of connection between retailer and supplier is “vertical integration”. Here, upstream supply chain tiers are directly owned by the retailer, whereby he has the strongest safe-guard against opportunistic behaviour and direct access to resources at upstream supply chain tiers. The “ethical sourcing performance” construct integrates the environmental and the social dimension. It is based on respondents’ assessments concerning the actual impact of companies’ sourcing activities, sourced products and (logistic) processes on the environment/society as compared to national competitors. While “monetary success” displays classical success indicators like turnover, profit and return on sales (cf. Conant/Smart/Solano-Mendez 1993), “non-monetary success” contains aspects like customer satisfaction and customer loyalty (cf. Homburg/Hoyer/Fassnacht 2002). Figure 1 summarises the constructs and relationships under examination in this study. Figure 1: Conceptual Model

sourcing goals

sourcing strategies

sourcing performance

company performance

vertical integration

environmentally responsible sourcing intention

monetary success

vertical cooperation ethical sourcing performance direct influence on suppliers` processes socially responsible sourcing intention

third party standards

non-monetary success

92

European Retail Research Vol. 25, Issue I, pp. 85-105

5.

Hypotheses

5.1

Influence of Ethical Sourcing Intentions on Sourcing Strategies

Since ethical surplus is characterised as a credence quality, it can be expected that the risk of opportunistic behaviour while sourcing ethical products is comparatively high. Salomone (2008) supports this assumption and states the existing problem of having to constantly monitor the entire supply chain especially regarding CSR issues. Portfolio approaches for organisational buying propose closer supplier relationships for situations with a high complexity of supply market and a high importance of purchasing (cf. Kraljic 1983; Olsen/Ellram 1997; Nellore/Söderquist 2000), which can be assumed for many situations of ethical purchasing in companies with a high ethical intention. While these portfolios usually focus on economic criteria, several studies also propose a relational approach for ethical supply chain initiatives (Krause/Ragatz/Hughley 1999; Emmelhainz/Adams 1999; Carter 2005, p. 182; Kambalame/de Cleene 2006). Gold/Seuring/Beske (2010, p. 239) expose the requirement of closer interactions between firms in supply chains while pursuing sustainability goals. These findings combined with the explained inferences of resource dependency theory and principal agent theory imply a tendency to a close retailer supplier relationship for companies with a high ethical sourcing intention or at least the use of monitoring or control mechanisms like external audits or certifications what “may be able to imitate controls inherent in vertical integration” (Zsidisin/Siferd 2001, 71). Thus we formulate the first two sets of hypotheses: H1:

The higher the environmentally responsible sourcing intention, the higher the H1.1: degree of vertical integration. H1.2: degree of vertical cooperation. H1.3: direct influence on suppliers’ processes. H1.4: the use of third party standards.

H2:

The higher the socially responsible sourcing intention the higher the H2.1: degree of vertical integration. H2.2: degree of vertical cooperation. H2.3: direct influence on suppliers’ processes. H2.4: the use of third party standards.

Bastian, J.; Zentes, J.

5.2

93

Effect of Different Purchasing Strategies on Ethical Sourcing Performance

The third set of hypotheses explores the suitability of relational sourcing strategies for improving ESP. The requirements demanded by consumers and law concerning environmental and social issues in Germany are comparatively high. The four strategic directions investigated should improve the direct or indirect control of retailers over production processes (including logistics) and also reduce the risk of opportunistic supplier behaviour. Corresponding with the principal agent theory, we expect that the strategy orientations described are suited to enhance ESP, which leads to the following hypotheses: H 3.1: A higher degree of vertical integration leads to a higher ESP. H 3.2: A higher degree of vertical cooperation leads to a higher ESP. H 3.3: A higher degree of direct influence on suppliers’ processes leads to a higher ESP. H 3.4: A higher use of third party standards leads to a higher ESP.

5.3

Ethical Sourcing Performance and Performance of the Firm

The reasons why firms engage in CSR activities are diverse and originate in economic as well as in ethical intentions of the acting parties. Concerning economic reasoning, firms regard CSR activities as a means to enhance their reputation (Fombrun 2001; 2005), pre-empt legal sanction (Parker 2002), respond to NGO actions (Spar/La Mure 2003), manage their risk (Fombrun/Gardberg/Barnett 2000; Husted 2005), and to generate customer loyalty (Sen/ Bhattacharya 2001; Bhattacharya/Sen 2004; Cruz 2009; Lacey/Kennett-Hensel 2010). For several companies, CSR activities are also a reflection of the morals of management or the owners of the company. In this case CSR activities are driven by an intrinsic motivation of the acting or rule-setting persons. Although many CSR activities do not originate from economic reasons, many studies have been conducted concerning the question of whether and how CSR affects corporate financial performance (cf. Carroll/Shabana 2010). However, empirical research has resulted in disparate and contradictory findings. Several researchers state that CSR initiatives result in additional costs, e.g. due to community development, maintaining plants in economically depressed locations, and establishing environmentally friendly policies (McGuire/Sundgren/Schneeweis 1988), which leads to an economic disadvantage for responsibly acting organisations (Ullmann 1985). Some, especially older studies, have found no relationship between CSR and firm performance (Alexander/Buchholz 1978; Abbott/Monsen 1979), whereas the proponents of a positive CSR-performance link commonly argue that firms with proactive CSR engage in managerial practices like environmental assessment and stakeholder management and tend to anticipate and reduce sources of business risk, such as potential governmental regulation, labour unrest, or environmental damage (Wood 1991; Orlitzky/Benjamin 2001; Zentes et al. 2009). For CSR in supply chains, Cruz (2009, p. 234) pointed out potential economic benefits like the reduction of excess input and waste, reduced

94

European Retail Research Vol. 25, Issue I, pp. 85-105

accident risk, decreased emissions as well as lower logistic costs. In sum, this could lower costs in the long run and improve products, thus leading to benefits for retailers as well as customers and vendors (Cruz 2009, p. 234). Pivato/Misani/Tencati (2008, p. 3) give an explanation for contradictory findings in literature by drawing attention to the role of intermediate performance measures, such as customer satisfaction. They found that social performance positively influences brand loyalty through building trust with consumers which leads to an indirect positive influence on financial performance. Consequently, the inclusion of mediating factors is more beneficial than a simplistic view that only recognises the direct CSRperformance relationship (Carroll/Shabana 2010, p. 94). We agree with the common opinion in literature that there are several business cases for CSR in retail supply chains. Furthermore, we also agree with the CSR sceptics that in many cases CSR entails higher costs. Also, firms truly engaged in ethical issues will accept economic disadvantages at least to some degree to fulfil higher CSR standards. Moreover, it must be stated that many cost reducing CSR measures, like reduced emissions, as well as lowered logistic costs, or a professional risk management do not require an ethical intention. Thus, (solely) shareholder-oriented retailers can also establish many of the CSR business cases just like ethically driven companies. But it should be expected that such an opportunistic integration of CSR issues will not lead to positive reputation and trust effects in the same way as it does for firms with truly responsible commitment. The assessments in our questionnaire regarded the last three years which is a relatively short timeframe concerning ethical strategies. On these grounds we propose the following hypotheses: H 4.1: In the short run, a higher ESP leads to a lower monetary success of the firm. H 4.2: A higher ESP leads to a higher non-monetary success of the firm.

6.

The Study

A mail questionnaire was constructed based on an extensive review of the relevant literature and findings from interviews with 15 CEOs, purchasing managers, and retail consultants. Valid and reliable existing scales where employed when possible and appropriate. The remaining scales for this study were developed based on the procedures recommended by Churchill (1979). Here, scale development evolved, and content and substantive validity were assessed, through interviews with purchasing managers, a comprehensive literature review and a pre-test with eight academics and consultants in the field of purchasing or supply chain management. Scale items that were ambiguous to pre-test participants were modified or deleted based on their comments. A factor analysis confirmed the chosen scales. The survey was sent to a sample of the 843 biggest German retailers in the segment of fast moving consumer goods listed in a commercial database. Furthermore, companies operating

Bastian, J.; Zentes, J.

95

C&C stores were addressed since the procurement processes in these companies are very similar to those of retail companies. Because the study examines the strategic level of the company and to ensure that the survey respondents were in fact knowledgeable and appropriate informants, the questionnaires were addressed to CEO’s (in small companies), purchasing managers and supply chain managers at top level (in larger firms). The respondents had the possibility to return the questionnaire via mail or to answer the questionnaire online. A total of 47 usable surveys were received. This results in an effective response rate of 5.57 %. An important reason for the comparatively low response rate might be the topic and the timeframe of our research. We asked for confidential data which are relevant for competition and concern antitrust law. Due to an unlucky coincident at the time our study took place, the German antitrust division carried out several investigations concerning illegal retail-manufacturer price agreements, well covered by the media, which led to considerable insecurities as evidenced by telephonic requests and written replies. About 36 % of the firms responding to the survey have revenues less than EUR 50 million, nearly 50 % have revenues between EUR 50 million and EUR 500 million, and about 15 % have revenues of over EUR 500 million. The percentage of respondents by industry is as follows (multiple answers were permitted): DIY and plants (15 %), electrical goods (21 %), drugstore products (13 %), cosmetics (15 %), food (36 %), beverages (15 %), furniture (9 %), sports goods/toys/leisure articles (23 %), fashion (34 %), footwear (15 %), and other fast moving consumer goods (13 %). 28 % of the respondents hold positions as CEO, 32 % of respondents are at board level, and the remaining 40 % hold C-level positions in the purchasing or supply chain department.

7.

Analyses

We used five point Likert scales to measure the respondents’ assessment of the respective issues, with “1” representing the lowest and “5” the highest approval. All performance dimensions were prompted as comparisons to the average national competitors in order to eliminate industry-specific differences. Measurement validation and model testing were performed by using the structural equation modelling tool SmartPLS 2.0.M3. We chose PLS because it is an approved algorithm, ideally suited for small sample research, and for reliably calculating our model with 47 cases (Chin/Newsted 1999, p. 326). The results of the calculation of the model are summarised in Table 2.

96

European Retail Research Vol. 25, Issue I, pp. 85-105

Table 2: Internal Consistency of Reflective Variables Variables

AVE

Composite Reliability

Cronbach’s Alpha

Environmentally responsible sourcing intention

0.78

0.88

0.73

Socially responsible sourcing intention

0.79

0.88

0.74

Vertical integration

0.67

0.89

0.84

Vertical cooperation

0.64

0.84

0.71

Direct influence on suppliers

0.66

0.85

0.75

Third party standards

0.75

0.90

0.84

Ethical sourcing performance

0.81

0.89

0.76

Monetary success

0.74

0.90

0.83

Non-monetary success

0.74

0.89

0.82

An average variance extracted (AVE) of at least 0.64, a composite reliability of at least 0.84, and a Cronbach’s Alpha of at least 0.71 indicate a high level of internal consistency across the model. Discriminant validity for the reflective constructs of the model was assessed with Fornell and Larcker’s (1981, 46) criterion. The results show satisfactory discriminant validity for the model.

8.

Results and Discussion

To test the hypotheses we calculated a PLS regression model. The correlation matrix for the model is shown in Table 3. Table 3: Pearson Correlation Matrix Variables Environmentally responsible sourcing intention Socially responsible sourc-

1**

2**

3**

4**

5**

6**

7**

8

1.00 0.78**

1.00

Vertical integration

0.30*

0.33*

1.00

Vertical cooperation

0.50**

0.53**

0.43**

1.00

Direct influence on suppliers

0.44**

0.40**

0.49**

0.53**

1.00

Third party standards

0.66**

0.55**

0.36*

0.54**

0.55**

1.00

Ethical sourcing performance

0.50**

0.56**

0.43**

0.60**

0.44**

0.51**

Monetary success

-0.31*

-0.25

-0.21

-0.28

0.00

-0.06

-0.07

1.00

Non-monetary success

-0.02

0.17

0.07

0.07

0.07

0.08

0.33*

0.46**

ing intention

9

** p < .01, * p < .05

1.00

1.00

Bastian, J.; Zentes, J.

97 2

Chin (1998a, p. 323) describes a coefficient of determination (R values) of 0.19, 0.33 respective 0.67 for endogenous constructs as weak, moderate respective substantial. Since the choice of sourcing strategies certainly does not only depend on the degree of ethical sourcing intention and since a firm’s performance does not only depend on ethical sourcing, substantial R2 values were not expected for most of our constructs. Nevertheless, the estimates of Chin are helpful as guidelines while verifying the hypotheses. Following Chin (1998b), standardised paths coefficients (ȕ values) should be at least 0.20 and ideally above 0.30 in order to be considered meaningful, while Lohmöller (1989, p. 60) determines values for ȕ up to a minimum of 0.1 to be an indicator for relevant correlation. The results of the PLS model are displayed in Figure 2. Figure 2: Results of Partial Least Squares R2=0.12 vertical integration Q2=0.05 environmentally responsible sourcing intention

R2=0.26 monetary success R2=0.30

Q2=0.17

vertical cooperation Q2=0.13

R2=0.44 ethical sourcing performance

R2=0.20 direct influence on suppliers` processes Q2=0.10

ȕ= 0.54***

Q2=0.26

socially responsible sourcing intention

R2=0.11 non-monetary success Q2=0.05

R2=0.43 third party standards Q2=0.28 Signif icance of t-values (bootstrapping procedure, m = 47; 100 samples): *** p < .01, ** p < .05, *p < .1; Q² values were calculated via the blind f olding procedure of Smart PLS

Hypotheses 1.1-1.4 investigate the influence of an environmentally responsible sourcing intention on sourcing strategies. The empirical findings support H 1.2, H 1.3, and H 1.4 (ȕ>0.2); however, only the positive effect on third party standards is statistically significant. The effect on vertical integration (H 1.1.) is positive as well, but not sufficient to support the hypothesis (ȕ<0.1). Concerning the effects of a socially responsible sourcing intention, H 2.1 and H 2.2 are supported; only the effect on vertical cooperation (H 2.2) is significant. The effect on direct influence is only very small (H 2.3) and the evaluated effect of using third party standards (H 2.4) is too small to support the hypothesis. It can be stated that there is a (significant) effect of ethical sourcing intention on the choice of sourcing strategies in retailing. Especially the R2 of vertical cooperation (0.30) and third party standards (0.43) reach considerable values which indicate a high explanatory power of the model for these constructs.

98

European Retail Research Vol. 25, Issue I, pp. 85-105 2

The relative low R of vertical integration is not surprising. Vertical integrations are extensive decisions which should depend on several other (dominant) factors, e.g. like company size or historical background of the retailer, so that a correlation with strategic orientations should only come into effect in the long run and only to a certain extent. While social issues imply primarily closer relations in form of integration or cooperation, environmental issues are first and foremost addressed through coordinating mechanisms like direct influence on suppliers’ processes or the use of third party standards. Reasons for the low effect of the social intention on the use of these coordinating mechanisms could be found in the difficulty of operationalising social issues in business connections. Apparently, retailers prefer close relationships while addressing social issues. Also, relationships could be seen as a social value itself. On the other hand, relationships are not so important for retailers focusing on environmental issues. Here, they prefer a direct influence on processes and especially the use of third party standards. It seems that standards concerning environmental issues are more suitable to meet the retailers’ needs. One reason for that could be the prevalence of labels first and foremost addressing environmental issues, as does the European organic label, whereas labels concerning social issues, like the fair trade label, are less popular in Germany (Lehnert 2009, pp. 5155). If this is true, the development of popular and trusted standards for social issues could probably change this situation. In sum, the assumption that an ethical purchasing intention supports relationally based sourcing, as well as coordinating mechanisms, can be answered positively, but a distinction in social and environmental intentions is appropriate. Hypotheses 3.1-3.4 propose a positive effect of the four investigated sourcing strategies on ESP. The data show a high and significant effect of vertical cooperation (H 3.2) and third party standards (H 3.4), while the effect of vertical integration (H 3.1) is positive but quite small and not significant. Surprisingly, there is nearly no effect of retailers’ direct influence on ESP (H 3.3). This implies that it is much more promising to build cooperative relationships or use third party certifications when striving for a higher ESP. One reason for this could be the buying manager’s dilemma between monetary issues and ethical sourcing. When retailers are able to take direct influence on their suppliers’ processes, other issues like cost reduction or (conventional) quality improvement seem to gain centre stage. A R2 of 0.44 for ESP also indicates a considerable explanatory power of the model for this construct. Hypotheses 4.1 and 4.2 show appropriate and statistically significant standardised path coefficients, hence they are supported by the data. This, combined with the positive effect of nonmonetary-success on monetary-success, shows that the total effect of ESP on financial performance is complex and may differ between companies or depend on periods investigated.

Bastian, J.; Zentes, J.

9.

99

Conclusion and Implications

The findings are highly relevant for retailers as well as all participants in the retail supply chain. The strategic goal of CSR changes the way retailers and producers are doing business. Besides the integration of ethical performance dimensions in supplier assessment, retailers and suppliers have to build up competence in managing intensive relationships. This requirement is even enhanced through the rising importance of third parties like professional certifiers and NGOs which are important trustees in ethical supply chains. Concerning the relationship between ESP and financial performance of the firm, the results imply that positive effects are achieved primarily in the long run due to the positive effect of ESP on the non-monetary-success. However, our results do certainly not exclude financial loss of companies ignoring ethical issues in procurement. Companies wanting to improve their ESP should develop consistent long term strategies and should be willing to accept financial losses in the short run. Further results with additionally integrated performance indicators like cost or quality of sourced products suggest that strategies which support ESP in retailing also lead to higher quality and availability of products but are in sum not helpful to lower costs. This should be taken into account while striving for ESP.

10.

Limitations and Further Research

The present research can be a starting point for a deeper investigation of the topic which could lead into several directions. First, the influence of ethical sourcing intention on further sourcing decisions like choice of geographical markets (regional/global sourcing), horizontal cooperations, or single versus multiple sourcing could be investigated. Moreover it would be interesting to integrate situational factors like company size, industry, or structure of company ownership. For these reasons and to improve validity, a bigger sample would be worthwhile. Another direction for future researches could be a deeper investigation of the mechanisms which cause the previously described effects of ethical sourcing intentions as well as the differences between social and environmental intentions. Hereto a more detailed alignment with case study research on ethical sourcing initiative could be valuable. Moreover, it would be interesting to prove the findings for other countries, especially for countries with a lower affinity for CSR like China or India. Finally, the findings are only a snap-shot in time, since our performance indicators refer to the period of the last three years. Investigations of longer periods could lead to different results regarding the ESP-financial performance relationship.

100

European Retail Research Vol. 25, Issue I, pp. 85-105

References Abbott, W.F.; Monsen, R.J. (1979): On the measurement of corporate social responsibility: Self-reported disclosures as a method of measuring corporate social involvement, in: Academy of Management Journal, Vol. 22, No. 3, pp. 501-515. Alexander, G.J.; Buchholz, R.A. (1978): Corporate social responsibility and stock market performance, in: Academy of Management Journal, Vol. 21, No. 3, pp. 479-486. Bhattacharya, C.B.; Sen, S. (2004): Doing better at doing good: When, why, and how consumers respond to corporate social initiatives, in: California Management Review, Vol. 47, No. 1, pp. 9-24. Bloemhof-Ruwaard, J.M.; van Beek, P.; van Wassenhove, L.N. (1995): Interactions between operational research and environmental management, in: European Journal of Operational Research, Vol. 85, No. 2, pp. 229-243. Boyd, B. (1990): Corporate linkages and organizational environment: A test of the resource dependence model, in: Strategic Management Journal, Vol. 11, No. 6, pp. 419-430. Carr, A.S.; Smeltzer, L.R. (1997): An empirically based operational definition of strategic purchasing, in: European Journal of Purchasing & Supply Management, Vol. 3, No. 4, pp. 199-207. Carroll, A.B.; Shabana, K.M. (2010): The business case for corporate social responsibility: A review of concepts, research and practice, in: International Journal of Management Reviews, Vol. 12, No. 1, pp. 85-105. Carter, C.R (2005): Purchasing social responsibility and firm performance: The key mediating roles of organizational learning and supplier performance, in: International Journal of Physical Distribution & Logistics Management, Vol. 35, No. 3, pp. 177-194. Carter, C.R.; Auskalnis, R.J.; Ketchum, C.L. (1999): Purchasing from minority business enterprises: Key success factors, in: Journal of Supply Chain Management, Vol. 35, No. 1, pp. 28-32. Carter, C.R.; Carter, J.R. (1998): Interorganizational determinants of environmental purchasing: Initial evidence from the consumer products industries, in: Decision Sciences, Vol. 29, No. 3, pp. 659-684. Carter, C.R.; Dresner, M. (2001): Purchasing's role in environmental management: Crossfunctional development of grounded theory, in: Journal of Supply Chain Management, Vol. 37, No. 3, pp. 12-27. Carter, C.R.; Ellram, L.M. (1998): Environmental Purchasing: Benchmarking our German counterparts, in: International Journal of Purchasing & Materials Management, Vol. 34, No. 4, pp. 28-38. Carter, C.R.; Jennings, M.M. (2002): Social responsibility and supply chain relationships, in: Logistics and Transportation Review, Transportation Research Part E, Vol. 38, No. 1, pp. 37-52. Carter, C.R.; Jennings, M.M. (2004): The role of purchasing in corporate social responsibility: A structural equation analysis, in: Journal of Business Logistics, Vol. 25, No. 1, pp. 145-86.

Bastian, J.; Zentes, J.

101

Chin, W.W.; Newsted, P.R. (1999): Structural equation modeling analysis with small samples using partial least squares, in: Hoyle, R.H. (ed.): Statistical strategies for small sample research, Thousand Oaks, pp. 307-342. Chin, W.W. (1998b): Issues and opinion on structural equation modeling, in: MIS Quarterly, Vol. 22, No. 1, pp. vii-xvi. Chin, W.W. (1998a): The partial least squares approach for structural equation modelling, in: Marcoulides, G.A. (ed.): Modern methods for business research, Mahwah, pp. 295-336. Churchill, G.A. (1979): A paradigm for developing better measures of marketing constructs, in: Journal of Marketing Research, Vol. 16, No. 1, pp. 64-73. Ciliberti, F., de Groot G.; de Haan, J.; Pontrandolfo, P. (2009): Codes to coordinate supply chains: SMEs' experiences with SA8000, in: Supply Chain Management, Vol. 14, No. 2, pp 117-127. Ciliberti, F.; de Haan, J.; de Groot, G.; Pontrandolfo, P. (2010): CSR codes and the principalagent problem in supply chains: Four case studies, in: Journal of Cleaner Production, in Press. Commission of the European Communities (2001): Green paper í promoting a European framework for corporate social responsibility, Brussels. Conant, J.S.; Smart, D.T.; Solano-Mendez, R. (1993): Generic retailing types, distinctive marketing competencies, and competitive advantage, in: Journal of Retailing, Vol. 69, No. 3, pp. 254-279. Cramer, J.M. (2008): Organising corporate social responsibility in international product chains, in: Journal of Cleaner Production, Vol. 16, No. 3, pp. 395-400. Cruz, J.M. (2009): The impact of corporate social responsibility in supply chain management: Multicriteria decision-making approach, in: Decision Support Systems, Vol. 48, No. 1, pp. 224-236. de Buck, A.J.; Hendrix, E.M.; Schoorlemmer, H.B. (1999): Analysing production and environmental risks in arable farming systems: A mathematical approach, in: European Journal of Operational Research, Vol. 119, No. 2, pp. 416-426. de Haan, J.; de Groot, G.; Loo, E.; Ypenburg, M. (2003): Flows of goods or supply chains; lessons from the natural rubber industry in Kerala, India, in: International Journal of Production Economics, Vol. 81-82, pp. 185-194. Dollinger, M.J.; Enz, C.A.; Daily, C.M. (1991): Purchasing from minority small businesses, in: International Journal of Purchasing & Materials Management, Vol. 27, No. 2, pp. 9-14. Drumwright, M.E. (1994): Socially responsible organizational buying: Environmental concern as a noneconomic buying criterion, in: Journal of Marketing, Vol. 58, No. 3, pp. 1-19. Dumond, E.J. (1994): Moving toward value-based purchasing, in: International Journal of Purchasing & Materials Management, Vol. 30, No. 2, pp. 2-8. Eisenhardt, K.M. (1989): Agency theory: An assessment and review, in: Academy of Management Review, Vol. 14, No. 1, pp. 57-74. Emmelhainz, M.A.; Adams, R.J. (1999): The apparel industry response to "sweatshop" concerns: A review and analysis of codes of conduct, in: Journal of Supply Chain Management, Vol. 35, No. 3, pp. 51-57.

102

European Retail Research Vol. 25, Issue I, pp. 85-105

Fombrun, C.J. (2005): The leadership challenge: Building resilient corporate reputations, in: Doh, J.P.; Stumpf, S.A. (eds.): Handbook on responsible leadership and governance in global business, Cheltenham, pp. 54-68. Fombrun, C.J.; Gardberg, N.A.; Barnett, M.L. (2000): Opportunity platforms and safety nets: Corporate citizenship and reputational risk, in: Business & Society Review, Vol. 105, No. 1, pp. 85-106. Fornell, C.; Larcker, D.F. (1981): Evaluating structural equation models with unobservable variables and measurement error, in: Journal of Marketing Research, Vol. 18, No. 1, pp. 39-50. Fry, L.W.; Keim, G.D.; Meiners, R.E. (1982): Corporate contributions: Altruistic or forprofit?, in: Academy of Management Journal, Vol. 25, No. 1, pp. 94-106. Gadde, L.E.; Håkansson, H. (1994): The changing role of purchasing: reconsidering three strategic issues, in: European Journal of Purchasing & Supply Management, Vol. 1, No. 1, pp. 27-35. Gold, S.; Seuring, S.; Beske, P. (2010): Sustainable supply chain management and interorganizational resources: A literature review, in: Corporate Social Responsibility & Environmental Management, Vol. 17, No. 4, pp. 230-245. Halldorsson, A.; Kotzab, H.; Mikkola, J.H.; Skjøtt-Larsen, T. (2007): Complementary theories to supply chain management, in: Supply Chain Management, Vol. 12, No. 4, pp. 284296. Haynes, P.J.; Helms, M.M. (1992): An ethical framework for purchasing decisions, in: Management Decision, Vol. 29, No. 1, pp. 35-38. Homburg, C.; Hoyer, W.D.; Fassnacht, M. (2002): Service orientation of a retailer's business strategy: Dimensions, antecedents, and performance outcomes, in: Journal of Marketing, Vol. 66, No. 4, pp. 86-101. Hong, L.; McGoldrick, P. (1996): International retail sourcing: Trend, nature, and process, in: Journal of International Marketing, Vol. 4, No. 4, pp. 9-33. Husted, B.W. (2005): Risk management, real options, and corporate social responsibility, in: Journal of Business Ethics, Vol. 60, No. 2, pp. 175-183. Ibarra, H. (1993): Personel networks of women and minorities in management: A conceptual framework, in: Academy of Management Review, Vol. 18, No. 1, pp. 56-87. Janz, M. (2004): Erfolgsfaktoren der Beschaffung im Einzelhandel, Universität des Saarlandes, Saarbrücken. Jennings, M.M.; Entine, J. (1998): Business with a soul: A reexamination of what counts in business ethics, in: Hamline Journal of Public Law and Policy, Vol. 20, No. 1, pp. 1-88. Kambalame, D.; de Cleene, S. (2006): Partnership building as an approach to addressing corporate social responsibility in the agriculture sector in Malawi, in: Development Southern Africa, Vol. 23, No. 2, pp. 281-287. Kampstra, R.P.; Ashayeri, J.; Gattorna, J.L. (2006): Realities of supply chain collaboration, in: International Journal of Logistics Management, Vol. 17, No. 3, pp. 312-330. Kolk, A.; van Tulder, R. (2002): The effectiveness of self-regulation: Corporate codes of conduct and child labour, in: European Management Journal, Vol. 20, No. 3, pp. 260-271.

Bastian, J.; Zentes, J.

103

Koplin, J. (2006: Nachhaltigkeit im Beschaffungsmanagement, Universität Oldenburg, Oldenburg. Kraljic, P. (1983): Purchasing must become supply management, in: Harvard Business Review, Vol. 61, No. 5, pp. 109-117. Krause, D.R.; Handfield, R.B.; Scannell, T.V. (1998): An empirical investigation of supplier development: reactive and strategic processes, in: Journal of Operations Management, Vol. 17, No. 1, pp. 39-58. Krause, D.R.; Ragatz, G.L.; Hughley, S. (1999): Supplier development from the minority supplier's perspective, in: Journal of Supply Chain Management, Vol. 35, No. 4, pp. 33-41. Lacey, R.; Kennett-Hensel, P. (2010): Longitudinal effects of corporate social responsibility on customer relationships, in: Journal of Business Ethics, Vol. 97, No. 4, pp. 581-597. Lehnert, M. (2009): Präferenzanalyse ethischer Produkte í Eine Verhaltenswissenschaftliche Analyse am Beispiel von Bio und Fairtrade, Universität des Saarlandes, Saarbrücken. Loew, T. (2006): CSR in der Supply Chain  Herausforderungen und Ansatzpunkte für Unternehmen, Berlin. Lohmöller, J.-B. (1989): Latent variable path modeling with partial least squares, Heidelberg. McGuire, J.B.; Sundgren, A.; Schneeweis, T. (1988): Corporate social responsibility and Firm financial performance, in: Academy of Management Journal, Vol. 31, No. 4, pp. 854-872. McLain, D.L. (1995): Responses to health and safety risk in the work environment, in: Academy of Management Journal, Vol. 38, No. 6, pp. 1726-1743. Min, H.; Galle, W.P. (1997): Green purchasing strategies: Trends and implications, in: International Journal of Purchasing & Materials Management, Vol. 33, No. 3, pp. 10-17. Möhlenbruch, D.; Wolf, A. (2009): Markenführung im Einzelhandel auf der Grundlage ökologischer und sozialer Verantwortung, in: Transfer Werbeforschung und Praxis, Vol. 55, No. 4, pp. 42-50. Montabon, F.; Melnyk, S.A.; Sroufe, R.; Calantone, R.J. (2000): ISO 14000: Assessing its perceived impact on corporate performance, in: Journal of Supply Chain Management, Vol. 36, No. 2, pp. 4-16. Moser, R. (2007): Strategic purchasing and supply management í a strategy-based selection of suppliers, European Business School, Oestrich-Winkel. Murphy, P.R.; Poist, R.F. (2002): Socially responsible logistics: An exploratory study, in: Transportation Journal, Vol. 41, No. 4, pp. 23-35. Nellore, R., Söderquist, K. (2000): Portfolio approaches to procurement: Analysing the missing link to specifications, in: Long Range Planning, Vol. 33, No. 2, pp. 245-267. Olsen, R.F.; Ellram, L.M. (1997): A portfolio approach to supplier relationships, in: Industrial Marketing Management, Vol. 26, No. 2, pp. 101-113. Orlitzky, M.; Benjamin, J.D. (2001): Corporate social performance and firm risk: A metaanalytic review, in: Business & Society, Vol. 40, No. 4, pp. 369-396. Parker, C. (2002): The Open Corporateion, Cambridge. Pfeffer, J.; Salancik, G. (1978): The external control of organizations: A resource dependence perspective, New York.

104

European Retail Research Vol. 25, Issue I, pp. 85-105

Pivato, S.; Misani, N.; Tencati, A. (2008): The impact of corporate social responsibility on consumer trust: The case of organic food, in: Business Ethics: A European Review, Vol. 17, No. 1; pp. 3-12. Pressey, A.; Tzokas, N.; Winklhofer, H. (2007): Strategic purchasing and the evaluation of "problem" key supply relationships: What do key suppliers need to know?, in: Journal of Business & Industrial Marketing, Vol. 22, No. 4/5, pp. 282-294. Qiu, Z.; Prato, T.; McCamley, F. (2001): Evaluating environmental risks using safety-first constraints, in: American Journal of Agricultural Economics, Vol. 83, No. 2, pp. 402-413. Quinn, B. (1999): E-system manages inventory to reduce risk, in: Pollution Engineering, Vol. 31, No. 9, pp. 27-28. Razzaque, M.A.; Hwee, T.P. (2002): Ethics and purchasing dilemma: A Singaporean view, in: Journal of Business Ethics, Vol. 35, No. 4, pp. 307-326. Rivoli, P. (2003): Labor standards in the global economy: Issues for investors, in: Journal of Business Ethics, Vol. 43, No. 3, pp. 223-232. Roberts, S. (2003): Supply chain specific? Understanding the patchy success of ethical sourcing initiatives, in: Journal of Business Ethics, Vol. 44, No. 2-3, pp. 159-70. Salomone, R. (2008): Integrated management systems: Experiences in Italian organizations, Journal of Cleaner Production, Vol. 16, No. 16, pp. 1786-1806. Sen, S.; Bhattacharya, C.B. (2001): Does doing good always lead to doing better? Consumer reactions to corporate social responsibility, in: Journal of Marketing Research, Vol. 38, No. 2, pp. 225-243. Shrivastava, P. (1995): The role of corporations in achieving ecological sustainability, in: Academy of Management Review, Vol. 20, No. 4, pp. 936-960. Spar, D.L.; La Mure, L.T. (2003): The power of activism: Assessing the impact of NGOs on global business, in: California Management Review, Vol. 45, No. 3, pp. 78-101. Svahn, S.; Westerlund, M. (2009): Purchasing strategies in supply relationships, in: Journal of Business & Industrial Marketing, Vol 24, No. 3/4, pp. 173-181. Trommsdorff, V.; Götze, F.; Herm, S. (2006): Konsumentenvertrauen und Marken, in: WISU, Vol. 35, No. 10, pp. 1264-1270. Ullmann, A.A. (1985): Data in search of a theory: A critical examination of the relationships among social performance, social disclosure, and economic performance of U.S. firms, in: Academy of Management Review, Vol. 10, No. 3, pp. 540-557. Vachon, S.; Klassen, R.D. (2006): Extending green practices across the supply chain, in: International Journal of Operations & Production Management, Vol. 26, No. 7, pp. 795821. Werner, H. (19969: Relationales Beschaffungsverhalten í Ausprägungen und Determinanten, Universität Mannheim, Mannheim. Wood, D.J. (1991): Corporate social performance revisited, in: The Academy of Management Review, Vol. 16, No. 4, pp. 691-718. Yuchtman, E.; Seashore, S.E. (1967): A system resource approach to organizational effectiveness, in: American Sociological Review, Vol. 32, No. 6, pp. 891-903.

Bastian, J.; Zentes, J.

105

Zentes, J.; Bastian, J.; Lehnert, F. (2010): HandelsMonitor 2010 í Strategien der Nachhaltigkeit: People í Planet í Profit, Frankfurt a. M. Zentes, J.; Schramm-Klein, H; Morschett, D.; Swoboda, B. (2009): Does corporate social responsibility pay for retailers?, Paper presented at American Marketing Association (AMA) 2009 Winter Educator`s Conference, Tampa, FL. Zsidisin, G.A.; Siferd, S.P. (2001): Environmental purchasing: A framework for theory development, in: European Journal of Purchasing and Supply Chain Management, Vol. 7, No. 1, pp. 61-73.

Retailing in India – Background, Challenges, Prospects Doreén Pick and Daniel Müller

Abstract India has become the second-largest consumer market and seventh-largest retail market worldwide. The prerequisite for this event was the liberalisation of several sectors of the economy, whereas the main driver was a continuous increase of the gross domestic product (GDP). As a consequence, the Indian retail market has shown impressive and constant growth. Although the global financial crisis affected the Indian economy, the prospects for the Indian retail market are encouraging. With a steadily growing economy, a broad middle class is evolving as progress is made in reducing poverty. Nevertheless, the knowledge about specific features of the Indian market is still unsatisfactorily low. Hence, our article provides a description of the status quo of India’s retail market, including an analysis of Indian consumers and the positioning of Indian and foreign retailers. It concludes with predictions of plausible further developments.

Keywords Indian Retailer, Indian Consumer, Shopping Behaviour, Rural India, Supply Chain

Doreén Pick (corresponding author) Business-to-Business Marketing, Freie Universität Berlin, Germany (Tel: +49 30 8385 4547; E-mail: [email protected]). Daniel Müller Asia Market Research, Berlin, Germany.

Received: October 9, 2010 Revised: December 31, 2010 Accepted: January 7, 2010

EUROPEAN RETAIL RESEARCH Vol. 25, Issue I, 2011, pp. 107-139

D. Morschett et al (eds), European Retail Research, DOI 10.1007/978-3-8349-6235-5_6, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

108

1.

European Retail Research Vol. 25, Issue I, pp. 107-139

Introduction

In the last few years, India has emerged as the seventh largest retail market in the world. Only two decades ago, market forces in India were in principle subordinated to political considerations, and the overall economic system was managed by the national government. Nearly every kind of economic activity required permission from state bureaucrats, with the result that the country was stalled by its “Hindu rate of growth”. This system, named “license raj”, was gradually eliminated beginning in the middle of the 1980s, ushering in a long and complicated process of economic liberalisation (Jenkins 1999) that continues today. Specifically, the government seeks to protect the Indian retail market, with its large number of small family retailers, from free competition. Even so, in recent years Indian politicians have become more willing to lift certain restrictions. Whereas governmental expenditures and investments were initially the main drivers of economic growth, the expanding middle class, with its consumption affinity and its pursuit of prosperity, has become more important. In addition, the high urbanisation rate, an increasing share of women in the workforce, a young population and the availability of personal credit are driving the Indian economy (Ghosh 2010). All of these factors contribute to the persistent growth of the Indian retail sector. This economic growth has raised the interest of domestic firms operating in industries such as telecommunication in starting retail units. Foreign retailers confronted with highly satiated home markets and searching for new growth opportunities are also showing interest. Some prior studies in the management literature have estimated that the leading retailers in India generate revenues of 10 to 50 billion USD per year (Giridharadas/Rai 2006). However, recent assessments call these numbers into question. The last two years have demonstrated that gaining access to the Indian retail market is much more difficult than was previously assumed. Several Indian retailers have had to concentrate on rationalising their processes and cutting back stores in some regions, leading researchers to conclude that their expansion strategies were too ambitious. However, in addition to the issues of optimal processes and key metrics, the Indian retail market is expected to keep growing by 10.0 to 13.0 % per year until 2012. It is assumed that the Indian retail market will surpass Japan’s in sales in 2010, Germany’s in 2011 and France’s in 2012 (Asipac 2010). Against this background, we expect that interest in the Indian retail market will remain high. Nevertheless, successful market entry and sustainable market operation in India require extensive information about the specifics of the market, competitors and customers. We intend to provide such information in this paper. First, we give an overview of the Indian retail market with a brief summary of relevant facts and data, including a snapshot of retail history in India. Second, we present the sections of the Indian retail market. Third, characteristics of Indian consumers are given, explaining their importance for domestic and foreign retailers. Fourth,

Pick, D.; Müller, D.

109

we delineate the traits of retail formats in India. Fifth, we introduce the main domestic retail players and the foreign retailers operating in India. We complete this article with a summary and outlook for the Indian retail market.

2

India – A Market Overview

2.1

India – A Country Profile

India has a population of about 1.21 billion people and is the second most populous country after China. About 70 % of Indian people, i.e. 743 millions, live in 138 million households in one of India’s 600,000 villages. The other 30 % of Indians live in one of the 5,170 cities, which are classified from Tier I to Tier IV according to their number of inhabitants. The growth of the population within the next five years is expected to amount to 1.3 %. India’s population is relatively young: the median age is 25.9 years, and 30.5 % of the population is younger than 14 years. About 64.3 % of Indians are between 15 and 64 years old. Only 5.2 % of India’s residents are older than 65 years (CIA Factbook India 2010). The life expectancy is quite low at 66 years. Despite the expected aging of the population, in 2020 the average Indian will be only 29 years old, and the percentage of people aged 15-49 years will still be high, at 53.0 %. Therefore, the Indian retail market will remain attractive for several decades. India’s gross domestic product (GDP) for 2009 is expected to be 3.57 trillion USD (in purchasing power parity), which corresponds to a GDP per capita of 1,031 USD (Gtai 2010). The share of agriculture in the GDP is 16.1 %, the share of industry is 28.6 % and, the share of services is 55.3 % (CIA Factbook India 2010). The outlook for the Indian GDP is optimistic. Hence, for the fiscal year 2010/2011, the growth rate is expected to be 8.5 %, according to the Reserve Bank of India. This indicates that the Indian economy is recovering quickly after the slowdown in the past two years caused by the global financial crisis. A recent report by Morgan Stanley predicts a potential growth rate of 9 to 9.5 % for India’s GDP for the period of 2013 to 2015. One of the main concerns in India is the enduring high inflation rate. The highest rate, 11.05 %, was measured in August 2008. Although inflation has decreased slightly within the last few months and was registered at 6.0 % at the end of March 2010 by the Reserve Bank of India, the inflation of food prices is distressing. From January 2009 to January 2010 food prices increased by 20 % (Bajaj 2010). Prices for cereals and milk have increased the most over the past two years. Because food prices are the major driver of inflation, the government is very concerned about securing affordable and sufficient access to food for all Indians. For the next fiscal year, the inflation rate for food prices is expected to partially relax to 13.1 %. Hence, the inflation of food prices in India will remain an issue in the coming months and years.

110

2.2

European Retail Research Vol. 25, Issue I, pp. 107-139

Snapshot of Retail History in India

Despite the importance of India as a country that produces many specialty goods, such as seasonings, that are traditionally exported, the retail sector was long directed primarily to satisfy the basic needs of the Indian people. Thus, the supplying of grains and pulses at low prices was the main concern of retail companies. To compensate for the shortcomings of a planned economy, the government was predominately engaged in the distribution of goods. Because of this limited focus on consumers and the sheer size of the country, most Indian consumers buy products to meet their daily needs in small stores (kirana stores), at wet markets and in bazaars near their living and working areas (Sengupta 2008). For decades, large retail formats such as supermarkets or malls were nearly unknown. Instead, government-run public distribution stores (PDS), fair price shops and co-operative stores have long been the prevailing retail formats, especially in rural India. In PDS, chiefly wheat, rice, sugar and kerosene are sold. These stores are more or less sufficient instruments of the government’s economic policy for ensuring the availability of staple foods to the public at affordable prices (Ahluwalia 1993; Howes/Jha 1994). Regardless of whether this retail model is considered to be old-fashioned, food shortages and inflation of food prices in recent years have led the government to reinforce its endeavors to strengthen the PDS system. Other important retail formats for Indian consumers have included paan-bidi shops, traditional convenience stores, street vendors and mobile vendors (Sengupta 2008). The history of modern retailing and organised retail in India began earlier than is often supposed. In 1971, India’s first “supermarket” was opened, in the form of Nilgiris at Bengaluru (Sengupta 2008). Modern Indian retail incorporates the concept of self-service and includes a chain of stores that are operated with modern management techniques. In the late 1970s, the number of brands, especially of daily cosmetics and sanitary articles, increased sharply (Sengupta 2008). This widening of the product range has intensified competition among manufacturers for shelf space in all retail formats. Apart from these early attempts, the modern retail sector in India took shape mostly at the beginning of the 21st century, when some domestic retailers set up their supermarket concepts. The sector gained significant momentum in 2006, when the Indian retail market was opened for FDI. In January 2006, the Indian retail market was opened for foreign companies to own up to 51 % in single-brand retail and 100 % in wholesale and cash and carry. For several years, multi-brand retail for foreign firms was restricted, leading to operations through franchises with Indian partners, such as Wal-Mart with Bharti Enterprises. In summer 2010, paramount steps were taken. In the near future, foreign retailers will be permitted to hold 49 % of multibrand retail in India. This change resulted from the expectation that further liberalisation might improve the disastrous existing infrastructure. In August 2010, the Consumer Affairs Ministry recommended that “A significant chunk of investments should be spent on back-end

Pick, D.; Müller, D.

111

infrastructure, besides logistics and agro-processing” (Menon 2010). There are no reliable sources by which to assess the development of retail sales and retail formats since 2000, but it can be said that some retail formats, such as supermarkets, have increased their store numbers tenfold between 2001 and 2006 (see table 1). In spite of the partial liberalisation of the market, the subsequent market entry of several retailers and new options for purchases, the buying patterns of the Indian consumer have not changed completely. In fact, the typical Indian consumer still buys in small shops in his/her neighbourhood (Anonymous 2007). Only 10 % are willing to travel more than five kilometres to purchase apparel. This pattern is reflected by the low share of organised retail (see chapter 2.4). The key question is whether organised retail will be able to raise its market share considerably. The success of this attempt is correlated with the progressive adoption of new retail formats by Indian consumers. However, it is unclear whether they will adopt the new retail formats. One empirical study has focused on the question of whether Indian consumers are likely to move from kirana stores to organised retail (Goswami/Mishra 2009). The study concluded that customer patronage of grocery stores is positively related to location, home shopping options, cleanliness and special offers. To sum up, since liberalisation in 2006, the Indian retail market has experienced a remarkable shift from small stores to modern large-scale formats such as supermarkets/hypermarkets, specialty stores, department stores, factory outlets and discounters. All of these concepts can be summarised under the label “organised retail”. Despite the enduring dominance of small stores in India, these new retail formats have increased their market shares. Although the global financial crisis has lowered the optimism of both domestic and foreign retailers, they are still confident that they will succeed, at least in the medium term. Particularly, Indian retailers are planning their next expansion phase (see chapter 4). To understand these growth plans, we describe in the following section the current situation of the Indian retail market.

2.3

Status Quo of the Indian Retail Market

Today, the Indian retail market accounts for 12 % of India’s GDP. The Indian retail market has grown from 201 billion USD in 1998 to an estimated 353 to 450 billion USD (271.5 to 346.2 billion EUR, using an exchange rate of 1.3 USD/EUR). The broad range of the estimated retail sales can be explained by two reasons. First, there are no official data on how many retailers operate in India and what they earn. Because the Indian retail market is driven by small retailers, the specific turnover is naturally small. The second reason for the broad range of values is that some major domestic retailers are subsidiaries of large enterprises. These companies often do not report sales and profit numbers for their retail units. Hence, both the size of the market and its growth can be only roughly appraised.

112

European Retail Research Vol. 25, Issue I, pp. 107-139

Overall, the Indian retail market has witnessed tremendous growth within the past five years. Consequently, from 2005 to 2007, India was rated as the most attractive country for retail investment (A.T. Kearney 2008). In 2009, the Indian retail market expanded by 12 %. However, the worldwide economic downturn has also impacted India and its retail industry. Therefore, several firms have adjusted their expansion plans. Despite several signs of market concentration and consumer satiation in the last 24 months, the outlook for the overall Indian retail market is promising. For 2010 and 2011, annual growth in retail sales of 6.0 to 6.5 % is forecasted (PriceWaterhouseCoopers 2009). Other retail experts expect that Indian retail sales will increase to 590 billion USD, and that the share of organised retail will increase to 16 % by 2011/2012 (Joseph et al. 2008). Even greater growth in sales is predicted by A.T. Kearney (2008), with an overall value of the Indian retail market of 833 billion USD in 2013, increasing to 1.3 trillion USD by 2018. Paradoxically, Indian organisations predict retail sales expansion to be much lower and expect sales of 543.2 billion USD in 2014 (IBEF 2010a) and 637 billion USD in 2015. These different metrics probably result from different motivations. Whereas foreign consultancy firms could be motivated to exaggerate India’s growth potential to attract investors (and consultancy projects), governmental organisations might be motivated to underestimate the market’s value reducing the interest of foreign retailers. Even with the impressive growth of the sector in recent years and its increasing concentration in some parts of the industry, India is still perceived as an attractive retail marketplace. The Global Retail Development Index 2009 again named India as the most attractive market (A.T. Kearney 2009). According to the report India is still a non-satiated retail market. In general, the Indian retail market can be split into two segments: The organised retail segment, which has a share of 3 to 6 % of total sales and is growing at more than 20 % a year (Asipac 2010; IBEF 2010a), and the unorganised retail segment, which dominates the Indian retail scene. In the next section, we will examine both retail segments in depth.

2.4

India’s Unorganised and Organised Retail Segments

The Indian retail market is highly fragmented into small, privately owned stores, the so-called unorganised retail or traditional retail. These unorganised retail stores account for 94 to 97 % of sales, compared to about 80 % in China, about 60 % in Thailand and about 15 % in the US (Srivastava 2008). These “Mom-and-Pop” stores comprise about 15 million businesses, from local kirana stores to footwear and apparel shops (Knight Frank 2010). They are usually run by the owner and one or two assistants. Approximately 95 % of these retail stores are smaller than 500 square feet (Srivastava 2008). India has the highest number of retail outlets worldwide, with an average size of 50 to 100 square feet, but the per capita retail space is amongst the lowest in the world. Many of these kirana stores are located in shopping centres. For 50 years, time has stood still in that “Each shop is typically about ten feet square, so that the pro-

Pick, D.; Müller, D.

113

prietor can sit in the middle of the floor and reach his entire stock” (Westfall/Boyd 1960, p. 14). In such stores, there is no self-service for customers. Organised retail, such as supermarkets and hypermarkets, has steadily gained market share at rates of about 35 % during the last five years. Organised retail now constitutes a 7.8 % share of the Indian retail sector (Asipac 2010) and accounted for 783 billion INR (13.5 billion EUR) in 2007. Apparel, food and grocery dominate organised retailing in India (IBEF 2010a) and account for circa 49.6 % of the sales. Previous judgments have been very enthusiastic about the prospects of organised retail, and the sector is expected to increase its share of the total retail trade to 16 % by fiscal year 2011/2012 (Joseph et al. 2008). However, recent research only expects organised retail to possess a share of 10.4 % of total retail sales in 2011/2012 (KPMG 2009). In contrast with other emerging countries in Asia, such as China or Vietnam, with organised retail shares of 18 and 23 %, respectively, India is thought to possess huge future growth potential (Asipac 2010). The development of organised retail can be captured quantitatively in several ways. In 1999, there were only three shopping malls in India totalling one million square feet. Only seven years later, in 2006, malls accounted for 28 million square feet, and in 2009 this value was 52 million square feet (Knight Frank 2010). The reason for the low share of organised retail in overall retail sales is the severe restriction on foreign direct investments (FDI) in retail that were in place until the end of 2005. At present, protests against organised foreign and domestic retailers are ongoing. However, it is not clear what the impact of organised retailers on small retailers might be. Some sources conclude the effect of a formal retailer to be a loss of about 23 % of sales within a year for nearby small shops. However, according to the Indian Council for Research on International Economic Relations (ICRIER), total sales returned to their previous level five years after the removal of the restrictions on FDI. Additionally, it was found that only 1.7 % of stores in the unorganised market close each year, and it is expected that by 2013, unorganised retail businesses will still account for 85 % of the Indian retail market (Salisbury 2010). Because the organised retail segment has only a modest market share in India, the development of this sector is unique. Table 1 shows how several retail formats developed between 2001 and 2006. It can be seen that the numbers of supermarkets and specialty stores increased nearly tenfold. Additionally, hypermarkets, which were nonexistent several years ago, now account for about 10 % of the space of organised retail. Although department stores have also gained in retail space (+ 540 %), their importance in organised retail has decreased. In recent months, several Indian retailers were forced to scale back their expansion plans and close down some stores. For example, Pantaloon, India’s largest retail company, cut its expansion plans through June 2010 from 4 million to 2 million square feet and has closed 103 of its stores. This consolidation can be interpreted as a consequence of the overly enthusiastic expansion phase in organised retail stores within the last few years. Nevertheless, it seems

114

European Retail Research Vol. 25, Issue I, pp. 107-139

that Indian retailers have learned from the past. Hence, recent press releases have announced a change of firm policies back to modest expansion after several restructuring programs. Table 1: Expansion of Organised Retail by Format, 2001 to 2006 2001 Retail format

Average size (sq.

No. of stores

feet)

Share of total space

feet)

(in %)

400

11.9

No. of stores

Area (in 1,000 sq.

Share of total space

feet)

(in %)

4,751

4,751

15.5 9.8

Supermarket/ convenience store

1,000

Hypermarket

40,000

0

0

0.0

75

3,000

Discount store

1,000

48

48

1.4

1,472

1,472

4.8

Specialty store

800

2,651

2,121

63.3

20,612

16,490

53.7

Department store

30,000

Total

400

2006

Area (in 1,000 sq.

26

780

23.3

166

4,980

16.2

3,125

3,349

100.0

27,076

30,693

100.0

Source: Anonymous (2010).

2.5

Sectors of Indian Retail

2.5.1 Food and Grocery Regardless of the continual rise of the Indian middle class and its new consumer demands for items in several categories, the Indian retail market is still dominated by sales of food and grocery, which account for 60 to 75 % of all retail sales (A.T. Kearney 2006). In 2007, sales of food and groceries accounted for 7,920 billion INR (ca. 135 billion EUR) (Gtai 2009). The share of total sales comprising food and grocery in organised retail is 11.5 %, meaning that this segment of Indian retail is mainly driven by unorganised retail stores. This pattern is reflected in the structure of the consumer shopping basket. In 2003/2004, food and groceries had a share of 42.1 % of the total consumer expenditures, and no significant changes can be expected in the coming years. The typical Indian consumer will continue to purchase food and groceries in small retail stores instead of supermarkets or other organised retail formats. The overall food and grocery market is expected to grow to 1.6 billion USD over the next five years (Srivastava 2008). Euromonitor International forecasts that grocery retailing in India will grow at about 17 % annually between 2009 and 2014, putting it among the fastest growing markets in the world (Chandran 2010). Organised food and grocery retail in India could nearly double to 1,750 billion INR (circa 30.2 billion EUR at current exchange rate) by 2015, representing 11 % of overall food and grocery sales (Tata 2010). Nevertheless, the food market strongly depends on the amount of the harvests and on price stability. As mentioned above, prices for some food categories have increased by up to 20 % per year. As a result, there is immense political interest in keeping prices for food and groceries low. In addition to subsi-

Pick, D.; Müller, D.

115

dies of several food items and selling selected items in PDS stores, some politicians believe that ongoing liberalisation of the market, such as lifting the buying restrictions for retailers, can contribute to a more efficient supply of food through better infrastructure and improved food processing. It is expected that Indian farmers and traders can benefit from the experiences of foreign retailers. 2.5.2 Apparel and Accessories Behind food and groceries, apparel and accessories is India’s second-largest retail category at 1,313 billion INR (22.6 billion EUR), representing 10 % of the Indian retail market (Gtai 2009). Most sales, 40.2 %, are generated in the men’s category, followed by 34.8 % in women’s apparel and 25.0 % in children’s apparel and uniforms. Compared with 2006, the sector has grown by 12.8 %. This is one of the highest growth categories, with a compound annual growth rate (CAGR) of 12 to 15 % (A.T. Kearney 2008). Apparel is moreover a segment that is mainly characterised by organised retail, which accounts for 20 % of the total apparel market (A.T. Kearney 2008; Gtai 2009). In 2007, sales of apparel and accessories in organised retail stores reached 298 billion INR (5.1 billion EUR) and increased by 35.5 %. In the coming years, annual growth of apparel and accessories sales in organised retail of 9.5 % is expected (Srivastava 2008). In sum, the overall share of apparel and accessories in the total apparel market in India is expected to triple to quadruple and to reach 35 to 40 % by 2013 (A.T. Kearney 2008). The manufacturers of apparel are challenged by customers’ strong store loyalty. Customers tend to be loyal to specific retailers, such as Shopper’s Stop, Westside or Pantaloon, instead of to a particular apparel brand. The consequence is an apparel market that is largely driven by private labels from retailers. 2.5.3 Consumer Electronics With rising disposable income, sales of consumer electronics have increased significantly. In the fiscal year 2008/2009, consumer electronics were sold with a total value of around 6 billion USD (Alex 2010). Another source, the Brand Marketing India (BMI), forecasts consumer electronic sales of 29.86 billion USD in 2010. The segment is expected to grow further at an annual rate of 15 to 20 % over the next three years (Alex 2010). The main growth driver is the low market penetration and low consumer satiation of products such as television and stereo systems, which are far below the numbers typical of other Asian countries. An important part of the consumer electronics industry is mobile phones and accessories, which accounted for a turnover of 272 billion INR in 2007 (4.7 billion EUR) (Gtai 2009). This group of products is growing at an annual rate of 25.6 %. The market leaders are Videocon, Croma (Tata Group), eZone (Future Group), Vivek’s and Reliance Digital. Among them, Videocon is the key player, with 526 stores across India. E-zone has 36 stand-alone stores and runs 16 shops-in-shops and is aiming for a turnover of 18 crore INR until June 2011. Vivek’s has 35

116

European Retail Research Vol. 25, Issue I, pp. 107-139

stores, and Reliance Digital sells in 39 stores while striving for sales of 90,000 crore INR by 2010 (15.5 billion EUR). 2.5.4 Watches, Eyeglasses and Jewellery The overall returns for watches and eyeglasses amount to 5,300 crore INR (913.8 million EUR). After the liberalisation in the 1990s, the watch industry saw many manufacturers and a variety of national and international brands attempting to open up this market. The industry is expanding by 11 to 12 % per annum. The primary reason for this rate of growth is the low ownership rate of watches in India. A total of 46 % of the market is organised. The dominant actor in this line of business is Titan, with a market share of about 65 % in the organised retail sector. Other players are Maxima, Timex and Citizen (Batra 2010). Jewellery has a pivotal position within the Indian consumer basket. About 5.9 % of India residents’ disposable income is spent on jewellery. The total market size is estimated to be 694 billion INR (11.9 billion EUR). Jewellery’s share of total retail sales is nearly 6 %. The market grew by about 10 % from 2006 to 2007. The leading manufacturer and retailer is the Gitanjali Group. Gitanjali has over 2,000 retail outlets across India and is in the process of expanding to Tier II and Tier III cities. The company plans to open 20 new stores by the end of 2010. In the coming three years, the firm has announced a turnover target of 1,000 crore INR (172.4 million EUR). 2.5.5 Health and Pharmaceuticals In 2007, the sales in the health and pharmaceuticals sector totalled 488 billion INR (8.4 billion EUR) (Gtai 2009). Other sources estimate the market size at around 21,000 crore INR (3.62 billion EUR). However, the first number seems to be more reliable, as the overall pharmaceutical market in India is ranked 14th in the world and is expected to have a turnover of 20 billion USD in 2015 (McKinsey & Company 2007). A total of 800,000 stores across India sell pharmaceuticals. The share of organised retail is 2 %. Over-the-counter (OTC) pharmaceutical retailers generate sales of 3.28 billion USD. The OTC sector is predicted to be the fastest growing retail sub-sector, and BMI forecasts that sales will reach 6.18 billion USD by 2014, an increase of 88.5 % (IBEF 2010a). Nevertheless, India has one of the lowest expenditures per capita for pharmaceuticals in Asia. The largest retailer in this sector is Apollo Pharmacy, a subsidiary of Apollo Hospitals. This retailer has over 1,000 outlets and is open for business 24 hours a day. Another important retailer is MedPlus, with nearly 800 stores. MedPlus began in 2006 in Hyderabad and has a market share of 30 % of organised pharmacy retail, with 5,000 employees. The third major player is Guardian Lifecare, with more than 200 stores in 26 cities, of which 90 % are owned and 10 % run via franchise. This retailer plans to double its number of stores by the end of 2010 by investing 100 crore INR (17.2 million EUR). In table 2, we give an overview of selected retail sectors in India.

Pick, D.; Müller, D.

117

Table 2: Summary of Retail Sectors in India Total market

Organised retail

Expected growth

Food and

7,920 billion INR (135 billion

grocery

EUR, 2007)

Apparel and

1,313 billion INR (22.6 billion

1.8 billion USD (20 % of total

Organised apparel retail by

accessories

EUR, about 10 % of the Indian retail market)

apparel market, expected to grow to 40 % in 2013)

9.5 % p.a. for the next three years

Consumer

6.0 billion USD (2008/2009);

electronics

other sources: 29.86 billion USD in 2010

-

Growth of 15 to 20 % p.a. for the next three years

Pharmaceuticals

488 billion INR (8.4 billion EUR); other sources: 3.62 billion EUR

2,0 % (168 million EUR)

In 2015, market size will be 14.3 billion EUR

53 billion INR (913.8 million EUR in 2009)

Jewellery: 6 %

11-12 % p.a.

Watches, eyeglasses and jewellery

11.5 % (15.5 billion EUR)

Organised retail in 2015 will reach 30.2 billion EUR

Note: INR: Indian rupees; p.a.: per annum

In all retail segments, firms are confronted with decelerating sales that have resulted in the closing of stores and restructuring of business operations. The “gold rush” years, when domestic retailers in particular broadened their networks of stores across India, seem to be over. Retailers’ response to the crisis was to rethink their business models. Now, several retail companies increasingly concentrate on strengthening existing operations and assessing options for growth through consolidation while continuing to innovate. Their first changes have been to renegotiate their rentals by moving toward hybrid rental models such as revenue sharing, rationalising store processes, managing working capital, optimising cost structures and resizing manpower (Bapna 2010; Jones Lang LaSalle Meghraj 2010; KPMG 2009).

2.6

The Indian Consumer

2.6.1 Overview – Numbers and Buying Behaviour Any attempt to classify the typical Indian consumer is exceedingly challenging if not impossible. In few other countries have the living conditions, income and consumption patterns changed to such an extent in recent years as they have in India. Furthermore, there is probably no other country in which the differences between regions, classes/castes and generations are as distinctive. The federal states and regions in India differ significantly in terms of their stages of development, growth rates, incomes and cultures. Whereas metropolitan areas like Mumbai and New Delhi are in the midst of a stage of intensifying economic and social modernisation, states like Uttar Pradesh and Bihar lag behind because of unfavourable political and social conditions (Bagchi/Kurian 2005). They are “poorhouses”, where incomes are considerably below the Indian average. In most publications, the Indian consumer is segmented simply by income. However, this approach has often been criticised as too simplistic. We be-

118

European Retail Research Vol. 25, Issue I, pp. 107-139

lieve that more valuable insights on Indian consumers can be generated by taking other variables, such as buying behaviour and brand awareness, into account. Consequently, in a first step we present characteristics of Indian consumers by income level. In a second step, we present generalised findings regarding common buying patterns and consumption behaviours of the Indian people. To a certain extent, this discussion is entering new territory because neither the theoretical nor the applied literature is very detailed in its description of the Indian consumer. Irrespective of the differences between the various regions in India, the combination of increasing incomes and a growing population makes the market in general very attractive for retailers. As mentioned before, the Indian retail market is expected to increase to 590 billion USD in 2011/2012 (450 billion EUR at current exchange rates). The average spending per capita is expected to reach 48,632 INR in 2025 (840 EUR in current exchange rates) (McKinsey Global Institute 2007). Although this number may seem to be quite low, the retail sector with its low satiation level í especially in new markets í will benefit significantly from consumers’ rising income and changing needs. The trend is in any case positive. Consumer spending has grown at an average annual rate of 11.5 % over the past decade. Nevertheless, the market’s potential should not be overestimated. Similar to circumstances 50 years ago, Indians still spend a great deal of their incomes on food. This means that there are only small modifications in their spending patterns so far, as demonstrated by the high demand for daily products and services. As an example, the consumption of milk increased by 9.4 % to 75.6 litres per person from 2004 to 2008 (PriceWaterhouseCoopers 2009). However, by analysing the available data, some alterations of the consumption patterns in India are apparent. In table 3, the per capita sales for non-food retailers are given. The average spending per capita has increased by 81.4 % to 4,117 INR. Since 1996, the average Indian consumer spends his/her money preferably in retail formats such as record and video retail, department stores, home furniture, pharmacies/chemists and apparel retail. Based on these numbers, we expect fundamental changes in the consumption behaviour of the majority of Indian consumers only in the long term. Meanwhile, significant changes in purchase behaviours are likely within the emerging middle class, although the majority of the Indian population will exhibit similar purchase and consumption behaviour as has historically been observed. Retailers and manufacturers should therefore carefully analyse their respective markets, including the specific needs of their customers, to adjust their offers and procedures to changed consumption behaviours. Considering the long history of India and its manifold traditions and diverse religious commandments, it is reasonable to assume that even the middle class cannot act absolutely free from the expectations of their family, caste or the society as a whole. This is not to say that Indian society is not experiencing value shifts that have consequences for the business world. In fact, a study from 2003 indicates, for example, that

Pick, D.; Müller, D.

119

Indian shoppers are more focused on the emotional or entertainment value of shopping than on its functional value (Sinha 2003). The study moreover showed that Indians search purposefully for information, are willing to switch stores, and talk with friends about their shopping experiences – as it can be observed in Western countries. Table 3: Per Capita Retail Sales for Non-food Retailers 1996 and 2001 in INR) 1996 Booksellers and stationers

2001

Change in %, 2001 to 1996

20.49

32.93

+60.71

Pharmacies, chemists and druggists

186.41

344.03

+84.56

Apparel and accessory outlets

545.16

982.93

+80.30

CTNs

109.50

170.05

+55.30

4.28

9.53

+122.66

Department stores Do-it-yourself, gardening and hardware outlets

39.88

54.26

+36.06

Electrical, electronic and computer outlets

170.95

270.31

+58.12

Home furniture and furnishing outlets

104.22

193.64

+85.80

Jewellers

283.23

242.63

-14.33

9.21

24.08

+161.74

27.30

39.91

+46.19

3.37

5.90

+75.07

Record/video game outlets Sporting goods outlets Toy Shops Other non-food specialists Other non-food retailers Total

42.40

66.84

+57.64

823.07

1,498.97

+82.12

2,269.48

4,117.98

+81.45

Source: Euromonitor (2003), p. 165.

Nevertheless, we theorise that Indian consumers are very dissimilar from their Western counterparts. One difference in the purchasing behaviour of Indian versus Western consumers is the tendency to hoard purchases in regions where the products need only basic storage, such as in Gujarat. Several product categories, such as oil, pulses and grains, are purchased only once a year (Dholakia/Sinha 2005). Retailers must be aware of this phenomenon to obtain adequate estimates of customers’ true loyalty and of the optimal ordering and stocking processes. Another difference in purchase behaviour is the expectation that prices are negotiable. Indian consumers are not accustomed to buying at fixed prices and may become embarrassed when treated otherwise. The acceptance of buying products without bargaining might take same time. A third difference from Western shoppers is that Indian shoppers choose stores based on the products or the brands that they offer. This means that the products instead of the retailer are in the consideration set of customers. A brand is perceived as a pivotal guarantee of the quality of a product and also has a flaunt appeal (Wharton 2009). Despite the lack of detailed data on consumer loyalty, it can be assumed the brand loyalty in India is in general

120

European Retail Research Vol. 25, Issue I, pp. 107-139

high. High brand loyalty also results from the fact that consumers enter stores with a prepared brand shopping list and only buy a different product if their first choice is sold out (Dholakia/ Shinha 2005). Spontaneous purchases are quite uncommon among Indian consumers. Despite the shift from saving to consuming and spending their disposable incomes, Indian consumers are still very budget conscious. Both low-income and middle-class Indian consumers prefer to buy only small quantities and search continually for the best value at the cheapest price. Further, the consumer tends to shop close to his/her home and is unwilling to travel long distances to purchase basic goods (Salisbury 2010). Shopping trips are seldom taken alone. An Indian consumer is normally accompanied by another person or by family members. Thus, intensive discussions about products are common in organised retail stores (Dholakia/ Sinha 2005). To meet the specific needs of the Indian consumer, several retailers, such as Pantaloon and Reliance Retail, have established private labels in their product portfolios. Local-based retailers in particular have recognised the potential to acquire and maintain customers with private labels that correspond with local tastes (Malhotra 2010b). Like Western retailers, Indian retailers benefit greatly from the margins offered by private labels. Private labels have gross margins that are 25 to 30 % higher than those of manufacturer brands, which offer gross margins of 12 to 17 %. This advantage is reflected by the share of private labels in overall sales of 10 to 12 % (Wharton 2009). In sum, the literature on consumer behaviour in India is severely limited, and existing studies have just begun to identify different customer segments and their varying attitudes, intentions and behaviours. Therefore, most articles segment Indian customers by their home region. 2.6.2 Segmentation by Region As just stated, the conventional approach to segmenting Indian consumers is to differentiate them according to the geographic regions in which they live. The literature distinguishes Tier I to Tier IV cities. The Tier I metropolitan cities and Tier II cities account for 44 % of the urban population. Table 4 shows the categorization of the main Indian cities. Of all Indian cities, Mumbai has emerged as the country’s biggest consumer market, with 86,140 crore INR (14.8 billion EUR) spent in 2007/2008. Mumbai is followed by Delhi (11.5 billion EUR), Kolkata (9.5 billion EUR), Bengaluru (4.5 billion EUR), Chennai (4.5 billion EUR), Hyderabad (3.7 billion EUR), and Surat (3.0 billion EUR). This enormous sales potential demonstrates the necessity for retailers to be present in Tier I cities. A focus on urban cities may be essential for retailers, given India’s steadily increasing urbanisation level. In 2025, around 45 % of the population will reside in urban areas, up from 30 % at present. Indian’s cities will gain 379 million residents in the next 40 years, which is more than the entire current US population (Dobhal 2008). This development has been acknowledged by several

Pick, D.; Müller, D.

121

retailers that aim to expand into Tier II and Tier III cities. Hence, for retailers, a presence in Tier I cities is obligatory to build up brand awareness and brand image, but the neglect of smaller cities can become problematic in the future. Table 4: Classification of Cities and Towns Classification

Cities

Tier I (major cities, metropolitan areas)

Mumbai, Kolkata, Delhi, Chennai, Bengaluru, Hyderabad, Ahmedabad, Pune

Tier II (average-sized cities)

Surat, Kanpur, Nagpur, Jaipur, Kochi, Madurai, Patna, Agra, Varanasi, Jabalpur, etc.

Tier III (climbers)

Amritsar, Allahabad, Gwalior, Jodhpur, Goa, Pondicherry, etc.

Tier IV (small towns)

Rothak, Udaipur, Faizabad, Shimla, Gurgaon, etc.

Source: Anonymous (2007), p. 90.

In contrast to the urban cities, India is characterised mainly by its rural regions. About 743 million people live in 138 million households in rural India (Shukla 2010). These households account for 56 % of India’s incomes, 64 % of expenditures and 33 % of savings (Bijapurkar/ Shukla 2008). Within the last decade, the income per capita in rural areas has grown by 50 % (Bapna 2010) and is expected to increase further by 3.6 % for two years (McKinsey Global Institute 2007). The sources of income have changed, and it is assumed that by 2015, the incomes of every second rural household will shift from agriculture to non-farm sources like construction or trading (Sharma 2010). As several studies have shown, some parts of the retail industry are dominated by rural consumer spending. For example, between 2005 and 2008, colour television sets’ penetration increased by 7 %, packaged biscuits’ by 10 %, and some categories, such as shampoo, increased their penetration rates by as much as 37 % (Bijapurkar/Shukla 2008). The consequence of this booming rural demand can be seen in the following example: A 1 % rise in refrigerator penetration over a five-year period would mean that more than 1.5 million additional refrigerators will be sold. Retailers are asked to analyse the consumer segments differently because the purchase structure and consumption patterns differ according to the living situations of particular segments of consumers. As illustrated above, India can be segmented into two areas – the rural and the urban. By comparing the distributions of sales, it can be shown that most sales of apparel and accessories and food are generated in rural areas, whereas entertainment and consumer services are preferentially sold in urban regions (see table 5). The obvious conclusion for retailers is to serve both markets by satisfying the needs of specific consumers. Our analysis has shown that foreign retailers such as Metro or Wal-Mart are only present in urban areas of India. To achieve a substantial market share and to operate efficiently, these retailers are, however, asked to expand also to rural regions. In this respect we

122

European Retail Research Vol. 25, Issue I, pp. 107-139

can assume that domestic retailers are well positioned by having several stores established in many different cities across India. Table 5: Distribution of Sales by Category in % Rural

Urban

Entertainment

33

67

Consumer services

44

56

Durables

50

50

Miscellaneous consumer goods

57

43

Apparel and accessories

61

39

Food

64

36

Source: Srivastava (2008).

2.6.3 Consumer Segmentation by Income Another way to classify Indian consumers is to take the common segmentation by income into account. The income of the Indian population has changed within the last few years. The number of very rich households has increased by 500 %, whereas the number of households earning less then 22,000 INR (380 EUR per year at current exchange rates) has decreased dramatically (PriceWaterhouseCoopers 2004). As a result, within the last ten years, a large middle class has emerged in India. Even with an average income of 1,031 USD per person, India is a country where some of the wealthiest people in the world live. In 2009, it was home to 52 billionaires, an increase from 27 in 2008. The accumulated wealth of these persons accounts for 276 billion USD, which is almost one fourth of the Indian GDP (Karmali 2009). However, for generating sales in the high-end consumer markets and luxury retail, a different consumer group is of higher interest – the high net worth individuals (HNWI). The growth of that segment reached 17.1 % in 2009, when its wealth rose by 18.9 % (Capgemini/Merrill Lynch 2010). This high number of wealthy persons provides a promising background for the luxury market. The Indian luxury goods market accounted for 14.8 billion USD in 2004/2005 (Anonymous 2007) and is expected to double in size by 2015 (Saraf 2010). The main jump in sales is expected for the period from 2015 to 2025. Nevertheless, major challenges for luxury retailers include finding the right mix of brands, location, customer service and pricing to attract customers (Saraf 2010). Table 6 shows the distribution of sales in the luxury market. The main segment is jewellery, which accounts for 27.6 % of overall sales. In response, several malls in big cities, such as the Palladium Mall in Mumbai, have specialized in selling only luxury goods, with a product portfolio of international brands such as Omega, Christian Dior and Bulgari.

Pick, D.; Müller, D.

123

Table 6: Sales in the Luxury Market in India 2004/2005 Sales, in million USD

% of total

Per household spent, in USD

Jewellery

4,088

27.6

2,555

Designer apparel

2,368

16.0

1,480

Digital accessories

1,917

12.9

1,198

Time wear

1,253

8.5

789

Cosmetics and skincare

1,139

7.7

712

Footwear

945

6.4

590

Accessories

874

5.9

546

Liquor

862

5.8

540

Fragrances

531

3.6

332

Crystal ware

256

1.7

160

Gourmet foods

250

1.7

157

Intimate wear

221

1.5

138

Tableware

100

0.7

Total

14,804.0

62 9,259.0

Source: Anonymous (2007), p. 279.

The second part of the income pyramid consists of the rising middle class. Figures indicating the concrete number of people belonging to that segment show poor reliability because there is no official definition of the middle class. One of the most used approaches is the McKinsey metric, which considers people with an annual income between 200,000 and 1 million INR (3,450 to 17,250 EUR) to be members of the middle class. In 2005, 50 million people were part of that group. In 2025, this customer segment is expected to account for about 41 % of the Indian population, or 583 million people (McKinsey Global Institute 2007). Another classification system considers ownership of specific goods, such as a car or scooter, colour television, or a telephone, as an appropriate indicator of status. According to that estimation, the middle class equals approximately 20 % of the population, or slightly over 200 million people. No matter which metric is used, the Indian middle class is still a minority segment of the Indian population. However, this segment is the main target group for most of the retailers in the organised sector. This finding derives from the observation that this group is growing faster than the total population and therefore presents attractive business opportunities. The third part of the pyramid consists of the poor and very poor people who live mostly in rural areas. This consumer segment is not targeted by organised retail because most of their shopping takes place in kirana stores. 2.6.4 Challenges for Retailers Serving the Indian Consumer The Indian consumer is quite diverse, and so is the Indian retail market. In view of the broad ranges of consumer segments and the lack of valid data on purchases and consumption pat-

124

European Retail Research Vol. 25, Issue I, pp. 107-139

terns, there are several challenges for retailers in serving Indian consumers. The main challenges can – in our opinion – be seen in the general access to consumer segments, the dealing with different segments, the supply of products, and finally the premature anticipation of the future demand and needs of the consumer. Challenge 1 – Access to Consumer Segments The chief obstacle to gaining access to all Indian consumers is the poor infrastructure, i.e., bad conditions of roads and streets and limited availability of transportation options and logistic systems. The average transport pace on Indian streets is only 20 to 30 kilometres per hour. Despite better circumstances in urban regions and the metropolitan cities, the access to consumers there is also complicated because of consumers’ refusal to travel longer distances for shopping. This factor makes it necessary to locate shops in high-density districts of a town. One possible solution to this problem could be to offer delivery services. Considering the small numbers of stores run by domestic retailers, it can be expected that the potential of all consumers is not already exploited. One cardinal problem in the context of consumer access is how to build up an adequate supply chain to connect rural stores with distribution centres. Another vital point is to make sure that consumers can be reached to create sufficient brand awareness. Here it is important to use different communication channels that are available in urban as well as in rural India. As described above, Indian consumers select products mainly from their preferred brands. If a brand is unknown, the purchase probability depends on whether the desired brand is in stock. Especially for foreign retailers, one crucial success condition in the Indian retail market is to realize that the consumers have a high demand for products in small sizes. Hence, Western retailers must offer their products in different sizes than they do in their home countries. Challenge 2 – Dealing with Different Consumer Segments In India, several languages, cultures and traditions have coexisted for centuries. Retailers have to know these languages to be able to launch adequate advertising campaigns that can be understood by the targeted consumers. Furthermore, they must bear in mind the specific features of the various local cultures to design successful private labels. In general, knowledge of the local conditions is very decisive in any attempt at gaining a realistic idea of the needs of Indian consumers, which is especially true in view of particular religious habits. One survey found that the annual planning of sales and customised seasonal activities based on geography and regional festivals is still at a nascent stage (KPMG 2009). Besides this requirement, there are very probably customer groups that have different expectations of the quality of products and retail services. These groups might include Westernized customers who have travelled around the world or worked in other countries and are therefore familiar with different levels of product quality and retail services.

Pick, D.; Müller, D.

125

Challenge 3 – Supply of Products Given the lamentable state of the infrastructure and the character of the value chain structure, it is quite clear that the optimal implementation of a supply chain is a pivotal step for domestic and foreign retailers that could be determinant for any market entry and market expansion strategy. Retailers in India suffer from miserable roads, an antiquated railroad network, regular interruptions of the electricity and water supplies as well as inadequate supplier storage and processing facilities. Other reasons for the bad supply chain are small farms, outdated processes, inconsistent governmental policies and incoherent tax regulations in different federal states. The consequences are manifold. Retailers have to face long transportation times, losses of products or high efforts to guarantee the quality of the goods. For example, the loss as a result of poor post-harvest management, including the lack of storage possibilities, is estimated to amount to 11 billion USD (Chandran 2010). Several retailers have started unconventional activities to deal with the various insufficiencies of the supply chain. Wal-Mart buys vegetables directly from farms, builds toilets to prevent soil contamination and teaches farmers about transplanting, nutrient management and the use of low-cost innovations to get a higher yield (Chandran 2010). Furthermore, Wal-Mart builds distribution centres to supply its stores within a radius of 200 km to keep products fresh (Bajaj 2010). The firm also sends its trucks every day to collect freshly picked vegetables and fruits in plastic cases and deliver them to the processing centre. In addition to the infrastructure, the structure of the value chain, which has not been refined over the last 50 years, is a serious challenge. One cause for the insufficiencies can be seen in the so-called mandi-system of many wholesalers and middlemen. Consequently, the supply chain of goods is excessively long (Robinson 2007). As a result, the prices for several goods are high, whereas the quality is often low. In response, foreign retailers especially have begun to buy directly from the producers. For example, Metro has convinced several Indian federal states to reform their legislation to allow retailers to purchase directly from farmers. Challenge 4 – Anticipating Future Demand and Needs of Consumers Over the last few decades, Western countries have experienced the gradual fragmentation of their societies, values, norms and lifestyles. For firms, the segmentation of consumers into manageable clusters is therefore much more difficult than it used to be. It is quite plausible to suppose that a changing of lifestyles will also occur in India. However, up to the present, the consumption behaviour of the Indian consumers has not changed substantially. In fact, slight modifications can only be noticed in some consumer segments. In this respect, it is complicated to foresee in what ways consumer attitudes in India might develop. With a view to the increasing environmental problems in India, for example, it will be crucial to offer environmentally friendly products and to establish sustainable production, transportation and consumption patterns (Kumar/Managi 2009). One approach might be to avoid allowing Indian

126

European Retail Research Vol. 25, Issue I, pp. 107-139

consumers to become accustomed to the Western style of waste production. In addition, the prediction of whether most Indian consumers will focus more on price or on service and convenience is of paramount importance. At the moment, there is a chance for retailers to build up value propositions for the middle class instead of singularly pursuing everyday-price discounts. However, the latest reorganisation of some domestic retailers has increased the pressure to lure customers with discounts. We therefore expect that retailers in India will finally decide to go the same price-fixated way as retailers have in industrialised countries. A further aspect is the anticipation of the course of private consumption expenditures. Although the content of the consumption basket has only marginally changed in the last years, some product categories – especially consumer electronics – have shown increased sales. In this context, several questions arise: How will the consumption basket for consumer segments look in ten years? Will jewellery still have such a high significance for Indians? Will consumer electronics, such as smart phones and electronic readers, receive higher status at the expense of other categories? Will the purchase of daily products such as cosmetics become more important, as in industrialised countries? It can be taken for granted that some product categories will increasingly win customers’ attention; the question is only when. It is crucial for retailers to answer these questions as soon as possible to start or accelerate necessary innovation processes.

3

Selected Retail Formats in India

3.1

Department Stores

As presented in table 1, between 2001 and 2006, the number of department stores increased from 26 to 166. Although the floor space also increased from 780,000 to 4,980,000 square feet, department stores’ share of organised retail space decreased. This finding does not indicate that department stores are in their maturing phase – the loss of market share is only driven by the tremendous growth of the other retail formats. In the coming decades, department stores will be an important retail format. Their broad product portfolio and location in shopping malls are highly attractive for consumers. In table 7, we present an overview of the main department stores in India. Table 7: Overview of Indian Department Stores Department Store Westside Lifestyle Globus Pantaloon Ebony

Company Name

Number of Outlets

Trent Ltd.

15

Sales in million INR, 2003/2004 1,555

Landmark Group

7

2,400

R Raheja

7

1,100

Reliance Retail

16

2,500

DS Group

8

820

Source: PriceWaterhouseCoopers (2004), p. 64-65.

Pick, D.; Müller, D.

3.2

127

Shopping Malls

Today, there are approximately 150 malls in India. The first malls were opened in 1999, and since then this retail format has gained tremendous interest. Mall development activity is being pursued aggressively across all metropolitan areas and high-growth cities, with significant investments upcoming. Indian malls with multiplexes, food courts and playgrounds for children have become destinations for family outings (Srivastava 2008). These attributes drive the attractiveness of the mall market, which is expected to grow at an annual rate of 35 to 40 % until 2013. Experts believe that customers become accustomed to this mall shopping culture. Most of the metropolitan cities are considered to have great potential for supporting additional malls (Srivastava 2008). However, several retail authorities estimate that only 20 to 25 % of all malls are profitable, indicating a consolidation process in the future. It is predicted that only 50 % of the malls built in 2010 will survive (Singh/Sharma/Nayar 2010). The main causes for this outlook can be seen in the short visit time: 75 % of consumers visit a mall for one to three hours (Srivastava 2008), and Indians prefer to buy clothes in small shops selling local brands. Department stores account for only 6 to 9 % of the total purchases of apparel (Anonymous 2007). In addition to the more common malls that serve the Indian middle class, luxury malls such as UB City in Bengaluru and DLF Emporio in Delhi are well positioned. Because of increased competition, several innovations in the mall market have arisen. One innovative mall format is the so-called “Wedding Mall”. Wedding malls stock the complete range of wedding product offerings, from apparel to jewelry. Another innovation is “Village Malls” with revamped fair price shops to cater to the changing needs of local populations. The government of Gujarat has spearheaded one such initiative, with 512 “Village malls” launched in the state and further plans for 508 more (IBEF 2010b).

3.3

Online Retail

The online retail business in India is relatively young, mostly as a result of the low penetration rate of desktop and laptop computers in India. Hence, domestic retailers have only recently started to introduce online platforms. For example, one of the biggest Indian retailers, the Future Group, opened its first internet store in 2006. Consequently, in fiscal year 2002/2003, only 1.3 billion INR (22.4 million EUR) were generated by online trade. For 2005/2006, sales are estimated to be 11.8 billion INR (Ossola-Haring/Raveendran 2008). In 2008, average online expenditures per customer rose by 42 % to 3,442 USD (Malhotra 2010a). Major online retailers are Rediff.com, Ebay.in, Indiatimes, Futurebazaar.com, Lifespace and i-choose. Futurebazaar.com, which began its online business four years ago, has revamped its online business portfolio and expects sales of 10 billion INR (172.4 million EUR) by the end of 2012. For the future, it might be worthwhile for retailers to investigate Indian consumers’ intentions of purchasing via mobile phones. Because of India’s high numbers of mobile

128

European Retail Research Vol. 25, Issue I, pp. 107-139

phones and their low costs for the user, shopping via mobile phone could be an interesting business area.

4

Main Indian Market Players and their Characteristics

4.1

Future Group

India’s largest retailer, the Future Group, was founded in 1987 under the name Manz Wear Private Limited. In the fiscal year 2009/2010, the consolidated turnover increased by 27.6 % to 9,786.9 crore INR (1.7 billion EUR). The consolidated annual profit was 76.4 crore INR (13.2 million EUR). Its principal formats include Pantaloon, a department store chain; Big Bazaar, a hypermarket chain; and Food Bazaar. The company operates over 16 million square feet of retail space, has more than 1,000 stores in 73 cities in India and employs over 30,000 people. A considerable 30 % of its turnover is generated by its private labels (Malhotra 2010b). The company established a joint venture with the US office stationary retailer Staples in 2006, running nine outlets in India. Another agreement exists with Celio, a French fashion retailer. Celio plans to increase its store numbers to 24 exclusive outlets and 70 shop-in-shop outlets by January 2011. For 2013, Celio plans to open 150 stores across India. Pantaloon Retail runs multiple retail formats in the value and lifestyle segment. Since the opening of its first store in 1997, Pantaloon has – in terms of financial size – raced to the forefront. With turnover of 87 million USD, it became the country’s largest public limited retail giant (Srivastava 2008). At present, Pantaloon has 48 stores. In the wake of the global financial crisis, Pantaloon has decided to slow down its expansion plans. Big Bazaar has 134 stores in 78 cities and a total retail area of over four million square feet (Balakrishnan 2010). The chain markets over 160,000 products in various segments, like apparel and accessories, electronics, toys and games and home and kitchen appliances. Big Bazaar reported a turnover of 3,600 crore INR in 2008/2009. For fiscal year 2010/2011, Big Bazaar aims to expand the chain to 300 supermarkets, with a target turnover of 13,000 crore INR (2.2 billion EUR) (Anonymous 2009). Food Bazaar, a supermarket chain launched in 2002, now has 185 stores. The total annual turnover is 250 to 500 crore INR (43-86 million EUR). In 2006, the Future Group introduced their online retail section, futurebazaar.com. The online retailer, whose product portfolio ranges from consumer electronics to home and kitchen appliances and home furnishings, expects a turnover of around 1,000 crore INR in the next 18 to 24 months.

4.2

Reliance Retail

Reliance Retail Limited (RRL) is a subsidiary company of the Reliance Group, which generated over 4,000 crore INR (690 million EUR) in sales in the fiscal year 2009/2010. Reliance Retail is the country’s second largest retailer by revenue. Some of the retail formats of Reliance are Reliance Fresh, Reliance Hypermart, Reliance Digital, and Reliance Brands. Food

Pick, D.; Müller, D.

129

and groceries account for 65 % of Reliance Retail’s overall sales. Within the last few years, the firm has undergone significant expansion and has opened more than 940 stores (Salisbury 2010). However, Reliance could not escape the negative consequences of the global financial crisis and has therefore closed several stores and fired 30,000 employees (Bapna 2010). At present, Reliance Retail has 660 stores in just under 100 cities. Even now, the firm does not operate profitably and needs monetary assistance from the holding company in the amount of total 5,200 crore INR (897 million EUR). In fiscal year 2008/2009, the firm accumulated a loss of around 20 crore INR. In 2009/2010, Reliance again reported a net profit of 18.22 crore INR (3.1 million EUR). Nevertheless, according to Reliance managers, the lean times seem to be over, and they plan to add 3,000 to 4,000 stores across all retail formats over 3-4 years. Reliance Retail also plans to enter the wholesale business sector by opening three outlets by the middle of 2011. They aim to challenge foreign cash-and-carry retailers such as the German Metro Group. A subsidiary of Reliance Retail, Reliance Brands, which was set up in 2007, aims to deepen its cooperation with foreign brands, such as Diesel, Zegna and Timberland, by enhancing their store presence. Reliance also cooperates with the British retailer Marks and Spencer by operating 17 stores in India (IBEF 2010b) and plans to increase its retail presence in India. The target is 50 stores in the next three years. Another cooperation exists with the UK-based toy retailer Hamleys, which has announced plans to open 20 toy stores across the country in the next seven years with a capital expenditure of 150 crore INR (Anonymous 2010). The British retailer’s role is to give support in store design, staff training and supplying products.

4.3

Trend Ltd.

The Tata Group, founded about 150 years ago, is one of the biggest companies in India. The group’s turnover in fiscal year 2008/2009 was 70.8 billion USD. The holding company operates in several lines of business, such as information systems and communications, engineering, energy, chemicals and consumer products. Tata’s retail subsidiary is Trent Ltd., which was established in 1998 and has its headquarters in Mumbai. Trend Ltd. accounts for sales of 9.7 billion USD and runs the Westside stores, which offer clothes, footwear and accessories, furnishings, art objects and a range of home accessories (Srivastava 2008). In addition to Westside, Trent Ltd. also runs Star Bazaar (hypermarket chain), Landmark (books and music) and Croma (consumer electronics). Under the label Westside, Trent runs 41 stores in 23 cities. The store sizes vary between 8,000 and 34,000 square feet. Star Bazaar is the hypermarket chain of Trent and was launched in 2004 in Ahmedabad as an exclusive franchise format with the British retailer Tesco. Star Bazaar runs seven stores in Mumbai, Chennai, Bengaluru and Ahmedabad. The store sizes vary between 50,000 and 75,000 square feet. In August 2010, Trent reported plans to invest 275 crore INR (47.4 million EUR) for opening 50 stores by fiscal year 2013/2014.

130

European Retail Research Vol. 25, Issue I, pp. 107-139

Under the label Landmark, which came to Trent in 2006, books and music are sold. Landmark commenced its operations in 1987 in Chennai. In 2008, Landmark's revenues were expected to be around 240 crore INR (41.4 million EUR). Currently, Landmark runs 16 stores in India, ranging in size from 12,000 to 45,000 square feet. In addition to these stand-alone stores, Landmark sells at eight hotel book stores and six airport stores. Via Croma, Trent sells consumer electronics and durables. Croma is run by Infiniti Retail Limited, a 100 % subsidiary of Tata Sons, and therefore is not directly managed by Trent Ltd. The retailer generated a turnover of around 1,000 crore INR (172.4 million EUR) in 2009/2010 and currently has 49 outlets in nine cities under the brands Croma Megastores and Croma Zip. Croma stores are between 15,000 and 20,000 square feet in size. In the current financial year, Croma has launched three stores every month. Infiniti foresees Croma to become a 100-store chain with a turnover of 3,000 crore INR by 2012. The Australian retailer Woolworths Ltd. provides technical support and strategic sourcing facilities to the stores from its global network. Trent operates a joint venture with the Spanish retailer Inditex, which has three Zara stores in India. The stores span 1,500 square feet, and the price range of Zara clothes is 990 to 8,990 INR, which should appeal to the upper middle class. The Inditex group is eager to expand, but their first goal is to understand customers’ needs and to find appropriate locations because of the Indian habit of transferring the image of a location to the brand image. The main challenge for Zara is thought to be India’s slow adoption of non-ethnic and casual clothing (i.e., Western wear). The ethnic wear market for women is almost three times larger than the Western wear market. Trent also operates franchise retail with Italian fashion label Sisley (Benetton Group). The first such store was opened in New Delhi in 2006. In India, Benetton generated a turnover of more than 100 million USD in 2009. The objective for the next four to five years is to earn 250 million US dollars by expanding to Tier III cities.

4.4

Aditya Birla Group

Worldwide, the Aditya Birla Group operates in more than 20 countries and has generated turnover of 29 billion USD. In 2007, the group started its retail activities with the acquisition of a South Indian-based supermarket chain and is now operating as Aditya Birla Retail Limited in the food and grocery retail sector. Its retail format for supermarkets and hypermarkets is “more”, which is India’s second largest supermarket chain. Within a few years, the group has opened 650 supermarket stores in 155 cities in 12 states. Aditya Birla Retail Limited also felt the effects of the financial crisis in recent years, and in 2009 about 120 stores were closed. Although only 70 % of its stores are profitable, the firm plans to get back on its expansion path by investing 1,500 crore INR (258.6 million EUR) in creating 1,000 stores by 2015. In fiscal year 2009/2010, its revenue was 1,130 crore INR (194.8 million EUR), and the firm employed more than 11,000 people. The company aims to achieve turnover of 8,000 crore INR by 2015. Seeking higher profitability, the Aditya Birla Retail Limited has increased its

Pick, D.; Müller, D.

131

private label portfolio. Currently, the retailer has more than 400 products under its private labels (Malhotra 2010b). Private labels for food are more, Feasters, Kitchen's Promise, and Best of India. In 2010, the private labels accounted for 19 % of the annual turnover, and the objective of the firm is to increase this share to 30 % within the next three years.

4.5

Bharti Retail

Bharti Retail Ltd. is a wholly-owned subsidiary of Bharti Enterprises, which is mostly known because of the market power of Bharti Airtel and its 100 million customers in India. Compared with other Indian retailers, Bharti entered the retail market in India in 2008. The retailer runs its own stores under the label “Easyday”. These stores are owned by Bharti and supplied by US retailer Wal-Mart. Currently, there are about 59 Easyday supermarket and hypermarket stores in India, mostly in the North. The sales numbers of Bharti Retail are unknown. Despite its late market entry, Bharti Retail has strengthened its position and plans to further expand by investing about 2.5 billion USD over the next five to six years to add about 10 million square feet of retail space (IBEF 2010b). For the wholesale retail format, Bharti has plans to add 10 to 15 cash-and-carry outlets over the next three years. The prospects for Bharti Retail are somewhat difficult to evaluate, but the frequent press releases indicate slowed expansion of Bharti Retail. Bharti Enterprises seems to have several deficiencies in retail expertise.

4.6

RPG Enterprises

Similar to Reliance, RPG Enterprises is one of India's largest industrial conglomerates and includes more than twenty companies across eight business sectors, with a total turnover of 17,000 crore INR (2.9 billion EUR). The main retail subsidiaries of RPG are Spencer’s Retail and Music World. Additional specialty retail formats are Food World (an alliance with Dairy Farm International), Health & Glow (cosmetics, health products and medicines) and Giant (hypermarkets). Spencer’s Retail Limited is one of India’s largest and fastest growing multi-format retailers with 220 stores, including 30 large-format stores across 35 cities in India. In fiscal year 2009/2010, Spencer’s Retail had a turnover of 1,000 crore INR (172.4 million EUR). The stores total about 900,000 square feet. The average sales per square foot per month were more than 800 INR, indicating a good performance (Paul 2010). The company runs two retail formats. Spencer's hyper stores are destination stores more than 15,000 square feet in size. The merchandise includes fruits and vegetables, groceries, meat, fish, garments, fashion accessories and consumer electronics. Spencer's stores are neighborhood stores ranging from 1,500 to 15,000 square feet in size. These stores stock the necessary range and assortment of fruits and vegetables, FMCG non-food, staples and frozen foods and cater to the daily and weekly topup shopping needs of the consumer. In 2009, Spencer’s Retail opened four to five store units and was expecting to add 15 big box stores in 2010 while expanding its trading area and

132

European Retail Research Vol. 25, Issue I, pp. 107-139

investing approximately 20 million USD (A.T. Kearney 2010). The firm plans to invest about 30 crore INR (5.2 million EUR) this fiscal year to increase its number of stores, including 30 to 35 new stores for the Beverly Hills Polo Club brand. Since 2009, RPG Enterprises has operated a joint venture with the US bakery café chain Au Bon Pain and plans to open ten cafes in Bengaluru itself and another 40 across South Indian cities.

4.7

Other Indian retailers

The Indian organised retail landscape is mainly characterised by small and local operating retailers. Because of the broad range of small retailers, we present some selected firms. The retailer Shopper’s Stop was established in 1991 with its flagship store, Shopper’s Stop, by the K. Raheja Group. The firm is a strong domestic competitor, with 1.9 million square feet of retail space across 88 stores in 12 cities. In 2008/2009, the firm had revenue of 1,578 crore INR (272.1 million EUR), indicating growth by 30 % relative to the previous year. To reach the sales goal of 1,800 crore INR in 2009/2010, the firm is looking to expand its total retail space to 3.5 million square feet in the next four years. Shopper’s Stop plans to invest 500 crore INR to introduce 40 new stores across the country. The discount retailer Vishal Retail began with a tiny apparel store in Kolkata in 1986. In 2009/2010, the turnover decreased by 16 % to 1,105 crore INR (190.5 million EUR) from 1,323 crore INR a year earlier. Vishal Retail, with 730 crore INR (125.9 million EUR) in debt, closed more than two dozen of its stores and warehouses in 2009 and plans to close an additional twelve stores (Salisbury 2010). Hence, Vishal Retail is in the midst of a corporate debt restructuring. Other Indian retailers that will not be discussed here are Liberty Retail, Adani Enterprise and Vivek’s.

5

International Retailers in India

5.1

Overview

As the saturation of Western retail and consumer markets has continued, market entry into emerging countries such as India has become an interesting option. Attracted by the huge growth rates of the Indian retail market, many retailers have entered the market or even planned expansion. However, market entry via FDI is heavily restricted despite the first steps to liberalise the system in 2006. Foreign retailers are only allowed to open single-brand stores (51 % ownership) and to operate wholesale retail (100 % ownership). On the other hand, the operation of multi-brand stores is still restricted and requires a domestic partner. This arrangement has resulted in several collaborations. The first international retailer to enter the Indian retail market was the German Metro Group with its wholesale format Cash & Carry in 2003. The US retailer Wal-Mart and the British retailer Tesco followed with their wholesale retail divisions. Apart from these big companies, several foreign retailers in apparel and fash-

Pick, D.; Müller, D.

133

ion retail such as Adidas, Nike, Zara and Oviesse are present in the Indian retail market. As the former chapter showed, the Indian retail market is dominated by domestic market players with many more stores than the Western retailers. In this chapter, we will briefly present the main foreign retailers operating in India, which are the Metro Group, Wal-Mart, Tesco and Carrefour, which intended to enter the market in 2010.

5.2

Metro Group

The German retailer Metro Group was the first foreign retailer to begin wholesale retail in India. In 2003, the firm opened two distribution centers in Bengaluru and now serves more than 150,000 businesses there. Both cash-and-carry markets have grown by 30 % per year. In 2006, they realized sales of 65 million EUR by selling 16,762 different products, mostly from Indian production (Kazim 2006). At present, Metro operates five cash-and-carry markets in India (two outlets in Bengaluru and one each at Hyderabad, Kolkata and Mumbai). Future plans for serving the Indian market include adding six more wholesale stores in Punjab (A.T. Kearney 2010). Following the needed minimum of 20 outlets to break even in India, Metro Cash & Carry plans to open 40 to 50 outlets in the long run. By the middle of 2011, it is planned to open two wholesale distribution centers at Zirakpur and Ludhiana. In addition, two distribution centres at Patiala and Jalandhar are intended to be launched by the end of 2011. The Metro Group currently has no formal plans to start an end-consumer retail business in India.

5.3

Wal-Mart

At the Indian retail market, the US retailer Wal-Mart is present in wholesale retail under the name “Best Price Modern Wholesale”, which started in Amritsar in 2009, and in endconsumer retail, both in cooperation with the Indian conglomerate Bharti. This cooperation began in summer 2007. The joint venture runs under the firm name Bharti Wal-Mart and now has 800 employees. Both companies operate retail stores under the name Easy Day grocery stores. Sales numbers of these stores are unknown. Wal-Mart provides back-end support for the Easy Day stores. Wal-Mart has invested heavily to enter the Indian market. A total of 11 million USD have been spent on lobbying since the joint venture agreement was signed in August 2007 (A.T. Kearney 2010). In wholesale retail, Wal-Mart operates two stores in Punjab. The stores offer an assortment of more than 6,000 food and non-food items. More than 90 % of these goods and services are sourced locally. Bharti Wal-Mart plans to open 10 to 15 wholesale cash-and-carry facilities and to employ approximately 5,000 people by March 2012. To strengthen their cooperation, in summer 2010, both firms launched the second Bharti WalMart Training Centre in New Delhi to bridge the shortage of skilled workers for their wholesale and end-consumer retail operations. Their first training center is situated in Amritsar (Punjab). Bharti Wal-Mart also runs the so-called “Mera Kirana” program, in which small

134

European Retail Research Vol. 25, Issue I, pp. 107-139

retailers such as kirana stores get support in managing their inventory and are educated about safe food handling and customer retention. With the liberalisation in 2006, Wal-Mart announced optimism for operating in India. However, in more recent years, the business came under pressure and the potential expansion became doubtful. Again in summer 2010, WalMart announced that it was highly confident about its own potential to open hundreds of new stores if FDI is relaxed.

5.4

Tesco

In 2008, the British retailer Tesco announced an initial investment of up to 60 million GBR in the cash-and-carry business over the first two years. For several years, Tesco planned the market entry for own-run Tesco stores. The first wholesale store is expected to open at the end of 2010. Currently, Tesco has a joint venture with Tata’s Trent to support the operations of the hypermarket Star Bazaar. Tesco also suffered from the global financial crisis and has consequently focused on its core business in Europe. Although Tesco reported a 10.4 % jump in profits for the last year, its prospects of becoming a strong wholesale retailer are expected to be low. Competitors such as Metro and Wal-Mart – while not the strongest themselves – occupy better positions in India then Tesco does. One advantage might be the representation of Tesco brands in the Star Bazaar stores.

5.5

Carrefour

The French retailer has planned its market entry in India for nearly a decade. Carrefour held talks with Bharti Enterprises Ltd., Wadia Group, and the real estate firms DLF Ltd. and MGF Ltd. to determine the opportunities of the market. In other areas in Asia, Carrefour is present with a total of 625 stores, but it now intends to exit from markets such as Singapore (2 stores), Malaysia (19 stores) and Thailand (40 stores) to focus on markets where it has a leading position (Thomas/Azhar 2010). Nothing is known about the reasons behind the long negotiations with different Indian retailers. Other options for market entry in the past included the acquisition of a majority in Spencer’s Retail Ltd, but the management of Spencer’s Retail declined at the beginning of 2010 (Paul 2010). This summer, Carrefour opened the first wholesale store in Seelampur in Delhi, with a size of 60,000 square feet and a stock of nearly 30,000 articles. According to Carrefour officials, the firm is still exploring options to directly sell to Indian consumers. One option could be the recently discussed cooperation with the Future Group. The Future Group plans to open between 150 and 300 Carrefour-branded franchise hypermarkets in the next five years (Bailay 2010). Carrefour has not commented on this information. Particularly because of its exit from some Asian countries, the broad market entry of Carrefour to India is somewhat uncertain and seems to have no future.

Pick, D.; Müller, D.

5.6

135

Other Foreign Retailers and their Expansion Strategies

In addition to the mentioned expansion strategies of the main foreign retailers that are focused on wholesale or cooperation with Indian retailers, there is an interesting development of smaller retailers, particularly in the apparel sector. Only a handful of international retailers publicise their strategies for expansion to India. Hence, compiling a market overview is challenging. In summary, compared with the size of the Indian market, the number of stores and the presence of foreign retailers are small. Nevertheless, within the last twelve months, several retailers have announced plans to enter the Indian retail market or to open stores soon. European retailers are in the forefront to expand to India. The Italian fashion retailer Oviesse has recently proclaimed its intention to cooperate with Brandhouse Retail in the joint venture of Brandhouse Oviesse by investing 150 crore INR (25.9 million EUR) to open 190 stores in the coming five years. The first ten stores will be opened by March 2011. Table 8 gives an overview of the expansion strategies of selected foreign retailers in India. Table 8: Expansion Strategies of Foreign Retailers in India Retailer

Category

No. of new stores

By year

Rosebys

Home Furnishing

500-700

2012

Adidas

Sportswear

600

2009

Oviesse

Apparel & Accessories

190

2015

Jockey

Apparel

100

unknown

Samsonite

Travel bags

50

2010

Miss Sixty

Apparel

32

2014

Lifestyle (Landmark)

Apparel

25

2012

Swatch

Watches

10-15

2010

Raymond Well

Premium Watches

10

2012

Admiral

Sportswear

10

2010

Cucine Lube

Kitchen Furnishing

9

2011

S. Oliver

Apparel

2

2010

Source: adapted from Jones/Lang/LaSalle/Meghraj (2010), p. 112.

6

Summary and Outlook

Within the last decade, the Indian retail market has shown remarkable development from a closed market to a sector of the economy that is partially open for FDI. The outlook for the Indian retail market is in general still encouraging because it is driven by a high growth rate of the GDP, increasing incomes for large subsets of the population, a rising and consumptionprone middle class and the sheer size of the country, with 1.2 billion people. When retail was opened for FDI in 2006, foreign firms were allowed to run retail businesses 100 % in the

136

European Retail Research Vol. 25, Issue I, pp. 107-139

wholesale segment and to hold a share of 51 % in single-brand stores. Although these changes are important, Western publications tend to ignore the fact that the Indian retail sector is mostly dominated by unorganised retail firms, which account for 94 to 97 % of total sales. Additionally, it is often overlooked that the organised retail industry is driven by the engagement of domestic retailers that are mostly subsidiaries of the big Indian industrial conglomerates (Reliance Group, Aditya Birla Group, Bharti Enterprises). With this fact in mind and in view of the various problems that complicate the endeavours of the domestic retailers in India, it is fair to assume that foreign retailers have to face even more serious challenges in the attempt to organise sustainable market operations in the Indian retail market. Whereas domestic retailers are already going through a phase of advanced learning, the foreign retailers must first develop a comprehensive notion of all of the difficulties that accompany market entry. At present, foreign retailers have a small share of the retail business in India. Despite the ambitious expansion plans of retail stores and wholesale operations, they may find it very challenging to defend and broaden their market positions. A main problem in estimating the best location, identifying promising customer segments and forecasting competitors’ actions is the great lack of trustworthy and usable information about the market. Also, Indian retailers are not very reliable in reporting their sales and store numbers over the years. Overall, the stable democracy, which provides a predictable framework for investments, the steady growth of the GDP and its related rising disposable incomes, and the advantageous demographics of the Indian population, has been helpful in the development of the Indian retail market. Particularly, if the weaknesses of the infrastructure and the lack of knowledge in supply chain management, customer insights and management are resolved, the trends observed in Indian retail can be expected to continue and accelerate.

Pick, D.; Müller, D.

137

References Alex, B. (2010): Unterhaltungselektronik in Indien mit hohen Zuwächsen, in: Gtai reports, April 22, 2010. Ahluwalia, D. (1993): Public distribution of food in India: Coverage, targeting and leakages, in: Food Policy, Vol. 18, No. 1, pp. 33-54. Anonymous (2010): Reliance Retail to invest Rs150 cr to open 20 Hamleys stores, in: The Wall Street Journal, April 8, 2010. Anonymous (2009): Future Group may hive off Big Bazaar: Biyani, in: Financial Express, September 21, 2009, under: http://www.financialexpress.com/news/future-group-mayhive-off-big-bazaar-biyani/519696/#, accessed September 10, 2010. Anonymous (2007): The marketing whitebook 2007-2008: The marketers’ essential handbook, New Delhi. Asipac (2010): Trading densities of organized retail formats, Bangalore. A.T. Kearney (2006): Retail in India: Getting organized to drive growth, Study. A.T. Kearney (2008): Emerging opportunities for global retailers: The 2008 A.T. Kearney global retail development report index, Study. A.T. Kearney (2009): Windows of hope: The 2009 A.T. Kearney global retail development report index, Study. A.T. Kearney (2010): Global retail newsletter. Bagchi, A.; Kurian, J. (2005): Regional inequalities in India: Pre- and post-reform trends and challenges for policy, in: Mooij, J. (Ed.): The politics of economic reforms in India, New Delhi, pp. 322-350. Bailay, R. (2010): Carrefour announces first wholesale outlet in India, in: The Wallstreet Journal, May 11, 2010. Bajaj, V. (2010): In India, Wal-Mart goes to the farm, in: New York Times, April 12, 2010. Balakrishnan, R. (2010): Futures gGroup aiming for Rs 200 cr from big bazaar alone, in: The Economic Times, August 25, 2010. Bapna, A. (2010): The emerging face of Indian retail, in: Storai Magazine, March/April, pp. 28-34. Batra, A. (2010): Watch out for watch retail, in: IndianRetailer.com, August 18, 2010. Bijapurkar, R.; Shukla, R. (2008): Spotlight on rural consumers, in: liveMint.com, December 3, 2008, http://www.livemint.com/2008/12/03224721/Spotlight-on-rural-consumers.html, accessed August 10, 2010. Capgemini; Merrill Lynch (2010): World wealth report 2010. Chandran, R. (2010): Build it and they will do it? Retailers’ India dilemma, in: The Wallstreet Journal, May 21, 2010. CIA Factbook India (2010): The World Factbook India, https://www.cia.gov/ library/ publications/the-world-factbook/geos/in.html, accessed August 10, 2010. Dobhal, S. (2008): Top 20 cities hold keys to urban growth, in: The Economic Times, August 8, 2008.

138

European Retail Research Vol. 25, Issue I, pp. 107-139

Dholakia, N.; Sinha, P.K. (2005): Observations on Observation in India's Dynamic Urban Markets, in: Forum Qualitative Sozialforschung, Vol. 6, No. 1, Art. 13, Ghosh, J. (2010): Die verdeckte Finanzkrise, in: Le Monde diplomatique, Special Issue on Indien. Die barfüßige Großmacht, pp. 40-45. Giridharadas, A.; Rai, S. (2006): Wal-Mart to open hundreds of stores in India, in: New York Times, November 27, 2006. Goswami, P.; Mishra, M.S. (2009): Would Indian consumers move from kirana stores to organized retailers when shopping for groceries?, in: Asia Pacific Journal of Marketing and Logistics, Vol. 21, No. 1, pp. 127-143. Gtai (2010): Wirtschaftsdaten kompakt: Indien, Köln. Gtai (2009): Boom im indischen Einzelhandel vorläufig gestoppt, February 27, 2009, http://www.gtai.de/DE/Content/__SharedDocs/Links-Einzeldokumente-Datenbanken/ fachdokument.html?fIdent=MKT200902268001, accessed August 5, 2010. Howes, S.; Jha, S. (1994): Public distribution of food in India: A comment, in: Food Policy, Vol. 19, No. 1, pp. 65-68. IBEF (2010a): Retail, http://www.ibef.org/industry/retail.aspx, accessed July 26, 2010. IBEF (2010b): Retail. Market & Opportunities, Study. Jenkins, R. (1999): Democratic politics and economic reforms in India, Cambridge. Jones Lang LaSalle Meghraj (2010): State of the retail sector, in: Storai, March/April, pp. 108-112. Joseph, M.; Soundararajan, N.; Gupta, M.; Sahu, S. (2008): Impact of organized retailing on the unorganized sector, Working Paper No. 222, September 2008, Indian Council for Research on International Economic Relations. Karmali, N. (2009): India's 100 richest, Special Report, in: Forbes, November 18, 2009. Kazim, H. (2006): Großes Land mit kleinen Lädchen, in: Spiegel Online, May 26, 2006, http://www.spiegel.de/wirtschaft/0,1518,414663,00.html., accessed August 31, 2010. Knight Frank (2010): Q1 2010 India organised retail market, in: Storai, May/June, pp. 66-82. KPMG (2009): Indian retail: Time to change lanes, Study. Kumar, S.; Managi, S. (2009): The economics of sustainable development: The case of India, Springer, Berlin et al. Malhotra, N. (2010a): Online retailing, in: IndianRetailer.com, April 6, 2010, http://retail.franchiseindia.com/articles/Retail-Trends/Retail-Industry/Online-retailing-9/, accessed August 31, 2010. Malhotra, N. (2010b): Private labels on an upswing, in: IndianRetailer.com, January 21, 2010, http://retail.franchiseindia.com/articles/Retail-Business-Practice/New-Trends/Privatelabels-on-an-upswing-160/, accessed August 31, 2010. McKinsey Global Institute (2007): The “bird of gold”: The rise of India’s consumer market, San Francisco. McKinsey & Company (2007): India pharma 2015: Unlocking the potential of the Indian pharmaceutical market, Study.

Pick, D.; Müller, D.

139

Menon, B.D. (2010): Consumer affairs ministry says yes to 49 % FDI in multi-brand Retail, in: Business Line, August 25, 2010. Ossola-Haring, C.; Raveendran, P.T. (2008): Indien auf einen Blick – mit vielen Facetten, in: Ossola-Haring, C. (Ed.): Wachstumsmarkt Indien  Das Investitionshandbuch für Unternehmen und deren Berater, München. Paul, A. (2010): Carrefour spurned as it wanted majority, in: The Wallstreet Journal, January 22, 2010. PriceWaterhouseCoopers (2009): Glimmers amid the gloom. The outlook for the retail and consumer products sector in emerging markets, Study. PriceWaterhouseCoopers (2004): 2004/2005 Global retail & consumer study from Beijing to Budapest, Study. Robinson, S. (2007): Food fight, in: TIME, May 31, 2007. Salisbury, T. (2010): The Future, on sale: The Indian retail market, in: South Asia Monitor, No. 137, January 6, 2010. Saraf, A. (2010): Richie rich. Luxury retail is gradually finding its feet in India, in: Business India, September 6, 2010, p. 115. Sengupta, A. (2008): Emergence of modern Indian retail: An historical perspective, in: International Journal of Retail & Distribution Management, Vol. 36, No. 9, pp. 689-700. Sharma, A. (2010): Research finds change in rural consumer patterns, in: The Economic Times, August 1, 2010. Shukla, R. (2010): India, sunny side up, in: The Financial Express, July 27, 2010. Singh, P.; Sharma, A.; Nayar, L. (2010): Therapy for retail, in: Outlook India, April 19, 2010. Sinha, P.K. (2003): Shopping orientation in the evolving Indian market, in: Vikalpa, Vol. 28, pp. 13-22. Srivastava, R.K. (2008): Changing retail scene in India, in: International Journal of Retail & Distribution Management, Vol. 36, No. 9, pp. 714-721. Tata (2010): Food retailing in India: Challenges and trends. Thomas, D.; Azhar, S. (2010): Retailers to submit bids for Carrefour Asia, in: The Wallstreet Journal, September 1, 2010. Westfall, R.; Boyd, H.W. (1960): Marketing in India, in: Journal of Marketing, Vol. 25, No. 2, pp. 11-17. Wharton (2009): More Choices in Store: India's Retailers Are Stocking Up on Private Label Brands, December 3, 2009, under: http://knowledge.wharton.upenn.edu/india/article.cfm? articleid=4432, accessed August 31, 2010.

Retail in Poland í New Challenges and New Strategies

Tomasz DomaĔski

Abstract From the European perspective, the Polish retail market is characterised by the significant presence of foreign retail chains. This phenomenon is very special when comparing Poland to other European Union (EU) countries that shaped their retail structure through evolutionary approaches with a predominant share of entities being locally owned. Foreign retail groups are also the largest retail companies in Poland. The evident presence of top European retail chains in Poland was mainly due to the radical liberalisation of the Polish economy after 1989, which accompanied by the large scale of the Polish market (40 million consumers), had to attract the top European operators and global retail chains within a short time. The Polish retail internationalization is visible in all formats of the large-format stores. Its highest level is observed in the group of hypermarkets and discount chains, where all the operators represent foreign groups. In comparison to major EU member states, the Polish retail market is characterised by its significant disintegration, which results in a large number of small and medium-sized retail companies. The specificity of Polish retail also involves its very strong polarization, as next to traditional retail, there are also many international retail chains, which manage modern formats of large-format stores. The Polish retail model will however remain unique with traditional retail undergoing rapid change. In the context of challenges related with economic recession, a further growth of importance of discount stores should be anticipated. This results from the permanent shift of demand towards this format. Operators of other formats will respond to this shift by creating and developing a broader discount offer and concentrating more on developing more private labels for their chains. Permanent changes in the behaviour of Polish consumers will involve increasing rationalisation and increased demand for cheap products. This factor will contribute favourably to increased customer demand for private labels. These phenomena will change the perception of chains’ private labels and contribute to the further improvement of their quality.

Keywords Poland, Retail Market, Internationalisation, Format Development, Retail Strategies, Hard Discount, Integration Strategies Tomasz DomaĔski Chair of International Marketing and Retailing, University of àódĨ, àódĨ, Poland (E-mail: [email protected]).

Received: October 29, 2011 Revised: February 2, 2011 Accepted: February 2, 2011

EUROPEAN RETAIL RESEARCH Vol. 25, Issue I, 201, pp. 141-180

D. Morschett et al (eds), European Retail Research, DOI 10.1007/978-3-8349-6235-5_7, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

142

1.

European Retail Research Vol. 25, Issue I, pp. 141-180

Introduction

This article aims to present the specificity of retail in Poland and the role that large international retail chains play in this sector. The role of the large retail chains is analysed in the context of traditional Polish retail and new integrated chains established by Polish entrepreneurs. This analysis is based on immediate observations of the author, who for nearly twenty years has been involved in the analysis of retail enterprises, and has placed special emphasis on the strategies of international food retail chains. The analysis has also covered selected categories of non-food products (such as DIY and garden; electronics; clothing and footwear). A range of statistical sources which characterise the evolution of the retail sector in Poland and demonstrate changes in behaviours of Polish consumers, have also been used in developing this paper. Processes of adaptation to the changing conditions on the Polish market that have been implemented by international chains have been presented. In 2009, the value of retail sales reached 580,964 million PLN (1 EUR= 4 PLN/Polish zloty), i.e. 15,227 PLN per capita (which indicates an increase of 2.8 % in relation to 2008). In 2009, the sale of consumer goods reached 455,164 million PLN, i.e. approximately 78.4 % of the total retail sales, including food products and non-alcoholic beverages (26.4 %), alcoholic beverages and tobacco products (9.0 %), and non-food goods (43.0 %). The remaining 21.6 % were shared by catering facilities (3.5 %) and non-consumer goods (18.1 %) (see Table A1 in the Appendix).

2.

The Specificity of Polish Retail in the Food Sector

In comparison to major EU member states, the Polish retail market is characterised by its significant disintegration, which results in a large number of small and medium-sized retail companies. Disintegration of Polish retail contributes to the fact that integration will become its key challenge in the future (see Table A2 in the Appendix). Almost all Polish retail is held by private owners: in 2009, 99.1 % of retail sales were carried out by the private sector (in 2000, this share reduced to 95.1 %). The specificity of Polish retail also involves its very strong polarization, as next to traditional retail, there are also many international retail chains, which manage modern large-format stores. In 2008, 385,663 stores operated in Poland, which indicates that one store provided service to 98.9 persons. Between 2005 and 2008, the total number of stores fell only by approx. 0.08 %, which indicates that retail continued to be the easiest form of conducting economic activity. Within the same period (2005-2008), the number of the largest stores with the selling surface of above 400 m2 increased from 6,258 to 8,301 (which indicates an increase by 33%!) (see Table A2 in the Appendix).

DomaĔski, T.

143

Although the degree of concentration in Polish retail continuously grows, it is still significantly lower than the degree observed in the largest old EU member states (France, Germany, Spain and the United Kingdom) (Pellegrini 2008, p. 101), and the neighbouring state of the Czech Republic. In this context, it is more symptomatic to compare Poland to the Czech Republic where the degree of concentration is already comparable to the degree in the old EU member states, although the role of independent businesses in the retail sector is very limited (see Table 1). In the case of Poland, the following companies were in the group of the 10 largest “general retailers”: Jeronimo Martins, Makro Cash and Carry, Real (the last two companies are owned by the Metro Group), Tesco, Carrefour, Auchan, Schwarz Group (Kaufland, Lidl), Selgros Cash and Carry, Intermarché and POLOmarket. Table 1: Degree of Concentration in the Polish Fast Moving Consumer Retail Sector in 2008 Country

Degree of turnover concentration (%)

France

89.3

Germany

73.4

Spain

72.6

Czech Republic

65.5

United Kingdom

62.6

Poland

30.8

Source: Planet Retail (2010); Badowski (2010).

The prominence of large foreign retail groups has increased in the ranking of the largest enterprises in Poland. At the same time, foreign retail companies have become the largest companies operating in all the sectors (i.e. manufacturing, trade and services) of the Polish economy. The data presented in Table 2 below indicates their very strong position and high dynamics of growth. In 2009, all the foreign groups presented in Table 2 noted significant growth; however the shift of the Metro Group to the 2nd position in the ranking of the largest companies is most spectacular. It results both from a very expansive policy of taking competition over and the multi-format strategy. It is particularly noticeable in the case of the Metro Group’s strategy, which manages four chains and three different formats of stores selling food and nonfood products. In the case of Jeronimo Martins, a Portuguese group, a progressing specialisation in the discount store format has been observed. By contrast, the Tesco chain manages three different formats, which are complementary under one integrated strategy. The stronger position of these largest retailers was also due to their very strong concentration in the developing Polish market, which they consider to be their strategic market. It was also a result of their consequent strategy of taking competition over: Real (taking Géant Casino hypermarkets over),

144

European Retail Research Vol. 25, Issue I, pp. 141-180

Biedronka (Plus í Tengelmann discount store chain) and Tesco (Leader Price discount store chain). Table 2: The Largest Retail Companies in Poland (in 2009)

Group name

The position in the ranking of 500 largest companies in Poland

Chains and formats included in the group

Turnover in 2008 (in million PLN)

2008

2009

Metro Group (Germany)

5

2

Real (hypermarket), Makro Cash and Carry, Media Markt and Saturn (specialised largeformat stores)

20,800

Jeronimo Martins (Portugal)

11

8

Biedronka (discount stores)

12,680

Tesco (United Kingdom)

15

13

Tesco (hypermarkets, supermarkets, convenience stores)

9,080

Source: Mapi (2010).

Polish retail of FMCG has undergone a very fast modernisation, which resulted both from development of the modern store formats and the decreasing role of traditional stores. An increased share in the general sales value has been observed in all the modern store formats (hypermarkets, supermarkets and discount stores). In 2009, the overall share of the “modern retail formats” in the total value of the FMCG sales increased to 60.7 %, in comparison to 57.3 % recorded in 2008. Thus a certain regular process of dynamic modernisation of Polish retail is taking place (see Table 3 below). Table 3: A Share of the Individual Store Formats in the Structure of FMCG Sales in Poland in the Quantitative Frame in % (in 2008 and 2009) Format of the stores

2008

2009

Small format and general stores

31.8

28.9

Supermarkets

16.6

17.2

Hypermarkets

21.1

21.6

Discount stores

19.6

21.9

Other stores

10.9

10.4

In total

100

100

Source: MikusiĔska-OzdobiĔska (2009); GFK Polonia (2009).

The changes observed are definitely the greatest in the case of the discount store format, which at the same time confirms the shift of demand of Polish consumers towards this dynamically growing format. This data also demonstrates that nearly two third of the FMCG

DomaĔski, T.

145

turnover has been earned in Poland through the Table 4 presented below, which illustrates the number of stores operating under the specific formats. Table 4: Changes in the Number of Grocery Stores in Poland Operating under the Specific Formats (2005-2009) Number of stores/per annum Store format 2005

2007

2009

Dynamics 2009/2005 (%)

Hypermarkets

204

245

282

+38.2

Supermarkets

1,478

1,991

2,160

+46.0

Discount stores

1,265

1,576

2,041

+61.3

Large-sized grocery stores 2 (101–300m )

5,021

5,430

5,425

+8.0

Medium-sized grocery stores 2 (41–100m )

31,924

31,951

31,556

–1.2

Small-sized grocery stores

73,720

66,773

54,045

–26.7

In total

113,612

107,966

95,509

–16.0

Source: Adapted from Badowski (2010).

In the modern store formats, between 2005 and 2009, the highest growth was observed in the discount store category (+62 %), followed by supermarkets (+46 %) and hypermarkets (+38 %). In the context of this category of modern large-format stores, within the past two years a clear slowdown in the development has been observed, which is obviously due to the deteriorating economic situation and more rigid administrative procedures required at the opening of new facilities í large-format stores in particular (DomaĔski 2008, p. 157). This phenomenon may also be explained by the very strong dynamics of development of the discount store chains, whose position is clearly strengthening. These stores are located much closer to their customers’ homes than hypermarkets are, and their image is also improving as they are presented as attractive places to shop. This data indicates that the highest growth dynamics can be found in stores with medium-size selling areas (such as discount stores and supermarkets), which follows tendencies observed in other EU states (Zentes 2008, pp. 58-60). Even so, three different tendencies have been noted in the remaining store formats: The group of large-sized grocery stores has undergone slow development, although recently it has reached a level of stability (+8 %); a slightly declining tendency has been observed in the group of medium-sized stores (-1.2 %); while a very clear and regular declining tendency has been observed in the group of small-sized grocery stores (-26.7 %). Analysing the changes taking place on the Polish FMCG market, it is evident that the leading position of several foreign chains is strengthening. This has been accompanied by a very strong disintegration of the whole food products’ market. Planet Retail’s own definition pro-

146

European Retail Research Vol. 25, Issue I, pp. 141-180

posed for this category (see Table 5 below) demonstrates the leading position of the Jeronimo Martins Group. It has consistently focused on developing a strong discount store chain with a strong and recognisable brand name and distinctive image throughout the domestic market. In relation to Table 3, it is also clear that the level of concentration of turnover on the food market is still significantly lower than on the FMCG market. Table 5: Poland í Food and Non-food Retail Market Concentration 2009 (mixture) Company

In million PLN

Market share (%)

Jerónimo Martins

14,0589

5.7

Metro Group

11,635.2

4.7

Tesco

8,962.6

3.7

8,487

3.5

Carrefour

7,703.9

3.1

Emperia Holding

7,531.7

3.1

Companies between 1.0–3.0% share

26,535.2

11.0

Companies between 0.5–1.0% share

10,192.6

4.2

Companies with 0.05–0.5% share

5,038.9

2.2

Remainder of market [<0.05% share]

145,263

59.2

Schwarz Group (Lidl/Kaufland)

245,409 Note: Based on the share of sales which are generated from groceries, from players in the modern grocery distribution market. The definition of groceries includes everything you would find in a standard supermarket (including some health and beauty products, OTC drugs and basic household non-food products). Modern grocery includes hypermarkets, supermarkets, discount stores, cash & carry, food halls in department stores, convenience stores and various specialised stores such as bakeries, delicatessens, health food stores, etc.

Source: Adapted from Planet Retail (2010); Barclays Capital (2009).

3.

Polish Retail Internationalisation

From the European perspective, the Polish retail market is characterised by the presence of the highest number of foreign retail chains. This phenomenon is very special when comparing Poland to other EU countries that shaped their retail structure through evolutionary approaches, with a predominant share of entities representing their domestic retail. These circumstances took place in such countries as France and Germany. However in Poland, rapid development of the retail sector took place in the late 1990s, mainly through entry of the major foreign retail chains and foreign retail groups (DomaĔski 2010, pp. 473-483). That applies both to retail of food and non-food products (see Table 6). Foreign retail groups that manage international retail chains are also the largest retail companies in Poland, which has been indicated above (see Table 2). Evident presence of the top European retail chains in Poland was mainly due to a radical liberalisation of the Polish economy after 1989, which together with the large size of the Polish market (approximately 40 million consumers) attracted the top European operators and global retail chains within a short time (DomaĔski 2002, pp. 266-280).

DomaĔski, T.

147

Table 6: Presence of the Largest European FMGG Retailers in Selected Countries 1.

Poland

2.

Spain

16 12

3.

Czech Republic

11

4.

France

9

5.

Belgium

8

6.

Italy

8

7.

Austria

7

8.

Hungary

7

9.

Holland

7

10.

United Kingdom

5

11.

Portugal

5

12.

Greece

4

13.

Germany

4

Source: PMR (2002).

The largest concentration of entries by foreign operators into the Polish market took place between 1994 and 1998 (12 chains from the FMCG sector). It was especially intensive in the 2nd half of that period, i.e. between 1996 and 1998 (9 chains from the FMCG sector). Table 6 presents the data for the peak period of the “market entries”, i.e. for 2002. This data confirm the thesis that in the case of markets which undergo radical changes, large international chains need at least four to five years to prepare a new market penetration strategy. In Poland this period was extended to additional six to seven years due to the fact that prior to the entry, the chains had been involved in international operations in other competitive markets (such as Latin America, Asia and Europe). The chains that were least experienced in internationalisation (HiT/Germany; Leclerc/France; Jeronimo Martins/Portugal and Casino/France), were the first to notice the benefits of penetrating the Polish market. The success of their strategies was mainly due to their earlier entry into Polish market, i.e. the advantage of being “the first” on the market (the lack of competition and access to attractive locations), as well as application of the concentration strategy focused on a single market. All the companies were employing the “trial and error method” to develop a hypermarket model adapted to Polish consumers. The Polish retail internationalisation is visible in all formats of the large-format stores. Its highest level is observed in the group of hypermarkets and discount chains, where all the operators represent foreign groups. Thus the level of internationalization is the highest in relation to the formats which previously (by 1989) did not operate in Poland (hypermarkets and discount stores), and to those whose development was very capital-intensive and complex during management of the chain and its accompanying logistics and distribution structure. In this context, Polish operators faced a capital-related barrier and limitations in the field of

148

European Retail Research Vol. 25, Issue I, pp. 141-180

infrastructure and competence. This type of limitation did not affect supermarkets which were based on a more diverse model of stores and were developed much later. This allowed Polish operators to gather necessary capital and experience in modern chain management.

4.

Development of Large-Format Retail in the Food Sector

4.1

Hypermarket Chain Development

Modern large-format retail in Poland mainly developed in the mid-1990s. Its development was sequential in nature, as the individual formats were implemented in stages. The first stage involved development of hypermarkets, and the next one, of supermarkets and discount stores. Table 7: Analysis of Development of Foreign Hypermarket Chains on Polish Market Number of hypermarkets (2009)

Name of the chain/group/ Country of origin

Date of entry

Model of development

HiT/Dohle/Germany*

1994

Organic growth

Mono-format

-

Leclerc/Leclerc/France

1995

Organic growth + acquisitions Billa/supermarkets

Mono-format

13

Géant/Casino/France*

1996

Organic growth

Multi-format: Hypermarket Discount stores

-

Auchan/Auchan France

1996

Organic growth + acquisitions

Multi-format: Hypermarket Supermarket

25

Jumbo/Jeronimo Martins/Portugal*

1996

Organic growth+acquisitions: Biedronka discount chain Plus discount chain

Real/Metro Group/Niemcy

1997

Organic growth + acquisitions Casino/ hypermarkets

Hypermarket

Carrefour/CarrefourFrance

1997

Organic growth + acquisitions: Ahold/Hypernova hypermarkets, Jeronimo Martins/Jumbo hypermarkets

Multi-format Hypermarket Supermarket Convenience

82

Ahold/Hypernova/Netherlands

1998

Organic growth+ acquisitions

Multi-format: Hypermarket Supermarket

-

Tesco/Tesco/United Kingdom

1998

Organic growth + acquisitions: HiT hypermarkets Leader Price discount stores

Multi-format: Hypermarket Supermarket

89

Kaufland/Schwarz Group/Germany

2001

Multi-format Hypermarket Discount (Lidl)

116

Organic growth

Strategy

Multi-format: Hypermarket Discount Cash & Carry

-

54

Notes: * Chains or formats that were taken over by competition between 2001 and 2008.

Development of hypermarkets in Poland was mainly based on the organic model (1995-2000). This was due to the fact that all the largest retail groups arrived on the Polish market at round the same time í when it was at the stage of a radical transformation, and the period prior to that when Poland did not know the form of large-format retail. Only after the year 2000, the first opportunities of taking competition over became available (acquisition strategy). Those

DomaĔski, T.

149

opportunities resulted both from deliberate strategic actions of some operators who had developed the chains in order to sell them later (Dohle), and wrong decisions of certain competing entities to exit the market (Casino). The organic development strategy at the same time enabled foreign operators of the retail chains to develop hypermarket format in a modern and innovative manner, to adapt it to the local environmental conditions and growing competition. In the phase of new hypermarkets’ development, apart from selecting the right location, selection of a model that would suit the expectations of Polish customers and match the strong international intra-format competition was the main dilemma. Paradoxically, foreign supermarket operators, even those holding major international experience, were not used to such intensive international competition as found in Poland. In the Polish conditions, the strategy of developing an optimum format aspired to answer the following questions: to what extent that format was to copy the previous formula and to what extent it should be adapted to new competitive conditions and a vision of this format’s evolution in the future (building competitive advantage in the perspective of 10 to 15 years). As opposed to the practices employed in the EU states, where most hypermarkets had been located in the city outskirts, in Poland there was a completely new opportunity of locating them in the city centres. On the one hand, this new opportunity was due to the availability of vacant land in the centres of large and medium-sized cities, and the process of constructing modern shopping centres in the city centres on the other. One may risk a thesis that the facilities built in Poland are nowadays among the most modern facilities in Europe. Adapting their size to the specifics of the Polish market was another strategic challenge. Some operators, Casino in particular, made wrong decisions and constructed hypermarkets that were too large (8,000-10,000 m2) in relation to expectations of the local markets. It seems that the compact hypermarket model (3,000-4,000 m2) developed by Kaufland after the year 2000 was much better adapted to the expectations of Polish buyers. The Leclerc chain implemented similar measures by constructing medium-sized facilities (5,000-6,000 m2), which were much more convenient and efficient for customers than Géant hypermarkets which were too large. Then in the period 2005-2010, adapting the acquired stores to the model format of a given chain became a key strategic challenge. For instance in 2007, the Carrefour chain took over stores owned by Ahold (hypermarkets and supermarkets), which necessitated implementation of a complex process of adaptation-based activities in 2008. A similar process took place after German Real (Metro Group) took over Géant hypermarkets owned by Casino, a French hypermarket chain. This process was even more complex because of its large scale and concentration within a limited time frame (mo 2009b; jn 2009b).

150

European Retail Research Vol. 25, Issue I, pp. 141-180

Observing development of the foreign chains, it should also be emphasised that the best results were attained by those companies that consequently departed from the model of excessive centralisation of decisions to delegated more responsibility to local managers in the field of their knowledge of the selected markets.

4.2.

Supermarket Chain Development

As opposed to the hypermarket format, which in Poland was completely dominated by foreign retail chains, in the case of supermarkets an opposite phenomenon has been observed. It involved a strong presence of Polish retail chains, which created unique formats of stores that were adapted to the specificity of the Polish market. This phenomenon may be justified by the smaller scale of supermarkets, their internal diversification and the possibility of developing unique strategies based on competitive positioning. Format diversification was also a factor that guaranteed a better adaptation-based capacity for the chains. Delicatessen supermarket chains (Piotr i Paweá and Alma) represent the most unique Polish supermarket formula, however their growth has been clearly impeded by the effects of the economic crisis (2008-2010) and the shift of Polish buyers toward cheaper substitutes (jn 2009, p. 8). POLOmarket, a Polish chain, has also developed a very interesting strategy: POLOmarket quickly entered into the markets of small and medium-sized towns in particular, and has reached the leading position in its category by offering its customers a product line at competitive prices in relation to the hypermarket chains. It also maintained good quality standards of its products. In 2008, POLOmarket owned nearly 270 stores which operated in 14 out of 16 regions, and their turnover exceeded 2,200 million PLN in 2008. “Managerial migrations” greatly determine strategies used by different store formats in Poland, as people managing the chains move to new companies owned by competitive chains that operate within the same or a competitive format. This includes homogenisation of specific innovation strategies by acquiring best practices of competition. This phenomenon was very important to the development of new forms of retail in Poland by entrepreneurs who had learnt how to manage the chains throughout the process of their development and by observing foreign competition (1995-2005). In this context, it is worth considering a unique format of a delicatessen supermarket, which was introduced by Alma, a Polish group. Alma í the Polish Chain of Delicatessen Supermarkets Alma is an original Polish format of modern large-format delicatessen stores offering “premium” food products. Its product range covers approximately 60,000 products. Many of these products are directly imported (approx. 3,000). A broad offer of Polish regional products is Alma’s another advantage. This format harmoniously combines the imported product lines (such as wine, beer and cheese) with the lines of Polish producers, and Krakowski Kredens,

DomaĔski, T.

151

which is Alma’s private label. Alma’s format provides a modern connection with the tradition of upmarket stores selling imported goods. One store’s area covers 2,000 m2, which emphasises the impression of “spaciousness” and eliminates the sense of crowding, which is typical for mass large-format chains. According to the assumptions of their founders, Alma delicatessen stores are located in the centres of large cities (above 100,000 residents) and in exclusive shopping centres, in premises acquired by lease contracts. Very good merchandising, which confirms the sense of the store’s exclusive nature, also determines Alma’s attractiveness. Their branding is addressed to customers with average and high levels of income, who appreciate comfort when shopping and a unique product range, and who mainly reside in larger cities. Alma’s strategic plan of development assumed that by the end of 2007, the chain should have held 18 outlets with the total space of 55,000 m2. Between 2008 and 2009, it was assumed that approximately 20 new outlets should be launched, and in 2011, the Alma chain was to manage 60 shopping facilities in Poland (Alma Market 2007, p. 5). The economic crisis radically stopped those ambitious plans. In 2009, Alma increased the number of stores to 27 delicatessens and supermarkets, totalling 80,000 m2 located in large Polish cities. In 2008, the sales structure of the holding company changed, with a clear dominance of delicatessen food products. These products constituted 84 % of the turnover (where 42 % were fresh products and 42 % durables). By the end of 2008, the net revenues from sales reached nearly 869 million PLN, in relation to 651 million PLN reached by the end of 2007. In 2009, Alma recorded the revenue reaching more than 1,009 million PLN. However it generated a 59 million PLN loss in comparison to 16.4 million PLN profit in 2008. Thus, it may be questioned whether the size of the stores adopted for this delicatessen format were not too large, taking into account both the overhead costs and the level of income of Polish consumers (Alma Market SA 2009). Appropriate location of new facilities, which determines the level of future sales and margins, is a key risk to Alma. Seasonality of retail is an additional factor that impacts the model of Alma’s chain development. Delicatessen products are mainly sold at the periods preceding holidays such as Easter and Christmas/New Year. The Alma chain is also vulnerable to currency exchange risk, as its competitive advantage is based on its importation of European regional products priced at medium to higher levels. Currency exchange rate fluctuations also affect the cost of store premises leased in large and modern shopping centres. Long-term lease contracts (often for the period of ten years) are paid in EUR, and are thus affected by currency fluctuations.

152

European Retail Research Vol. 25, Issue I, pp. 141-180

Where zloty currency exchange rate fluctuations are unfavourable, Alma’s large dependence on imports is reflected by increased prices of its products or the necessity to lower the chain’s margins. This is very bad for the company due to its high level of debt caused by the consequences of fast development. Alma’s delicatessen development strategy is very sensitive to changes in the immediate environment. The Alma delicatessen chain intended to be perceived as the “most expensive Polish delicatessen chain”. In the economic crisis, and given the small size of the Polish middle class, this positioning may be too limited in the medium-term. It seems that in Poland the “democratisation of luxury” at the level of purchase of expensive food products will occur significantly more slowly than it has occurred in the West. The store retail area, which is relatively large, generates high leasing costs in modern shopping centres, and poses another risk. The chain’s positioning based on “life style” (travelling and openness) still “precedes” the contemporary Polish customer, however the opposing values on which Alma’s format and brands (modernity and tradition, or internationality and locality) have been based will gain popularity in the future. It seems that the assumed dynamics of the chain’s development has been assessed too optimistically by the owners while the significance of worsening negative externalities has been underestimated (DomaĔski/Bryáa 2010, pp. 211-222).

4.3.

Discount Chain Development

Similarly to hypermarkets, discount chain development in Poland was exclusively based on investments of foreign operators (DomaĔski 2005, pp. 35-51). Jeronimo Martins Group (JM), a Portuguese operator, became the leader of this format. The success of this strategy confirms the thesis that the right moment of market entry (“first mover”) combined with concentration on this particular market and mono-format specialisation, are of key importance to JM’s successful internationalisation. JM’s competition includes: Lidl, Netto and Aldi. French Leader Price was bought out by Tesco. By contrast, Aldi which is the world discount leader, due to its very late entry into the Polish market in 2008 still holds a marginal position in Poland, which does not correspond with its European and international position in this format (DomaĔski 2005, pp. 25-29). On the other hand, the early moment of entry into the Polish market provided the JM Group with a permanent competitive advantage. The lack of previous experience and very large openness to innovative tactics enabled JM to adapt to the specificity of the Polish market much better than its competition. Paradoxically, the lack of a standard format combined with greater openness and the process of having to quickly learn the intricacies of the new market, helped JM to build an effective competitive advantage.

DomaĔski, T.

153

Table 8: The Leading Discount Store Chains in Poland (2009) Name of the chain/group/ country of origin

Date of entry

Number of stores in 2009

Netto/Dansk Supermarkt/Denmark

1995

Biedronka/Jeronimo Martins/Portugal

Model of development

Strategy

200*

Organic -regional range

Mono-format

1995

1,400

Buy-out and organic development - nationwide range

Shift from multi-format to mono-format strategy

Lidl/Schwarz/Germany

2003

214

Organic - national at present

Multi-format/Schwarz Group

Atak/Auchan/France

2005

10

Organic -regional range

Multi-format/Auchan

Aldi/Aldi-Nord/ Germany

2008

32

Organic -regional range

Mono-format

Notes: *2010.

The case of the Aldi-Nord chain also confirms the thesis that late market entry, even if a specific chain holds the position of the leader in a given format on the European market, makes competition difficult. This is of special importance where a company intends to develop exclusively on the basis of the organic growth model. This model is both time-consuming and it strongly depends on environmental conditions. The market environment in Poland has become more restrictive and rigorous with respect to legal and administrative regulations that inhibit new entities and new facilities from joining the retail sector. These factors are often bureaucratic in nature, which slows the market entry process down. Next to specialised discount chains, this format is also developed by hypermarket chains, which under their large-format strategies offer a broad range of “discount products” (Tesco and Carrefour). For instance Carrefour’s offer of discount products in Poland is significantly broader than it is in France. It includes more than 700 products that are sold under the Carrefour brand name. The Polish market has also become a source of many important innovations for foreign retail chains. Auchan is a good example of this. Similarly to Carrefour, in March 2009, Tesco Poland introduced nearly 700 food, cosmetic and chemical products under 40 private label names. New discount brands respond to customers’ growing interest in the lowest prices. According to Nielsen’s analysis, in 2008, the share of discount stores in sales of food products in Poland reached 13 %, and 10 % in the category of cosmetic and chemical products.

154

European Retail Research Vol. 25, Issue I, pp. 141-180

Biedronka (Jeronimo Martins) í a Study of the Largest Discount Chain in Poland The Biedronka chain is both the largest retail store chain and the largest discount store chain in Poland. In 2009, it owned more than 1,400 stores supplied by seven modern distribution centres owned by JM. The chain operates in over 500 Polish towns and cities, which enables most of Poland’s population to do their every-day shopping in its outlets. The data presented in Table 9 (below) demonstrates the strong position of this group in comparison to its main foreign competitors that represent competitive formats and strategies (Tesco and Metro Group). The JM Group’s strong position results from a very consistent mono-format strategy (discount stores) and steady development of the chain throughout the domestic market. The dynamically growing turnover results both from the chain’s fast development and repositioning of the Biedronka discount store format to make it more modern and attractive, which helps to obtain new and wealthier customers. Table 9: Tesco, Metro* and Biedronka sales growth 2004-2008 2004

2005

2006

2007

2008

Absolute sales (in million PLN) Tesco Makro Cash and Carry* Real*

4,616

5,348

6,530

7,934

8,695

6,912

6,929

6,940

7,393

7,780

2,929

2,952

3,524

5,222

5,463

Biedronka

4,789

5,419

6,685

9,025

12,391

Tesco

9.4

15.9

22.1

21.5

9.6

Makro Cash and Carry*

4.5

0.2

0.2

6.5

5.2

Absolute sales growth (%)

Real*

8.2

0.8

19.4

48.2

4.6

Biedronka

17.6

13.2

23.4

35.0

37.3

Notes: Average currency exchange rate 1 EUR = 4 PLN (Polish currency).*Metro Group (Makro Cash and Carry, Real).

Source: Company data; Barclays Capital (2009).

The Biedronka chain also holds a very strong Polish image, although it is managed by JM, a Portuguese group. It started to operate in Poland in 1995. It developed its activity under the Biedronka brand name in 1998, following the purchase of 243 Biedronka chain stores by JM. Between 1995 and 2010, the JM Group invested over 750 million EUR in the process of establishing a modern chain of discount stores. The JM Group’s success in Poland is worth emphasising as it did not have any previous significant experience in internationalisation nor management of large discount chains. Its success was based on the strong concentration in the Polish market and specialization in management of a single store format (i.e. the discount store format). The group also adequately

DomaĔski, T.

155

assessed the attractiveness of the discount format to Polish consumers. The right moment of entry into the Polish market (1995), before foreign competition, was another important factor (DomaĔski/Bryáa 2010, pp. 199-211). The Biedronka discount store chain was established as a result of consistent acquisitions of existing stores, such as co-op stores and local shops (acquisition strategy combined with organic development). As opposed to the hypermarket and supermarket format, it was possible to develop the discount format successfully based on acquisitions. A strong internal diversification of the chain structure was the main issue of that strategy, which undoubtedly complicated the process of management and building the chain’s uniform image within the initial period of its operation (1997-2002). Only consolidation and modernisation of the chain according to a consistent standard enabled it to develop its uniform and attractive image (20082010). Through its strategy, the JM Group demonstrated huge innovation by building a unique store format and adapting it to the changing conditions of the Polish market. The Biedronka chain also is one of the largest Polish employers, as it employs over 27,000 members of staff. The group holds great experience in cooperation with suppliers with respect to inspecting the sources of supply and creating private labels. In 2008, in the ranking of the 500 strongest Polish enterprises, JM was ranked as 8th (based on sale revenues), and has improved its position from 2007 when it was ranked as 11th (see Table 2 above). The chain regularly cooperates with over 400 Polish suppliers. In its marketing communications, the company strongly emphasises the fact that out of 900 products permanently offered to its customers, approximately 95 % are produced in Poland. This message is very clear and helps to strengthen the Polish image of the chain. Names of all the brands are typically Polish and the style of packaging also refers to Polish associations and Polish culinary traditions. Few products are imported, and then only due to their unique place of origin and production. They mainly include wines or other products labels manufactured by foreign suppliers, and certain textile products ordered from Chinese suppliers by the Portuguese company. Within fifteen years, JM was able to create a very distinctive brand, which is recognised by Polish consumers and associated with the discount store format offering attractively priced products. Associating this lower and more affordable price with the good quality of products is an important element of this strategy. Aldi, the leader of the international discount store format, applied a similar strategy in the past: emphasising a symbiosis between low prices and the good (and guaranteed) quality of products. Fifty five percent of Poles regularly visit this chain, i.e. 2 million customers per day and approx. 500 million per annum. In 2009, a GfK’s public opinion survey concerning preferences of customers of retail chains in Poland indicated that the Biedronka chain is the most

156

European Retail Research Vol. 25, Issue I, pp. 141-180

rooted retail chain in the customers’ awareness. A survey concerning top-of-mind awareness of retail chain brands indicated that Biedronka was declared as the first by 25 % of respondents, which was the top result. The great majority of respondents (41 %) also indicated Biedronka as the outlet where “they spent most money on food products”. At the same time, 61 % respondents declared that they bought Biedronka’s private label products (Biedronka 2010). According to the Shopping Monitor survey, also delivered by GfK, the top-of-mind brand awareness reached 45 %, and prompted brand awareness, reached 92 %.

5.

Integration Processes in Polish Retail

5.1.

Strategies Initiated by Wholesalers

Polish retail will undergo fast integration processes (DomaĔski 2003, pp. 189-195). Currently, approx. 15 % of all stores on average are owned by various chain organisations. The stores with the smallest retail areas (up to 40 m2) are least willing to operate as members of chains. Surveys delivered in Poland by the GfK Polonia indicated that 90 % of small retailers do not intend to join any integrated chains and plan to continue their independent operations (Niewiadomska/Hamdan 2010, p. 26). Conversely, stores with the largest selling areas (ranging from 100 to 300 m2) are most willing to joining integrated chains í they constitute nearly 50 % of stores. Both large warehouses selling food products and small voluntary associations of retailers may become integrators of a retail group. Wholesalers acting as suppliers hold a natural advantage over retailers, which they try to use in the processes of contractual vertical integration. Wholesalers mainly develop various forms of vertical integration on the basis of the relations they already have with a number of small retailers. By contrast, for many years, retailers have established regional associations in the form of contractual horizontal or vertical integration. They establish procurement hubs and teams responsible for private label development in order to meet the needs of their members. Models of integration of small retailers in Poland demonstrate that Polish chains are successful integrators as they have a better knowledge of the market situation and due to this, they gain integrated entities confidence faster than foreign chains. In the conditions of the Polish market, integration of food retail is mainly developed by largeformat wholesale warehouses or other wholesale facilities. Specialised units of wholesalers become natural integrators of disintegrated retailers operating in the field of traditional retail. The activity of the Makro Cash and Carry chain, owned by Metro Group, is an example of such a strategy. During their sixteen year operations on the Polish market, Makro warehouses

DomaĔski, T.

157

have developed a very strong network of links with independent retailers for whom they constitute a key and strategic supplier. In recent years (2008-2010), Makro has offered its best customers with a system of cooperation, which is constantly enhanced and covers advisory services and training programmes to help the retailers to manage their stores better and improve their selling efficiency. This type of a “rather casual integration” mainly enables the building of a partnership based on mutual links in the field of organisation of supply streams and their flows. These solutions are implemented under EU funded training programmes in particular. Makro also consistently develops a range of its private labels offered to retailers. Its product range includes almost a thousand food and non-food products sold under the “Aro” brand name, and small retailers may be sure that they will pay the lowest purchase price for the products falling under a specific category. Sales of private labels constitute almost 10 % of the total turnover earned by Makro. The Eurocash chain is the second operator that consistently develops new forms of retailers’ integration. In 2010, Eurocash integrated the largest number of small shops operating on the Polish market (approx. 3,500) with the total sale revenues exceeding 5,000 million PLN, applying the model of “soft franchise”. “Soft” and “hard” franchises differ by the scope of retailers’ integration carried out by the integrator. In case of the “soft” franchise, the scope of the retailers’ integration is smaller thus the degree of their independence higher. In the case of the “hard” franchise, the scope of integration is very broad while the retailer receives very little freedom as regards the form of managing his/her shop. The Emperia/Lewiatan Holding Group is the third major integrator in the category of wholesalers. It is a good example of combining contractual vertical integration with the horizontal integration of associations of retailers operating in various regions (regional companies). In 2009, the group associated almost 2,400 small and medium-sized retailers (including mini supermarkets) with the total turnover value reaching almost 5,800 million PLN. This is a hybrid strategy which involves integrating a very large number of various retail groups operating in various regions in a single large structure. The key challenges for Polish retail in 2011 includes consolidation of the largest integrators at the level of wholesale, i. e. Eurocash and Emperia. Integration of these entities will contribute to establishing the strongest integrator on the Polish retail market, which will control more than 5,000 small retailers of food products on the domestic market, and hold huge bargaining power with respect to potential suppliers. Eurocash intends to take over Tradis, a food product warehouse, from Emperia, which owns approximately 4,000 facilities and manages two supermarket chains, i.e. Stokrotka (170) and Delima (10). The strategy provides typical defensive measures to protect the companies from increasing threats posed by large international retail chains which manage large-format stores. At the same time, this strategy is offen-

158

European Retail Research Vol. 25, Issue I, pp. 141-180

sive. As a result of the merger and integration of the companies owned by these two entities, the largest distributor of brand grocery products that intends to offer best purchase conditions to the cooperating retailers, will be established in Poland. There is a chance that the integrated group may become the second largest, following JM, FMCG distributor in Poland, the largest warehouse, and one of the ten largest Polish companies as regards revenue. Estimated turnover of the group integrated in this way may reach 14,000 million PLN in 2011/12 (Money.pl 2011).

5.2.

Strategies Initiated by Retailers

Polish chains established on the basis of voluntary associations of retailers prove most successful in the Polish conditions. Integrating activities of Lewiatan, a Polish chain, which consequently integrated entities of independent retailers under the common name of Lewiatan, is an example of such a strategy. In 2009, the Lewiatan chain associated more than 2,000 small shops throughout Poland, which operated under regional companies with a very different degree of standardisation of their strategy. This is an example of a combination of contractual horizontal integration (voluntary associations of retailers operating under Lewiatan name) with vertical integration (joint source of supplies, purchase negotiating and contracting production under the association’s name) (see Table 10 below). Integration links in this type of association are usually quite informal, and joint purchasing of products at more competitive prices is the main driving force of their operations. Regional groups of independent retailers usually associate with between 100 and 300 stores, and they conclude agreements of cooperation as associations, which gradually allows for the extension of integration to the national dimension. However, Polish small retailers still demonstrate a very strong aversion to integration processes. They continue to perceive them more as a threat than opportunity. It seems that aspirations of small retailers in Poland are significantly stronger than it has been observed in better developed EU states. Polish owners of small shops still prefer integration links based on “soft franchise” as they are reluctant to rigorous franchise systems, which limit the freedom of their business. Reluctance towards hard franchise often results from negative experience of many retailers caused by unfavourable relations with the integrating entity or unequal bargaining position, and unfavourable wording of franchise contracts. It also has to be emphasised that the franchise chain model is much more often promoted by brand new companies and chains with a lineage or with a share of foreign capital. In the latter case, it is related with the transfer of specific “know-how” in management held by the integrated retail network that has been earlier developed abroad.

DomaĔski, T.

159

Table 10: Ranking of the Largest Integrated Retail Chains in Poland in 2009 Total selling surface (in 1,000 m²)

Number of stores

Sales revenues in million PLN

Name of the chain

Name of the operator

1

PSH Lewiatan

ZKiP Lewiatan’94 Holding/Emperia

460

2,334

5,724

2

ABC

Eurocash Cash & Carry

267*

3,424

5,128* 3,000*

3

Sieü 34

Rabat Pomorze/Bomi

106

1119

4

Intermarché

Grupa Muszkieterów

140

145

2,610

5

Delikatesy Centrum

Eurocash/Franchising

95

465

2,200

Source: Niewiadomska/Hamdan (2010), p. 24.

It seems that new retail chains, which in the future will be developed by foreign operators as brand new chains, will be based on hard franchise principles. In order to join the system, the entities will have to fully comply with the specific principles distinguishing a given chain from competitive groups. The future model of franchise in Poland will be based on a more selective choice of new retail entities for the retail chains developed. Larger stores that will fully meet the criteria characteristic for a specific chain will be preferred and the general tendency will include consistent shift from the soft to hard franchise model. It seems that future integration processes in Polish retail will involve mergers of the main market operators í Eurocash and Emperia, which will be able to combine the area of wholesale and retail, and supply their cooperating retailers under retail chains whose integration will be progressing (chains of grocery stores, convenience stores, mini supermarkets and so on).

6.

Retail in Non-food Sector í Selected Product Categories

6.1.

Introduction

The analysis presented below concerns three selected categories of products: DIY and garden, electronic products, and clothing and footwear. The choice of these categories results from their significance to consumers, and is linked with the development of modern retail formats in Poland and their internationalisation. The analysis of these categories does not fully correspond with the analysis of the store format, which usually involves more categories than the ones presented in the compilation. This form of presentation was selected mainly due to the availability of data. The compilation presented below concerns the key product categories and was provided to the author by Planet Retail. This information has enabled a much more detailed analysis and comparison of data available under the same category than the analysis and comparison of estimated turnover of foreign chains operating in Poland. Planet Retail’s

160

European Retail Research Vol. 25, Issue I, pp. 141-180

own definition of each category has been presented together with the corresponding results of the analysis. The compilation of the companies always includes Polish companies operating under a specific category, and that is why additional remarks have been presented for the results. Planet Retail presented the estimated turnover values in EUR, which may lead to certain deviations, as many products were imported and the currency exchange rate in a given year underwent significant fluctuations. For the needs of this analysis, the values in EUR have been converted into Polish zloty according to the standard currency exchange rate prescribed at the end of the specific year (2008 and 2009), in order to ensure consistency of the data compiled. The currency exchange rate fluctuations have undoubtedly affected the level of turnover.

6.2.

DIY and Garden Category

According to the Planet Retail’s definition, the DIY and garden category mainly includes: home and garden maintenance and improvement products, i.e. tools, power tools, tool boxes, hardware, workbenches, storage units and containers; paints and coatings, brushes, rollers and decorating tools and accessories; building materials and supplies, plumbing, doors, windows; kitchen sinks, sink waste disposers, kitchen fixtures and fittings; bathroom units (baths, tubs, showers, sinks, toilets, bidets), faucets/taps/mixers and fittings, shower panels, towel rails, toilet roll holders wall tiles, wallpaper; heating fixtures and fittings, fireplaces, hearths, fitted radiators; torches, extension cables; shower curtains, blinds, curtain rails; security cameras, burglar alarms, fire alarms, doorbells, home automation systems; garden furniture, garden sheds, ladders, greenhouses, fencing, gates; aggregate, garden rocks, paving; garden tools, garden power tools, garden ornaments, plant pots, watering systems, gazebos, awnings; outdoor plants, soil, fertiliser, seeds, weed killer, garden pesticides; outdoor saunas, spas, hot tubs; outdoor lighting and heating; grills/barbeques and accessories. This category excludes: Bathroom and kitchen furniture í see Furniture and floorcoverings; bathroom and kitchen accessories í see other non-food products. It is estimated that this category constitutes approx. 52 % of Castorama’s turnover and between 37 and 39 % of the remaining operators (Leroy Merlin, OBI, Praktiker and Bricomarché) (see Table 11 below). This category clearly remains the basic category considering differences related with defining the product range of a given format by each of the chains. According to the estimates calculated by Praktiker’s experts (Praktiker Annual Report 2009), Castorama (10.4 %), Leroy Merlin (5.3 %), OBI (3.4 %) and Praktiker (2.2 %) hold the largest shares in this segment of the Polish market. According to Praktiker’s analysts, in 2009, a 13 % decline in the value of single transaction has been noted on average on all the chain’s foreign markets, including Poland.

DomaĔski, T.

161

Table 11: Sales of the DIY and Garden Product Category on Polish Market (2008 and 2009) Turnover in million PLN

Name of chain/group

Date of the first store opening

Number of stores (2009)

Model of development

Strategy

Castorama/Kingfisher

1997

47

Organic

Mono-format*

Leroy Merlin/Adeo

1996

33

Organic

OBI/Tengelmann

1997

30

Organic

Praktiker

1997

21

Bricomarché/ITM

2000

71

2008

2009

2,626.05

2,592.02

Mono-format*

692.9

808.11

Mono-format

749.48

528.08

Organic

Mono-format

380.89

311.6

Organic

Mono-format

202.54

223.04

Note: *Mono-format strategy complemented with a new format of specialised discount stores.

Source: Adapted from Planet Retail (2010).

This category concerns large-format stores referred to as home improvement and construction stores; Home and Garden or Do-It-Yourself. In Poland, this category was significantly dominated by foreign retail chains, all of which entered the Polish market more or less at the same time (1994/1995). Two years after their market entry, they launched their first large-format stores. In the case of home improvement and construction hypermarkets, the time and model of market entry were very similar. It seems that these chains observe their competition’s strategic operations very closely and follow one another on strategically important markets. The strongest competition is observed among the chains which originally came from France (Castorama, currently Kingfisher), Leroy Merlin/Adeo and Bricomarché/ITM, and German chains, such as OBI/Tengelmann and Praktiker. Their strategies are very similar. The standard format of the home improvement and construction hypermarket covers approx. 8,500 m2 of selling space, although in Warsaw Leroy Merlin operates its largest hypermarket in Europe covering nearly 18,000 m2. The product line ranges from 50,000 (Castorama) through 60,000 (Leroy Merlin) to 70,000 products (OBI) í depending on the importance of their interior décor sections and diversification of the product range offered. Shifting from the mono-format to the multi-format strategy is a new phenomenon. This strategy is focused on complementing the leading format, i.e. a classic home improvement and construction hypermarket with a new format of Brico Dépôt discount shop for Castorama, or Bricoman for Leroy Merlin. In this context we are dealing with a similar phenomenon already observed in the sector of food hypermarkets. It is in line with a new tendency where the range of the formats is extended by discount stores adapted to customers’ changing expectations. In the context of locating new facilities, two parallel tendencies have been observed. The first one involves establishing “stand-alone” type of facilities, while the other, locating hypermarkets in larger shopping centres and “retail parks”. It seems that such decisions are generally

162

European Retail Research Vol. 25, Issue I, pp. 141-180

related with attractiveness of a specific location in the context of future strategy. Differences in strategic operations of individual chains also result from selecting the size of the towns and cities where new facilities are then constructed. For instance Castorama selects towns with populations of 50,000 inhabitants for its target market, while its competition focuses more on the largest urban centres, with populations over 200,000 (Leroy Merlin, Praktiker and OBI). Bricomarché/ITM stores, which are under the home improvement and construction hypermarket category and are located in medium-sized cities with populations ranging from 20,000 to 70,000 residents, are the only exception. However their product offer is quite narrow and does not exceed 15,000 products. It is also consistent with the strategy of locating Intermarché grocery markets owned by the ITM Group, which are often located in the immediate vicinity on the same plot. According to the data presented for this category in Table 11, Kingfisher (Castorama) is its absolute leader. Kingfisher’s turnover is over three times higher than the turnover of Adeo (Leroy Merlin), its immediate competition. This difference results both from the chain’s fast development (the number of stores is approx. 50 % higher), and importance of this category in the chain’s turnover. In comparison to Kingfisher, the position of such German chains as OBI and Praktiker is clearly lower. In 2010, the number of OBI stores increased to 34, which indicated its further development. It is also worth emphasising that all five key competing entities view the internationalisation process as an important element of their overall development strategies. Each entity is strongly rooted in the markets of new Europe and other EU markets. OBI operates on thirteen European markets (including five markets of the new EU member states), and Praktiker on ten foreign markets (including four markets of the new EU member states). The second phenomenon concerns the fact that the next ranks (6-11) are held by large-format non-specialised chains. Although this product category only complements their product line, the large-format non-specialised chains are serious competition to specialised chains. In 2008, the turnover of JM, Metro Group, Schwarz Group, Auchan, Carrefour and Tesco reached 656 million PLN, and was comparable to the turnover of ADEO, the market’s second most dominant retailer. In practice it means that in the group of large-format stores, a significant share of turnover is still generated by non-specialised chains. In 2008, almost 86 % of turnover for this category under the modern formats was generated by specialised chains, with 14 % by nonspecialised chains í for this category and for large-format stores (the group of the largest markets). At the same time, due to the crisis impacts observed in 2009, almost all large-format store chains recorded decreased turnover in comparison to 2008. The decrease was the lowest for Kingfisher group (-1.3 %), and the highest for Tengelmann (OBI/-29.5 %), although in the

DomaĔski, T.

163

latter case, a negative effect of structural changes in Tengelmann Group should be taken into account. By contrast, positive results indicating turnover increase were recorded by ADEO Group and Bricomarché, which should be interpreted as an effect of the opening of new stores and selecting appropriate locations. When analysing fluctuations in the level of turnover, one should consider the fact that the crisis first affected the construction sector, interior design and garden expenditure (which is not treated as the expenditure incurred to satisfy basic needs and is relatively easy to abandon). It may be anticipated that in the future Polish companies will also become competitive with regards to this category of products. The PSB SA Group has operated in Poland since 1998, in the segment of building material sales, and now sells products in 420 stores. The group offers nearly 155,000 product items. In 2009, the turnover of the whole group in Poland reached 4,100 million PLN (including wholesale turnover). The group associates almost 300 Polish small and medium-sized family wholesale businesses that are its shareholders. The PSB SA Group is the only group from the Central Europe that is a member of the EURO-MAT European Federation, which associates 4,000 outlets with the total turnover value of 22,900 million EUR. The PSB Group has gradually developed a chain of medium-sized Home and Garden format outlets, and already holds 59 stores with the surface space ranging 800 to 4,500 m2 depending on the location. The stores are usually located in smaller municipalities and towns with populations up to 100 thousand inhabitants. This new positioning is based on a narrow and well selected product range located in a relatively small space (800 to 1,200 m2 in smaller centres) and professional advisory service, and in future it may become a unique format in the market of tools and power tools, building materials and supplies, including heating and sanitary equipment, lighting, paints, construction and household chemicals, ceramic tiles, garden and home environment, and so on. Products are supplied by 150 contract suppliers of the PSB SA Group. In 2009, the shareholders’ revenues from sales of the products purchased from the group’s procurement hub reached 1,177 million PLN, which was similar to the level achieved in 2008. These activities demonstrate that preferential linkage developed with a carefully selected group of suppliers, as it is done in the EURO-MAT European Federation, may provide the foundation to establish a unique retail chain by wholesalers (vertical integration with the association of wholesalers acting as the integrator). Each store is managed by a group’s shareholder by virtue of a contract (the “contractual vertical integration” type).

164

6.3.

European Retail Research Vol. 25, Issue I, pp. 141-180

Electronic Products Category

According to the Planet Retail’s definition, this category is referred to as “Electricals (brown goods)”. Its main elements include household electrical entertainment goods, which cover: TVs, DVD players, home theatre equipment, TV digital and satellite set-top boxes, radios, alarm clock radios, VCRs; cameras, digital cameras, camcorders, memory cards and cassettes, camera bags; hi-fis, MP3 players, personal audio and video equipment and accessories; games consoles and accessories; digital photo frames; car audio, GPS and navigation, in-car DVD players. It excludes: Electronic musical instruments í see other non-grocery. Significance of this category to the global turnover of individual operators differs greatly and may even reach 68 % in case of the leaders of this ranking (Media Markt and Saturn), while in case of the other operators, it may range from 30% (Euronics/Avans International), 34 % (Neonet) to 45 % (Mix Electronics). Table 12: Top Retailers in the Electronic Products Category in Poland (2008/2009) Name of the chain/group

2008 Turnover in million PLN

2009 Turnover in million PLN

Change 2009/2008 in %

2877.38

2295.18

-20.2

749.89

717.91

-4.3

906.1

716.68

-21

488.72

-0.75 -12.6

1.

Media Markt and Saturn/Metro Group

2.

Euronics/Avans International

3.

RTV Euro AGD

4.

Mix Electronics/Electro World

492.41 555.14

485.03

430.5

354.24

-17.7

355.88

308.32

-13.4

5.

Auchan

6.

Vobis (PL)

7.

Neonet

8.

Carrefour

354.24

304.22

-14.1

9.

Schwarz Group

330.04

286.59

-13.3

10.

Tesco

311.19

269.37

-13.4

Source: Planet Retail (2010).

In the electronic products category a clear domination of Metro Group, which manages specialised large-format chains, such as Media Markt and Saturn, is observed. In 2009, the turnover of both of these chains reached nearly 2,295.18 million PLN and was more than 20 % lower than the turnover in 2008. In 2008, in this category the Metro Group produced turnover that was almost 4 times higher (3.69) than the turnover reached by the Euronics group, which was ranked as second and which, in Poland, was a partner to the Avans International chain. In 2009, as a result of Metro’s lower turnover level, that advantage was 3.19 times smaller, however it was still huge. Fluctuations of the currency exchange rates for Polish zloty, the dollar and euro also contributed to that decline, especially that many of their products were imported. For certain groups of products, a disaggregated quantitative analysis of retail sales would be more reliable than a value-based analysis.

DomaĔski, T.

165

It is generally observed that effects of the crisis contributed to a clear decrease in the turnover in the whole category. That decrease mostly affected the largest chains (Metro Group and RTV Euro AGD), where it exceeded the level of 20 %. By the end of 2009, Media Markt held 41 modern large-format stores with surface space ranging from 3,500 to 8,000 m2. They offered approx. 45,000 products under all categories. On the other hand, the Saturn chain owned 15 modern stores. In 2010, the Metro Group held 60 stores operating in this category, which were owned by these two chains. Its advantages include excellent location of the stores and a very distinctive and efficient marketing campaign, Media Markt’s in particular, and aggressive price and promotion strategy. Generally we may conclude that specialised chains based on various forms of contractual integration, including franchise links, such as Euronics/Avans International (600 stores operating in Poland and owned by independent retailers), were significantly weaker than large specialised chains and hypermarket chains, although the decrease recorded in their turnover in 2009 (4.3 % in this case) was not very high. In case of the Euronics chain this decrease was comparable to the average decrease recorded for the whole purchasing group. It also seems that the formula of stores involving independent retailers cooperating with such major European purchasing groups as Euronics provides them with a relatively better market stability based on the benefits resulting from international vertical integration with respect to supply. Support of such a large integrating entity as Euronics (global turnover in 31 countries í 14.1 billion EUR earned by 11,000 stores owned by 6,400 independent retailers) provides more stable and competitive business conditions also for Polish retailers. In the Polish conditions many specialised store chains, of medium-size in particular, were established as a result of the vertical integration processes initiated by wholesalers. It was also related with the fact that since the early 1990s, Polish retail of electronic products was strongly controlled by wholesalers, who were also involved in the import of these products from Asian markets. Examples of this integration include Mix Electronics, a Polish chain, ranked as the fourth in sales of this product category and holding 200 stores selling consumers electronics and household electrical appliances. This chain has operated since 1990 and acquired the Elektro World chain in 2009. In the group of non-specialised hypermarket chains, Auchan holds the highest position in this product category, and is followed by Carrefour, Schwarz Group (Kaufland and Lidl) and Tesco, respectively. All large-format non-specialised chains recorded a decrease in their turnover in this category, which ranged from 12.6 % for Auchan to 14.1 % for Carrefour. Analysis of this data indicates that the greatest decrease was recorded by large-format stores, both specialised (see above) and non-specialised (hypermarkets). It may also indicate that in the conditions of crisis, customers have less confidence in private labels offered by large-format and non-specialised stores in particular.

166

6.4.

European Retail Research Vol. 25, Issue I, pp. 141-180

Clothing and Footwear Products Category

According to Planet Retail’s definition, this category includes clothing, footwear and accessories. It also includes: men’s, women’s, children’s and baby clothing, footwear and accessories; outer garments, underwear, nightwear, bath robes; gloves, scarves, hats, umbrellas, parasols and other accessories; costume jewellery and other fashion accessories; handbags, purses, wallets, briefcases, bags, shopping bags; uniforms and workwear; fancy dress costumes; clothing fabric; clothing and footwear with a sports/leisure profile which however are or could be used as, casualwear. It excludes: Sports and leisure clothing, footwear and accessories which are positioned for sports/leisure use and would normally be used only for sports and leisure activities í see sports and leisure; kitchen aprons, oven gloves í see household textiles. The standard list of foreign retailers developed by Planet Retail has been complemented with case studies of two leaders of the Polish clothing market, i.e. the LPP Group and Vistula Group. In the clothing and footwear product category, a clear tendency of dominance of modern large-format specialised clothes and shoes stores is observed. As regards the level of turnover, the following stores are members of this group (see Table 13 below): H&M, Inditex, Deichmann and C&A, respectively. They are followed by large retail groups which manage large-format store portfolios (Jeronimo Martins, Schwarz Group, Metro Group), and which offer their private labels or products without a brand. In comparison to 2008, very different results were recorded for individual retailers. The highest turnover increase (37.7 %) was recorded for Inditex Group, which may be explained by its consistent and dynamic development in relation to all its store formats and brands. The stores are usually located in the best and most modern shopping centres, and their image is very good. As in most European countries, in Poland the Inditex Group operates through a chain of its own stores, which reflect its developed portfolio of brands. These brands are positioned at slightly higher prices than in the Western Europe, which may constitute a certain barrier to an increase in demand in the shortterm perspective. H&M and C&A chains recorded a decrease in their turnover: -10.8 % and -4.6 %, respectively. A very clear decrease was also observed in German Schwarz and Metro groups, which may indicate that non-specialised stores are more affected by the crisis than the specialised ones holding a strong brand. Weaker positions of Tesco and Carrefour chains (11th and 12th in this ranking) indicate that hypermarkets’ private labels í in the clothing sector í weaken their position. Private labels of large retail chains have not achieved a significant position in Poland.

DomaĔski, T.

167

Table 13: Top Foreign Retailers in the Category of Clothing and Footwear Products in Poland (2008/2009) Name of the group

2008 Turnover in million PLN

2009 Turnover in million PLN

Change 2009/2008 in %

1.

H&M

988.1

880.68

-10.88

2.

Inditex

617.46

850.34

+37.7

3.

Deichmann

523.98

567.85

+8.3

4.

C&A

528.08

503.89

-4.6

5.

Jeronimo Martins

432.14

456.33

+5.0

6.

Schwarz Group

259.12

621.5

-10.6

7.

Metro Group

254.2

214.02

-15.8

8

Oxylane

133.66

200.08

+49.7

9

Auchan

201.31

175.07

-13.04

10

Marks and Spencer

114.39

153.34

+34.0

Source: Planet Retail (2010).

JM is a specific positive exception: it increased its turnover as a result of establishing new facilities, re-positioning of discount stores and taking competition over (Plus í Discount). This chain’s repositioning and good policy in the field of selecting suppliers may make these positive trends permanent in the future. The Oxylane Group, which distributes clothing and footwear through the Decathlon store chain, has also recorded a very positive result. Decathlon places great emphasis on cooperation with suppliers of the chain’s private labels, and the data presented in this paper correspond with the dynamic development of the Decathlon chain, which in 2009 already held 14 large-format specialized sports stores, and 21 by 2010. Deichmann, a German chain of shoe stores, which has operated in Poland since 1997 and whose style perfectly corresponds with the discount format preferred by Polish customers, has achieved very good results. Deichmann has consistently developed its market, and managed a chain of 140 stores in Poland in 2009 which increased to 168 in 2010. This growth indicates the high dynamics of this chain’s development, reaching a level of over 10 % quantitative increase per year. The chain’s development strategy in Poland is a part of a broader strategy of internalization of the company which operates in 20 countries, including 8 states of the “new EU” (Deichmann 2011b).

168

European Retail Research Vol. 25, Issue I, pp. 141-180

LPP í Case Study of the Largest Polish Clothing Group The LPP Group has not been included in the compilation of the foreign retailers operating on the Polish clothing and footwear market as it is a Polish retail company. LPP has operated since 1995 and it is a model example of a company which has become a key player in the field of design and distribution of clothes and accessories as a result of its consequent strategy. It is a model company which has followed strategies of the top global manufacturers and retailers of clothing (Inditex í Zara, GAP), and within the period of 20 years was able to develop the position of the strongest Polish clothing business. In its strategy, LPP followed the strategies of its foreign “model examples” creatively, and transferred this experience onto the Polish market. In July 2009, the merger of LPP SA with Artman SA, its largest Polish competitor, was completed. The aim of the merger was to establish the largest company offering clothing and accessories, and the largest clothing retail chain in Central and Eastern Europe. The financial results in 2009 were mainly affected by recession and weak exchange rate of PLN in relation to USD, which resulted in lower return on sales (LPP SA 2009e). LPP manages a group of six unique and very distinctive clothing brands addressed to young women and men, which are designed by the in-house team of designers and contracted for production in China (approximately 70 % of all the products) by the company’s trade office in Shanghai and other Asian countries (22 %). The company has integrated the whole production and distribution process vertically by establishing its own chain of brand stores and franchise stores in Poland. In 2009, its total selling surface in Poland covered almost 200,000 m2, and including its overseas properties, 288,000 m2. The dynamics of growth of the selling surface reached 29 %. The company does not have its own manufacturing facilities, i.e. the relevant overhead costs have been eliminated, which allows it to increase its selling capacity and develop its own and strong distribution chain in the clothing market. The LPP Capital Group estimates its share in the Polish clothing market at the level of approx. 4 % (3 to 5%). The company has developed its own distribution chain in new shopping centres. Its development occurred in parallel with development of modern retail and the entry of best-known international clothing brands onto the Polish market. LPP’s private labels, which bear universal and international names (Reserved, Cropp, Esotiq, Mohito) and the brands acquired from its competition (House), are perceived by Polish customers as “foreign” but their prices are more affordable. In Poland LPP manages the chain of its own stores of Reserved (140), Cropp (168), House (177), Mohito (53) and Esotiq (58). Development of the retail chain was financed with the capital earned from sales of the stock when the company was registered on the Warsaw Stock Exchange in 2001.

DomaĔski, T.

169

LPP also sells its retail products in more than 300 brand stores based abroad; Reserved (130), Cropp (70) and House (110). It is a sustainable development retail model which will apply equally to foreign markets in the EU states (Estonia, the Czech Republic, Slovakia, Hungary, Lithuania, Latvia, Romania and Bulgaria) and in Russia and the Ukraine. The value of clothing and footwear sold by LPP in Poland in 2008 reached 1,218.4 million PLN, and in 2009, 1,525.73 million PLN, which indicates an increase in turnover of 25.22 %. A similar, although slightly lower level, was recorded for the company’s global turnover (23.4 %), which indicates that the dynamics of growth in export sales (18 %) was lower than the dynamics of increase in turnover on the domestic market (LPP SA 2010c, p. 71). In 2008, LPP was an absolute leader on the Polish retail market of clothing, before H&M (988.1 million PLN) and Inditex (617.46 million PLN). In 2009, the level of turnover of these two foreign competitors reached 880.68 million PLN and 850.34 million PLN respectively. LPP increased its level of turnover and held advantage over its key competition, i.e. the advantage increased from 23.3 % to 73.2 % in case of H&M, and fell from 97.2 % to 79.4 % in case of Inditex. Such large differences indicate how strong the LPP brands’ portfolio is, and the strong position of its retail chain. In 2009, retail in Poland constituted 76.1 % of the whole company’s sales. In 2008, it had reached 75 %. Because of increased export volume, LPP’s retail share on the domestic market remains high and indicates a small increase, which indicates the strength of its brand position and a very well developed retail chain. LPP is an example of a Polish company that consistently built a family of strong brands and unique system of retail carried out through brand stores. Each brand is sold in a separate chain of brand stores and addressed to a separate segment of the market. The LPP’s strategy was based on a combination of organic development (its own stores), taking over its competitors and their retail chains (House and Mohito brands), and development of retail chains based on franchise. Depending on the brand, the company searchers for franchise purchasers in the cities with populations of over 30,000 inhabitants, who already have stores with selling spaces ranging from 350 to 700 m2 (Reserved); 150 to 300m 2 (Cropp, House, Mohito) and 50-150 m2 (Esotiq). The stores have to be located in top locations. Combining corporate and contractual (franchise) retail systems, has provided the company with very good conditions to cover the whole domestic market and access to the best locations (LPP SA 2011f). Vistula Group SA í Manufacturer and Distributor of Polish and Foreign Brands of Clothing Products Vistula Group SA should also be listed among the leading Polish clothing companies, which manufacture and distribute clothing products. In 2008, it managed a chain of 165 brand clothes own stores, which mainly offered Vistula brand clothes for men (suits, overcoats,

170

European Retail Research Vol. 25, Issue I, pp. 141-180

jackets, accessories and footwear), and Wólczanka (shirts), and a whole range of brands addressed to various segments of customers of fashion market for men and women (Deni Cler). Vistula and Wólczanka brand stores sell their products, but selected collections and models have also been offered in partner stores and independent chains of clothes stores. The Group also managed Galeria Centrum, a retail chain, which offered ladies fashion, however in April 2009, that chain was declared insolvent, which significantly affected the results of the whole Group. In 2008, turnover of the whole group in the clothing segment in Poland reached 350 million PLN. In 2009, following the bankruptcy of Galeria Centrum, the turnover reached 133.4 million PLN (fashion segment) (Vistula Group 2010, p. 31). The data indicates that the best known and classic brands of men’s clothing in Poland undergo constant erosion despite its continuously strong market position (approx. 7 % decline in turnover recorded in 2009). It may indicate that the brands are becoming outdated and that the demand shifts toward new and “young” brands, both Polish (LPP) and foreign (Inditex). This is clearly the case of generation changes, which take place although such well known Polish brands as Vistula and Wólczanka have a well developed distribution chain.

7.

Future Challenges for Polish Retail

In the future, the process of concentration (further mergers and acquisitions) of Polish retail will continue, while the number of market players will be falling. As a result, the FMCG sector will note strengthening of the position of the strongest foreign retail chains (DomaĔski 2008, p. 170-171). The Polish retail model will however remain quite unique, with a strong presence of traditional retail undergoing fast changes. This results from habits of Polish consumers and very strongly developed independent entrepreneurship in Polish retail. The Polish retail model will remain closer to the Italian model (Pellegrini 2008, p. 97) than to the German or Scandinavian ones (Colla 2008, p. 196). In traditional trade, a process of fast modernisation related with generation changes will be observed, i.e. small stores will be taken over by a new generation of young and well educated entrepreneurs. These young entrepreneurs are also more open to operate under vertically or horizontally integrated professional chains based on franchise. They are also ready to manage chains composed of several stores operating under modern franchise system. The processes of succession in retail will clearly contribute to a broader use of franchise systems. As regards the largest FMCG retailers (Carrefour, Tesco and Metro Group), changes occurring on the Polish market will involve further extension of large-format strategies. Looking for inter-format synergy in the field of logistics, supplies, marketing communication and development policy for private labels offered by the formats managed, will become their key

DomaĔski, T.

171

challenge. Efficiency of this process will also depend on the skill to adapt stores acquired from the competition to the model of the format typical for a given chain. The mono-format strategy will become an alternative. It seems that these chains that consistently apply mono-format strategies, such as JM, reach market success faster by mastering specialization under a selected format. This results from the scale of operations, enhanced knowledge of Polish buyers’ behaviours and improving management of the narrow range of quickly rotated products (DomaĔski/Bryáa 2010, p. 199-210). Dissemination of modern technologies and the complementarities of various strategies, modern large-format retail and Internet trade in particular, will face significant challenges. It seems that Polish retail has only just started to use Internet channels, however due to a great openness of the new generation of consumers, it may quickly make up for this delay. Recession and growing rationalisation will cause consumers to be increasingly selective in choosing where they shop. It will significantly hamper the process of building permanent loyalty of customers towards the place of their shopping and specific store formats. In the Polish conditions, a further growth of importance of discount stores will be observed, which will result from the permanent shift of demand towards this format. The discount format will also undergo a very strong “in-plus” repositioning. This will contribute to consistent improvement of its image and attracting better earning customers (the middle segment) who prefer price-focused rationalisation of their shopping. The policy of discount segment repositioning will mainly involve modern marketing communication, change of this format’s image, attractive merchandising, and private label offer developed on the basis of partnership-based links with manufacturers of well-known brand products. Operators of other formats will respond to this shift by developing a broader discount offer and by more intensive development of their private labels. Changes in behaviours of Polish buyers will involve their growing rationalisation and increased demand for cheap products. Customers become more rational and sensitive to prices, so they search for cheaper substitutes. This factor will stimulate demand for the chains’ private labels and contribute to their improved quality (DomaĔski 2005, p. 259-260). Due to the limitations in the context of revenues, it should be expected that the dynamics of development of delicatessen chains and selective products (non-food) chains will slow down. Polish chains operating under this format will be forced to adapt their development strategies to the changes in market conditions (Alma, Piotr i Paweá, Bomi and so on). A partial pricebased repositioning of this format, in the conditions of the narrow niche market, is a potential strategy (DomaĔski/Bryáa 2010, p. 211-222). It will involve extending the offer of lower priced products and private labels in order to become more competitive in relation to other formats. However delicatessen formats may lose their specificity and original and distinctive

172

European Retail Research Vol. 25, Issue I, pp. 141-180

positioning as a result of this strategy On the other hand, a “greater democratisation of prices” may attract new customers from the “middle” segment, which is expected by operators of this format. For many foreign chains, Polish retail will remain a place for testing development of a range of innovations, which will then be selectively introduced into other markets, especially in relation to the discount and “convenience” formats (DomaĔski 2010, p. 478-479). Foreign operators will also be able to benefit from great opportunities in the field of developing modern franchise systems in the non-food products, where a new generation of entrepreneurs holding adequate capital and experience are searching for the prospective formats which in future will be attractive to the market. As regards non-food product retail, it should be expected that foreign chain operators dealing with large-format retail will strengthen their position as they already hold an appropriate base (Vianelli/Dianoux/DomaĔski/Herrmann 2007, p. 124-125). As regards specialized retail, selective and exclusive distribution systems using franchise models may develop. Two opposite tendencies may be anticipated in the field of the clothing market: strong expansion of new domestic and foreign brands, and the weakening position of traditional brands (due to declining preference for known brands which is typical for generation changes).

DomaĔski, T.

173

Appendix Table A1: Retail Sales 2000

2005

Specification

2008

2009

in million PLN

TOTAL

360,318

433,255

564,665

580,964

In retail outletsa

345,610

416,159

544,461

560,812

Consumer goods

291,810

34,3882

436,876

455,164

Food and non-alcoholic beverages

102,861

125,553

146,495

153,087

Alcoholic beverages, tobacco products

32,833

38,839

47,530

52,236

non-food products

156,116

179,490

242,851

249,841

non-consumer goods

53800

72,277

107,585

105,648

In catering facilities

14,708

17,096

20,204

20,152

9,419

11,353

14,814

15,227

b

PER CAPITA

Note: a Including retail sales conducted at wholesale stores and manufacturers.

b

in PLN.

Source: The Concise Statistical Yearbook of Poland (2010), p. 205.

174

European Retail Research Vol. 25, Issue I, pp. 141-180

Table A2: Shops and Petrol Stations (as of 31 December) Specification

2005

2007

2008

Stores

385,990

371,364

385,663

Including private sector

384,673

370,033

384,301

6,258

7,304

8,301

116,094

105,509

98,460

Including stores with selling surface exceeding 2 400 m General grocery stores Fruit and vegetables

5,222

4,778

4,407

Meat

13,072

12,448

11,966

Fish

1,106

974

932

Bakery and confectionery products

5,520

6,033

6,508

Alcoholic beverages

2,545

2,768

2,723

Cosmetics and personal care products

8,211

8,421

8,407

Textile products

4,904

4,693

4,923

Clothing products

39,375

37,809

40,795

Footwear and leather products

8,129

7,980

8,696

Furniture and lighting appliances

7,085

7,925

8,356

Radio, TV and household appliances Books and stationery

6,844

7,484

8,216

8,479

6,781

7,678

Mechanical vehicles

12,634

12,107

13,399

a

146,861

145,654

160,197

Petrol stations

10,086

9,831

10,073

Including private sector

9,805

9,553

9,814

Other shops

Note: a Shops with various specialization not listed above, In 2008, 98.9 persons per one store, compared to 103 persons in 2007, and 98.9 persons in 2005.

Source: The Concise Statistical Yearbook of Poland (2010), p. 207.

DomaĔski, T.

175

References ABC (2011): ABC, http://www.sklepyabc.pl/, accessed January 17, 2011. Adea Group (2011): Goupe Adeo, http://www.groupe-adeo.com/index.php?id=36&L=2, accessed January 17, 2011. Ahold (2011): Ahold, http://www.ahold.com/, accessed January 17, 2011. Albert (2011): Albert Polska, http://www.albert.com.pl/, accessed January 17, 2011. Aldi Market (2011): Aldi, http://www.aldi.pl/, accessed January 17, 2011. Aldi-Nord (2011): Aldi, http://www.aldi-nord.de/, accessed January 17, 2011. Alma Market SA (2007): „Alma Market” í Raport Analityczny of 12/04/2007/Dom Maklerski PKO BP, p. 5. Alma Market SA (2009): Alma Market S.A. í skonsolidowane sprawozdanie finansowe za 2009 Report, http://www.almamarket.pl/www/media/files/raporty_okresowe/Roczny2009/ sprawozdanie_finansowe_skonsolidowane_roczne_2009.pdf, accessed January 17, 2011. Alma Market (2011): Delikatesy, http://www.almamarket.pl/, accessed January 17, 2011. Aro (2011): Produkty Aro, http://www.aro.makro.pl/, accessed January 17, 2011. Artman SA (2011): LPP SA: Home, http://www.lpp.com.pl/, accessed January 17, 2011. Auchan Polska (2011): Auchan, http://www.auchan.pl/, accessed January 17, 2011. Avans International (2011): Avans, http://www.avans.pl/kontakt.html, accessed January 17, 2011. Badowski, K. (2010): Model organizacyjny jako czynnik sukcesu w branĪy detalicznej, in: Poland & CEE Retail Summit 2010, Warsaw. Barclays Capital (2009): European food retail: initiation of coverage. Scanning the investment horizon, in: Equity Research, September 8, 2009, pp. 165-178. bem (2009): Nowy prezes Polomarketu o strategii, in: Handel, March 18, 2009, p. 4. Biedronka (2010): Kim jesteĞmy, http://www.biedronka.pl/o_nas/biedronka/kim_jestesmy/, accessed January 17, 2011. Biedronka (2011): Biedronka, http://www.biedronka.pl/, accessed January 17, 2011. Billa (2011): Billa, http://www.billa.pl/home.php, accessed January 17, 2011. Bomi (2011): Bomi, Shopping with taste, http://www.bomi.pl/, accessed January 17, 2011. Bricomarché (2011): Bricomarche, http://www.bricomarche.pl/, accessed January 17, 2011. bw/jn (2009): Aldi siĊ nie poddaje, in: Handel, September 23, 2009, p. 4. C&A (2011): Moda C&A, http://www.c-and-a.pl/, accessed January 17, 2011. Carrefour Polska (2011): Carrefour, http://www.carrefour.pl/page/pl/tanie-promocje/stronaglowna/, accessed January 17, 2011. Casino (2011): Votre magasin Casino, http://www.casino.fr/, accessed January 17, 2011. Casino Group (2011): Groupe Casino, http://www.groupe-casino.fr/, accessed January 17, 2011.

176

European Retail Research Vol. 25, Issue I, pp. 141-180

Castorama (2011): Castorama, http://www.castorama.pl/, accessed January 17, 2011. Colla, E. (2008): Analyse de la réglementation française du commerce et de ses effets, in: Colla, E. (ed.): Réglementation et commerce en Europe: Les effets de la réglementation sur les stratégies et les performances des entreprises, Paris, pp. 177-206. Cropp (2011): Cropp, http://sklep.cropp.com/, accessed January 17, 2011. Cushman & Wakefield (2010): Marketbeat. Raport o rozwoju rynku centrów handlowych w Europie. Publikacja dziaáu doradztwa firmy Cushman & Wakefield. Decathlon (2011): SprawdĨ cenĊ i dostĊpnoĞü http://www.decathlon.com.pl/PL/, accessed January 17, 2011.

produktów

on

line,

Deichmann (2011a): Grupa Deichmann, http://www.deichmann.pl/site/pl/index.php, accessed January 17, 2011. Deichmann (2011b): Geschichte, http://www.deichmann.com/DE/de/corp/geschichte.jsp, accessed January 17, 2011. Delikatesy Centrum (2011): Delikatesy Centrum, http://www.delikatesy.pl/, accessed January 17, 2011. Delima (2011): Delima, http://delima.pl/, accessed January 17, 2011. Deni Cler (2011): Deni Cler, http://www.denicler.pl/2011_charmetta/, accessed January 17, 2011. DomaĔski, T. (2002): Foreign investment in Poland in the field of retailing, in: Liuhto, K. (ed.): Business in Poland: Experiences of Fininsh companies in doing business in the largest eastern EU candidate Country, Lappeenranta University of Technology: Studies in Industrial Engineering and Management, Vol. 18, Lappeenranta, pp. 266-285. DomaĔski, T. (2003): The development of the retail sector in Poland since 1990, in: Stüting, H.-J.; Dorow, W.; Claassen, F.; Blazejewski, S. (eds.): Change management in transition economies: Integrating corporate strategy, structure and culture, London et al., pp. 189-202. DomaĔski, T. (2005): Strategie rozwoju handlu, Polskie Wydawnictwo Ekonomiczne, Warszawa. DomaĔski, T. (2008): Révolution commerciale, entre libéralisme et interventionnisme en Pologne, in: Colla, E. (ed.): Réglementation et commerce en Europe: Les effets de la réglementation sur les stratégies et les performances des entreprises, Paris, pp. 147-173. DomaĔski, T. (2010): Strategie internacjonalizacji duĪych sieci handlowych na polskim rynku: Nowe wyzwania dla marketingu miĊdzynarodowego, in: Wiktor, J. W.; ĩbikowska, A. (eds.): Marketing miĊdzynarodowy: Uwarunkowania i kierunki rozwoju, Polskie Wydawnictwo Ekonomiczne, Warszawa, pp. 473-483. DomaĔski, T.; Bryáa P. (2010): Marketing produktów ĪywnoĞciowych. Wyzwania rynku europejskiego, Polskie wydawnictwo ekonomiczne, Warszawa. Electro World (2011): Electro World, http://www.electroworld.pl/, accessed January 17, 2011. Emperia Holding (2011): Grupa Handlowa Emperia, http://www.emperia.pl/, accessed January 17, 2011. Esotiq (2011): Esotic, http://www.esotiq.pl/, accessed January 17, 2011. Eurocash Grupa (2011):Grupa Eurocash, http://www.eurocash.pl/, accessed January 17, 2011.

DomaĔski, T.

177

EURO-MAT (2011): Euro-Mat S.A., http://www.euro-mat.com/, accessed January 17, 2011. Euronics (2011): Euronics International, http://www.euronics.com/, accessed January 17, 2011. GAP (2011): Gap, http://www.gap.com/, accessed January 17, 2011. Géant Casino (2011): Géant Casino, http://www2.geantcasino.fr/pages/welcome.aspx, accessed January 17, 2011. Grupa Muszkieterów (2011): Strona Startowa, muszkieterowie/index.php, accessed January 17, 2011.

http://www.muszkieterowie.pl/

Handel (2009): Nadzieje i obawy. Wypowiedzi: Pedro Pereira da Silvy í Jeronimo Martins Dystrybucja, Andrzeja Wojciechowskiego í Bomi, Ryszarda, Tomaszewskiego í Tesco, Daniela Praáata í Intermarché, Uwe Hölzera í Cash & Carry, Artura Kawy í Emperia Holding, Piotra Wosia í Piotr i Paweá, Jacka Roszyka í ĩabka, January 21, 2009, pp. 4-8. HiT (2011): Hit, http://www.hit.de/, accessed January 17, 2011. H&M (2011): Entrance, http://www.hm.com/, accessed January 17, 2011. House (2011): House, http://www.house.pl/, accessed January 17, 2011. Hypernova (2011): Promo Ceny, http://hypernova.promoceny.pl/, accessed January 17, 2011. Inditex (2011): Inditex, http://www.inditex.com/en, accessed January 17, 2011. Intermarché (2011): Supermarkety Intermarché, http://www.intermarche.pl/, accessed January 17, 2011. Jeronimo Martins Dystrybucja (2011): Jeronimo Martins, http://www.karierawjmd.pl/, accessed January 17, 2011. jn (2009): Dyskontowe Auchan, in: Handel, July 8, 2009, p. 8. jn (2009a): Straty i ciĊcia Almy, in: Handel, September 23, 2009, p. 8. jn (2009b): Metro ostroĪnie w 2009 r., in: Handel, January 21, 2009, p. 8. Jumbo (2011): Jumbo, http://www.jumbosupermarkten.nl/, accessed January 17, 2011. LPP (2009a): Annual report 2009, http://www.inwestor.lpp.com.pl/uploads/ /periodical_reports/RR2009/consolidated-annual-report-of-lpp-s_en.pdf, accessed January 17, 2011. LPP (2009b): Supplementary report on the audit of the consolidated financial statements for the year 2009, http://www.inwestor.lpp.com.pl/uploads//periodical_reports/RR2009/ gk_lpp_raport.pdf, accessed on January 17, 2011. LPP SA (2010c): Skonsolidowany Roczny Raport GK LPP S.A. za 2009 rok, GdaĔsk, 15 kwietnia 2010 r. LPP SA (2009d): Sprawozdanie z dziaáalnoĞci Grupy Kapitaáowej Alma Market SA za rok 2008, Kraków, 26 marzec 2009 r. LPP SA (2009e): ZakoĔczenie fuzji Artman  LPP SA, 2009-07-03, accessed January 24, 2011. LPP SA (2011f): Franczyza, http://www.lpp.com.pl/onas/franczyza.htm, accessed January 24, 2011. Kaufland (2011): Home, http://www.kaufland.pl/Home/index.jsp, accessed January 17, 2011.

178

European Retail Research Vol. 25, Issue I, pp. 141-180

Kingfisher (2011): Kingfisher, http://www.kingfishershopping.com/, accessed January 17, 2011. Krakowski Kredens (2011): Krakowski Kredens, http://www.krakowskikredens.pl/index.php, accessed January 17, 2011. Leader Price (2011): Leader Price, http://www.monleaderprice.fr/, accessed January 17, 2011. Leclerc (2011): E. Leclerc, http://www.lodz.e-leclerc.pl/, accessed January 17, 2011. Leroy Merlin (2011): Leroy Merlin, http://www.leroymerlin.pl/, accessed January 17, 2011. Lewiatan Polska Sieü Handlowa (2011): Lewiatan, http://www.lewiatan.pl/, accessed January 17, 2011. Lidl (2011): Lidl, http://www.lidl.pl/cps/rde/xchg/lidl_pl/hs.xsl/index.htm, accessed January 17, 2011. LPP SA (2011): LPP S.A., http://www.lpp.com.pl/, accessed January 17, 2011. Makro Cash and Carry Polska SA (2011): Makro, http://www.makro.pl/pl/, accessed January 17, 2011. Mapi (2010): Rankig „Polityki”: problemy lidera, sieci handlowe mocno w górĊ, http://gospodarka.gazeta.pl/firma/1,31560,6553335,Ranking__Polityki___Problemy_lidera __sieci_handlowe.html, accessed on January 17, 2011. Marks and Spencer (2011): http://www.marks-and-spencer.com.pl/, accessed January 17, 2011. Media Markt (2011): Marks & Spencer, http://www.mediamarkt.pl/, accessed January 17, 2011. Metro C&C (2011): Metro, http://www.metro-cc.com/, accessed January 17, 2011. Metro Group (2011): Metro, http://www.metrogroup.pl/servlet/PB/menu/-1_l10/index.html, accessed on January 17, 2011. Mix Electronics (2011): Mix Electronics, http://www.mixelectronics.pl/, accessed January 17, 2011. MikusiĔska-OzdobiĔska, B. (2009): OstroĪny klient, in: Handel, October 7,2009, p. 19. MikusiĔska-OzdobiĔska, B. (2010): Rozmowa z Arturem Kawą, prezesem zarządu Emperia Holding SA, in: Handel, July-August, p. 31. MikusiĔska-OzdobiĔska, B. (2009a): Siáa Lewiatana: Rozmowa z Wojciechem Kruszewskim, prezesm Lewiatana Holding, in: Handel, Magazyn Handlu, November 18, 2009, pp. 12-13. MikusiĔska-OzdobiĔska, B. (2009b): Skupiamy siĊ na klientach: Rozmowa z Gilles’em Roudy, prezesem Carrefour Polska, in: Handel. Magazyn Handlu, March 18, 2009, pp. 1415. MikusiĔska-OzdobiĔska, B.; Hamdan, J. (2009): We franczyzie wrze (wypowiedzi: Pedro Martinho í Eurocash Franczyza, Wojciecha Kruszewskiego í ZKiP Lewiatan’94 Holding, BoĪeny Lesiewicz í Rabat Detal), in: Handel, Magazyn Handlu, April 15, 2009, pp. 11-15. Mo (2009a): Dobre wyniki Delikatesów Centrum, in: Handel, February 4, 2009, p. 4. Mo (2009b): Real skoncentruje siĊ na sieci, in: Handel, February 4, 2009, p. 6. Mo (2009): Tesco rzuca rĊkawicĊ Biedronce i Lidlowi, in: Handel, March 18, 2009, p. 4

DomaĔski, T.

179

Money.pl (2011): Emperia i Eurocash do koĔca czerwca chcą zrealizowaü waĪną umowĊ, http://www.money.pl/gielda/wiadomosci/artykul/emperia;i;eurocash;do;konca;czerwca;chc a;zrealizowac;wazna;umowe,180,0,747700.html, accessed January 17, 2011. Mohito (2011): Mohito, http://mohito.pl/, accessed January 17, 2011. Neonet (2011): Neonet, http://www.neonet.pl/, accessed January 17, 2011. Netto (2011): Netto, http://www.netto.pl/, accessed January 17, 2011. Niewiadomska, J. (2010a): Razem w Lewiatanie, in: Handel, March, pp. 40-41. Niewiadomska, J. (2010b): Sieü, nie Sieü, in: Handel, April, pp. 36-37. Niewiadomska, J.; Hamdan, J. (2010): Co dwie gáowy, to nie jedna, in: Handel, No. 5, May 2010, pp. 24-26. OBI (2011): Obi, http://www.obi.pl/pl/, accessed January 17, 2011. Oxylane (2011): Oxylane, http://www.oxylane.com/, accessed January 17, 2011. Pellegrini, L. (2008): Libertés ou contraintes dans le commerce italien? L’Etat contre les regions. in: Colla, E. (ed.): Réglementation et commerce en Europe: Les effets de la réglementation sur les stratégies et les performances des entreprises, Paris, pp. 95-122. Piotr i Paweá (2011): Piotr i Paweá, http://www.piotripawel.pl/, accessed January 17, 2011. Plus (2011): Plus, http://www.plus.pl/oferta_indywidualna/, accessed January 17, 2011. PMR (2002): The list Top 50, in: Handel, Vol. 1, Warsaw, http://www.pmrpublications.com/ceemarket.com/2023/TOP_50_by_Handel.shtml, cessed January 17, 2011.

16; ac-

Polityka (2010): NajwiĊksze polskie przedsiĊbiorstwa 2009, http://www.lista500.polityka.pl/, accessed January 17, 2011. POLOmarket (2011): POLOmarket, http://www.polomarket.pl/, accessed January 17, 2011. Praktiker (2009): Annual Report 2009, http://www.praktiker.com/pb/site/praktiker_com/ get/433914/100326_Praktiker_annual_report_2009.pdf, accessed January 17, 2011. Praktiker (2011): Praktiker, http://www.praktiker.pl/praktiker-international/html/pl_PL/859/ index.html, accessed January 17, 2011. PSB Polskie Skáady Budowlane (2009): Annual Report 2009: http://www.grupapsb.com.pl/ grupa-psb/raporty-roczne/rok-2009-1, accessed January 17, 2011. PSB Mrówka (2011): Stona glowna, http://psb-mrowka.pl/, accessed January 17, 2011. PSB SA. (2011): Polskie Skáady Budowlane, http://www.grupapsb.com.pl/, accessed January 17, 2011. Rabat Pomorze (2011): Rabat service, http://www.rabatpomorze.pl/?page_id=110, accessed January 17, 2011. Real (2011): Real, http://www.real.pl/, accessed January 17, 2011. Reserved (2011): Reserved, http://www.reserved.com/, accessed January 17, 2011. RTV Euro AGD (2011): RTV Euro AGD, http://www.euro.com.pl/, accessed January 17, 2011. Saturn Polska (2011): Saturn, http://www.saturnpolska.com/, accessed January 17, 2011.

180

European Retail Research Vol. 25, Issue I, pp. 141-180

Selgros Cash and Carry (2011): Selgros, http://www.selgros.com.pl/, accessed January 17, 2011. Sieü 34 (2011): Sieü 34, http://www.siec34.pl/, accessed January 17, 2011. Stokrotka (2011): Stokrotka, http://www.stokrotka.pl/, accessed January 17, 2011. Tengelmann Group (2011): Unternehmensgruppe Tengelmann, http://tengelmann.de/, accessed January 17, 2011. Tesco (2011): Tesco, http://www.tesco.pl/, accessed January 17, 2011. Central Statistical Office (ed) (2010): The concise statistical yearbook of Poland 2010, Year LIII, Warsaw, pp. 205-207. Vianelli, D.; Dianoux, C.; DomaĔski, T.; Herrmann; J. L. (2007): In-Store shopping behaviour: A cross-cultural comparison in Italy, France and Poland, in: Journal of East-West Business, Part II: Market Entry and Operational Decision Making in East-West Business Relationships, Vol. 13, No. 2/3, pp. 115-142. Vistula Group (2009a): Vistula Group, http://www.vistulagroup.pl/files/reportsperiodical/ R_2009.pdf, accessed January 17, 2011. Vistula Group (2009b): Vistula Group, http://www.vistulagroup.pl/files/reportsperiodical/ RS_2009.pdf, accessed January 17, 2011. Vistula Group (2009c): Vistula Group Spóáka Akcyjna. Sprawozdanie Zarządu z DziaáalnoĞci Vistula Group SA w 2008 r., Kraków, 30 kwietnia 2009. Vistula Group (2010): Vistula Group Spóáka Akcyjna. Sprawozdanie Zarządu z DziaáalnoĞci Vistula Group SA w 2009 r., Kraków, 22 marca 2010. Vistula Group SA (2011): Vistula Group, http://www.vistulagroup.pl/, accessed January 17, 2011. Vobis (PL) (2011): Vobis, http://www.vobis.pl/oferta/notebooki/lista.aspx, accessed January 17, 2011. Wólczanka (2011): Wólczanka, http://www.wolczanka.com.pl/, accessed January 17, 2011. Zara (2011): Zara International, http://www.zara.com/webapp/wcs/stores/servlet/worldwide, accessed January 17, 2011. za (2009): Biedronka nie zmienia planów, in: Handel, January 21, 2009, p. 11. Zentes, J. (2008): Reglementation, hard discount et guerre de des prix en Allemagne, in: Colla, E. (ed.): Reglementation et commerce en Europe: Les effects de la reglementation sur les strategies et les performances des enterprises, Paris, pp. 57-76.