Final Thesis

Antecedents of customer loyalty at Kong Fui supermarket on Aruba

by

Bsc. Rignald J. F. Martis

A thesis submitted in partial fulfillment of the requirements for

the degree of

Master in Business Administration

University of Groningen

2006

Approved by ________________________________________________________Chairperson of Supervisory Committee

Mrs. Dr. J.A. Voerman

and

_______________________________________________________

Assistant Supervisor

Ms. Drs. S.T. M. Kremer

Program Authorized to Offer Degree______________________________________________________

Date _______________________________________________________________

UNIVERSITY OF GRONINGEN

(RIJKSUNIVERSITEIT GRONINGEN)

ABSTRACT


by Bsc. Rignald J. F. Martis

Chairperson of the Supervisory Committee: Mrs. Dr. J.A. VoermanMarketing Department

A thesis presented on the antecedents of customer loyalty at Kong Fui

Supermarket on Aruba, D.C.

In grocery retailing, consumers have a great variety of supermarkets to patronize. This brings forth an important issue as to what can be done by supermarket managements to let their customers feel at home so they can come back. In this regard, customer satisfaction, acculturation preference, shopper characteristics, shopping motivations, customer characteristics and loyalty program adoption have been assessed as the predictors of loyal behavior and –attitude of customers. Furthermore, the effects of idiosyncrasy, privacy concerns, customer characteristics, loyalty program design and – enjoyment on loyalty program adoption have been studied. The potential effects were assessed through techniques to measure both linear –and non-linear dependencies. Finally, the results provide mixed support for the impact of the studied antecedents on both customer loyalty and loyalty program adoption respectively.

Keywords: Customer loyalty; Customer satisfaction; Loyalty program


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Marketing ManagementUniversity of Groningen

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TABLE OF CONTENTS

Number Page

Table of Contents ____________________________________________ i

List of figures _______________________________________________iii

Acknowledgments ___________________________________________ iv

Summary___________________________________________________ v

Chapter 1 Introduction __________________________________ - 1 -

1.1 Background of the study___________________________________ - 1 -

1.2 Kong Fui Supermarket & Wholesale ________________________ - 4 -

1.3 Problem statement and research questions __________________ - 6 -

1.4 Methodology and limitations _______________________________ - 7 -

1.5 Report structure __________________________________________ - 7 -

Chapter 2 Literature review _____________________________ - 8 -

2.1 State of the art of customer loyalty__________________________ - 8 -

2.2 Factors affecting loyalty __________________________________ - 10 -

2.3 Antecedents and moderating factors of customer loyalty for food retailers____________________________________________________ - 13 -

2.4 Factors affecting loyalty program adoption _________________ - 17 -

2.4 Antecedents and moderating factors of loyalty program adoption for food retailers _______________________________________________ - 19 -

2.5 Conceptual model________________________________________ - 22 -

Chapter 3: Research methodology ________________________ - 24 -

3.1 Introduction_____________________________________________ - 24 -3.1.1 Operationalization of model___________________________________- 24 -3.1.2 Questionnaire _______________________________________________- 28 -

3.2 Sample and data collection________________________________ - 28 -

3.3 Analytical method _______________________________________ - 29 -


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Chapter 4: Research results ___________________________ - 38 -

4.1 Representativity of sample________________________________ - 38 -

4.2 General findings _________________________________________ - 39 -4.2.1 Basic descriptive statistics for customer loyalty predictors _______- 39 -4.2.2 Basic descriptive statistics of loyalty program adoption predictors - 53 -4.2.3 Reduction of multi-item measures _____________________________- 54 -

4.3 Non-linear influence of antecedents________________________ - 57 -4.3.1 Influence of the binary customer characteristics ________________- 57 -4.3.2 Influence of non-customer characteristics ______________________- 59 -4.3.3 Influence of the customer characteristics with manifold categories - 60 -

4.4 Linear influence of the antecedents of both layers ___________ - 65 -4.4.1 Influence on word-of-mouth ___________________________________- 65 -4.4.2 Influence on attitudinal loyalty________________________________- 66 -4.4.3 Influence on repeat purchases_________________________________- 67 -4.4.4 Influence on loyalty program adoption _________________________- 68 -

4.5 Further analysis of the influences _________________________ - 70 -

Chapter 5: Conclusions and implications __________________ - 74 -

5.1 Conclusions and discussion _______________________________ - 74 -

5.2 Implications and evaluation_______________________________ - 76 -5.2.1 Implications for further research and evaluation ________________- 76 -5.2.2 Managerial implications ______________________________________- 79 -

Bibliography_________________________________________________ i


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LIST OF FIGURES

Number PageFigure 2.1: The four-stage loyalty model __________________________________ - 9 -Figure 2.2: Conceptual model ___________________________________________- 22 -Figure 3.1: Overview of studied influence of customer characteristics _______- 31 -Table 4.1: Sample proportions based on age category ______________________- 38 -Table 4.2: Sample proportions based on gender ___________________________- 38 -Table 4.3: Sample proportions based on number of household members _____- 39 -Table 4.4: Descriptive statistics of shopper characteristics items____________- 40 -Table 4.5: Descriptive statistics related to the shopping motivation items ___- 42 -Table 4.6: Descriptive statistics of the 5-point scale customer satisfaction measure______________________________________________________________________- 43 -

Table 4.7: Descriptive statistics of the 10-scale customer satisfaction items __- 43 -Table 4.8: Descriptive statistics of the measured customer satisfaction aspects - 46 -Table 4.9: Descriptive statistics of focal – and competitors’ loyalty programs adoption ______________________________________________________________- 47 -Table 4.10: Descriptive statistics of the customer characteristics’ items _____- 49 -Table 4.11: Descriptive results of acculturation preference items ___________- 50 -Table 4.12: Descriptive results of attitude – and WoM items _______________- 51 -Table 4.13: Descriptive results of repeat purchase behavior items __________- 52 -Table 4.14: Descriptive results of enjoyment items & idiosyncratic fit measure - 53 -Table 4.15: Descriptive results of design items & privacy measure __________- 54 -Table 4.16: Overview of factor analysis and reliability analysis for customer satisfaction____________________________________________________________- 55 -Table 4.17: Chi-square test results; influence binary (gender) items on loyalty program adoption ______________________________________________________- 57 -Table 4.18: Mann-Whitney U test results for behavioral loyalty ____________- 58 -Table 4.19: Student t- test results for head of HH _________________________- 59 -Table 4.20: Chi-square test results; influence marital status & gross income on card ownership ________________________________________________________- 61 -Table 4.21: Summary of Kruskal-Wallis tests’ results _____________________- 62 -Table 4.22: Results from different analyses of variance ____________________- 64 -Table 4.23: Summary of stepwise multiple linear regression analysis for WoM and attitude _______________________________________________________________- 66 -Table 4.24: Summary of stepwise logistic multiple regression analysis for variables predicting repeat purchase behavior ____________________________- 68 -Table 4.25: Summary of stepwise logistic multiple regression analysis for card adoption ______________________________________________________________- 69 -


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ACKNOWLEDGMENTS

This report is the result of a research project performed to complete my

Marketing Management study at the faculty of Economics at the

University of Groningen (RuG). Therefore, at this point, I would like to

acknowledge the persons who have contributed in one way or the other in

making it a success.

Thereby, I want to thank the Almighty God for the perseverance and

strength given in writing this thesis. Furthermore, wishes of sincere

appreciation to my supervisor Dr. J.A. Voerman for her assistance in the

preparation of this manuscript by providing useful comments on the

submitted drafts. Undoubtedly, the realization of this final product would

have been impossible without her inputs.

Additionally, special thanks to my father, mother, and my sister for their

moral support and help. My cousins C. J. Henriquez, A. G. Tromp and A.

L. Tromp, my aunts E. M. Tromp and M. A. Krozendijk-Tromp and my

lovely mom many thanks for your cooperation in the data collection

process.

Furthermore, I am very thankful to Mr. Errol Henriquez (Kong Fui

supermarket) and Ms. M. Vigelandzoon (CBS Aruba) for her great

support and patience during these last months of research.

Additionally, I would like to use this opportunity to thank both Mrs. P.

Paula-Croes and Mr. R. Sharp for their great work and dedication

regarding the translation and back-translation of the questionnaire. I

would also like to use this opportunity to thank all the customers of Kong

Fui supermarket who have filled in the questionnaire on which this

investigation is based. Last but not least, I am very thankful to all the

other persons who in one way or the other were there with their support

and words giving me extra strength to march on with this research

project.

“From the bottom of my heart, thanks, you really deserve it!”


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SUMMARY

Retailing is an always interesting field, since we get in touch with it in our

daily lives. However, the existence of too many retailers in today’s

marketplace makes the playing field of grocery retailers in general ever

more competitive. Consequently, this high level of competition makes it

necessary for managers to look for ways to encourage relationship

building with customers. This may look like an easy task, but without

knowing what is valued by ones’ customers and what their preferences

are, nothing can be done to satisfy them and try to bind them to ones

store.

It was this lack of knowledge about customer wishes of the management

combined with the churn of customers at Kong Fui Supermarket on Aruba

that lead to the following management question:

What can be done to increase the purchases of their current customers?

In order to answer this question it was chosen to search for predictors of

repeat purchase behavior. Thereby, existing literature shows that repeat

purchase behavior is a measure of customer loyalty. However, several

writings suggest that customer loyalty has two dimensions and (i.e.

Reinartz and Kumar (2002) – point out that it would be useful to

complement purely purchase behavior measures of customer loyalty with

attitudinal measures. Therefore, and in order to get a broader view as to

the antecedents of customer loyalty in general the following closely related

research question was formulated:

Which are the shopping motivators affecting customer loyalty?

An extensive literature review was done, which provided most of the

constructs to be studied under the population. This review also showed

that customer satisfaction and loyalty card adoption are quite significant

predictors of customer loyalty. Consequently, the conceptual model was


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constructed with a double layer. In this the predictors for the adoption of

loyalty cards was also assessed. Furthermore, the aspects presumed to

affect customer loyalty were also added to the questionnaire.

With the help of a survey under the customers of Kong Fui it was tried to

come up with answers to the hypotheses which were broadly based on

prior research. Consequently, the ultimate goal of the survey was to

provide an answer to the research question.

In this regard, the findings showed support for the existence of an effect of

customer satisfaction, card ownership, shopping motivation, shopper

characteristics, acculturation preference and customer characteristics on

customer loyalty. However, it should be noted that not all these predictors

of customer loyalty have the same type of effect on customer loyalty; some

have linear influences, whereas others have non-linear relationships

towards customer loyalty. Furthermore, some appear to have independent

effects on customer loyalty, whereas others seem to have significant

effects only when assessed communally. It is also remarkable that the

effects differ per measure and/or dimension of customer loyalty.

On the contrary, regarding the antecedents of loyalty program adoption,

the evidences were not sufficient as to support the existence of significant

effects of loyalty program design and privacy concern on the adoption of

these programs. Additionally, not each of the measures of customer

characteristics appeared to be significantly influential. However, loyalty

program enjoyment seems to have the highest ability of predicting the

adoption of loyalty programs as compared to idiosyncrasy.


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CHAPTER 1 INTRODUCTION

1 . 1 B a c k g r o u n d o f t h e s t u d y

Retail patronage issues have engaged academic minds ever since the dawn of marketing as a scientific discipline (Bhatnagar and Ratchford 2004). Every day we get in touch with retailing. Retailing is the set of business activities that adds value to the products and services sold to consumers for their personal or family use (Levy and Weitz, 2004). Another possible definition for retailing is the following: ‘Retailing consists of those business activities involved in the sales of goods and services to consumers for their personal, family or household use. It is the final stage in the distribution process’ Berman and Evans (1992: 3). Therefore, whether doing our daily grocery purchases, buying plane tickets or such diverse transactions, we are dealing with the retail world. Retailing is such a part of our everyday lives that it is often taken for granted (Levy and Weitz, 2004). There are too many retailers in today’s marketplace. Since the playing field of grocery retailers in general is becoming ever more competitive, it is necessary to look for ways to encourage relationship building with customers. It is necessary to build, evaluate and retain the loyalty of customers (Sawmong and Omar, 2004). By building relationships with customers it is presumed that retailers could enhance the likelihood of customer repurchase, maximize the value of future purchases of these customers and even reduce customer churn rates (Rust, Lemon and Narayandas, 2005). Furthermore, customer loyalty can result in the following advantages, namely more natural interactions, complaints being expressed earlier, more knowledge of customer wishes, referrals, lower price sensitivity, and larger profits and last but not least customer loyalty represents value (Reichheld 2003; Reinartz and Kumar 2002). However, it is not that simple to build lasting relationships with customers. Still, due to the various advantages brought by loyal customers it should be tried to build these relationships. In this regard, Duffy (1998) lists –after an extensive literature review – various economic- and competitive advantages that are brought by loyal customers, including1:

1 Note that Duffy (1998) comprised a longer list, however these extra advantages were at the brand level and

this study focuses on the retail store level

Greater sales: loyal customer buy more; they increase sales by purchasing a wider variety of products, making more frequent purchases and buying more

expensive goods; Higher prices: due to their lesser price sensitivity loyal customers provide the

ability to set premium prices; Word-of-mouth: loyal customers provide the best available advertising a

company can get by spreading word-of-mouth; Entry barrier: the existence of a loyal customer base is seen as a substantial

entry barrier to competitors; Energy devotion: a loyal customer base allows a company to devote its energy to

other matters like quality improvement; Sensitivity to marketing efforts: marketing efforts of competitors like marketing

communication or a decrease in price are less likely to influence loyal customers.




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For the purpose of this study, the category of food retailing stores was chosen. Since this study deals with a supermarket, the definition of supermarkets as used in this study follows. McClelland (1962) cited the trade journal Self Service and Supermarket for the following definition of supermarkets “stores of not less than 2,000 sq. ft. sales area, with three or more checkouts and operated mainly on self-service, whose range of merchandise comprises all food groups, including fresh meat and fresh fruit and vegetables, plus basic household requisites (i.e. soaps and cleaning materials)”. It should be mentioned that this definition was adopted from secondary sources and is generally accepted in Britain. However, there appear to be three sorts of difficulties when defining supermarkets, namely those relating to the minimum size requirements, to the characteristics and extent of self-service, and to the stock assortment (McClelland, 1962). This paper focuses on the food retailing industry on Aruba. Aruba is an island in the Caribbean where almost no products are produced domestically. On the island almost every product category lacks a domestic product. In this regard, the study could be related to the study of Nijssen and Douglas (2004), who studied the impact of the availability of both domestic and foreign brands, on consumers’ attitudes towards the purchase of foreign products in a country with a high level of foreign trade. That research was performed in the Netherlands, where quite a few product categories are well represented by domestic products. Furthermore, according to Jamal et al (2006) there is a paucity of research examining shopper profiling in non-Western contexts. They referred to literature stating that the need for such a research is highlighted through the fact that the aggressive, geographic market expansion of successful retail organizations, the internationalization of retail practices and the development of a global consumer market has led many to call for investigating consumer behaviors in specific cultural contexts. However, there appeared to be another stream of authors arguing that the management of retail firms in other cultures requires an understanding of, and responding to, the local consumers’ motives, value, lifestyles, perceptions, attitudes and needs.

Food retailing on Aruba Food retailers on Aruba can be grouped into three types of outlets; large supermarkets, mini-markets, and small shops. On the island, the large or chain supermarkets are used to have stores with areas over 30,000 sq. ft., selling both food and non-food items2. The layout of these stores are similar to US supermarkets, with separate deli, bakery, meat, frozen, seafood and produce sections, as well as scanners and electronic inventory control systems. These supermarkets have well over three registers- as stipulated in the definition to be classified as supermarket- and over 10 aisles, ample parking space and a trained management staff. Competition among supermarkets is fierce with promotion and advertisement playing a key role in marketing strategies. However, an interview with the general manager of Kong Fui revealed that the focus in this battle field for customer purchases is on price, assortment and cleanliness of the store. Although the company has adopted a loyalty program, the company has recently seen a drop in its visitors and customers’ purchases. This fact can be counter balanced either by focusing on the current customers or by looking for ways to attract new customers and/or bring back the ones who have crunched. However, it is widely known that it is much more

2 Source: Export Guide to the Consumer Food Market, September 1997; prepared for the U.S. Department of

Agriculture by Fintrac Inc.




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expensive to attract customers than to keep the existing ones (e.g. Liebermann 1999). The costs of attracting new customers may even be as big as five times that incurred for retaining an old customer (e.g. Reichheld, 2003). Therefore, this investigation will focus on a strategy whereby the company focuses on the existing customers. By focusing on these customers the company should look for ways to enhance the spending of these customers. This research tries to give them an insight into what could be possible actions to undertake to make that possible. According to Sharp and Sharp (1997) the usage of loyalty programs is a defensive strategy, because they aim to keep the current customers in the face of future competitive offers, rather than to gain market share. For that reason, it is assumed that a better fit of the loyalty program design and customers’ preferences concerning loyalty programs could enhance this defense. Furthermore, due to the existence of multi-store loyalty in grocery retailing both on the island and in general, reacting on competitors’ loyalty programs is presumed to be helpful. This is extremely important, since the provision of loyalty incentives and rewards through loyalty programs of competitors may loose the competitive advantage of a company. This would not be an exception on Aruba.

Contribution of this studyThis study contributes to the literature by investigating the determinants of customer loyalty in a grocery context for a small economy, namely Aruba. Thereby, it investigates the effects of customer satisfaction, loyalty card ownership, acculturation preference, shopper characteristics, shopping motivation and some consumer characteristics including some moderating factors on loyalty in the retail food market on Aruba. These effects are particularly studied for Kong Fui and both card-holders and non-card-holders are surveyed. Furthermore, according to Jamal et al. (2006) food and grocery shopping is an effective context to study consumers and their shopping motivations, values and decision making styles for a number of reasons. After an extensive literature review Jamal et al. (2006) list the following reasons:

Each of these three motivations is expected to hold in this situation as well, however, it will be left to the results of this study to prove it. Before continuing, some information about the focal company in this research will be presented in the following section.

The ability to contrast their findings against previous research, due to the existence of previous research that examined shopping motivations in a

grocery context; grocery shopping is an ongoing and essential activity whereby consumer decision-making within the grocery environment can be highly involving; and while grocery shopping in a Western context is often perceived as task

oriented, routine, and non-recreational in nature, their preliminary discussions with some of the local shoppers revealed that the grocery shopping

in the local context (where supermarkets and shopping malls are a recent phenomenon) was associated with a number of hedonic feelings




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1 . 2 K o n g F u i S u p e r m a r k e t & W h o l e s a l e

This study investigates particularly consumers of Kong Fui Supermarket & Wholesale on Aruba (hereafter abbreviated to Kong Fui). Kong Fui is a supermarket with its core business being the sales of both food and non-food merchandise. The company is a wholly owned organization of a Chinese businessman, as most of the supermarkets on Aruba. The company has been operating on the Aruban market since 1992. During those years of its existence the company has coped with stable growth in both sales and customers visiting the supermarket. During the years it has been transformed from a mainly food retailer into a supermarket selling both food and non-food items. It currently has a department for ready-to-eat food take-away, a cosmetics department, and a department with fresh meat, another with fresh fruits, a department with household products and the department of canned food. The company operates both as a retailer and as a wholesaler. According to the general manager the margins on retail sales on Aruba are far lower than those in big countries. In an effort to balance these small margins they started directly importing some of the products they sell. By importing themselves they can buy the products against lower prices which would ultimately translate into higher margins. He continued pointing out that to do this you would need wholesale operations as well. The implementation of wholesale operations would increase the logistical speed and bring continuity in the arrival of imported goods. Besides that, they can buy in on larger scales, which they sell to other smaller players on the market on Aruba.

Membership card of Kong FuiThe membership card of Kong Fui supermarket is for both clients of the supermarkets as well as wholesale members. The clients of the supermarket become class-A members after completing the registration, while the wholesale members are categorized as class-B members. In order to sign up for the membership card, which is free of charge, one should provide one’s ID number, address, home telephone number and those types of general information. The program functions as follows: each month the purchases of each member will be aggregated and after reaching the amount of a particular amount in Aruban guilders in purchases for that month, the corresponding member will start getting 2% discount on each purchase during that month. This purchase amount aggregation method starts back at the beginning of every month. At the end of the year, the total purchases for that year are calculated and the member will get baskets with a particular value of merchandise based on its yearly purchases. Thus, in short this program awards points for purchases that entitle to additional discounts when reaching a certain level. There are eight grocery retailers making use of loyalty programs on Aruba. Out of these eight programs five have been running for a merely three to four years. Certified Super Center, with the longest running program, gives its members gifts at the end of the year. This gift is in proportion of the purchase of these members during the year. The second longest running program of the three is the one of Kong Fui. The functionality of this program has already been covered in the preceding paragraphs. Finally, there is the youngest loyalty program of the three; the one of PriceSmart. In order to participate in this program customers are required to pay a registration/annual membership fee. As reward




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for their membership, members of this program get discounts on the prices of the whole assortment of the retailer. The other five programs can be described briefly as follows:

In this regard, it is assumed that especially PriceSmart’s program is especially attractive to households with large purchase volumes. Consequently, it would be important for Kong Fui to react on this program to prevent further customer defection; thereby playing defense. In this it should not be forgotten that it is easier and less costly to keep customers than to get them back or even attract new ones.Having and, more importantly, retaining loyal customers is extremely important for the continuity and growth of any company and Kong Fui is no exception. Satisfying their loyal customers has a twofold importance. Besides providing Kong Fui with the advantages listed above, satisfying loyal shoppers is extremely important in the market in which Kong Fui operates. It is a fact that Aruba is an island of no more than 69.5 square miles. On this small area, there are over 250 food retailers offering a general assortment to the roughly 100,000 inhabitant of the island (in 2004 there were 98,829 inhabitants on the island3). All these food retailers and other retailers offering food- and/or non-food items are competing to get a share of the purchases made by these 100,000 inhabitants. Thus, it is an intensive competition. This fierce competition is intensified by the ever growing number of players in the market. Just in the first four months of 2006 there were six entrants to the market of food retailing offering a general assortment, which registered at the Chamber of Commerce on Aruba4. In this regard, the distance from home to the food retailer is becoming continuously less; putting more stress on the size of the supermarket’s selling area. Therefore, having a loyal customer base would be a very valuable asset for Kong Fui.

3 Source: www.arubaeconomicaffairs.aw4 Source: www.arubachamber.com

Hong Kong supermarket: There are no registration fees and no discounts on purchases or merchandise, but the customers get gifts at the end of the year.

Super Food: The membership registration is free and each week there is a new set of merchandise available with discounts on them. Customers are

eligible to get gift certificates of AFL. 25, 50, 75 or 100 and at years end they can get one of these gift certificates or a basket with merchandise depending

on the points they have accumulated during the year. Kong Hing Super Center: The membership registration is free of charge. At

years end customers are eligible to get a gift certificate as a present. Morning Supermarket: Also has a free membership fee. Customers

accumulate points and get baskets filled with gifts at years end. Mundo Nobo Supermarket: There are no registration fees either. At years

end, the customers are eligible to get baskets filled with gifts.




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1 . 3 P r o b l e m s t a t e m e n t a n d r e s e a r c h q u e s t i o n s

Hence, the management at Kong Fui is dealing with the question:

If some families are more loyal than others, a retailer might be able to secure a larger share of these more profitable customers or perhaps the loyalty of present customers could be upgraded, according to Cunningham (1961). Either possibility is presumed to help improve market position. However, the usual competitive efforts to build volume through higher traffic may merely be able to support the patronage of different stores by low-loyalty customers (Cunningham, 1961). Accordingly, the purpose of this study is assessing the effect of some shopping motivators on customer loyalty. Furthermore, this study evaluates whether there is a difference in loyalty towards Kong Fui between its members and non-members; thereby assessing whether membership affects customer loyalty. Consequently, the problem statement is:

In order to facilitate the research process the research problem will be broken down into some sub-problems, namely:

In solving the main problem, the sub-problems will be used as guidelines. Furthermore, it will be determined whether the hypotheses are true or false5.

5 These hypotheses are based on literature and are therefore presented in the next chapter.

1. Which are the factors and the moderating factors affecting customer loyalty according to theory?

2. Which is a good set of factors and moderating factors influencing customer loyalty for a food retailer?

3. Which are the factors and the moderating factors affecting loyalty program adoption according to theory?

4. Which is a good set of factors influencing loyalty program adoption for a food retailer?

5. What is the effect of each of the factors in the above stated set on customer loyalty for the customers of Kong Fui?

6. What is the effect of each of the factors in the above stated set on loyalty program adoption for the customers of Kong Fui?






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1 . 4 M e t h o d o l o g y a n d l i m i t a t i o n s

This study covers the investigation of factors affecting customer loyalty and of the antecedents of the adoption of loyalty programs by customers; thereby having a double layer structure. In order to come up with these potential factors – of both layers of the study – a literature review will be done. This literature review would lead to a conceptual model. There after an empirical research will follow, whereby a survey will be used. Respondents will be asked to fill in a questionnaire covering all the constructs of the conceptual model. In gathering respondents no limitations will be held concerning membership. Both card-holders and non-card-holders are approached to participate. However, the survey would limit itself to adults, since children are not presumed to have disposable incomes and are therefore not part of the population as such. As stated earlier, the aim is to study the customers of Kong Fui; therefore the data collection limits itself to these customers. For further details concerning the methodology please refer to chapter 3.

RestrictionsThis study will be subject to the following restrictions:

1 . 5 R e p o r t s t r u c t u r e

The report will be set up as follows:Chapter two –2– addresses the general background theory. Before coming up with a conceptual model literature related to customer loyalty in general and the adoption of loyalty programs, and specifically related to factors directly and moderately affecting them would be assessed. This chapter therefore deals with sub-problems one through four. Chapter three –3– covers the research methodology explicitly. This chapter covers topics such as sample, data collection method, the questionnaire and the method of analysis. Chapter four –4– presents a general overview of the results of the survey and the results related to the different relationships as illustrated in the conceptual model presented in chapter two. Subsequently, chapter five –5– considers all prior chapters in order to draw conclusions6 and make suggestions in the form of both research implications and managerial implications. 6 Concerning the acceptance and rejection of each the tested hypotheses, among others

The study will be limited to Kong Fui only. The respondents will be limited to current customers. However, in the data

collection no distinction will be made between members and non-members. Only for the analysis this difference will be assessed; whenever it is considered appropriate to do so.

The study focuses on assessing the effects between the studied constructs. No efforts will be made to validate measures for these constructs. It is therefore, that it is tried to use as much existing measures as possible.

This study limits itself to the antecedents of customer loyalty. The claimed effect of customer loyalty on profitability lies outside the scope of this study. However, from the four companies studied by Reinartz and Kumar (2003) the grocery retailer had the strongest association between customer longevity and company profits.




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CHAPTER 2 LITERATURE REVIEW

This chapter covers the results of secondary research to point out the factors influencing customer loyalty. It starts by providing a definition for loyalty in paragraph 2.1. Thereafter, the developments regarding research about the antecedents of loyalty is presented as the state of the art of customer loyalty. In assessing the developments in literature dealing with this topic, the available factors influencing loyalty will be gathered. It should be noted here, that since most of these constructs are well established in prior research, we include them in our model without explicit further discussion. Finally, the paragraph ends with the coverage of the second sub-question. The subsequent paragraph deals with the six factors presumed to have a direct effect on customer loyalty. In these sections the theory will be both analyzed and evaluated. Paragraph 2.3 will present the developments regarding studies covering the antecedents of the adoption of loyalty programs. The next paragraph covers the factors presumed to influence the adoption of loyalty programs. Thereafter, paragraph 2.5 presents the conceptual model and the corresponding explanations. But, the state of the art on loyalty follows.

2 . 1 S t a t e o f t h e a r t o f c u s t o m e r l o y a l t y

The studies in customer loyalty over the last five decades can be categorized into three types of studies. First, the period 1960-1990 had studies with a focus on the definition and operationalization of customer loyalty (i.e. Jacoby and Chestnut 1978). Jacoby and Chestnut (1978) have explored the psychological meaning of customer loyalty in an effort to distinguish it from behavioral (i.e. repeat purchase) definitions. Their analysis concludes that consistent purchasing as an indicator of customer loyalty could be invalid because of happenstance buying or a preference for convenience and that inconsistent purchasing could mask customer loyalty if consumers were multi-brand loyal. Due to these possibilities the authors concluded that it would be unwise to infer loyalty or disloyalty solely from repetitive purchase patterns without further analysis. Oliver (1999) builds on this suggestion by pointing out that all three decision making phases must point to a focal brand preference if true brand loyalty exists. Hereby, he refers to beliefs, attitude and conation. Secondly, there were the studies pertaining to the period 1980-2000. In this period the focus was on the antecedents of self-reported customer loyalty (i.e. Anderson and Sullivan, 1993). Anderson and Sullivan (1993) developed a model to link explicitly the antecedents and consequences of satisfaction in a utility-oriented framework. The data were obtained by a computer-aided telephone survey, whereby the customers were required to provide their satisfaction, repurchase intentions, expectations, perceived quality, degree of disconfirmation/confirmation (the extent to which perceived quality fails to match pre-purchase expectations), and ease of evaluating quality, all on a 10-point scale. Consequently, the respondents directly answer their repurchase intentions. Finally, there are studies focusing on antecedents of objective customer loyalty, the so-called customer asset management studies (i.e. Bolton and Lemon 1999). Bolton and Lemon (1999) based their research on panel data in which the same customers are re-interviewed over time so that changes in their opinions could be assessed. The databases used contained both actual as well as self-reported measures of usage levels. However,




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they used the actual measures since these are statistically related to satisfaction measures according to Bolton and Lemon (1999; cited Collopy 1996). In this it is assumed that light users tend to over-report their usage, whereas heavy users tend to under-report their usage.

Two dimensions of loyaltyHowever, although the concept of customer loyalty has been extensively discussed in traditional marketing literature, there is no universally accepted definition for loyalty. There are people, who claim that it should be measured by customers’ share of wallet while others say it should be based on the customer retention rate. There are still others proclaiming frequency as the best measure, while others claim it's the customers' attitude towards the company that best describes loyalty (Woolf, 2002). Still, the main emphasis has been on two different dimensions of the concept, namely behavioral- and attitudinal loyalty. Before the 1970s, the majority of researchers measured loyalty as a pattern of repeat purchasing behavior. The attitudinal perspective on customer loyalty appeared after that period. A highly relevant model for the measurement of grocery store loyalty in this respect is the one proposed by Oliver (1997). In this work the four-stage loyalty model was proposed, as illustrated in figure 2.1.

Figure 2.1: The four-stage loyalty model

Source: Oliver (1997)

Keller (2003) applies behavioral loyalty as a dimension of brand resonance; however, defining behavioral loyalty at the brand level. At the store level behavioral loyalty would mean repeat purchases and the amount or share of category volume attributed to the store. Hereby, it encompasses both how often customers visit a store (and make their purchases there) and how much they purchase at that store. Conversely, Reichheld (2003) defines customer loyalty from a ‘relationship’ perspective. In this respect, he defines customer loyalty as “the willingness of someone – a customer, an employee, a friend – to make an investment or personal sacrifice in order to strengthen a relationship”. For a customer, this can mean sticking with a supplier who treats him well and gives him good value in the long term even if the supplier does not offer the best price in a particular transaction. Building on this definition, he argues that customer loyalty is much more than repeat purchases. Inertia, indifference or exit barriers may exist. According to Noordhoff, Pauwels and Odekerken-Schröder (2004) behavioral store loyalty is expressed by the actual revisiting of the store and the total budget ratio spent at a single

Cognitive loyalty

Affective loyalty

Conative loyalty

Action loyalty




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store; the so-called share-of-wallet. As a central dependent construct, behavioral store loyalty is still popular even though authors as Reinartz and Kumar (2002) – to name a few – expressed fundamental criticism. In fact, purchase behavior does not always provide the accurate loyalty measure, given that other moderating variables such as social norms and situational influence, both studied by Huddleston et al. (2004), manipulate a decision to patronize a store. Therefore, some authors – i.e. Reinartz and Kumar (2002) – point out that it would be useful to complement purely purchase behavior measures of customer loyalty with attitudinal measures. Attitudinal loyalty refers to “the consumer’s predisposition towards a store as a function of psychological processes, [which] includes attitudinal preference and commitment towards the store” (Jacoby and Chestnut, 1978). The addition of attitudinal measures could help in making a clear distinction between inertia and convenience against pure behavioral loyalty. Jacoby and Chestnut (1978) investigated both behavioral- and attitudinal loyalty thereby finding out that the effects of loyalty are much lower in case of only behavioral loyalty. They suggest that for companies “to identify the real apostles, they need to judge customers by more than just their actions”. The ambassadorship of customers showed quite a big increase when customers were both behaviorally- and attitudinally loyal.Measures for attitudinal loyalty are trust, commitment, satisfaction, attractiveness and switching costs among others. Conversely, the measures for behavioral loyalty can be divided into transactions and non-transactions. In the category of transactional measures there are items such as retention, cross-buying, usage/repeat purchase, upgrading and customer share/share of wallet. Furthermore, Rowley (2005) suggests relationship continuance, increased scale or scope of relationship, and recommendations to be loyalty behaviors as well. Regarding the non-transactional measures there are customer referrals/word-of-mouth and supportive behavior/complaints.

This study builds on the second type out of the three mentioned in the beginning of this paragraph. This study tries to assess the antecedents of self-reported customer loyalty. Hereby the data will be collected by gathering answers directly from respondents. Leenheer (2004) points out – based on literature review – that the marketing literature provides a wide range of customer loyalty measures. The usefulness of these measures appears to be dependent on the specific market and study objective. Leenheer (2004) continues citing that in grocery retailing, purchase behavior is characterized by high buying frequency and variation in basket sizes. Additionally, consumers are often polygamous loyal. Based on these characteristics, share-of-wallet is the most suitable measure for behavioral loyalty (Mägi, 2003). However, due to the strong subjectivity of this measure it is not used in this study.

2 . 2 F a c t o r s a f f e c t i n g l o y a l t y

A quite extensive literature review revealed that there are different ways in which the factors presumed to affect both dimensions of customer loyalty are classified. Some researchers just leave the studied factors unclassified, while others group them into multi-attribute constructs based on e.g. factor analysis and still others use the categorization of Oliver (1997) as starting point. By basing studies on this model, it is meant that the researchers do not use the exact same items as the original model; however, the division of loyalty into the natures of loyalty is maintained (for more details see e.g. Dick and Basu




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1994). Examples of studies based on the four-stage loyalty model of Oliver (1997) include Huddleston et al. (2004) and Sawmong and Omar (2004).Secondly, there is the stream of researches which study the items, affecting both dimensions of customer loyalty, separately. Examples of this stream are Huddleston et al. (2004) where focus group interviews revealed promotions, price, convenience, location, product, atmosphere and service to be the factors that respondents liked about their retailer and Bellizi and Bristol (2004) which study revealed check-out, variety of fresh produce, location, fresh meat department, fresh bakery, deli counter and low prices as the most important loyalty influencing factors. Finally, there is the category of studies using multi-attribute constructs. Although most of these multi-item constructs have been studied in the grocery retail industry, the multi-item constructs perceived trust, perceived value, overall satisfaction and future Internet Service Provider expectancy have been studied for an Internet service provider (Chiou, 2004) not in a grocery retail context as far as this review goes. Additionally, the service quality performance, value and customer satisfaction multi-attribute constructs have been studied for six service industries (Cronin Jr., Brady and Hult, 2000). So, customer satisfaction is a construct that has been studied both in grocery retailing and in non-grocery retailing studies. Cortiñas, Elorz and Villanueva (2004) also used multi-attribute constructs. However, these were investigated as antecedents of satisfaction, which was further studied as an antecedent of customer loyalty. The added-value of that study is that it investigated the relations both in the grocery industry and at petrol stations. Majumdar (2005) also takes a new dimension in this type of studies by studying stores in a shopping mall. Based on the setting of the study, the loyalty of shoppers towards the shopping mall was also taken into account as an antecedent of store loyalty intention. Besides shopping mall loyalty, value perception, sales promotion and overall impression of the store have also been incorporated in that particular study as antecedents of store loyalty intention. Still, a strange part of the data collection was that only garment store shoppers were intercepted, although a shopping mall generally has a wide variety of stores in it. Other studies belonging to this category include Verhoef, Franses and Hoekstra (2002), Verhoef, Franses and Donkers (2002), Seiders et al. (2005) and Verhoef and Franses (2003). Verhoef, Franses and Hoekstra (2002) studied the effect of relational constructs on customer referrals and number of services purchased. This study was performed on the customers of an insurance company. They included relationship age as a moderator on the relationship between the relational constructs and loyalty. The relational constructs included satisfaction, both affective- and calculative commitment, and payment equity. The loyalty measures were customer referrals and number of services purchased. Verhoef, Franses and Donkers (2002) also included referrals and number of services in their study. However, they restricted the studied relationships to the effects of satisfaction and payment equity on these loyalty measures. Note that this study was also performed in the insurance branch. Verhoef and Franses (2003) studied the effects of commitment, satisfaction and word-of-mouth on behavior. In this regard, both stated and revealed behavior was investigated and the measures of behavior in this study were customer retention, cross-buying and customer share. Seiders et al. (2005) also studied both self-reported and objective repurchase behavior. Their study investigated the effect of satisfaction on both number of visits and dollars spent in a specialty retail chain. Besides investigating that direct effect, they studied the moderating effect of customer-, relational-




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and marketplace characteristics. The customer characteristics studied included both involvement and household income, whereas the relational characteristics were relationship age and program participation and the marketplace characteristics were competitive intensity and convenience offering.

Retail/food multi-attribute antecedentsThese multi-attribute constructs presumed to influence loyalty include customer satisfaction, customer-card possession, service quality and value (Sawmong and Omar, 2004; Noordhoff, Pauwels and Odekerken-Schröder, 2004; Chiou, 2004). Besides these presumed antecedents of loyalty, an extensive literature review by Landsverk, Hughes and Fearne (2003) revealed that there are seven key elements in the retail brand. These seven elements are said to be physical store, service products, fresh food concepts/products, own label products, communication, price positioning and quality standards in different product areas, and access attributes (Landsverk, Hughes and Fearne, 2003). Piron (2001) studied the predictability of location, store image, merchandise price, merchandise assortment, merchandise quality, service and advertisement and promotion for store loyalty in the grocery retail industry under Singaporean shoppers. In addition, Sirohi, McLaughlin and Wittink (1998) studied three important links based on their model, namely the effects of extrinsic cues on merchandise quality perceptions, the antecedents of perceived value and the determinants of the store loyalty intentions. Thereby they used eight constructs to measure respondents’ perceptions. These measures were store operations perception, store appearance perception, personnel service perception, sales promotion perception, merchandise quality perception, perceived relative price, perceived value and perceived value of competitor. The effects of all but three constructs on store loyalty intentions were separately assessed. In this regard, they have combined store operations perception, personnel service perception and store appearance perception into a single item and assessed its effect on store loyalty intentions.

Yet another categoryBesides these categories of antecedents, length, depth and breadth of the relationship have also been studied as antecedents of loyalty (Verhoef, 2001). In this regard, it is assumed that this study belongs to yet another category of studies. These antecedents’ effect on purchase behavior was thereby assessed in service industries. The author points out that the studied antecedents of purchase behavior are reflected in different purchase behaviors in the investigated industries.




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2 . 3 A n t e c e d e n t s a n d m o d e r a t i n g f a c t o r s o f c u s t o m e r l o y a l t y f o r f o o d r e t a i l e r s

In short, the antecedents of both attitudinal- and behavioral loyalty can be divided into the following general categories (adapted from Verhoef, 2003):

From these antecedents – presented above – this study assesses customer satisfaction (perceptions of relationship), loyalty programs adoption (marketing instruments), customer characteristics, shopper characteristics and shopping motivation as antecedents of customer loyalty towards their grocery store; particularly towards Kong Fui. So, the aim was to include items covering each of the six general categories affecting both attitudinal-and behavioral loyalty. However, past behavior was not assessed. Although frequency of purchase – in the past – was indirectly assessed it was studied as a measure of behavioral loyalty, not as a potential predictor. Consequently, potential antecedents – as discussed in the previous paragraph – which are assumed to be irrelevant due to some reason have been left out of this analysis. These potential antecedents include location and communication. Location is irrelevant, since the supermarket is already settled there for years and at the moment the only way to change it is to open the store somewhere else. The marketing activities of the supermarkets on Aruba are minimal, so it is presumed as irrelevant to incorporate communication as an antecedent in the study. The availability of own label products is also irrelevant in this study, since Kong Fui does not carry private label brands/products. Finally, both merchandise assortment and -quality are considered as irrelevant due to the competitive pressure that in some degree dictates them. However, to have a better insight into the aspects of customer satisfaction at Kong Fui, these items are incorporated into the study as well, but as single-items. However, these items are not illustrated in the conceptual model of figure 2.2, since their influence on customer satisfaction and/or customer loyalty is beyond the scope of this study.

Shopper characteristicsAccording to Mägi (2003), a number of studies on grocery shopping behavior have related customer share in the primary store to a range of consumer characteristics. She points out that although her research conveys a somewhat fragmented picture of consumer-level

Perceptions of relationship or offerings of suppliers. In this category items such as commitment, satisfaction, and trust are represented.

Marketing instruments. Here it can be thought of instruments like direct marketing, loyalty programs and advertising.

Past behavior. Items belonging to this category are relationship age, number of products sold and RFM (recency, frequency and monetary value) among others.

Customer characteristics. This category encompasses items such as age, income, education and household size among others.

Shopper characteristics. The three shopping motivations of Mägi (2003) belong to this category.

Shopping motivation. Hereby a distinction is made between utilitarian shopping motives and hedonic shopping motives.




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correlates with customer share, some results stand out as fairly consistent across studies. She used three shopper motivations, which are shopper types identified by other researchers as well. Therefore, it is presumed that they represent some of the core motivational drivers of grocery shopping behavior. Jamal et al. (2006)’s literature review revealed a stream of authors arguing that the management of retail firms in other cultures requires an understanding of, and responding to, the local consumers’ motives, value, lifestyles, perceptions, attitudes and needs. This need is assumed to be especially important for the personalizing shopping oriented shoppers. With this in mind this section covers shopper characteristics, whereby a distinction will be made between economic-, personalizing- and apathetic shopping orientation. According to Mägi (2003) these characteristics represent some of the core motivational drivers of grocery shopping behavior. Mägi (1999) points out that the consumers who appreciate the social dimension of shopping tend to concentrate their purchases at a specific store, since that would make it easier to build and maintain relationship with store personnel. Therefore, it is expected that the personalizing shopping orientation has a positive effect on both behavioral- and attitudinal loyalty. That expectation leads to:

Furthermore, Mägi (1999) points out that the negative effect of being a price-oriented consumer on customer share in the primary store is intuitive. Consumers who perceive benefits from comparing prices across stores would be more likely to spread their purchases evenly across stores in their pursuit of good deals than consumers who do not find across-store price comparisons worthwhile. Therefore,

Similarly, the degree of apathetic shopping or un-interest in shopping has some effect on behavioral loyalty. According to Williams et al. (1978) the more apathetic the consumer, the likelier that he/she would concentrate purchases to one store to minimize the energy spent on grocery shopping. This concentration of purchases can be seen as a form of behavioral loyalty. Additionally, it is expected here, that this same un-interest in shopping would influence the attitude of consumers towards shopping negatively. Therefore,

Shopping motivationExisting literature has sought to develop typologies of shoppers based either on shopping motives (Arnold and Reynolds, 2003) or on decision-making styles (Lysonski et al., 1996) among others. Arnold and Reynolds (2003) came up with a six-factor hedonic shopping motivation profile classification. However, due to the magnitude of this investigation the

H1.3a: Apathetic shopping orientation has a positive effect on behavioral loyalty.H1.3b: Apathetic shopping orientation has a negative effect on attitudinal loyalty.

H1.2: Economic shopping orientation has a negative effect on behavioral loyalty.

H1.1a: Personalizing shopping orientation has a positive effect on behavioral loyalty.H1.1b: Personalizing shopping orientation has a positive effect on attitudinal loyalty.




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shopping motives will be restricted to a general classification of hedonic and utilitarian motivations. Additionally, the aim of this investigation is to come up with consumer profiles based on a combination of factors instead of really going into details for each of these factors. Therefore, it was assumed unnecessary to use that six-factor hedonic shopping motivation profile instead of only dividing shopping motivation into hedonic shopping and utilitarian shopping. Since hedonic shopping motives deal with the basic premise that shoppers are motivated by non-product related attributes, it is assumed in this study that hedonic shoppers would be more willing to become loyal to a firm. Consequently, it is expected that the following hypotheses hold:

Customer satisfactionCustomer satisfaction is widely assumed to be an antecedent of behavioral loyalty. In Oliver (1997) satisfaction is defined as pleasurable fulfillment. That is, the consumer senses that consumption fulfills some need, desire, goal or so forth and that this fulfillment is pleasurable. It is presumed that the higher the degree of satisfaction of a customer the stronger his/her behavioral loyalty. Anderson and Sullivan (1993) found that the elasticity of repurchase intentions with respect to satisfaction is lower for firms that provide high satisfaction. According to them this implies a long-run reputation effect insulating firms which consistently provide high satisfaction. This implies that there is a relationship between customer satisfaction and behavioral loyalty. Even stronger their findings support the implication that there is a positive relation between satisfaction and behavioral loyalty. However, Mägi (2003) found only a moderate effect of customer satisfaction on share of wallet. Consequently, that study did not get significant evidence for a strong relation between customer satisfaction and behavioral loyalty. Furthermore, it was found that this effect is influenced negatively by both the economic orientation and personalizing shopping behavior of customers. However, aggregate purchase volume seemed to have a positive influence on that effect. Therefore, the following hypotheses are expected to hold:

H3.1a: Customer satisfaction has a positive effect on behavioral loyalty. H3.1b: Customer satisfaction has a positive effect on attitudinal loyalty.

H3.2a: The effects of customer satisfaction on behavioral loyalty are moderated by shopper characteristics.

H3.2b: The effects of customer satisfaction on attitudinal loyalty are moderated by shopper characteristics.

H2.1a: Hedonic shopping motivation has a positive effect on behavioral loyalty. H2.1b: Hedonic shopping motivation has a positive effect on attitudinal loyalty.

H2.2a: Utilitarian shopping motivation has a negative effect on behavioral loyalty. H2.2b: Utilitarian shopping motivation has a negative effect on attitudinal loyalty.




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Loyalty program adoption There are different actions which firms can take to enhance relationship equity. Rust, Lemon and Narayandas (2005) defined relationship equity at the brand level. At the store level, it would be defined as the tendency of the customer to stick with the store, above and beyond the customer’s objective and subjective assessments of the store. Research in relationship equity revealed loyalty programs, affinity programs, community building programs and knowledge building programs as the key drivers of relationship equity. Loyalty programs are the set of actions taken by firms to reward their customers for specific behaviors (Rust, Lemon and Narayandas 2005). These rewards may be both tangible- and intangible benefits. Altogether, these drivers may help firms to maximize the likelihood of customer repurchase, maximize the value of a customer’s future purchases or even reduce customer churn. Therefore, loyalty programs are thought to have a positive effect on behavioral loyalty. It is assumed that due to the benefits that someone gets by participating in a loyalty program, people are eager to become loyal to firms with these programs. Mägi (2003) provided evidence that the presumed positive relationship between loyalty program and behavioral loyalty can not be supported. This lack of support was however only found at the store level. At the chain level however, a positive effect was found for members holding only the focal chain’s card. Thus, although loyalty card ownership does not seem to affect share of wallet at the store level, there was enough evidence for it to be influential at the chain level. Therefore, it is not surprising that Leenheer (2004) also found evidence supporting the influence of loyalty programs on share of wallet. Consequently, it is assumed here that there is a relationship between loyalty program adoption and behavioral loyalty. This assumed relationship translates into the following hypotheses:

Customer characteristicsLast but not least, it is assumed intuitively that customer characteristics, being gender, age, education level, income and household size would influence store loyalty under customers. Therefore, the following set of hypotheses is expected to hold.

H5.1a: Customers’ gender influences behavioral loyalty.H5.1b: Customers’ gender influences attitudinal loyalty.

H5.2a: Customers’ age influences behavioral loyalty.H5.2b: Customers’ age influences attitudinal loyalty.

H5.3a: Customers’ educational level influences behavioral loyalty.H5.3b: Customers’ educational level influences attitudinal loyalty.

H5.4a: Customers’ income level influences behavioral loyalty.H5.4b: Customers’ income level influences attitudinal loyalty.

*** Note: the last four of the set are shown on the next page ***

H4a: Loyalty program adoption has a moderate effect on behavioral loyalty.H4b: Loyalty program adoption has a moderate effect on attitudinal loyalty.




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Acculturation preferenceAnother attribute of customers to be studied here is the one presented by Swaidan et al (2006). They investigated the role of acculturation in shaping consumers’ views of ethics. Specifically, their study examined the relationships between the desire to keep one’s original culture, the desire to adopt the host culture, and the four dimensions of consumer ethics scale of Muncy and Vitell (1992). In this study instead of assessing the role of acculturation, consumers’ preference towards acculturation of the food retail staff will be investigated. The aspect is incorporated in the study, since e.g. by adopting the host culture the language barrier could be bridged whereby it would be easier for the consumer to build and maintain a relation with store personnel.

Furthermore, most of the supermarkets on Aruba are owned and operated by Chinese.Consequently, it is assumed that the preference of consumers towards the adoption of the host culture by these Chinese could have some influence on customer loyalty. Hereby it can be thought of the communication barrier that would exist when there is direct contact between the consumers and (one of) these Chinese and when the latter can not understand the host language. Consequently, it is expected that both behavioral- and attitudinal loyalty are influenced to consumers’ acculturation preference. This assumption translates into the hypotheses:

2 . 4 F a c t o r s a f f e c t i n g l o y a l t y p r o g r a m a d o p t i o n

For a better insight into loyalty program (LP) adoption, the reasons for people to adopt loyalty programs are incorporated in this study as proposed by Mägi (2003). The primary aim of loyalty programs is to stimulate customers to patronize the same retail store in a structural way (Leenheer, 2004). Its ultimate goal is to improve both attitudinal- and behavioral loyalty. Sharp and Sharp (1997) defined a loyalty program as a supplier’s structural effort to increase customers’ attitudinal- and behavioral commitment to the supplier’s market offering. Consumers perceive this loyalty program as an organized marketing activity which offers (some of) the customers additional rewards or benefits (DeWulf et al., 2003 in Noordhoff, Pauwels and Odekerken-Schröder, 2004). However, the extant literature does not show agreement on the minimal characteristics of a loyalty program (Noordhoff, Pauwels and Odekerken-Schröder, 2004). When deciding whether to participate in a company’s loyalty program, a customer generally compares the expected benefits and cost (Leenheer, 2004). In this regard, Graeff and Harmon (2002) implied that loss of privacy, loss of control over personal information, cumulative amount of money spent and possible subscription fees are the main obstacles

H5.5a: Customers’ household size influences behavioral loyalty.H5.5b: Customers’ household size influences attitudinal loyalty.

H5.6a: Customers’ income inflow frequency influences behavioral loyalty.H5.6b: Customers’ income inflow frequency influences attitudinal loyalty.

H6a: Acculturation preference influences behavioral loyalty. H6b: Acculturation preference influences attitudinal loyalty.




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for a customer to adopt a loyalty program. Therefore, it can be presumed that there are more aspects taken into consideration when taking this decision than only the cost-benefit ratio. Nevertheless, customers should be given some sort of compensation for disregarding these obstacles. The most obvious compensation in this regard is the rewards which customers are given for their participation in the scheme. But, are these enough to compensate the customers for their participation albeit these barriers. There are at least five other elements which are supposed to help companies compensate this action of their customers. In this regard, O’Brien and Jones (1995) identified cash value, choice of redemption options (the range of rewards offered); aspirational value (how much the customer wants the reward); relevance (the extent to which rewards are achievable); and convenience (ease of participation in the scheme) as elements that contribute to the value of a loyalty scheme. Consequently, it can be mentioned that Kivetz and Simonson (2002 and 2003) assess the relevance-element of O’Brien and Jones (1995) by their examination of the effect of the level of effort required to obtain an LP reward on consumers’ perception of the LP’s attractiveness. Consumers are often promised a delayed reward, contingent on the performance of future effort. According to Soman (1998), in such situations, at the time of brand choice, the future effort is underweighted, relative to the future savings. Consequently, the time interval between the times of brand choice – in this regard, time of store choice – and of redemption could make an attractive incentive seem unattractive or vis-à-vis. Additionally, Leenheer (2004) studied the effect of instrumental variables, store characteristics, loyalty program design and initial share of wallet on loyalty program adoption by customers. The instrumental variables investigated were loyalty program enjoyment and privacy concerns, whereas the store characteristics were distribution density and price level. Regarding the design of the loyalty program, she studied the effect of the promotion- and saving rewards. Furthermore, the moderating effect of household characteristics on the link between store characteristics and loyalty program adoption was assessed. Regarding loyalty program design, Leenheer and Bijmolt (2003) examined the effect of household size on the type of loyalty program adopted. Thereby a distinction was made between loyalty programs giving price discounts, those where points are earned and those giving special service.Related to the adoption of loyalty programs, Graeff and Harmon (2002), examined the extent to which consumers are concerned with how their personal information is collected and used, their awareness and knowledge of data collection practices using loyalty cards and the relationship between demographics and privacy concerns. They measured the familiarity with loyalty programs and the level of knowledge about how these cards are used. Regarding the assessment of the familiarity, they asked consumers whether they use grocery loyalty cards or not. Moreover, the privacy concerns of customers have been investigated. Privacy concerns were assessed both regarding the information and the comfort using credit cards. Furthermore, there are studies which examined the influence of demographic characteristics on loyalty program adoption. Cunningham (1961) examines the relation between size of food expenditures and loyalty. Thereby, it can be assumed that the size of food expenditures is closely related to family size. Vakratsas (1998) assesses the effect of demographic variables including employment status of female head, household size and household income on household purchases. The measure used to assess household




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purchases was household purchase timing decisions and in particular purchase acceleration due to promotions.

Although an extensive set of factors affecting loyalty program adoption have been outlined in the lines above, this study would focus on the effect of loyalty program enjoyment, idiosyncratic fit, loyalty program design, household characteristics and privacy concerns on loyalty program adoption at Kong Fui. This set of factors has been chosen, since it is assumed that these are the factors relevant in the retail food context. Note, however, that as was the case with the antecedents of customer loyalty, the expectations related to these factors will be adopted as much as possible from prior research.

2 . 4 A n t e c e d e n t s a n d m o d e r a t i n g f a c t o r s o f l o y a l t y p r o g r a m a d o p t i o n f o r f o o d r e t a i l e r s

Loyalty program enjoymentLoyalty program enjoyment stimulates participation in loyalty programs, according to Leenheer (2004). Thereby it has been shown that some customers derive benefits, which are beyond purely economic benefits, from a loyalty program. Therefore, it is proposed here that:

Idiosyncratic fitThe logic of idiosyncratic fit is that the consumer’s perception of the LP’s attractiveness will have a positive effect on the shopping motive of the consumer. Kivetz and Simonson (2003) propose that in certain conditions, increasing program requirements can enhance consumers’ likelihood of joining the program, thus leading consumers to prefer a dominated option. In their study they obtained evidence for their hypothesis that “consumers often evaluate LPs on the basis of their individual effort to obtain the reward relative to the relevant reference effort (e.g. the effort of typical other consumers)”. Furthermore, “when consumers believe they have an effort advantage over typical others (i.e. an idiosyncratic fit with the LP), higher program requirements magnify this perception of advantage and can therefore increase the overall perceived value of the program”.Therefore, this consumers’ idiosyncratic fit heuristic will be assessed in the study for presumably having some positive influence on the loyalty card ownership. This translates into the following hypothesis:

HA2: Consumer perception of idiosyncratic fit influences the participation in loyalty programs positively.

HA1: Consumers’ loyalty program enjoyment influences the participation in loyalty programs positively.




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Loyalty program designFurthermore, Leenheer and Bijmolt (2003) propose that large households tend to adopt loyalty programs giving price discounts over those where points are earned and those giving special service, which translates into the following hypothesis:

Customer characteristicsSoman (1998) demonstrates empirically that face value, level of effort and temporal delays have effects on choice, redemption and profits. Results of that study show that temporal delay between choice and redemption causes a systematic underweighting of future effort, which mediates the increased attractiveness of alternatives with delayed incentives. Cunningham (1961) illustrates that there are no significant correlation between total food expenditures and loyalty. Store loyalty appears to be independent of the total amount spent for food purchases by a particular family. Vakratsas (1998) suggests that all the three demographic variables, she used in her study, have a significant effect on purchase acceleration. The study categorizes these three demographic variables as household characteristics and based on these results assesses their effect on loyalty programs adoption. Households of larger size have higher consumption rates. Since these big household buy more, they would get more rewards by participating in loyalty programs. Their ability to get more rewards would then be presumed to influence their adoption of loyalty programs, which leads to the following hypothesis:

However, Leenheer and Bijmolt (2003) propose that households with higher incomes value the economic benefits of loyalty programs less than households with lower incomes. Therefore, a higher income household is expected to be more inclined to adopt a loyalty program, which leads to:

Similarly, Blattberg et al. (1978) point out that higher income households have access to more information and generally have more resources. This reasoning is in line with the empirical results of their study where upper income households (without controlling for other demographics) were shown to be more deal-prone than low income households. However, generally speaking, high income households are less price sensitive than low income households (Blattberg et al., 1978). Furthermore, high income households are expected to have higher opportunity costs of time with income being considered a time constraint variable. Assuming that these opposite expected effects of income can be offsetting, both high and low income households would have low deal-proneness, whereas only the middle incomes would have high deal-proneness. It is presumed that the effect of household income is also related to the degree of participation in loyalty programs. Since the relation between deal-proneness and household income has been supported by

HA4.2a: Household income influences loyalty program adoption.

HA4.1: Household size has a positive effect on loyalty programs adoption.

HA3: Loyalty program design influences consumers’ adoption of loyalty programs.




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Vakratsas (1998), it is expected that the following hypothesis – which takes the last one a step further – holds:

Mägi (2003) expected elderly to have more loyalty cards than younger people. This was expected due to the availability of time that the elderly have to visit different stores. Consequently, these elderly are also expected to have higher participation rates of loyalty programs than younger customers, leading to the hypothesis:

The employment status of the female head of the household can signal a household's opportunity costs of time. Blattberg et al. (1978) found empirical support for the proposition that households with non-working housewives are more deal-prone. In this regard it is assumed in this investigation that households with non-working housewives would participate in different loyalty programs. Narasimhan (1984) was based on a slightly different context – the study of coupon usage as an aspect of deal-proneness. However, similar results were obtained. In situations where the female head of the household is unemployed or not employed in a full-time capacity, there is more time for that household to evaluate a promotion and assess whether or not purchase acceleration would lead to considerable savings. Consequently, also supporting the assumption that when the female head of the household is unemployed or working part-time the household would be participating in different loyalty programs. This assumption is based on the logic that since they would have more time to evaluate promotions or look for better prices, they would know how to distribute their purchases between the different firms with loyalty programs thereby getting most rewards possible. Consequently, it is hypothesized that:

Privacy concerns

Yet another determinant of loyalty program adoption by customers is privacy concerns. Leenheer (2004) found – as stated earlier – that customer’s privacy concerns withhold them from registering for loyalty programs, leading to the hypothesis:

HA5: Privacy concerns have a negative effect on loyalty program adoption by customers.

HA4.4: Head of household employment status influences their participation in loyalty programs.

HA4.3: Customers’ age influences loyalty program adoption.

HA4.2b: Middle income households have a greater rate of participation in loyalty programs than low and high income households.




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2 . 5 C o n c e p t u a l m o d e l

In the previous sections, the state of art on both customer loyalty and loyalty program adoption were covered. Furthermore, the antecedents of both customer loyalty and loyalty program adoption – as to be investigated here – have been extensively discussed. However, in order to give an illustration of the relationships that will be investigated, a conceptual model has been constructed. A conceptual model connects the theories that play an important role in the research objective (De Leeuw, 1996).

Figure 2.2: Conceptual model

H6

HA4a -- HA4e

HA1

Competitive loyalty programsLP design

HA5

HA2

Loyalty program design

HA3

Idiosyncratic fit

H5a -- H5e

H2 Shopping motivation

H4

Privacy concerns

H1a -- H1d

Acculturationpreference

Loyalty program

enjoyment

H3a -- H3cCustomerLoyalty

Customer Satisfaction

Shoppercharacteristics

Loyalty program adoption

Customer characteristics




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As has been explained earlier, the conceptual model has a double layer set of relationships. The layer dealing with antecedents of loyalty program adoption can be further divided into a set of direct and indirect effects. Concerning the main layer, the direct relationships to be studied are between:

The direct relationships to be studied – as illustrated in the second layer – are between:

Besides these direct effects, the second layer covers the following moderating effects:

Loyalty program design, and Competitors’ loyalty program adoption

Loyalty program enjoyment and loyalty program adoption Idiosyncratic fit and loyalty program adoption Loyalty program design and loyalty program adoption Customer characteristics and loyalty program adoption Privacy concerns and loyalty program adoption

Shopper characteristics and customer loyalty; both attitudinal – and behavioral loyalty

Shopping motivation and customer loyalty; both attitudinal – and behavioral loyalty

Customer satisfaction and customer loyalty; both attitudinal – and behavioral loyalty

Loyalty program adoption and customer loyalty; both attitudinal – and behavioral loyalty

Customer characteristics and customer loyalty; both attitudinal – and behavioral loyalty

Acculturation preference and customer loyalty; both attitudinal – andbehavioral loyalty




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CHAPTER 3: RESEARCH METHODOLOGY

This chapter covers the design of the consumer survey. This is one component of the most essential part of the research. The other component of this essential part encompasses the results of the consumer survey and is covered in the next chapter. This chapter starts with the operationalization of the model, followed by a description of the sample. Thereafter, a discussion of the questions that are used in the quantitative research follows. The chapter ends with a discussion of the methods of analysis.

3 . 1 I n t r o d u c t i o n

The study is restricted to the determinants and moderators influencing customer loyalty and loyalty program adoption as proposed by prior studies and where presumed necessary complemented by observations concerning the situation in food retail on Aruba. In this regard, both behavioral- and attitudinal loyalty antecedents are studied.

3.1.1 Operationalization of model

Shopper characteristicsThe items used to measure the three orientations part of the shopper characteristics of Mägi (2003) provided a high internal consistency. Therefore, these items will be used in this investigation as well. It should be mentioned that since they are stated in a general way, there was no need to adapt their wording very much to the situation under consideration. However, whereas she used primary grocery store in her items, here the items will be specified to Kong Fui, since Kong Fui is the focal grocery store under investigation. However, the 10-point scale she used will be adapted to a 9-point scale. This is done to be able to give respondents the availability of an average score. Furthermore, the 5-point or 7-point scale was not adopted, in order to prevent the loss of even more information as a result of range restrictions and coarseness which can attenuate the ability to detect significant interaction effects that truly exist in the population (Russell and Bobko, 1992 in Seiders et al., 2005). In this, range restriction occurs when information is lost due to the inability of the highest and lowest point on the scale to accurately capture extreme variations in the construct of interest. Similarly, coarseness refers to the loss of information when one-point scale variations do not accurately capture within-range variation in the construct of interest (Seiders et al, 2005). However, range-restricted and coarse scales may capture direct linear relationships with other constructs, especially if the two measure share common method variance and response bias (Bolton, 1998). Consequently, there will be a total of 12 questions in the survey which will be used to assess the different shopper characteristics. In this it is assumed that the customers of Kong Fui have the same three different characteristics as the customers investigated by Mägi (2003).

Antecedents of customer loyalty




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Shopping motivationDue to the unsuitability of the items used in prior research such as Arnold and Reynolds (2003), the items used to assess shopping motivation are based on intuitive in this study. The items used in this regard are I enjoy shopping for groceries, I feel thrilled to shop for groceries, I feel freed from domestic chores when shopping for groceries and I go grocery shopping exclusively to get the groceries I need. For all these four items a 9-point scale of “completely disagree” to “completely agree” will be used. The reason for the adoption of the 9-point scale is the same as the one stated above and cited from Seiders et al. (2005).

Customer SatisfactionThe antecedents of customer satisfaction are to be assessed by an adapted version of the items as used by Gómez et al. (2004). Gómez et al. (2004) expressed changes in customer satisfaction as a function of a vector of changes in the three factors (customer service, quality and value). The adaptations include accuracy of scanning process at checkout,cleanliness of parking lot, prices of loyalty card specials, availability of loyalty card specials and variety of advertised loyalty card items. Both accuracy of scanning process at checkoutand cleanliness of parking lot will be replaced in the survey. The decision to replace these two items was made due to the fact that they are the two items with the smallest factor loadings towards the customer service construct anyway. However, instead of these two items the items service provided by the fresh meat department personnel and service provided by the cosmetics’ department personnel will be added. The addition of these two items is based on the implicit purpose of this study to assess whether there is some dissatisfaction with the service provided where there is direct contact between the customers and the personnel in the supermarket. The other three items will be taken out of the survey, since they are unsuitable for the supermarket-loyalty card combination under investigation. Conversely, merchandise quality and merchandise assortment were added to the set. It is assumed that the 18 items which will be utilized to assess the antecedents of customer satisfaction would be reduced to the same three factors as was the case in the study of Gómez et al. (2004). However, the 6-point scale that was used there is adapted here to a 7-point scale in order to have an average option and in order to prevent loss of information as discussed previously. Furthermore, the wording of the scales is slightly adapted from ‘very poor to excellent’ into ‘very poor to very good’.

Conversely, the measures for customer satisfaction as such have been adopted from Mägi (2003). For the items “How well does your primary grocery store match your expectations?” and “Imagine a perfect grocery store. How close to this ideal is your primary grocery store?” the same 10-point scale was maintained. However, for the item “How satisfied are you with your primary grocery store?” the scale was reduced to a 5-point scale. The reason for this reduction was to give the customer a concrete label with each of the scaling-points. This is assumed to be more comfortable for them, when answering the question.

Loyalty program adoption The respondents will be asked whether they hold a membership card of one of the grocery stores on Aruba having loyalty programs. Whether the respondents have the loyalty card of Kong Fui or not in their household is used as the item corresponding to the card ownership construct. And whether the respondents have the loyalty card of competitors’




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programs or not will be utilized as the assessment of the competitive loyalty programs’ construct.

Customer characteristicsFor the household characteristics the demographic characteristics as studied by Bhatnagar and Ratchford (2004) will be utilized plus some additional characteristics. Apart from the household annual income as used in the referred study, the items will be copied without further discussion. However, for this item another categorization would be used, since a currency other than the US$ is used on Aruba. Regarding the additional items, the items that will be added are income frequency during a month, gender of the head of household, employment status of the head of household and number of employees in the household having an income. The addition of the gender and employment status of the head of household was based on the findings of Blattberg et al. (1978). The number of employees in the household which are receiving income is incorporated to assess the likelihood of the household to have best-offer search behavior. The income frequency during a month item is incorporated to investigate whether there is some difference in loyalty based on differences in this frequency.

Acculturation preferenceAs far as the literature review goes, no direct measures could be found for the acculturation preference item. And based on its presumed importance in the grocery retail industry on Aruba, it has been considered important to include it anyhow. Therefore, a measure has been intuitively created to measure it. The measure consists of three statements regarding acculturation by the staff of the grocery store and the scale used is a 9-point scale, ranging from ‘completely agree’ to ‘completely disagree’.

Customer loyaltyAlthough Lin and Wang (2006) provide items that are presumed to be good to assess customer loyalty, it is assumed that their items would not be suitable in this study, due to the presumed multi-store loyalty. The items used by Lin and Wang are based on a single website loyalty. Therefore, it is chosen to utilize the items as investigated in the studies ofSelin et al. (1988) and Muncy (1983) for assessing loyal attitude. The items used in both these studies will be adapted to incorporate them into the questionnaire of this study. This adaptation is merely the switch of XYZ airline into Kong Fui. Furthermore, Day’s (1969; 30) equation will be used as the basis for this study’s investigation of loyalty. Respectively, it is tried to assess both behavioral and attitudinal loyalty, since Dick and Basu (1994) suggested that true costumer loyalty requires the assessment of both behavioral- and attitudinal loyalty. Another measure of loyalty to be used is word-of-mouth. The items used to measure word-of-mouth are the items used by Baumann, Burton and Elliott(2005). They studied the determinants of customer loyalty and share of wallet in retail banking in Australia. In that study share of wallet was assessed by intentions to recommend (i.e. word-of-mouth), as well as self-stated short-and long-term intentions to remain a customer of the bank. The items measuring word-of-mouth will be incorporated in this survey with minor changes to them. However, it should be noted that the scale used in the study of Baumann, Burton and Elliott (2005) is not stated in the report. Therefore,

Customer loyalty




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in this study a suitable scale will be utilized independent of the scale of Baumann, Burton and Elliott (2005). Furthermore, Reichheld (2003) looked for a single question, which assesses consumers’ loyalty to a company. Thereby he did a research linking survey responses with actual consumer behavior and ultimately with company growth. This study provided the author with that single survey question useful to predict growth. That particular question came out to be about customers’ willingness to recommend a service or product to someone else. There appeared to be a direct correlation between customers’ enthusiasm to refer a friend or colleague and the differences in growth rates among competitors in most of the investigated industries. Therefore, the customers’ willingness to recommend Kong Fui to someone else would be incorporated in this research as well.

Loyalty program enjoymentThe measures for the construct assessing enjoyment of the loyalty program are investigated with the items as used in the study of Leenheer (2004). These items will be incorporated into the survey related to this study without further discussion.

Idiosyncratic fitThe idiosyncrasy construct will be assessed by a single item. This item is based on the relevance element (O’Brien and Jones, 1995). In this regards, the customer will be asked to rate the statement ‘I consider the extent to which rewards are achievable with the loyalty program of Kong Fui is very important’. The rating uses a 7-point scale with 1= completely disagree to 7= completely agree.

Loyalty program designLoyalty program design will be based on the modified framework of reward schemes of Yi and Jeon (2003). It will be tried to evaluate the preference of customers with regard to the timing of the reward and the type of the reward. These preferences will be translated into the items “I prefer a direct reward for my participation in a loyalty program” and “I prefer a delayed reward for my participation in a loyalty program”. Additionally, consumers’ thoughts about membership fees for loyalty programs will be assessed. This will be measured by the item “I do not like to pay fees to participate in a loyalty program”. This item was incorporated due to the existence of a loyalty program on Aruba, where the consumers are obliged to pay a fee to participate in the program. Subsequently, it was chosen to use a 5-point scale ranging from ‘strongly disagree’ to ‘strongly agree’ to measure the construct.

Customer characteristicsThe measures concerning the customer characteristics’ construct have been discussed above already.

Privacy concerns

Just as the measure for loyalty program adoption, the measure for privacy concerns is adopted from Leenheer (2004).

Antecedents of loyalty program adoption




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3.1.2 Questionnaire

The compilation of the measures as discussion previously leads to the construction of the questionnaire7 to be used in this study. The questionnaire consists of several questions concerning both the antecedents of customer loyalty and the antecedents of loyalty program adoption. In appendix 18 (tables A-1 through A-3) an overview is given of the questions that were used in the questionnaire including the constructs that these questions are supposed to measure. Some questions form multi-item constructs. Section B encompasses the questions related to household characteristics9. This section includes questions covering the number of household members, frequency of income-inflow in the household, gender of the head of the household, employment status of head of household, street name and number of household members receiving some sort of income. Finally, the general socio-demographic questions9 are listed in section C. This section includes questions related to the gender, age category, educational level, marital status and household’s monthly-income category of the respondents. It is important to present these specific questions thoroughly, due to the reason that the answers to these questions are used to investigate possible similarities or differences between different groups under the respondents. These variables will therefore be mainly important for the chi-squared tests. All questions of section A except questions 1, 2, 5, 6 and 7 were ordinal variables and had to be answered with a Likert-scale. These Likert-scales varied from 5-point, 7-point and 9-point scales. Both questions 5 and 6 had a 10-point scale. Finally, questions 1, 2 and 7 were nominal variables. Concerning section B of the survey, both questions 1 and 5 were ratio variables. Questions 2 through 4 were nominal variables as were all the questions of section C.

3 . 2 S a m p l e a n d d a t a c o l l e c t i o n

This section covers the sample and data collection method as planned prior to the investigation. Regarding the population of the study, it consisted of both male and female supermarket shoppers (18 and above) on Aruba, specifically at Kong Fui Supermarket. The potential respondents were approached when visiting Kong Fui for their grocery purchases at the entrance of the supermarket.

The model for modeling customer loyalty depends on three aspects, namely:

7 This questionnaire is displayed in the appendices (appendix 2 in English and appendix 3 in Papiamento).8 Note that the tables whose numbers start with an ‘A’ are depicted in appendix 19 Note that in this study both household characteristics and socio-demographic characteristics are represented

by the term ‘customer characteristics’.

Type of loyalty measure: here self-reported behavior is used. Available data: due to the magnitude of the databank of this company, the

data were not readily available to be analyzed. Longitudinal nature of data: this study has a static nature.




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Data will be collected through a mall intercept survey since that method had been used by previous research (see for instance, Reynolds et al. 2002; Babin and Darden, 1996). During the survey no distinction will be made between members and non-members. Consequently, both groups of shoppers will be approached to collaborate with the study. It will be only in the analysis that the members and non-members will be separated to see whether there are differences between these two groups of customers, thereby assessing the influence of card ownership. Regarding the data collection period, it will cover one weekend and some week-days of the subsequent week. To be specific, with weekends it is referred to both Friday and Saturday both before and after the afternoon (opening hours) and on Sunday only before the afternoon due to opening hours’ restrictions. Thereby the chance of getting consumers with different shopping frequencies to participate in the survey is expected to be greater. This selection was made based on the flow of shoppers during the weekends at the supermarket. Specifically, the weekend will be the last weekend of the month, since during that weekend a larger flow of consumers is expected. Regarding these different shopping frequencies a distinction will be made between monthly, bi-monthly and weekly shoppers. This distinction is thought to be important for the analysis as well, since there is expected to be some difference between the degree of store patronage between especially monthly shoppers and weekly shoppers. Since the weekly shoppers are supposed to buy less during each visit, it is assumed that their degree of store patronage would be significantly different from at least the monthly shoppers.Furthermore, week-days were chosen as well, since there are possibly difference in purchase behavior and/or loyalty between customers visiting the supermarket during week-days and those during weekends. The sample will be performed at the end of June 2006. The specific dates of the sample were originally set to be from Saturday to Thursday of the next week, at the beginning of July 2006. Thereby, responses could be compiled of customers shopping both in the weekend and those shopping during the week-days. The daytime on which the sample was compiled was differentiated between the days as well, to minimize possible biases, caused by the part of day and day on which the grocery shopping takes place, in the data possible.For the coverage of the representativity of the sample refer to the following chapter.

3 . 3 A n a l y t i c a l m e t h o d

Different analytical methods will be applied in order to come up to an answer to the research question and the last two sub-questions, and test the hypotheses. Prior to the application of these different analytical techniques, the general results of the survey will be described. Thereafter, a factor analysis (including reliability tests) has to be performed to reduce the number of items, due to the multi-item character of the questionnaire for some of the constructs. Factor analysis provides the tools for analyzing the structure of the interrelationships (correlations) among a large number of variables, in this case questionnaire responses, by defining sets of variables that are highly interrelated (Hair et al., 2006). These sets of highly interrelated variables are then specified as factors. They are assumed to represent dimensions within the data. The concern here is to reduce the number of variables. Thereby, the dimensions can guide in creating new composite measures. These composite




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measures represent the constructs in the conceptual model, which were assessed by multiple items in the survey. Prior to performing the factor analyses, some important decisions had been made regarding missing data. In this regard, two questions concerning missing data, to address any problems it may create (Hair et al., 2006), were answered:

1. Is the missing data sufficient and non-random so as to cause problems in estimation or interpretation?

2. If missing data must be remedied, what is the best approach?

Most notably, missing data must always be addressed if the missing data are in a non-random pattern or more than 10 percent of the data items are missing (Hair et al, 2006). There are three basic methods available for solving the missing data problem: the complete case approach (known as listwise deletion), the all-variable approach (known as pairwise deletion) and model-based imputation techniques. Although listwise deletion has been considered most appropriate for structural equation modeling (SEM) traditionally, pairwise deletion has been applied more recently. Pairwise deletion allows the use of more data. However, both of these procedures can produce problems (Allison 2003 in Hair et al, 2006). Still when the missing data are random, less than 10 percent of observations, and the factor loadings are relatively high (.7 or greater), any of these approaches can be expected to provide good results (Enders and Bandalos, 2001). The pairwise approach appears to be a good solution for the missing data problem when the sample sizes exceed 250 and the total amount of missing data involved among the measured variables is below 10 percent. Conversely, the model-based approaches become superior when samples are small and when the amount of missing data becomes large. However, conclusions drawn from any sample that contains large amounts of missing data should be handled cautiously (Hair et al, 2006). Therefore and due to the relatively small differences between the results of these three different approaches regarding the influence of the missing data in this sample, it was chosen to follow the model-based approach. In this regard, the approach of replacing the missing data with the mean of these variables was selected since this was the other approach available in SPSS, besides the pairwise and the listwise approaches. As mentioned previously, factor analyses were performed on the multi-item questions to reduce the number of variables. However, prior to assessing the factors, it has to be checked whether it is favorable to perform a factor analysis on these items. This check will be performed by the KMO-test and Bartlett's Test of Sphericity. After checking whether the application of factor analysis on these items is supported, a decision has been made regarding the number of factors to be retained. According to Hair et al. (2006), this decision can be based on different considerations. However, in this study it was chosen to base the number of factors to be retained on two considerations out of the list of Hair et al. (2006). The maintained considerations are:

1. factors with eigenvalues greater than 1.0, and2. enough factors to meet a minimum of 60% of variance explained

After coming up to the factors and labeling them, a reliability test is performed to test the internal consistency of the items belonging to each separate factor. Hereby the lower limit for Cronbach’s Alpha being .60 was kept, as suggested by Hair et al. (2006).




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Thereafter, the application of the main tests could start. First, the influence of the different customer characteristics’ variables on customer loyalty and the adoption of loyalty programs will be studied. The influences of the other antecedents studied – besides the customer characteristics’ measures – were assessed in similar ways. However, due to the similarity these results – as depicted in appendix 1 – will not be explicitly discussed in this report10. To assess each of these influences a distinction is made between two independent samples tests and more independent samples tests, depending on the customer characteristic under consideration. Besides this division into two classes there is a division into three types of tests depending on the type of dependent variable investigated. Figure 3.1 illustrates the different types of the dependent variables that are investigated in these tests.

Figure 3.1: Overview of studied influence of customer characteristics

Two independent samples tests11

Regarding the two independent samples tests a distinction is made between Chi-square test, the Mann-Whitney test and the Student t-test. It depends on the dependent variables which of these three tests is the appropriate one. The Chi-square test is used with nominal dependent variables, whereas the Mann-Whitney test is used when dealing with ordinal dependent variables. Finally, the student’s t-test is used when dealing with interval/ratio scaled variables (De Vocht, 2000). Since this study deals with each one of these types of variables, as can be seen in figure 3.1, each of these three different tests would have to be performed.

10 For the results of these tests refer to appendix 1 – tables A-22 through A-2511 For further details about these techniques refer to De Vocht, 2000, Hair et al., 2006 or Baarda and De Goede,

2001

Adoption of the loyalty program

Adoption of competitive loyalty programs

Customer Characteristics

Nominal Nominal

Behavioral loyalty

Ordinal

Attitudinal loyalty&

Word of mouth

Interval/Ratio




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Chi-square testChi-square test is a method of analyzing data in a contingency table by comparing the actual cell frequency to an expected cell frequency. It is a standardized measure of actual cell frequencies compared to expected cell frequencies (De Vocht, 2000). In this study, the Chi-square test will be used to study the influence of the binary customer characteristics in this study on both the adoption of competitive loyalty programs and the adoption of the loyalty program, since these are two nominal variables (see figure 3.1). Before performing each of the Chi-square tests in this research it was checked whether two requirements are met regarding the expected cell frequencies. These requirements are:

1. All expected cell frequencies should be equal to or greater than 1, and2. Maximum 20 % of the expected cell frequencies could lie between 1 and 5

Mann-Whitney U testThe Mann-Whitney U test is a non-parametric test that can be performed when not all the requirements of a Student t-test are met (De Vocht, 2000). Instead of these requirements the Mann-Whitney U test requires only an ordinal measuring scale. The null-hypotheses, that two samples originate from identical populations – thus that the distributions are identical – are tested by the Mann-Whitney test. Here, the Mann-Whitney U test will be used to uncover the influence of the binary customer characteristics in this study on behavioral loyalty, since behavioral loyalty was measured by an ordinal variable.

Student t-testThe t-test for two independent samples tests whether the means of an interval- or ratio variable in two groups (populations) are the same. The t-test includes the Levene’s test for Equality of variances. The null-hypothesis of the Levene’s test states that the variances of both populations are the same (12=22). If the null-hypothesis is not rejected the Equal variances assumed should be used, otherwise the Equal variances not assumed (De Vocht, 2000). Here this test will be used to discover the influence of the binary customer characteristics in this study on word-of-mouth and attitudinal loyalty, since these are interval/ratio variables.

More independent samples tests11

When dealing with more independent samples tests a distinction could be made between Chi-square test, Kruskal-Wallis test and the analysis of variances (ANOVA). It depends on the dependent variables which of these three tests is most appropriate. The Chi-square test is used with nominal dependent variables, whereas the Kruskal-Wallis test is used when dealing with ordinal dependent variables. Finally, ANOVA is used when dealing with interval/ratio scaled variables (De Vocht, 2000). As was the case with the two independent samples tests the character of the variables – as illustrated in figure 3.1 –make the utility of each of these three tests necessary. Since the technique essence of Chi-square tests was extensively covered earlier, it will not be discussed here.




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Kruskal-Wallis testAs the Mann-Whitney U test, the Kruskal-Wallis test is a non-parametric variance analysis. Hereby it is solely required that the dependent variable is measured on an ordinal scale. With this test, the null-hypothesis that the samples originate from identical populations – thus the distributions are equal – is tested for more than two samples (groups). Here the Kruskal-Wallis test will be used to assess the influence of different customer characteristics on behavioral loyalty, since behavioral loyalty is an ordinal variable.

Analysis of variance Variance analysis (ANOVA) is a technique to test the equality of the means of different interval- or ratio-scales. ANOVA is used to determine the probability that differences in means across several groups are due solely to sampling error. Because the mean squares between groups (MSB) are inflated by differences between the groups, large values of the F-statistic lead to rejection of the null-hypothesis (

0H ), which states that there is no

difference in means across groups (Hair et al, 2006 and Baarda and De Goede, 2001). The groups are differentiated by an independent nominal (or ordinal) variable that is named the factor-variable. Accordingly, the F-statistic does not address the question of which means are different, even though it assesses the null-hypothesis of equal means (Hair et al., 2006). To assess these differences Hair et al (2006) suggest the use of either planned comparisons or post hoc tests. However, this investigation is limited to the assessment of equal means.The null-hypothesis in variance analysis is always that the population means of all (k) groups are the same; 1 = 2 = 3 = …. k. Variance analysis is based on the variation in the sample data. Variation refers to the squared deviation of all observations from the mean (Sum of Squares). The variance is obtained by dividing the variation by the degrees of freedom (De Vocht, 2000).In variance analysis the total variance is split into two components, namely the within groups variance and the between groups variance. The testing is based on the F-test. The F-value is calculated by dividing the between groups variance by the within groups variance. When there is a great amount of between groups variance, the majority of thespread would be caused by differences between the groups; then the F-value is (clearly) greater than 1.The null-hypothesis – that the population means of all groups are equal – is rejected or not through the assessment of the significance level ( .Sig ). Hereby, the significance value has to be greater than 0.05 in order not to reject the null-hypothesis. Consequently, if the significance is smaller than the required 0.05, the null-hypothesis can be rejected with a reliability of 95% ( 05.0. Sig ).

Variables’ transformation In order to take the customer characteristics’ variables into the multiple regressions, some had to be transformed into variables with only two options, where after they had to be recoded into dummy variables. The transformed variables12 include:

12 Note that the list here will include the abbreviations for the variables as listed in table A-3 in the appendix 1.




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Additionally, the variables gender and head of HH were only recoded. The binary coding of these variables was changed from a 1-2 code into a 0-1 code as necessary for a dummy variable (De Vocht, 2000).

Income frequencyPrior to transforming the variable “income frequency” an analysis of variance was performed to see whether frequency of income inflow has some effect on attitudinal loyalty and/or word-of-mouth. The results of these analyses illustrated that there is no significant difference between the different frequencies of income inflow. Therefore, the choice has been made arbitrary to make a distinction between weekly inflows on the one hand and both bi-weekly and monthly inflows on the other hand.

EducationRegarding educational level, the two options encompassing an educational level below college degree have been reduced to one group and the three options above high school have been combined into one group. This distinction was also based on the assumption that people with an educational level above high school would have considerably more disposable income than those with a maximum of high school education.

Marital statusThe three studied categories of marital status were reduced to two categories based on the assumption that singles would have a different degree of loyalty compared to those people who either have a partner or are married. Thereby the responses of this variable were divided into those of singles and those of the people who are not singles.

AgeIn order to transform the age category into a dummy variable an analysis of variance was performed, thereby assessing the relation between the age categories and the level of customer satisfaction. The results of that test are displayed in table A-5 in appendix 1. Due to the low frequencies for some age categories the decision was made to maintain a lower confidence level in the analysis of variance. With 91% confidence it can be concluded that the population means of the age categories do differ – based on the significance level ( .Sig ). Therefore, the variable was arbitrarily split into two groups. The binary groups which will be held for age category are ‘under 25 years’ and ’25-35 years’ forming one group and the other three categories forming the other group.

EmploymentRegarding the employment status of the head of the household, again an analysis of variance was performed, thereby addressing the relation between employment status and the level of customer satisfaction. The results are depicted in table A-5. The population

Income frequency Education Marital status Age Employment Gross income




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means of the employment status of head of household appear to differ at the 99% confidence level. Consequently, the variable was arbitrarily split into two groups, where the households with an unemployed head of household and those where the head is retired were grouped into one group. Thereby the assumption maintained was that both these groups would have less disposable income than in situations where the head of the household is employed.

Gross incomeFinally, the four studied categories of monthly-income were also reduced to two. In this reduction those with a monthly-income below modal were kept apart and all the other categories were grouped together.

Additionally, adoption of loyalty programs and behavioral loyalty – both ordinal scales –were transformed into dichotomous variables, thereby making the performance of a logistic regression analysis possible. These transformations were necessary in order to be able to assess the effect of the antecedents of loyalty on behavioral loyalty – as measured by repeat purchases – and the effect of the antecedents of loyalty program adoption on the adoption of the loyalty program of Kong Fui.

Similarly, each of the other antecedents of customer loyalty and loyalty program adoption were recoded into binary variables – based on their medians – in order to access their non-linear influence on either customer loyalty or loyalty program adoption respectively.

Regression analysisThe analysis concludes with the application of different regression analyses to evaluate the effects of the antecedents of customer loyalty and those of loyalty program adoption. Hereby, a distinction was made between dichotomous dependent variables and interval/ratio-scaled dependent variables. This distinction made the application of both linear regression analysis and logistic regression analysis necessary. Furthermore, the effect of these antecedents will be assessed through single regression analysis as well as multiple regressions. Consequently, the effect of each antecedent will be assessed independently13 (single regression analysis) as well as when all the potential predictors are combined in the multiple regression, whereby the influence of all the other independent variables are controlled for (see De Vocht, 2000).

Linear regression analysisThe statistical test of the regression coefficients and the constant is to ensure the estimated parameters would be different from zero within a specified level of acceptable error. Although not a test of validity, this test determines whether the impacts represented by the coefficients are generalizable to other samples from the customers of Kong Fui –being the population under investigation. However, it should not be forgotten that the coefficients can vary quite remarkably from sample to sample. It is this variation that leads to the need to validate any regression analysis on a different sample (s). But that validation lies outside the scope of this particular research.

In interpreting the regression variates attention was given to the assessment of multicollinearity, which is labeled as a key issue by Hair et al. (2006). Multicollinearity




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refers to the correlation among the independent variables. Although the ideal situation would be to have a number of independent variables highly correlated with the dependent variable and with little correlation among themselves, this is hardly ever the case. In most situations, particularly when dealing with consumer response data, some degree of multicollinearity is unavoidable. The simplest and most obvious way to identify collinearity is by examining the correlation matrix for the independent variables. Thereby, the .Sig (2-tailed) value would be used to determine whether the different correlations are significant. The null-hypotheses thereby are that the correlation coefficients are zero (or close enough to be taken as zero). These null-hypotheses will be rejected at the 5% level if the significance values are less than 0.05. The presence of high correlations (generally .90 and higher) is the first indication of substantial collinearity (Hair et al. 2006). De Vocht (2000) suggests leaving one of any couple of variables that have a correlation of 9.0|| r out from the model. A measure expressing the degree to which each independent variable is explained by the set of other independent variables is needed in order to assess multicollinearity. Note hereby that multicollinearity is the collinearity that may be due to the combined effect of two or more other independent variables. Tolerance and its inverse – being the variance inflation factor (VIF) – are the two most common measures used for assessing both pairwise and multiple-variable collinearity. Tolerance is a direct measure of multicollinearity. It is defined as the amount of variability of the selected independent variable not explained by the other independent variables. The tolerance value should be high, which means a small degree of multicollinearity, i.e. the other independent variables do not collectively have any substantial amount of shared variance. A common cutoff threshold is a tolerance value of .10 (Hair et al. 2006). VIF, on the other hand, is calculated simply as the inverse of the tolerance value. Consequently, instances of higher degrees of multicollinearity are reflected in lower tolerance values and higher VIF values. The VIF translates the tolerance value into an impact on the estimation process. As the standard error is increased, it makes the confidence intervals around the estimated coefficients larger, thus making it harder to demonstrate that the coefficient is significantly different from zero (Hair et al. 2006). Generally, the tasks which should be performed in this regard include:

However, in this investigation the task is limited to the assessment of the degree of multicollinearity. The determination of the impact of multicollinearity on the results and the application of the necessary remedies are both suggested to be incorporated in further research.

Logistic regression analysisAccording to Hair et al. (2006), logistic regression tests hypotheses about individual coefficients just as it is done in multiple regression analysis. Hereby, a statistical test is used to see whether the logistic coefficient is different from 0. However, it should not be

assessment of the degree of multicollinearity, determination of its impact on the results, and application of the necessary remedies if needed

(Hair et al. 2006)




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forgotten that in this case a value of 0 corresponds to the odds of 1.00 or a probability of .50. This .50 indicates that the probability is equal for each group (i.e. again no effect of the independent variable on predicting group membership). Furthermore, here the Wald statistic is used to assess the significance of the different coefficients individually. This statistic provides the statistical significance for each estimated coefficient so that hypothesis testing can occur just as in multiple regression analysis. Consequently, a statistically significant logistic coefficient can be interpreted in terms of how it impacts the estimated probability and thus the prediction of group membership (Hair et al., 2006).Concerning model estimation fit, the basic measure of how well the maximum likelihood estimation procedure fits is the likelihood value ( 2LL- value). A perfect fit correspondents to a 2LL- value equal to 0. Consequently, a lower 2LL- value corresponds to a better model fit. The chi-square test and the associated test for statistical significance are used to evaluate the reduction in the log likelihood value (for further details refer to Hair et al., 2006). The measure that will be used to assess overall model fit is the Nagelkerke 2R , since the usage of the Cox and Snell 2R is excluded, due to its limitation – that it can not reach the maximum value of 1. Conversely, the Nagelkerke 2R has a range of 0 to 1. Still, both of these measures reflect the amount of variation accounted for by the logistic model, whereby 1.0 indicates a perfect model fit (Hair et al., 2006).




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CHAPTER 4: RESEARCH RESULTS

As already stated in the previous chapter, this chapter covers the results of the consumer survey. The chapter starts with the assessment of the representativity of the sample. Next, it covers the general findings of the survey. And these general findings will give way to the more specific analysis and results.

4 . 1 R e p r e s e n t a t i v i t y o f s a m p l e

The target group of Kong Fui supermarket consists of consumers of all ages. However, here the decision was made to concentrate on consumers of 20 years of age and above. This decision was made, since it is assumed in this study that Arubans younger than 20 years generally are still living with their parents and are therefore not independent grocery shoppers. Thereby, as can be seen in table 4.1, there is only a small proportion of respondents in the age category below 25 years as is also the case with the age category of 55 years and above. This is quite understandable, since a substantial proportion of the people below 25 years are either still living with their parents or studying abroad. On the other hand, the people of 55 years or older are possibly helped by their family members with the purchase of groceries.

Table 4.1: Sample proportions based on age category

Age categories Sample proportionsUnder 25 years 1,3 %25 to 34 years 21,6 %35 to 44 years 46,4 %45 to 54 years 25,5 %55 years and above 5,2 %

Furthermore, it is assumed that the big majority of these consumers would be females, either shopping on their own or accompanied by their husbands or partners. Therefore, it is not surprising that almost 87 per cent of the sample was females (see table 4.2).

Table 4.2: Sample proportions based on gender

Gender Sample proportionsFemale 86,9 %Male 13,1 %

Finally, table 4.3 depicts that the single-member households and households with more than 5 members in this sample are remarkably fewer than the other sizes of households. But a similar distribution appeared to exist under the customers of Kong Fui. The single households and those with over five members are relatively underrepresented in the customer base.

However, an analysis of variance for the different household sizes against the measures of customer satisfaction towards Kong Fui shows that there are no significant differences based on household size. Therefore, and based on both age categories and gender it can be concluded that this sample is a good representation of the population, being the public shopping for groceries at Kong Fui.




- 39 -

Table 4.3: Sample proportions based on number of household members

Number of household members Sample proportions1 6,5 %2 24,2 %3 24,8 %4 24,8 %5 14,4 %6 4,6 %9 0,7 %

4 . 2 G e n e r a l f i n d i n g s

This paragraph shows the general findings of the empirical research. Thereby, some basic descriptive statistics14 are provided as a first illustration of the data that will be analyzed thereafter.

4.2.1 Basic descriptive statistics for customer loyalty predictors

Shopper characteristicsTable 4.4 shows the distribution of the importance of each item measuring shopper characteristics. It is illustrated that the majority of the respondents (72.5%) completely agree with the statement “I choose to shop at the grocery store that has the best deals at the time”. Subsequently, a total of 79.8% of the respondents agree with the statement in some degree. Conversely, only 8.0% (5.3% + .7% + 1.3% + .7%) of the respondents appear to be against this statement.Similarly, it is depicted that a vast 63.6% of the respondents completely agree with the statement “I compare what I get for my money in different stores”. Furthermore, a total of 77.5% of the respondents do agree with the statement in some degree. On the contrary, only 13.9% appear to be in disagreement with the statement. Regarding the statement “You profit from comparing prices across stores”, the percentage of respondents, who completely agree with it is relatively lower, as compared with the two previous statements. However, still a majority of 51.0% stated to be in complete agreement with this statement. When all the levels of agreement are grouped, this adds up to a great 66.2% of the respondents. On the other hand, a summation of the levels of disagreement with this statement, adds up to 17.2% of the respondents. However, what is presumed worth mentioning is that this statement has a greater percentage of respondents that are completely in disagreement with it (13.9%). There are 66.7% of respondents that completely agree with the statement “I choose what store to go to on the basis of where I find what I need for the best prices”. Conversely, there is a total of 10.6% of the respondents that have some degree of disagreement with the statement. Still, this percentage is irrelevant as compared with the great 81.8% of respondents, who support the statement.

14 Note that the responses to most of the questions are depicted in the corresponding tables in more details per

category. However, to keep the coverage short the necessary groupings of categories were done in the text




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A vast majority of the respondents (65.8%) completely agree with the statement “I think personal contact with store personnel is important”. Subsequently, an extremely high total of 90.8% of the respondents agree with the statement in some degree. Conversely, a mere 2.7% of the respondents appear to be against this statement.It is also shown that a great 52.0% of the respondents completely agree with the statement “I think it is important to be recognized by the store's personnel”. In addition, an absolute majority totaling 84.8% of the respondents do agree with the statement in some degree. On the other hand, only 4.0% appear to be in disagreement with the statement.

Table 4.4: Descriptive statistics of shopper characteristics items

Shopper characteristicsC

ompl

etel

y di

sagr

ee

Dis

agre

e --

-

Dis

agre

e --

Dis

agre

e -

Nei

ther

dis

agre

e, n

or

agre

e

Agr

ee +

Agr

ee +

+

Agr

ee +

++

Com

plet

ely

agre

e

N (t

otal

res

pon

ses)

I choose to shop at the grocery store that has the best deals at the time

5.3 .7 1.3 .7 11.8 2.0 2.0 3.3 72.5 152

I compare what I get for my money in different stores

9.9 1.3 .7 2.0 8.6 2.6 4.0 7.3 63.6 151

You profit from comparing prices across stores

13.9 1.3 .7 1.3 16.6 2.6 6.0 6.6 51.0 151

I choose what store to go to on the basis of where I find what I need for the best prices

8.5 .7 .7 .7 7.8 2.0 4.6 8.5 66.7 153

I think personal contact with store personnel is important

2.0 - - .7 6.6 2.6 9.2 13.2 65.8 152

I think it is important to be recognized by the store's personnel

2.6 - .7 .7 11.2 2.6 13.8 16.4 52.0 152

I only shop in stores where I know the staff is friendly

.7 - - .7 9.9 2.0 5.3 11.8 69.7 152

I think it is important there are staff members to talk to in the store in which I shop

2.6 - 1.3 1.3 8.5 .7 6.5 19.0 60.1 153

I want to spend as little effort as possible on grocery shopping

5.3 1.3 - 1.3 9.9 1.3 4.6 17.9 58.3 151

I think grocery shopping is a necessary evil

23.0 2.0 - .7 10.8 .7 2.7 8.1 52.0 148

I enjoy shopping for groceries 5.3 3.3 1.3 .7 6.0 1.3 4.7 12.7 64.7 150I spend as little time as possible on grocery shopping

6.8 2.0 .7 1.4 14.3 3.4 4.1 12.2 55.1 147




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Regarding the statement “I only shop in stores where I know the staff is friendly”, as much as 69.7% of the respondents are in complete agreement with it. Summing all the levels of agreement up, adds to a grand total of 88.8% of the respondents agreeing with this statement. Conversely, a summation of the levels of disagreement with this statement, adds up to an almost irrelevant 1.4% of the respondents. There are 60.1% of respondents that completely agree with the statement “I think it is important there are staff members to talk to in the store in which I shop”. Conversely, there is a total of 5.2% of the respondents that have some degree of disagreement with the statement. However, this percentage is irrelevant as compared with the great 86.3% of respondents who said to be in agreement with the statement. As much as 58.3% of the respondents completely agree with the statement “I want to spend as little effort as possible on grocery shopping”. Subsequently, a total of 82.1% of the respondents agree with the statement in some degree. Conversely, a relatively small 7.9% of the respondents appear to have a differing opinion.Then, it can be noticed that a great 52.0% of the respondents completely agree with the statement “I think grocery shopping is a necessary evil”. Additionally, 11.5% of the respondents have some degree of agreement with the statement adding up to a total of 63.5% of the respondents, who are supportive of this statement. On the contrary, a slight high 25.7% of the respondents appear to be in disagreement with the statement. Regarding the statement “I enjoy shopping for groceries”, there is a great majority of 64.7% of the respondents, who are in complete agreement with this statement. The addition of the other levels of agreement adds up to a total of 83.4% of the respondents supporting this statement. Still, there are 10.6% of the respondents, who have a different opinion in this regard. Finally, there are 55.1% of the respondents that completely agree with the statement “I spend as little time as possible on grocery shopping”. Conversely, there is a total of 10.9% of the respondents that have some degree of disagreement with the statement. However, this percentage is irrelevant as compared with the 74.8% of the respondents, who agree with the statement.

As is illustrated implicitly in table 4.4 and explicitly in table A-6, the items related to the personalizing shopping motivation of Mägi (2003) have the strongest negatively skewed distributions. Each of the four items belonging to that shopping motivation has more than 84 percent of the respondents rating them as important shopping motivators; rates 6 or higher. Of the items belonging to the other two shopping motives, the same can not be said. These motives have some items that give the impression to be less important as motivators for the respondents. The respondents seem to consider the personalizing shopping motivation-items relatively more important than the items belonging to the other two shopping motivations of Mägi (2003). This impression is supported by the fact that three of the items related to the personal interaction between the personnel and the shopper have the top three means.

Shopping motivationA vast 66.2% of the respondents completely agree to enjoy shopping for groceries (see table 4.5). Further analysis shows that an even greater 78.1% of the respondents appear to enjoy shopping for groceries. On the contrary, a relatively small proportion equaling 13.9% of the respondents do not enjoy shopping for groceries.




- 42 -

Furthermore, 67.3% and 33.6% of the respondents completely agree that they feel thrilled to shop for groceries and that they feel freed from domestic chores when shopping for groceries respectively. Further analysis illustrates that a bigger 80% of the respondents feel some degree of thrill to shopping for groceries. Additionally, it can be observed that 59.8% of the respondents feel that in some way they are freed from domestic chores when shopping for groceries. Still, this percentage is relatively small compared with the percentage of respondents either enjoying shopping for groceries or feeling thrilled to shop for groceries. But this low percentage appear to be the consequence of a relatively high 21.5% of the respondents having no clear opinion concerning the feeling of being freed from domestic chores when shopping for groceries. Conversely, the percentages of respondents not feeling thrilled to shop for groceries and not felling freed from domestic chores when shopping for groceries are respectively 14.0% and 18.8%.Finally, there are 78.1% of the respondents who go grocery shopping exclusively to get the groceries they need. These are the ones, who go grocery shopping out of a utilitarian motive. Of these a relatively big 56.3% are in complete agreement that they go grocery shopping for that exclusiveness. However, there are still 13.2% of the respondents who do not share this exclusiveness.

Table 4.5: Descriptive statistics related to the shopping motivation items

Fre

quen

cy

Per

cen

t

Fre

quen

cy

Per

cen

t

Fre

quen

cy

Per

cen

t

Fre

quen

cy

Per

cen

t

Completely disagree 11 7.3 11 7.3 17 11.4 12 7.9Disagree

---8 5.3 7 4.7 9 6.0 2 1.3

Disagree--

2 1.3 - - 1 .7 3 2.0

Disagree-

- - 3 2.0 1 .7 3 2.0

Neither disagree, nor agree

12 7.9 9 6.0 32 21.5 13 8.6

Agree+

3 2.0 3 2.0 9 6.0 7 4.6

Agree++

7 4.6 4 2.7 11 7.4 7 4.6

Agree+++

8 5.3 12 8.0 19 12.8 19 12.6

Completely agree

I en

joy

shop

ping

for

groc

erie

s

100 66.2

I fe

el t

hrill

ed t

o sh

op fo

r gr

ocer

ies

101 67.3 I fe

el fr

eed

from

dom

esti

c ch

ores

whe

n sh

oppi

ng fo

r gr

ocer

ies

50 33.6

I go

gro

cery

sho

ppin

g ex

clus

ivel

y to

get

the

gro

ceri

es I

nee

d

85 56.3

Customer SatisfactionConcerning customer satisfaction towards Kong Fui, there are a great 73.2% of the respondents, who are satisfied with the supermarket (see table 4.6). Besides, there are still 13.1% more that are very satisfied with the supermarket. However, there appears to be 13.1% of the respondents also that do not have a clear-cut opinion regarding their




- 43 -

satisfaction with Kong Fui. Still, it is important to note that a mere .7% of the respondents reported to be very dissatisfied with the supermarket.

Table 4.6: Descriptive statistics of the 5-point scale customer satisfaction measure

How satisfied are you with Kong Fui supermarket?

Frequency Percent

Very dissatisfied 1 .7Dissatisfied - -

Neither dissatisfied, nor satisfied 20 13.1Satisfied 112 73.2

Very satisfied 20 13.1

Table 4.7 illustrates that Kong Fui matches the expectations of the absolute majority of the respondents (92.7%). Conversely, the supermarket does not meet the expectations of only 7.2% of the respondents. However, further analysis shows that although the supermarket meets the expectations of the absolute majority of the respondents there is still some room for improvement. This can be concluded when taking a closer look at the scores. Only 5.2% of the respondents gave the supermarket the absolute score. Next, there were a relatively low 17.6% of them who gave the supermarket a 9. However, the responses were more concentrated on the scores of 8 (34.6%) and 7 (26.8%). Furthermore, the closeness of Kong Fui towards the ideal supermarket according to the respondents has a distribution quite similar to the ability of Kong Fui to match the expectations of the respondents. Still, the scores 7 and 8, 24.2% and 26.1% respectively are most frequently given by the respondents. Thus, although the great majority of the respondents (90.2%) picture Kong Fui as being close to their ideal supermarket, there is still room for improvement to get the supermarket there. Still it is remarkable that fewer respondents reported that Kong Fui is able to match their expectations completely than that Kong Fui is close to their ideal supermarket. This could be an indication that the respondents do not expect their ideal supermarket to match their expectations completely anyhow.

Table 4.7: Descriptive statistics of the 10-scale customer satisfaction items

How well does Kong Fui supermarket match your expectations?

How close to ideal is Kong Fui supermarket?

Score Frequency Percent Frequency PercentNot at all 1 3 2.0 1 .7 Not close at allDisagree ---- 2 - - - - Far ----Disagree --- 3 - - - - Far ---Disagree -- 4 2 1.3 2 1.3 Far --Disagree - 5 6 3.9 12 7.8 Far -Agree + 6 13 8.5 22 14.4 Close +Agree ++ 7 41 26.8 37 24.2 Close ++Agree +++ 8 53 34.6 40 26.1 Close +++Agree ++++ 9 27 17.6 28 18.3 Close ++++Completely 10 8 5.2 11 7.2 Very close




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Besides the overall measures of customer satisfaction, some items were assessed which could be influential on customer satisfaction. Table 4.8 gives an overview of the responses of these items which were measured. It is clear that the great majority (80.9%) of the respondents value the service provided by the personnel of the fresh meat department as satisfactory. However, most of that proportion is concentrated on the score 6. Therefore it can be concluded that there is still for improvement assessed from the customer perspective. Regarding the service in the cosmetics’ department 82.8% of the respondents do value it as satisfactory. Here the majority of the responses are almost equally distributed between the scores of 6 and 7. Therefore, it can be said that as compared to respondents’ valuation of the service provided by the personnel of the cosmetics’ department, the service in the fresh meat department is lacking. Service provided at the check-out point, however, has a clear concentration of respondents scoring it as very good. The majority of the respondents thus score perceive it as if there is little or no room for improvement. Furthermore, a high 85.2% of the respondents value the service provided at the check-out point as pleasing. Hereby, it is important to notice that although the service in the cosmetics’ department appears to be better than the service in the fresh meat department; both departments perform worse than the check-out point service-wise. Furthermore, the great majority (93.9%) of the respondents value the friendliness of cashiers as adequate. Of those 77.7% (73.0/93.9) value that friendliness even as very good. A slight 2.1% of the respondents value the friendliness of the cashiers below average. Both service provided by baggers and overall friendliness of the associates have similar distributions. A slight 1.3% of the respondents value the service provided by baggers below average, whereas a slightly higher 2.1% of the respondents score the overall friendliness of the associates below average. Conversely, the majority of the respondents score both items above average. The service provided by baggers is even scored as very good by 68.5% of the respondents, while 63.3% of the respondents score the overall friendliness of the associates as very good. Similarly, speed of check-out, overall store service and variety of the fresh vegetables and –fruit department have comparable distributions. Only 1.4% of the respondents score the speed of check-out and the overall store service respectively below average. The variety of the fresh vegetables and – fruit department performs slightly worse with a mere 2.0% of the respondents valuing it below average. However, the great majority of the respondents (87.7%, 88.5% and 88.5% respectively) values speed of check-out, overall store service and variety of the fresh vegetables and – fruit department as suitable. For each of these three items the responses concentrated on the extreme being “very good”. This concentration was 63.0% for speed of check-out, a slightly fewer 61.9% for overall store service and yet a slightly fewer 59.7% for the variety of the fresh vegetables and – fruit department. The percentages of respondents, who have rated these items below average are 1.4% for both quality of the fresh vegetables and – fruit department and variety of fresh meat items, while a slightly higher 4.1% have rated the overall store cleanliness inside as performing poorly. On the other hand, 89.9% of the respondents rated the quality of the fresh vegetables and – fruit department as being satisfactory. The variety of fresh meat items was rated satisfactory by 91.9% of the respondents. And relatively fewer respondents – a mere 79.9% – rated the overall cleanliness of the supermarket inside of it as suitable.




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There appears to be an observable difference related to the distribution of overall store cleanliness inside compared to the other two items. For both the other two items there is a big difference in frequency between the modal option and the second most frequent option (59.5% versus 20.3% and 52.0% versus 28.4%). For both this difference is that the modal option is about two times the next. However, for overall store cleanliness inside this difference is much smaller. Still, each of these three items has the ‘very good’ option as the modal option. The aspects ‘quality of the fresh meat items’, ‘availability of everyday grocery items’ and ‘overall value for your money’ have similar distributions of the frequencies also. Of these three items, quality of the fresh meat items is the item which has less negative responses; only .7% of the respondents rated it below average. Availability of everyday grocery items performs slightly worse with 1.4% of the respondents rating it as unsatisfactory. At last, overall value for your money performs yet slightly worse with 3.4% of the respondents, who believe it is below average. Conversely, 91.1% of the respondents believe that the quality of the fresh meat items at Kong Fui is above average. It was even rated as very good by 55.1% of the respondents. A slightly higher percentage of the respondents (91.7%) believe that the availability of everyday grocery items at Kong Fui is better than average. A high 61.6% of the respondents even think that Kong Fui performs very well on this item. Last, the overall value for money perception at Kong Fui performs slightly worse as compared as compared to the other two items in table 4.15. A slightly lower percentage of the respondents (87.8%) value this item as being above average for Kong Fui. However, a pretty high 57.8% of the respondents perceive the value for money at Kong Fui very well. The frequencies for the different possible responses of overall prices as compared to the competition, merchandise assortment and merchandise quality are also very similar to each other. Of these three items, merchandise quality performs marginally better, on the below average side of the responses, with only .7% of the respondents rating it below average, there after overall prices as compared to the competition (2.0%). Merchandise assortment has a slightly bigger percentage of respondents valuing it below average (2.7%). On the above average side of the responses, overall prices as compared to the competition takes the middle position with 86.4% of the respondents valuing this item above average. Merchandise assortment was rated above average by 86.0% of the respondents, whereas merchandise quality was rated above average by a slightly higher percentage of the respondents (88.6%). Although merchandise assortment has the lowest percentage of the respondents rating it as above average, it is the item – out of the three in table 4.16 – that has the highest percentage of respondents that rated it as very well (63.8%). Then comes merchandise quality with 61.1% of the respondents sharing the perception that the quality of the merchandise at Kong Fui is very well. At last, 53.7% of the respondents perceive the overall prices at Kong Fui very well as compared to the competition.




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Table 4.8: Descriptive statistics of the measured customer satisfaction aspects

Customer satisfaction aspects

Ver

y po

or (1

)

Poo

r +

(2)

Poo

r –

(3)

Nei

ther

po

or,

nor

go

od (4

)

Goo

d –

(5)

Goo

d +

(6)

Ver

y go

od (7

)

N (t

otal

res

pon

ses)

Service provided by the fresh meat department personnel 3.4 - .7 14.3 19.0 42.2 19.7 147

Service provided by the cosmetics' department personnel 2.8 1.4 2.1 11.0 16.6 32.4 33.8 145

Service provided at the check-out point 2.0 - .7 11.5 6.1 23.0 56.1 148Friendliness of cashiers - .7 1.4 4.1 8.1 12.8 73.0 148Service provided by baggers - 1.3 - 6.0 9.6 13.7 68.5 146Overall friendliness of our associates - 1.4 .7 12.9 6.8 15.0 63.3 147Speed of check-out - .7 .7 11.0 5.5 19.2 63.0 146Overall store service - .7 .7 10.2 8.2 18.4 61.9 147Variety of our fresh vegetables and -fruit department .7 1.3 - 9.4 11.4 17.4 59.7 149

Quality of our fresh vegetables and -fruit department .7 .7 - 8.8 10.1 20.3 59.5 148

Overall store cleanliness inside .7 - 3.4 16.1 24.8 21.5 33.6 149Variety of fresh meat items 1.4 - - 6.8 11.5 28.4 52.0 148Quality of our fresh meat items .7 - - 8.2 5.4 30.6 55.1 147Availability of everyday grocery items .7 - .7 6.8 7.5 22.6 61.6 146Overall value for your money .7 - 2.7 8.8 11.6 18.4 57.8 147Overall prices as compared to competition - .7 1.3 11.6 11.6 21.1 53.7 147Merchandise assortment - .7 2.0 11.4 7.4 14.8 63.8 149Merchandise quality .7 - - 10.7 10.1 17.4 61.1 149

Since there appears to be a relatively immense similarity in the distribution of the responses for the eighteen customer satisfaction aspects, a further analysis was performed on these items. Table A-7 depicts the results of this further analysis. Based on these results it can be stated that Kong Fui performs best – based on the means – in service provided at check-out point, closely followed by friendliness of cashiers, service provided by baggers and availability of everyday grocery items among others. However, as could be expected –based on the general description of the results above – the respondents perceive the performance of Kong Fui on the service provided by the cosmetics’ department personnel, the overall store cleanliness inside and the service provided by the fresh meat department personnel relatively worse. When taking the percentiles into account, it can be seen that the lower 16%15 of the responses for service provided by the cosmetics’ department personnel, the overall store cleanliness inside and service provided by the fresh meat department personnel were scores of 4 or less. Kong Fui performs relatively better on the other fifteen items depicted

15 sX ; conversely, the 84th percentile is sX




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in table A-7 with at least a score of 5 for the same lower 16% of the responses. When taking a closer look to the column of the 50th percentile for example, it can be seen that the three items with the lowest means are the ones with the lowest median as well.

Loyalty program adoption In assessing loyalty program adoption a distinction was made between the adoption of the focal loyalty program and the adoption of competitors’ loyalty programs. The results for the adoption of the loyalty program of Kong Fui are depicted on the left side in table 4.9, whereas the results for the adoption of competitors’ loyalty programs are shown on the right. As much as 56.2% of the respondents appear to have a membership card of Kong Fui in their household. The great majority (49% of the 56.2%) of those having a loyalty card of Kong Fui had it of their own. Only 7.2% overall had a loyalty card of Kong Fui at home, while the card was of another member of their household. However, it should be noticed that there is still a relatively high proportion of the respondents (43.8%) that do not have a loyalty card of Kong Fui. There are 31.6% of the respondents, who possess membership cards of other supermarkets on Aruba. Actually, it was the aim to get to know cards of which supermarkets the respondents do possess. However, assuming that not every respondents would be open to state the name of the supermarkets issuing the loyalty cards they possess, the decision was made to incorporate the option ‘yes’ as well. Afterwards, this appears to have been a good decision, since one-third of the respondents, who have competitors’ loyalty cards did fill in the option ‘yes’. Furthermore, of those who chose the option ‘yes, namely __’ there were quite a few that did not fill in the name.

Table 4.9: Descriptive statistics of focal – and competitors’ loyalty programs adoption

Do you have a membership card of Kong Fui supermarket in your household?

Do you have membership card in your household of any other supermarket on

Aruba?N (cases) 153 152 N (cases)

Frequency Percent Frequency PercentYes, I have one myself 75 49.0 16 10.5 YesYes, another household member has one

11 7.2 32 21.1Yes, namely ____

No 67 43.8 104 68.4 No

Customer characteristicsTable 4.10 depicts the responses of the questions of part B and C of the survey, measuring customer characteristics. The percentage (24.8%) of respondents with a household size of 3 members is the same as the one for households with 4 members. The next most frequent household size under the respondents was a household consisting of 2 members. There are twenty two respondents with 5-member households equaling 14.4% of the sample, seven 6-member households (4.6%) and only one respondent pertaining to a 9-members household representing .7% of the sample. Finally, there were also ten single-member households, accounting for 6.5% of the sample. Additionally, it is illustrated that the majority of the respondents (75.2%) have a monthly income inflow in their household, while the percentages of the respondents that have a more frequent income inflow in their household are illustrated accordingly. It is also depicted that for the great majority of the respondents the head of their household is a




- 48 -

male (80.4%). The proportion of respondents with a female head of household is 19.6% of the sample. Of the 153 respondents, 86.9% were females, whereas 13.1% were males. Regarding the employment status of the head of household, only 3.9% are retired and 2.0% unemployed, whereas a considerable 94.1% is employed. Most of the respondents belong to households with two members earning some sort of income (63.4%). There are also a relatively high percentage of households with only one member earning some sort of income (28.1%). However, households where there are more than two members earning some sort of income are relatively rare; a mere 5.9% of the respondents belong to households with three members earning income, whereas only one respondent (.7%) is member of a household with four members earning some income.The category of age of 35 to 44 years was the modal response (46.4%). The other two categories that were also well represented in the sample were the categories from 25 to 34 years (21.6%) and from 45 to 54 years (25.5%). Only 1.3% of the respondents were younger than 25 years and merely 5.2% of them were 55 years or older. Regarding the educational level of the respondents, relatively few respondents had less than high school as their highest level; merely 4.6% of the sample that is equal to seven respondents. On the other extreme of educational level, there were relatively few respondents as well; a mere 2.0% representing only three respondents had a post-graduate degree. There were considerable more respondents with either their high school completed (17.8%), with some college education (37.5%) or with a graduate degree (38.2%). The modal marital status under the respondents was the status of being married. Although there were a quite high proportion of the respondents, who claimed to be single (22.9%), the proportion of those that are married was well higher being 63.4%. Besides, 13.7% of the respondents had a marital status other than single or married. However, it would be worth mentioning that most of the twenty one respondents, who choose ‘other’ as their marital status, were either living together or stated that they have a partner. Thus, in both cases they were not singles. The modal response concerning the category of the households’ gross monthly-income was ‘about 2-times modal’, with sixty-five respondents filling in that option. Those sixty-five respondents were good for 42.5% of the sample. Besides them 15.0% of the respondents filled in that they had a gross monthly-income below modal for their household. Another 33.3% of them had a gross monthly-income about modal for their household. Finally, there were fourteen respondent, good for 9.2% of the sample that had a household’s gross monthly-income surpassing two times the modal gross income of AFL. 2700.




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Table 4.10: Descriptive statistics of the customer characteristics’ items

HH members EarnersNumber of members Frequency Percent Number of members Frequency Percent

1 10 6.5 0 3 2.02 37 24.2 1 43 28.13 38 24.8 2 97 63.44 38 24.8 3 9 5.95 22 14.4 4 1 .76 7 4.6 Employment9 1 .7 Categories Frequency Percent

Income frequency Employed 143 94.1Categories Frequency Percent Unemployed 3 2.0

Weekly 9 5.9 Retired 6 3.9Bi-weekly 29 19.0 GenderMonthly 115 75.2 Gender Frequency Percent

HH gender Female 133 86.9Gender Frequency Percent Male 20 13.1Female 30 19.6 EducationMale 123 80.4 Level Frequency Percent

Age Less than high school 7 4.6Category Frequency Percent Completed high school 27 17.8Under 25 2 1.3 Some college education 57 37.5

25-34 years 33 21.6 Graduate degree 58 38.235-44 years 71 46.4 Post-graduate degree 3 2.045-54 years 39 25.5 Gross income

55 years or older 8 5.2 Category Frequency PercentMarital status Below modal 23 15.0

Status Frequency Percent About modal 51 33.3Single 35 22.9 About 2-times modal 65 42.5

Married 97 63.4 > 2-times modal 14 9.2Other 21 13.7

Note: The abbreviations in bold refer to survey questions as listed below.

HH members = What is the number of members in your household?Earners = How many members of your household are receiving some sort of income?Income frequency = What is the frequency in which there is an inflow of income in your household?HH gender = What is the gender of the head of your household?Age = What is your age category?Marital status = What is your marital status?Employment = What is the employment status of the head of your household?Gender = What is your gender?Education = What is your highest educational level?Gross income = What is the category of your household’s gross monthly-income?

Acculturation preferenceTable 4.11 depicts the frequencies and percentages of the responses to the three questions related to acculturation preference. As can be seen 66.4% of the respondents completely support the idea that the personnel of the grocery where they do their shopping should




- 50 -

adopt their culture. One hundred and thirteen respondents, representing 75.3% of the sample, consider it extremely important for the personnel of the grocery where they shop to talk their language. And one hundred and eleven respondents, accounting for 74.0% of the sample, think it is extremely important for the personnel of the grocery store where they shop to understand their language. When taking a broader picture the respondents, who consider the adoption of their culture by the personnel of the grocery store where they shop as important account for 83.2% of the sample. These figures are respectively 86.6% for the ability of the personnel to talk the host language and 87.3% for the ability of the personnel to understand the host language. The respondents, who consider the adoption of their culture by the personnel of the grocery store where they shop as unimportant, represent a mere 5.6% of the sample. Those respondents considering it unimportant for the same personnel to talk the host language account for a little 7.3% of the sample, whereas the proportion equals 8.7% for the respondents that consider the ability to understand the host language by those same personnel as unimportant.

Table 4.11: Descriptive results of acculturation preference items

Acculturation preference items

Com

plet

ely

disa

gree

(1)

Dis

agre

e --

- (2)

Dis

agre

e --

(3)

Dis

agre

e - (

4)

Nei

ther

di

sagr

ee,

nor

ag

ree

(5)

Agr

ee +

(6)

Agr

ee +

+ (7

)

Agr

ee +

++ (8

)

Com

plet

ely

agre

e (9

)

N (t

otal

res

pon

ses)

I consider it important for the personnel, of the grocery store where I shop, to adopt

the host culture4.9 - - .7 11.2 4.9 1.4 10.5 66.4 143

I consider it important for the personnel of the grocery store where I do my shopping to talk the host language

5.3 .7 - 1.3 6.0 2.7 3.3 5.3 75.3 150

I consider it important for the personnel of the grocery store where I shop to

understand the host language6.7 .7 - 1.3 4.0 2.7 3.3 7.3 74.0 150




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Attitudinal loyalty and word-of-mouthHere follows two frequency tables where the distribution of respondents’ agreement with some statements regarding customer loyalty is shown. For instance, table 4.12 shows that the majority of the respondents (64.7%) perceive themselves as loyal patrons of Kong Fui. A little more than 10% of the respondents do not consider themselves as loyal patrons of Kong Fui; to be exact they represent 11.6% of the sample. The responses as to the patronage of another supermarket in case that they would have to do it again, were less unequally distributed. More respondents appear to be willing to patronize another supermarket (43.6%) as compared to the ones that are not willing to do so (30.3). What is remarkable is that the mode for this statement is the average option, in which the respondents do not agree neither disagree with the statement. However, a great proportion of the respondents are willing to stick to Kong Fui for their grocery purchases, due to their perception that that is their best choice (65.6%). Moreover, a large 35.9% of them strongly agree to be willing to keep that bond with Kong Fui, since it is their best choice. A quite irrelevant proportion of the respondents strongly disagree in this regard (1.4%).

Table 4.12: Descriptive results of attitude – and WoM items

Attitudinal loyalty –

&

WoM items

Str

ongl

y di

sagr

ee

Dis

agre

e

Nei

ther

dis

agre

e, n

or

agre

e

Agr

ee

Str

ongl

y ag

ree

N (t

otal

res

pon

ses)

I consider myself to be a loyal patron of Kong Fui supermarket 4.1 7.5 23.8 32.7 32.0 147

If I had to do it over again, I would patronize another supermarket 8.5 21.8 26.1 19.0 24.6 142

I try to stick to Kong Fui supermarket for my grocery purchases, since it is the best

choice for me1.4 5.5 27.6 29.7 35.9 145

To me, Kong Fui supermarket is the same as other supermarkets 4.7 8.7 24.8 30.2 31.5 149

If other people inquired about my grocery store then I would recommend Kong Fui 3.4 6.1 27.0 33.1 30.4 148

I am happy to voluntarily recommend Kong Fui to others 3.4 5.4 27.9 32.0 31.3 147

Next, only a slight 4.7% of the respondents seem to perceive Kong Fui as a supermarket different from its competitors. Much of the respondents (31.5%) really do not see Kong Fui as a supermarket that stands out compared to its competitors.

Customer loyalty




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Concerning the respondents’ willingness to recommend Kong Fui to other people, they appear to be willing to do it both inquired and voluntarily in similar proportions. For example, whereas forty-five respondents (30.4%) would be totally willing to recommend Kong Fui if inquired by other people, forty-six respondents (31.3%) are absolutely enthusiastic to recommend the supermarket voluntarily. Furthermore, forty respondents (27.0%) are indifferent to pass word-of-mouth when asked for, whereas forty-one respondents (27.9%) are indifferent to do it voluntarily.

Repeat purchase behaviorConcerning the frequency of visits during last month, Kong Fui appears to have received a visit of 30.7% of the respondents once a week (see table 4.13). Sixty-two respondents (40.6%) visited Kong Fui more often than once a week during last month. Eighteen of these respondents seem to have visited Kong Fui even more than three times a week during lastmonth. However, supermarkets in general have been visited once or twice last month by forty-five respondents, which accounts for 29.4% of the sample and is actually the mode. A minimum of nine respondents (5.9%) visited supermarkets in general more than three times a week during last month. Finally, most respondents (30.7%) visited grocery stores in general once or twice last month as well. A mere 7.2% of the respondents visited grocery stores in general more than three times a week during last month. However, it is remarkable that this proportion is greater here than it is for the visits of supermarkets in general, but still smaller than it is for the visits of Kong Fui supermarket during last month. Thereby, it can be concluded that Kong Fui was more often picked for grocery shopping trips that were more often than three times a week, as compared to both supermarkets in general and grocery stores in general16. And apparently Kong Fui is more sensitive for customers going to another type of grocery store than going to another supermarket when shopping that often.

Table 4.13: Descriptive results of repeat purchase behavior items

Repeat purchase behavior items

Les

s th

an

once

a

mon

th

On

ce

or

twic

e a

mon

th

On

ce a

wee

k

2-3

tim

es a

wee

k

Mor

e th

an 3

tim

es

a w

eek

N (t

otal

res

pon

ses)

How many times have you visited Kong Fui supermarket last month? 12.4 16.3 30.7 28.8 11.8 153

How many times have you visited supermarkets in general last month? 28.8 29.4 22.2 13.7 5.9 153

How many times have you visited grocery stores in general last month? 30.1 30.7 20.9 11.1 7.2 153

16 This implicitly coincides with the findings of González-Benito, Muñoz-Gallego and Kopalle, 2005.




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4.2.2 Basic descriptive statistics of loyalty program adoption predictors

Loyalty program enjoymentTable 4.14 depicts that most of the respondents strongly enjoy participating in loyalty or saving programs (33.3%), perceive loyalty and saving programs as truly offering attractive benefits (31.3%) and really appreciate being selected for loyalty programs and special offers (34.0%). A mere 0.7% of the respondents really do not appreciate to be selected for loyalty programs or special offers, whereas 1.4% of the respondents think that loyalty and saving programs truly offer attractive benefits and three respondents (2.1%) absolutely do not enjoy participating in loyalty or saving programs.

Table 4.14: Descriptive results of enjoyment items & idiosyncratic fit measure

Loyalty program enjoyment –

&

idiosyncratic fit items

Str

ongl

y di

sagr

ee

Dis

agre

e

Nei

ther

dis

agre

e, n

or

agre

e

Agr

ee

Str

ongl

y ag

ree

N (t

otal

res

pon

ses)

I enjoy participating in loyalty or saving programs 2.1 9.0 16.0 39.6 33.3 144

Loyalty and saving programs offer attractive benefits 1.4 6.8 18.4 42.2 31.3 147

I appreciate being selected for loyalty programs and special offers .7 6.9 15.3 43.1 34.0 144

I consider the extent to which rewards are achievable with the loyalty program

of Kong Fui is very important2.8 7.0 21.7 31.5 37.1 143

Idiosyncratic fitThe extent to which rewards are achievable with the loyalty program of Kong Fui is perceived as very important by 37.1% of the respondents and a slightly fewer 31.5% of them consider it important (see table 4.14; above). A mere 2.8% of the respondents think the extent to achieve rewards through the loyalty program of Kong Fui is completely unimportant, whereas a slightly larger 7.0% of them consider it unimportant. Still thirty-one respondents (21.7%) are indifferent regarding the importance of the achievability of rewards through the loyalty program of Kong Fui.

Program designDirect rewards from loyalty programs are truly preferred by 29.1% of the respondents (see table 4.15). Still, forty-nine respondents (34.8%) seem to prefer direct rewards. Conversely, a mere 3.5% of the respondents truly do not prefer direct rewards. Most of the respondents (33.8%) absolutely detest participating in loyalty programs where a fee has to be paid. A relatively small proportion equaling 14.8% of the respondents would not mind to participate in loyalty programs where fees have to be paid. However, there are




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still twenty-nine respondents – representing 20.4% of the sample – who are indifferent towards the existence of membership fees for loyalty programs. Delayed rewards are preferred by 65.2% of the sample; half of that proportion even strongly prefers to get delayed rewards for their participation in loyalty programs. Thirty-three respondents – representing 22.9% of the sample – are indifferent towards receiving delayed rewards for their participation in loyalty programs. And a mere 2.1% of the respondents strongly dislike receiving delayed rewards.

Table 4.15: Descriptive results of design items & privacy measure

Loyalty program design –

&

Privacy concern items

Str

ongl

y di

sagr

ee

Dis

agre

e

Nei

ther

di

sagr

ee,

nor

agr

ee

Agr

ee

Str

ongl

y ag

ree

N (t

otal

res

pon

ses)

I prefer direct reward for my participation in a loyalty program 3.5 9.9 22.7 34.8 29.1 141

I do not like to pay fees to participate in a loyalty program 2.8 12.0 20.4 31.0 33.8 142

I prefer a delayed reward for my participation in a loyalty program 2.1 9.7 22.9 32.6 32.6 144

The registration systems of loyalty programs infringe on my privacy

10.6 17.6 30.3 26.1 15.5 142

Privacy concernsMost of the respondents (30.3%) are indifferent towards the violation of their privacy by the registration systems of loyalty programs (see table 4.15; above). Additionally, fifty-nine respondents (41.6%) perceive the registration systems of loyalty programs as a violation of their privacy, whereas forty respondents (28.2%) do not perceive it as a violation of their privacy.

4.2.3 Reduction of multi-item measures

Factor analyses were performed as described previously – in the analytical method section – to reduce the multi-items of questions three, eight, nine and ten. Prior to assessing the factors, it was checked whether it was favorable to perform a factor analysis on these items. The results of the KMO-test and Bartlett's Test of Sphericity supported the application of factor analysis on the items – on which factor analysis was performed (see table A-8).

As illustrated in table A-9, the three shopping motivations as suggested by prior research (i.e. Mägi, 2003) were supported by the results of this research. The three factors all together explain as much as 65.43% of the variance related to shopper characteristics. The results further show that the variance explained by each of the three factors are respectively 23.45% by economic shopping motivation, 25.58% by personalizing shopping




- 55 -

motivation and 17.17% explained by apathetic shopping motivation. Consequently, the internal consistency each of the three factors was evaluated. This reliability test resulted in an internal consistency of .8497 for economic shopping motivation, .8448 for personalizing shopping motivation and .6908 for apathetic shopping motivation. However, it is worth mentioning that the item “I enjoy shopping for groceries” had a high load towards the factor representing personalizing shopping motivation. This could be explained by the fact that the higher the personalizing motivation for shopping the more a customer would enjoy shopping for groceries. Still, the item was included in the economic shopping motivation factor, since it has the minimally acceptable factor loading of +/- .30 as proposed by Hair et al. (2006). Therefore, three four-item factors were maintained. Next, a new set of factor analyses were performed to assess the variance that each set of items explains for the factor to which they belong. These tests showed that 69.29% of the variance in the economic shopping motivation factor is explained by the four items that comprise that factor (see table A-9). Subsequently, the four items comprising the factor personalizing shopping motivation explain 69.03% of the variance in that factor, while 52.74% of the variance in the factor apathetic shopping motivation is explained by the items comprising it.

Table 4.16: Overview of factor analysis and reliability analysis for customer satisfaction

Variables Factor 1How satisfied are you with Kong Fui supermarket?How well does Kong Fui supermarket match your expectations?Imagine a perfect grocery store. How close to this ideal is Kong Fui supermarket?

Customer satisfaction

Eigenvalues 79.915Cronbach -score .822417

Table 4.16 depicts the results of both the factor analysis and the reliability analysis. As can be seen by the eigenvalues, a great proportion of the variance of customer satisfaction is explained by these three items. When the internal consistency of the factor is assessed, it can be seen that the deletion of the item ‘How satisfied are you with Kong Fui supermarket?’ would lead to a higher level of internal consistency. The suggestion to delete that item may be caused by the fact that although the items were based on prior research (i.e. Mägi, 2003), the scaling of this particular item was adapted.

Regarding the aspects of customer satisfaction no support could be found for prior research (see table A- 10). Although the items from prior research (i.e. Gómez et al. 2004) were adapted and some items were added, it was expected to get similar results. However, this was not the case. Apparently, the respondents do not see so much difference between the different aspects of customer satisfaction delivery. This could be expected following the general results – covered previously – were there was so much similarity in the distribution of the responses for these items. This expectation is further supported by the suggestion to delete the item ‘Service provided by the cosmetics’ department personnel’ in order to get a higher internal consistency for factor 1. A further look at the items that were

17 Becomes .9035 if ‘How satisfied are you with Kong Fui supermarket?’ is deleted




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either not included in the set pertaining to factor 1 or suggested to be deleted shows that they are precisely the three items on which Kong Fui scored worst and the item on which it scored best.

Although the items used to measure loyalty program enjoyment and loyalty program design were not directly based on prior research – as discussed previously in the operationalization of the model – both factors performed really good on the factor analysis and the reliability analysis (see table A-11). Concerning the items used to assess loyalty program enjoyment the three items appear to explain as much as 88.86% of the variance of loyalty program enjoyment. These three items even seem to be strongly internal consistent with a Cronbach’s alpha of .9404 and no suggestion were made to delete any of them for a higher internal consistency.As for loyalty program design, the three items belonging to this factor also explain a high proportion of variance in this factor (77.22%). Moreover, the results of the reliability analysis show that these three items are highly internal consistent with a Cronbach’s alpha as high as .8576. And as was the case with the reliability test for the construct loyalty program enjoyment, no suggestion has been made to delete any item here. Regarding the construct ‘attitudinal loyalty’ the results of the factor analysis suggest that the four items used pertain to two different factors – factors C and D as shown in the appendix (table A-11). This result was unexpected, since prior research used all these for items in assessing attitudinal loyalty (i.e. Pritchard et al., 1999). The fact that the results suggest the combination of items from different studies (Selin et al., 1988 and Muncy, 1983) into one factor made the decision to perform a reliability test one these four items undeniable. Contrary, to the results of the factor analysis the results of the reliability analysis were as expected. The internal consistency of the factor – based on four items –resulted being pretty high (.74) and no suggestion was made to delete any of the items. Still, it should be noted that the internal consistency is relatively low as compared to the other internal consistencies – based on the Cronbach’s alphas in the same table. However, further analysis will be done with attitudinal loyalty comprising all these four items, since the Cronbach’s alpha is still well above the required score. As illustrated in table A-12, the three items related to hedonic shopping motivation explain 77.15% of the variance in that factor. However, the results of the reliability test, suggest that the inclusion of the item ‘I feel freed from domestic chores when shopping for groceries’ reduces the internal consistency of the factor. However, since the factor analysis results pointed out the existence of a sole factor for these items, the decision was made to stick to one factor based on all three items. Moreover, the internal consistency of the factor – based on the three items – is considerably higher than the minimally required score. The three items, measuring acculturation preference, seem to explain a considerable 89.28% of the variance in that factor (see table A-12). The internal consistency of the factor – based on these three items – is considerably high with a Cronbach’s alpha of .9569. However, the reliability test results suggest the deletion of the item ‘I consider it important for the personnel, of the grocery store where I shop, to adopt the host culture’ to get a higher internal consistency. But based on the same justification as for the hedonic shopping motivation construct, it was decided to hold on to all three items.




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4 . 3 N o n - l i n e a r i n f l u e n c e o f a n t e c e d e n t s

4.3.1 Influence of the binary customer characteristics

Influence on loyalty program adoptionThe Chi-square test was used to assess the influence of the binary customer characteristics’ variables – related to gender – on loyalty program adoption18. First, the influence of the gender of the head of household on the adoption of the focal loyaltyprogram was analyzed. As illustrated at the top in table 4.17, the value of chi-square equals .584 with a significance level of .445. Therefore, the chance on such a chi-square is .445 if the null-hypothesis – that the variables are statistically independent – is true. Consequently, if a confidence level of 95 % is maintained ( = 0.05), the null-hypothesis would not be rejected. With 95% confidence there is no statistical significant relation between the gender of head of household and focal card ownership. Next, the influence of the gender of the head of household on adoption of competitors’ loyalty programs was evaluated. Here, a chi-square of 2.911 corresponding to a significance level of .088 was obtained. Thereby, it can be concluded that the chance on such a chi-square is relatively small. However, it is not small enough when the requirement of a 95% confidence level is maintained. Consequently, it can be stated with 95% confidence that there is no statistical significant influence of the gender of head of household on competitors’ card ownership.

Table 4.17: Chi-square test results; influence binary (gender) items on loyalty program adoption

Focal loyalty card adoptionCompetitors’ loyalty card adoption

Female Male Female MaleYes 15 71 Yes 13 35No 15 52 No 16 88

2 .584 2 2.911

Sig. NS Sig. +

Focal loyalty card adoptionCompetitors’ loyalty card adoption

Female Male Female MaleYes 80 6 Yes 43 5No 53 14 No 89 15

2 6.421 2 .461

Sig. * Sig. NS

Note: NS = Not SignificantTop: Gender of head of household / Bottom: Respondent’s gender

001.***.01.**.05.*.1. pppp

Then, an analysis was made of the influence of the gender of the respondent on adoption of the loyalty program. This analysis delivered a chi-square of 6.421 corresponding to a significance level of .011 (see table 4.17; bottom). These results support the existence of

18 Note that if the test for any bivariate analysis does not meet any of the two requirements as stated earlier,

the results of that particular test will be provided in the appendix. Furthermore, for a description of the abbreviations used for the variables in the subsequent sections refer to table A-4 in appendix 1




- 58 -

statistically significant influence of gender of the customer on focal card ownership. Hence, one can be 95% sure of the existence of that influence. At last, an analysis of the influence of gender of the respondent on adoption of competitors’ loyalty programs, delivered the results as illustrated in table 4.17. Based on the significance level of .497 obtained here, it can be concluded with 95% confidence that the gender of the customer does not influence the adoption of competitors’ loyalty programs.

Influence on repeat purchase behaviorIn order to assess the influence of both gender related customer characteristics on behavioral loyalty the Mann-Whitney U-test was used. As depicted in table 4.18, the null-hypothesis – that samples come from identical populations – can not be rejected based on the two-tailed significance level of .089 with a confidence of 95%. Therefore, the gender of head of household does not have a statistical significant influence on behavioral loyalty. This means that the distribution of behavioral loyalty does not differ between households depending on the gender of the head of household. Additionally, similar results are illustrated (table 4.18) for the influence of gender of the respondent on behavioral loyalty. It can be stated with 95% confidence that there is no influence of respondents’ gender on behavioral loyalty.

Table 4.18: Mann-Whitney U test results for behavioral loyalty

Gender of head of household Gender of respondentFemale Male Female Male

Mean rank 65.05 79.91 78.94 64.13Sum of ranks 1951.50 9829.50 10498.50 1282.50

U 1486.5 1072.5Sig. + NS

Note: NS = Not Significant

001.***.01.**.05.*.1. pppp

Influence on attitudinal loyalty and word-of-mouthIn assessing the influence of both gender of the head of household and the gender of the respondent on attitudinal loyalty and word-of-mouth the student t-test was used. The F-value and its corresponding significance show that the variances differed significantly from each other (see table 4.19). Therefore, the statistics of Equal variances not assumed were used. The t-value for Equal variances not assumed is .939. The significance level (Sig. 2-tailed) equals .354; at 36.454 degrees of freedom. Therefore, the null-hypothesis (1=2) can not be rejected with a confidence of 95%. The mean attitudinal loyalty scores for male – and female heads of household does not differ significantly from each other. Consequently, it can be said that there is no influence of gender of head of household on attitudinal loyalty.




- 59 -

Table 4.19: Student t- test results for head of HH

Attitude WoM2 2 2 2

Mean difference .1441 .1441 -.091 -.091S.E. difference .1248 .1535 .2133 .1937

F-value 7.291* .725t-value .939 -.424

Sig. NS NSNote: NS = Not Significant

001.***.01.**.05.*.1. pppp

Next, the influence of gender of head of household on word-of-mouth was also assessed. The F-value and its corresponding significance show that the variances do not differ significantly from each other (see table 4.19). This resulted in the use of the statistics of Equal variances assumed. The t-value for Equal variances assumed is -.424 and the significance level is .672; at 146 degrees of freedom. These statistics show that there is no influence between gender of head of household and word-of-mouth. Similarly, table A-13 in appendix 1 depict that there is no influence of gender of the respondent for neither attitudinal loyalty nor word-of-mouth.

4.3.2 Influence of non-customer characteristics

Influence on loyalty program adoptionThe Chi-square test was used to assess the influence of enjoyment, design, privacy and idiosyncrasy on loyalty program adoption. Since similar results have been discussed previously and none of these influences appeared to be significant, they will not be discussed explicitly. For an overview of the results refer to table A-14.

Influence on repeat purchase behavior In order to assess the influence of economic, personalizing, apathetic, hedonic, utilitarian, satisfaction, acculturation and card ownership on behavioral loyalty the Mann-Whitney U-test was used. As depicted in table A-15, the null-hypotheses – that samples come from identical populations – can be rejected based on the two-tailed significance levels of each of these variables with a reliability of at least 90%. Therefore, each of these variables does have a statistical significant influence on behavioral loyalty.

Influence on attitudinal loyalty and word-of-mouthIn assessing the influence of economic, personalizing, apathetic, hedonic, utilitarian, satisfaction, acculturation and card ownership on attitudinal loyalty and word-of-mouth the student t-test was used. As discussed previously, the F-value and its corresponding significance were evaluated to see whether the Equal variances not assumed or the Equal variances assumed statistics should be examined. Consequently, the t-values and their corresponding significance levels ( tailedSig 2. ) were examined to see whether significant influences of the different antecedents do exist on either attitudinal loyalty or word-of-mouth. The only variable that appeared to have a significant influence on attitudinal loyalty was economic – with 90% reliability; as depicted in tables A-16 and A-17. On the other hand, different variables appeared to be significant in influencing word-of-mouth. The only ones that were insignificant were hedonic and card ownership. Apathetic and




- 60 -

satisfaction resulted in the ones with the highest reliability, which equaled 99.9%; for an illustration refer to tables A-16 and A-17.

4.3.3 Influence of the customer characteristics with manifold categories

Influence on loyalty program adoptionChi-square tests were used to assess the influence of the customer characteristics with more than two (manifold-) categories on loyalty program adoption19. Here again a distinction was made between the adoption of the focal loyalty program and the adoption of competitors’ loyalty programs. The chance on a chi-square of 2.886 is .236 if the null-hypothesis – that marital status and the adoption of competitors’ loyalty programs are statistically independent –is true (see table 4.20). Maintaining a reliability of 95% ( = 0.05) the null-hypothesis can not be rejected. Consequently, with 95% reliability there is no statistical significant relationship between marital status and adoption of competitors’ loyalty programs. Therefore, it can be concluded that there is no influence of marital status on the adoption of competitors’ loyalty programs. Then, the chance on a chi-square of 2.299 is .317 when the null-hypothesis – that marital status and the adoption of the focal loyalty program are statistically independent – is true. Therefore, again with a reliability of 95% it can be concluded that marital status does not have an influence on the adoption of the focal loyalty program. On the contrary, the chance on a chi-square of 12.424 is .006 with a null-hypothesis – that the category of households’ gross monthly-income and the adoption of competitors’ loyalty programs are statistically independent – that holds (see table 4.20). Therefore, it can be concluded here with 99% (Sig. = .006) reliability that the category of households’ gross monthly income influences the adoption of competitors’ loyalty programs. Furthermore, there is sufficient evidence to reject the null hypothesis – that the category of households’ gross monthly-income and the adoption of the focal loyalty program are statistically independent. To be specific, the figures show that the chance on a chi-square of 12.430 is only .006 if the null-hypothesis is true. Therefore, the null-hypothesis can be rejected with 95% confidence and it can be concluded that the category of households’ gross monthly-income influences the adoption of the focal loyalty program as well.

19 If the test for any bivariate analysis does not meet any of the two requirements as stated earlier, the results

of that particular test are provided in appendix 1.




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Table 4.20: Chi-square test results; influence marital status & gross income on card ownership

Competitors’ loyalty programs adoption

Focal loyalty program adoption

Single Married Other, _ Single Married Other, __Yes 14 26 8 18 38 11No 20 71 13 17 59 10

2 2.886 2.299

Sig. NS NS

Competitors’ loyalty programs adoption

Focal loyalty program adoption

Belowmodal

About modal

About 2-times modal

More than 2-times modal

Below modal

About modal

About 2-times modal


Yes 11 19 11 7 13 25 45 3No 12 31 54 7 10 26 20 11

2 12.424 12.430

Sig. ** **

Note: NS = Not SignificantTop: Marital Status

Bottom: Gross income

001.***.01.**.05.*.1. pppp

Influence on repeat purchase behavior The influence of the customer characteristics with more than two (manifold –) categories on behavioral loyalty was assessed by performing some Kruskal-Wallis tests20. The assessment of the influence of employment status of the head of household on behavioral loyalty produced a chi-square of 8.336 as depicted in table 4.21 (at the top) coupled with a significance value of .015; at 2 degrees of freedom. The null-hypothesis – that the three samples come from identical populations – is rejected with a reliability of 95% ( 05.0.. SigAsymp ). Therefore, it can be stated that the distribution of behavioral loyalty of the different employment status’ are not (all three) equal to each other. The statistics obtained from the evaluation of the influence of highest educational level on behavioral loyalty show that this influence does exist as well (see table 4.21; center). The Chi-square is 10.037 coupled with a significance value of .040; at 4 degrees of freedom. These figures support the rejection of the null-hypothesis that the five samples are from the same population. The null-hypothesis was also rejected with 95% reliability. Consequently, educational level influences behavioral loyalty. The assessment of the influence of households’ gross monthly-income category on behavioral loyalty produced a Chi-square of 12.607 together with a .006 significance value; at 3 degrees of freedom (see table 4.21; bottom). Consequently, the null-hypothesis – that the four samples come from the same population – can be rejected with a reliability of 99%

20 Note that mostly the significant results are depicted in tables throughout this section; the other results are

illustrated in appendix 1.




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( 01.0.. SigAsymp ). So, households’ gross monthly-income influences repeat purchase behavior.

Table 4.21: Summary of Kruskal-Wallis tests’ results

Employment on repeat purchaseEmployment status of the

head of householdN Mean Rank

Employed 143 74.36Unemployed 3 80.50

Retired 6 125.582 = 8.336* Sig. = .015 (df. = 2)

Education on repeat purchaseHighest level of education N Mean Rank

Less than high school 7 101.21Completed high school 27 67.41Some college education 57 66.82

Graduate degree 58 86.64Postgraduate degree 3 88.67

2 = 10.037* Sig. = .040 (df. = 4)

Gross income on repeat purchaseGross income N Mean RankBelow modal 23 94.93About modal 51 67.01

About 2-times modal 65 83.62More than 2-times modal 14 53.21

2 = 12.607** Sig. = .006 (df. = 3)

001.***.01.**.05.*.1. pppp

Influence on attitudinal loyalty and word-of-mouthThe influence of the customer characteristics with more than two (manifold -) categories on both attitudinal loyalty and the behavioral loyalty measure word-of-mouth was assessed by performing some analyses of variance (ANOVA)21. In this regard, the first null-hypothesis that is rejected stated that the population means of the all the groups are equal. This means that indifferent of the frequency on which the respondents have an inflow of income in their household, their mean for attitudinal loyalty are the same. As shown in table 4.22 (top-left), the means do differ, with those with a bi-weekly income inflow having the highest mean for attitudinal loyalty. Furthermore, the significance value of .054 claims that the null-hypothesis can be rejected with 90% reliability. This means that although a little weaker than the 95% confidence interval, there is support for the existence of an influence of income frequency on attitudinal loyalty. Concerning the null-hypothesis stating that the population means of word-of-mouth are equal for all the categories of frequency of income inflow, ANOVA shows that this statement can be rejected with 95% reliability. The significance value of .015 shows that 21 Note that mostly the significant results are depicted in tables throughout this section; the other results are

illustrated in appendix 1.




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the reliability for such a rejection is 98.5%. Furthermore, it can be seen that the mean for word-of-mouth in the category of those with a weekly income inflow is considerably higher than for the other two categories. Consequently, this supports the existence of an influence of frequency of income inflow on word-of-mouth. Table 4.22 (top-right) also depicts the results for the analysis of variance (ANOVA) for the two null-hypotheses – the population means of attitudinal loyalty is equal for all the employment status and the population means of word-of-mouth are equal for all the employment status. These results show a significance level of .019 for the first null-hypothesis, thereby indicating that the null-hypothesis can be rejected with a reliability of 95%. Conversely, the results show a significance level of .093 for the second null-hypothesis, thereby indicating that that null-hypothesis can also be rejected. However, the reliability for this rejection would be a lower but still high 90%. A closer look to the table illustrates that the respondents who are retired are the most attitudinally loyal to Kong Fui, whereas those that are employed as the least attitudinally loyal. On the other hand, the unemployed respondents are the least behaviorally loyal (word-of-mouth) ones, whereas the retired ones show the highest level of behavioral loyalty through word-of-mouth. Therefore, there appear to be an influence of employment status of the head of household on both attitudinal loyalty and behavioral loyalty through word-of-mouth. However, the influence of employment status on attitudinal loyalty is considerably stronger. The results of the ANOVA concerning the two null-hypotheses – the population means of attitudinal loyalty is equal for all the age categories and the population means of word-of-mouth are equal for all the age categories are illustrated in table 4.22 (left-center). As can be seen, the respondents under the age of 25 years are the most attitudinally loyal ones, whereas the ones between 35 and 44 years of age are the least attitudinally loyal ones. The significance value of .023 shows that the null-hypothesis in this case can be rejected with 95% reliability. Thereby, it is illustrated that age category does influence attitudinal loyalty. On the contrary, the respondents 55 years of age or older are the ones who are the most behaviorally loyal for passing on word of mouth, whereas the ones between 25 and 34 years of age are the least behaviorally loyal ones to pass on word of mouth. The significance value for this analysis equals .308 and illustrates that the null-hypothesis tested in this regard can not be rejected with 95% reliability. Consequently, there is no support for the existence of an influence of age category on word-of-mouth. The results of the analyses concerning the two null-hypotheses – the population means of attitudinal loyalty is equal for all the education levels and the population means of word-of-mouth are equal for all the education level are summarized in table 4.22 (right-center). As can be seen, the respondents with the lowest level of education are the most attitudinally loyal ones, whereas the ones with some college education are the least attitudinally loyal ones. The significance value obtained in this case equals .040, thereby supporting the rejection of the null-hypothesis with a reliability of 95%. Therefore, there is enough support for the claim that education level does influence attitudinal loyalty.




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Table 4.22: Results from different analyses of variance

Income frequency on attitude and WoM Employment on attitude and WoM

Income frequency

Attitudinal loyalty

Word-of-mouth

Employment Attitudinal loyalty

Word-of-mouth

Mean Mean Mean MeanWeekly 3.14 4.67 Employed 3.12 3.79

Bi-weekly 3.40 3.98 Unemployed 3.25 3.67Monthly 3.09 3.70 Retired 3.90 4.80

Sig. .054 .015 Sig. .019 .093

Age on attitude and WoM Education on attitude loyalty and WoM

Age Attitudinal loyalty

Word-of-mouth

Education Attitudinal loyalty

Word-of-mouth

Mean Mean Mean MeanUnder 25 yrs 4.00 4.00 Less than high

school3.92 4.25

25-34 yrs 3.10 3.53 Completed high school

3.14 3.64

35-44 yrs 3.08 3.85 Some college education

3.10 3.69

45-54 yrs 3.19 3.89 Graduate degree 3.12 3.9855 yrs and

older3.71 4.36 Postgraduate

degree3.25 3.50

Sig. .023 .308 Sig. .040 .354

Gross income on attitude loyalty and WoM

Gross income

Attitudinal loyalty

Word-of-mouth

Mean MeanBelow modal 3.48 3.89About modal 3.08 3.62

About 2-times modal

3.14 4.04


2.93 3.33

Sig. .029 .052

In contrast, there is not enough support for the existence of a relation between education level and word-of-mouth. The figures illustrate that although the people with the lowest level of education are the most behaviorally loyal ones – for passing word of mouth – and the respondents with a postgraduate degree the least behaviorally loyal ones, the reliability to reject the null-hypothesis is low. The significance value is a considerable .354, thereby showing support for the claim that the population means of word-of-mouth for all the groups of education levels. Table 4.22 (bottom-left) summarizes the results of the analyses concerning the two null-hypotheses – the population means of attitudinal loyalty is equal for all the gross income categories and the population means of word-of-mouth are equal for all the gross income categories. Whereas the first null-hypothesis can be rejected with a reliability of 95% ( 05.0. Sig ), the second null-hypothesis can be rejected with 90% reliability ( 10.0. Sig ).




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Consequently, gross income category influences both attitudinal loyalty and word-of-mouth. A closer look at the statistics illustrates that the respondents with a gross income below modal have the highest scores for attitudinal loyalty (3.48), whereas the respondents with the highest gross incomes show the lowest scores for attitudinal loyalty (2.93). Conversely, the respondents with a gross income about 2-times modal have the score higher on word-of-mouth (4.04), whereas the respondents with the highest gross incomes score the lowest on word-of-mouth (3.33).

4 . 4 L i n e a r i n f l u e n c e o f t h e a n t e c e d e n t s o f b o t h l a y e r s

In this section the correlation matrix for the independent variables is examined first. Subsequently, the results of the regression analyses – both linear regression and logistic regression – are performed and the antecedents of loyalty according to this particular sample are discussed. Thereby attention is drawn to the tolerance and VIF of the selected independent variables, which are assumed to be antecedents of customer loyalty. Afterward, similar examinations are performed for the antecedents of loyalty program adoption.

4.4.1 Influence on word-of-mouth

The effects of the antecedents of loyalty on word-of-mouth are covered first. Prior to the regression analysis, a correlation matrix of the independent variables was made and examined, thereby trying to disclose strong correlations. The examination showed that the highest correlation was between the independent variables was .693, which was between acculturation preference and utilitarian shopping motive. Consequently, no independent variable was dropped for the regression analysis.The coefficients of the stepwise multiple regression analysis for the dependent variable word-of-mouth are summarized in table A-26, whereas a summary is depicted in table 4.23. The correlation between the dependent variable word-of-mouth and the set of independents – satisfaction, personalizing and income frequency – is .503. The coefficient of determination shows that 25% of the variation of word-of-mouth is explained by the three independent variables. Additionally, the analysis of variance used to evaluate whether the model is significant ( 0:0 multipleH ), provided an F-value of 16.795. Based

on this F-value the null-hypothesis can be rejected (Sig=.000). Consequently, the model is significant with a reliability of 99.9%.

Antecedents of customer loyalty




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Table 4.23: Summary of stepwise multiple linear regression analysis for WoM and attitude

Dependent variables:WoM Attitude

B BSE B BSEConstant 1.235* .616 2.428*** .284

Personalizing .179*** .054 - -Apathetic - - -.136*** .029

Satisfaction .309*** .068 .211*** .043Card ownership - - .311*** .093

Income frequency -.842** .304 - -Gender - - .264* .128

R .50322 .54523

R2 .253 .297F-value 16.795*** 15.602***

Note: BSE = Std. Error of B

001.***.01.**.05.*.1. pppp

The results of the single linear regression analysis between word-of-mouth and each of the potential predictors individually are depicted in table A-27. These statistics illustrate that although not all the studied predictors had a significant linear relation with word-of-mouth. However, age of the respondents seems to be 90% reliable to have a linear influence on word-of-mouth. Additionally, acculturation preference and hedonic shopping motivation show a 95% reliable linear relation towards word-of-mouth, whereas utilitarian shopping motivation and income frequency illustrate reliability as high as 99%. Finally, the reliability of personalizing shopping motivation, customer satisfaction and loyalty card adoption is even higher (99.9% reliable). Furthermore, the correlations between word-of-mouth and each of these predictors individually appeared to be as high as .385 between word-of-mouth and customer satisfaction and as low as .139 between word-of-mouth and age of the respondents. On the contrary, the independent variables which came out being insignificant delivered a high .127 correlation between word-of-mouth and apathetic shopping motivation and a low .013 correlation between word-of-mouth and gender of the respondent. Subsequently, the coefficients of determination show that the proportion of variation in word-of-mouth explained by these individual significant predictors ranges from a low 1.9% to a high 14.8%. Consequently, customer satisfaction seems to be the most important antecedent of word-of-mouth individually explaining 14.8% of its variation.

4.4.2 Influence on attitudinal loyalty

The coefficients of the stepwise multiple regression analysis for the dependent variable attitudinal loyalty are summarized in table A-28. The assessment of the predictors of attitudinal loyalty provided similar results as those for the predictors of word-of-mouth (see table 4.23; above). However, the set of independent variables that were significant

22 Predictors: (Constant), satisfaction, personalizing and income frequency23 Predictors: (Constant), satisfaction, apathetic, card ownership and gender




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changed considerably. The correlation between attitudinal loyalty and the set of independents – satisfaction, apathetic, adoption and gender – is .545. These four independent variables appear to explain 29.7% of the variation in attitudinal loyalty as depicted by the coefficient of determination. Moreover, the analysis of variance used to test the whether the model is significant ( 0:0 multipleH ), provided an F-value of 15.602.

Based on this F-value the null-hypothesis can be rejected (Sig=.000). Consequently, the model is significant with a reliability of 99.9%.

Additionally, the results of the single linear regression analysis between attitudinal loyalty and each of the potential predictors individually are depicted in table A-29. The correlations between attitudinal loyalty and each of these predictors individually appeared to be as high as .369 between attitudinal loyalty and customer satisfaction and as low as .136 between attitudinal loyalty and marital status of the respondents. On the contrary, the independent variables which came out being insignificant delivered a slightly lower correlation of .114 between attitudinal loyalty and education level of the respondent as the highest correlation in that set. Subsequently, a look at the coefficients of determination shows that the proportion of variation in attitudinal loyalty explained by these individual significant predictors ranges from a low 1.8% to a high 13.6%. Consequently, customer satisfaction seems to be the most important antecedent of attitudinal loyalty individually explaining 13.6% of its variation. Consequently, the Beta-coefficients shows that marital status of the respondent and personalizing shopping motivation can be considered as predictors of attitudinal loyalty with the minimally required reliability of 90%. Of the other significant predictors, customer satisfaction and loyalty card adoption appear to have the highest reliabilities to be considered as predictors of attitudinal loyalty.

4.4.3 Influence on repeat purchases

The coefficients of the logistic regression analysis (standard method) for the dependent variable repeat purchases are summarized in table A-31. A model summary for the step-wise method is depicted in table 4.24. The LL2 value 99.817 shows that the model has quite an imperfect fit. However, the Nagelkerke 2R shows that as much as 56.9% of the variation in repeat purchases is explained by the three independent variables. Therefore, it can be concluded that the three independent variables – economic, apathetic and card adoption – do affect behavioral loyalty as measured by repeat purchases. When the standard method is used the variable economic seems to loose its significance, however, the variables earners, acculturation and personalizing appear to be significant at the 90% confidence level (see table A-31). Furthermore, it can be seen at the LL2 value that the model constructed through the standard method is less imperfect than the one by the step-wise method. The 2R illustrates that the total set of antecedents under investigation explains 68.6% of the variation in repeat purchase behavior. However, this higher 2R should be interpreted cautiously since it can be the result of the higher number of independent variables in the model. De Vocht (2000) suggests that the use of the adjusted 2R would be a better measure in such instances; however, the logistic regression does not provide that statistic.




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Table 4.24: Summary of stepwise logistic multiple regression analysis for variables predicting repeat purchase behavior

Dependent variable repeat purchasesPredictor B BSE eB

Economic .2821* .1182 1.3259Apathetic .4955** .1643 1.6413

Card ownership 3.3139*** .6245 27.4919Constant -5.2307*** 1.2626 -

2 8.7259

Df 7% of repeat purchase

(above median) 71.9%

LL2 value99.817

Nagelkerke 2R.569

Note: eB= exponentiated B & BSE =Std. Error of B001.***.01.**.05.* ppp

As resulted from the single logistic regression personalizing shopping motivation and apathetic shopping motivation as well as customer satisfaction each is significant (with 99.9% reliability) for predicting repeat purchase behavior (see table A-32). Furthermore, it is depicted that customer satisfaction has the ability to explain as much as 26.1% of the variation in repeat purchase behavior. Personalizing shopping motivation takes 21.9% of that variation individually on its part, whereas apathetic shopping motivation is able to explain 14.4% of that variation individually. Still, none of these three predictors produced a significant chi-square. But as shown by the LL2 value these three predictors have the lowest scores24. Consequently, they seem to be the ones with the less imperfect model fits.

The results of the logistic regression (standard method) for the dependent variable loyalty program adoption are presented in this section. The model summary statistics for the results of the step-wise method are depicted in table 4.25, whereas the results of the standard method are summarized in table A-34.

4.4.4 Influence on loyalty program adoption

As was the case with the LL2 value for the results discussed in the previous section, the LL2 value (161.030) in table 4.25 shows that this model does not have a quite perfect fit

either. However, contrary to the predictors of repeat purchase behavior, the Nagelkerke 2R shows here that a relatively few 19.3% of the variation in loyalty program adoption is

explained by the sole significant independent variable. Still, loyalty program enjoyment seems to affect loyalty program adoption.

24 Note hereby that the independent variables of which no chi-square could be produced are not considered in

the comparison of the likelihood values ( LL2 ).

Antecedents of loyalty program adoption




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Table 4.25: Summary of stepwise logistic multiple regression analysis for card adoption

Dependent variable loyalty program adoption25

Predictor B BSE eB

Enjoyment .9577*** .2313 2.6056Constant -3.3274*** .9281 -

2 11.5476**

Df 4% of adoption of focal

loyalty program 61.0%

LL2 value161.030

Nagelkerke 2R.193

Note: eB= exponentiated B & BSE = Std. Error of B001.***.01.**.05.* ppp

When the standard method is used the variable enjoyment seems to loose its significance, however, it is still the only significant predictor found for loyalty program adoption as depicted in table A-34 ( 05.0. Sig ). Additionally, the constant – being the variableadoption – also looses its significance. As a matter of fact, the constant looses that much that is significant merely at the 90% confidence level. Furthermore, there is a drop in the

LL2 value as well. When the model is constructed with the standard method this relatively smaller value of 150.436 shows that the model becomes less imperfect than the one of the step-wise method. The 2R value demonstrates that the total set of antecedents under investigation explains a quite higher percentage (28.0%) of the variation in loyalty card adoption. However, again caution has to be taken when interpreting this higher

2R since it can be the result of the higher number of independent variables in the model aswell. Consequently, De Vocht (2000) suggests that a better measure for such instances would be the adjusted 2R ; however, as stated previously the logistic regression does not provide that statistic.

On the other hand, the results of the single logistic regression show that gender of the respondents, loyalty program enjoyment, idiosyncratic fit and loyalty program design each is significant with at least 95% reliability for predicting loyalty card adoption (see table A-35). Of the four predictors, gender of the respondent has the lowest reliability as predictor, whereas loyalty program design is 99% reliable and loyalty program enjoyment and idiosyncratic fit each is 99.9% reliable. Furthermore, it is depicted that loyalty program enjoyment and idiosyncratic fit each individually and independently explains 15.8% of the variation in loyalty card adoption. Both gender of the respondent and loyalty program design perform weaker in explaining variations in loyalty card adoption, with gender of the respondent taking care of explaining 5.5% and loyalty program design covering 9.5% of the explanation. Still, only two of these four predictors produced a significant chi-square. Each loyalty program enjoyment and idiosyncratic fit has a 95% significant chi-square, whereas the chi-square corresponding to loyalty program design resulted being insignificant. Finally, the significance of the chi-square for gender of the respondent was not given in the output; since the degrees-of-freedom was lower than 1. Consequently, the Hosmer and Lemeshow goodness-of-fit test was skipped. As shown by the LL2 values the two predictors with significant chi-square scores also have the lowest LL2 scores. In this case 25 Predictors: (Constant) and enjoy




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these two had the lowest LL2 values overall, indifferent whether the chi-squares were and their significance were calculated or not. Consequently, these two predictors seem to be the ones providing the less imperfect model fits.

4 . 5 F u r t h e r a n a l y s i s o f t h e i n f l u e n c e s

Due to the limitations of regression analysis, which only assesses the existence of linear relationships, this section discusses the differences between the linear relationship tests and the non-linear relationship tests and gives some further comparisons between the two linear relationship assessment techniques used here as a discussion.

Word-of-mouthIn order to facilitate the comparison between the results of the singular- and the multiple regression analyses a combined overview of the influences was made as depicted in table A-30. Regarding the influence of the antecedents of customer loyalty on word-of-mouth, the results of the multiple linear regression analysis are quite similar to those of the single linear regression analysis. The analysis of a single linear relationship between satisfaction and word-of-mouth produced a model that was significant with 99.9% reliability – as shown by the F-value. This was also the case for the linear relationship between personalizing and word-of-mouth. Furthermore, the Beta-coefficients of both these independents plus those of the corresponding constants were also 99.9% reliable. Therefore, these independents were highly expected to be in the list of independent variables with the most significant predictable ability for word-of-mouth as obtained through the multiple linear regression analysis. Similarly, income frequency had such high reliabilities. Only the reliability of its predicting ability was below the 99.9% reliability point – the other two, as depicted in the table, were 99.9%. Consequently it was highly anticipated to see income frequency on the list of reliable predictors of word-of-mouth. However, what caused a surprise was that card ownership was not included in this set of predictors. This was bizarre, especially seeing the inclusion of income frequency, which had a less reliable coefficient. Still, the difference in reliability between the two was minimal. Based on the deletion of card ownership, the deletion of the potential predictors –as was supported by the single linear regression models – hedonic, utilitarian, acculturation and age, was quite expectable.

The insignificance of a potential predictor of word-of-mouth based on regression analysis results does not mean that there is no type of relationship between that particular independent and word-of-mouth. Therefore, the possibility of existence of any other type of relationship between word-of-mouth and potential predictors should be addressed. This assessment was quite extensively addressed in the previous section; therefore, it will be done really short here. In this regard, income frequency is the only customer characteristic measure that appeared to have a significant influence on word-of-mouth as measured both through linear relationship tests and non-linear relationship tests. Its reliability in the non-linear relationship test was as high as 95%. On the contrary, gross income and employment each had a non-linear relationship reliability of 90%, which is less than that of income frequency.




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Attitudinal loyaltyRegarding the antecedents of attitudinal loyalty, the significances of the different determinants in the multiple linear regression analysis are quite analogous to those of the single linear regression analysis (see table A-30). As was the case with word-of-mouth, the analysis of a single linear relationship between satisfaction and attitudinal loyalty produced a model that was significant with a reliability of 99.9% – as suggested by the F-value (see table A-29). This was also the case for the linear relationship between card ownership and attitudinal loyalty. Furthermore, the Beta-coefficients of both these independents plus those of the corresponding constants were also 99.9% reliable – similar to the relationship between satisfaction and attitudinal loyalty. Therefore, it was no surprise to see these independents in the list of independent variables with the most significant predictable ability for attitudinal loyalty as obtained through the multiple linear regression analysis. Similarly, apathetic that had a model that is 99% reliable coupled with 99% reliability for its predicting ability towards attitudinal loyalty, was no surprise either on the list.Contrarily, the results of the multiple linear regression analysis concerning the reliabilities of apathetic, personalizing, employment, gender, marital status and gross income were not as was anticipated. Here a distinction should be made between those variables that did well in the single linear regression analysis and lost their predictable ability in the multiple linear regression analysis and those that performed better in the multiple linear regression analysis as compared to the single linear regression analysis. In this regard, the variables personalizing, employment, marital status and gross income had significant models, significant independent coefficients and dependent coefficient (constant) with a minimum of at least 90% reliability each – in the single linear regression analyses. However, in the multiple linear regression analysis none of these four independently significant predictors of attitudinal loyalty were reliable enough to pass at least the 90% reliability cut-off point. On the contrary, gender – only having a significant dependent coefficient – made even the 95% reliability cut-off point. This shows that although there was some degree of consistency between the two regression analyses, there were some shuffling in the reliable predictors of attitudinal loyalty as well. Similarly, income frequency, education and age each had an insignificant model fit in the single linear regression analysis and were also left out of the set of reliable predictors of attitudinal loyalty through multiple linear regression analysis. Nevertheless, each appears to have some type of influence on attitudinal loyalty, with a reliability of at least 90% – as shown by the none-linear relationship tests. Contrarily, employment and gross income each has significant single linear model fits; the model fit for employment was 95% reliable, whereas the one for gross income was as high as 99% reliable. Therefore, the 95% reliability for the existence of some kind of relationship between each of these variables and attitudinal loyalty is not surprising.




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Repeat purchase behaviorFurther comparison between the results of the single logistic regression analysis and the standard logistic multiple regression analysis shows that there are quite some other differences concerning the significant independent variables – as depicted in table A-33. These differences are supposed to be caused by the low fit of the model as depicted by the significance of the chi-square. Earners for example which was not significant in the single logistic regression analysis can be considered as a predictor of repeat purchase behavior with 90% reliability – based on the results of the standard logistic multiple regression analysis. Similarly, acculturation and card ownership can be considered as predictors of repeat purchase behavior with the same levels of reliability – from results of the standard logistic multiple regression analysis as compared to those of the single logistic regression analysis – where the constants appeared to be insignificant. A comparison – between the results of the standard logistic multiple regression analysis and its stepwise counterpart – illustrates that there are quite some differences between these two analysis as well. Economic becomes significant – at the 95% confidence level – in the stepwise method, while it was not significant in the standard method. Similarly, the constant becomes significant even at the 99.9% confidence level, while in the standard method it was insignificant. Card ownership maintains its reliability between the two different analyses, producing the same significance results. The three variables –personalizing, acculturation and earners – that could be considered significant with 90% reliability as illustrated by the standard logistic multiple regression analysis, are each insignificant as appears from the stepwise method. Last but not least, apathetic switches from 95% reliability in the standard method to 99% reliability in the stepwise counterpart thereby maintaining its influence on repeat purchase behavior between the methods.

At first site the results of the standard logistic multiple regression analysis concerning the significance of the coefficients for personalizing and apathetic, appear strange. The higher significance for apathetic may be questionable, since single logistic regression analysis showed that the significance of the constant coupled with apathetic is lower. However, a closer look to the ‘critical values of the chi-square distribution’ table26 shows that a chi-square of 11.07 is required to be significant at the 5% significance level. As is shown in table A-32, the chi-square corresponding to apathetic comes closer to this requirement. This could be the explanation for the results of the standard logistic multiple regression analysis regarding the coefficients of the two independent variables apathetic and personalizing. Remember that the relationships as discussed previously are all linear relationships. Although the variables head of HH, employment, education and gross income do not have any linear relationship with repeat purchase behavior, the same statement does not hold for any type of relation. As illustrated in table A-33, with 99% reliability there is an apparent influence of gross income on repeat purchase behavior. Similarly, there are apparent influences – at the 95% confidence level – of both education and employment on repeat purchase behavior. Additionally, there seems to be 90% reliability for the influence of head of HH on repeat purchase behavior.

26 Source: Cooper and Schindler (2003) exhibit G-3




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Card adoptionThe results of the multiple logistic regression analysis are as expected from the logistic –single – regression analysis with enjoyment as the sole predictor of card ownership based on the existence of a linear relationship between the two. This exclusiveness in predicting ability was anticipated, since the Beta-coefficient for enjoyment (.8509) was a bit higher than that for idiosyncrasy (.7253). However, in the logistic –single – regression analysis idiosyncrasy and enjoyment seemed to explain the same amount of variance in the dependent variables’ variation (15.8%). And the difference in the LL2 values – enjoyment (182.380) compared to idiosyncrasy (175.967) – is quite small, however the LL2 values show that the model with idiosyncrasy as independent variable has a better fit. Conversely, the stepwise method logistic multiple regression analysis illustrates that the significance of both variables – the dependent loyalty program adoption and the independent enjoyment – increase as compared with the results of the standard method. Both variables coefficients become 99.9% reliable. Furthermore, albeit the single logistic regression analysis results show that idiosyncrasy also has a logistical linear relation with loyalty program adoption, this relationship is not supported by either logistic multiple regression analyses. However, again a closer look to the ‘critical values of the chi-square distribution’ table27 shows that chi-square values of 9.49 and 5.99 are required for enjoyment and idiosyncrasy respectively to be significant at the 5% significance level. The chi-square corresponding to enjoyment is relatively greater compared to the corresponding required chi-square statistic (see table A-35). Further statistics of interest – as depicted in table A-36– relate to privacy, gender and design. When testing the logistic linear relationship between gender and card ownership the chi-square could not be computed due to the existence of less than 1 degree of freedom. However, the coefficients of both gender and the dependent card ownership seem to be at least 90% reliable. Similarly, the coefficients of both design and the dependent card ownership are reliable even at a higher 99% confidence level. But, in the analysis of this relationship no significant chi-square statistic was found. Conversely, the analysis of the relationship between privacy and card ownership produced a significant chi-square statistic at the 90% confidence level. But in this case the coefficient for the dependent card ownership was not significant. Only the significance of the coefficient of privacy was 90% reliable.

Again, it should be noted that the apparent relationships as discussed above were all linear relationships. Although gross income do not have a linear relationship with card ownership and it was hard to find a linear relationship between gender and card ownership, this same inexistence or difficulty does not hold for the existence of other types of relationships. As illustrated in table A-36, with 99% reliability there is an apparent –probably non-linear – influence of gross income on card ownership. Similarly, there is an apparent influence – at the 95% confidence level – of gender on card ownership.

27 Source: Cooper and Schindler (2003) exhibit G-3




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CHAPTER 5: CONCLUSIONS AND IMPLICATIONS

In this chapter the conclusions and implications that coincide with the covered topic are discussed. These conclusions and implications should solve the main problem. Hence, for the comprehensibility it is started by repeating the problem statement here:

5 . 1 C o n c l u s i o n s a n d d i s c u s s i o n

ConclusionsConcluding, a number of significant conclusions can be drawn from the results discussed previously. Firstly, it was clear that Kong Fui had quite high overall customer satisfaction scores. However, there are some aspects of customer satisfaction that performed less good then other. These aspects on which Kong Fui performs relatively bad are service provided by the cosmetics’ department personnel, the overall store cleanliness inside and the service provided by the fresh meat department personnel. On the contrary, service provided at check-out point, closely followed by friendliness of cashiers, service provided by baggers and availability of everyday grocery items among others resulted in the customer satisfaction aspects on which Kong Fui was relatively strong. Secondly, not all the hypotheses28 related the antecedents of customer loyalty could be rejected29. Still, a distinction is made between those related to the behavioral part of customer loyalty and those related to the attitudinal part of it. Both dimensions had some hypotheses that could not be rejected. Concerning behavioral loyalty, there was sufficient evidence for the existence of a positive effect of personalizing shopping orientation on behavioral loyalty (hypothesis H1.1a). Similarly, behavioral loyalty seems to be influenced positively by apathetic shopping motivation (hypothesis H1.3a). Customer satisfaction also resulted to have a positive influence on behavioral loyalty (hypothesis H3.1a). Furthermore, there was sufficient evidence for the existence of an effect of customers’ income level on behavioral loyalty (hypothesis H5.4a). Acculturation preference also resulted having an influence on behavioral loyalty (hypothesis H6a). On the other hand, attitudinal loyalty seemed to be affected by customers’ age (hypothesis H5.2b). Similarly, sufficient evidence was gotten for the existence of the influence of educational level on attitudinal loyalty (hypothesis H5.3b). Customers’ income level also appeared to have effects on attitudinal loyalty (hypothesis H5.4b). Last but not least, the influence of income inflow frequency also seemed to be significant (hypothesis H5.6b).

28 Note that H3.2a and H3.2b were excluded from the analysis, due to the magnitude of the research and their

presumed lack of added-value for the completeness of this particular research. However, for the completeness of the conceptual model, they were considered value additive. Refer to table A-37 for an overview of the rejected and supported hypotheses.

29 Note that although regression analyses were used the hypotheses-testing were based on the non-linear relationship tests.





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The other hypotheses were rejected for differing reasons; 1. some hypothesized an influence of the predictor on customer loyalty on the opposite

direction as shown by the results, e.g. the utilitarian shopping motive appeared to have a positive influence on behavioral loyalty as opposed by the hypothesis, therefore the hypothesis was rejected

2. others just did not get sufficient evidence to support them, e.g. customer satisfaction appeared to have no effect on attitudinal loyalty, thereby the likelihood of a positive effect was even less, and

3. still others were only partially true – as far as behavioral loyalty is concerned, e.g. the presence of an influence of hedonic shopping motivation on behavioral loyalty was supported only by repeat purchase behavior; word-of-mouth seemed to be unaffected by the hedonic shopping motives

Thirdly, not all the propositions regarding the antecedents of loyalty program adoption could be rejected. However, it is particularly peculiar that only propositions related to customer characteristics were not rejected. In this regard, only gender and income level resulted in predictors of loyalty program adoption. Thus, although the gender of the head of household does not seem to be influential on the adoption of loyalty programs, the gender of the customer does. However, household income appeared to be significantly influential on loyalty program adoption (hypothesis HA4.2a). Even stronger, sufficient evidence was gathered for the more focal proposition that middle income households are more inclined to adopt loyalty programs than low – and high income households (hypothesis HA4.2b). Fourthly, the assessments of linear relationships between the antecedents of customer loyalty and customer loyalty also produced mixed results. Although there were great similarities in the results of singular – and multiple regression analyses, there were some remarkable differences as well. In this regard, personalizing shopping motivation, apathetic shopping motivation, customer satisfaction, loyalty card ownership, employment status, marital status and income level each separately seemed to have significant linear influences towards attitudinal loyalty. However, when all the studied potential predictors are regressed against attitudinal loyalty only apathetic shopping motivation, customer satisfaction and loyalty card ownership seem to maintain their importance for influencing attitudinal loyalty. Additionally, gender of the customer becomes significantly influential on attitudinal. Similar relationships between the singular – and multiple regression analysis were obtained in the case of the different dimensions of behavioral loyalty. When measured singularly personalizing shopping motivation, hedonic shopping motives, utilitarian shopping motives, customer satisfaction, acculturation preference, loyalty card ownership, income frequency and age each appeared to be influential for word-of-mouth. However, when regressed as a set of independents only income frequency, customer satisfaction and personalizing shopping motivation were strong enough to claim their linear influential effect. Conversely, personalizing shopping motivation, apathetic shopping motivation and customer satisfaction showed significant linear influential effects, when their predictability for repeat purchase behavior was evaluated singularly. However, when the overall model was evaluated by the set of independents only apathetic shopping motivation had enough importance in predicting repeat purchase behavior as to be selected as a predictor. And loyalty card ownership appeared to be a strong predictor when its influential ability was assessed together with the other potential predictors.




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Fifthly, the evaluation of the linear relationships between loyalty card adoption and its antecedents produced mixed results as well. Whereas the gender of the customer, loyalty program enjoyment, loyalty program design and idiosyncrasy seemed to have individual linear effects on loyalty program adoption, when their influence was assessed mutually only loyalty program enjoyment showed significant influence.

DiscussionIn this regard, the results are discussed in light of the goal of the research and the research question. The data provided the ability to answer the research question. Although the different techniques used provide some differences as to which are the predictors of customer loyalty, some insight has been obtained as to which variables influence customer loyalty. In this regard, it can be stated that the antecedents of customer loyalty are each of the three shopper characteristics, both utilitarian – and hedonic shopping motives, customer satisfaction, acculturation preferences, loyalty card ownership, and some customer characteristics. These customer characteristics are employment status of the head of the household, frequency income inflow, level of education of the customer, gross income of the household, age category of the respondent, gender of the head of household, marital status, number of earners of income in the household and gender of the customer. Thus although the each of these antecedents have different levels of influence and appearto be insignificant in some of the analyses overall each of them appear to have some kind of influence on customer loyalty. Although some of the hypotheses were rejected, it is considered appropriate not to try to put any link with the studies on which this one is based. This inappropriateness is based on the fact that the results of this study were not validated. Furthermore, this study was aimed to give some preliminary insight into the antecedents of customer loyalty and of loyalty program adoption on Aruba and specifically at Kong Fui. Another important issue concerning this study is that a distinction has to be made between the non-linear relationship tests and the linear relationship tests assessed through regressions. The regressions were used to test the fit of the conceptual model, whereas the non-linear relationship tests were to assess whether there is any type of relationship between the dependent variables customer loyalty and loyalty card adoption and their respective antecedents. Therefore, the hypotheses were tested based on the non-linear relationship test. The hypotheses did not propose the existence of linear relationships between the dependents and their respective antecedents. They only suggested some kind of effect. What was remarkable as well was the influence of gender of the customer on the adoption of loyalty programs. However, this can be assumed to the human nature of people, where men are less inclined to bind themselves by contractual things such as membership cards as opposed to women.

5 . 2 I m p l i c a t i o n s a n d e v a l u a t i o n

5.2.1 Implications for further research and evaluation

For a preliminary study, the results seem quite satisfactory. However, there is always room for improvement due to the limitations. In this regard, there are some limitations related to the collection of data and interpretation of the results. A first limitation might be the exclusion of some important variables. In this regard, it would be suggested to




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look for ways of incorporating the assessment of the influence of some antecedents of customer loyalty as studied in other contexts in the grocery retailing sector.

Secondly, this study focused on consumer-specific moderators of customer loyalty, but it would be challenging to assess the role of other contingency- and moderating factors. For example, it might be interesting to do some additional studies over different and several grocery stores. This would benefit the ability to generalize the findings. Furthermore, the relationships between customer loyalty and its antecedents are probably much more complex than initially assumed. Therefore, apparently this study has looked only at a part of the relationships. The way in which these relationships are moderated by e.g. the product category – including the distinction between food – and non-food section of a grocery store – or the buying and usage process for that category is likely to influence these relationships. Consequently, one suggestion for further research would be to study the moderating power of such effects. Such studies would advance customer loyalty research as well as be of great value for managers. Another important issue is the effect of the customer satisfaction aspects – as evaluated in this study – directly or indirectly on customer loyalty. In the food and grocery context it is possible that improvements in friendliness of the cashiers, for example, would have a large effect through customer satisfaction or even directly on customer loyalty for some shoppers. Consequently, a better understanding of such interrelationships, coupled with a good knowledge of the customers visiting the individual store, would be of great help for managers to efficiently increase customer loyalty towards their store. Similarly, the assessment of customer loyalty per product category as such could enhance the understanding of such interrelationships as well.A third potential limitation is related to the way that behavioral loyalty was measured here. Since it was based on self-reports the true meaning may be only partially captured. Therefore, and based on different studies questioning the predictive validity of self-reported behavior (i.e. Morwitz, Steckel and Gupta, 1997), it would be advised to assess behavioral loyalty through longitudinal studies or database information. In this regard, it should be remembered that the results could be strengthened with access to behavioral data on customer purchase histories that are not subject to potential recall loss (as advised by DeWulf, Odekerken-Schröder and Iacobucci, 2001). The use of longitudinal data would be particularly important to assess the effect of particular improvements related to the offering of the store (s) under investigation. Thereby, the effect on behavioral loyalty could be more directly discovered. Fourth, another shortcoming in the study is common method bias (as in DeWulf, Odekerken-Schröder and Iacobucci, 2001). This study based itself on one questionnaire to measure all the constructs represented in the conceptual model. So, perhaps the strength of the relationships among these constructs may be somewhat inflated. Therefore, another suggestion would be to take the necessary steps in correcting this possible bias or at least investigate whether the bias really exists. A fifth shortcoming would lie in the analytical techniques used. Thereby, the use of more advanced statistical applications, such as LISREL, as well as the validation of the model is advised. These applications could possibly provide a better assessment of the overall model fit and its validity. Additionally, wider samples – both in terms of number of clubs included and number of respondents – will probably facilitate improved results. Similarly, some issues were raised by the findings related to loyalty card adoption as well. In this regard, especially




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consumers’ reasons for acquiring cards should be further assessed. Additionally, the further investigation of the prevalence of multiple memberships and its influence on customer loyalty is also suggested. Another suggestion for further research would be the assessment of card loyalty e.g. as assessed by Mauri (2003). The determination of the impact of multicollinearity on the results and the application of the necessary remedies –which lied outside the scope of this study, but were suggested by Hair et al. (2006) in their coverage of multicollinearity – are both suggested to be incorporated in further research as well.Consequently, these recognized limitations of this study together with the additional advises based on potential room for improvement, could inspire researchers to define their future research agendas.

Ultimately, the study report can be as a means for contrasting its findings against previous research as stated previously. Consequently, its findings are subject to comparisons and improvements when necessary – e.g. the application of the necessary remedies concerning multicollinearity if needed; as suggested by Hair et al. (2006). Regardless, the idea of this writing has been to stimulate further advances in this important and challenging area on Aruba and around the world.

EvaluationAlthough the relationships identified by study mostly fit the theoretical predictions, there is some degree of contradiction as well. Support was found for various hypotheses as adopted from prior research. Still, some seemed insignificant in this context – as covered previously. Hence, this is precisely one of the contributions of this research. It further expands the extensive set of studies on food and grocery shopping thereby further enhancing the ability to contrast findings against previous research in the same branch, as reasoned by Jamal et al. (2006). This contrasting ability is further enhance by the main contribution of this research as to it was based on the investigation of determinants of customer loyalty in a grocery context in a small economy. Referring to the other reason of Jamal et al. (2006) – grocery shopping is an ongoing and essential activity whereby consumer decision-making within the grocery environment can be highly involving – to study food and grocery shopping it can not be said with precision whether this is the case in this population or not. Still, it was not the objective either to investigate this. However, their reasoning that is well supported in this research is that the Western grocery shopping context is influenced by hedonic feelings.Last but not least, the study gives useful insight into the antecedents of customer loyalty in a grocery context on a small island where the vast majority of products are being imported. However, a validation of the results would have further embraced the strength of the study-report.

In evaluating the research process, it should be noted that no matter how good things are planned, there will almost always be deviations from the plans. For example, although the idea was to use the equation for assessing loyalty as proposed by Day (1969), this was not possible due to the categorical measure for behavioral loyalty used. Still, the determinants of loyalty have been assessed here, by looking at the determinants of both behavioral- and attitudinal loyalty separately, was necessary anyhow to test the hypotheses. Concerning the data collection, it was planned to hold the survey during a weekend and some days of the subsequent week. Furthermore, it was thought that the period – being the end of the month – would guarantee sufficient respondents in that short period.




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However, that turned out not to be the case. Therefore, the decision had to be made to prolong the survey period. The adaptation in the scaling related to the item “How satisfied are you with Kong Fui Supermarket?” appeared not to be a good option for the analysis. Due to this adaptation in the scaling, it was difficult to construct a combined variable representing the mean of the three items of customer satisfaction. Therefore, a solution was found in recoding the answers of that particular question into a new 5-point scale, whereby a new classification30

was maintained. The idea behind the usage of such a classification was not to bias the distribution of the combined measure, whereby class’ means are used for the item “How satisfied are you with Kong Fui Supermarket?”

5.2.2 Managerial implications

As far as the managerial implications are concerned, the results of the analysis revealed different options available to the management of Kong Fui to answer the problem they are dealing with. However, for clarity reasons this problem is presented again here, prior to the discussion of the possible actions to be undertaken.

In order to increase the purchases of their current membership-card owners, the following options seem to be available and the corresponding recommendations are as follows:

Recommendation 1:

Firstly, it is recommended to the management of Kong Fui to make sure that a shopping experience is offered in the supermarket. This is extremely important, since the shoppers of Kong Fui do not consider the core product as the reason for coming back. Instead of basing their decision to come back on the product they are shopping for, they rate both personalizing – and apathetic shopping motivation as determinants for their repeat visit. This is extremely important in this context, since there is such a concentration of supermarkets on the small island of Aruba, that it is vital for a supermarket striving for continuity to present a value-offering to the customers, rather than the core product-offering. This need for offering service as supplementary to the core product further supports the necessity of applying the necessary chances as would be advised in the next recommendation.

Recommendation 2:

The second recommendation concerns the customer satisfaction enhancing aspects. Due to the importance of customer satisfaction for customer loyalty as resulted from this study and is supported by many prior studies, it is advised to concentrate on the aspects where Kong Fui is not performing so well. These customer satisfaction enhancing aspects include service provided by the cosmetics’ department personnel, the overall store cleanliness inside and the service provided by the fresh meat department personnel. Consequently, it

30 1-2-3-4-5 1.5-3.5-5.5-7.5-9.5





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is advised to take care especially of these two service aspects. Both are related to departments of the grocery store where the customer comes in direct contact with the personnel. Thereby, there is communication between the customer and your personnel. Apparently the dissatisfaction with these services is caused by this communication. Thereby, it would be recommended to take a closer look at these aspects. And if communication is really the cause and more specifically the language barrier than it would be suggested to at least make sure that the personnel in these departments can understand and talk the host language by providing them teaching possibilities. The essentiality of this suggestion further lies in the in the importance that is given to acculturation preference and its influencing power towards repeat purchase behavior and word-of-mouth. Similarly, it is recommended to take a closer look to the overall cleanliness aspect in the store and make the necessary changes to provide an overall clean shopping environment. On the contrary it is recommended to make sure that the relatively strong position of the remaining aspects is guarded and if possible even improved.The lack of guaranteeing an improvement in the customer satisfaction aspects performing poor would possibly have a direct impact on the value-proposition since the total value offering would not be suitable without the necessary improvements. Consequently, it will lead to further drop in the repeat purchases and possibly even lead to clients leaving Kong Fui and patronizing other supermarkets

Recommendation 3:

Furthermore, related to who are the behaviorally loyal customers, it is suggested to do some further analysis in order to assess this. This would be important since this study shows that customers with lower levels of income are more inclined to come back and to spread word-of-mouth. Consequently, although the high earners are the ones who possibly would spend more with each visit, on the long run it is possible that the low earners are more profitable for the supermarket anyhow.

Recommendation 4:

Last but not least, it is advised to try everything that is manageable to get more program subscribers. Especially, the saving abilities of the program would be crucial in this regard. Therefore, it is suggested to come up with campaigns to make customers and potential customers aware of the benefits of subscription to the program. Once the customers know that they get discounts besides the gifts that are available at years’ end the chances are considerable that they will stick to Kong Fui for their periodic purchases. Although this does not have any instantaneous income potential such as subscription fees, the results showed that card ownership is a significant predictor for repeat purchase behavior.


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