Master Thesis Bjorn Nijmeijer final version

GOING FOR VALUE

OR

GOING FOR DISCOUNT

A research on pricing strategies

and

store loyalty implications

Bjorn Nijmeijer

February 2010

GOING FOR VALUE OR

GOING FOR DISCOUNT

A research on pricing strategies

and

store loyalty implications

Author: Faculty of Economics and Business

Bjorn Nijmeijer (s1537024) Master thesis: Marketing Management

Petrus Driessenstraat 7 February 2010

9714 CA Groningen Supervisors:

[email protected] Drs. J. Berger

0612115360 Dr. J.E.M. van Nierop

© Bjorn Nijmeijer 2010 - 1 -

MANAGEMENT SUMMARY

Pricing strategies are an important issue in retail marketing nowadays. The Dutch supermarket industry is

characterized by different pricing formats such as EveryDay Low Pricing (EDLP) and High/Low Pricing (HILO). In

addition, hybrid structures of these pricing formats are also present in this industry. During the pas decades,

few studies have addressed the implications of executing these specific types of pricing strategies. Those that did

address its implications merely focused on retailer profitability and competitive forces. Thus far, no attempts

have been made to assess the implications of executing such a pricing strategy on customer store loyalty

intentions. In addition, it would be of great value to identify those customers that prefer a specfic pricing format

in favor of the other ones. Therefore, the purpose of this research is twofold: Firstly, we will make an attempt to

identify those factors that influence the process of store loyalty creation in case of either a HILO- or an EDLP-

pricing strategy. Secondly, we will segment customers based on their scores on the relevant variables that we

identified in the first part of this research. This brings us to the following problem statement:

What factors influence the creation of store loyalty under different pricing strategies and which loyalty

segments can be distinguished for each pricing strategy?

Literature review revealed that both pricing strategies have their own advantages and disadvantages when it

comes to consumer attractiveness. HILO-retailers should be especially aware of consumer price consciousness

and the importance that consumers attach to visiting this type of retailer. On the other hand, literature review

suggests that EDLP-retailers have to cope with consumer deal proneness, consumer scepticism, and the quality

of its merchandise and service.

To test the specific store loyalty contributions of the previously suggested factors, 300 respondents

were asked to fill in a questionnaire. These 300 respondents consist of 150 HILO-shoppers and 150 EDLP-

shoppers. Regression analysis revealed that in the case of a HILO-pricing strategy, the factors ‘price

consciousness’ and ‘merchandise and service quality’ both significantly facilitate the creation of store loyalty

intentions. Implications are that HILO-retailers should especially pay attention to price promotions and the

quality of both its products and service environment in order to contribute to the development of loyal

customers. For the EDLP-pricing strategy, ‘deal proneness’ and ‘merchandise and service quality’ both

significantly facilitate the process of store loyalty creation. These results are somewhat suprising, since

literature review suggested that EDLP-shoppers could develop a perception of low merchandise and service

quality, due to the advertised consistently low prices. Literature review also suggested the emergence of deal-

loyal customers in reaction to the promotional deals advertised by the EDLP-retailer. However, we could not

find any evidence for this statement. Additionally, the factor ‘consumer scepticism’ was found to impede the


creation of store loyalty under an EDLP-pricing format. A possible explanation for the existence of this

phenomenon may be the fact that EDLP-claims are difficult to verify and therefore impede the creation of a

relationship based on trust and honesty. Perhaps the most surprising result is the fact the factor ‘merchandise

and service quality’ turned out to be of particular important influence for both pricing strategies. This result is in

line with previous research on the relationship between a satisfactory in-store shopping experience and retailer

loyalty. It seems that the wellknown SERVQUAL-model is yet again confirmed.

The second part of this study focused on the creation of loyalty-based segments. By combining a cluster

analysis with multiple discriminant analyses, different loyalty-based segments could be established for both

pricing formats. Results show that for both pricing strategies, families with a household size of 4 or more

persons turn out to be the most store loyal. A possible explanation for the presence of stronger store loyalty

intentions among these customers is the fact that in most traditional Dutch families, women are the ones who

do the weekly grocery shopping. This recurring event may lead to a more convenience-based shopping pattern

as these women patronize the retailer on a weekly basis. In addition, those households that consist of merely 1

or 2 young persons turn out to be the least loyal with regard to either the HILO- or EDLP-retailer. These weak

store loyalty intentions may be the result of the flexibility and individuality that these consumers experience

nowadays.

Both pricing strategies have their own managerial implications for the type of retailer concerned. HILO-

retailers should pay special attention to the price consciousness of its most promising customers, families with

children. Cents-off deals and rebates on specific merchandise may attract these type of customers to its store.

Another possibility exists in the introduction of a so-called “Loss Leader”, in combination with specific family

merchandise to generate larger sales. On the other hand, EDLP-retailers should focus on the deal proneness of

its customers by emphasizing its product deals in conjunction with its dedication to undersell the competitors.

But, since we have to deal with well-informed customers these days, caution is advised. A possibility here is to

offer ‘packaging deals’, fine-tuned for its most promising customers. Here, one could think of promotions in the

form of “buy 2 boxes of diapers, get one for free” or “buy 6 bottles of soda and receive a free box of candy”.

Finally, as a general recommendation to both HILO- and EDLP-retailers: The quality of merchandise and service

turned out to be of surprising importance to the creation of store loyalty intentions. Therefore, we advice both

HILO- and EDLP-retailers to closely monitor the quality of the different aspects of their store and merchandise,

before going deeper into segment-specific marketing communication- and promotiontools.


PREFACE

This is it! This final thesis marks the end of my study at the University of Groningen. The past four years have

been exciting in many different ways. From my first lectures to my internship at L’Oréal, up to the last exams, I

definitely wouldn’t have missed it.

Almost ready to enter the labour market, I can look back with much satisfaction on the progress of establishing

this final piece of work. This thesis offered me the opportunity to apply several of those theories and techniques

that I have learned throughout the past years, and experience how it is to conduct research in the field. I have

experienced this graduation project as a unique learning experience and a perfect preparation for my

professional development in the upcoming years.

My sincere thanks go out to all those teachers and fellow students that have contributed to both my personal

and professional development. A special word of thanks goes out to Dr. J.E.M. van Nierop for his feedback and

advice regarding the completion of my thesis. But most of all I would like to thank my supervisor, Drs. J. Berger. I

want to thank him for his instructive insights, clear advices and guidance throughout the process of conducting

this research.

Finally, I would like to thank my parents and my girlfriend Lieke for their financial and social support during the

years that I studied in Groningen.

Bjorn Nijmeijer

Groningen, February 2010


TABLE OF CONTENTS

MANAGEMENT SUMMARY ................................................................................................................ 1

PREFACE ........................................................................................................................................... 3

1 INTRODUCTION.......................................................................................................... 5

1.1 Pricing formats....................................................................................................................... 5

1.2 Relevancy............................................................................................................................... 6

1.3 Customer satisfaction ............................................................................................................ 7

1.4 Problem statement ................................................................................................................ 8

1.5 Overview and contribution.................................................................................................... 9

2 THEORETICAL FRAMEWORK........................................................................................ 10

2.1 High/Low Pricing.................................................................................................................... 10

2.1.1 Definition of High/Low Pricing.............................................................................. 10

2.1.2 Implications of executing a HILO pricing strategy ................................................ 11

2.2 Everyday Low Pricing ............................................................................................................. 15

2.2.1 Definition of Everyday Low Pricing ....................................................................... 16.

2.2.2 Implications of executing an Everyday Low Pricing strategy................................ 16

2.3 Conceptual model.................................................................................................................. 22

3 RESEARCH DESIGN...................................................................................................... 23

3.1 Data collection ....................................................................................................................... 23

3.2 Design of the questionnaire................................................................................................... 24

3.3 Plan of analysis....................................................................................................................... 25

3.4 Quality demands.................................................................................................................... 27

4 RESULTS..................................................................................................................... 28

4.1 Descriptives and representativeness of the sample.............................................................. 28

4.2 Regression analysis ................................................................................................................ 31

4.2.1 HILO-pricing strategy............................................................................................ 32

4.2.2 EDLP-pricing strategy ........................................................................................... 34

4.3 Cluster analysis ...................................................................................................................... 35



4.4 Discriminant analysis I ........................................................................................................... 36



4.5 Discriminant analysis II .......................................................................................................... 40



4.6 Profiling the loyalty clusters .................................................................................................. 43



5 CONCLUSIONS AND RECOMMENDATIONS................................................................... 46

5.1 Conclusions ............................................................................................................................ 46

5.2 Limitations and future research............................................................................................. 50

REFERENCES...................................................................................................................................... 53

APPENDICES...................................................................................................................................... 57


1 INTRODUCTION

‘Every Day: Euro’s Cheaper’ is one of the 7 so-called ‘Daily Certainties’ offered by the Dutch retail chain Jumbo.

The idea behind this certainty is that Jumbo promises to offer the lowest price on specific A-brand products in

the Netherlands. If consumers do find a lower price at competitors, Jumbo will correct its price and offer the

specific consumer a free copy of the product concerned. This phenomenon is not only applicable to the Dutch

market, but does also apply for US- and UK-supermarkets. Perhaps the most well-known example of a retailer

offering its products for a consistent low price is Wal-Mart, worldwide number one in retail. Under the slogan

“We Sell for Less. Always”, Wal-Mart tries to convince its customers of its dedication to undersell the

competition (Keller, 1998).

1.1 Pricing formats

The similarity between previous examples can be found in the way these retailers try to offer products for a

consistent low price. According to Levy and Weitz (2009), this concept is called ‘everyday low pricing’ (EDLP).

This pricing strategy emphasizes the continuity of retail prices at a level somewhere between the regular non-

sale price and the deep-discount sale price of so-called ‘high/low’ (HILO) retailers. These HILO retailers form the

opposite when it comes to adopting a retail pricing strategy; retailers using a HILO strategy tend to discount the

initial prices for merchandise –often weekly- through frequent sales promotions (Levy and Weitz, 2009).

According to Bailey (2008), HILO and EDLP form the two opposite bases of pricing strategy formulation. With the

former strategy, retailers seek to response on competitive moves and a positive response of value-conscious

customers, while the latter one is incorporated by retailers trying to cope with the problem of consumer-

scepticism with respect to initial retail prices (Bailey, 2008).

Additionally, both pricing approaches include advantages and disadvantages for the retailer. Firstly,

retailers pursuing a HILO pricing strategy have an opportunity to increase profits through price discrimination.

Higher prices can be charged to customers who are not price sensitive and are willing to pay the “high” price and

lower prices to price-sensitive customers who will wait for the “low” sale price. Furthermore, HILO pricing

strategies do create excitement and a “get them while they last” atmosphere. Retailers even augment low prices

and advertising with special in-store activities, like product demonstrations, giveaways, and celebrity

appearances (Levy and Weitz, 2009). In addition, frequent sales promotions allow retailers to get rid of slow-

selling merchandise. Some retailers have found, however, that adopting a HILO pricing strategy can be

dangerous. As customers learn to expect frequent sales, many simply wait until the merchandise they want goes

on sale (Levy and Weitz, 2009). EDLP pricing, on the other hand, assures customers that they will get the same

low prices every time they patronize the EDLP retailer. The term everyday low pricing is in this case somewhat


misleading, because low doesn’t mean lowest. At any given time, a sale price at a HILO retailer may be the

lowest price available in a market (Levy and Weitz, 2009). Additionally, executing an EDLP pricing strategy

encompasses several operational advantages; stable prices limit the need for (weekly) sale advertising and

incurrence of labor costs of changing price tags and putting up sales signs. In addition, Levy and Weitz (2009)

state: “EDLP reduces large variations in demand caused by frequent sales with large markdowns. As a logical

result, fewer stock outs mean more satisfied customers and higher sales.”

The concept of pricing strategies has been researched various times during the last decades. In 1991, Bucklin and

Lattin researched the effect of in-store price promotions, as executed by HILO-retailers. By proposing a two-state

model of purchase incidence, the authors found that consumers which have not planned their purchase in a

category (called opportunistic information processors) may be strongly influenced by in-store price promotions,

in contrast to consumers which in turn did plan their purchase in a category (called planned information

processors). The implication of this effect may be that the successful execution of a HILO-pricing strategy is

availed at the unplanned shopping behavior as executed by the consumer. In addition, Kahn and Schmittlein

(1992) conducted research in the field of pricing strategies in the United States. By analyzing shopping trip data

and the purchasing process, the authors tried to explain shopping trip behavior and the decisions made by

consumers. Among others, the outcomes showed that consumer tendencies to use coupons, which reveal that

these consumers shop at a HILO-retailer, are greater on “major” shopping trips, in contrast to so-called “quick”

shopping trips. This outcome leads to the consideration that there is a positive relationship between the

importance of a shopping trip and the chances that consumers are willing to use coupons and thus visit a HILO-

retailer (Kahn and Schmittlein, 1992). Finally, several studies on consumer responses to alterations in pricing

strategies are insightful. Mulhern and Leone (1990) conducted an event study of a discrete change in a store’s

pricing strategy. Their results suggested that a change from EDLP to a HILO-strategy increased sales but

decreased the traffic generated to the store. Additionally, Hoch, Drèze, and Purk (1994) investigated the impact

of category-level pricing strategy changes on sales responses and found that EDLP gave a small win to

manufacturers, but represented a big loss for the retailer in profits. Paradoxical, Ailawadi, Lehmann, and Neslin

(2001) revealed that a change from HILO to EDLP pricing strategy led to a decrease in market share, exacerbated

by increases in advertising. Backed by the results from previous studies, we are curious about the underlying

factors that accounted for these consumer responses to a change in pricing strategy. Under which conditions

might consumers respond differently to a HILO or EDLP strategy?

1.2 Relevancy

At the moment of writing, the Dutch supermarket industry is experiencing a race for market share. Market

leader Albert Heijn (31% share, September 2009) and runner-up C1000 (12% share, September 2009) started


with enormous promotional activities to attract new customers. Albert Heijn set up a promotion which should

seduce consumers to buy for at least 10 Euros in order to receive a package with puzzle pieces. After collecting

all 24 packages, a soccer puzzle can be completed (www.ah.nl). Furthermore, competitor C1000 established a

promotion targeted at families with young children. For each 10 Euros spend at a C1000 supermarket, the

consumer receives a so-called ‘GOGO’; a small toy for children in the age of 3 to 12 years old. In total, 301

different ‘GOGOs’ can be collected at C1000 supermarkets (www.c1000.nl). It should become clear that both

supermarkets carry out symptoms of a HILO-strategy. By aggressively advertising its promotions and creating a

“get them while they last” atmosphere, both retailers try to capture market share in the Dutch supermarket

industry. A totally different strategy for capturing market share is executed by EDLP-retailer Jumbo

supermarkets (5% share, September 2009). On October 19 2009, Jumbo spread the news that it would take over

one of its competitors, Super de Boer (6.8% share, September 2009). If Jumbo succeeds in this acquisition, it will

grow to about 12% market share, thereby becoming the second largest players in the Dutch supermarket

industry (www.distrifood.nl).

1.3 Customer satisfaction

From the preceding, it should become clear that the Dutch supermarket industry is experiencing turbulent times

in the run for the consumer. Different pricing strategies are being carried out to win the trust of the consumer.

One field of interest which has not been addressed frequently in the literature is the effect of these different

pricing strategies on customer satisfaction. Thus far, research on this topic has revealed that consumers have

expressed preference for a specific pricing format, based on the size of their shopping basket and the frequency

of store visits. Moreover, Bell and Lattin (1998) suggested that consumer demographics can have a moderating

influence on the relationship between pricing format, store preference, and in addition customer satisfaction.

Furthermore, a study performed on the effectiveness of marketing expenditures conducted by Kumar and Basu

(2008) shows contradictory results. The authors revealed that on the one hand frequent sales promotions and

on the other hand the use of consistent low prices both positively influences customer satisfaction. Taken as a

whole, it should become clear that research has been conducted on the relationship between different pricing

formats and the facilitation of customer satisfaction; however, no clear advice can be given regarding which

pricing strategy to incorporate in which situations.

Furthermore, several researchers have investigated the topic of customer satisfaction and the way in

which it facilitates loyalty (Bloemer and Kasper, 1995; Bloemer and de Ruyter, 1998; Omar and Sawmong, 2007;

Vesel and Zabkar, 2009; Lai, Griffin, and Babin, 2009). These studies all show that satisfaction is a key antecedent

of store loyalty, irrespective of the industries in which the research took place. Additionally, store loyalty can be

defined as “the conscious buying behavior of a consumer expressed over time with respect to one store out of a

set of stores and which is driven by commitment to this store” (Odekerken-Schröder et al., 2001). Moreover, the


authors operationalized store loyalty by means of two constructs; store commitment and buying behavior,

whereby store commitment executes a strong positive influence on buying behavior. Odekerken-Schröder et al.

(2001) conclude their investigations with the judgment that there is a strong relationship between buying

behavior and bottom-line profits.

1.4 Problem statement

When we apply the previous findings with regard to the creation of customer satisfaction and store loyalty to

the Dutch supermarket industry, we can state that it may be of particular interest of a retailer how to adapt its

pricing strategy to respectively acquire and retain potential and existing customers, in order to capture market

share. Taking this in advance, we can state that the purpose of this study is twofold; Firstly, we will try to gain

insight in the consequences of executing a specific pricing strategy and the factors that either facilitate or

impede the creation of store loyalty. Secondly, we will take a look at how retailers can use this information as a

basis for segmentation. This brings us to the following problem statement:



Furthermore, the following research questions will be incorporated in this study to generate an answer on the

problem statement and to give direction to the research:

1. Which factors play a role in the creation of store loyalty in case of a HILO pricing strategy?

2. Which factors play a role in the creation of store loyalty in case of an EDLP pricing strategy?

3. Which loyalty segments can be distinguished in case of a HILO pricing strategy?

4. Which loyalty segments can be distinguished in case of an EDLP pricing strategy?

In order to answer the first two research questions, a literature study will be performed on the topics of EDLP-

and HILO pricing strategies and the implications of these strategies on the creation of store loyalty. Moreover,

prior studies in the field of consumer behavior will be studied in order to identify factors that may play a key role

in the process of loyalty creation. By using the outcomes of this literature study, a conceptual model will be

developed which, in turn, will be tested using hypotheses. The data collection method used here is a

questionnaire. The collected data will be analyzed by applying a multiple regression analysis. For a detailed


overview of the applied statistical tests and corresponding plan of analysis, see chapter 3. Next, a segmentation

will be conducted which, in turn, will result in different consumer segments for both pricing strategies based on

contextual- and individual difference factors.

With the aim of conducting research in an effective and structured way, several side conditions have to be taken

into consideration:

� The research should be completed within 6 months, starting at the 1st

of September 2009

� There is no budget available for the execution of the research

� During the research process, meetings with the supervisor should occur on a regular basis

1.5 Overview and contribution

This article adds to the discussion on the impact of executing an EDLP- or a HILO pricing strategy by identifying

and examining factors that may facilitate of impede the creation of store loyalty. As far as we know, this is the

first time that contingency factors are being reviewed with respect to their influence on the creation of store

loyalty under different pricing formats. Therefore, a contribution to the growing body of knowledge on the topic

of pricing is the result of this study. Moreover, a unique contribution will be made by attempting to use these

factors as a basis for segmentation. By developing pricing format-specific segments, retailers should be able to

better understand the underlying factors that attract or distract customers to their stores. The first part of this

study will focus on the identification of the contingency factors that play a role in the execution of the different

pricing strategies. Due to the fact that this is the first time that these factors are identified in this retail setting, a

qualitative approach will be enhanced while reviewing the existing scientific literature. Next, the empirical

research will be based on the real case of two supermarket chains that can be classified as an EDLP-retailer on

the one hand and a HILO-retailer on the other hand. Results of this study have implications for these retailers

and other marketers who often implement these strategies that they believe are in their and their consumers’

best interest. If consumers respond differently to these pricing strategies, based on individual and/or

environmentally difference factors, then it behooves retailers to take these factors into account in designing

price- and promotional strategies to reach different groups of consumers.

The paper proceeds as follows. In chapter 2 we will critically review the available scientific literature on

the topic of research. Next, we will explain the used methodology in chapter 3, followed by chapter 4 in which

the results will be presented. Finally, chapter 5 will contain conclusions, limitations and recommendations for

further research.


2. THEORETICAL FRAMEWORK

As one of the elements of the marketing mix, the concept of ‘pricing’ has received much attention from

scientists and practitioners throughout the years (Keller, 1998). It is viewed as one of the “top five priorities in

retail management” (Bell and Lattin, 1998). Adopting an effective pricing strategy is required to attract

consumers, increase store patronage and shopping frequency, and increase quantity purchased (Pechtl, 2004).

As part of an overarching pricing strategy, price promotions account for a significant portion of the marketing

budget of most companies (Levy and Weitz, 2009). Marketing managers usually have a number of objectives for

price discounts and sales. These include, among others, increasing or maintaining sales, getting shelf attention or

building customer loyalty. A number of studies revealed that consumers respond differently to these types of

promotional activities, which leads to the hesitation of the effectiveness of these activities on consumers (Keller,

1998; Bell and Lattin, 1998; Levy and Weitz, 2009). As mentioned in the introduction, two opposing pricing

strategies can be identified in modern retail landscape (Bailey, 2008). Among other researchers, Yavas and

Babakus (2009) address that it may be of particular interest to gain insight into the factors that may function as

an antecedent of store loyalty. This literature study will start with an exploration in the field of pricing strategies

in order to gain a better understanding of both pricing formats and its implications in the field of store loyalty

creation. Paragraph 2.1 will go deeper into the concept of HILO pricing and its implications on loyalty creation,

whereas paragraph 2.2 contains a critical review of the existing theories and concepts in the field of EDLP pricing.

To end this chapter, a conceptual model will be proposed which will graphically display the relationships

between the different concepts.

2.1 High/Low Pricing

The concept of HILO pricing has been researched numerous times (Bucklin and Lattin, 1991; Kahn, 1992; Hoch,

Drèze, and Purk, 1994; Ailawadi, Lehmann, and Neslin, 2001; Pechtl, 2004; Bailey, 2008). In order to gain a better

understanding of the concept of HILO pricing, research will be conducted in a structured way. Firstly, we will

come up with a definition of the HILO concept which, in turn, will be used to explore the implications of

executing a HILO strategy on the creation of store loyalty. The paragraph will be completed by giving direction to

the empirical research by proposing one or more hypotheses regarding the implications of executing a HILO

strategy on the facilitation of store loyalty.

2.1.1 Definition of High/Low Pricing

Within the modern world of retailing, many terms have been used to describe the effect of retailers tending to

discount the initial prices for merchandise through frequent sales promotions (Levy and Weitz, 2009). Hoch,


Drèze, and Purk (1994) are one of the first authors to give a clear and relevant definition of the concept of the

High/Low pricing-format in relation to the EDLP format. The author states that “the HILO-retailer charges higher

prices on an everyday basis but then runs frequent promotions in which prices are temporarily lowered below

the EDLP-level.” Additionally, Pechtl (2004) defines the HILO format as a pricing strategy where temporary price

discounts for selected items occur for some days, followed by weeks with ‘normal’ prices. Moreover, Kukar-

Kinney, Walters, and MacKenzie (2007) state that HILO retailers offer temporary deep discounts in specific

categories, thereby creating excitement and opportunities to increase profits through price discrimination.

It should become clear that previous definitions show similarities in defining the HILO pricing strategy. A

few points to note here are that:

� The HILO-format contains price promotions

� Promotions are temporarily and frequent

� Promotional prices lie under the EDLP price level

� Normal prices are higher than the EDLP price level

In order to have a comprehensive definition, we will formulate it as follows:

“A High/Low pricing strategy is a pricing strategy based on frequent, temporarily price promotions with

promotional prices below the average EDLP price level and normal prices above the EDLP price level.”

2.1.2 Implications of executing a HILO pricing strategy

As stated in the introduction of this paper, executing a HILO strategy has both advantages and disadvantages.

Levy and Weitz (2009) state that these retailers have an opportunity to increase profits through price

discrimination, as higher prices can be charged to customers who are not price sensitive and are willing to pay

the “high” price, and lower prices to price-sensitive customers who will wait for the “low” sale price. A recent

study that examined the effect of pricing techniques on consumer shopping behavior has been conducted by

Kukar-Kinney, Walters, and MacKenzie (2007). The authors state that practitioners and researchers in marketing

consider pricing policies and -techniques as marketing tools designed to stimulate price competition in the

marketplace by signaling to consumers that the retailer offers low prices, as well as to increase store traffic,

purchasing and facilitate store loyalty (Kukar-Kinney, Walters and MacKenzie, 2007). Based on the manipulation

of the following pricing characteristics: (1) the depth of the promotion in terms of discount percentage, (2) the

promotional period, which is the length of the promotion in days, and (3) the scope of promotional activities,

defined as the geographic area in which promotions are held, the authors suggests that consumers may react

differently to promotional activities based on perceptions and behavior. The findings indicate that price

consciousness is a key consumer trait, interacting with all of the previously mentioned pricing characteristics

studied. Moreover, if the retailer that offers promotional prices wants to increase store patronage and facilitate


the creation of store loyalty, a pricing policy that promises to beat rather than match competitive prices should

be considered (Kukar-Kinney, Walters, and MacKenzie, 2007). Finally, results indicate that highly price conscious

consumers, who likely face low individual search costs, have a greater intention to increase and prolong their

search for promotions and attractive prices, in contrast to low price conscious consumers. Implications of these

findings are that HILO-retailers face a major challenge in attracting and retaining highly price conscious

consumers in order to contribute to the creation of store loyalty (Kukar-Kinney, Walters and MacKenzie, 2007).

Similar results with respect to the influence of price consciousness on search behavior are found in a

study performed by Alford and Biswas (2002). The underlying thought is that the use of an advertised reference

price with an advertised sale price (as carried out by HILO retailers) focuses consumers’ attention on the

difference between the two prices. This leads to a perception of greater value concerning the purchase of the

product. In addition, consumers are less likely to search for other retail locations and have an increased

likelihood of purchase. It is here that price consciousness, defined as “the degree to which the consumer focuses

exclusively on paying a low price”, is expected to have influence on individual search intentions. The results of

the study show that highly price-conscious consumers expressed higher search intention than low price-

conscious consumers, irrespective of the level of sale proneness (Alford and Biswas, 2002) (Figure 2.1).

Figure 2.1- The relationship between sale proneness (SP) and search intention under low- and high price

consciousness (Alford and Biswas, 2002).

The relationship between price consciousness, search intention, and store loyalty is not as

straightforward. Sirohi, McLaughlin and Wittink (1998) conducted research in the field of store loyalty within the

supermarket industry. The authors used the concept of store loyalty intentions to portray the interaction

between the two constructs store commitment and buying behavior as defined by Odekerken-Schröder et al.

(2001) in the introduction of this paper. Measurement of store loyalty intentions is done by: (1) intention to

continue shopping, (2) intention to increase purchases, and (3) intention to recommend the store. By examining

several antecedents of store loyalty intentions, Sirohi, McLaughlin, and Wittink (1998) propose a model of store

loyalty intentions for supermarket retailers. Findings reveal that in a highly competitive retail market, store

loyalty intentions depend mainly on service quality (measured by store operations perception, store appearance

perception and personnel service perception) and perceived value for money (measured by having sales (in stock)


and perceived relative price). Furthermore, the effectiveness of promotional activities as an antecedent of store

loyalty intentions relies on the presence of search intentions carried out by consumers (Figure 2.2).

Figure 2.2 - Antecedents of store loyalty intentions (Sirohi, McLaughlin, and Wittink, 1998)

It should become clear that on the one hand Alford and Biswas (2002) as well as on the other hand

Sirohi, McLaughlin, and Wittink (1998) address the problem of attracting high price-conscious consumers in

competitive modern retail markets. While the former authors state that these consumers should be attracted by

beating rather than matching competitors’ prices, Sirohi, McLaughlin, and Wittink (1998) advice to emphasize

the perceived value for money which will be received by the consumer. Moreover, Sirohi, McLaughlin, and

Wittink (1998) state that this perceived value exists of multiple factors, namely (1) having sales or special offers

and having these in stock, and (2) having a lower perceived price compared to competitors.

The above reasoning, backed by the results from the study by Kukar-Kinney, Walters, and MacKenzie

(2007) suggests differences in the way high price-conscious and low price-conscious consumers carry out store

loyalty intentions. Hence, we hypothesize that:


H1: Consumer price consciousness will moderate the effects of a HILO-pricing strategy on store loyalty intentions.

Specifically, price consciousness will have a positive influence on the creation of store loyalty intentions under a

HILO-pricing strategy.

Coupons, a central concept in executing a HILO pricing strategy, can be seen as one of the main

promotion activities of retailers (Keller, 1998). Levy and Weitz (2009) addressed the importance of ‘creating

excitement’ and a “get them while they last”-atmosphere by reviewing different marketing mediums. For

retailers, coupons are seen as relatively cheap, having a great reach, and being effective with respect to

increasing sales. A clear definition of the word coupon in this retail setting is provided by Bell and Lattin (1998):

“a coupon is a printed form, often distributed as part of an advertisement, entitling the bearer to purchase a

specific item of merchandise at a discount.” The emphasis in this definition is on the fact that the purpose of a

coupon is to trigger the consumer to buy merchandise at discount, which accentuates the fact that we are

dealing with the HILO pricing strategy. According to Keller (1998), coupons deliver multiple advantages to

consumers. Firstly, coupons function as a medium by telling the consumer what’s on discount and facilitating

shopping intentions. Also, recurring use of coupons facilitates the creation of a relationship between the retailer

and its customers. Finally, coupons support the overall store price image of a retailer, thereby encouraging

customers to increase their purchases.

Several authors addressed the importance of promotional activities when executing a HILO pricing

strategy (Buckling and Lattin, 1991; Hoch, Drèze, and Purk, 1994; Alford and Biswas, 2002; Qiang and Moorthy,

2007). Among other findings, coupons were defined as embracing low-uncertainty for consumers and offering a

finetuned control over whom to serve for the retailer (Qiang and Moorthy, 2007). The low-uncertainty is a result

of the fact that coupons offer deals up front, in contrast to for example rebates which can only be redeemed

after purchase. When consumers experience uncertain redemption costs, this difference translates to a

difference in when uncertainty is resolved. With coupons, this uncertainty is resolved before purchase, while

rebates handle the uncertainty after the purchase has been made (Qiang and Moorthy, 2007) (Figure 2.3).

Figure 2.3 - Sequence of Consumer Decisions: Coupons versus Rebates (Qiang and Moorthy, 2007)


In addition to the work of Qiang and Moorthy (2007), several other authors studied the effects of using

coupons as a marketing instrument (Kahn and Schmittlein, 1989; Kahn and Schmittlein, 1992; Bell and Lattin,

1998). Kahn and Schmittlein (1989) conducted research in the field of shopping trip behavior based on empirical

observations. The authors provided evidence of a significant day-of-the-week phenomenon, which means that

the decision to go shopping is dependent on the particular day of the week. Moreover, by examining the work of

Frisbie (1980), the authors explain that shopping trips can be classified as either ‘regular’ or ‘quick’, depending

upon the amount of money spent per trip. However, there also appears to be ‘regular’ and ‘quick’ consumers,

where the quick ones make many trips to the store, purchasing only a small amount on each trip, and the regular

ones are more apt to have a once-a week regular shopping day. The study is concluded with the managerial

advice that these findings may have implications for the effectiveness of promotional activities (Kahn and

Schmittlein, 1989). By taking these implications into account, the authors addressed the effectiveness of

coupons and features by using shopping trip data in conjunction with panel purchase data. Outcomes reveal that

the propensity to purchase when coupons are available is more associated with major trips than fill-in trips,

while features worked better in the case of fill-in trips (Kahn and Schmittlein, 1992). Besides, the effect of

features seemed to be stronger in nonfavorite stores, while for coupons the reverse was found: the pattern was

more pronounced in the household’s favourite store.

After reviewing different studies on the relationship between shopping trip magnitude and coupon

usage, we developed the thought that the importance of a given shopping trip may have influence on

consumers’ willingness to use coupons. Combining this thought with our present knowledge on the impact of

coupon usage, we theorize that:

H2: Shopping trip importancy will moderate the effects of a HILO-pricing strategy on store loyalty intentions.

Specifically, shopping trip importancy will have a positive influence on the creation of store loyalty intentions

under a HILO-pricing strategy.

2.2 Everyday Low Pricing

Now that we have reviewed the existing literature on the implications of executing a HILO pricing, it’s time to

turn our heads to the other side of the pricing spectrum and have a close look at the Everyday Low Pricing (EDLP)

format. In order to gain a sufficient understanding of the EDLP concept and its implications, we will start with

formulating a clear, comprehensive definition. Subsequently, we will take a look at the implications of this

strategy on the creation of store loyalty intentions by critically evaluating literature on this topic. The paragraph

will be completed by proposing one or more hypotheses.


2.2.1 Definition of Everyday Low Pricing

The term Everyday Low Pricing has become more and more popular during the last decades. Mastered by giant

multinationals like Wal-Mart and Procter&Gamble, EDLP has grown to a fully accepted pricing strategy in

modern retail landscape (Keller, 1998). The strategy lives on the promise that consumers will pay the lowest

available price without coupon clipping, waiting for discount promotions, or comparison shopping; it’s also

called value pricing (Ailawadi, Lehmann, and Neslin, 2001). As stated in the introduction of this paper, the term

everyday low pricing is in this case somewhat misleading, because low doesn’t mean lowest. At any given time, a

sale price at a HILO retailer may be the lowest price available in a market. Additionally, Levy and Weitz (2009)

state that “implementing an EDLP pricing strategy creates a no-nonsense image of consistent consumer value, in

contrast with so-called TPRs (temporary price reductions) and noisier sales gimmicks.” When adding these

findings to the body of knowledge that we acquired in the previous paragraph, it can be stated that:

� EDLP pricing promises to maximise value for its customers

� The EDLP-format (at least in its original form) contains no price promotions

� It guarantees its customers that they pay the lowest price on certain merchandise

� Prices lie below the regular prices of the HILO price level, but may be higher than promotional

prices offered by HILO competitors

In order to have an ample definition, we will formulate it as follows:

“An Everyday Low Pricing strategy is a pricing strategy based on creating value without price promotions by

guaranteeing its customers that they pay the lowest price on certain merchandise. Prices lie in between the

regular- and promotional prices of HILO-retailers.”

2.2.2 Implications of executing an EDLP pricing strategy

Throughout the years, the supermarket industry has become extremely competitive. Retailers are struggling to

determine the most efficacious policies to attract and hold consumers who patronize their stores (Hoch, Drèze,

and Purk, 1994; Alford and Biswas, 2002; Kukar-Kinney, Walters, and MacKenzie, 2007; Bailey, 2008).

Furthermore, changes in pricing policies as a strategic move show contrary results; Mulhern and Leone (1990)

advocate that a change from HILO to EDLP pricing decreased sales but increased the traffic generated to the

store, while Ailawadi, Lehmann, and Neslin (2001) revealed that a change from HILO to EDLP pricing led to a

decrease in market share. Another interesting study on the effects of implementing an EDLP pricing format was

conducted by Bailey (2008). By setting up an experiment around the effects of sale proneness and patronage

intentions on consumer attitude regarding an EDLP policy, the author discovered that sale proneness was indeed

a significant influencer of consumer attitude towards an ELDP pricing policy. In a follow-up study, a link between

income and the EDLP format was expected due to the fact that higher income consumers were expected to be


less price sensitive and, consequently, less responsive to EDLPs. However, no relationship could be discovered

(Bailey, 2008). These results may establish support for the fact that consumers, regardless of income levels, like

to shop around for deals. Bailey (2008) finalizes the study with future research directions pointing towards a

deeper understanding of the moderating role of deal proneness on consumer attitude towards an EDLP pricing

policy (Bailey, 2008).

This standpoint is also covered in the work of Pechtl (2004), which states that EDLP retailers promote a

basket of products with the argument to offer attractive low prices which will be constant for a longer period.

Moreover, as we’ve included in our definitions of both pricing strategies, these EDLP-prices are lower than

normal prices in HILO stores, but not as low as their price discounts. Thus, where HILO retailers can compete on

prices in the short term, EDLP retailers are required to search for other ways of competing (Pechtl, 2004).

A possible solution is proposed by the work of Lichtenstein, Netemeyer, and Burton (1990). By stating

that EDLP-retailers cannot seduce sale prone consumers with discount prices in the short run, the authors

suggest that anticipation on the inability of deal prone customers to resist a bargain could be a profitable

strategy. In this light, they define deal proneness as “a general propensity to respond to promotions

predominantly because they are in deal form.” In addition, Levy and Weitz (2009) point out those EDLP-retailers

in the modern supermarket industry mainly compete on quantity packages. These ‘deals’ usually come in the

form of special offers (‘pay for 2, get 3 products’) or special packages (‘buy 6 bottles of soda and receive a free

glass’).

One of the earliest studies that investigated the concept of deal proneness in a retail setting was

conducted by Blattberg et al. (1978). By developing a model of consumer buying behavior, the authors tried to

identify household characteristics that should affect deal proneness. Usage of panel data for five frequently

purchased products in the supermarket industry revealed that home ownership and automobile ownership

affected deal proneness in a positive way. Another interesting study on the topic of deal proneness is conducted

by Gázquez-Abad and Sánchez-Pérez (2009). These authors shed more light on the issue of deal prone

consumers by profiling and segmenting consumers based on their response to retail promotions, which are

further divided into price- and non-price promotions. The authors explain that deal proneness is the

psychological propensity to buy, not the actual purchase of goods on promotion. Thus, deal-prone consumers

value the transaction utility rather than, or in addition to, the acquisition utility associated with buying on deal

(Gázquez-Abad and Sánchez-Pérez, 2009). One assumption made in this study is the existence of a relationship

between price-sensitivity and deal proneness. Without a doubt, observations revealed that consumers who are

most price-sensitive have a greater change to respond to promotions, due to the fact that these promotions

appear in deal form. In addition, the relationship between deal proneness and loyalty intentions is tested using a

multinomial logistic latent class model in combination with point-of-sale (hypermarket) scanner data. Results

indicate that a higher level of deal proneness is associated with customers who are less loyal to both brands and

stores, since these customers attach more importance to the price and/or deal than to the product or store


(Gázquez-Abad and Sánchez-Pérez, 2009). Moreover, results of this study are displayed graphically below (Figure

2.4).

Figure 2.4 - Segments of deal proneness (Gázquez-Abad and Sánchez-Pérez, 2009)

Another interesting study that addresses the effect of deal proneness on store loyalty intentions is

conducted by Miranda and Kónya (2007). Findings prove that deal prone consumers are especially interested in

store flyers to be informed of deal specials that a store has to offer. In contrast, non-deal prone consumers are

less likely to be interested in these flyers, because they will patronize their favorite store anyway. Moreover, as

Rothschild (1987) shows, deal-prone consumers reinforce the search for more deals and this, in turn, leads to

deal prone behavior rather than loyalty intentions.

From the preceding literature review, it should become clear that many researchers agree that deal

proneness influences the effect of promotions on store loyalty intentions in a negative way. Therefore we

hypothesize that:

H3: Consumer deal proneness will moderate the effects of an EDLP-pricing strategy on store loyalty intentions.

Specifically, deal proneness will have a negative influence on the creation of store loyalty intentions under an

EDLP-pricing strategy.

Along with deal proneness, Bailey (2008) suggests that there are also additional individual difference

factors that could have an influence on the loyalty creation process under an EDLP pricing format. For example,

consumer scepticism is a factor that could impact store loyalty intentions, given that consumers are increasingly

‘suspicious of retailers’, high ‘regular’ prices and frequent ‘sales’ (Bailey, 2008). Throughout the last decades,

consumers have become more and more informed and educated about marketing- and pricing techniques

incorporated by retailers. Guided by the shared knowledge of consumer associations, the modern consumer has

been informed very well on the topic of retailer promotion activities (Levy and Weitz, 2009). These ideas are also


covered in the work of Forehand and Grier (2003), which studied the topic of consumer scepticism as a result of

company business practices. Fundamental in this research is the belief that consumer attribution of marketer

intent guides consumer behavior and can, in turn, influence consumer satisfaction and loyalty (Forehand and

Grier, 2003). Moreover, prior research has defined consumer scepticism as ‘a trait that predisposes individuals

to doubt the veracity of various forms of marketing communication, including advertising and public relations’.

In addition, this scepticism also comes to light when reviewing consumer evaluations of Corporate Societal

Marketing (CSM). Although CSM can benefit both the firm and society, most firms promote these campaigns

solely in terms of their benefits to society. However, consumers are likely to know that firms have ulterior

motives such as profit or image management and may be more distrustful of firms that profess purely public-

serving motives (Forehand and Grier, 2003).

An additional study in the light of this research is conducted by Ford, Smith, and Swasy (1990), which

studied consumer reactions to different types of claims made by retailers. Starting point in this research is the

theory of economics of information (EOI), which predicts that when consumers can easily evaluate the

truthfulness of advertising claims before purchase, the claims will most often be true because the market will

discipline advertisers who are untruthful. Moreover, EOI theory advances that consumers will be most sceptical

of advertising claims they can never verify and least sceptical of claims they can easily and inexpensively verify

prior to purchase. Findings of the study confirm the statements of the EOI theory and show confirmative results

(Ford, Smith, and Swasy, 1990). When applying these results to the retail landscape, we theorize that EDLP-

claims are more difficult to verify than HILO-claims are. Reasons for this thought are that promotional claims by

HILO-retailers can be easily verified before making the actual purchase, for example by studying and verifying

claims made in flyers and advertisements. On the other hand, EDLP-claims are much more difficult to check in

advance, due to the complexity of these claims, which involve price comparisons with (many) competitors. This

line of reasoning is in full conformity with the outcomes of the previously cited research by Ford, Smith, and

Swasy (1993), which conclude their study by stating that consumers are more sceptical in case of claims that

require relatively more time and effort to verify.

Now that we’ve seen that EDLP-retailers can face major problems in convincing consumers of its

dedication and efforts to underprice its competitors, our interest focuses on the implications of this dilemma. A

good starting point here is the psychological study conducted by Gahwiler and Havitz (1998). By examining

several antecedents of store loyalty, defined here in the context of social sciences as ‘site loyalty’, the authors

identified that trust and honesty were key antecedents of both attitudinal- and behavioral loyalty (Gahwiler and

Havitz, 1998). Corresponding results are found by Auh (2005), which made an attempt to explain the factors that

facilitate the creation of loyalty intentions in a service environment. By differentiating between so-called ‘hard’

and ‘soft’ service attributes which stand for core- and non-core aspects of the service, Auh (2005) discovered

that, among other interesting findings, both aspects facilitated loyalty intentions through trust and honesty.

Moreover, several ‘soft’ service attributes were highlighted due to their surprising contribution to loyalty


intentions. Along with others, conviction and benevolence are referred to as foundations for strong loyalty-

based relationships (Auh, 2005).

When applying these findings to the retail industry, we can state that EDLP-retailers face major

difficulties in winning the consumers’ confidence. Compared to HILO-retailers, EDLP practitioners face much

greater challenges in convincing customers of its dedication to undersell competitors. Additionally, Gahwiler and

Havitz (1998) and Auh (2005) found comparable results with respect to the implications of this hampered

conviction of consumers. Both studies revealed that trust and honesty were key requirements for consumers to

carry out store loyalty intentions. In conclusion, we developed the thought that consumer scepticism may harm

the creation of store loyalty intentions in an EDLP-retail setting, due to the fact that EDLP-claims are difficult to

verify and impede the creation of a relationship based on trust and honesty. Therefore, we hypothesize that:

H4: Consumer scepticism will moderate the effects of an EDLP-pricing strategy on store loyalty intentions.

Specifically, consumer scepticism will have a negative influence on the creation of store loyalty intentions under

an EDLP-pricing strategy.

In order to realize sustainable gains from an EDLP-pricing policy, retailers should manage different

aspects of executing this strategy. One feature that may be particularly important in case of an EDLP-retailer

may be the perceived merchandise quality and assortment, since sometimes consumers associate (everyday)

low prices with low quality merchandise. They may also associate low prices with poor or no customer service

(Bailey, 2008), while these aspects are numerous times cited in consumer surveys of retail patronage intentions.

These surveys repeatedly have found that location/convenience is the most important aspect, followed in order

of mention by low prices, assortment, courteous service, good-quality merchandise, and fresh meat (Arnold,

Oum, and Tigert, 1983). Moreover, Rajagopal (2006) confirms the idea that merchandise quality is an important

factor in a value-based retail setting. By investigating customer value in a Mexican retail setting, the authors

discovered that, measured by sales- and customer growth, merchandise quality exerts a significant influence on

store performance. In addition, extensive research by Sirohi, McLaughlin, and Wittink (1998) revealed that the

perceived relative price in a retail store is a significant determinant of merchandise quality perceptions by

consumers. Also, a clear, direct link between merchandise quality perceptions and perceived value is established.

In summary, Sirohi, McLaughlin, and Wittink (1998) revealed that perceived low prices can lead to perceived low

merchandise quality, which, in turn, can lead to perceived low value with all negative consequences that will

follow.

After addressing the importance of the perceived merchandise quality in a retail setting, Sirohi,

McLaughlin, and Wittink (1998) continued their research by investigating whether and which consumer

perceptions function as antecedents of store loyalty intentions. The authors consider three measures of store

loyalty intentions: (1) customers’ intent to continue purchasing, (2) their intense to increase future purchases,


and (3) their intent to recommend the store to others. The latter measure is also relevant to customer retention

in the sense that customers' intentions to recommend a retailer to others would not be consistent with

inclinations to switch from the same retailer. Results of the investigation show that service quality and

merchandise quality influence store loyalty intentions in a direct and indirect way, in that order. Similar results

with respect to factors that play a role in the establishment of loyal customers are found by Terblanche and

Boshoff (2006). By establishing a relationship between a satisfactory in-store shopping experience and retailer

loyalty, the authors confirm that, among others, merchandise value and customer service contribute to

satisfactory- and, in turn, loyal customers. By building forth on the well known SERVQUAL-model (Parasuraman

et al., 1988), a new model of dimensions and outcomes of the in-store shopping experience was constructed

(Figure 2.5).

Figure 2.5 - The dimensions and outcomes of the in-store shopping experience (Terblanche and Boshoff, 2006)

In the light of the model above, the authors argue that “it is not only service quality or merchandise

value that will drive consumer loyalty – it is a combination of various factors that influence each other and

combine into a whole that will determine the loyalty of a retail shopper. Consumer loyalty is preceded by a

multitude of experiences and perceptions.”

One small remark that has to be made here is the fact that Terblanche and Boshoff (2006) use a

different definition of loyalty than Sirohi, McLaughlin, and Wittink (1998) do. Where the former authors define

loyalty as ‘an interaction between behavioural and attitudinal measures’, the latter one focuses on the

‘intentions’ to carry out loyalty regarding the retailer. Nevertheless, both studies conclude that loyalty is a

combination of a cognitive construct (attitude) and a shopping behaviour, which additionally lead the same

purpose. Summarizing, we’ve seen that specifically EDLP-retailers may face problems in guaranteeing the quality

of its merchandise and customer service. This challenge is due to the fact that consumers may perceive a low

price with low quality. Eventually, this drawback can lead to a decreased chance of provoking store loyalty

intentions from its customers. Therefore, we hypothesize that:


H5: Merchandise quality and service quality will moderate the effects of an EDLP-pricing strategy on store loyalty

intentions. Specifically, merchandise quality and service quality will have a negative influence on the creation of

store loyalty under an EDLP-pricing strategy.

2.3 Conceptual model

Based on the findings resulting from our theoretical framework, a conceptual model is developed. The model

will incorporate all factors that have been studied in the previous paragraphs. Expected relationships between

variables are displayed by a dotted line (Figure 2.6).

Figure 2.6 – Conceptual model presenting the expected relationships found in the literature

H4 (-)

HILO- PRICING

PRICE

CONSCIOUSNESS

SHOPPING TRIP

IMPORTANCY

DEAL

PRONENESS

CONSUMER

SCEPTICISM

EDLP- PRICING

MERCHANDISE

QUALITY &

SERVICE

QUALITY

H1 (+) H2 (+)

H3 (-) H5 (-)

STORE

LOYALTY


3. RESEARCH DESIGN

Throughout the previous chapter, an exploratory approach is used to identify factors that may enhance or

impede the creation of store loyalty under different pricing formats. The insights gained from the literature

review will be verified by conducting conclusive research. According to Malhotra (2006), the objective of

conclusive research is to test hypotheses and examine specific relationships. We will test the hypotheses

formulated in the previous chapter by appyling several statistical techniques.

As mentioned in the introduction of this paper, the second part of this research will focus on the

establishment of loyalty-based segments. This will be done for both pricing strategies, resulting in two sets of

segments.

3.1 Data collection

The two relevant groups in this research are those consumers that visit either a HILO-store or an EDLP-store.

Information from the sample will be obtained just one time, thereby defining the type of research as single-

cross-sectional design (Malhotra, 2006). The collection of information will be done by using a questionnaire (for

details, see paragraph 3.2). The questionnaire will be conducted by asking respondents which visit a HILO- or an

EDLP-retailer to fill in the questionnaire. Two major players in the retail industry in The Netherlands are HILO-

retailer C1000 and EDLP-retailer Jumbo. These retailers will be incorporated in this study, due to the fact that

they differ in their promotion strategy, which was operationalized by the advertising format both stores used in

their weekly flyers (Pechtl, 2004). Supermarket C1000 could be classified as an HILO-store, presenting the

reduced and normal prices of merchandise side by side in the flyers. The second supermarket, Jumbo, follows an

EDLP-strategy, because merchandise is advertised with slogans like ‘low prices every day’. Although both stores

also occasionally use the other promotion format for some products, especially for durables or advertise

products in an unspecified manner, Pechtl (2004) states that it is possible to distinguish a predominantly HILO-

and EDLP-oriented store for frequently purchased items which dominate shopping in a grocery store.

The focus of this study is on the behavior expressed by the Dutch supermarket consumers with respect

to the pricing strategy incorporated by the retailer concerned. Therefore, the population of interest concerns all

Dutch inhabitants in the age of 18 and older. The sample size will consist of 300 respondents, made up by 150

HILO-shoppers and 150 EDLP-shoppers. The actual data collection took place during November and the first half

of December 2009, thereby avoiding those weeks during the Christmas period, which could possibly bias the

results. During the 6 weeks of data collection, respondents were approached ‘on the street’, where the

researcher met the respondents and personally asked them to fill out the questionnaire. The fill-in of the

questionnaire took merely 5 minutes per respondent; thereby creating the opportunity to receive 300


respondents within the period of 6 weeks. The process was repeated in different Dutch cities, namely Groningen,

Amsterdam, Amstelveen and Enschede. The outcome is that the results of this study are more representative to

the Dutch population than in the case of merely one place or moment of data collection.

3.2 Design of the questionnaire

In order to generalize the results from the sample to the population of interest, a quantitative research based on

a questionnaire is conducted. Within this questionnaire, the research question is operationalized into questions

to the target group. With our eye on the side conditions for conducting this research, there are a few reasons for

using a questionnaire in this study:

1) Questionnaires provide an efficient way to collect responses from a large sample

2) Questionnaires are (relatively) inexpensive and are (relatively) little time-demanding

3) It contains standardised questions that are most likely to be interpreted in the same way by the different

respondents

The questionnaire can be classified as a self-administered structured questionnaire, as there is no

interviewer involved in the completion process (Malhotra, 2006). The development of the questionnaire is based

on the construction of the conceptual model as well as the research questions defined in the introduction of this

study. Moreover, the questionnaire consists to a large extent of fixed-alternative questions using an itemized

rating scale. In this case, a five-point Likert scale will be used (1= strongly disagree, 5= strongly agree).

According to Malhotra (2006), the type of information obtained from a questionnaire may be classified

as: (1) basic information, which relates directly to the research problem, (2) classification information, consisting

of socioeconomic and demographic characteristics, and (3) identification information, which includes name,

address, e-mail address, etc. As a general guideline, the previously mentioned types of information should be

collected in the order stated above. This is due to the fact that basic information is of greatest importance to this

research project and should be obtained first, before we risk alienating the respondents by asking a series of

personal questions (Malhotra, 2006). The questions in the questionnaire are a mixture of self-formulated

questions and questions from previous research projects (Bloemer and de Ruyter, 1998; Sirohi, McLaughlin and

Wittink, 1998; Bailey, 2008; Orth and Green, 2009). These questions have already been tested, which increases

the validity of this research. Table 3.1 presents an overview of the questions and statements used in the

questionnaire and the related variables which are tested, except for questions regarding individual difference

factors. For a complete overview of the questionnaire used, see the appendix under the header

‘QUESTIONNAIRE’.


Variable Question

Price consciousness 1, 2, 3, 4

Shopping trip importancy 5, 6, 7

Deal proneness 8, 9, 10, 11, 12

Consumer scepticism 13, 14, 15, 16

Merchandise quality & service quality 24, 25, 26, 27, 28

Store loyalty 17, 18, 19

Time pressure 20, 21

Store image 22, 23

Table 3.1 - Overview of the relationships between variables tested and corresponding questions and statements

used in the questionnaire.

In addition, prior to applying the questionnaire to the data collection, it was pilot tested among ten

persons, which are drawn from the same population as will be done in the data collection. This pretesting was

done with the purpose of improving the questionnaire by identifying and eliminating potential problems. The

pre-test was conducted by personal interviews, thereby enabling the possibility of observing respondents’

reactions and attitudes (Malhotra, 2006). The conclusion of this pretesting was that several questions should be

formulated in a different way, thereby guaranteeing that all respondents interpreted the questions in the same

way.

3.3 Plan of analysis

As stated in the previous paragraph, the research will be conducted by asking visitors of either a HILO- or an

EDLP-retailer to fill in a questionnaire. The results of the questionnaire will be coded by using the statistical

computer program SPSS version 16.0. After having codified the data, several statistical tests will be conducted in

order to formulate an answer on the research questions. Unless otherwise stated, the p-value should be smaller

or equal to α = 0.05 (significance level), in order for a reported difference or result to be considered significant.

As a first step, descriptives will be presented in order to be sure that the sample represents the

population of interest, which is the Dutch population of 18 years and older. We will use data from the Dutch

Bureau for Statistics (CBS) in order to analyze the representativeness of our sample. This will be done by

applying the Chi-square test to statistically analyze if the sample corresponds with the population of interest.

Additionally, descriptives will contribute to the overall picture of the shoppers of both retail chains.

With the research purpose of explaining the factors that influence the creation of store loyalty under

different pricing formats, it sounds logical to conduct a regression analysis. Multiple regression analysis assumes


1. Formulate the

problem

2. Select a distance

measure

3. Select a clustering

procedure

4. Determine the

amount of clusters

5. Interpret and

profile clusters

6. Criticize the validity

a causal relationship between a dependent variable and multiple independent variables. The regression analysis

is allowed to use here though we use ordinal data. This allowance is due tot the fact that previous research in

the field of social sciences has shown that consumers interpret a 5- or 7-point Likert scale on an interval

measurement scale. Moreover, this allowance is facilitated by the use of a visual scale where equal spacing of

response levels is clearly indicated (Malhotra, 2006). In order to construct variables out of the questionnaire,

several questions will be combined. To guarantee that the questions are consistent in what they indicate about

the variable, Cronbach’s alpha will be determined to assure the internal consistency reliability of the variables

concerned (Malhotra, 2006). In this study, store loyalty intentions will be incorporated to represent the

dependent variable whereas other variables embody the independent variables. This test will be conducted for

both pricing strategies, thereby creating the opportunity to differentiate between the relevant factors that

create store loyalty intentions under either a HILO- or an EDLP-pricing strategy (Malhotra, 2006). A small note

here is that multiple regression analysis is complicated by the presence of multicollinearity. This problem arises

when intercorrelations among the predictors (the independent variables) are very high. In order to cope with

this problem, we will carefully check the Variance Inflation Factor (VIF) to identify potential collinearity problems.

Finally, in order to conduct a segmentation of consumers based on their preference for a store price format, we

will apply a cluster analysis. According to Malhotra (2006), cluster analysis is ‘a class of techniques used to

classify objects or cases into relatively homogeneous groups called clusters.’ Moreover, as Malhotra (2006)

explains: “Objects in each cluster tend to be similar to each other and dissimilar to objects in the other clusters.”

The process of conducting a cluster analysis is presented below (Figure 3.1).

Figure 3.1 - Process of conducting a cluster analysis

Perhaps the most important step in conducting a cluster analysis is the selection of the variables on

which the clustering will be based. The inclusion of irrelevant variables may distort an otherwise useful

clustering solution. Malhotra (2006) suggests that variables should be selected based on past research or theory.

Therefore, respondents will be clustered based on their response to those questions from the questionnaire that

represents the variable ‘store loyalty intentions’.


Finally, in order to profile the clusters that emerged from the cluster analysis, a discriminant analysis

will be performed twice. The first discriminant analysis will include the variables that we identified in the

literature review, whereas the second one will apply the individual difference factors and situational factors as

independent variables. By doing so, a comprehensive picture of the different loyalty-related clusters and their

characteristics can be established.

3.4 Quality demands

As noted by various researchers in the field of marketing (Keller, 1998; Malhotra, 2006), scientific research

should meet several quality demands in order to be credible. Among others, the next three quality demands are

universally applicable to scientific research. Therefore, we will assess the way in which this study copes with

these quality demands.

� Reliability

In order to be sure that this study enhances a consistent measurement, Cronbach’s alpha (α) is

determined multiple times to check for internal consistency. Moreover, the data collection took place in

different cities on different times of the day. This behavior will enhance the maximisation of the reliability of the

quantitative research. Finally, as stated in paragraph 3.3, we will cope with the problem of multicollinearity by

checking for high intercorrelations among the independent variables in the multiple regression analysis.

� Validity

In the light of this research, validity assesses the degree to which the indicators used to make our

theory measurable represent the theory well. It’s about the degree to which our variables have been well

operationalized in the questionnaire. Since the questions in our questionnaire are to a large extent formulated

based on previous research, an attempt is made to guarantee a satisfying degree of validity.

� Generalizability

According to Malhotra (2006), generalizability refers to ‘the degree to which one can generalize from

the observations at hand to a universe of generalizations.’ This definition clearly addresses the problem of the

applicability of the results to other settings. As this study has been conducted among Dutch supermarket

consumers, it is merely representative for the Dutch supermarket industry. Though, as the concepts and

variables used in this research are internationally applicable in retail landscape, it may sounds likely that the

results of this study can be extended to other supermarket industries worldwide.


Age distribution of EDLP-shoppers

12 13 12 13

3

169

33

21

18

0

5

10

15

20

25

30

35

40

45

50

18-25 25-35 35-50 50-65 >65

age categories

# r

es

po

nd

en

ts

female

male

Age distribution of HILO-shoppers

2219

13 11

1

819

38

12

7

0

10

20

30

40

50

60

18-25 25-35 35-50 50-65 >65

age categories

# r

es

po

nd

en

ts

female

male

4. RESULTS

This chapter will encompass the results of the statistical analyses which have been performed in order to test

the hypotheses formulated as a result of the literature review. The outline of this chapter will look as follows:

firstly, descriptives will be presented in order to be sure that the sample represents the population of interest.

Secondly, a regression analysis will be applied to test whether and how the identified factors that emerged from

the literature review contribute or impede the creation of store loyalty intentions. Thirdly, a cluster analysis will

be conducted to form loyalty-based clusters for both pricing strategies. Next, a discriminant analysis will be

performed twice in order to determine by which variables the loyalty-based cluster differ from each other and

how these cluster look like in terms of demographic and situational variables. A complete overview of the

statistical output can be found in the appendix.

4.1 Descriptives and representativeness of the sample

A total of 300 respondents successfully completed the questionnaire. This sample consists of 150 EDLP-shoppers

and 150 HILO-shoppers. To be able to know which consumers took part in the research, various characteristics

will be presented. The output of the statistical tests can be found in the Appendix, section A.

Figure 4.1 – Age and gender distributions

The age distribution among consumers shopping at an EDLP-retailer is to some extent different than

those of the HILO-retailer. EDLP-shoppers are relatively old compared to HILO shoppers (Figure 4.1). Whereas

almost 80% of the shoppers of the HILO-retailer are between the age of 18 and 50 years old, merely 63% of the

EDLP-shoppers fall into this age category. Furthermore, the number of respondents with an age above 65 years

is much lower at the HILO-retailer (8 respondents; 5.3%) than at the EDLP-retailer (21 respondents; 14%).


income distribution

0

10

20

30

40

50

60

<20K 20-40K 40-60K 60-80K >80K

income

# respondents

EDLP

HILO

Applying the Chi-square test to compare the sample with the Dutch population shows that the observed

frequency in the different categories of the age variable correspond with the expected frequency (p= .166).

About the gender distribution; 65% of the EDLP respondents are female, whereas for HILO-respondents

this percentage is 56%. The binomial test reports that for the EDLP-shoppers, the observed frequency does not

correspond with the expected frequency (p= .000). For the HILO-shoppers, the frequency distribution is as

expected based on the data from the CBS (p= .165). A possible explanation for the larger amount of women

compared to men patronizing these retailers may be that in most (traditional) Dutch families, women are the

ones who do the weekly groceries. Due to this ‘tradition’, no hard consequences will be involved with regard to

the differences in gender distributions.

Figure 4.2 – Income distributions

Concerning the yearly gross income, it can be stated that 88% of the EDLP-shoppers earns less than 60K

on an annual basis. For HILO-shoppers, this percentage rises to almost 93%. There were no HILO-shoppers who

earned more than 80K on an annual basis, in contrast to 4 EDLP-shoppers (2.7%). The Chi-square test shows that

the observed income distribution does not correspond with the expected distribution (p= .000). A possible

explanation may be the relatively large quantity of students who participated in the sample. These consumers

are known for their low incomes, thereby explaining the large amout of consumers with an income less than 20K

annually. (Figure 4.2).


Education level distributions

0

10

20

30

40

50

60

LBO

Mid

delbar

e sc

hool

MBO

HBO

Univ

ersi

teit

# r

esp

on

den

ts

EDLP

HILO

Figure 4.3 – Household size distributions

The data shows us that no less than 68% of the total respondents (both EDLP- and HILO-store visitors)

belong to a household consisting of 1, 2 or 4 persons. The Chi-square test shows that the observed household

sizes are slightly (p= .053) equivalent to the predicted household sizes. Furthermore, the distributions of the

household size are quite similar, revealing that no clear, visible differences exist in the household sizes of EDLP-

and HILO-shoppers (figure 4.3).

Figure 4.4 – Education level distributions

Like the distributions of household size and income, education level shows relatively the same

distributions among EDLP- and HILO-shoppers. More than 58% of the 300 respondents are highly educated,

whereas 27% is moderately- and almost 15% is lower educated. The Chi-square test confirms these figures by

revealing that the observed education levels do not correspond to the expected education levels (p= .000). This

bias of relatively much high-educated respondents is again due to the fact that a considerable part of the


respondents can be typified as ‘students’; having a lower income, household size being 1, and showing a higher

education level (Figure 4.4).

4.2 Regression analysis

This section will cover the testing of the hypotheses which have been formulated in chapter 2. In order to test

the specific variables, questions from the questionnaire are recoded into new variables representing the

different variables. One critical condition for the construction of new variables is the reliability. Malhotra (2006)

explains that Cronbach’s Alpha (α) should be at least 0.6 in order to guarantee a sufficient internal consistency of

the construct. An overview of the different variables, related questions, and corresponding Cronbach’s alphas is

given below (Table 4.1) and in the Appendix, section B.

Table 4.1 - Overview of the variables tested, corresponding questions and statements, and Cronbach’s alphas

As can be seen, Cronbach’s alpha did turn out to be sufficient for all researched variables. The variables ‘store

image’ and ‘merchandise quality & service quality’ show a Cronbach’s alpha slightly above 0.6, noting that these

variables are the least reliable with respect to their internal consistency. Concluding, the items that measure a

specific variable are combined into one single variable. The variable represents the average scores of the items

belonging to that specific variable.

In order to define the specific contributions of the factors that we tested in the previous paragraph, a

linear regression analysis will be conducted. Regression analysis assumes a causal relationship between a

dependent variable and (one or more) independent variables. In this study, ‘store loyalty intentions’ will be the

dependent variable due to the fact that we are interested in the factors that either facilitate or impede the

Variable # of Items Question Cronbach’s

alpha (α)

Price consciousness 4 1, 2, 3, 4 0.773

Shopping trip importancy 3 5, 6, 7 0.780

Deal proneness 5 8, 9, 10, 11, 12 0.828

Consumer scepticism 4 13, 14, 15, 16 0.841

Merchandise quality & service quality 5 24, 25, 26, 27, 28 0.622

Store loyalty 3 17, 18, 19 0.785

Time pressure 2 20, 21 0.730

Store image 2 22, 23 0.610


creation of store loyalty. The independent variables consist of the relevant factors that we identified in the

literature study. In addition, a secondary regression analysis will be conducted including those variables that are

not included the first time. This way, we will be able to come up with two comprehensive regression analyses

that clarify the factors that either facilitate or impede the creation of store loyalty under a HILO- and an EDLP-

pricing strategy. An overview of the output of the regression analysis can be found in the Appendix, section C.

4.2.1 HILO-pricing strategy

The regression model of the HILO-pricing strategy reports the predictability of the dependent variable ‘store

loyalty intentions’ based on the independent relevant variables that we identified in the literature section of this

study. Firstly, by including the relevant independent variables ‘price consciousness’ and ‘shopping trip

importancy’, the regression output reports an R-value of .598. However, since we apply multiple independent

variables, we are more interested in the R² values. Our model reports an adjusted R² of .348. The model reveals

that almost 35% of the variation in store loyalty intentions under a HILO-pricing strategy can be explained by the

two independent variables ‘price consciousness’ and ‘shopping trip importancy’. The estimated regression

coefficients (B) report that price consciousness significantly (p= .000) positively contributes to the model

(B= .548). For shopping trip importancy, the positive (B= .022) contribution is not significant (p= .751). An

implication is that ‘price consciousness’ is the single factor that explains the variation in store loyalty intentions.

More specifically, if ‘price consciousness’ rises by a factor of 1, ‘store loyalty intentions’ will rise by a factor

of .548. In addition, the fact that both independent factors do not correlate high with respect to each other

results in a rather low Variance Inflation Factor (VIF) of 1.005, meaning that we are not dealing with

multicollinearity problems (Table 4.2).

Unstandardized coefficients

Standardized

Coefficients

Collinearity Statistics

Model B Std. Error Beta

t

Sig. Tolerance VIF

1 (Constant)

Price consciousness

Importancy

1.785

.548

.022

.303

.061

.070

.596

.021

5.894

8.986

.318

.000

.000

.751

.995

.995

1.005

1.005

Table 4.2 - Regression analysis regarding store loyalty intentions in case of a HILO-pricing strategy

Subsequently, we will include those factors that are identified in the literature section, but haven’t

proven to be influencing store loyalty intentions in case of a HILO-pricing strategy. The results of this regression

analysis indicate an adjusted R² of .511. This means that more than 51% of the variation in store loyalty

intentions can be explained by the factors ‘price consciousness’, ‘shopping trip importancy’, ‘deal proneness’,

‘consumer scepticism’, and ‘merchandise and service quality’.


When looking at the regression coefficients (B), it can be concluded that merely the factors ‘price consciousness’

(p= .000) and ‘merchandise and service quality’ (p= .000) significantly positively contribute to the model (Table

4.3).


Standardized

Coefficients



t

Sig. Tolerance VIF

1 (Constant)

Price consciousness

Importancy

Deal proneness

Consumer scepticism

M&S Quality

.746

.358

.010

.004

-0.79

.549

.497

.060

.062

.066

.069

.084

.390

.009

.003

-.072

.431

1.500

5.936

.158

.055

-1.137

6.525

.136

.000

.875

.956

.257

.000

.760

.963

.906

.827

.753

1.315

1.038

1.104

1.209

1.328

Table 4.3 – Regression analysis regarding store loyalty intentions in case of a HILO-pricing strategy, including

those factors that have not been reviewed in the literature regarding HILO-pricing

In order to come up with an optimized regression model, we will conduct a final regression concerning

the factors that significantly contribute to store loyalty intentions under a HILO pricing format. This time, merely

the significant factors from the previous regression analyses will be included. This regression analysis results in

an adjusted R² value of .517, thus explaining almost 52% of the variance in store loyalty intentions under the

HILO pricing format. The regression coefficients (B) report a positive significant contribution of ‘price

consciousness’ (B= .372, p= .000) and ‘merchandise and service quality’ (B= .576, p= .000) (Table 4.4).


Standardized

Coefficients



t

Sig. Tolerance VIF

1 (Constant)

Price consciousness

M&S Quality

.426

.372

.576

.276

.058

.080

.404

.451

1.545

6.417

7.166

.124

.000

.000

.817

.817

1.224

1.224

Table 4.4 – Optimized regression analysis regarding store loyalty intentions under a HILO-pricing strategy

By including ‘merchandise and service quality’, the adjusted R² rose from .348 to .517. Therefore, we

will accept this regression outcome. This result leads to the thought that in a HILO-retail setting, merely price

promotions and quality function as antecedents of store loyalty intentions. The causal relationships and its

strengths are graphically displayed below (Figure 4.7).


0.372*

0.576*

Figure 4.7 – Causal relationships regarding store loyalty intentions in case of a HILO-pricing strategy

4.2.2 EDLP-pricing strategy

The regression model of the EDLP-pricing strategy reports the predictability of ‘store loyalty intentions’ based on

the independent variables that we identified in the literature section. The model reports an adjusted R² value

of .302, meaning that 30,2% of the variance in ‘store loyalty intentions’ can be explained by the coefficient

factors ‘deal proneness’ (B= .339), ‘consumer scepticism’ (B= -.149), and ‘merchandise and service quality’

(B= .226) (Table 4.5).


Standardized

Coefficients



t

Sig. Tolerance VIF

1 (Constant)

Deal proneness

Consumer scepticism

M&S Quality

2.244

.339

-.149

.226

.339

.072

.049

.077

.363

-.208

.225

6.610

4.719

-3.021

2.935

.000

.000

.003

.004

.792

.992

.797

1.263

1.009

1.254

Table 4.5 - Regression analysis regarding store loyalty intentions in case of an EDLP-pricing strategy

Subsequently, we will include those factors that are identified in the literature section, but haven’t

proven to be influencing store loyalty intentions in case of an EDLP-pricing strategy. The regression results report

an adjusted R² of .310, meaning that adding the variables ‘price consciousness’ and ‘shopping trip importancy’

results in an increase in contribution to the model of only .008 compared to only including the relevant variables

as done previously. In addition, the variables ‘shopping trip importancy’ (p= .338) and ‘price consciousness’

(p= .081) turn out to be insignificant. Therefore, we decide to go on with the previous regression model

containing the variables ‘deal proneness’, ‘consumer scepticism’ and ‘merchandise and service quality’. This leads

to the causal model displayed below (Figure 4.8).

* = significant at 0.05 level

Price Consciousness

Merchandise and

service quality

Store

loyalty

intentions


0.339*

-0.149*

0.226*

Figure 4.8 – Causal relationships regarding store loyalty intentions in case of an EDLP-pricing strategy

4.3 Cluster analysis

In order to classify the respondents into various clusters based on their store loyalty intentions, a hiearchical

cluster analysis is conducted for both the EDLP- and HILO-pricing strategy. An overview of the output of the

cluster analysis can be found in the Appendix, section D.


For the EDLP-strategy, the agglomeration schedule reveals that the coefficients value substantially increases

between stages 147 (3 clusters) and 148 (2 clusters). Consequently, we will be able to determine 3 clusters

differing on the amount of store loyalty intentions. It appears that cluster 1 contains 17 respondents, cluster 2

contains 44 respondents, and cluster 3 contains 89 respondents. The means from each cluster concerning the

variables used in the cluster analysis are displayed below. A One-Way Anova test reports that at least two

clusters significantly (p= .000) differ from each other. The post hoc Scheffe test indicates which clusters differ

from each other (Table 4.6).

Cluster

1 (n=17) 2 (n=44) 3 (n=89)

If I buy groceries, I usually go to Jumbo* 2.18 3.45 4.56

I see myself as a loyal customer of Jumbo** 2.65 2.86 4.43

I recommend Jumbo to others* 1.88 3.82 4.27

* All three clusters significantly differ from each other

** Cluster 1 significantly differs from cluster 3; cluster 2 significantly differs from cluster 3

Table 4.6 – Cluster means concerning the items used in the cluster analysis of the EDLP-pricing strategy

* = significant at 0.05 level

Deal Proneness

Consumer

scepticism Store

loyalty

intentions

Merchandise and

service quality



In addition, the agglomeration schedule of the cluster analysis regarding the HILO-pricing strategy shows that

between stages 147 (3 clusters) and 148 (2 clusters) the coefficients value substantially increases, resulting in

the acceptance of 3 clusters. Descriptives reveal that the clusters 1, 2, and 3 contain 28, 87, and 35 respondents

respectively. The One-Way Anova reports that at least two clusters significantly (p= .000) differ from each other.

Moreover, the post hoc Scheffe test indicates the significant differences between the clusters (Table 4.7).

Cluster

1 (n=28) 2 (n=87) 3 (n=35)

If I buy groceries, I usually go to C1000* 2.43 4.13 3.60

I see myself as a loyal customer of C1000* 2.46 4.66 3.83

I recommend C1000 to others** 2.64 4.37 2.97

* All three clusters significantly differ from each other

** Cluster 1 significantly differs from cluster 2; cluster 2 significantly differs from cluster 3

Table 4.7 – Cluster means concerning the 3 items used in the cluster analysis of the HILO-pricing strategy

4.4 Discriminant analysis I

Now that we have clustered the respondents based on their store loyalty intentions, it is time to profile the

clusters in order to identify the variables by which these clusters differ from each other. The three different

clusters per pricing strategy that we identified previously will be used as the grouping variable, whereas the

variables that emerged from the literature review will function as independent variables. An overview of the

output of the first discriminant analysis can be found in the Appendix, section E.


By assessing the equality of group means, it comes forth that the variable ‘shopping trip importancy’ is the only

insignificant (p= .086) variable which, therefore, does not differ across the three clusters (Table 4.8).

Table 4.8 – Tests of equality of group means

Wilks’ Lambda F Df1 Df2 Sig.

Price consciousness .932 5.347 2 147 .006

Importancy .967 2.490 2 147 .086

Deal proneness .803 18.068 2 147 .000

Consumer scepticism .811 17.169 2 147 .000

M&S Quality .817 16.428 2 147 .000


The discriminant analysis generated two functions which correctly determine 91% and 9% of the

variance respectively. Moreover, the large Eigenvalue of function 1 (.742) reveals that we are dealing with a

strong function. On the other hand, function 2 has a very low Eigenvalue, resulting in a less reliable function. This

is also emphasized by the Canonical Correlations for both functions. While the Canonical Correlation of function

1 (.653) reports that function 1 discriminates well between the 3 loyalty-based EDLP-clusters, the opposite is

found for function 2, which reports a Canonical correlation of merely .261 (Table 4.9).

Function Eigenvalue % of Variance Cumulative % Canonical

Correlation

1 .742 91.0 91.0 .653

2 .073 9.0 100.0 .261

Table 4.9 – Eigenvalues of discriminant functions 1 and 2

Before we can interpret the results of the discriminant analysis, Wilks’ Lambda should be assessed for

both functions. This assessment will provide us with insights in the proportion of the total variance in the

discriminant scores not explained by differences among the three clusters. The Lambda’s for function 1 and 2

are .535 (p= .000) and .932 (p= .037) respectively, revealing that for both functions the cluster means appear to

differ significantly. It should be noted that function 2 again shows a very small explanatory power, which is in

line with earlier findings concerning the Eigenvalue and Canonical Correlation of this function (Table 4.10).

Function Wilks’ Lambda Chi-square df Sig.

1 through 2 .535 90.697 10 .000

2 .932 10.214 4 .037

Table 4.10 – Wilks’ Lambda of discriminant functions 1 and 2

The standardized canonical coefficients for the variables on the two functions yielded by the

discriminant analysis are given in Table 4.11. Function 1 is strongly dominated by a positive loading on ‘deal

proneness’. At a lower level, ‘price consciousness’ and ‘merchandise quality and service quality’ also contribute to

function 1. On the other hand, function 2 is mostly typified by ‘shopping trip importancy’, which confirms our

previously developed thought that function 2 and the corresponding variable ‘shopping trip importancy’ are less

useful and applicable in this analysis.


Function

1 2

Price consciousness .236 .168

Shopping trip importancy -.112 .865

Deal proneness .430 -.732

Consumer scepticism -.732 -.373

Merchandise quality and service quality .509 .337

Table 4.11 – Standardized Canonical Discriminant Function Coefficients

Now that we have assessed the various variables with respect to the two functions, further insights can

be developed by investigating the structure matrix of the discriminant analysis. The structure matrix reports the

correlations between the different variables and the discriminant functions. The structure matrix is constructed

in order of importance, where the most important variables are noted on top and the least important variables

at the bottom (Table 4.12).

Function

1 2

Deal proneness .562* -.392

Consumer scepticism -.555* -.260

Merchandise quality and service quality .547* .140

Price consciousness .312* -.067

Shopping trip importancy -0.38 .671*

Table 4.12 - Structure Matrix of discriminant functions 1 and 2

From the preceding, it should become clear that the variation between the three different EDLP-

clusters is mainly explained by the strong function 1. The variables that best explain the differences between the

clusters are (1) Deal proneness, (2) consumer scepticism, (3) merchandise quality and service quality, and to a

lesser extent (4) price consciousness. By assessing the scores of each cluster on function 1, it is possible to assign

a (strongly limited) profile to each cluster. One of these (preview) results encompasses the positive scoring of

cluster 3 (=most store loyal, see Table 4.6) on function 1. Below are the scores of each cluster on the

discriminant function (Table 4.13).

Cluster Function 1

1 -1.891

2 -.543

3 .630

Table 4.13 – EDLP Group centroids for function 1


The final step in this cluster analysis regarding the EDLP-pricing strategy encompasses the determination of

the validity. This will be done by using the leave-one-out method. Results show that 61.3% of the EDLP

respondents are correctly assigned to a cluster. According to Malhotra (2006), this percentage is high enough

(>50%) to guarantee a satisfactory validity. Moreover, the reported 61.3% can be explained by referring to the

cluster analysis which has been performed. Due to the fact that the same variables were used to assign

respondents to a cluster, it sounds logical that some sort of ‘fit’ arises.


By assessing the previously defined three loyalty clusters for the HILO-pricing strategy (see figure 22) as the

grouping variable, we can expect two functions as the result of the discriminant analysis. However, the results

show us that the second function is not significant (p= .878).

The equality of group means test reports that the variables ‘shopping trip importancy’ (p= .758) and

‘deal proneness’ (p= .182) are not significant and thus do not differ across the three previously identified HILO-

clusters. The discriminant analysis reports two functions, determining 99.1% and 0.9% of the variance

respectively. Eigenvalues of the two functions are .962 for function 1, and .008 for function 2. Moreover,

Canonical Correlations for function 1 and 2 are determined as .700 and .091 respectively. Due to the fact that

function 2 was founded not significant, we will continue by describing the first and only function. Wilks’ Lambda

of this significant (p= .000) function is found to be .505, meaning that the cluster means appear to differ

significantly. The standardized canonical coefficients for the variables are presented below (Table 4.14).

Table 4.14 – Standardized Canonical Discriminant Function Coefficients

‘Price consciousness’, ‘merchandise quality and service quality’, and ‘consumer scepticism’ are heavily loaded in

the function. By combining these results with the structure matrix, a more comprehensive picture of the

discriminating function of the variables can be established (Table 4.15).

Function

Price consciousness .456

Shopping trip importancy .002

Deal proneness .026

Consumer scepticism -.324

Merchandise quality and service quality .694


Function

Merchandise quality and service quality .813

Price consciousness .609

Consumer scepticism -.478

Deal proneness .149

Shopping trip importancy .051

Table 4.15 – Structure Matrix of the discriminant function

Now that we have assessed the variables that account for the variance between the three loyalty-

based clusters, it is time to summarize our findings with regard to the HILO-pricing strategy: The variables that

best explain the differences between the HILO-clusters are (1) merchandise quality and service quality, (2) price

consciousness, (3) consumer scepticism, and to a lesser extent (4) deal proneness. Shopping trip importancy is

found to be of very little variance explaining power. Additionally, by assessing the scores of the three different

HILO-clusters to the function, it becomes possible to assign a limited profile to each cluster. Results of this

assessment report a positive score of cluster 2 (= most store loyal, see Table 4.7) on the function (Table 4.16).

Cluster Function 1

1 -1.899

2 .674

3 -.157

Table 4.16 – HILO Group centroids for function 1

Finally, the leave-one-out method shows us that 59.3% of the HILO respondents were correctly

assigned to a cluster. This percentage is appropriate for guaranteeing a satisfactory reliability. Again, this

relatively high percentage is due to the fact that the previously conducted cluster analysis made use of the same

variables for clustering the respondents.

4.5 Discriminant analysis II

In the previous paragraph, we researched the relevant variables that discriminate between the loyalty-based

clusters. This research has been performed for both the EDLP- and the HILO-pricing strategy. This paragraph will

cover the second discriminant analysis, in which we will study the individual difference factors and situational

factors that discriminate between the clusters. By doing so, a more comprehensive picture of the different

loyalty-related clusters and their content can be established. Output of the second discriminant analysis can be

found in the Appendix, section F.



The equality of group means test reveals that 6 out of the 7 variables are significant and thus differ across the

three EDLP-clusters. The variable ‘education’ is found to be insignificant (p= .100). The discriminant analysis

reports two functions which determine 60.4% and 39.6% of the variance respectively. Corresponding

Eigenvalues for both functions are found to be .389 for function 1 and .255 for the second function. These

Eigenvalues indicate the proportion of variance explained by the function (between-groups sums of squares

divided by within-groups sums of squares). Additionally, the Canonical correlations are determined as .529 for

function 1 and .451 for function, thereby confirming the discriminatory power of both functions across the three

loyalty-based EDLP-clusters. Moreover, Wilks’ Lambda reports that for both functions, the group means of the

clusters appear to differ. The Lambda’s for function 1 and 2 are found to be .574 (p= .000) and .797 (p= .000)

respectively. By taking a close look at the structure matrix, one can assess the amount of correlation between

the variables involved and the discriminant function (Table 4.17).

function

1 2

Age -.528* .351

Gender .409* -.313

Income -.308* .158

Education .265* .134

Time pressure .375 .645*

Household size -.094 .452*

Store image .339 .441*

Table 4.17 – Structure Matrix of the discriminant functions 1 and 2

It should become clear that the variation between the three different EDLP-clusters can be explained by

interpreting both functions. Function 1 is dominated by ‘age’, ‘gender’ and ‘income’, and at a lower level by the

‘education level’ of EDLP-shoppers. Conversely, function 2 is characterized by the variables ‘time pressure’,

‘household size’, and ‘store image’. Furthermore, by assessing the scores of the three different EDLP-clusters to

the function, it becomes possible to assign a limited profile to each cluster. Results of this assessment report a

positive score of cluster 3 (= most store loyal, see Table 4.6) on function 2 (Table 4.18). In addition, cluster 2 is

found to score strongly positive on function 1 and negative on function 2, while cluster 1 scores negative on both

functions (Table 4.18).


Table 4.18 – EDLP Group centroids for function 1

Finally, the leave-one-out method shows us that 60.7% of the EDLP respondents were correctly

assigned to a cluster. According to Malhotra (2006), a correct classification of at least 50% of the respondents is

appropriate to guarantee the validity of the analysis, ensuing the acceptability of the results.


By assessing the previously defined three loyalty clusters for the HILO-pricing strategy (see Table 4.7), we can

expect two functions as the result of the discriminant analysis. However, the results show us that the second

function is not significant (p= .229). Therefore, we will conduct the discriminant analysis once again, including

the subcommand for a maximum number of functions of 1 by moderating the syntax function of SPSS.

The equality of group means test reveals that merely two variables are significant, namely ‘age’ (p= .009)

and ‘household size’ (p= .001). The variable ‘time pressure’ is slightly insignificant (p= .076). The discriminant

analysis reports that the generated function determines 78.4% of the variance. The Eigenvalue of .211 in

combination with a Canonical correlation of .417 reveal that we are dealing with a relatively weak discriminating

function. This is also emphasized by Wilks’ Lambda which has been determined at .781 (p= .001) for this function,

meaning that a large portion of the total variance in the discriminant scores is not explained by differences

among the three loyalty-based HILO-clusters. The structure matrix shows us that the variables ‘household size’

and ‘age’ are the most discriminating variables across the three clusters (Table 4.19).

Table 4.19 – Structure Matrix of the discriminant function

Cluster Function 1 Function 2

1 -1.617 -.489

2 .533 -.645

3 .045 .412

Function

Household size .699

Age .546

Store image .346

Time pressure -.247

Gender -.223

income .207

education -.134


In addition, the group centroids reveal that cluster 2 (=most store loyal, see Table 4.7) is the only cluster

that scores positive on the function (Table 4.20). Moreover, the leave-one-out method shows that merely 44%

of the respondents were correctly assigned to a cluster.

Table 4.20 – HILO Group centroids for function 1

4.6 Profiling the loyalty clusters

The final step in the assessment of the different loyalty-based clusters is providing a clear picture of the clusters.

For both pricing strategies, three different loyalty-clusters have been established in paragraph 4.5. These

clusters have been distinguished from each other by means of discriminant analyses in the previous paragraph.

In this paragraph, the results of both the cluster- and discriminant analyses will be combined into a more

comprehensive, practical segmentation. The clusters will be profiled by describing the respondents by the

significant variables of the discriminant analysis. We will start with assessing the clusters of the EDLP-pricing

strategy, followed by the clusters of the HILO-pricing strategy.


The cluster analysis revealed that we are dealing with three clusters exhibiting different levels of loyalty. As can

be seen, cluster 3 has the highest average store loyalty intentions, while cluster 1 exhibits the lowest average

store loyalty intentions. Moreover, by including the relevant variables from the cluster analysis we are able to

show that cluster 3 has indeed the greatest potential for the EDLP-retailer due to its lowest consumer scepticism

and its highest quality judgments. Moreover, it looks like that the most store loyal customers are also the most

deal prone- and price conscious ones (Table 4.21). See also the output in the Appendix, section G.

Cluster

1 (n=17) 2 (n=44) 3 (n=89)

Store loyalty intentions* 2.24 3.38 4.42

Deal proneness 2.87 3.57 3.87

Consumer scepticism 3.87 3.42 2.77

Merchandise quality and service quality 3.13 3.51 3.93

Price consciousness 2.93 3.33 3.61

* = the average store loyalty intentions regarding the three loyalty items applied in the cluster analysis

Table 4.21 – Average EDLP-cluster scores on store loyalty intentions and other significant variables

Cluster Function

1 -.538

2 .387

3 -.530


Now that we know that cluster 3 is the most attractive cluster and cluster 1 the least attractive one, we

will include the individual difference factors and situational factors in order to come up with a more detailed

description of the respondents belonging to each cluster.

EDLP CLUSTER 1 (n=17) low loyalty

variable description

Age Relatively old; 71% is > 50 years old.

Gender 59% male vs. 41% female

Income 82% earns < 60K annually

Household size Relatively even distributed from 1 to 5 persons

Time pressure Relatively disloyal under time pressure; 88% switches easily under time pressure

Store image 76.4% rates the EDLP-retailer above average on store image; mean = 3.50

EDLP CLUSTER 2 (n=44) medium loyalty


Age Relatively young; 47% is < 35 years old, 86.3% is < 50 years old

Gender Mostly women; 84%

Income 68% earns < 40K annually. Average income lies between 20k and 40K annually

Household size 73% consist of 1 or 2 persons

Time pressure moderately loyal under time pressure; 66% is relatively loyal vs. 44% relatively disloyal

Store image 86% rates the EDLP-retailer above average on store image; mean = 3.90

EDLP CLUSTER 3 (n=89) high loyalty


Age Normally distributed, 68% is between 25 and 65 years old

Gender 41% male vs. 59% female

Income 61% earns <40K annually, 85% earns <60K annually, average income lies between 20K and 40K annually

Household size Relatively large households; 57% consists of ≥ 4 persons. Median = 4 persons

Time pressure Relatively loyal under time pressure; 72% carries out above average store loyalty intentions under time

pressure.

Store image 99% rates the EDLP-retailer above average on store image. 70% rates the EDLP-retailer ‘good’ or ‘very

good’ on store image. mean = 4.08



The cluster analysis revealed cluster 2 has the highest average store loyalty intentions, while cluster 1 exhibits

the lowest average store loyalty intentions. Moreover, by including the relevant variables from the cluster

analysis we are able to show that cluster 3 has the greatest potential for the HILO-retailer due to the best quality

judgements, and the relatively low consumer scepticism which it has to cope with. Surprisingly, cluster 2 also

shows the greatest consumer price consciousness. This leads to the thought that those consumers that exhibit

the highest store loyalty intentions regarding the HILO-retailer are also the most price conscious ones (Table

4.22).

Cluster

1 (n=28) 2 (n=87) 3 (n=35)

Store loyalty intentions* 2.51 4.39 3.47

Consumer scepticism 3.41 2.66 2.83

Merchandise quality and service quality 2.89 3.82 3.51

Price consciousness 2.87 3.85 3.47

* = the average store loyalty intentions regarding the three loyalty items applied in the cluster analysis

Table 4.22 – Average HILO-cluster scores on store loyalty intentions and other significant variables

The final step involves the inclusion of the individual difference factors and situational factors in order

to come up with a more detailed description of the respondents belonging to each cluster. Unfortunately, only

two variables proved to be significant in the discriminant analysis. Therefore, we will profile the clusters based

on the variables ‘age’ and ‘household size’.

HILO CLUSTER 1 (n=28) low loyalty


Age Relatively young consumers. 89% is <50 years old. No customers >65 years old

Household size 43% consists of 1 person

HILO CLUSTER 2 (n=87) high loyalty


Age Normally distributed, 75% is between 25 and 65 years old

Household size Distributed from 1 to >6 persons. 71% consists of 1, 2, or 4 persons

HILO CLUSTER 3 (n=35) medium loyalty


Age Relatively young. 60% is <35 years old. 94% is <50 years old

Household size Relatively small. 63% consists of 1 or 2 persons. 89% consists of ≤3 persons


5. CONCLUSIONS AND RECOMMENDATIONS

This chapter will cover conclusions regarding the research that has been conducted in the previous chapters. An

attempt will be made to formulate a comprehensive answer on the problem statement and corresponding

research questions. In addition, practical relevancy and applicability of the findings will be provided. The chapter

will be closed by a critical reflection on the research, and recommendations for further research directions will

be provided.

5.1 Conclusions

Different pricing strategies are expected to attract different types of consumers (Bell and Lattin, 1998) with

different shopping motives (Mulhern and Leone, 1990). In the introduction, we stated that we are curious about

the underlying factors that accounted for these consumer responses to a specific pricing strategy. Particularly

the conditions under which these consumers create store loyalty intentions are subjective to our interest.

Therefore, we formulated the following problem statement:



In order to comprehensively solve the problem statement, the research questions formulated in the introduction

will be answered.

1) Which factors play a role in the creation of store loyalty in case of a HILO pricing strategy?

‘Price consciousness’ is found to be significantly influencing store loyalty intentions in case of a HILO pricing-

format. This finding is in line with the results of the study by Alford and Biswas (2002), which state that

consumer price conscious in a HILO-retail setting leads to a perception of greater value, resulting in diminished

search intentions and greater store loyalty intentions. In addition, ‘shopping trip importancy’ did not turn out to

be of significant influence on store loyalty intentions. Therefore, we cannot confirm the study on the

relationship between shopping trip importancy and store loyalty as conducted by Kahn and Schmittlein (1992).

Moreover, ‘merchandise and service quality’ also significantly contributes to store loyalty intentions among

HILO-shoppers. This result is in line with the research of Terblanche and Boshoff (2006), which researched the

relationship between a satisfactory in-store shopping experience and retailer loyalty. It seems that the

wellknown SERVQUAL-model as developed by Parasuraman et al., (1988) is yet again confirmed.


2) Which factors play a role in the creation of store loyalty in case of an EDLP pricing strategy?

In an EDLP-retail setting, the factors ‘deal proneness’, ‘consumer scepticism’, and ‘merchandise and service

quality’ are found to significantly influence store loyalty intentions among EDLP-shoppers. ‘Deal proneness’ was

expected to negatively influence the creation of store loyalty intentions among EDLP-shoppers, due to the

existence of so-called deal-loyalty, which proposes that deal-prone consumers attach more importance to the

deal than to the product or store (Gázquez-Abad and Sánchez-Pérez, 2009). Our research findings suggest

different. Deal proneness was found to significantly positively influence store loyalty intentions among EDLP-

shoppers. This result leads to the thought that the deal-prone behavior as it is called by Rothschild (1987)

doesn’t apply here, or at least not in the Dutch supermarket industry.

Furthermore, the literature review strongly supports the thought that ‘consumer scepticism’ negatively

influences the creation of store loyalty in an EDLP-retail setting (Gahwiler and Havitz, 1998; Auh, 2005). Our

empirical results confirm this thought as EDLP-claims are relatively difficult to verify and impede the creation of

store loyalty intentions among consumers.

The final variable empirically tested in an EDLP-retail setting is ‘merchandise and service quality’.

Terblanche and Boshoff (2006) pointed out that the lowest-price claim by EDLP-retailers may result in a lower

perception of merchandise and service quality and eventually in a decreased chance of provoking store loyalty

intentions. Our empirical results prove the opposite, namely that ‘merchandise and service quality’ positively

influence store loyalty intentions. This leads to the thought that the relationship between quality and loyalty

intentions is stronger than the consequences of a potential decreased quality rating due to low prices.

Summarizing, the variables that prove to significantly influence the creation of store loyalty intentions under

either a HILO- or an EDLP-pricing format are graphically displayed below.

Figure 5.1 – Conceptual model presenting the relationships found in the empirical research

+

STORE

LOYALTY

+

HILO- PRICING

PRICE

CONSCIOUSNESS

DEAL

PRONENESS

CONSUMER

SKEPTICISM

EDLP- PRICING

MERCHANDISE

QUALITY &

SERVICE

QUALITY

+

+ -


(n=28)

• 71% is between 25-50 years old

• 43% of the households consist of

1 person

(n=35)

• Relatively young, 60% < 35 years

old, 94% < 50 years old

• 63% consists of ≤ 2 persons, 89%

consists of ≤ 3 persons

(n=87)

• Relatively old, 83% is > 25 years

old, 30% > 50 years old

• Large households, 55% consists of

≥ 4 persons

Which loyalty segments can be distinguished in case of a HILO pricing strategy?

Based on the variables that we researched beforehand, three different consumer loyalty segments can be

identified in a HILO-retail setting. The discriminant analysis revealed that these segments significantly differ from

each other on merely two factors, namely ‘age’ and ‘household size’. Consequently, segments can hardly be

described into detail (figure 5.2).

Figure 5.2 – Consumer loyalty segments in a HILO-retail setting

From the empirical research comes forth that the segment containing large households is the most attractive

segment (the family), followed by a segment (the couple) typified by mostly 2-persons households in the age of

18 – 50 years old. The third segment (the loner) is exhibiting the weakest store loyalty intentions. These results

lead to the thought that HILO retailers - with their price conscious, quality-driven customers- should focus on

families with children. Additional comprehensive and detailed recommendations with regard to the marketing

practices that a HILO-retailer may apply in practice are offered below.

Which loyalty segments can be distinguished in case of an EDLP pricing strategy?

For the EDLP-retail setting, again three loyalty-based clusters are identified. In contrast to the HILO-setting,

these segments can be described in a more comprehensive way (figure 5.3).


(n=17)

• Relatively old, 71% ≥ 50 years old

• 59% male vs. 41% female

• 65% earns between 20 and 40K

annually

• Household size is relatively even

distributed from 1 to 5 persons

• Disloyal under time pressure

• Inferior store image

(n=44)

• Relatively young, 48% ≤ 35 years

old, 86% ≤ 50 years old


• 68% earns ≤ 40K annually

• Small households, 73% consists of

1 or 2 persons

• Moderately loyal under time

pressure

• Temperate store image

(n=89)

• Middle aged, 67% is between 25

and 65 years old


• 40% earns > 40K annually

• Large households, 57% consists of

≥ 4 persons

• Loyal under time pressure

• Superior store image

Figure 5.3 – Consumer loyalty segments in an EDLP-retail setting

The loyalty segments of the EDLP-retail landscape reveal that, in line with the sementation results of the HILO-

retailer, families appear again to be the most attractive segment for retailers (the faithful family). The smallest

segment (the grumpy old men) does not turn out to be that loyal; therefore this segment is the least attractive

for the EDLP-retailer. In addition, the segment containing a huge amount of younger females (the young women)

exhibits moderate store loyalty intentions relative to the other segments. It turns out that, similar to the HILO-

retail setting, families are the most attractive segment when it comes down to store loyalty intentions.

It should become clear that both pricing strategies have their own managerial implications for the

exposed store loyalty intentions by consumers. Although differences exist between the factors that either

facilitate or impede the creation of store loyalty, similarities are also present. Whereas the HILO-retailer should

focus on its price conscious customers in order to facilitate the creation of store loyalty intentions, the EDLP-

retailer should provide attention to multiple factors like the dealproneness of its customers and, even more


important, scepticism among its customers with regard to prices and promotions. Perhaps the most important

factor to consider is the quality of merchandise and service in this retail setting. It turned out that for both

pricing strategies, the quality factor was one of the leading predictors of store loyalty intentions. Regarding the

loyalty segments that can be identified; it turns out that for both retail pricing-strategies families are the most

loyal, and in the view of this research, most attractive segment. The relatively older adults in combination with a

larger household size (≥ 4 persons) reveal that we are dealing with families with children. A possible explanation

for the presence of stronger store loyalty intentions among these customers is the fact that in most traditional

Dutch families, women are the ones who do the weekly grocery shopping. This recurring event may lead to a

more convenience-based shopping pattern as these women patronize the (same) retailer on a weekly basis. On

top, these “major” shopping trips lead to increased consumer tendencies to use coupons which, in turn,

facilitate the creation of store loyalty intentions in case of a HILO-retailer.

Moreover, since retailers are armed these days with a wealth of information on the shopping patterns

of their customers, marketers should be able to target specific segments of its customers based on their price

consciousness. Marketing communication tools should also be aligned in order to attract the most promising

customers. For the HILO-retailer on an operational level, this can result in the communication of its price

promotions targeted at families. Cents-off deals and rebates on specific merchandise may attract the right

customer to the store. Another marketing price promotion technique that may attract the most promising

customers is the application of a so-called ‘Loss Leader’, which stands for a (popular) product that is sold at a

(very) low price to stimulate other, profitable sales. The HILO-retailer may combine this loss leader with specific

family merchandise to generate larger sales. In case of an EDLP-pricing strategy, we have seen that deal

proneness is one of the significant antecedents of store loyalty intentions. Contrary, consumer scepticism was

found to be a barrier to facilitate store loyalty intentions. Implications are that a specific EDLP-retailer should

attract the right customers by emphasizing its product deals in conjunction with its dedication to undersell the

competitors. As consumers are nowadays well-informed about competitive prices and quality claims, caution is

advised. A possibility here is to offer ‘packaging deals’, fine-tuned for its most promising customers with respect

to loyalty intentions. Here, one could think of promotions in the form of “buy 2 boxes of diapers, get one for

free” or “buy 6 bottles of soda and receive a free box of candy”.

5.2 Limitations and future research

As with any study, this research also has its limitations. This paragraph will go deeper into these restraints and

suggests how these can be overcome in future research. In addition, an attempt will be made to provide some

general recommendations for future research directions.

As noted in the introduction of this study, this research consists of two parts; 1) the identification of

variables that facilitate or impede the creation of store loyalty intentions under a specific pricing strategy, and 2)


the segmentation of consumers based on their store loyalty intentions. With respect to the former, it can be

stated that we made an attempt to include the most relevant variables to explain the concept of store loyalty

intentions. Although we did extensive literature research on this topic, there is a multitude of factors potentially

responsible for clarifying store loyalty intentions. It is nearly impossible to explain all of these factors in a

turbulent FMCG-market. Therefore, one should clearly align the chosen variables with the purpose of the

research. Moreover, the data collection method used in this research – a questionnaire – has some drawbacks in

the light of this study. Due to the fact that one of the side conditions of this study is the maximum time usage of

six months, merely 150 respondents per pricing format could be approached. This small sample size results in a

few small segments of no more than 20 to 30 respondents, which can hardly be generalized across the entire

Dutch supermarket industry. Also the construction of the questionnaire raises some questions in the light of this

research. The statements used for measuring the factor ‘merchandise and service quality’ can be interpreted as

leading questions, since the formulation of these statements is positively biased.

Also the second part of this study – the segmentation of customers based on their expressed store

loyalty intentions- raises some critical questions. In the cluster analysis, we chose to use 3 loyalty clusters based

on the agglomeration schedule. In this case, it would have been better to identify richer and detailed clusters in

order to segment consumers based on their store loyalty intentions. For future research attempts, this could be

established by making use of a larger and more comprehensive dataset based on a larger number of

respondents. Moreover, maybe the largest drawback of this study is the small amount of demographic data that

we acquired with the help of the questionnaire. An implication is that we could hardly describe the loyalty-based

segments in terms of demographics. By including more questions regarding demographic variables in the

questionnaire, a more comprehensive picture of a retailers’ most promising customers can be established, which,

in turn, leads to a segment of customers which is easier to target by marketing communication and promotion

plans.

Unfortunately, due to a lack of time, it was not possible to research all the relevant factors that impede

or facilitate the creation of store loyalty under different pricing strategies. A recommendation for future

research is to examine more (in)direct antecedents of store loyalty intentions in order to extend the model that

we constructed in this study. Perhaps this leads to some new insights in consumer shopping behavior.

Furthermore, this research has focused on store loyalty inplications in the Dutch supermarket industry. We could

ask ourselves to what extent our developed model also applies to other industries which make use of EDLP- and

HILO-pricing strategies, like the consumer electronics industry. It could also be of particular interest to research

the applicability of our model in a B2B-setting. Additionally, this research has proven that the quality-dimension

is of major importancy with respect to the creation of store loyalty in both an EDLP- and HILO-retail setting.

Future research should investigate the underlying factors of this quality-dimension, thereby unraveling the

dimensions of quality in a retail-setting that facilitate store loyalty intentions. The final suggestion for future


research concerns the degree to which this research can be duplicated to other countries. Differences in

consumer behavior or attitude regarding the shopping proces could result in different loyalty-based segments. In

turn, these different segments could have implications for the marketing communication and promotion tools

that a retailer can apply to attract consumers to its store.


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APPENDICES

These appendices contain the questionnaire that is used in the data collection process. Although we researched

shoppers at two different retailers, both sets of respondents received the same questionnaire. The only

substantive difference between the two questionnaires is the name of the retailers. In addition, statistical output

of the applied statistical tests is provided in a structured way.

Table of contents:

QUESTIONNAIRE ............................................................................................................................... 58

STATISTICAL OUTPUT ........................................................................................................................ 61

A) Descriptives........................................................................................................................................ 61

B) Variable construction ......................................................................................................................... 66

C) Regression analysis............................................................................................................................. 67

D) Cluster analysis .................................................................................................................................. 71

E) Discriminant analysis I ........................................................................................................................ 74

F) Discriminant analysis II ....................................................................................................................... 77

G) Profiling the clusters .......................................................................................................................... 80


QUESTIONNAIRE

Bij onderstaande vragen is het de bedoeling dat u aangeeft in welke mate u het eens bent met de desbetreffende

stelling.

Bijvoorbeeld: Stel dat u het helemaal eens bent met de volgende uitspraak: “Als ik boodschappen doe, wil ik waar

voor mijn geld”, dan kruist u het bijbehorende hokje aan onder ‘helemaal mee eens’.

Helemaal

mee oneens

Oneens Noch eens,

noch

oneens

Eens Helemaal

mee eens

1. Als ik boodschappen doe, dan kies ik een

supermarkt op basis van de prijzen voor de

producten die ik wil gaan kopen

2. Ik baseer mijn supermarktkeuze op basis van de

prijzen van de producten

3. Als ik boodschappen doe, wil ik waar voor mijn

geld

4. Ik vergelijk supermarkten op basis van de prijzen

die voor producten gevraagd worden

5. Ik doe de ‘grote boodschappen’ altijd bij C1000

6. Ik besteed bij C1000 gemiddeld meer geld dan dat

ik bij andere supermarkten doe

7. De boodschappen voor feestdagen, verjaardagen

en andere belangrijke gebeurtenissen koop ik bij

C1000

8. Als een product in de aanbieding is, koop ik meer

dan ik normaal zou doen

Mevrouw, Mijnheer,

Mijn naam is Bjorn Nijmeijer en in het kader van mijn afstudeeronderzoek aan de Rijksuniversiteit

Groningen vraag ik u vriendelijk deze vragenlijst zo volledig en waarheidsgetrouw mogelijk in te

vullen.

Het invullen van de vragenlijst zal plusminus 5 minuten in beslag nemen.

Uw medewerking wordt erg op prijs gesteld en zal in grote mate bijdragen aan de kwaliteit van dit

onderzoek.

Bij voorbaat dank voor uw medewerking!


9. Als een product in de aanbieding is, kan dat voor

mij een reden zijn om het te kopen

10. Ondanks dat ik een aantal favoriete merken heb,

koop ik meestal het merk dat in de aanbieding is

Helemaal

mee oneens

Oneens Noch eens,

noch

oneens

Eens Helemaal

mee eens

11. Een gratis cadeau bij aanschaf van een product

kan voor mij de reden zijn om het product

daadwerkelijk te kopen

12. Ik vind het moeilijk om aanbiedingen te

weerstaan

13. Sommige supermarkten beweren de laagste

prijzen te hanteren. Ik betwijfel dit soort claims ten

zeerste

14. Supermarkten zijn niet te vertrouwen

15. Supermarkten verspreiden folders om klanten te

misleiden in plaats van informeren

16. Supermarkten zijn slechts geïnteresseerd in het

maken van winst in plaats van het bedienen van de

klanten

17. Als ik boodschappen doe, ga ik doorgaans naar

C1000

18. In zie mijzelf als een ‘vaste klant’ van C1000

19. Ik raad anderen aan ook bij C1000 te winkelen

20. Als ik haast heb, ben ik geneigd een supermarkt

te bezoeken waar ik bekend ben

21. Voor ‘kleine, snelle boodschappen’ ga ik naar

C1000

22. C1000 is een verzorgde, nette supermarkt

23. C1000 heeft een goede reputatie

Bij onderstaande vragen is het de bedoeling dat u een score geeft voor C1000 op de genoemde aspecten.

Bijvoorbeeld: Stel dat u het personeel van C1000 als redelijk vriendelijk ervaart, dan kruist u het bijbehorende

hokje aan onder ‘redelijk’.

Zeer slecht Slecht

Gemiddeld Goed Zeer goed

24. Vriendelijkheid van het personeel

25. Snelheid waarmee klanten worden geholpen

26. Kwaliteit van de aangeboden producten

27. Versheid van groente & fruit

28. Grootte van het aangeboden assortiment


Tot slot volgen nu een aantal vragen over uw leefomstandigheden. Omcirkel a.u.b. het juiste antwoord.

Uw leeftijd? 18-25jr 25-35jr 35-50jr 50-65jr >65jr

Uw geslacht? Man Vrouw

Uw jaarlijkse bruto inkomen (x1000)? < €20 €20 - €40 €40 - €60 €60 - €80 > €80

Uit hoeveel personen bestaat uw huishouden? 1 2 3 4 5 6 >6

Wat is uw hoogst genoten opleiding? LBO Middelbare school MBO HBO Universiteit

Dit is het einde van de vragenlijst. Bedankt voor uw deelname!


STATISTICAL OUTPUT

A) Descriptives

A.1) Age

age29 * sex30 Crosstabulation (EDLP)

Count

sex30

male female Total

18-25 12 16 28

25-35 13 9 22

35-50 12 33 45

50-65 13 21 34

>65 3 18 21

age29

Total 53 97 150

age29 (EDLP)

Frequency Percent Valid Percent Cumulative Percent

18-25 28 18,7 18,7 18,7

25-35 22 14,7 14,7 33,3

35-50 45 30,0 30,0 63,3

50-65 34 22,7 22,7 86,0

>65 21 14,0 14,0 100,0

Valid

Total 150 100,0 100,0

age29 * sex30 Crosstabulation (HILO)

Count

sex30

male female Total

18-25 22 8 30

25-35 19 19 38

35-50 13 38 51

50-65 11 12 23

>65 1 7 8

age29

Total 66 84 150


age29 * sex30 Crosstabulation (HILO)

Count

sex30

male female Total

18-25 22 8 30

25-35 19 19 38

35-50 13 38 51

50-65 11 12 23

>65 1 7 8

age29

Total 66 84 150

A.2) Gender

sex30 (EDLP)


male 53 35,3 35,3 35,3

female 97 64,7 64,7 100,0

Valid

Total 150 100,0 100,0

sex30 (HILO)


male 66 44,0 44,0 44,0

female 84 56,0 56,0 100,0

Valid

Total 150 100,0 100,0


A.3) Income

income31 (EDLP)


<20K 40 26,7 26,7 26,7

20-40K 51 34,0 34,0 60,7

40-60K 41 27,3 27,3 88,0

60-80K 14 9,3 9,3 97,3

>80K 4 2,7 2,7 100,0

Valid

Total 150 100,0 100,0

income31 (HILO)

Frequency Percent Valid Percent

Cumulative

Percent

<20K 56 37,3 37,3 37,3

20-40K 40 26,7 26,7 64,0

40-60K 43 28,7 28,7 92,7

60-80K 11 7,3 7,3 100,0

Valid

Total 150 100,0 100,0


A.4) Household size

household32 (EDLP)


1 27 18,0 18,0 18,0

2 45 30,0 30,0 48,0

3 9 6,0 6,0 54,0

4 28 18,7 18,7 72,7

5 27 18,0 18,0 90,7

6 13 8,7 8,7 99,3

>6 1 ,7 ,7 100,0

Valid

Total 150 100,0 100,0

household32 (HILO)


1 37 24,7 24,7 24,7

2 36 24,0 24,0 48,7

3 15 10,0 10,0 58,7

4 37 24,7 24,7 83,3

5 17 11,3 11,3 94,7

6 4 2,7 2,7 97,3

>6 4 2,7 2,7 100,0

Valid

Total 150 100,0 100,0


A.5) Education level

education33 (EDLP)


LBO 4 2,7 2,7 2,7

Middelbare school 21 14,0 14,0 16,7

MBO 40 26,7 26,7 43,3

HBO 41 27,3 27,3 70,7

Universiteit 44 29,3 29,3 100,0

Valid

Total 150 100,0 100,0

education33 (HILO)


LBO 6 4,0 4,0 4,0

Middelbare school 13 8,7 8,7 12,7

MBO 41 27,3 27,3 40,0

HBO 50 33,3 33,3 73,3

Universiteit 40 26,7 26,7 100,0

Valid

Total 150 100,0 100,0


B) Variable construction

STORE IMAGE

Cronbach's Alpha N of Items

,610 2

SHOPPING TRIP IMPORTANCY


,780 3

PRICE CONSCIOUSNESS


,773 4

DEAL PRONENESS


,828 5

STORE LOYALTY


,785 3

CONSUMER SCEPTICISM


,841 4

TIME PRESSURE


,730 2

MERCHANDISE QUALITY &

SERVICE QUALITY


,622 4


C) Regression analysis

C.1) HILO-pricing strategy

(Dependent variables: price consciousness + shopping trip importancy)


(Dependent variables: price consciousness + shopping trip importancy + deal proneness + consumer

scepticism + merchandise & service quality)

(Dependent variables: price consciousness + merchandise & service quality)


C.2) EDLP-pricing strategy

(Dependent variables: deal proneness + consumer scepticism + merchandise & service quality)


(Dependent variables: deal proneness + consumer scepticism + merchandise & service quality + price

consciousness + shopping trip importancy)


D) Cluster analysis

D.1) EDLP-pricing strategy


D.2) HILO-pricing strategy


E) Discriminant analysis I

E.1) EDLP-pricing strategy


E.1) HILO-pricing strategy


F) Discriminant analysis II

F.1) EDLP-pricing strategy


F.2) HILO-pricing strategy


G) Profiling the clusters

G.1) EDLP-pricing strategy

Cluster 1 - “The grumpy old men”


Cluster 2 – “The temperate women”


Cluster 3 – “The faithful family”


G.2) HILO-pricing strategy

Cluster 1 – “The loner”

Cluster 2 – “The family”


Cluster 3 – “The couple”

Master Thesis Bjorn Nijmeijer final version

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Transcript of Master Thesis Bjorn Nijmeijer final version