Master Thesis Bjorn Nijmeijer final version
Transcript of 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
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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.
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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
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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
4.3.1 EDLP-pricing strategy ........................................................................................... 35
4.3.2 HILO-pricing strategy............................................................................................ 36
4.4 Discriminant analysis I ........................................................................................................... 36
4.4.1 EDLP-pricing strategy ........................................................................................... 36
4.4.2 HILO-pricing strategy............................................................................................ 39
4.5 Discriminant analysis II .......................................................................................................... 40
4.5.1 EDLP-pricing strategy ........................................................................................... 41
4.5.2 HILO-pricing strategy............................................................................................ 42
4.6 Profiling the loyalty clusters .................................................................................................. 43
4.6.1 EDLP-pricing strategy ........................................................................................... 43
4.6.2 HILO-pricing strategy............................................................................................ 45
5 CONCLUSIONS AND RECOMMENDATIONS................................................................... 46
5.1 Conclusions ............................................................................................................................ 46
5.2 Limitations and future research............................................................................................. 50
REFERENCES...................................................................................................................................... 53
APPENDICES...................................................................................................................................... 57
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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
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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
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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
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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:
What factors influence the creation of store loyalty under different pricing strategies and which loyalty
segments can be distinguished for each pricing strategy?
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
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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.
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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,
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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
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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)
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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:
© Bjorn Nijmeijer 2010 - 14 -
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)
© Bjorn Nijmeijer 2010 - 15 -
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.
© Bjorn Nijmeijer 2010 - 16 -
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
© Bjorn Nijmeijer 2010 - 17 -
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
© Bjorn Nijmeijer 2010 - 18 -
(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
© Bjorn Nijmeijer 2010 - 19 -
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
© Bjorn Nijmeijer 2010 - 20 -
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,
© Bjorn Nijmeijer 2010 - 21 -
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:
© Bjorn Nijmeijer 2010 - 22 -
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
© Bjorn Nijmeijer 2010 - 23 -
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
© Bjorn Nijmeijer 2010 - 24 -
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’.
© Bjorn Nijmeijer 2010 - 25 -
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
© Bjorn Nijmeijer 2010 - 26 -
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’.
© Bjorn Nijmeijer 2010 - 27 -
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.
© Bjorn Nijmeijer 2010 - 28 -
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%).
© Bjorn Nijmeijer 2010 - 29 -
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).
© Bjorn Nijmeijer 2010 - 30 -
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
© Bjorn Nijmeijer 2010 - 31 -
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
© Bjorn Nijmeijer 2010 - 32 -
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’.
© Bjorn Nijmeijer 2010 - 33 -
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).
Unstandardized coefficients
Standardized
Coefficients
Collinearity Statistics
Model B Std. Error Beta
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).
Unstandardized coefficients
Standardized
Coefficients
Collinearity Statistics
Model B Std. Error Beta
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).
© Bjorn Nijmeijer 2010 - 34 -
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).
Unstandardized coefficients
Standardized
Coefficients
Collinearity Statistics
Model B Std. Error Beta
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
© Bjorn Nijmeijer 2010 - 35 -
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.
4.3.1 EDLP-pricing strategy
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
© Bjorn Nijmeijer 2010 - 36 -
4.3.2 HILO-pricing strategy
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.
4.4.1 EDLP-pricing strategy
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
© Bjorn Nijmeijer 2010 - 37 -
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.
© Bjorn Nijmeijer 2010 - 38 -
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
© Bjorn Nijmeijer 2010 - 39 -
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.
4.4.2 HILO-pricing strategy
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
© Bjorn Nijmeijer 2010 - 40 -
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.
© Bjorn Nijmeijer 2010 - 41 -
4.5.1 EDLP-pricing strategy
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).
© Bjorn Nijmeijer 2010 - 42 -
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.
4.5.2 HILO-pricing strategy
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
© Bjorn Nijmeijer 2010 - 43 -
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.
4.6.1 EDLP-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
© Bjorn Nijmeijer 2010 - 44 -
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
variable description
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
variable description
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
© Bjorn Nijmeijer 2010 - 45 -
4.6.2 HILO-pricing strategy
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
variable description
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
variable description
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
variable description
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
© Bjorn Nijmeijer 2010 - 46 -
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:
What factors influence the creation of store loyalty under different pricing strategies and which loyalty
segments can be distinguished for each pricing strategy?
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.
© Bjorn Nijmeijer 2010 - 47 -
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
+
+ -
© Bjorn Nijmeijer 2010 - 48 -
(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).
© Bjorn Nijmeijer 2010 - 49 -
(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
• 16% male vs. 84% female
• 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% male vs. 60% female
• 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
© Bjorn Nijmeijer 2010 - 50 -
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)
© Bjorn Nijmeijer 2010 - 51 -
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
© Bjorn Nijmeijer 2010 - 52 -
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.
© Bjorn Nijmeijer 2010 - 53 -
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© Bjorn Nijmeijer 2010 - 56 -
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© Bjorn Nijmeijer 2010 - 57 -
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
© Bjorn Nijmeijer 2010 - 58 -
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!
© Bjorn Nijmeijer 2010 - 59 -
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
© Bjorn Nijmeijer 2010 - 60 -
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!
© Bjorn Nijmeijer 2010 - 61 -
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
© Bjorn Nijmeijer 2010 - 62 -
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)
Frequency Percent Valid Percent Cumulative Percent
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)
Frequency Percent Valid Percent Cumulative Percent
male 66 44,0 44,0 44,0
female 84 56,0 56,0 100,0
Valid
Total 150 100,0 100,0
© Bjorn Nijmeijer 2010 - 63 -
A.3) Income
income31 (EDLP)
Frequency Percent Valid Percent Cumulative Percent
<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
© Bjorn Nijmeijer 2010 - 64 -
A.4) Household size
household32 (EDLP)
Frequency Percent Valid Percent Cumulative Percent
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)
Frequency Percent Valid Percent Cumulative Percent
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
© Bjorn Nijmeijer 2010 - 65 -
A.5) Education level
education33 (EDLP)
Frequency Percent Valid Percent Cumulative Percent
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)
Frequency Percent Valid Percent Cumulative Percent
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
© Bjorn Nijmeijer 2010 - 66 -
B) Variable construction
STORE IMAGE
Cronbach's Alpha N of Items
,610 2
SHOPPING TRIP IMPORTANCY
Cronbach's Alpha N of Items
,780 3
PRICE CONSCIOUSNESS
Cronbach's Alpha N of Items
,773 4
DEAL PRONENESS
Cronbach's Alpha N of Items
,828 5
STORE LOYALTY
Cronbach's Alpha N of Items
,785 3
CONSUMER SCEPTICISM
Cronbach's Alpha N of Items
,841 4
TIME PRESSURE
Cronbach's Alpha N of Items
,730 2
MERCHANDISE QUALITY &
SERVICE QUALITY
Cronbach's Alpha N of Items
,622 4
© Bjorn Nijmeijer 2010 - 67 -
C) Regression analysis
C.1) HILO-pricing strategy
(Dependent variables: price consciousness + shopping trip importancy)
© Bjorn Nijmeijer 2010 - 68 -
(Dependent variables: price consciousness + shopping trip importancy + deal proneness + consumer
scepticism + merchandise & service quality)
(Dependent variables: price consciousness + merchandise & service quality)
© Bjorn Nijmeijer 2010 - 69 -
C.2) EDLP-pricing strategy
(Dependent variables: deal proneness + consumer scepticism + merchandise & service quality)
© Bjorn Nijmeijer 2010 - 70 -
(Dependent variables: deal proneness + consumer scepticism + merchandise & service quality + price
consciousness + shopping trip importancy)
© Bjorn Nijmeijer 2010 - 71 -
D) Cluster analysis
D.1) EDLP-pricing strategy
© Bjorn Nijmeijer 2010 - 72 -
D.2) HILO-pricing strategy
© Bjorn Nijmeijer 2010 - 73 -
© Bjorn Nijmeijer 2010 - 74 -
E) Discriminant analysis I
E.1) EDLP-pricing strategy
© Bjorn Nijmeijer 2010 - 75 -
E.1) HILO-pricing strategy
© Bjorn Nijmeijer 2010 - 76 -
© Bjorn Nijmeijer 2010 - 77 -
F) Discriminant analysis II
F.1) EDLP-pricing strategy
© Bjorn Nijmeijer 2010 - 78 -
F.2) HILO-pricing strategy
© Bjorn Nijmeijer 2010 - 79 -
© Bjorn Nijmeijer 2010 - 80 -
G) Profiling the clusters
G.1) EDLP-pricing strategy
Cluster 1 - “The grumpy old men”
© Bjorn Nijmeijer 2010 - 81 -
Cluster 2 – “The temperate women”
© Bjorn Nijmeijer 2010 - 82 -
Cluster 3 – “The faithful family”
© Bjorn Nijmeijer 2010 - 83 -
G.2) HILO-pricing strategy
Cluster 1 – “The loner”
Cluster 2 – “The family”
© Bjorn Nijmeijer 2010 - 84 -
Cluster 3 – “The couple”