GOING FOR VALUE
OR
GOING FOR DISCOUNT
A research on pricing strategies
and
store loyalty implications
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:
bjorn_nijmeijer@hotmail.com Drs. J. Berger
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.
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.
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
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 ... 81.5 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
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 non-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).
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
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.
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?
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.
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
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
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 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).
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.
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).
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
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
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’).
(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.
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-EDLP-claims are. Reasons for this thought are that promotional EDLP-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.
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.
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.”
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
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-HILO-retailer Jumbo. These HILO-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.
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).
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.
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
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
Age distribution of EDLP-shoppers 12 13 12 13 3 16 9 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 e s p o n d e n ts female male
Age distribution of HILO-shoppers
22 19 13 11 1 8 19 38 12 7 0 10 20 30 40 50 60 18-25 25-35 35-50 50-65 >65 age categories # r e s p o n d e n 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
incom e dis tribution 0 10 20 30 40 50 60 <20K 20-40K 40-60K 60-80K >80K incom e # r e s p o n d e n ts 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
Education level distributions 0 10 20 30 40 50 60 LBO Mid delb are scho ol MB O HBO Uni vers iteit # r e s p o n d e n 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
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
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
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
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).
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
