Consequences of Value in Retail Markets

Journal of Retailing 85 (3, 2009) 406–419

Consequences of Value in Retail Markets夽 Arjun Chaudhuri ∗ , Mark Ligas 1 Charles F. Dolan School of Business, Fairfield University, North Benson Road, Fairfield, CT 06824-5195, USA

Abstract We suggest that merchandise value, affect and two types of loyalty are related in the retail domain. Based on ideas from cognitive psychology on the structure of value we suggest that merchandise value is directly related to repurchase loyalty but indirectly related to attitudinal loyalty via the construct of store affect. Additionally, our model proposes that attitudinal loyalty is related to willingness to pay a price premium while repurchase loyalty is not. We also control for the effects of store familiarity and convenience. We test our hypotheses in three studies and find that, in general, our model is well supported at the level of both individual consumers and stores. Additionally, we find that perceived retailer differentiation moderates the effect of merchandise value on store affect. © 2009 New York University. Published by Elsevier Inc. All rights reserved. Keywords: Value; Affect; Loyalty; Price premium

Consumer evaluations of retail price and quality have been found to be related to important retail outcomes (Hardesty, Bearden, and Carlson 2007; Hardesty and Bearden 2003; Krishna et al. 2002; Ofir et al. 2008). For instance, it has been found that perceived value (an evaluation of the fit between quality and price) is related to willingness to pay a price premium (WPP) (Netemeyer et al. 2004). However, it is unclear why consumers would be willing to pay more for good value. We know from previous research that price premiums (WPP) can accrue from the type of loyalty-relationship that consumers have with a brand (Chaudhuri and Holbrook 2001). Recent research has also shown that developing affective bonds and connections between consumers and firms establishes committed relationships (Yim, Tsee, and Chan 2008). Further, merchandise value has been related to the pleasure resulting from obtaining a good deal (Grewal, Monroe, and Krishnan 1998a). Accordingly, we posit a model in which merchandise value can lead to different consequences in retailing with regard to the level of the

夽

This research was funded by a grant from the Fairfield University Research Committee to the first author. We would like to thank Ed Blair, Ruth Bolton, Dhruv Grewal and Jeffrey Inman for comments on previous versions of this paper. We are also very grateful to the three reviewers and the special issue editors for their guidance and support of this paper. ∗ Corresponding author. Tel.: +1 203 254 4000x2823; fax: +1 203 254 4105. E-mail addresses: [email protected] (A. Chaudhuri), [email protected] (M. Ligas). 1 Tel.: +1 203 254 4000x3117; fax: +1 203 254 4105.

loyalty-relationship that it generates between the consumer and the store. Thus, it can lead to a transactional exchange (repurchase loyalty without commitment) but also to a more enduring committed relationship that includes some relational bond or connectedness with the store (attitudinal loyalty). We suggest in this paper that the first consequence can exist with or without the generation of positive affect, while the second type must include some level of positive affect that precedes it. This, in turn, leads to a willingness to pay a price premium as an expression of a committed relationship. There has been a long-standing controversy concerning the nature of loyalty (Assael 1998). Behavioral proponents of loyalty have espoused consistent repurchase over time as the sole criteria for the measurement and understanding of loyalty (Foxall 1999; Tucker 1964). Cognitive theorists, on the other hand, have suggested that repeat purchase alone could indicate spurious loyalty to some other factor, such as low price, and have advocated the use of both repurchase and attitudinal dimensions of loyalty to measure true loyalty (Jacoby and Kyner 1973; Oliver 1999b). However, there is very little evidence, in the retail domain, of the differential effects of these two dimensions of loyalty, viewed simultaneously, on important outcome variables such as WPP. Importantly, we contribute to the literature by addressing this issue. If managers understand the chain of effects from merchandise value to store affect, attitudinal loyalty and WPP, they may want to emphasize the affect arising from in-store merchandise value in their out-of-store communications. Or, they may want to investigate

0022-4359/$ – see front matter © 2009 New York University. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jretai.2009.05.006

A. Chaudhuri, M. Ligas / Journal of Retailing 85 (3, 2009) 406–419

alternative, perhaps more cost effective, means of creating store affect. Thus, our incremental contribution in this paper is to study the simultaneous effect of merchandise value and store affect on two types of loyalty and WPP in three retail studies at both the individual level of consumers and the aggregate level of stores or firms. Previous empirical studies on value in retailing have not considered all these consequences of merchandise value simultaneously and have focused largely on the antecedents of merchandise value. For instance, Baker et al. (2002) explored the relationship of merchandise value with store patronage intentions (similar to repurchase loyalty) along with several antecedents of merchandise value, including psychic costs or negative affects. From a pricing perspective, Grewal et al. (1998a) demonstrated the effect of perceived acquisition value (similar to merchandise value in our study) on consumers’ willingness to buy and search intentions along with several antecedents including perceived transaction value or the resultant pleasure from receiving a good deal. However, none of these studies have examined the effects of merchandise value on two types of loyalty (repurchase and attitudinal) and WPP. In an online context, Srinivasan, Anderson, and Ponnavolu (2002) found that e-loyalty was positively related to willingness to pay more but their study was not designed to examine the antecedents and consequences of both types of loyalty. Similarly, Jones and Reynolds (2006) looked at positive affect and only one type of loyalty in their structural model. We also contribute to the literature by demonstrating that perceived retailer differentiation (at the store level) may act as a boundary condition that moderates the linkage of value to affect in our model. The relationship between merchandise value and WPP is an important consideration for retailers in the long term. Two ways that retailers create greater merchandise value in the short term are to increase the quality of their merchandise (e.g. J.C. Penney; see Levy and Weitz 2009) while maintaining the price, or to lower the price while maintaining quality (e.g. Saks; see Porter and Helm 2008). Both of these strategies may not be profitable strategies in the long run. For instance, deep discounting in retail stores has been known to cause retail profits to dwindle (Forest 2003) and offering quality merchandise at a lower price at the Old Navy chain of stores has led to the cannibalization of the high margin sales of a sibling store such as Gap (Lee 2003). Consider however, that Coach, Starbucks, Whole Foods and other retailers offer examples of merchandise value that can lead to higher prices and profits (Barbaro 2006; Holmes 2003; McNatt 2003; Waters 2007). However, there are also pitfalls in achieving higher prices as exemplified by Wal-Mart’s lack of success in deemphasizing everyday low pricing in order to cater to upscale buyers (Elliott and Barbaro 2006; Vranica and McWilliams 2006). We propose in this paper that merchandise value can lead to WPP because of store affect and attitudinal loyalty. We contend that store merchandise value is directly related to repurchase loyalty but only indirectly related to attitudinal loyalty via store affect. We further contend that store affect can create WPP through the creation of attitudinal loyalty but not repurchase loyalty. In this manner, we try to provide some answers to the

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puzzle of why people are willing to pay more to get “a good deal”. Accordingly, in this paper, we present a parsimonious conceptual model of the consequences of consumer value and we empirically test the model at the level of both individuals (Studies 1 and 2) and stores (Study 3) to provide evidence for our theory. We end with a discussion of our findings, their managerial implications and suggestions for future research. Theory Definitions The marketing literature has viewed perceived value as a function of both quality and price (Dodds, Monroe, and Grewal 1991; Johnson, Herrmann, and Huber 2006; Lichtenstein, Netemeyer, and Burton 1990; Zeithaml 1988). In other words, value is based on the extent of the “deal’ that the consumer is getting (Thaler 1985). While such a deal may constitute various configurations of quality and price, high quality and low price are two basic value strategies that firms employ (Grewal et al. 1998a). Further, Zeithaml (1988) defines perceived value as “the consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given” (p. 14). Conceptualizations like these adopt a quality-price, benefits versus costs, give-up versus get-back orientation to consumer value (see also Gale 1994). In retail markets, prior research (Baker et al. 2002; Cronin, Brady, and Hult 2000; Dodds et al. 1991; Grewal et al. 1998a,b; Harris and Goode 2004; Sirohi, McLaughlin, and Wittink 1998; Sweeney and Soutar 2001; Sweeney, Soutar, and Johnson 1999) also uses the “give-get” notion of value. In sum, the literature on perceived value has usually viewed good value as a favorable match between quality and price. Accordingly, we define the merchandise value of a store as the consumer’s overall evaluation of the store’s merchandise based on the perceived fit between the overall quality and price of the merchandise. We define store affect as consumers’ self-reported level of the intensity of the subjective experience of positive feelings towards a store in general. Affect is an integral aspect of the conceptualization of value in social psychology (Mandler 1982). Consumer affect in the store environment has also been viewed as a necessary factor for the development of attitudinal preference towards a store (Bitner 1992; Hui and Bateson 1991). Hence, such store affect is expected to be the link between merchandise value and the two types of loyalty in our model. Two aspects of loyalty, repurchase and attitudinal, have been well established in the marketing literature (Aaker 1991; Chaudhuri and Holbrook 2001; Dick and Basu 1994; Jacoby and Chestnut 1978; Oliver 1999b; Tucker 1964). The repeat purchase side of loyalty is represented in our model as repurchase loyalty and the attitudinal side of loyalty is represented as attitudinal loyalty. Repurchase loyalty is defined as a basic level of interest in a store that is limited to an intent to re-buy from the particular store at a future date. Attitudinal loyalty is defined as a level of attitudinal interest in a store that indicates some level of an existing bond or relationship with the store. We model willingness to pay a higher price as a purely endogenous variable in our model

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Fig. 1. A model of the effects of merchandise value.

and we define it as the propensity of a consumer to pay a higher price at a particular store for an item despite the availability of the item elsewhere at a lower price. Hypotheses We suggest, first, that one of the consequences of merchandise value is a simple interest in returning to the store (in order to regain such value) which we have defined earlier as repurchase loyalty. Previous work (Baker et al. 2002) has investigated this linkage at the individual level of consumers. We contribute to the literature by examining this relationship at both the individual level and the aggregate level of the store. Additionally, in terms of our contribution, we demonstrate that while merchandise value is related to one aspect of loyalty (repurchase), it is not simultaneously related directly to the other aspect of loyalty (attitudinal) at any of the two levels of analysis. To our knowledge, previous studies have not attempted these tasks. In constructing our hypotheses, we use a theoretical framework (Fig. 1) based on Mandler’s (1982) theory of value from social psychology and also the literature in marketing on the disconfirmation of expectations (Oliver 1996; Wood and Moreau 2006). According to Mandler, stimuli that fulfill our expectations are positively evaluated (“good” merchandise value, for instance) but do not result in intense affect. On the other hand, stimuli that are somewhat different from our expectations are stronger in affect intensity. When expectations are positively or negatively disconfirmed, the result can be high levels of affect. Accordingly, we propose that when the benefits and costs of obtaining merchandise from a store meets consumers’ expectations, merchandise value leads to a basic level of interest in the store but it is not evocative of affect. Thus, we postulate that merchandise value will be directly related to repurchase loyalty since, according to Mandler, such basic positive evaluations are associated with a simple preference for the object of evaluation. An acceptable match between an individual’s expectations of

price and quality and a store’s offerings on these same features result in a willingness or interest in obtaining the same value at the store in the future (repurchase loyalty). Recall also that we defined attitudinal loyalty to include a certain level of bonding or relationship with the store. This type of loyalty-relationship requires the development of affective bonds (Yim et al. 2008) and, thus, we suggest that merchandise value (by itself without store affect) will not be directly related to attitudinal loyalty. Instead, merchandise value that fulfills expectations will be directly related to repurchase loyalty since such basic positive evaluations of value are associated with a simple preference for the object of evaluation. H1. Merchandise value will be positively related to repurchase loyalty. Our next hypothesis concerns the relationship of merchandise value and store affect. Previous work (Grewal et al. 1998a) has tested the relationship of merchandise value and perceived transaction value which measures affect ensuing from a particular transaction but not affect ensuing from the store. The literature on disconfirmation of expectations suggests that such disconfirmation leads to emotion (or specific affects in our case) (Oliver 1996; Wood and Moreau 2006). Wood and Moreau use the term emotion to refer to global feelings that are either positively or negatively valenced. We use the term affect to refer to a specific and qualitatively different feeling such as happiness or joy. As discussed above, a scenario of good perceived value, in terms of the benefits and costs being equal as expected, will lead to a simple interest in rebuying from the store in the future (repurchase loyalty). Similarly, when consumers encounter merchandise value in the form of unexpected benefits that are greater than the costs, they will continue to consider it to be good perceived value. However, the difference between the two scenarios is that only the latter case will lead to positive affect (a specific feeling of happiness). According to Mandler (1982) as well, people have schemas or expectations of

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value. When a stimulus goes against an individual’s schema the discrepancy causes affect. If the discrepancy/disconfirmation is favorable (i.e. it can be assimilated into the schema), positive affect will ensue and the extent of affect that is generated will depend on the extent of the disconfirmation. Even in cases where the consumer is familiar with a store, we suggest that disconfirmation still plays a part based on the principle of scarcity (Cialdini 2007; Lynn 1991; Stock and Balachander 2005).2 Superior merchandise value that stems from high quality or low price is difficult to find (perhaps the very reason that it is considered to be a “good” deal). The awareness of such value that is exclusive to a source (and, thus, scarce) is also accompanied by the realization that this value has the potential to be lost (Cialdini 2007). This expectation of loss which is averted every time that the deal is, in fact, obtained, results in positive disconfirmation and positive affect. Thus, the process is still similar to that for consumers who have newly discovered the same deal. Consumers do not get jaded by familiarity with a valued object as long as they are kept aware that the value (say, a price promotion) could be withdrawn at any time. H2. Merchandise value will be positively related to store affect. In conjunction with H1 and H2, our third and fourth hypotheses contribute to the literature by suggesting that while merchandise value is both directly and indirectly (via store affect) related to repurchase loyalty; it is only indirectly related to attitudinal loyalty through the concept of store affect. To our knowledge, no previous retail studies have tried to study these constructs (merchandise value, store affect, repurchase loyalty, attitudinal loyalty) together so as to understand the unique relationships among these various constructs. We have defined store affect as the self-reported intensity of the subjective experience of positive feelings towards a store and loyalty as the level of interest, repurchase or attitudinal, in the store. Affect is also the subjective experience associated with arousal. According to Mandler (1982), the relationship between value and interest will depend on the degree of arousal that is generated by an unexpected stimulus and by the particular meaning analysis that is engendered by the situation. Thus, affect, which is the individual’s description of arousal, should mediate the relationship of merchandise value and loyalty. Further, merchandise value of the store and interest in the store (loyalty) may be related but they are distinct concepts since, as Mandler (1982) points out, interesting objects may not always be valuable and valuable objects may not always be interesting. At the same time, one reason why something valuable (store merchandise value) becomes something interesting (store loyalty) is the level of affect associated with it. Thus, positive feelings towards a store should engender an interest in continuing both a behavioral and attitudinal relationship with the store.

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WPP or the notion of premium prices as an outcome in marketing has been studied in the services literature (Zeithaml, Berry, and Parasuraman 1996), the branding literature (Chaudhuri and Holbrook 2001) and in the emerging literature on trust (Ba and Pavlou 2002) but rarely in the retail area. In the retail area, Srinivasan et al. (2002) have studied the effect of e-loyalty on willingness to pay a price premium but not the effect of two types of loyalty that are examined in this paper. Further, there are some inconsistencies in the results of studies of the effect of loyalty on price premiums. For instance, while Yoo, Donthu, and Lee (2000) found no relationship between loyalty and price perceptions, Chaudhuri and Holbrook (2001) found a significant positive relationship at the aggregate level. We contribute to the retailing literature by demonstrating that, at both the individual and the aggregate level, there is, indeed, a positive relationship but for a certain type of loyalty-relationship only. As discussed below, we suggest that only attitudinal loyalty leads to WPP. Consumers who derive affect from merchandise value may be willing to return to the store but not necessarily to pay a higher price at the same store, since this would, in fact, reduce the nature of the deal and the ensuing affect. Thus, affect alone is not always a sufficient condition for a willingness to sacrifice resources and pay a higher price (WPP). At the same time, consumers who are interested in having an ongoing relationship (attitudinal loyalty) with a store may be willing to sacrifice resources to maintain such a valued relationship. Thus, they may be willing to pay a higher price at the relational store than at other similar stores. Accordingly, we predict that when store loyalty is attitudinal, it will lead to a willingness to pay a higher price at the store. The case for attitude strength leading to premium prices has been presented in earlier theoretical work (Keller 1993). On the other hand, repurchase loyalty is a simple behavioral interest in revisiting the store at a future date. It is not indicative of any attitudinal or relational motivation and, thus, is not capable of eliciting the sacrifice entailed in a willingness to pay a higher price at one store over other similar stores. In this sense, the two aspects of loyalty are dissimilar. H5. Attitudinal loyalty will be positively related to WPP. As emphasized in the foregoing discussion, implicit in our set of hypotheses pertaining to the model in Fig. 1 is the prediction that the following four non-hypothesized paths will not be significant: (a) the path from merchandise value to WPP, (b) the path from merchandise value to attitudinal loyalty, (c) the path from store affect to WPP, (d) the path from repurchase loyalty to WPP. In a later section we empirically test these four non-hypothesized paths along with the hypothesized paths in our model. Perceived store differentiation as a moderator

H3. Store affect will be positively related to repurchase loyalty. H4. Store affect will be positively related to attitudinal loyalty.

2

We are grateful to one of the reviewers for suggesting this point.

Previous research by Voss and Seiders (2003) found no relationship between retailer differentiation and price variation at the aggregate level of stores. In this context, we contribute to the retail literature by suggesting that, instead of a direct effect of retailer differentiation on price promotions (one aspect of mer-

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chandise value), retailer differentiation at the aggregate level acts as a moderator of the effect of merchandise value on store affect. Retailer differentiation is usually considered to be a combination of the objective attributes of a store based on various aspects of the retail mix such as service quality, atmosphere, and so forth (Levy and Weitz 2009; Voss and Seiders 2003). Such environmental cues have been established as crucial aspects of retailing and servicescapes (Bitner 1992; Kotler 1973). Since our model is constructed at the level of individual consumers’ perceptions, feelings and intentions, our notion of perceived retailer differentiation is based on consumers’ subjective perception of a store’s attributes utilizing various aspects of the retail mix. We look at two types of perceived retailer differentiation, high and low. High perceived retailer differentiation provides a hedonic and pleasurable store environment derived from greater sensory cues among store attributes (such as an appealing store atmosphere, friendly service, etc.) while low perceived differentiation provides a utilitarian and functional store environment utilizing fewer sensory cues in the store. Kaltcheva and Weitz (2006) provide evidence that hedonic and utilitarian orientations among consumers act as moderators that influence positive feelings. Their results indicate that when consumers have a hedonic orientation, positive feelings increase. On the other hand, when consumers have a utilitarian orientation, positive feelings decrease. Similarly, we expect that when consumers are primed with a hedonic (high perceived differentiation) orientation in a store, the effect of merchandise value on positive store affect increases. Conversely, when consumers have a utilitarian (low perceived differentiation) orientation, this decreases the effect of merchandise value on store affect. These differences may be attributed to differences in sensory responses produced by hedonic and utilitarian store environments. A hedonic environment based on a larger number of concrete, sensory cues produces greater sensory responses, and resultant affect, than a utilitarian environment. Ceteris paribus, the perception of high-differentiation stores fosters more positive store affect from merchandise value than perceptions of low-differentiation stores due to the additional sensory input and resultant pleasure that consumers derive from being surrounded by high service quality, an entertaining environment, and so forth. H6. The path from merchandise value to store affect will be greater for high perceived differentiation stores compared to low perceived differentiation stores. Control variables It is important that our findings can demonstrate that the effects postulated in our hypotheses are present even when store familiarity and store convenience are controlled for. In other words, any findings pertaining to our hypotheses should be obtained in addition to the effects of these control variables. For instance, store familiarity could increase or decrease store affect. According to Zajonc (1980), affect and cognition have separate processes, and affect can be caused independently of cognition (merchandise value in our case) based on familiarity with a stimulus. We define familiarity as “the level of prior expe-

rience with the store”. Although it is not a path of theoretical interest, we include the effect of store familiarity on store affect since it could increase or decrease affective responses towards the store. Accordingly, we present no hypotheses in this regard but control for the effect of store familiarity on the relationships of theoretical interest in our study. Prior research shows that convenience is a vital part of the retail experience (Berry, Seiders, and Grewal 2002; Brooks, Kaufman, and Lichtenstein 2004) and, thus, it is likely that convenience will have an effect on a shopper’s level of loyalty to a store. In our study, convenience refers to the physical location of a store in terms of its accessibility to the customer. We control for convenience and its effects on both aspects of loyalty, repurchase and attitudinal, although neither path is of theoretical interest in our studies. Next, we present the results of three studies designed to test our hypotheses. The first study was conducted at a food store with unique merchandise. The second study was conducted in a traditional grocery store with a large assortment of national brands. These two studies help us to understand if our model (H1–H5) can be replicated across different stores. Our third study used a dataset with stores as the units of analysis and helps us to understand if our model can be replicated across a large variety of stores and store types. Moreover, it allows us to test H6, which deals with store differentiation at the store level. Study 1 Sample and procedure For Study 1, we chose a specialty food store that provides high merchandise value. In addition to carrying a limited number of brand name grocery items, this store specializes in providing quality meat and seafood, a variety of farm-fresh dairy products, and numerous baked-on-the-premises items. Although the store touts high-quality offerings, the store’s brisk sales and plans for expansion suggest that the marketplace does not perceive the retail prices as overly high. Further, the store runs regular sales, which it advertises in the local papers. Additionally, as customers work their way through the aisles, animated puppetry and vivid displays provide entertainment. After checking out, customers can purchase ice cream cones made from the store’s dairy products, while children participate in the outdoor petting zoo. The quality of the offerings, combined with the interactive and inviting atmosphere, make this store an extremely popular destination. We obtained permission from the store’s owner to allow a research assistant, trained in administering questionnaires, to approach customers as they left the store. After identifying himself and determining whether the customer would be interested in participating, the assistant administered a brief (5–7 min) questionnaire. Willing respondents were handed a card containing a 7-point scale, which they referred to as the assistant read each question and noted their responses. At the end of the interview, the assistant thanked the respondent for his/her time and moved on to the next customer. Data collection occurred over a 1-month period until the assistant had 150 completed surveys. 72 percent

Table 1 Measurement results: Studies 1 and 2. Constructs

Items

Sources

λ Study 1

Merchandise Value

Study 2

Adapted from Dodds et al. (1991) Baker et al. (2002) Overall, the merchandise in this store is at a fair price. The merchandise in this store is a good value. The merchandise in this store is economical.

.84

.86

.86

.76

.79

Adapted from Berscheid (1983) Adapted from Richins (1997) I love this store. I feel good when I shop at this store. I enjoy my visits at this store. This store puts me in a good mood.

Repurchase Loyalty

.69 .89 .87 .84

Attitudinal Loyalty

.82

.77

.65

.87

Adapted from Jacoby and Chestnut (1978) Adapted from Price and Arnould (1999) I am committed to this store. I have a close relationship with this store. I have a connection with this store.

WPP

.78 .87

.81 .78

.82

.80

Adapted from Chaudhuri and Holbrook (2001) I would be willing to pay a higher price at this store over other similar stores. I prefer to shop at this store, even if another store advertises a lower price.

.83

.73

.78

.76

This store is conveniently located. This store is accessible.

.88 .86

.86 .80

I shop at this store frequently. I am familiar with this store.

.74 .70

.69 .69

Store Convenience

AVE

Study 1

Study 2

Study 1

Study 2

Study 1

Study 2

.85

.87

.85

.87

.65

.69

.89

.87

.89

.81

.70

.62

.70

.80

.53

.71

.54

.69

.86

.84

.85

.83

.68

.63

.78

.72

.65

.59

.71

.55

.86

.82

.75

.40

.76

.72

.68

.65

.52

.56

.52

.48

.68 .86 .83 .78

Adapted from Jacoby and Chestnut (1978) I am willing to buy from this store again. I am likely to shop at this store in the future.

Measure of associationa

Adapted from Seiders et al. (2005)

Store Familiarity

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Store Affect

.82

Composite reliability

Note: All items measured on 7-point scales. a Cronbach alpha reported for Merchandise Value, Store Affect, and Attitudinal Loyalty. Pearson correlation coefficient reported for Repurchase Loyalty, Willingness to Pay a Price Premium (WPP), Store Convenience, and Store Familiarity. 411

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Table 2 Structural results: Studies 1 and 2. Study 1 Structural coefficients

Study 2 Structural coefficients

Paths H1: Merchandise Value → Repurchase Loyalty H2: Merchandise Value → Store Affect H3: Store Affect → Repurchase Loyalty H4: Store Affect → Attitudinal Loyalty H5: Attitudinal Loyalty → WPP

.30 .35 .42 .60 .80

.01 (ns) .55 .76 .79 .69

Controls Store Familiarity → Store Affect Store Convenience → Repurchase Loyalty Store Convenience → Attitudinal Loyalty

.44 .23 .31

.43 .03 (ns) .10 (ns)

Non-hypothesized paths Merchandise Value → WPP Merchandise Value → Attitudinal Loyalty Store Affect → WPP Repurchase Loyalty → WPP

.15 (ns) .14 (ns) .07 (ns) −.16 (ns)

−.11 (ns) .10 (ns) .39 (ns) −.05 (ns)

(ns) indicates a non-significant (p > .05) coefficient.

of the respondents were female and the majority of respondents (39 percent) were 36–45 years of age. Measures In Table 1, we present the items (with corresponding sources) used to measure the exogenous (merchandise value), four endogenous (store affect, repurchase loyalty, attitudinal loyalty, willingness to pay a price premium), and two control (store convenience, store familiarity) variables. All items for each variable are measured on 7-point scales, where 1 = “completely disagree” and 7 = “completely agree.” In order to assess item reliability, we report either the Cronbach alpha or the Pearson correlation coefficient in Table 1. All Cronbach alphas are greater than .80 and all Pearson correlations are significant at p ≤ .01. Results We tested the measurement model using structural equation modeling (SEM). Despite a statistically significant Chi-square, our model fits the data (χ2 = 268.13, df = 114; NFI = = .85; CFI = .91, IFI = .91). Table 1 provides the factor loadings for each item, as well as the composite reliabilities, measures of association, and average variance extracted (AVE) for each construct. All the indicators loaded higher than .60 on their respective constructs and all were significant (p ≤ .05), thus demonstrating convergent validity. To test for discriminant validity, we first used a method recommended by Anderson and Gerbing (1988). One by one, we constrained the correlation between each pair of constructs so that it equaled 1. A statistically significant Chi-square difference between a single-factor model and a two-factor model indicates discriminant validity, as those measures not theoretically associated with a specified construct should “diverge” from it (Netemeyer, Bearden, and Sharma 2003). For example, the Chi-square difference of 156.02, df = 1 between Merchandise Value and Store Affect,

as a two-factor versus a single-factor model, is statistically significant at p ≤ .05 and, thus, we are able to discriminate between both constructs. This occurred in the case of all construct pairings. The second test of discriminant validity involves comparing the average variance extracted (AVE) for each construct to the squared correlation of each construct with every other construct (Fornell and Larcker 1981). Discriminant validity is achieved when the AVE of the construct is greater than the squared correlation of that construct with each of the other constructs. For example, the AVE for Merchandise Value is .65, while the squared correlation with Merchandise Value and each of the other constructs is as follows: with Affect .36, with Repurchase Loyalty .38, with Attitudinal Loyalty .31, with WPP .25, with Store Convenience .17, and with Store Familiarity .35. Discriminant validity occurred among all the constructs of theoretical interest in Study 1. Table 2 provides the results of the structural model (χ2 = 291.67, df = 123; NFI = .84; CFI = .90, IFI = .90) and shows the significant structural coefficients (p ≤ .05) for the paths shown in Fig. 1. The results support all five hypothesized paths in our model. As customers’ perceive greater merchandise value, their repurchase loyalty (H1) and affect toward the store (H2) increases. Further, increased affect for the store causes increased repurchase loyalty (H3) as well as increased attitudinal loyalty (H4). Finally, an increase in customer attitudinal loyalty triggers a propensity to pay a premium price (H5). As stated in the hypothesis section, implicit in our model are the assumptions that the following paths will not be significant: Merchandise Value → WPP, Merchandise Value → Attitudinal Loyalty, Store Affect → WPP, and Repurchase Loyalty → WPP. When included in the model, none of the four pathways are statistically significant, thus adding credence to our model as the more parsimonious alternative. Table 2 also provides the non-significant coefficients for each of these paths. Finally, since the same questionnaire was used to measure both the exogenous and endogenous constructs, we utilized a

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procedure of comparing “simpler” models with more complex ones, in order to identify any influence of common method variance-CMV (Iverson and Maguire 2000; Korsgaard and Roberson 1995; Mossholder et al. 1998; Podsakoff et al. 2003). If CMV exists, a simpler model (fewer factors) should fit the data as well as or better than a more complex one. Results from a series of Chi-square difference tests indicate that model fit increases significantly by adding additional constructs. For example, a four-factor model has a Chi-square of 504.90, df = 129, while the Chi-square for a five-factor model is 432.70, df = 125 (χ2 = 72.2, df = 4, which is significant). The bestfitting model results when we specify all five constructs of theoretical interest, as well as the two control variables (Fig. 1).

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none were significant (non-significant path coefficients appear in Table 2). Therefore, other than H1, our model appears to also be a more parsimonious fit for the traditional grocery store data (Study 2). As was the case in Study 1, comparisons of simpler models with more complex ones in the Study 2 data suggested the absence of common method variance. Model fit increased significantly with the addition of more constructs, and the best-fitting model for the Study 2 data was the one we hypothesized in Fig. 1. Study 3 Sample and procedure

Study 2 Sample and procedure The intent of Study 2 was to see whether our model and findings would replicate in a different retail environment. As stated earlier, the store used in Study 1 was a specialty food store. In contrast, Study 2 focuses on a “traditional” grocery store. This regional chain store offers perishable and nonperishable food items, household cleaners, personal health and hygiene products, as well as a full selection of meat and dairy products and baked goods. The store follows traditional grocery store merchandising and layout patterns, namely long aisles filled with a variety of brand name products. The store advertises regularly via newspaper circulars, television and radio broadcasts. As was the case in Study 1, we obtained permission from store management to allow our research assistant to administer surveys as customers exited the store with their purchases. Data collection occurred over the course of a few weeks, until 150 surveys were completed. 67 percent of the respondents were female, and 50 percent were 55+ years of age. Measures We used the same measures from Study 1 in Study 2. As was the case in Study 1, all Cronbach alphas are greater than .80 and all Pearson correlations are significant at p ≤ .01, signaling item reliability. Results The hypothesized model also fit the Study 2 data (χ2 = 195.74, df = 114; NFI = .88; CFI = .94, IFI = .95). The items’ lambda loadings, as well as the constructs’ composite reliabilities, measures of association, and AVE are provided in Table 1. As in Study 1, all the indicators loaded higher than .60 on their respective constructs and all were significant (p ≤ .05). All but one of the hypotheses for our model were supported in Study 2 (χ2 = 215.89, df = 123, NFI = .87; CFI = .94, IFI = .94). Only H1, the merchandise value → repurchase loyalty path, was not supported in Study 2. Testing of the four non-hypothesized paths revealed the same results as in Study 1, namely that

The objectives for Study 3 were twofold: (1) verify whether our model (Fig. 1) applied to different types of stores (beyond specialty food and grocery), and (2) assess if perceived retailer differentiation would moderate the Merchandise Value → Store Affect (H6) path. In order to achieve these objectives, we utilized a previously collected, aggregate level dataset with stores as the units of analysis. Except for a conference proceedings abstract (Chaudhuri and Ray 2002), this dataset has not been published as part of a completed manuscript. In this dataset we aggregated consumers’ individual level scores regarding a particular store and used these aggregate scores to construct a dataset consisting of stores as the units of analysis. This approach of aggregating consumer-level scores to represent store-level attributes has been used, in a similar manner, for brands and advertisements in previous research (Chaudhuri and Holbrook 2001; Holbrook and Batra 1987; Olney, Holbrook, and Batra 1991; Smith and Park 1992; Stewart and Furse 1986). Utilizing this approach allows us to bring the store-level concept of retailer differentiation into the same plane as the consumer-level constructs and relationships in our model. All the constructs in Studies 1 and 2 were operationalized in this aggregate data set, albeit with fewer indicators for the constructs of interest in some cases. Table 3 identifies the 38 store types comprising the dataset, which were identified from the North American Industry Classification of retail categories (Office of Management and Budget 1998), as well as the number of each type of store (data was collected from 71 retail locations). Data collection involved administering surveys (using the same format as in Studies 1 and 2) to customers who were about to enter a particular retail store. It was felt that surveying customers who were entering the store would provide a better overall evaluation of the store based on an aggregate of previous experiences at the store. Note that in Studies 1 and 2 consumers were surveyed after leaving the store, thus their evaluations may have been more heavily based on their last experience at the store (we discuss this limitation later). The final sample consisted of 1966 respondents (mean age = 35), with a minimum of 20 completed surveys for each store (maximum 35). We aggregated consumers’ individual level scores regarding a particular store and we used these aggregate scores to construct a dataset consisting of stores as the units of analysis.

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Table 3 Store categories and number of stores researched: Study 3. Category

Number of stores

Category

Number of stores

1. Automobile Dealership 2. Tire Dealership 3. Home Furnishings 4. Household Appliances 5. Paint and Wallpaper 6. Nursery/Garden Centers 7. Convenience 8. Baked Goods 9. Health and Personal Care 10. Cosmetics, Beauty Supplies and Personal Care 11. Gas Stations and Convenience 12. Women’s Clothing 13. Shoes 14. Leather Goods 15. Sporting Goods 16. Toys 17. Books 18. Warehouse Clubs 19. Art Dealers and Paintings

2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1

20. Automotive Parts/Accessories 21. Furniture Store 22. Electronics 23. Camera and Photographic Supplies 24. Hardware 25. Grocery 26. Specialty Foods 27. Beer, Wine, and Liquor 28. Pharmacies/Drug Stores 29. Optical Goods 30. Men’s Clothing 31. Children’s and Infant’s Clothing 32. Accessories and Jewelry 33. Luggage 34. Music 35. Musical Instruments and Supplies 36. Department Stores 37. Pet and Pet Supplies 38. Office Supplies/Stationary

2 2 2 2 3 2 2 2 2 2 2 1 2 1 1 1 2 3 1

Measures All items in Study 3 were measured with 7-point scales, where 1 = “very strongly disagree,” and 7 = “very strongly agree.” With some minor differences, the construct measures in this dataset closely approximated the measures used in Studies 1 and 2. Table 4 identifies the items used to measure each construct, as well as the appropriate measure of association for each set of items. Results We aggregated the indicators of the respective constructs in Study 3. This path-analytic procedure has been used in previous research (see Li and Calantone 1998 and the references they cite) in which a small sample size (71 in our case) prevents the use of the full structural equation model. Table 5 provides the results of the structural model (χ2 = 70.58, df = 9, NFI = .81; CFI = .83, IFI = .83) and shows the significant structural coefficients (p ≤ .05) for the paths shown in Fig. 1. It is probable that the small sample size is at least partially to blame for the reduced fit statistics; however, it is also important to note that “. . .the NFI may underestimate the fit of the model in good-fitting models with small samples,” (Tabachnick and Fidell 1996, p. 749; see also Bearden, Sharma and Teel 1982). A similar phenomenon can occur with the incremental fit index (IFI). Importantly, the comparative fit index (CFI) result, which does a better job of estimating fit in small-sample models, is closer to the .90 accepted threshold. The results obtained from the Study 3 dataset supported all five hypothesized paths in our model. When considering the four non-hypothesized pathways, all five hypothesized paths remain significant and in the same direction. However, unlike the findings in Studies 1 and 2, one non-hypothesized path, from Store Affect → Willingness to Pay a Price Premium, is also significant in Study 3. The path coeffi-

cients for the four non-hypothesized paths are also available in Table 5. To test for the moderating effect of perceived retailer differentiation, we began by calculating a perceived retailer differentiation score for each store from the five items and used it to perform a median split. This procedure divided the 71 stores into two different datasets, one comprised of stores with high perceived differentiation scores (N = 36), the other containing the stores with low perceived differentiation scores (N = 35). Next, we conducted multigroup analyses using the two datasets, to test for significant differences with regard to the hypothesized path of interest (H6). The first step was to identify the path coefficients of interest for each group (βlow diff = .54, βhigh diff = .10). We then performed a Chi-square difference test, in order to determine whether the hypothesized path differed across the two groups. To begin, we assumed that all the paths in the hypothesized model differ across the two groups (baseline model: χ2 (18) = 67.57, p = 0.0). Next, we constrained the hypothesized path of interest so that it would be equal across both groups. The Chi-square difference between the constrained model and the baseline model indicates whether the path of interest differs significantly between the two groups. For the Merchandise Value → Store Affect path, the Chisquare for the constrained model was χ2 (19) = 72.08, p = 0.0, and the difference between the constrained and baseline model was χ2 (1) = 4.51, p ≤ .01. Thus, the Merchandise Value → Store Affect path differs across the high and low perceived retailer differentiation groups. However, contrary to H6, the coefficients indicate that the path is greater for low-differentiation retailers (βlow diff = .54 versus βhigh diff = .10). To summarize, the results obtained from analyses of the Study 3 data show that our hypothesized model holds across a variety of store types with one exception, namely the significance of one non-hypothesized path (Store Affect → WPP). With regard to the moderating effect, the Merchandise Value → Store Affect

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Table 4 Measurement results: Study 3. Constructs

Measure of associationa

Items

Merchandise Value

.62 This store gives me good value for the money. This store is economical.

Store Affect

.95 I love this store. This store makes me happy. I feel good when I shop at this store.

Repurchase Loyalty

.90 I intend to return to shop at this store. I will use this store the next time I buy . I would recommend this store to my friends and relatives.

Attitudinal Loyalty

– I am committed to this store.

WPP

.79 I would be willing to pay a higher price at this store over other similar stores. I prefer to shop at this store even if another store advertises some deal.

Store Convenience

.51 This store is convenient for me to travel to. This store is easy to shop in.

Store Familiarity

.64 I am familiar with this store. I shop at this store frequently.

Perceived Store Differentiation (moderator)

.93 This store has good overall service. This store has high quality merchandise. This store is a pleasant place to shop. This store has a nice atmosphere. This store has a good image.

Note: All items measured on 7-point scales. a Cronbach alpha reported for Store Affect, Repurchase Loyalty, and Perceived Store Differentiation. Pearson correlation coefficient reported for Merchandise Value, Willingness to Pay a Price Premium (WPP), Store Convenience, and Store Familiarity.

path differs significantly between the high and low perceived retailer differentiation groups and is greater for consumers who perceive low differentiation. In the next section we discuss these results in conjunction with the findings from Studies 1 and 2 in greater detail. Table 6 summarizes the findings across all three studies with regard to each hypothesis for the proposed model. Table 5 Structural results: Study 3. Structural coefficients Paths H1: Merchandise Value → Repurchase Loyalty H2: Merchandise Value → Store Affect H3: Store Affect → Repurchase Loyalty H4: Store Affect → Attitudinal Loyalty H5: Attitudinal Loyalty → WPP

.37 .52 .41 .71 .82

Controls Store Familiarity → Store Affect Store Convenience → Repurchase Loyalty Store Convenience → Attitudinal Loyalty

.31 .09 (ns) .09 (ns)

Non-hypothesized paths Merchandise Value → WPP Merchandise Value → Attitudinal Loyalty Store Affect → WPP Repurchase Loyalty → WPP

−.07 (ns) .07 (ns) .46 .04 (ns)

(ns) indicates a non-significant (p > .05) coefficient.

Discussion Our model is well supported by the results of all three studies. In study one, which was conducted in a food store, we found support for each of our hypotheses. In Study 2, which was conducted in a traditional grocery store, we found support for all our hypotheses except H1. This last result is very interesting and discussed later in detail. In Study 3, which was conducted across a wide variety of stores and store types, we once again found support for all our hypotheses. Importantly, studies one and two were at the level of individual consumers while Study 3 was at the level of individual stores. Most studies of consumers are at the individual level. Seldom, if ever, are the relationships in a model tested at both the individual and aggregate levels, as in the present paper. This is an important aspect of our work since it gives managers greater confidence in carrying out strategies based on changing consumer perceptions by altering store attributes. For instance, at the individual level it is useful for managers to know which consumers find merchandise value in a store and express commitment to the store because of their positive feelings arising out of the merchandise value of the store. However, it is equally, if not more, important for managers to know which stores with high merchandise value also have high levels of commitment from their consumers because such stores are characterized by high (or low) levels of affect, differentiation, and so forth. While managers cannot change consumers’

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Table 6 Summary of findings. Hypotheses

Study 1, specialty food store

Study 2, traditional grocery store

Study 3, variety of retail stores

H1: Merchandise Value → Repurchase Loyalty H2: Merchandise Value → Store Affect H3: Store Affect → Repurchase Loyalty H4: Store Affect → Attitudinal Loyalty H5: Attitudinal Loyalty → WPP H6: The following path will be greater for high perceived differentiation stores: Merchandise Value → Store Affect

Supported

Not Supported

Supported

Supported Supported Supported Supported –

Supported Supported Supported Supported –

Supported Supported Supported Supported Not Supported

characteristics, they can change their own store characteristics in an attempt to change consumers’ perceptions. Consonant with H1, and consistent with past research (Baker et al. 2002), the results from Studies 1 and 3 show that, for a variety of stores, merchandise value leads to repurchase loyalty. However, we did not find support for H1 in the traditional grocery store (Study 2) so that, in this store, merchandise value only indirectly affected repurchase and attitudinal loyalty via the mediation of store affect. One explanation for this may be that grocery stores carry similar branded merchandise in the form of national brands and, thus, merchandise value is not very different between such stores. Accordingly, merchandise value becomes a factor in repatronage only when such value can be differentiated, perhaps by price promotions. Unique merchandise value produces affect (due to disconfirmation of expectations, as stated earlier) and, in turn, repatronage intentions. In contrast, merchandise value in the food specialty store in Study 1 directly produced both repurchase loyalty and store affect because the quality of the specialty food products was perceived to be unique to the store and, thus, deserving of repeat visits regardless of the affective response. Overall, with this one exception in Study 2, all the paths in our model are supported in all three studies. H2 indicated a path between merchandise value and store affect and this path was significant in all three studies. Our model also suggested that store affect directly impacts both repurchase loyalty (H3) and attitudinal loyalty (H4) and our findings support the existence of both paths in all three studies. Further, H5 identified the path from attitudinal loyalty to WPP and our research finds support for this path in all instances. As importantly, all but one (again, in only one of the three studies) of the non-hypothesized paths are non-significant (p > .05). We find in one of the studies (Study 3) that, in addition to its indirect effect, store affect also leads directly to WPP. Study 3 used 71 different stores and, thus, we suggest that additional relational mediators of store affect and WPP may have emerged in this study due to the particular strategies conducted by the individual stores. For instance, a store that offers easy returns of its merchandise could create trust among its consumers and trust could act as an intervening construct in the linkage of store affect and WPP. Previous research supports the notion that trust, in the context of loyalty, leads to higher price tolerance (DelgadoBallester and Munuera-Aleman 2001). On the whole, however, in terms of both hypothesized and non-hypothesized paths, our model appears to be well suited to the data in all three studies.

We had expected that high-differentiation stores would derive more positive store affect from merchandise value than lowdifferentiation stores due to the greater pleasure consumers should derive from being surrounded by high service quality and a pleasurable environment. Interestingly, we found that the link was stronger for low-differentiation stores rather than for high-differentiation stores. One explanation for this unexpected result may be that less differentiated stores which provide merchandise value in terms of good deals and bargains may allow consumers to derive greater positive affect since there are no other sources of affect which might otherwise overpower or subsume the affect derived from the low-price value aspects of the store. We offer this explanation by drawing on the literature on schema-triggered affect in consumer behavior (Myers-Levy and Tybout 1989; Sujan, Bettman, and Sujan 1986), stemming from the ideas of Fiske (1982). According to Fiske (1982, p. 60), people have schemas (“existing knowledge structures”) which they use to evaluate new stimuli. Some of these schemas may already be associated with extreme affect as in the case of schemas for high-differentiated stores. Such schema-triggered affect could, then, dominate over the affect which is generated as a result of cognitive processing and elaboration (Myers-Levy and Tybout 1989) conducted by consumers when inside a store, as in the case of merchandise value evaluations. Thus, high-differentiation stores could produce less positive affect from merchandise value than low-differentiation stores. Low-differentiation stores may have less pre-existing affect from aspects of the environment (service, atmosphere) which could get in the way of a shopper’s enjoyment of a bargain. This is important since it suggests that low-differentiation stores, which endeavor to provide merchandise value with lower prices, may not want to undertake expensive renovations that differentiate their store since this could lower consumers’ perceptions of merchandise value. This and other managerial implications are provided next. Managerial implications Our model, in Fig. 1, would suggest that retailers have at least three different choices in developing strategies of merchandise value. First, except for the grocery store in Study 2, we found that merchandise value leads directly to repurchase loyalty. Thus, one managerial strategy can be directed towards consumers who follow the “rational” route leading from merchandise value to repurchase. This strategy assumes that there are consumers who

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evaluate merchandise value based on sound economic reasons and who will return to a store that offers good quality at a fair price. Accordingly, store promotions could emphasize improvements in quality such as offering organic, natural products at the same price over past products in the same store. Alternatively, consumers could be encouraged to undertake a between store or inter-store comparison (Krishnan, Biswas, and Netemeyer 2006; Oliver 1999a) resulting in an assessment of the relative value of the store versus other stores. Thus, for example, relative value could be created by using both in-store and out-of-store communications to compare competitors’ offerings (“their price”) (“their quality”) with those of the focal store (“our price”) (“our quality”). Second, our results show that there is an “emotional” route from merchandise value to affect and, in turn, to repurchase loyalty. Thus, a second strategy drawn from our results caters to consumers who are happily enjoying a low-price deal and who are reluctant to pay higher prices (even though affect is evoked) since this would reduce the value of the deal. These consumers are not willing to pay a higher price because this would reduce the positive affect (pleasure, etc.) that they derive from a good deal and, consequently, reduce the value of the deal. Store communications should encourage these consumers to feel smart or lucky when enjoying a good deal and hence experience pleasurable feelings as a result of merchandise value. Promotions that encourage the “thrill of the hunt” and show the intense pleasure arising from successful foraging in the store are recommended in this context. Finally, our results suggest a third route from merchandise value to WPP via store affect and attitudinal loyalty. Managers of retail establishments typically lower prices to increase merchandise value for their customers. In contrast, the results of our studies suggest that managers may be able to obtain higher prices over their competitors by providing merchandise value that creates positive affect and leads to an emotion-based bond with their consumers. Our findings further demonstrate that these effects are present even when store familiarity (and store convenience) is controlled for. Thus, for instance, a hardware store can provide repair services for small household products (lamps, vacuums, etc.) and create loyalty and a willingness to pay a higher price for replacement parts. Even national chain stores such as Home Depot provide home improvement classes for their customers so as to develop a bond and a connection with the store. Similarly, Williams Sonoma provides free recipes in their stores and catalogues to create a closer relationship with consumers culminating in a readiness to buy premium priced products. We found that low-differentiation stores generate greater positive affect from merchandise value than high-differentiation stores. Managers of low-differentiation stores may want to consider our explanation for why low-differentiation stores may produce more affect than high-differentiation stores. We have suggested, based on the literature on schema-triggered affect that this may be so because low-differentiation stores usually do not have other hedonic features that may overpower the pleasure of obtaining a bargain. The implication, then, is that music and other in-store entertainment may actually interfere with the pleasure received from merchandise value. This last result from

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our study suggests that consumers who shop at BJs, Costco and other warehouse stores, for instance, derive greater happiness from the value of the merchandise not despite a bare, austere environment but, perhaps, as a consequence of it. Limitations and future research This last suggestion is speculative since our research did not directly test such a cause and effect relationship between store environment and store affect from merchandise value. Future research using an experimental manipulation may want to investigate whether consumers who come back from a bargain basement “no frills” environment with “a deal” are happier than those who come back with the same deal from a high-end retailer which provides a more differentiated environment. In addition to positive affect, a potential mediator of such effects may be negative affects. If negative retail outcomes are also considered, it is possible that positive and negative affects work independently of each other, depending on the types of environments and consumer motivations. Future work could consider, for instance, those retail contexts where increased differentiation may generate negative store affect. In general, other retail contexts than food and grocery stores need to be studied. Our studies, like most studies, have certain limitations which should be acknowledged. For instance, there were anomalies between the three studies due to circumstances beyond our control. As an example, the management of the stores in Studies 1 and 2 insisted that we conducted the surveys after consumers had left the store while the data set in Study 3 was collected from consumers who were about to enter the store. Another anomaly concerns the differences in some of the wording and items between the questionnaires in the studies. While Study 1 and 2 used the same questionnaire, Study 3 used a questionnaire that was slightly different in certain areas. For instance, the questionnaire in Study 3 used items for the convenience construct which included more than just the notion of location convenience measured in Studies 1 and 2. As a result of these anomalies, the three studies were not identical in every sense. Nevertheless, the consistent nature of the findings in all three studies suggests that the differences between the studies did not affect the robustness of the results across all three studies. Additionally, our study used a limited number of theoretical and control variables to develop a manageable model for data collection purposes. Future work needs to consider other factors than merchandise value that might impact the customer’s decision to pay higher prices. For example, would customers be willing to pay higher prices if say, product assortment was greater (creating a one-stop shopping environment)? Is WPP influenced by social factors (e.g., being seen with items from the store, awareness of others who shop at that store)? In other words, what other kinds of value encourage consumers to pay more? WPP is an important dependent variable in retailing and worthy of further examination. Our research has shown that WPP is the result of one type of consumer loyalty but not of another and that merchandise value results in one type of loyalty directly and only indirectly (through affect) in another type of loyalty. Our work casts loyalty

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