A Model of Consumer Perceptions and Store Loyalty Intentions for a Supermarket Retailer

A Model of Consumer Perceptions and Store Loyalty Intentions for a Supermarket Retailer

NIREN SIROHI Cornell University

EDWARD W. MCLAUGHI.IN Cornell University

DICK R. WITTINK Cornell University

Slow growth and intense competition in retail markets in recent years increases the need for retailers to use strategies focused on retaining and attracting the right customers. However, a strategy that is effective in acquiring new customers may not be the most effective in retaining current customers. In order to understand the effectiveness o f activities designed to retain customers, we study the store loyalty intentions of current customers for a multi- store grocery, retailer. Using Partial Least Squares, on data averaged across at least 100 customers per store for each of about 160 stores, we find that service quality is by far the most critical determinant o f merchandise quality perception. Perceived value for money depends on perceived relative price and sales promotion perceptions, and to a lesser extent on service quality and merchandise quality perceptions. Store loyalty intentions, measured by intent to continue shopping, intent to increase purchases and intent to recommend the store, depend on service quality and merchandise quality perception. By separating the stores according to average consumer perceptions o f competitor attractiveness, we further find that perceived value does play an important role in the determination of store loyalty intention if there is a high degree of competitor attractiveness. When this attractiveness is low, our results fail to show a relevance for perceived value for money.

Niren Sirohi is a doctoral candidate in marketing at Cornell's Johnson School <[email protected]>; Edward W. McLaughlin is Professor of Marketing and Director of the Food Industry Management Program, Cornell Univer- sity <[email protected]>; Dick R. Wittink is the Henrietta Johnson Louis Professor of Management, and Pro- fessor of Marketing and Quantitative Methods, Johnson Graduate School of Management, Comell University <[email protected]>.

Journal of Retailing, Volume 74(2), pp. 223-245, ISSN: 0022-4359 Copyright © 1998 by New York University. All rights of reproduction in any form reserved.

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224 Journal of Retailing Vol. 74, No. 2 1998

INTRODUCTION

In recent years retail competition has intensified, generally as a consequence of new tech- nologies, more sophisticated management practices and industry consolidation. These trends have been particularly pronounced in the food channel. For example, Walmart and Kmart have added grocery sections to their outlets in many regional markets. Supermarket profits have been flat or declining for nearly a decade (Progressive Grocer Annual Report, 1996). In this environment, it is essential for firms to focus on the "fight" current customers. In the face of slow growth and highly competitive markets, a good defense is critical (Fornell, 1992). Marketing tools such as coupons and promotions are not only often mini- mally effective, but also attract the wrong customers by adverse selection (Reichheld, 1996). For example, temporary price cuts and coupons tend to attract cherry pickers, whose purchases often actually detract from profits. Recent evidence suggests that profits may be enhanced when strategies focus on retaining current customers. Even small increases in retention rates can dramatically increase profits (Reichheld and Sasser, 1990; Fornell and Wernerfelt, 1987, 1988). Relative retention has been shown to explain profits better than market share, scale, cost position, or any of the other variables usually associated with competitive advantage (Reichheld, 1996). At least indirectly, these findings help explain the recent proliferation of retailer loyalty and frequent shopper programs (see, for example, Raphel, 1995).

Increased customer retention has two important effects: (1) it can lead to a gradual increase in the firm' s customer base which is vital in an era of low sales growth, and (2) the profits earned from each individual customer grow the longer the customer remains loyal to the firm. Existing customers also tend to purchase more than new customers (Rose, 1990). In addition, according to a study by the U.S. Department of Consumer Affairs (Peters, 1988), costs to retain customers are about 80% lower than the costs to acquire new customers.

A focus on one's current customers, if it results in increased satisfaction, may also generate other benefits, for example, the generation of positive word-of-mouth (see Urbany et al., 1996 about the role of word of mouth with respect to prices). And with enhanced loyalty the prevailing practice of offering costly loss leaders to generate store traffic may become less necessary. However, how customers develop loyalty to a particular store and how that loyalty can be maintained are open questions. An understanding of current customers' store loyalty intentions and their determinants is an important basis for the identi- fication of optimal retailer actions. Uncertainty and incorrect beliefs about what matters to customers seem to be present, especially in the grocery retail industry. For example, it has been shown that executives in that industry tend to overestimate the proportion of consumers who actively search and who respond to advertised price information (Urbany et al., 1991).

The focus of this study is to examine the links between and the effects of various antecedents of current customers' store loyalty intentions in the supermarket channel. We consider three store loyalty intentions: customers' intent to continue purchasing, their intent to increase future purchases, and their intent to recommend the store to others. We also examine the effects of shoppers' perceived value for money for the focal store and the percep-

A Model of Consumer Perceptions and Store Loyalty Intentions for a Supermarket Retailer 225

tions of value for money for the second preferred chain on their store loyalty intentions. Our empirical results are based on responses averaged across at least 100 customers for each of about 160 stores belonging to a single grocery retailer. This retailer follows a marketing strategy that is intended to appeal to a broad set of consumers. The stores offer a conventional set of items including large assortments of fresh fruit and vegetables, and medium-to-high quality meat and fish. Different items are promoted in different weeks to maintain interest and curiosity of loyal customers and to provide them a sense of satisfaction about their purchases.

Our study goes beyond the existing literature in several ways. First, most of the existing consumer intention research has concentrated on service industries (one exception being LaBarbera and Mazursky, 1983). Instead, we consider a supermarket setting in which both product- and service-quality play important roles. Second, very few micro-level studies exist on the store loyalty intention measures considered here. Although Zeithaml et al. (1996) have included such measures, they only examine the relation between service quality and store loyalty intentions. Third, by studying the antecedents of perceptions of quality and value for money, in addition to the antecedents of store loyalty intentions, we obtain a more complete understanding of the process by which store loyalty intentions are formed. Fourth, by incorporating process-level measures for constructs such as service quality, our research not only provides a richer understanding of the antecedents of store loyalty intentions but we can also make realistic suggestions for managers. Finally, by modeling the relations among the different constructs as a system (by means of structural equation modeling), we avoid potential problems of low reliability and misspecification.

The remainder of the paper is organized as follows. In section two, we discuss the conceptual model and develop testable hypotheses. We outline the sample and the methodology used in section three. In section four, we provide the results of the measurement and structural models. In section five, we provide a discussion of the results, their limitations, and suggest future research directions.

STORE LOYALTY INTENTIONS MODEL

We test the conceptual model introduced below on data collected by a large supermarket chain from their own shoppers. Management of the supermarket chain identified both the general areas and the specific questions to be asked using a telephone survey technique.1 The initial model (Figure 1) has nine constructs based on the various areas in which the survey questions were asked. The first eight constructs measure respondent perceptions of:

Store operations (SOP): Operational issues such as store hours, training and staffing of employees. Store appearance (SAP): Physical appearance and facility organization. Personnel service (PSP): Services provided by department managers, clerks, cashiers, baggers, etc. Sales promotion (SPP): Sales/specials in store. Relative price (PRP): Price relative to prices of similar products in other stores.


Merchandise quality (MQP): Overall quality perceptions of merchandise and variety in brands/categories. Perceived value (PV): Value for money at the focal store. Perceived value of competitor (PVC): Value for money at competitor.

The ninth construct is: Store loyalty intention(SI). This construct reflects the store loyalty intentions of current

customers.

Multiple measures were used for most constructs. These are elaborated in the Appendix. The measures used for SOP, SAP, and PSP are very similar to those used for measuring "service quality" in other studies. Therefore, it is of interest to determine whether SOP, SAP, and PSP are three separate constructs or whether they are part of a single "service quality" construct. We address this question by testing for discriminant validity of the constructs. Until we introduce the test results, we refer to SOP, SAP, and PSP as the three service quality constructs.

The model in Figure 1 is an adaptation of a model proposed by Dodds and Monroe (1985). The unique features of this model are: introduction of a service quality construct in addition to the merchandise quality construct, the inclusion of a perceived value of competitor construct, and the focus on current customers' store loyalty intentions. There are three important links 2 in Figure 1:

1. Effects of extrinsic cues on merchandise quality perceptions, 2. The antecedents of perceived value, and 3. The determinants of the store loyalty intentions.

We discuss the rationale for our emphasis on these linkages next.

Effect of Extrinsic Cues On Merchandise Quality Perceptions

Perceived quality is defined as the consumer's judgment about the extent of superiority or excellence of the product (Zeithaml, 1988). This is a user-based approach as suggested in Garvin (1983). It is widely believed that consumers use cues to infer quality (Zeith- am1,1988; Olshavsky,1985). These cues typically are classified as intrinsic or extrinsic (Olson and Jacoby, 1972). Intrinsic cues involve the physical composition of the product (for example, flavor and color in beverages) while extrinsic cues include other, generally controllable, aspects (for example, price and brand name). Once the types and varieties of brands have been selected, retailers cannot generally influence the intrinsic cues of grocery products because these cannot be changed without alterations in the product ingredients. Thus, it is important for retailers to understand the effects of extrinsic cues on shoppers' perceived merchandise quality. The degree of importance associated with extrinsic cues depends of course on the effect of perceived merchandise quality on shoppers' store loyalty intentions.


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* This link represents only the direct effect.

FIGURE 1

Conceptual Model of Store Loyalty Intentions as a Function of Perceptual Constructs

Extrinsic cues are relevant in a supermarket setting for several reasons. First, the evaluation of intrinsic cues by consumers may require more time and effort than the consumer perceives is worthwhile (Zeithaml, 1988). Working women, men, and single shoppers, for example, have been reported to use supermarket product information significantly less than other demographic segments (Zeithaml, 1985). For such consumers, extrinsic cues provide a signal. Second, extrinsic cues become more important when quality is difficult to evalu- ate as in the case of experience goods such as food products (Zeithaml, 1988). Most of the research on extrinsic cues has focused on price, brand name, store name and level of advertising (Dodds et al., 1991; Mazursky and Jacoby, 1985; Nelson, 1974; Rao and Monroe, 1989). However, the focus has been almost exclusively on the perceived-price quality rela-


tionship, even though it has been demonstrated that the availability of other cues typically reduces the importance of price as a cue (Olson, 1977; Bonner and Nelson, 1985; Dodds et al., 1991). Based on Monroe and Krishnan (1985) we expect to see a positive relation between perceived price and perceived quality. However, we do not expect price to be a very important extrinsic cue given that other cues are readily available.

Service quality may also be used as an extrinsic cue in consumers' evaluations of the overall merchandise quality in a supermarket. We note that our focus is on overall merchandise quality rather than the quality of specific product categories or brands because typically consumers purchase a basket of goods rather than a single item during a regular shopping trip.

The three "service quality" constructs of perception of store operations (SOP), perception of store appearance (SAP), and perception of personnel service (PSP), all provide distinct cues about service quality as further discussed below. Scales with items similar to those used to measure SOP, SAP, and PSP have been widely used to measure the "service quality" construct (Parasuraman et al., 1985, 1988; Cronin and Taylor, 1992). Perceptions of store appearance, which are essentially perceptions of physical attributes of the service delivery system, provide tangible clues about service quality (Bitner, 1990; Bitner, 1992; Baker et al., 1992; Donovan and Rossiter, 1982). For example, proper layouts can decrease shopper search time, improve processing efficiency, increase service consistency and reliability, and facilitate customer orientation within the service delivery system (Chase and Hayes, 1991; Bitner, 1992; Sulek et al., 1995). Similarly, perceptions about personnel formed during service encounters have been found to have an impact on evaluations of service quality and value (Bolton and Drew, 1992).

If consumers use extrinsic cues to infer quality, and service is an important factor con- tributing positively to the shopper's overall experience, we expect the three constructs of SOP, SAP and PSP to have a positive influence on perceptions of overall merchandise quality. In fact, focus-group research (Sweeney et al., 1995) has shown that aspects of service which contribute to product knowledge can have a positive effect on perceptions of merchandise quality. This effect can work in several different ways. For example, good service quality can improve a shopper's perceptions of the image of the store which can improve the shopper's perceptions of overall merchandise quality. However, to the best of our knowledge, such an effect has not been demonstrated empirically.

Hypothesis 1: The three service quality constructs (if they are empirically distinct), store operations perception, store appearance perception and personnel service perception, have a positive effect on perceptions of merchandise quality.

Antecedents of Perceived Value

Value is very important to marketers (Dodds, 1991; Oesterreicher, 1993; Fredericks and Salter, 1995; Vantrappen, 1992), especially in the 1990s. It can be defined in several ways (Zeithaml, 1988). We define value as "what you get for what you pay." This is similar to


the utility per dollar measure of value used by Hauser and Urban (1986). In this definition of value for money, there is an implicit tradeoff between money and the "benefit" compo- nents of the exchange. Value, in supermarket shopping, consists of several benefit compo- nents such as variety, service, facilities, quality, nutrition, convenience, and freshness (Bishop, 1984; Doyle, 1984; Schechter, 1984). Superior service quality has been described as the third ring of perceived value (Clemmer, 1990), the first two being the basic product/ service and extended support services. The presence of service can reduce the nonmone- tary sacrifices made by shoppers (e.g., time) and also increase the benefits of shopping (e.g. convenience through facility design etc.). Perceived service quality has been found to have a positive impact on perceived service value (Bolton and Drew, 1991).

It is reasonable to assume that consumers take competing alternatives into account while forming their perceptions. Thus, perceived value for money of the competitor should be an important construct for many consumers. We expect that as the perceived value for money at the most relevant competitor increases, the consumers' perceptions of value for money at the focal store decrease.

Hypothesis 2: Perceived value of competitor is negatively related to perceptions of value for money at the focal store.

Determinants of Store Loyalty Intentions

The three measures for the construct of store loyalty intentions of current customers' are: willingness to repurchase, willingness to purchase more in the future, and willingness to recommend the store to others. Most previous research in this area focuses either on the impact of service quality and/or customer satisfaction on store loyalty intentions or the impact of perceived value on store loyalty intentions. Some researchers (Cronin and Tay- lor, 1992; Taylor and Baker, 1994) treat service quality and customer satisfaction as distinct constructs, in the sense that service quality is an attitude while customer satisfaction is (often) a transaction-specific measure. Cronin and Taylor (1992), using a single-item purchase intention scale, find that service quality affects customer satisfaction but do not find a significant effect of service quality on purchase intention. However, they do find that customer satisfaction affects purchase intention. Taylor and Baker (1994), using a three- item purchase intention scale, obtain significant effects for service quality, satisfaction and an interaction term on purchase intention. Other researchers (Boulding et al., 1993; Zeith- aml et al., 1996) do not distinguish between service quality and consumer satisfaction, and treat these as one and the same. Boulding et al. (1993) find a significant relationship between service quality and a two-item measure of repurchase intention and willingness to recommend. Zeithaml et al. (1996), using five different behavioral intention measures, find a significant relationship between service quality and all five behavioral intention measures.

In the second category of research, the focus is on the effect of perceived value on behavioral intention (Dodds et al., 1991; Sweeney et al., 1995; Zeithaml, 1988; Dodds, 1991). Perceived value has been proposed as a mediating construct in the effects of price and other


information on purchase intention (Dodds and Monroe, 1985; Zeithaml, 1988). Perceived value is an important construct to consider because consumers often have an acceptable price range outside which products may not be considered (Dodds, 1991). Furthermore, most of the research in this area (Dodds et al., 1991; Sweeney et al., 1995) focuses only on the 6willingness to buy6 behavioral intention, and does not consider other intention measures.

To our knowledge, there exists no work on the relationships between service quality, perceived value, and store loyalty intention in a single setting. In this research, we examine the impact of the perception of value for money (with a richer set of antecedents) and service quality on store loyalty intention. We hypothesize:

Hypothesis 3: The perception of value for money has a positive effect on the store loyalty intentions of current customers.

Hypothesis 4: The three service quality constructs (store operations, store appearance, and personnel service) have both a positive direct effect on store loyalty intention and a positive indirect effect through perceived value.

If a competing alternative (supermarket) is considered to provide high value for money, this will lower the value perception of the store under consideration and decrease the store loyalty intention of current customers. To our knowledge, there exists no work on the impact of the perceived value of competitor on store loyalty intention, except for Ping (1993) who focused on the relation between alternative attractiveness (satisfaction with the best alternative) and behavioral intention in the retailer-supplier relationship.

Hypothesis 5: Perceived value of competitor has a negative effect on store loyalty intentions.

The previous hypotheses are tested with data from all the stores in our sample. However, one might imagine that different stores exhibit fundamentally different effects for the relationships in our model. For example, in stores operating in highly competitive local retail environments, perception of value may be a more important factor in determining store loyalty intentions than in stores operating in less competitive local retail environments. In the absence of strong competitors, a favorable store loyalty intention will depend less on value for money than on other aspects (e.g., geographic location). Such a dependency may reflect the idea that a customer is more inclined to make 'value for money' assessments of multiple stores, if there is a strong competitor who pursues the customer. Thus, perception of value may be a more relevant predictor of loyalty intentions in the presence of a highly competitive local retail environment than in its absence.

Hypothesis 6: The impact of perception of value for money on store loyalty intention is moderated by the competitive intensity of the local retail environment: the higher the competitive intensity, the stronger the impact of perception of value on store loyalty intention.


METHODOLOGY

Operationalization Of The Latent Constructs

For the three constructs of service quality (store operations, store appearance, and personnel service), we use perception measures rather than the gap between perceptions and expectations. This is because the evidence is that perception measures 3 typically explain more of the variation in overall service quality than the gap model does (Cronin and Tay- lor, 1992, 1994; Parasuraman et al., 1991). Note that our perception measure is a cumula- tive measure which also captures the effect of experience.

In order to avoid confounding the price and value constructs, a relative price concept identical to Conover (1986) is used. We show the operationalizations of all latent constructs in the Appendix.

Sample

Data on the measures were collected by a market research supplier for a large, east coast supermarket chain via phone interviews of 16,096 shoppers. 4 The chain was selected for this research in part due to its representativeness of a variety of store conditions. For example, store sizes, merchandising approaches, ethnic diversity among customers, pricing strategies and store formats all had a substantial amount of variation such that the results reported here should have some generalizability. The supermarket identified 1500 names for each of about 160 stores. These names were drawn randomly from a frequent shopper database in which shopper name, address and telephone number were listed. After the list of names was cleaned, to delete homes without phones, etc., phone interviews were conducted until 100 were successfully completed for each store (the response rate was 50-60 percent). Respondents completed a lengthy interview encompassing factors to describe their normal shopping behavior and perceptions of their preferred supermarket. The customers were asked how often they shopped at the participating chain and were also asked for their first- and second-preferred chain. For the data used in our analysis, all respondents indicated the store we focus on as their first preferred store and some other chain as the second preferred (respondents were selected based on this condition). The second preferred chain was treated as the competing alternative about which the 'perceived value of competitor' question was asked.

Apart from this survey, we asked three senior managers familiar with all the stores in the sample to judge overall store quality and the intensity of local retail competition for each store. They provided these judgments on five-point scales ranging from low to high.

232

Methodology

Journal of Retailing Vol. 74, No. 2 1998

We specify a model to explain store loyalty intention and other aspects relevant to shopper behavior as a function of the constructs described earlier (defined in the Appendix and illustrated in Figure 1 ). The model parameters are estimated using Lohmoller's (1981) Par- tial-Least-Squares (PLS) algorithm. PLS is a powerful multivariate causal modeling technique for relations between multiple dependent and independent latent constructs. In PLS, the model parameters are divided into subsets estimated with ordinary least squares that involve the values of parameters in other subsets (Fornell and Bookstein, 1982). The latent constructs are estimated as linear combinations of their empirical indicators. This approach avoids the problems of improper solutions and factor indeterminacy which are common in factor-analysis based approaches. 5 Parameters are estimated from average values at the store level for all the measures, which means that all differences between customers patronizing the same store are removed. Thus, all estimated effects reported here are based on differences between stores.

In PLS, the latent constructs can be treated as underlying factors (measures treated as reflective) or viewed as indices produced by the observable measures (measures treated as formative) (Fornell and Bookstein, 1982). In our model, we use both reflective and formative indicators. Since our interest is in explaining the variation in the store loyalty intention measures by way of the latent constructs, we model the measures of the store loyalty intention construct as reflective and the measures of all other constructs as formative. 6

PLS provides factor loadings between the observed measures and latent constructs (when measures are in the reflective mode), weights (when measures are in the formative mode), and standardized path coefficients between the latent constructs. Since PLS estimation involves no assumptions about the population or scale of measurement, there are no distri- butional requirements. For that reason nonparametric jackknife estimates of standard errors are utilized.

To test Hypothesis 6, competitor attractiveness is used as a proxy for competitive intensity of the local retail environment. We first calculated an average competitor attractiveness score 7 for each respondent (based on measures obtained on the following six items with respect to the second preferred chain: overall quality of products and services, value for money, rating on store appearance, rating of sales and specials, rating of personnel service, and rating of store operations). We then split the sample into those stores which had low average competitor attractiveness (average < 3.24) and those which had high average competitor attractiveness (average > 3.36), in order to do a separate analysis for each sub-sample. 8 These splits were based on the empirical distribution of the average competitor attractiveness for the stores. 9

RESULTS

Measurement Model

To test for the reliability and validity of the constructs, we use the following measures (Fornell and Larcker, 1981):


1. The reliability (RHO) of construct 11 is:

/ P q = ~'rli) /[~i~=l~'rli + Y, Var(ei) i 1 i=1 J

where the )~li are the loading coefficients for construct q and measure i, and pq is the number of indicators for construct q. Nunnally (1978) recommends that this measure be greater than 0.70.

2. The average amount of variance (AVE) that is captured by construct q, relative to the total amount of variance in the measures, is:

Pq F Pn Pq

A V E ( q ) = i~=lK2i/Li~l K 2 i + i = I y~ Var(e i ) j = i=1 ~ K2i/Prl (since PLS standard-

izes constructs and indicators). If this measure is less than 0.50, then the variance due to error is larger than the variance captured by the construct. In that case, the validity of both the measure and the construct are in question.

Discriminant validity is established if AVE > T 2 where gamma-squared is the square of the path coefficient linking two constructs being examined for discriminant validity. If the path coefficient for a particular pair of constructs is not available, we use the correlation between these constructs in lieu of the path coefficient. In a model with only two constructs, the path coefficient is the same as the correlation coefficient when the constructs are standardized.

Tests for discriminant validity indicate that SOP, SAP, and PSP are not distinct constructs and thus their measures were pooled together as formative indicators of a single service quality (SQ) construct. We do not know whether the stores belonging to the chain are truly similar in quality across the three dimensions. An alternative interpretation is that respondents were unable or unwilling to recognize systematic differences between these dimensions, for a given store. In any event, all the constructs are unidimensional based on the covariance matrix of the residuals of the measures. None of the covariances were large enough to warrant consideration of more than one dimension for any construct. In our application, the store loyalty intention construct is also unidimensional. Thus, it does not make sense to assume that the three measures of store loyalty intention (willingness to repurchase, willingness to purchase more in the future, and willingness to recommend store) relate to distinct constructs.

In order to improve AVE (average variance extracted) of the SQ and PRP constructs, measures with small loadings were dropped. As a result, six indicators from the model were eliminated. An examination of the excluded indicators showed that the substantive meaning of the constructs did not change. The measurement properties of the revised model are shown in Table 1. The five constructs shown in Table 1 all have reliabilities greater than 0.85. The smallest AVE value is 0.55.

For clarity, we present results separately for the measurement model and the structural model. However, the estimation of the entire structural system is carried out simulta- neously, i.e. the measurement and structural parts are not estimated separately.


TABLE 1

Measurement Properties of the Revised Model

Construct Reliability AVE

Service Quality (SQ) Sales Promotion Perceptions (SPP) Perceived Relative Price (PRP) Merchadise Quality Perceptions (MQP) Store Loyalty Intentions (SI)

.97 .65

.87 .77

.86 .55

.97 .72

.87 .70

Structural Model

The standardized structural parameter estimates (total effects) are shown in Table 2. We show in Figure 2 both direct and indirect effects that are significant (p < 0.05) based on jackknifed standard errors.

Hypothesis 1: (Service quality ~ Merchandise quality) is supported by the data. Service quality has the largest effect on perceptions of merchandise quality.

Hypothesis 2: (Perceived value of competitor ~ Perceived value of the focal store) is not supported by the data. The effect is not statistically significant (and the magnitude of the effect, which is not shown, is the least of all effects on perceived value).

TABLE 2

Structural Model Results (Total Effects)

Merchandise Quality Perceived Value Store Loyalty Source~Target Perception (MQP) (PV) Intentions (SI)

Service Quality (SQ) .75 Merchandise Quality Perceptions (MQP) Sales Promotion Perceptions (SPP) .16 Perceived Relative Price (PRP) .05 Perceived Value (PV) Perceived Value of Competitor (PVC)

R-sq 0.78 Q-sq

.18 .67

.32 .47

.40 .12"

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-.13

0.69 0.66 0.38

Notes: Redundancy Coefficient = 0.23. RMS Cov (Inner residual, Outer residual) = 0.06. * Indirect Effect Only.


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Structural Model

Hypothesis 3: (Perceived value of the focal store ~, Store loyalty intention) is supported by the data. We note, however, that the magnitude of this effect is the smallest of the direct effects on store loyalty intention.

Hypothesis 4: (Service quality ~ Store loyalty intention) is supported by the data. Service quality has the largest effect among all the effects on store loyalty intention. The effect magnitude of 0.67 is the sum of a direct effect of 0.33 and an indirect effect of 0.34.


Hypothesis 5: (Perceived value of competitor ~ Store loyalty intention) is supported by the data. Competitor attractiveness has a significant negative effect on store loyalty intention: the higher the perceived value of competitor, the lower the store loyalty intention.

Hypothesis 6: (Value for money ~ Store loyalty intention; moderated by competitor attractiveness) is supported by the data. In the high attractiveness sample, the magnitude of the effect of perceived value on store loyalty intention is 0.45, (the second largest effect after service quality), while in the low attractiveness sample perceived value has no significant effect on behavioral intention.

In addition to the path coefficients, Table 2 also shows R 2 values for the merchandise quality perception, perceived value, and store loyalty intention constructs and a Q2 value for the store loyalty intention construct. The R 2 value is the percent of variation in the endogenous latent constructs explained by the exogenous latent constructs, while Q2 is the predictive relevance of the model for the endogenous latent constructs with reflective indicators. Specifically, the Q2 value indicates the percentage of the blind-folded data that can be recreated by the specified path model. 1° For finite samples, Q2 in general has a smaller value than the R 2 (Quan, 1988). To judge the fit of the overall model, Lohmoller (1981) suggests the use of two additional measures, namely the redundancy coefficient and the covariance of the inner (structural) model (Figure 1) residuals and the outer (measurement) model residuals.ll A high redundancy coefficient and a low covariance indicate a good fit. The redundancy coefficient and the root mean squared covariance of the inner and outer residuals we obtain for our model (provided in Table 2) suggest a good fit.

DISCUSSION

The use of defensive strategies by retailers increases the importance of understanding the effects of alternative strategies on retaining customers. Thus, the store loyalty intentions of current customers become a central focus for retailers. In this paper we used the following store loyalty intention measures at a supermarket retailer: "intent to continue purchasing," "intent to increase future purchases," and "intent to recommend 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. We model the linkages between antecedents (formulated as latent constructs) such as the focal store's service quality, price, merchandise quality and perceived value as well as the best competitor's perceived value, and estimate their effects on current customers' store loyalty intentions, using Partial Least Squares. The empirical results from this latent variable structural equation model provide useful insights to supermarket managers.

Our results highlight the importance of service quality as an extrinsic cue in the formation of perceptions of overall merchandise quality for a supermarket retailer. Past research on service quality has been limited to service industries and has not included a focus on this


relationship in the grocery retailing sector. The large and significant impact of service quality indicates that a good facility design and service provision by customer-contact employees leads to enhanced perceptions of overall merchandise quality. Our results also indicate that price does not play an important role in customers' perceptions of merchandise quality, especially when other cues are readily available to consumers. The magnitude of the effect of perceived relative price is the smallest of all effects on merchandise quality perception. Since extrinsic cues are the only variables over which the retailer has direct control, once product selection has been made, these results suggest several courses of action for retail managers who wish to improve consumers' perceptions of overall merchandise quality. Such improvement is critical to retailers because we find that perceptions of overall merchandise quality have significant direct impacts on overall customer store loyalty intentions. The magnitude of the path coefficient for merchandise quality perception on store loyalty intention is the second largest effect after that of service quality.

Previous research results about the determinants of customer intention to purchase or willingness to buy suggest that one of the most effective ways of improving purchase intention is to improve the perception of value. Our study may be the first one to focus on the determinants of customers' store loyalty intentions beyond the 6intent to buy/5 decision (since respondents were selected based on the criterion of the store being their most preferred store). A somewhat unexpected finding is that perceptions of value do not appear to be important in determining store loyalty intentions if the intensity of local retail competition is low. Thus, for consumers shopping at their preferred store, the improvement of value perceptions may not be needed to increase their intentions to continue purchasing, to purchase more often, or to recommend the store to others, if alternative competitors are not attractive. Of course, this result must be interpreted with caution. For example, if value perceptions for the retailer are low, the opportunity for a new competitor to enter the market is high. And if entry occurs, the retailer may discover there is a lack of customer goodwill and insufficient competency to compete.

Our results confirm that the path coefficient for price on value perceptions is very large. However, there is no direct price impact on store loyalty intention (and only a modest indirect price effect). By modeling the relationships within a system using PLS, and accounting for both direct and indirect effects, the results of our model shed new light on the strength of relations for various constructs. Mostly, the indirect effects are either insignificant or very small in magnitude compared to the direct effects. However, this is not so for the effect of service quality on store loyalty intention for which the direct and indirect effects have about the same magnitude. Ignoring either effect would lead to a substantial underestima- tion of the strength of the total effect (0.67) of service quality on store loyalty intention.

PLS appears to be a useful predictive tool. The Q2 value for behavioral intention is 0.38 (Table 2), indicating that the predictive relevance of this model is good. Therefore, this model should be useful to managers for determining the effects of various strategies (e.g., improving perceptions of assortment, improving perceptions of personnel service by baggers) on the three store loyalty intention measures of willingness to repurchase, willingness to purchase more often in the future and willingness to recommend store. Conventional approaches generally measure individual antecedent variables in isolation and try, retro- spectively, to estimate the relationship to some criterion variable like loyalty or store loyalty intentions. Such approaches often show strong bias and low reliability because of


misspecification (Fornell, 1992). Our model can also be used to track customer scores on the constructs over time which is difficult to do in models estimated with factor-analysis based approaches such as LISREL.

Limitations and Future Research

Since our analysis was conducted exclusively on shoppers who mentioned the chain as their most preferred outlet, it is possible that this introduces a bias. For example, the responses may be biased upward because of customers' loyalty to this chain, relative to what applies to all shoppers. However, this effect, if it exists, applies to all respondents, and it may have little impact on the results from an analysis of differences between stores.

To determine if the store loyalty intention measures are overstated, it would be useful to study the impact of the various antecedent variables on actual behavior rather than just on store loyalty intentions of consumers. Our model of survey data could be expanded by incorporating supermarket scanner purchase data. As more supermarkets offer frequent shopper programs and track purchases at the individual household level, an integration of purchase and survey data may soon be commonplace.

Although the store data are based on a large number of customers per store, our sample is limited to one supermarket chain. However, the chain was selected for this research in part because of its representativeness. On many differentiating characteristics between supermarket retailers, the participating chain is in the mid-range. Its pricing strategy could be described as "middle-of-the-road," it has a typical array of conventional formats, the customers have approximately average demographic diversity for the US, and the stores are situated in many types of socio-economic locales. Although we do not believe the results apply directly to the universe of all supermarket types, we do believe that our results are likely to apply to a considerable array of supermarket companies operating conventional store formats. Nevertheless, it will be useful to extend our approach to a broader group of retailer ~ompanies.

Another fru: ul area for future study consists of the use of multiple years of data. With data for addiaonal years, one can test the predictive validity of the model. If retail management implements a variety of new initiatives based on model results such as those reported here, it will be instructive to see to what extent future customer evaluations of the store rep- resent predicted changes. An alternative procedure to examine this is to test the stability of the effects over time. Thus, if there are changes on various perceptual measures, and the endogenous variables do not change in a manner consistent with the estimated model, then the effects are not stable over time.

More attention in future research should be allocated to a determination of the causal paths of the relationships that we studied. It is plausible that the direction of causality we assume in our model is wrong. For example, some research suggests that overall prefer- ences for an outlet influence the ratings shoppers provide on the characteristics of their preferred stores, a sort of 6halo6 effect (Arnold et al., 1996) Because our results are based entirely on between-store differences and halo effects apply especially to within-store differences between customers, we believe that our results may have external validity.


Finally, we believe it will be instructive to measure and incorporate various objective store characteristics in a model. This could, for example, offer management the opportunity to learn how consumer perceptions are influenced by objective characteristics. The importance of this type of research is underscored by the fact that consumers' perspectives typically diverge from what managers believe. To illustrate, for the research reported here we asked three senior managers of the chain to independently rate each store on quality. Each manager was familiar with each store, and we obtained acceptable inter-rater reliability on this measure. Yet we obtained no support for the hypothesis that store quality would be less relevant in the subset of stores rated as being of low quality than in high-quality stores. It will be informative to determine what influences managers' perceptions of store quality, and to compare this with an explanation of customers' store quality perceptions.

APPENDIX

The operationalization of all proposed latent constructs is given below:

1. Store operations perception (SOP). This is determined by six indicators all measured on a five-point scale, ranging from excellent to poor. The indicators are:

a. quality of operations(hours, training, and staffing of employees) b. staffing enough employees to meet customer needs c. offering convenient hours of operation d. keeping deli, bakery open and providing services for extended hours e. providing adequate training of employees f. ability of manager to resolve questions and problems

. Store appearance perception (SAP). This is determined by eight indicators measured on a five-point scale, with seven scales ranging from excellent to poor and the remaining one ranging from strongly agree to strongly disagree. The indicators are:

a. rating of overall appearance of store b. providing a clean shopping environment c. having wide, open aisles d. having various departments in appropriate places in the store e. having well-marked aisle directories f. having a safe parking lot g. providing clean restrooms h. providing a pleasant shopping environment

. Personnel service perception (PSP). This is determined by eleven indicators measured on a five-point scale, with nine scales ranging from excellent to poor and the remaining two ranging from strongly agree to strongly disagree. The indicators are"


.

.

.

a.

b. C.

d. e .

f. g. h. i. j. k.

overall quality quality quality quality quality quality quality

quality of services provided by personnel the customer interacts with of services provided by cashiers and baggers of services provided by customer service desk of services provided by employees at the deli counter of services provided by employees at the in-store bakery of services provided by employees in the meat department of services provided by employees at the seafood counter of services provided by employees in the produce department

quality of services provided by store manager customer orientation of the supermarket provision of friendly and responsive service by the supermarket

Merchandise quality perception (MQP). This is determined by fifteen indicators, all measured on a five-point scale, ranging from excellent to poor. The indicators a r e :

a. overall quality of merchandise purchased b. quality of produce department c. quality of meat department d. quality of deli e. quality of grocery items f. quality of in-store bakery g. quality of seafood department h. quality of frozen food section i. quality of dairy section j. quality of health and beauty aid department k. 1.

quality of private-label items rotation of perishables, so that they are always displayed flesh

m. wide brand selection of grocery items n. variety of grocery items o. presence of items appropriate for a supermarket

Sales promotion perception (SPP). This is determined by two indicators, both measured on a five-point scale, ranging from excellent to poor. The indicators are:

a. sales or specials offered b. having sale items in stock

Perceived relative price (PRP). This is determined by five indicators, all measured on a five-point scale, ranging from much lower priced to much higher priced. The indicators are:

a. comparison with charges made by alternative supermarket for similar products b. comparison with charges made by stores other than supermarkets for similar products


c. comparison of meat prices with other supermarkets

d. comparison of produce prices with other supermarkets

e. comparison of health and beauty aid prices with other supermarkets

7. Perce ived value (P¥) . This is determined by a single indicator (comparing what you pay for what you get) measured on a five-point scale, ranging from excellent value for money to poor value for money.

8. Perce ived value of compe t i t o r (PVC). This is determined by a single indicator, perceptions of value for money at the competi tor 's store, measured on a five-point scale ranging from excellent to poor.

9. S tore loyal ty in ten t ions (SI). This is determined by three indicators, all measured on a five-point scale, ranging from extremely likely to not at all likely. The indicators are:

a. l ikelihood to continue shopping

b. l ikelihood to use the store for more of your grocery needs in the next twelve months

c. l ikelihood to recommend supermarket to a friend

NOTES

1. The authors had no involvement in the design of the study. After the data had been collected, they assisted management of the retail chain in data analysis and interpretation of results.

2. Only some of the linkages in Figure 1 have hypotheses identified as H. This is because we restrict ourselves to hypotheses for which the insights we provide are new. However, all path coefficients in the system are estimated.

3. Only perceptions of objective store characteristics were used in the model. Examining the relationship between objective characteristics and perceptions, although interesting, is beyond the scope of our study.

4. Survey available from authors on request. 5. Indeterminacy need not be limited to exploratory analyses (Mulaik, 1976). Indeterminant

factors can have improper loadings which lead to negative variances. PLS provides determinant factor scores which improve prediction and control.

6. This mixed-mode estimation cannot be performed with popular programs such as LISREL. 7. In the model shown in Figure 1, we use only the perceived "value for money" of the second

preferred store. However, when we split the sample to categorize stores according to competitive intensity, we use an average of six measures. We do this because this average provides a better proxy for "intensity of local retail competition" than "value for money" alone does.

8. The mean and standard deviation are 3.1 and 0.1 for the low, and 3.5 and 0.1 for the high average 'competitor attractiveness' groups.

9. We also examined sample splits by other variables, for example: overall store quality judgments by the manager and frequency of shopping of consumers. However, these analyses did not yield meaningful differences, and hence are not reported here. We did not analyze splits by manage- rial judgments of intensity of local retail competition because of low inter-rater reliability.


10. Blindfolding means to omit a part of the data matrix while estimating model parameters, and then to reconstruct the omitted part by the estimated parameters. This procedure is repeated until each data point is omitted and reconstructed once. The resulting tolerance measure Q2 has the form Q2 = l - (E/O) where E is the error sum of squares, and O is the observed values sum of squares. Since the error is the difference between the observed and reconstructed values, this measure is conceptually similar to goodness of fit.

I I. The redundancy coefficient is defined as follows:

Let the vector of indicator variables be y and the vector of latent constructs be Y. For the outer (measurement) model one can write:

y = PY + e, where P is the loading pattern matrix. For the inner (structural) model one can write:

Y= BY+ u, where B is the path coefficients matrix; that is, given two endogenous latent constructs, we have:

Y2 = B22 Y2 + u2

Y 31 B32 Y

Substituting the above into the measurement model relation one gets: y = PBY+ Pu + e =y** + (Pu + e)

The diagonal of F 2, the covariance matrix of y** divided by the variances of the indicator variables, contains the redundancies which are the proportion of the variance of the indica-

tor variables reproduced by the predictor latent constructs of the indicator' s own latent constructs. The redundancy coefficient is defined as: (trace F2/k) where k is the total number of indicator variables.

REFERENCES

Arnold, Stephen J., Jay Handelman, and Douglas J. Tigert. (1996). "Organizational Legitimacy and Retail Store Patronage," Journal of Business Research, 35(3): 229-239.

Baker, J., M. Levey, and D. Grewal. (1992). "An Experimental Approach to Making Retail Store Environmental Decisions," Journal of Retailing, 68(4): 445-461.

Bishop, W.R.J. (1984). "Competitive Intelligence," Progressive Grocer, (March): 19-20. Bitner, M.J. (1990). "Evaluating Service Encounters: The Effects of Physical Surroundings and

Employee Responses," Journal of Marketing, 54(2): 69-82. . (1992). "Servicescapes: The Impact of Physical Surroundings on Customers and Employ-

ees," Journal of Marketing, 56: 57-71. Bolton, R.N. and J.H. Drew. (1991). "A Multistage Model of Customers' Assessments of Service

Quality and Value," Journal of Consumer Research, 17 (March): 375-384. . (1992). "Mitigating the Effect of Service Encounters," Marketing Letters, 3(1): 57-70. Bonner, P. Greg and Richard Nelson. (1985). "Product Attributes and Perceived Quality: Foods." Pp.

64-79 in J. Jacoby and J. Olson (Eds.), Perceived Quality. Lexington, MA: Lexington Books.


Boulding, William, Ajay Kalra, Richard Staelin, and Valarie A. Zeithaml. (1993). "A Dynamic Pro- cess Model of Service Quality: From Expectations to Behavioral Intentions," Journal of Marketing Research, 30 (February): 7-27.

Chase, R.B. and R.H. Hayes. (1991). "Beefing up Operations in Service Firms," Sloan Management Review, 32: 15-26.

Clemmer, J. (1990). "The Three Rings of Perceived Value," Canadian Manager, (Summer): 12-15. Conover, J.N. (1986). "The Accuracy of Price Knowledge: Issues in Research Methodology." Pp.

589-593 in Lutz, R.J. (Ed.), Advances in Consumer Research, Vol. 13: Association for Consumer Research.

Cronin, J.J. and S.A. Taylor. (1992). "Measuring Service Quality: A Reexamination and Extension," Journal of Marketing, 56 (July): 55-68.

• (1994). "SERVPERF versus SERVQUAL: Reconciling Performance-Based and Percep- tions-Minus-Expectations Measurement of Service Quality," Journal of Marketing, 58 (1): 125- 131.

Dodds, William B. (1991). "In Search of Value: How Price and Store Name Information Influence Buyer's Product Perceptions," Journal of Services Marketing, 5 (3): 27-36.

Dodds, William B. and Kent B. Monroe. (1985). "The Effect of Brand and Price Information on Sub- jective Product Evaluations." Pp. 85-90 in Elizabeth C. Hirschman and Morris B. Holbrook (Eds.), Advances in Consumer Research, Vol. 12. Provo, UT: Association for Consumer Research.

Dodds, William, B., Kent B. Monroe, and Dhruv Grewal. (1991). "Effects of Price, Brand, and Store Information on Buyers' Product Evaluations," Journal of Marketing Research, 28(August): 307- 319.

Donovan, R. and J. Rossiter. (1982). "Store Atmosphere: An Environmental Psychology Approach," Journal of Retailing, 58: 334-357.

Doyle, Mona. (1984). "New Ways of Measuring Value," Progressive Grocer-Value, Executive Report: 15-19.

Fornell, Claes. (1992). "A National Customer Satisfaction Barometer: The Swedish Experience," Journal of Marketing, 56 (January): 6-21.

Fornell, Claes and Fred L. Bookstein. (1982). "Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory," Journal of Marketing Research, 19 (November): 440- 452.

Fornell, Claes and David F. Larcker. (1981). "Evaluating Structural Equation Models with Unob- servable Variables and Measurement Error," Journal of Marketing Research, 18 (February): 39- 50.

Fornell, Claes and Birger Wernerfelt. (1987). "Defensive Marketing Strategy by Customer Com- plaint Management: A Theoretical Analysis," Journal of Marketing Research, 24 (November): 337-346.

. (1988). "A Model for Customer Complaint Management," Marketing Science, 7 (Summer): 271-286.

Fredericks, Joan O. and James. M-II Salter. (1995). "Beyond Customer Satisfaction," Management Review, 84 (5): 29-32•

Garvin, David A. (1983). "Quality on the Line," Harvard Business Review, 61 (September-October): 65-73.

Hauser, John. R. and Glen Urban. (1986). "The Value Priority Hypotheses for Consumer Budget Plans," Journal of Consumer Research, 12 (March): 446--462.

LaBarbera, Priscilla A. and David Mazursky. (1983). "A Longitudinal Assessment of Consumer Sat- isfaction/Dissatisfaction: The Dynamic Aspect of the Cognitive Process," Journal of Marketing Research, 20(November): 393-404.


Lohmoller, Jan-Bernd. (1981). LVPLS 1.6:Latent Variables Path Analysis with Partial Least- Squares Estimation. Munich, Federal Republic of Germany: University of the Federal Armed Forces.

Mazursky, David and Jacob Jacoby. (1985). "Forming Impressions of Merchandise and Service Quality." Pp. 139-154 in J. Jacoby and J. Olson (Eds.), Perceived Quality. Lexington, MA: Lex- ington Books.

Monroe, Kent B. and R. Krishnan. (1985). "The Effect of Price on Subjective Product Evaluations." Pp. 209-232 in J. Jacoby and J. Olson (Eds.), Perceived Quality. Lexington, MA: Lexington Books.

Mulaik, Stanley A. (1976). "Comments on the Measurement of Factorial Indeterminacy," Psy- chometrika, 41: 249-262.

Nelson, Philip. (1974). "Advertising as Information," Journal of Political Economy, 81(4): 729-754. Nunnally, Jum C. (1978). Psychometric Theory. New York: McGraw-Hill. Oesterreicher, James. (1993). "Attracting the Value-Oriented Retail Customer," Stores, 75 (10):

RR4-RR6. Olshavsky, Richard W. (1985). "Perceived Quality in Consumer Decision Making: An Integrated

Theoretical Perspective." Pp. 3-29 in J. Jacoby and J. Olson (Eds.), Perceived Quality. Lexington, MA: Lexington Books.

Olson, Jerry C. (1977). "Price as an Informational Cue: Effects in Product Evaluation." Pp. 267-286 in Arch G. Woodside, Jagdish N. Sheth, and Peter D. Bennet (Eds.), Consumer and Industrial Buy- ing Behavior. New York: North Holland Publishing Company.

Olson, Jerry C. and Jacob Jacoby. (1972). "Cue Utilization in the Quality Perception Process." Pp. 167-179 in M. Venkatesan (Ed.), Proceedings of the Third Annual Conference of the Association

for Consumer Research. Iowa City: Association for Consumer Research. Parasuraman, A. (1988). "SERVQUAL: A Multiple-Item Scale for Measuring Consumer Percep-

tions of Service Quality," Journal of Retailing, 64 (Spring): 12-40. Parasuraman, A., Leonard L. Berry, and Valarie A. Zeithaml. (1991). "Refining and Reassessment of

the SERVQUAL Scale," Journal of Retailing, 67 (Winter): 420-450. Parasuraman, A., Valarie A. Zeithaml, and Leonard L. Berry. (1985). "A Conceptual Model of Ser-

vice Quality and its Implications for Future Research," Journal of Marketing, 49 (Fall): 41-50. Peters, Thomas J. (1988). Thriving on Chaos. New York: Alfred A. Knopf. Ping, Robert A. (1993). "The Effects of Satisfaction and Structural Constraints on Retailer Exiting,

Voice, Loyalty, Opportunism, and Neglect," Journal of Retailing, 69(3): 320-352. Progressive Grocer Annual Report. (1996). "The Challenges Mount." Pp. 8-13 in 63rd Progressive

Grocer Annual Report. Stamford, CT: Maclean Hunter Media. Quan, N.T. (1988). "The Prediction Sum of Squares as a General Measure for Regression Diagnos-

tics," Journal of Business and Economic Statistics, 6: 501-504. Rao, Akshay R. and Kent B. Monroe. (1989). "The Effect of Price, Brand Name and Store name on

Buyers' Perceptions of Product Quality: An Integrative Review," Journal of Marketing Research, 26 (August): 351-357.

Raphel, Murray. (1995). "Frequent Shopper Clubs: Supermarkets' Newest Weapon," Direct Market- ing, 58( 1 ): 18-20.

Reichheld, Frederick F. (1996). The Loyalty Effect: The Hidden Force Behind Growth, Profits, and Lasting Value. Boston, MA: Harvard Business School Press.

Reichheld, Frederick F. and W. Earl Sasser, Jr. (1990). "Zero Defections: Quality Comes to Ser- vices," Harvard Business Review, 68 (September-October): 105-111.

Rose, S. (1990). "The Coming Revolution in Credit Cards," Journal of Retail Banking, 12 (Summer): 17-19.


Schechter, L. (1984). "A Normative Conception of Value," Progressive Grocer, Executive Report: 12-14.

Sulek, Joanne M., Mary R. Lind, and Ann S. Maruchek. (1995). "The Impact of a Customer Service Intervention and Facility Design on Firm Performance," Management Science, 41 (11): 1763- 1773.

Sweeney, Jill, Geoffrey Soutar, and Lester Johnson. (1995). "The Influence of Retail Service Quality on Perceived Value," Working Paper, The University of Western Australia.

Taylor, S.A. and Thomas L. Baker. (1994). "An Assessment of the Relationship Between Service Quality and Customer Satisfaction in the Formation of Consumers' Purchase Intentions," Journal of Retailing, 70 (2): 163-178.

Urbany, Joel E., Rosemary Kalapurakal, and Peter R. Dickson. (1996). "Price Search in the Retail Grocery Market," Journal of Marketing, 60 (April): 91-104.

Urbany, Joel E., Peter R. Dickson, and Rosemary Key. (1994). "Do Pricing Executives Overestimate the Price-Driven Segment?," Stores, 76 (I): RR4-RR5.

Vantrappen, H. (1992). "Creating Customer Value by Streamlining Business Processes," Long Range Planning, 25 (February): 53-62.

Zeithaml, Valarie A. (1985). "The New Demographics and Market Fragmentation," Journal of Mar- keting, 49 (Summer): 64-75.

. (1988). "Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Syn- thesis of Evidence," Journal of Marketing, 52 (July): 2-22.

Zeithaml, Valarie A., Leonard L. Berry, and A. Parasuraman. (1996). "The Behavioral Consequences of Service Quality," Journal of Marketing, 60 (April): 31-46.

A Model of Consumer Perceptions and Store Loyalty Intentions for a Supermarket Retailer

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