modelo de percepcion

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A Model of Consumer Perceptions andStore Loyalty Intentions fo r a Supermarket RetailerNIREN SIROHICornell UniversityEDWARD W. MCLAUGHLINCornell UniversityDICK R. WITTINKCorn ell University "

Slow growth an d intense competition in retail market.s in recent years increases ihe need forretailers to use strategies focused on retaining and attracting the right customers . H owever,a strategy that i.s effective in acquiring new customers may not be the most effective inretaining current cu.Ktomers. In order to understand the effectiveness of activities designedto retain customers, we .study the store loyalty intentions of current cu.stomers for a multi-store grocery retailer. Using Partial Least Squares, on data averaged across at least 100customers per .store for each of about 160 stores, w e find that service qua lity is by far themos t critical determinant of merchandise qu ality perception. P erceived value for moneydepends on perceived relative price and .sales promotion perceptions, and to a lesser e.xtenton service quality and merchandise quality perception.s. Store loyalty intentions. mea.suredhv intent to continue .shopping, intent to increase purchases and intent to recommend the.store, depend on service quality and merchandise quality perception. By separating thestores according to average consumer perceptions of competitor attractiveness, we furtherfind that perceived value does play an important role in the determination of store loyaltyintention if there is a high degree of com petitor attractiveness. Wh en this attractiveness islow, our results fail to show a relevance for perceived value for mon ey.

Niren S irohi is a doctoral candidate in marketing at Co rne ll 's Johnson School ; Edw ard W .McLaughlin is Professor of Marketing and Director of the Food Industry Matiagement Paigram, Cornell Univer-sity : Dick R. Wilt ink is the Henrietta Johnson Louis Professor of Management, and Pro-fessor of Marketing and Quantitative Methods. Johnson Graduate School of Management, Cornell University.


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22 4 Journal of Retailing Vol. 74, No . 2 199 8INTRODUCTION

In recent years retail competition has intensified, generally as a consequence of new tech-nologies, more sophisticated management practices and industry consolidation. Thesetrends have been particularly pronounced in the food channel. For example. Walmart andKm art have added grocery sections to their outlets in many regional markets. Supermarketprofits 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 tbe "right" current custom-ers. In the face of slow growth and highly competitive markets, a good defense is critical(Fom ell, 1992). M arketing tools such as coupons and prom otions are not only often mini-mally effective, but also attract tbe wrong customers by adverse selection (Reicbbeld,1996). For example, temporary price cuts and coupons tend to attract cherry pickers, w hosepurchases often actually detract from profits. Recent evidence suggests that profits may beenhanced when strategies focus on retaining current customers. Even small increases inretention rates can dramatically increase profits (Reichheld and Sasser. 1990; Fomell andW emerfelt, 1987, 1988). Relative retention has been shown to explain profits better thanmarket share, scale, cost position, or any of the other variables usually associated withcompetitive advantage (Reichheld, 1996). At least indirectly, these findings help explainthe 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 gradualincrease in tbe firm's customer base which is vital in an era of low sales growth, and (2) theprofits earned from each individual customer grow the longer the customer remains loyalto the firm. Existing customers also tend to purchase more than new customers (Rose,1990). In addition, according to a study by the U.S. Depa rtment of Co nsum er Affairs(Peters, 1988), costs to retain cu stomers are about 80% lower than the costs to acquire newcustomers.A focus on one's current customers, if it results in increased satisfaction, may also gen-erate other benefits, for example, tbe generation of positive word-of-mouth (see Urbany etal., 1996 about the role of word of mouth with respect to prices). And with enhanced loy-alty the prevailing practice of offering costly loss leaders to generate store traffic maybecome less necessary. However, how customers develop loyalty to a particular store andhow that loyalty can be maintained are open questions. An understanding of current cus-tomers' store loyalty intentions and their determinants is an important basis for the identi-fication of optimal retailer actions. Uncertainty and incorrect beliefs about wbat matters tocustomers seem to be present, especially in the grocery retail industry. For example. It hasbeen shown that executives in that industry tend to overestimate the proportion of consum -ers who actively search and who respond to advertised price information (Urbany et al1991).The focus of this study is to examine the links between and the effects of various ante-cedents of current customers" store loyalty intentions in tbe supermarket chann el. We con-


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A Modei of Consumer P erceptions and Store Loyalty Intentions fora Supermarket Retailer 225tions 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 foreach of about 160 stores belonging to a single grocery retailer. This retailer follows a mar-keting strategy that is intended to appeal to a broad set of consumers. The stores offer aconventional set of items including large assortments of fresh fruit and vegetables, andmedium-to-high quality meat and fish. Different items are promoted in different weeks tomaintain interest and curiosity of loyal customers and to provide them a sense of satisfac-tion about their purchases.

Our study goes beyond the existing literature in several ways. First, most of tbe existingconsumer intention research has concentrated on service industries (one exception beingLaBarbera and Mazursky, 1983). Instead, we consider a supermarket setting in which botbproduct- and service-quality play important roles. Second, very few micro-level studiesexist on the store loyalty intention measures considered here. Although Zeithaml et al.(1996) have included such m easures, they only examine the relation between service qual-ity and store loyalty intentions. Third, by studying the antecedents of perceptions of qualityand value for money, in addition to tbe antecedents of store loyalty intentions, we obtain amore complete understanding of tbe process by whicb store loyalty intentions are formed.Fourth, by incorporating process-level measures for constructs such as service quality, ourresearch not only provides a richer understanding of the antecedents of store loyalty inten-tions but we can also make realistic suggestions for managers. Finally, by modeling therelations among the different constructs as a system (by means of structural equation mod-eling), we avoid potential problems of low reliability and misspecification.The remainder of the paper is organized as follows. In section two, we discuss the con-ceptual model and develop testable hypotheses. We outline the sample and the methodol-ogy used in section three. In section four, we provide the results of the measurement andstructural 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 supermarketchain from their own shoppers. Management of the supermarket chain identified both thegeneral areas and the specific questions to be asked using a telephone survey technique.The initial model (F igure 1) has nine constructs based on the various areas in which the sur-vey questions were asked . Tbe first eight constructs measure respondent perceptions of:Store operations (SO P): Operational issues such as store hours, training and staffingof employees.Store appearance (SAP): Physical appearance and facility organization.Personnel service (PSP): Services provided by department managers, clerks, cash-


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226 lournal of Retailing Vol. 74, No. 2 1998Merchand ise quality (MQ P): Overall quality perceptions of merch andise and varietyin brands/categories.1 Perceived value (PV ): Value for money at the focal store.Perceived value of com petitor (PVC): Value for money at com petitor,

i'The ninth construct is:

Store loyalty intention(SI). This construct reflects the store loyalty intentions of currentcustomers.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 "servicequality " construct. We address this question by testing for discriminant validity of the con-structs. Unti! we introduce the test results, we refer to SO P, SA P. and PSP as the three ser-vice quality co nstructs.

The m odel in Figure 1 is an adaptation of a model proposed by D odds and M onroe(1985). The unique features of this model are: introduction of a service quality construct inaddition to the merchandise quality construct, the inclusion of a perceived value of com pet-itor construct, and the focus on cunrenl customers' store loyalty intentions. There are threeimpo rtant links^ in Figure I: ,

1. Effects of extrinsic cues on m erchandise quality p erceptions,2. The antecedents of perceived value, and3. The determinants of the store loyalty intentions.

We discuss the rationale for our emphasis on these linkages n ext.

Effect of Extrinsic Cues On Merchandise Quality Perceptions

Perceived quality is defined as the consumer's judgment about the extent of superiorityor excellence of the product (Zeithaml, 1988). This is a user-based approach as sug gestedin Garvin (1983). It is widely believed that consumers use cues to infer quality (Zeith-aml,1988; OIshavsky,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, generallycontrollable, aspects (for example, price and brand name). Once the types and varietie.s ofbrands have been selected, retailers cannot generally influence the intrinsic cues of groceryproducts 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'


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A Model of Consumer Perceptions an d Store Loyalty Intentions for a Supermarket Retailer 227

of ffnuved Viluf

Note: *This link represents only the direct effect.FIGURE 1

Conceptual Model of Store Loyalty Intentions as aFunction of Perceptual Constructs

Extrinsic cues are relevant in a supermarket setting for several reasons. First, the evalu-ation of intrinsic cues by consumers m ay require more time and effort than the consum erperceives is worthwhile (Zeitham l, 1988). Working wom en, men, and single shoppers, forexam ple, have been reported to use supermarket product information significantly less thanother demographic segments (Zeithaml, 1985). For such consu me rs, extrinsic cues providea signal. Second, extrinsic cues become more important when quality is difficult to evalu-ate as in the case of experience goods such as food p roducts (Zeitham l, 1988). Most of the


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228 !. Journal of Retailing Vol. 74, No. 21998tionship. even though it has been dem onstrated that the availability of other cues typicallyreduces the importance of price as a cue (Olson, 1977; Bonner and Nelson. 1985; Dodds etal., 1991). Based on Monroe and Krishnan (1985) we expect to see a positive relationbetween perceived price and perceived quality. However, we do not expect price to be avery important extrinsic cue given that other cues are readily available.

Service quality may also be used as an extrinsic cue in consumers' evaluations oftheoverall merchandise quality in a supermarket. We note that our focus is on overall mer-chandise quality rather than the quality of specific product categories or brands becausetypically consumers purchase a basket of goods rather than a single item during a regularshopping trip.The three "service quality" constructs of perception of store operations (SOP), percep-tion of store appearance (SAP), and perception of personnel service (PSP), all provide dis-tinct cues about service quality as further discussed below. Scales with items similar tothose used to measure SOP, SAP, and PSP have heen widely used to measure the "servicequality" construct (Parasuramanetal., 1985. 1988; Cronin and Taylor, 1992). Perceptionsof store appearance, which are essentially perceptions of physical attributes of the servicedelivery system, provide tangible clues about service quality (Bitner, 1990; Bitner, 1992;Baker et a l . 1992; Donovan and Rossiter, 1982). For examp le, proper layouts can dec reaseshopper search time, improve processing efficiency, increase service consistency and reli-ability, and facilitate customer orientation within the service delivery system (Cha.se and

Haye s, 1991; Bitner, 1992; Suiek et al., 1995). Similarly, perceptions about personnelformed during service encounters have been found to have an impact on evaluations of ser-vice 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 ofSOP, SAP and PSP to have a positive influence on perceptions of overall merchandisequality. In fact, focus-group research (Sweeney et al., 1995) has shown that aspects of ser-vice which contribute to product knowledge can have a positive effect on perceptions ofmerchandise quality. This effect can work in several different ways. For example, goodservice 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 ofour knowledge, such an effect has not been demonstrated emp irically.Hyp othesis 1: The three service quality constructs (if they are empirically distinct),store operations perception, .store appearance perception and per-sonnel service perception, have a positive effect on perceptions ofmerchandise quality.

Antecedents of Perceived Value \


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A Model of Consumer Perceptions and Store Loyalty Intentions for a Superm arket R etailer 229the utility per dollar measure of value used by Hauser and Urban (1986). In this definitionof value for money, there is an implicit tradeoff between money and the "benefit" compo-nents of the exchange. V alue, in supermarket shopping, consists of several benefit com po-nents such as variety, service, facilities, quality, nutrition, convenience, and freshness(Bishop. 1984; Doyle, 1984; Schechter. 1984). Superior service quality has heen describedas the third ring of perceived value (Clem mer, 1990), the first tvyo being the basic prod uct/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 havea positive impact on perceived service value (Bolton and Drew, 1991).

It is reasonable to assum e that consume rs take comp eting alternatives into account w hileforming their perceptions. Thus, perceived value for money of the competitor should be animportant construct for many consumers. We expect that as the perceived value for moneyat the most relevant competitor increases, the consumers' perceptions of value for moneyat the focal store decrease.Hypo thesis 2: Perceived value of comp etitor is negatively related to perceptions of

value for mone y at the focal store.

Determinants of Store Loyalty Intentions- I

The three m easures for the construct of store loyalty intentions of current cus tom ers' a re:willingness to repurchase, willingness to purchase more in the future, and willingness torecommend the store to others. Most previous research in this area focuses either on theimpact of service quality and/or customer satisfaction on store loyalty intentions or theimpact 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 dis-tinct constructs, in the sense that service quality is an attitude while customer satisfactionis (often) a transaction-specific measure. Cronin and Taylor (1992), using a single-itempurchase intention scale, fmd that service quality affects customer satisfaction but do notfind a significant effect of service quality on purchase intention. H owever, they do find thatcustomer satisfaction affects purchase intention. Taylor and Baker (1994), using a three-item purchase intention scale, obtain significant effects for service quality, satisfaction andan interaction term on purchase intention. Other researchers (B ouiding et al., 1993; Zeith-aml et al., 1996) do not distinguish between service quality and consumer satisfaction, andtreat these as one and the same. Bouiding et al. (1993) find a significant relationshipbetween .service quality and a two-item measure of repurchase intention and willingness torecommend. Zeithaml et al. (1996), using five different behavioral intention measures, finda significant relationship between service quality and all five behavioral intention mea-sures-


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230 . . Journal of Retailing Vol. 74, No . 2 1998information on purchase intention (Dodds and Monroe, 1985; Zeithaml. 1988). Perceivedvalue is an important construct to consider because consumers often have an acceptableprice range outside which products may not be considered (Dodds, 1991). Furthermore,most of the research in this area (Dodds eta l., 1991; Sweeney etal., 1995) focuses only onthe owillingness to buyo behavioral intention, and does not consider other intention mea-sures.

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 examinethe impact of the perception of value for money (with a richer set of antecedents) and ser-vice quality on store loyalty intention. We hypothesize:Hypothesis 3: The perception of value for money ha.s a pcsitive effect on the storeloyalty intentions of current customers.Hypothesis 4: The three service quality constructs (store operation.'!, store appea r-ance, and personnel service) have hoth a positive direct effect onstore loyalty intention and a positive indirect effect through perceivedvalue.

If a competing altema tive (supermarket) is considered to provide high value for m oney,this will lower the value perception of the store under consideration and decrease the storeloyalty intention of current customers. To our knowledge, there exists no work on theimpact of the perceived value of competitor on store loyalty intention, except for Ping(1993) who focused on the relation between alternative attractiveness (satisfaction with thebest altemative) and behavioral intention in the retailer-supplier relationship.Hypothesis 5: Perceived value of competitor has a negative effect on store loyaltyintentions.

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 rela-tionships in our model. For example, in stores operating in highly competitive local retailenvironments, perception of value may be a more important factor in determining storeloyalty intentions than in stores operating in less competitive local retail environments. Inthe absence of strong competitors, a favorable store loyalty intention will depend less onvalue for money than on other aspects (e.g., geographic loca tion). Such a dependency mayreflect the idea that a customer is more inclined lo make 'value for money' assessments ofmultiple stores, if there is a strong competitor who pursues the custom er. Th us, perceptionof value may be a more relevant predictor of loyalty intentions in the presence of a highlycompetitive local retail environment than in its absence.Hypothesis 6: The impact of perception of value for money o n store loyalty intention


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A Model of Consutr^er Perceptions and Store Loyalty Intentions fora Superma rket Retailer 231M E T H O D O L O G Y

Operationalization Of The Latent Constructs

For the three constructs of service quality (store operations, store appearance, and per-sonnel service), we use perception measures rather than the gap between perceptions andexpectations. This is because the evidence is that perception mea.sures- typically explainmore ofthe 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 expe rience . ,

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

Sample

Data on the measures were collected by a market research supplier for a large, east coastsupermarket chain via phon e interviews of 16,096 shop pers. The chain was selected forthis re.search in part due to its represen tativeness of a variety of store conditions. For exam -ple, store sizes, merchandising approaches, ethnic diversity among customers, pricingstrategies and store formats all had a substantial amount of variation such that the resultsreported here should have some generalizability. The supermarket identified 1500 namesfor each of about 160 stores. These names were drawn randomly from a frequent shopperdatabase in which shopper name, address and telephone number were listed. After the listof names was cleaned, to delete homes without phones, etc., phone interviews were con-ducted until 100 were successfully completed for each store (the response rate was 50-60percent). Respondents completed a lengthy interview encompassing factors to describetheir normal shopping behavior and perceptions of their preferred supermarket. The cus-tomers were asked how often they shopped at the participating chain and were also askedfor their first- and seco nd-preferred chain . For the data used in our analy sis, all respond entsindicated the store we focus on as their first preferred store and som e other chain as the sec-ond preferred (respondents were selected based on this condition). The second preferredchain w as treated as the com peting alternative about which the 'perceived value of com pet-itor' question was asked.

Apart from this survey, we asked three senior managers familiar with all the stores inthe sample to judge overall store quality and the intensity of local retail competition for


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232 Journal of Retailing Vol. 74, No. 2 1998Methodology

W e specify a model to explain store loyalty intention and other asp ects relevant to shop-per behavior as a function of the constructs described earlier (defined in the Appendix andillustrated in Figure 1). The model parameters are estimated using Lo hm oller 's (19 81 ) Par-tial-Least-Squares (PLS) algorithm. PLS is a powerful multivariate causal modeling tech-nique for relations between multiple dependent and independent latent constructs. In PLS.the model parameters are divided into subsets estimated with ordinary least squares thatinvolve the values of param eters in other subsets (Fornell and B ook stein. 1982). The latentconstructs are estimated as linear com bination s of their empirical ind icators. This approachavoids the prohlems of improper solutions and factor indeterminacy which are common infactor-analysis based approaches.^ Parameters are estimated from average values at thestore leve! for all the measures, which means that all differences between customerspatronizing the same store are remov ed. Thu s, all estimated effects reported here are basedon differences between stores.

In PLS, the latent constructs can be treated as underlying factors {measures treated asrefiective) or viewed as indices produced by the observable measures (measures treated asformative) (Fom ell and Books tein, 1982). In our m odel, we use both reflective and formativeindicators. Since our interest is in explaining the variation in the store loyalty intention mea-sures by way ofthe latent constructs, we model the measures ofthe store loyalty intentionconstruct as reflective and the m easures of all other constructs as form ative.

PLS prov ides factor loadings between the observed m easures and latent constructs (wh enmeasures are in the refiective mode), weights (when measures are in the formative mode),and standardized path coefficients between the latent constructs. Since PLS estimationinvolves no assumptions about the population or scale of measurement, there are no distri-butional requirem ents, For that reason non parametric jackkn ife estim ates of standard errorsare utilized.

To test Hyp othesis 6, competitor attractiveness is used as a proxy for com petitive intensityofthe local retail environm ent. W e first calculated an average com petitor attractiveness score^for each respondent (based on measu res obtained on the following six items w ith respect tothe second preferred chain: overall quality of products and serv ices, value for m oney, ratingon store appearance, rating of sales and specials, rating of personnel service, and rating ofstore operation s). W e then split the samp le into those stores which had low average com petitorattractiveness (average < 3.24) and those which had high average com petitor attractiveness(average > 3.36), in order to do a separate analysis for each sub-sample.** These splits werebased on the em pirical distribution of the average com petitor attractiveness for the stores.^

RESULTS 'Measurem ent Mode l ' ' ' '


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A Mod el of Consum er Pe rceptions and S tore Loyalty Intentions fora Superm arket Retailer 2331. The reliability (RHO) of construct t) is: '

where the X, are the loading coefficients for construct ri and m easu re /, and p^, is the num-ber of indicators for construct T|. Nunnaliy (1978) recom mends that this measure be greaterthan 0.70.2. The average amount of variance (AVE) that is captured by construct rj , relative to thetotal amount of variance in the measures, is:

/'i= 1

/'I Var(e,)= I

(since PLS standard-( = I

tzes constructs and indicators). If this measure is less than 0.50, then the variance due toerror is larger than the variance captured by the construct. In that case, the validity of boththe measure and tbe construct are in question.Discriminant validity is establisbed if AVE > Y where gamma-squared is the square ofthe path coefficient linking two constructs being exam ined for discriminan t validity. If thepath coefficient for a particular pair of constru cts is not availab le, we use the correlation

between these constructs in lieu of tbe path coefficient. In a model with only two con-structs, the path coefficient is the same as the correlation coefficient when the construc tsare standardized.Tests for discriminant validity indicate that SOP, SAP, and PSP are not distinct con-structs and thus their measures were pooled together as formative indicators of a single ser-vice quality (SQ) construct. We do not know whether the stores belonging to the chain aretruly similar in quality across Ibe three dimensions. An altemative interpretation is thatrespondents were unable or unwilling to recognize systematic differences between thesedimensions, for a given store. In any event, all the constructs are unidimensional based ontbe covariance m atrix of tbe residuals of the mea sures. None of the covariances were largeenough to warrant consideration of more than one dimension for any construct. In ourapplication, the store loyalty intention construct is also unidimensional. Thus, it does notmake sense to assume that the three measures of store loyalty intention (willingness torepurchase, willingness to purchase more in the future, and willingness to recommendstore) relate lo 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 modelwere eliminated. An examination of the excluded indicators showed that the substantivemeaning of the constructs did not change. The measurement properties of the revised

mode! are sbown in Tab le 1. The five constructs shown in Table 1 all bave reliabilitiesgreater than 0.85. The sm allest AV E value is 0.55.


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234 Journal of Retailing Vol. 74, No . 2 1998TABLE 1 , . , . ' Me asurem ent Properties of the Revised M od el

Construe!Service Quality (SQ)Sales Pro mo tion Percep tions (SPP)Perceived Relative Price (PRP)Merchadise Qual i ty Percept ions (MQP)Store Loyalty Intent ion s (SI)

Reliabilily,97,87,86,97,87

AVE.65.77.55.72.70

Structural Model

The standardized structural parameter estimates (total effects) are shown in Table 2. Weshow in Figure 2 hoth direct and indirect effects that are significant ip < 0.05) based onjackknifed standard errors.Hypo thesis I: (Service quality -* Mer chandise quality) i.s supported hy the data.

Service quality has the largest effect on perceptions of merchandisequality.

Hypo thesis 2: (Perceived value of com petitor -> Perceived value of the focal store)is not supp orted by the data. The effect is not statistically significant(and the mag nitude ofthe effect, which is not shown, is the least of all

. 'f ., effects on perceived value).

TABLE 2 ^Structural Model Results (Total Effects)

Source/TargetService Quality (SQ)Merchandise Qual i ty Percept ions (MQP)Sales Pro mo tion Perce ptions (SPP)Perceived Relative Price (PRP)Perceived Value (PV)Perceived Value of Co mp et i tor (PVC)R-sq 1Q-sq

Merchandise QualityPerception (MQP)

.75

.1 6

.05

0,78

Perceived Value(PV),1 8.32.40.4 7

0.69

Store LoyaltyIntentions (SI)

,6 7. ,47.12*.08*.13- .130.660.38


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A Model of Consumer Perceptions an d Store Loyalty Intentions for a Supermarket Retailer 235

\ -.13*

a( PfruLvtdDolBiininnnu o


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23 6 t- , ., . ,. Journa l of Re tail ing Vo l. 74 , N o. 2 19 98Hypothesis 5: (Perceived value of competitor -* Store loyalty intention) is sup-

ported by the data. Competitor attractiveness has a significant nega-tive effect on store loyalty intention: the higher the perceived value ofcompe titor, the lower the store loyalty intention.

Hypothesis 6: (Value for mon ey -* Store loyalty intention; moderated b y compe titorattractiveness) is supported by the data. In the high attractivenesssample, the m agnitude of the effect of perceived value on store loyaltyintention is 0.45, (the second largest effect after sen'ice quality),while in the low attractiveness sample perceived value has no signif-icant effect on behavioral intention. ' iIn addition to the path coefficients. Table 2 also shows R values for the merchandisequality perception, perceived value, and store loyalty intention constructs and a Q^ valuefor the store loyalty intention construct. The R^ value is the percent of variation in theendogenous latent constructs explained by the exogenous latent constructs, w hile Q~ is thepredictive relevance of the model for the endogeno us latent constructs w ith reflective indi-cators. Specifically, the Q" value indicates the percentage of the blind-folded data that canbe recreated by the specified path model. "* For finite samples, Q^ in general has a smallervalue than the R^ (Quan, 1988). To judge the fit of the overall model. Lohmoller (1981)suggests the use of two additional measures, namely the redundancy coefficient and thecovariance of the inner (structural) model (Figure I) residuals and the outer (m easurem ent)model residua ls. 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 outerresiduals 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 theeffects of alternative strategies on retaining customers. Thus, the store loyalty intentions ofcurrent customers become a central focus for retailers. In this paper we used the followingstore loyalty intention measures at a supermarket retailer: "intent to continue purcha sing,""intent to increase future purcha ses," and "intent to recomm end store to others ." The lattermeasure is also relevant to custome r retention in the sense that custom ers' intentions to rec-ommend a retailer to others would not be consistent with inclinations to switch from Ihesame retailer. We m odel the linkages between antecedents (formulated as latent co nstructs)such as the focal store 's service quality, price, merchandise quality and perceived value aswell as the best com petitor's perceived value, and estimate their effects on current custom -ers' store loyalty intentions, using Partial Least Squares. The empirical results from thislatent variable structural equation model provide useful insights to supermarket managers.


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A Model of Consum er Perceptions and Store Loyalty Intentions for a Supermarket Retailer 237relationship in the grocery retailing sector. The large and significant impact of service qual-ity indicates that a good facility design and service provision by customer-contact employ-ees leads to enhanced p erceptions of overall merchand ise quality. Our results also indicatethat price does not play an important role in custom ers' perceptions of merchandise q uality,especially when other cues are readily available to consumers. The magnitude of the effectof 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, onceproduct selection has been made, these results suggest several courses of action for retailmanagers who wish to improve consumers' perceptions of overall merchandise quality.Such improvement is critical to retailers because we find that perceptions of overall mer-chandise quality have significant direct impacts on overall customer store loyalty inten-tions. The magnitude of the path coefficient for merchandise quality perception on storeloyalty intention is the second largest effect after that of service quality.

Previous research results about the determinants of customer intention to purchase orwillingness to buy suggest that one of the most effective ways of improving purchase inten-tion is to improve the perception of value. Our study may be the first one to focus on thedeterminants of customers' store loyalty intentions beyond the ointent to buyo decision(since respondents were selected based on the criterion of the store being their most pre-ferred store). A somewhat unexpected fmding is that perceptions of value do not appear tobe important in determining store loyalty intentions if the intensity of local retail competi-tion is low. Thus, for consumers shopping at their preferred store, the improvement ofvalue perceptions may not be needed to increase their intentions to continue purchasing, topurchase more often, or to recommend the store to others, if alternative competitors are notattractive. Of course, this result must be interpreted with caution. For example, if value per-ceptions for the retailer are low, the opportunity for a new competitor to enter the market ishigh. And if entry occurs, the retailer may discover there is a lack of customer goodwill andinsufficient competency to com pete.

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 indi-rect price effect). By modeling the relationships within a system using PLS, and accountingfor both direct and indirect effects, the results of our model shed new light on the strengthof relations for various constructs. Mostly, the indirect effects are either in.significant orvery small in magn itude com pared to the direct effects. However, this is not so for the effectof service quality on store loyalty intention for which the direct and indirect effects haveabout 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 Q^ value for behavioral intention is 0.38(Table 2), indicating that the predictive relevance of this model is good. Therefore, thismodel should be useful to managers for determining the effects of various strategies (e.g.,improving perceptions of assortment, improving perceptions of personnel service by bag-

gers) on the three store loyalty intention measures of willingness to repurchase, willingnessto purchase more often in the future and willingness to recommend store. Conventional


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23 8 s. , . Journal of Retai l ing Vol. 74 , No . 2 199 8misspecification (Fomell, 1992). Our model can also be used to track customer scores onthe constructs over time which is difficult to do in models estimated with factor-analysisbased approaches such as LISREL.

Limitations and Future Research

Since our analysis was conducted exclusively on shoppers who mentioned the chain astheir most preferred outlet, it is possible that this introduces a bias. For example, theresponses may be biased upward because of customers' loyalty to this chain, relative towhat 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 tostudy the impact of the various antecedent variables on actual behavior rather than just onstore loyalty intentions of consumers. Our model of survey data could be expanded byincorporating superm arket scanner purchase data. As more superm arkets offer frequentshopper programs and track purchases at the individual household level, an integration ofpurchase and survey data may soon be commonplace.Although the store data are based on a large number of customers per store, our sampleis limited to one supermarket chain. However, the chain was selected for this research in

part because of its representativeness. On many differentiating characteristics betweensupermarket retailers, the participating chain is in the mid-range. Its pricing strategy couldbe described as "middle-of-the-road," it has a typical array of conventional formats, thecustomers have approximately average demographic diversity for the US, and the storesare situated in many types of socio-economic locales. Although we do not believe theresults apply directly to the universe of alt supermarket types, we do believe that our resultsare likely to apply to a considerable array of supermarket companies operating conven-tional store formats. Nevertheless, it will be useful to extend our approach to a broadergroup of retailer companies.Another fru' -ul area for future study cons ists of the use of multiple yea rs of data. Withdata for addinonal years, one can test tbe predictive validity of the model. If retail manage-ment implements a variety of new initiatives based on model results sucb as those reportedhere, it will be instructive to see to what extent future customer evaluations of the store rep-resent predicted ch anges . An alternative proce dure to exam ine this is to test the stability ofthe effects over time. Thus, if there are changes on various perceptual measures, and theendogeno us variables do not change in a manner consistent w ith the estimated model, thenthe effects are not stable over time.More attention in future research should be allocated to a determination of the causalpaths ofthe relationships that we studied. It is plausible that the direction of cau.sality 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 pre-


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A Model of Consumer Perceptions and S tore Loyalty Intentions for a Supermarket Retailer 239Finally, we believe it will be instructive to measure and incorporate various objective

store characteristics in a model. This could, for example, offer management the opportu-nity to leam how consumer perceptions are influenced by objective characteristics. Theimportance of this type of research is underscored by the fact that consum ers' perspectivestypically diverge from what managers believe. To illustrate, for the research reported herewe asked three senior managers of the chain to independently rate each store on quality.Each m anager was familiar w ith each store, and we obtained acce ptable inter-rater reliabil-ity on this measure. Yet we obtained no support for the hypothesis that store quality wouldbe less relevant in the subset of stores rated as being of low quality than in high-qualitystores. It will be informative to determine what influences managers' perceptions of storequality, and to compa re this with an explanation of custom ers' store quality percep tions.

APPENDIX

The op erationalization of all proposed latent constructs is given below:1. Store operations perception (SOP). This is determined by six indicators all mea-sured on a five-point scale, ranging from excellent to poor. The indicators are;

a. quality of opera tions(h ours, training, and staffing of em ploye es)b. staffing enough em ployees to meet customer needsc. offering convenient hours of operationd. keeping deli, bakery open and providing services for extended hourse. providing adequate training of employeesf. ability of manager to resolve questions and problems

2. Store appearance perception (SAP). This is determined by eight indicators mea-sured on a five-point scale, with seven scales ranging from excellent to poor and theremaining one ranging from strongly agree to strongly disagree. The indicators are:a. rating of overall appearance of storeb. providing a clean shopping environmentc. having wide, open aislesd. having various depa rtme nts in approp riate places in the storee. having well-marked aisle directoriesf. having a safe parking lotg. providing clean restroomsh. providing a pleasant shopping environment


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24 0 , . lournal of Retailing Vol. 74, No . 2 1998a. overall quality of services provided by personnel the customer interacts withb. quality of services provided by cashiers and baggersc. quality of services provided by custom er service deskd. quality of services provided by employees at the deli countere. quality of services provided by employees at the in-store bakeryf. quality of services provided by employees in the meat department t).g. quality of services provided by employees at the seafood coun ter fth. quality of services provided by employees in the produce departmen t i ,i. quality of services provided by store man ager

j . custom er orientation of the superm arket ' .. 'k. provision of friendly and responsive service by the supermarket

4. Merchandise quality perception (MQP). This is determined by fifteen indicators,all measured on a five-point scale, ranging from excellent to poor. The indicatorsare:a. overall quality of merchand ise purchased . , ,b. quality of produce departmentc. quality of meat departm ent j c i-tjlii'. \d. quality of deli . r . ,e. quality of grocery items ,f. quality of in-store bakeryg. quality of seafood departmenth. quality of frozen food section ,. ,i. quality of dairy section

j . quality of health and beauty aid departmentk. quality of private-label items1. rotation of perishables, so that they are always displayed fresh ^m. wide brand selection of grocery items in. variety of grocery itemso. presence of items appropriate for a supermarket

5. Sales promotion perception (SPP). This is determined by two indicators, bothmeasured on a five-point scale, ranging from excellent to poor. The indicators are :a. sales or spec ials offeredb. having sale items in stock ' '

6. Perceived relative price (PR P). This is determined by five indicators, all measuredon a five-point scale, ranging from much lower priced to much higher priced. Theindicators are:


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A Model of Consumer Perceptions andStore Loyalty intentions fora Supe rmark et Retailer 241c. comparison of meat prices with other supermarketsd. comparison of produce prices with other supermarketse. comparison of heahh and beauty aid prices with other supermarkets

I

7. Perceived value (PV). This is determined by a single indicator (comparing whatyou pay for what you get) measured on a five-point scale, ranging from excellentvalue for money to poor value for money.

8. Perceived value of competitor (PVC). This is determined by a single indicator,perceptions of value for money at the competitor's store, measured on a five-pointscale ranging from excellent to poor.

9. Store loyalty intentions (SI). This is determined by three indicators, all measuredon a five-point scale, ranging from extremely likely to not at all likely. The indicatorsare:a. likelihood to continue shoppingb. likelihood to use the store for more of your grocery needs in the next twelvemonthsc. likelihood to recommend supermarket to a friend

NOTES

1. The authors had no involvement in the design of the study. Afler ihe data had been collected,they assisted management of the retail chain in data analysi.s and interpretation of results.2 . Only some of the linkages in Figure 1 have hypotheses identified as H. This is because werestrict ourselves to hypotheses for which the insights we provide are new. How ever, all path coeffi-cients 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 thescope of our study.

4. Survey available from authors on request.5. Indeterminacy need not be limited to exploratory analyses (Mulaik, 1976). Indeterminantfactors can have improper loadings which lead to negative variances. PLS provides determinant fac-tor scores which improve prediction and control.6. This mixed-mode estimation cannot beperformed with popular programs such as LISREL.7. In the model shown in Figure I. we use only the perceived "value for money" of the second

preferred store. However, when we split the sample to categorize stores according to competitiveintensity, we use an average of six measures. We do this because this average provides a better proxyfor "intensity of local retail comp etition" 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 O.i for the highaverage 'competitor attractiveness' groups.9. We also examined sample splits by other variables, for example: overall store quality judg-


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242 Journal of Retai l ing Vol. 74, No. 2 199810. Blindfolding means to omit a part of the data matrix while estimating modei parameters, andthen to reconstruct the omitted part by the estimated param eters. This procedure is repeated until eachdata point is omitted and reconstructed once. The resulting tolerance measure Q' has the form Q- =

\- (E/O) where E is the error sum of squares, and O is the observed values sum of squares. Since theerror is the difference between the observed and reconstructed values, this measure is conceptuallysimilar to goodness of fit.11. The redundancy coefficient is defined as follows:

Let the vector of indicator variables he > and the v ector of latent constructs be Y.For the outer (measurement) model one can write:I

3J = PK+e, where P is the loading pattern matrix. >i 'For the inner (structural) model one can write:

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

0'l

y,=

10

.'531

0^22^32

000

Substituting the above into the measurement model relation one gets:y = PBY+Pu + e = y** + {Pu+ e)The diagonal of F^, the covariance matrix of v** divided by the variances ofthe indicatorvariables, contains the redundancies which are the proportion ofthe variance ofthe indica-tor variables reproduced by the predictor latent constructs ofthe Indicator's own latent con-structs. The redundancy coefficient is defmed as: (trace F^/k) where k is the total numberof Indicator variables.

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