Linking Perceived Quality and Customer Satisfaction to Store Traffic and Revenue Growth

8/14/2019 Linking Perceived Quality and Customer Satisfaction to Store Traffic and Revenue Growth

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Decision SciencesVolume 35 Number 4Fall 2004Printed in the U.S.A.

Linking Perceived Quality and CustomerSatisfaction to Store Trafc and RevenueGrowthEmin Babakus Department of Marketing & Supply Chain Management, Fogelman College of Business & Economics, The University of Memphis, Memphis, TN 38152, e-mail: [email protected]

Carol C. Bienstock Department of Management & Marketing, Radford University, P.O. Box 6954, Radford,VA 24142, e-mail: [email protected]

James R. Van Scotter Department of Information Systems & Decision Sciences, E. J. Ourso College of Business Administration, Louisiana State University, 3190 CEBA, Baton Rouge, LA 70803,e-mail: [email protected]

ABSTRACT

Effects of perceived merchandise and service quality, relative to competition, on retailstore performance are investigated using store trafc and revenue growth as outcomevariables. A model is proposed and tested using aggregate customer data and storeperformance outcomes from a group of stores owned by a national retail organization.Results suggest that both service and merchandise quality exert signicant inuence onstore performance, measured by sales growth and customer growth, and their impactis mediated by customer satisfaction. Implications of the results and future researchdirections are discussed.

Subject Areas: Aggregate Survey Data, Causal Models, Consumer Behavior,Customer Satisfaction, Merchandise and Service Quality, and Retail Store Performance.

INTRODUCTION

There has been an explosion of interest in quality and its impact on a rms successin a ercely competitive global market. Grandzol and Gershon (1997) reported thatmore than half of all corporate training dollars are spent on quality issues. Giventhis level of spending, decision makers are understandably eager to learn howquality improvement efforts are related to performance measures such as return

We keep the identity of the organization that provided the data condential to honor the wishes of the


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714 Perceived Quality and Customer Satisfaction

on investment, net revenues, customer growth, and sales (Sousa & Voss, 2002).Researchers have described understanding the nature of the relationship betweena rms nancial outcomes and its customers perceptions of quality as . . . the

issue of highest priority (Zeithaml, Berry, & Parasuraman, 1996) and the qualityof goods and services, and customer satisfaction have come to occupy a prominentposition in the research literature (Ahire & Dreyfus, 2000; Hardie, 1998; Reeves& Bednar, 1994).

Much of this attention is motivated by the premise that improving the qual-ity of goods and services will increase customer satisfaction and loyalty, decreasecosts, and ultimately will lead to better nancial performance. Yet, despite nearlytwo decades of research on quality improvement efforts, the relationships betweencustomer perceptions of quality and nancialoutcomes are still being debated (Das,Handeld, Calantone, & Ghosh, 2000). Although previous research has exploredboth the cost reduction and revenue enhancement avenues that link quality to busi-ness performance, ndings in both areas are mixed and inconsistent (Hardie, 1998).Some studies have produced results consistent with the assumption that improvingquality and customer satisfaction lead to better performance outcomes for the rm(Buzzell & Gale, 1987; Fornell, 1992; Ittner & Larcker, 1998; Kordupleski, Rust,& Zahorik, 1993; Nelson et al., 1992). Evidence from other studies suggests thatquality and customer satisfaction do not always lead to better performance andthat results may even be negative (Grandzol & Gershon, 1997; Ittner, Larcker, &Meyer, 2003; Tornow & Wiley, 1991; Yavas & Burrows, 1994).

There are several potential explanations for these seemingly inconsistentndings. First, a number of studies used perceived quality to predict consumerbehavior directly. This approach ignores the cognitive and affective processes thatare responsible for the relationships, and it does not consider how quality and rmoutcomes are related. This omission may be important because leading theoriessuggest that the in uence of customers quality perceptions on rm outcomes ismediated by customer satisfaction (Fornell, 1992; Fornell, Johnson, Anderson,Cha, & Bryant, 1996; Oliver, 1997, 1999). Thus, customer perceptions of qual-ity may not affect rm outcomes directly. Second, researchers have often reliedon cross-sectional data. If improved quality leads to higher performance, it does

so over time. Although businesses higher in quality should exhibit higher perfor-mance, improvements in quality and increases in performance cannot be measuredcross-sectionally. Snap-shots of cross-sectional data are incapable of modelingthe process or extent of improvements. For this reason, researchers have arguedthat future studies must move beyond cross-sectional designs (Bernhardt, Donthu,& Kennett, 2000; Zeithaml, 2000) to capture the impact of quality on rm perfor-mance. A third problem is that inconsistencies in the de nition and measurementof quality and customer satisfaction contribute to the mixed ndings regardingrelationships among quality, satisfaction, and performance (Choi & Eboch, 1998;Hardie, 1998; Sousa & Voss, 2002). For example, customers judgments of relativequality (compared with competitors) have been found to be positively related tomarket share (Buzzell & Gale, 1987), but, due to higher costs, measures of the


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Babakus, Bienstock, and Van Scotter 715

conformance to speci cations, and meeting/exceeding expectations) and their listis by no means exhaustive.

Objectives of the StudyWhat is the nature of the relationship between customer perceived quality and -nancial outcomes? The answer to this question has strong managerial implicationsfor the allocation of scarce resources as well as for developing executive compen-sation packages that include non nancial measures such as customer satisfactionand perceived quality (Banker, Potter, & Srinivasan, 2000). These non nancialperformance measures are expected to provide timely information regarding anorganization s progress toward its strategic goals and future nancial performance.Yet, the majority of companies do not realize such bene ts due to their failure

to demonstrate the impact of improvements in perceived quality and customersatisfaction on nancial performance (Ittner & Larcker, 2003).Our study addresses the preceding question and contributes to the litera-

ture in at least three ways. First, we use Bagozzi s (1992) appraisal affectiveresponse behavior framework to develop and test speci c research hypotheseslinking customer satisfaction, perceived merchandise and service quality, and retailstore performance (growth in sales and store traf c). This framework proposes thatcustomer evaluations of a retail store s merchandise and service quality in uencecustomer satisfaction and customer satisfaction, in turn, in uences store traf c andsales growth (Figure 1 presents the conceptual model).

Second, the study tests the usefulness of an alternative approach toward mea-suring quality dimensions, which calls on customers to evaluate quality relative tocompetition (Gale, 1994). In prior research, alternative evaluation standards with-out explicit reference to competition have been used more frequently than relativeapproaches in perceived quality measurement studies (Olsen, 2002). However, in acompetitive environment the relative approach seems more consistent with the wayconsumers make purchase decisions (Gale, 1994). Thus, our instrument explicitly

Figure 1: A conceptual model of perceived quality and retail store performance.


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asks customers to compare the focal store s quality with a list of its competitors.The resulting data are used to determine the relative importance attached to thequality of merchandise and the quality of services offered by the retailer.

Third, our study contributes to the research literature by using the appropriatelevel of analysis the storeand introducing the use of criteria to justify aggre-gating perceptual data at the store level. Conceptually, differences in the levelsof perceived quality and customer satisfaction among the stores may occur as afunction of management practices; employee attitudes, motivation, and skills; andthe physical characteristics of each store. However, using average scores of per-ceived quality or customer satisfaction to re ect a stores characteristics requires justication that is based on rigorous empirical assessment (James, Demaree, &Wolf, 1984; Klein, Dansereau, & Hall, 1994; Schneider, White, & Paul, 1998). Byintroducing criteria to demonstrate the level of agreement regarding perceptions of quality and satisfaction among each store s customers, we demonstrate the ef cacyof using these aggregate scores.

CONCEPTUAL FRAMEWORK AND MODEL DEVELOPMENT

The literature identi es two primary mechanisms through which quality relates toperformance: production/operations and market (Hardie, 1998; Rust, Zahorik, &Keiningham, 1993; Sousa & Voss, 2002). The production/operations mechanismimproves process and design quality, reduces waste, and realizes operational ef-

ciencies by ne-tuning internal operations. Quality improvements here reducethe number of returns, complaints, and labor needed to x problems with prod-ucts. These ef ciencies improve nancial performance by reducing costs and byincreasing product reliability, making products more attractive to customers.

The market route focuses primarily on using improved quality to increaserevenues, leading to higher pro ts. Ceteris paribus , customers are expected tocompare the quality of products offered by competing companies, and choosethe product that they believe to be the best. Quality improvements also attract newcustomers, enhance retention and loyalty of existing customers, and lure customersaway from competitors whose products are perceived as lower in quality. Thus, theultimate judgment about quality is rendered by the customer and it is relative to thecompetitors offerings (Gale, 1994). In addition, customers may be willing to paypremium prices and increase their purchases because of improved quality. Thus,improvements in product quality increase revenues, increase market share, and leadto higher pro ts (Sousa & Voss, 2002).

A Model of Perceived Quality and Store Performance

Our model is based on Bagozzi s (1992) reformulation of attitude theory. Afterreviewing existing attitude theories, Bagozzi (1992) argued that attitudes and sub-

jective norms are not suf cient determinants of intentions, and intentions are notsuf cient determinants of behavior. He proposed that self-regulating processes,


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to a strong commitment such that consumers continue patronizing the same retailerand resist competitors enticements (Oliver, 1999).

Conceptual and empirical works by Fornell (1992) and Fornell et al. (1996)

assign customer satisfaction a central role as a direct determinant of customer be-havior and organizational performance. After an extensive review of competingmodels followed by a comprehensive empirical study across six service industries,Cronin, Brady, and Hult (2000) concluded that customer satisfaction has a direct ef-fect on behavioral intentions. In a recent study, Brady and Cronin (2001a) providedfurther empirical evidence regarding the direct in uence of customer satisfactionon behavioral outcomes in two of the three industry sectors investigated. Mittal andKamakura (2001) showed that customer satisfaction has a strong direct impact oncustomer loyalty as measured by repeat purchase behavior in the automobile sector.More importantly, a number of studies showed a direct linkage between customersatisfaction and rm performance (e.g., Das et al., 2000; Ittner & Larcker, 1998). Inaddition to strongtheoretical arguments, there is ample empirical evidencesuggest-ing a direct inuence of customer satisfaction on retail store performance. Hence,we hypothesize that:

H3: Customer satisfaction has a positive in uence on store traf cgrowth.

H4: Customer satisfaction has a positive in uence on store salesgrowth.

Causal Order and MediationThe causal sequence of perceived quality, customer satisfaction, and customer pa-tronage behavior shown in Figure 1 is not without controversy. Questions havebeen raised about the causal order of perceived quality and customer satisfaction(e.g., Dabholkar, Shepherd, & Thorpe, 2000) and the way perceived quality andcustomer satisfaction in uence nancial outcomes (e.g., Bernhardt et al., 2000;Rust, Zahorik, & Keiningham, 1995; Zeithaml et al., 1996). There are two majortheoretical perspectives. One argues that customer satisfaction is an outcome of

the consumer s favorable evaluations of the quality of goods and services, and thuscan be inuenced by managerial interventions that emphasize quality. The secondperspective builds on the premise that customer satisfaction/dissatisfaction experi-ences determine perceived quality of goods and services (e.g., Bitner, 1990; Bolton& Drew, 1991). This view suggests that the customer s affective state resulting froma retail transaction or encounter in uences his/her appraisal of quality. Customersexperiencing positive affect judge things to be higher in quality, and those expe-riencing dissatisfaction judge things to be lower in quality. Therefore, satis edcustomers will make favorable quality judgments about goods and services. Onbalance, there is considerably more empirical support for the perspective that per-

ceived quality precedes customer satisfaction(e.g., Anderson,Fornell,& Lehmann,1994; Brady & Robertson, 2001; Cronin et al., 2000; Dabholkar et al., 2000;


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H5: A model in which perceived quality customer satisfaction store traf c and sales growth will t the data better than a model inwhich customer satisfaction perceived quality store traf c

and sales growth.

An implication of H5 is that customer satisfaction fully mediates the relation-ship between perceived quality and store performance, and that perceived qualitydoes not have a direct in uence on performance. This is based on strong theo-retical arguments and empirical evidence (Bagozzi, 1992; Fornell, 1992; Fornellet al., 1996; Oliver, 1999). However, the literature also suggests a direct impactof perceived quality on customer behavior (Cronin et al., 2000). For example, re-search in a retail grocery context found both merchandise and service quality hadsignicant direct as well as indirect effects on customers store loyalty (Sirohi,

McLaughlin, & Wittink, 1998). Similarly, a study in the automotive sector foundbrand loyalty and purchase intentions were signi cantly affected by customers perceptions of goods and service quality (Devaraj, Matta, & Conlon, 2001). There-fore, a test of H5 involves addressing the role of customer satisfaction as a com-plete or partial mediator of the relationship between perceived quality and storeperformance.

METHODOLOGY

Overview of the Study and Group

The study was sponsored by a large retail organization, which specializes in provid-ing a major merchandise line and related services. The merchandise and servicesare primarily targeted to individual consumers although the company was also test-ing offerings to industrial customers at the time of the study. The research teamconsisted of three faculty members and two doctoral students at a southern univer-sity. In addition, the VP for human resources and director of IT of the sponsoringretailer served as facilitators and members of the extended team.

Procedures : Initially, one of the faculty members on the research team con-ducted a series of focus groups with current and lost customers to understand how

consumers de ne quality with respect to this line of merchandise and services,and how they evaluate retailers that provide similar merchandise and services.Sixteen customer focus group sessions were conducted at locations across eightstates. Two sessions were devoted to potential industrial customers and three ses-sions were conducted with defected customers. Group sizes ranged from 6 to 13.Participants were chosen randomly from the retailer s database across regions.

Subsequent to the focus groups and using the information gleaned from them,the research team and a group of managers from the sponsoring retailer workedtogether to develop an initial measurement instrument. This initial survey wasmailed to 200 randomly selected current customers in one of the markets served

by the sponsor. The response rate for the pretest was 20%. Based on the resultsof the pretest, minor changes were made in the wording of some of the questions.


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the sponsor, as a reminder of various providers. A ve-point response format wasprovided with labels XYZ is much better to XYZ is much worse. The middlepoint was labeled as about the same. For instance, respondents rated the spon-

soring retailer relative to competition regarding the variety of products to choosefrom, on this ve-point scale one of the six indicators of perceived merchandisequality. A complete list of items is provided in the Appendix.

Store performance variables : Several months after completion of the cus-tomer survey, the sponsor provided two store-level performance measures: cus-tomer count and revenue. In addition, information on monthly payroll expensesfor each store was provided. These measures were used to develop two store-levelperformance measures: quarterly change (as percentage change from Quarter 1 toQuarter 2, the two consecutive quarters following the completion of the customersurvey) in sales volume and customer count (store traf c) per payroll dollar foreach store. The adjustment of sales and customer count by the amount of payrollfor a particular store was an attempt to make the performance measures comparableamong stores by controlling differences due to store size. The average number of customers per payroll dollar ranged from .25 to .92 for the rst quarter and from .24to .95 for the second quarter. Average sales per payroll dollar ranged from $3.59to $11.06 and $3.68 to $11.55 for the rst and second quarters, respectively.

Assessment of Measures

The items representing customer measures (i.e., 6 perceived merchandise qualityitems, 11 perceived service quality items, and 3 customer satisfaction items) wereexamined at the individual response level before conducting the store-level analy-sis. The total sample was randomly divided into two groups of approximately equalsize (approximately 8,500 observations each). One group was used for exploratoryanalysis and the development of the measurement model (calibration group), andthe other group was used to validate (validation group) the initial results. An ex-ploratory maximum likelihood factor analysis using the calibration group was rstconducted to test the dimensionality and factor structure of the service quality, mer-chandise quality, and customer satisfaction measures. Since these constructs are

conceptually related, solutions with oblique rotation were examined. (The contentsof items representing perceived merchandise quality, service quality, and customersatisfaction scales are presented in the Appendix.)

The exploratory factor analysis results showed that merchandise quality, ser-vice quality, and customer satisfaction items produced three-factor solutions witheigenvalues greater than 1.0 in both calibration and validation groups. The three-factor solutions accounted for 69% and 66% of the variance, respectively, in thecalibration and validation groups. Results supported the a priori expected three-factor solution. That is, customer satisfaction, service quality, and merchandisequality items loaded heavily on the appropriate factor, while the cross-loadings

were relatively weak with one exception. One of the satisfaction items (satisfac-tion with the service) had a relatively high cross-loading on the service quality


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ranged from .56 to .93 with a median value of .70. The loadings for the validationgroup ranged from .54 to .85 with a median value of .69. Based on exploratoryfactor analysis results it was decided to keep the entire set of items for further

analysis.Next, a conrmatory factor analysis was performed using LISREL 8

(Joreskog & Sorbom, 1993) on the calibration group with the same set of 20items. The analysis was repeated for the validation group. The Appendix showsthe results for both groups.

With the exception of the 2-statistic, the overall t indices suggest thatthe hypothesized measurement model ts the data reasonably well in both groups( 2176 = 6,626; 6,517 and p = 00, NFI = .92; .92, NNFI = .91; .91, and CFI =.92; .92 for the calibration and validation group, respectively; Hu & Bentler, 1999).Researchers suggest that for sample sizes larger than 200, 2-tests are not reliablein assessing the overall goodness-of- t of a model (Cudeck & Henly, 1991). Co-ef cient alphas ranged from .88 to .93 indicating that the measures were highlyreliable. All factor loadings were signi cant (t -values were well above 2.0 and p < .01) and nearly all were above .70, which shows that the measures exhibitconvergent validity (Anderson & Gerbing, 1988). Discriminant validity was as-sessed by conducting a series of 2 difference tests using measures of each pairof constructs (Anderson & Gerbing, 1988; O Leary-Kelly & Vokurka, 1998). Thatis, for each pair of constructs, we rst tested a two-factor CFA model and thenwe imposed a single-factor solution. In each case, the single-factor model re-

sulted in a signi cantly larger (larger than 3.84 with 1 df ) 2

-value. This wasthe case for all pairwise tests in each group. Hence, when a single factor solutionwas imposed on the two sets of measures, the model t deteriorated signi cantly( p < .05). These results suggest that customer satisfaction, perceived service, andmerchandise quality exhibited strong discriminant validity.

Store-Level Aggregation of Individual Responses

Since the research model required store-level analysis, perceived merchandise andservice quality, and customer satisfaction scale items had to be aggregated to form a

single measure for each store. Concerns about the logicaland empirical justi cationforaggregating individual responses to theorganizational level havereceived a greatdeal of attention recently. The strongest conclusion is that individual responsesshould not be averaged to form organizational-level indices without conceptualand statistical support (James et al., 1984; Klein et al., 1994).

In our study, aggregation of perceived quality and customer satisfaction datato the store level can be justi ed on conceptual, operational, and statistical grounds.First, the study s conceptual focus is on the relationships of customer perceivedquality and customer satisfaction with store traf c and sales at the store level.Second, constructs were operationalized and measured appropriately to support

inferences at the store level. Instruments focused on perceived service quality, mer-chandise quality, and customer satisfaction at the store level and were premarked


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and marketing activities. Third, the empirical evidence presented below shows thatthe customer satisfaction and quality ratings provided by customers from eachstore exhibit adequate reliability to justify aggregation at the store level. Following

the approach used by Schneider et al. (1998), we computed r wg( j) statistics for thecustomer measures from each store. This index is a measure of interrater reliabilityfor each retail store. It compares the amount of variance observed in responses withwhat would be obtained if responses were random. The more consistent responsesare, the closer r wg( j) is to 1.0. The more random responses are, the closer r wg( j)is to zero (see James et al., 1984, for details). We obtained average r wg( j) valuesof .87 for merchandise quality, .91 for service quality, and .79 for customer satis-faction, and .95 across all variables. All of these statistics are greater than the .60cutoff recommended by James (1982) and compare favorably with results found inother studies. For example, Schneider et al. (1998) reported average r wg( j) valuesof .82.88 for customer variables measured in a study of bank branches.

These interrater reliability statistics provide empirical support for aggregat-ing customers responses at the store level and are consistent with the idea thatmerchandise quality, service quality, and customer satisfaction vary at the storelevel and represent phenomena that customers of a particular store can agree upon.Hence, for each group of respondents that was identi ed with a particular store, anaggregate score for each item was created by dividing the total score of the groupon that item by the number of respondents in the group. Stores with at least vecustomer responses were considered for the store-level analysis, which resulted in

an overall sample size of 1,100 stores. The data were randomly divided into twogroups of approximately equal size for cross validation.For each item (representing perceived service quality, merchandise quality,

and customer satisfaction constructs), store-level (aggregate)scoreswere computedas the sum of scores of the respondents identi ed with a particular store divided bythe number of respondents for that store. Store-level item scores were combinedto create composite scale scores (summated item scores for each construct dividedby the number of items in the scale). Correlations, means, standard deviations,r wgs and Cronbach s alphas (Cronbach, 1951) of the aggregated measures arepresented in Table 1. Results for the calibration and validation groups were similar.

The relatively high levels of customer satisfaction and quality perceptions are notsurprising, since the retailer dominates the markets in which it operates. Table 1also indicates that the payroll adjusted average customer growth was 5%, whileaverage sales growth was 19%. All of the correlations are signi cant ( p < .05)in both groups, although the strength of correlations between perceptual and storeperformance variables are modest.

TESTS OF THE PROPOSED MODEL AND HYPOTHESES

The proposed model in Figure 1 was rst tested using the calibration group. The

group covariance matrix was input to LISREL (Joreskog & Sorbom, 1993). Sinceour focus at this stage was on the structural relationships, we used the store-level


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Table 1: Means, standard deviations, and correlations of aggregate observed vari-ables (store level) a.

SAT SQ MQ CGR SGR r bwg

c

Customer Satisfaction (SAT) d 1.00 .73 .69 .14 .15 .79 .89 (.89)Service Quality (SQ) d .77 1.00 .78 .14 .15 .91 .94 (.95)Merchandise Quality (MQ) d .68 .80 1.00 .10 .16 .87 .92 (.91)Customer Growth (CGR) e .13 .12 .09 1.00 .52 N/A N/ASales Growth (SGR) e .09 .10 .13 .57 1.00 N/A N/ACalib. Group ( n = 547)

Mean 4.36 3.82 3.87 .05 .19SD .31 .29 .31 .10 .10

Valid. Group ( n = 553)

Mean 4.37 3.82 3.88 .05 .19SD .29 .30 .28 .10 .10

aAbove diagonal elements are calibration group and below diagonal elements are validationgroup correlations. All correlations are signi cant at the .05 level.bThe r wg values are for the entire sample of participating stores.cReliability coef cients ( ) for the validation group are in parentheses.dPerceptual variables, which are on a ve-point scale, higher scores indicating morefavorable responses.eCustomer Growth (CGR) and Sales Growth (SGR) are measured as percent increase (ordecrease) in sales and store traf c adjusted for payroll expenses for each store.

alpha (Cronbach, 1951) was used as an estimate of the reliability of each compositesingle indicator. The reliabilities and the composite score variances were used to seterror variances of the composite indicators to (1 )s2 and the factor loadings to = ( s2)

12 for testing the structural model, where is Cronbach s (1951) reliability

coef cient and s2 is the variance of composite scale (Bishop, Scott, & Burroughs,2000; Bollen, 1989; Joreskog & Sorbom, 1982). Because the two performancevariables (sales growth and customer growth) were single item measures to beginwith, the reliability estimates were obtained from a ML factor analysis of thesemeasures along with aggregate indicators of customer satisfaction, service and

merchandise quality. Communality estimates were used as estimates of reliability(Wanous & Hudy, 2001), and the error variances for sales growth and customergrowth measures were xed using the same procedure.

The results from the calibration group indicated that the proposed model tsthe data well and all hypothesized linkages in the model were signi cant( t -values >2.00). The results are summarized in Table 2. The overall model t was excellent onthe basis ofa number of t indicatorsas showninTable 2 ( 24 = 4.2, p = .382, NFI =1.00, NNFI = 1.00, CFI = 1.00, and RMSEA = .009). Of these t measures, theroot mean square error of approximation (RMSEA) is the most stringent t statistic,which measures the discrepancy per degrees of freedom between the covariance

matrixpredictedby themodel andsamplecovariance matrix. A RMSEA value < .05indicates close t (Joreskog & Sorbom, 1993, p. 124), which is the case with


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2 : C r o s s - v a l i d a t i o n t e s t s o f t h e s t r u c t u r a l m o d e l .

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Figure 2: Path diagram of the research model and cross-validation test results. a, b,c

aH5 states that this model ts the data better than a model in which SAT (MQ, SQ) (CGR, SGR), where SAT, MQ, SQ, CGR, and SGR are latent variablesof customer satisfac-

tion, merchandise quality, service quality, customer growth, and sales growth, respectively.Each latent variable is represented by a single indicator (composite single indicator in thecase of SAT, MQ, and SQ) as shown in the path diagram.bModel t statistics: 219 = 41.7( p = .002), SRMR = .04, NFI = .98, NNFI = .99, CFI =.99, RMSEA = .046 (Model t statistics for testing H5: 218 = 276.8 ( p = .00), SRMR =.05, NFI = .89, NNFI = .88, CFI = .90, RMSEA = .17). Variance explained ( R2) inSAT = .69, CGR = .08, and SGR = .06.cRegression coef cients ( 1, 2 , 3, and 4) are common metric completely standardizedestimates ( t -values are in parentheses) and they are based on two-group cross-validation(simultaneous analysis of calibration and validation groups). All regression coef cientsare signicant at the .05 level. The variances of the measurement errors and factor load-ings of the indicators were xed by using reliability information and variances of theindicator scores. These a priori set values were: 1 = .007, 2 = .005 , 1 = .011 , 2 =.0073 , 3 = .0067, and 1 = .96, 2 = .97, 3 = .51, 4 = .56.

Using the calibration group data, further tests were conducted by allowingperceived service quality and merchandise quality to directly in uence sales andcustomer growth, in addition to their indirect effects through customer satisfaction.Service and merchandise quality did not show a signi cant direct impact on either

of the outcome variables and adding these paths did not provide a signi cantimprovement in model t. Hence, the proposed model could not be rejected. AsTable 2 shows, almost identical results were obtained when the model was testedusing the validation group ( 24 = 7.9, p = .096, NFI = .99, NNFI = .99, CFI =1.00, and RMSEA = .042).

The two groups were analyzed simultaneously to subject the model to a strin-gent cross-validation test. The cross-validation results are presented in Table 2 andalso in Figure 2 as part of the path diagram of the research model. The resultsindicate that the proposed model ts the data well ( 219 = 41.7, p = .002, NFI =.98, NNFI = .99, CFI = .99, and RMSEA = .046) and all of the hypothesized

linkages are signi cant. The direct effects of service and merchandise quality onsales and customer growth were not signi cant. Further examination of Figure 2


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more than two-thirds of the variance in customer satisfaction ( R2 = .69). Cus-tomer satisfaction has a slightly stronger impact on customer growth than it hason sales growth (common metric regression coef cients were .28 and .24, respec-tively). Customer satisfaction accounts for 8% of the variation in customer growth( R2 = .08) and 6% of the variance in sales growth ( R2 = .06).

Since there is an alternative perspective in the literature about the causal orderof perceivedquality andcustomer satisfaction, a nal analysis was conductedto testH5 by switching the order between customer satisfaction and perceived quality. Inthis model both merchandise quality and service quality were posited as mediatorsbetween customer satisfaction and store performance variables. This alternativemodel was tested using both calibration and validation groups simultaneously. Thealternative model does not t the data well ( 218 = 276.8, p = 00, NFI = .89,NNFI = .88, CFI = .90, and RMSEA = .17) in comparison to the proposed model( 219 = 41.7, p = .002, NFI = .98, NNFI = .99, CFI = .99, and RMSEA = .046).Hence, the empirical results most strongly support theproposed model in this study.Collectively, these results indicate that the proposed model is viable and each oneof the ve specic research hypotheses is supported by the data.

DISCUSSION AND IMPLICATIONS

We examinedtheconsequencesof customer perceived service quality andmerchan-dise quality using store-level analysis of data representing 1,100 stores of a nationalretail chain. We adopted Bagozzi s (1992) appraisal affective response be-havior framework to study the relationships among service quality, merchandisequality, customer satisfaction, and customer and revenue growth at the store level.The proposed model, in which customer satisfaction completely mediates the rela-tionship between perceived quality and store performance variables, was supportedby the data using relatively stringent evaluation procedures. In addition, an alterna-tive model, in which customer satisfaction preceded both merchandise and servicequality, was not supported by the data. These ndings give further credence to thetheoretical proposition that perceived quality is an antecedent to customer satis-

faction and that perceived quality in uences store performance indirectly throughcustomer satisfaction.The ndings in this study are consistent with a number of previous studies

in terms of the causal sequence of quality, customer satisfaction, and customerbehavior (Cronin & Taylor, 1992; Cronin et al., 2000; Dabholkar et al., 2000; Daset al., 2000; Gotlieb et al., 1994; Hand eld, Ghosh, & Fawcett, 1998). However,prior research primarily relied on customers behavioral intentions or self-reportmeasures of performance. Very few prior studies have used objective performancevariables (e.g., Fornell et al., 1996; Ittner & Larcker, 1998) in the context of a soundtheoretical model. This study moved one step further by using objective data to link

quality to actual customer behavior in the form of store traf c and sales growth.The proportion of variance explained in the store performance variables is


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many other factors including location, competitive intensity, and seasonal uctua-tions in demand. However, even without incorporating these additional factors, theexplained variance in store performance translates into a large number of customers

and signi cant revenues for the retail organization studied. Hence, the model pro-vides a simple but informative tool for managers to gauge progress and determinethe impact of improvement efforts on performance. The present study also makes acontribution by simultaneously examining the relative in uence of perceived ser-vice quality and merchandise quality on store performance. The ndings indicatethat service quality plays a more critical role in in uencing both customer sat-isfaction and, consequently, store performance than does perceived merchandisequality. However, we refrain from making generalizations to other retail settingsfollowing Ittner and Larcker s (2003) suggestion that each organization must de-termine the effects of quality and customer satisfaction on its performance basedon its own unique context instead of using off-the-shelf templates or blind faithabout the impact of these variables on performance.

The store performance measures used in this study, particularly sales growth,can also be viewed as labor productivity measures, since both customer countand sales gures were adjusted by payroll expenses for each store. The fact thatboth quality and customer satisfaction are positively related to labor productiv-ity provides additional insights and raises new questions. Questions have beenraised about the compatibility of pursuing productivity and customer satisfactionobjectives simultaneously in some industry sectors. For instance, Anderson, For-

nell, and Rust (1997) found that furniture stores, banks, and airlines that earnedgreatest average return on investment (ROI) were those with low productivity andhigh customer satisfaction, while clothing stores with high ROI showed high pro-ductivity and high customer satisfaction. The retail organization in the presentstudy falls into high productivity and high customer satisfaction category and itsROI is well above industry average. This means that some retail organizations canachieve higher ROI by pursuing a strategy of high productivity and high customersatisfaction. The present study demonstrates the importance of establishing the ex-istence and strength of linkages between perceived quality, customer satisfaction,and productivity outcomes.

Managerial Implications

Today it is almost impossible to nd an organizational mission statement that doesnot mention a commitment to quality and customer satisfaction. Most companiesengage in some sort of measurement to gauge their customers perceptions of qual-ity. Since perceived quality and customer satisfaction may be considered leadingindicators of nancial performance (Ittner & Larcker, 1998), systematic and validmeasurement is critical. More importantly, it is essential for each organization toestablish empirical linkages between these indicators and nancial performance

measures. Our results provide support for the impact of customer perceptions of quality and customer satisfaction on retail store performance, measured as store


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be more useful as a guide to improvement efforts than data re ecting consumerperceptions of quality without reference to competitive offerings. The use of aquality measure that makes explicit reference to competition should help an or-

ganization get where it needs to be ahead of the competition. The service andmerchandise quality measures developed in this study may prove highly practicalfor a variety of retailers in terms of content and the number of items.

Sales growth and customer growth measures appear to be very appropriateas intermediate term performance measures for evaluating quality improvementefforts. Such evaluations should reveal the relative importance of merchandiseand service quality in in uencing customer satisfaction and store performance.Repeated collection of customer data should allow calibration of model parametersand long-term assessment of the impact of quality on pro tability measures inaddition to growth measures. In a retail business where tangible goods constitutea part of the offerings, the importance of merchandise and service quality maychange over time as a result of competitive actions and the introduction of newproducts. Such a systematic approach helps managers to detect changes in therelative contributions of service and merchandise quality on customer satisfaction.

Assuming perceived merchandise quality remains high and continues to bea signicant predictor of performance, the retailer has an opportunity to intro-duce store brands and capitalize on its quality reputation. The introduction of quality store brands, along with national brands, can help retailers to differentiatethemselves, strengthen store loyalty andstore performance (Corstjens & Lal, 2000).

Similarly, service quality should be monitored over time to make the necessaryadjustments in training, empowering, and rewarding frontline employees. For in-stance, in the present study customers rely on high quality service and expert advicefrom store employees about the proper selection and use of the merchandise offeredby the retailer. As customers become more knowledgeable about various goods, theneed for expert advice may diminish. This is already happening in the consumerelectronics sector. As a result, retailers such as Circuit City Stores began replacinghighly knowledgeable but costly sales associates with self-service technologiesand a smaller number of lower paid hourly oor employees ( Retail Worker News ,Wednesday, February 5, 2003). Even if all customers are knowledgeable and do

not need any advice for choosing merchandise, some customers may still expecthigh quality service to satisfy their needs for interpersonal contact (Dabholkar& Bagozzi, 2002). Hence, careful and routine recalibration of the model basedon customer segments with different service expectations can provide managerswith valuable signals regarding the proper balance between standardization versuspersonal attention to customers.

Future Research Directions

Our results support suggestions in the literature that customer satisfaction has a di-rect inuence on store-level performance outcomes (Ittner & Larcker, 1998; Oliver,

1997) and fully mediates the relationship between perceived quality and perfor-mance. The literature also suggests that perceived quality may have a signi cant


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While the appraisal affective response behavior framework appearsto work well even when customer responses are used in an aggregate fashion,the present study did not include behavioral intentions, a critical element of the

model, which mediates the relationship between customer satisfaction and actualbehavior. Zeithaml et al. (1996) highlighted the importance of incorporating acomprehensive behavioral intentions construct in the study of perceived qual-ity. Rede ning the intentions construct to include multiple dimensions (loyalty,switching, and willingness-to-pay more) might provide additional insights regard-ing the underlying process by which perceived quality in uences organizationalperformance.

The use of comparative measures of perceived quality (e.g., using compe-tition as a comparison standard) should be examined further. One advantage of these comparative measures might be that they provide a context for a customer sdecision to choose one retailer over another. Customers rated the store s serviceand merchandise quality relative to its competitors, and they did this with the listof competitors names in front of them. The management team of the sponsoringorganization insisted on this approach, and there is prior research favoring the useof competitors as a standard of comparison (Gale, 1994; Olsen, 2002). Results of a recent study by Dabholkar et al. (2000) demonstrated the superiority of directmeasures over difference scores. The direct measures of quality relative to com-petition used in our study exhibited good psychometric properties. However, thisapproach should be compared with other approaches currently in use.

The present study provided conceptual and empirical evidence to supportaggregation of individual responses for each store. Not only were the internal con-sistency reliabilities acceptable, but we also found within-store agreement indices(r wg) met the criteria for aggregation. The issue of aggregation for organizationalor business unit level of analysis is an important one for future research, especiallywhen attempting to relate individual responses to organizational outcomes.

The measurement of service quality as a one-dimensional construct in thepresent study may be considered a limitation in light of current conceptualizationsof it as a multidimensional construct (e.g., Brady & Cronin, 2001b; Dabholkar,Thorpe, & Rentz, 1996; Parasuraman, Zeithaml, & Berry, 1988). Service quality

items in the present scale represent higher levels of abstraction as opposed to morespecic lower level attributes. For instance, the item quality of people workingin the store could be represented with a number of lower level attributes suchas courtesy, expertise, willingness to help, knowledge, appearance, and so on.Similarly, other items could also be expanded into more speci c attributes. Theresulting measure could produce a number of dimensions to allow comparisonswith current conceptualization and measurement of service quality. Future researchshould be able to provide additional insights into the level of granularity requiredfor measuring perceived service quality.

The store performance variables in the current study were based on store-level data for consecutive quarters, which were measured following the completionof the customer survey. These performance variables re ect change from one time


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APPENDIX

Maximum Likelihood Con rmatory Factor Analysis of Customer Measures a

StandardizedCustomer Satisfaction ( = .89, .88)b Loadings c t -Values c

How satis ed have you been with XYZ? (verysatisedvery dissatis ed)SAT1. Your overall satisfaction .92 (.91) 87.9 (86.0)SAT2. Products you have purchased .82 (.80) 73.3 (70.5)SAT3. Services you have received .83 (.84) 75.5 (75.1)

Perceived Service Quality: ( = . 93, .93)c

Please rate XYZ relative to its competitors (XYZ muchbetter much worse)PSQ1. Always improving their customer service .78 (.78) 69.0 (69.5)PSQ2. Quality of the people working in the store .57 (.59) 76.4 (47.7)PSQ3. Providing customer service faster than in the past .77 (.77) 67.8 (68.0)PSQ4. Providing accurate information to you .80 (.79) 71.8 (70.4)PSQ5. Quality of the service provided .78 (.76) 68.7 (66.1)PSQ6. Willing to try new things in the way to do business .79 (.78) 70.9 (68.9)PSQ7. Having your best interest in mind .76 (.76) 66.6 (66.5)PSQ8. Quality of the store .84 (.83) 78.0 (76.2)PSQ9. Living up to the promise they make in their

advertising.74 (.74) 64.4 (64.3)

PSQ10. Informing you about new products and services .64 (.64) 52.9 (52.9)PSQ11. Actively seeking your feedback about the way

they do business.76 (.77) 66.7 (68.0)

Perceived Merchandise Quality: ( = .91, .92)b

Please rate XYZ relative to its competitors (XYZ muchbetter much worse)PMQ1. Variety of products to choose from .67 (.65) 55.4 (53.4)PMQ2. Being able to get the products you need .81 (.81) 72.1 (72.1)PMQ3. Overall quality of products .85 (.85) 78.6 (77.9)PMQ4. Having products in stock .82 (.83) 74.3 (75.3)PMQ5. Quality of the product warranties .75 (.75) 64.4 (64.3)PMQ6. Carrying name brands .79 (.77) 69.9 (67.6)

Fit Statistics (Model df = 176) 2 = 6626 (6517)NFI = .92 (.92)NNFI = .91 (.91)CFI = .92 (.92)

aCustomer measures are scored on a ve-point scale, where a higher score indicates a morefavorable evaluation.bReliability coef cient ( ) for the calibration group is listed rst, followed by the validationgroup.cValidation group results are in parentheses.

EminBabakus is professor of marketing in the Department of Marketing & Supply


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Business Research, and Journal of Advertising Research, among others. He serveson the editorial review boards of several journals.

Carol C. Bienstock is on the faculty of the College of Business & Economics inthe Department of Management & Marketing at Radford University. Her researchhas been published in the International Journal of Research in Marketing, Journalof Business Logistics, Supply Chain Management Review, International Journal of Physical Distribution and Logistics Management, Journal of Services Marketing, Journal of the Academy of Marketing Science, Transportation Journal, Quarterly Journal of Electronic Commerce, and Business Horizons. She is also coauthor, withJohn T. Mentzer, of the book Sales Forecasting Management.

James R. Van Scotter is assistant professor in the Information Systems & De-cision Sciences Department at Louisiana State University. His research has beenpublished in the Journal of Applied Psychology, International Journal of ElectronicCommerce, and Human Resource Management, among others.


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Documents

Linking Perceived Quality and Customer Satisfaction to Store Traffic and Revenue Growth