Determinants of retail patronage: A meta-analytical perspective

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Journal of Retailing 82 (3, 2006) 229–243

Determinants of retail patronage: A meta-analytical perspective�

Yue Pan a,∗, George M. Zinkhan b,1

a 812 Miriam Hall, Department of Management & Marketing, University of Dayton, Dayton, OH 45469-2271, United Statesb Department of Marketing, Terry College of Business, University of Georgiam, Athens, GA 30602, United States

Accepted 11 November 2005

bstract

The retail patronage idea includes such key concepts as store choice and frequency of visit. In this study, the authors synthesize previousmpirical studies through a formal, critical review of retailing literature. The meta-analysis suggests that various predictors (e.g., service,
roduct selection, quality) are strongly related to shoppers’ retail choice, whereas others (e.g., store attitude, store image) are importantntecedents of shopping frequency. However, the relationships between the predictors and retail patronage vary according to the studyharacteristics (e.g., experimental vs. other designs). The authors offer implications for retailing research and practice.
2006 New York University. Published by Elsevier Inc. All rights reserved.

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eywords: Retail patronage; Meta-analysis; Store choice; Frequency of vis

Introduction

An understanding of patronage behavior is a critical issueor retail managers because it enables them to identify andarget those consumers most likely to purchase. Reflectinghis managerial need, one research stream has focused onxplaining retail patronage with respect to various elementse.g., store, frequency of visit, store choice). The practical andonceptual importance of this topic has been underscored byhe substantial volume of studies published in leading jour-als. Decades of research efforts have produced a rich body
f empirical data associated with a variety of research designsnd study contexts. However, a review of the literature revealsarked differences in both the direction and the magni-
� This article is based on the first author’s dissertation and was the 2002MS Mary Kay Doctoral Dissertation Competition winner. The authors

cknowledge the helpful input of Don Lehmann, David Henard, Randyparks, and Ashutosh Dixit. They also thank the editors and the anonymousR reviewers for their valuable comments on previous drafts of this article.his work was supported by summer research grants from the University ofayton.∗ Corresponding author. Tel.: +1 937 229 1773; fax: +1 937 229 3788.

E-mail addresses: [email protected] (Y. Pan),[email protected] (G.M. Zinkhan).1 Tel.: +1 706 542 3757; fax: +1 706 542 3738.

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022-4359/$ – see front matter © 2006 New York University. Published by Elsevieoi:10.1016/j.jretai.2005.11.008

ude of the effects for the same predictor variables acrosstudies.

Consider “store price level” as an example. Although it isell documented that low prices accelerate retail purchases

e.g., Tigert 1983; Walters and Rinne 1986), some researchas found a positive relationship between monetary pricend perceptions of product quality (e.g., Dodds et al. 1991;ao and Monroe 1989). Shoppers with limited sources ofiagnostic information tend to make more use of price asquality cue (Rao and Monroe 1988). Following this logic,

ome consumers may choose a retailer that offers high-pricedroducts to enhance their expected quality (Tellis and Gaeth990).

In addition to conflicting evidence about the directionf the relationship, the findings pertaining to the strengthf the relationship also vary. For example, though someesearchers report no evidence of a significant relationshipetween low-price offerings and retail choice (e.g., Lumpkinnd Burnett 1991–1992), others suggest a significantly posi-ive relationship (e.g., Thelen and Woodside 1997). Becausef heterogeneous findings and diverse study conditions in the
xtant empirical literature, the relationship between variousredictors and a shopper’s retail patronage are unclear, whichn turn, complicates our efforts to develop a comprehensivenderstanding of what affects shoppers’ decisions to patron-
r Inc. All rights reserved.

mailto:[email protected]

mailto:[email protected]

dx.doi.org/10.1016/j.jretai.2005.11.008

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30 Y. Pan, G.M. Zinkhan / Journa

ze a retail outlet. Given this limitation, it is somewhat difficulto translate academic findings into a form that is useful foretailing managers.

Despite the importance of retail patronage, no compre-ensive work has attempted to assess the general findingscross academic studies. We seek to fill that void by conduct-ng a meta-analysis of empirical findings on the predictorsf retail patronage. Our dependent variable, retail patronage,ncludes two dimensions: (1) store choice (i.e., a consumer’shoice to patronize a particular store) and (2) frequency ofisit (i.e., how often a shopper patronizes that store). Such aesearch effort appears useful for at least two reasons. First,e seek to reconcile inconsistent findings and establish theeneralizability of the relationships between retail patronagend its correlates. Second, research on retail patronage haseen conducted in various methodological contexts, yet nottempt has been made to evaluate the robustness of the effectscross study conditions. Here, we attempt to explain differ-nces in the results of previous studies by investigating var-ous study characteristics that could moderate the effects ofnterest.

In terms of organization, we begin by presentingntecedents to retail patronage that are frequently reportedn prior research, followed by a discussion of the theoreticalackground of these variables. Then, we discuss the samplerame for the study. We subsequently present the researchethod and data analysis procedure, followed by a discus-

ion of the results. We conclude with some implications anduggestions for further research.

Antecedents of retail patronage: theoretical foundations

A review of the literature reveals 16 antecedents reportedrequently enough (n ≥ 4) to be included in a formal meta-nalysis. To organize the large number of antecedents, weategorize them into three groups: (1) product-relevant fac-ors, which pertain to product features and attributes, such asroduct quality and price; (2) market-relevant factors, whichertain to the retailer of interest, such as the service pro-ided by the store; and (3) personal factors, which pertaino consumer characteristics, such as demographics. To checkhe coding quality of this categorization, two independentnvestigators who were familiar with retail patronage liter-ture placed specific predictors within each category andeviewed the final taxonomy for completeness and appropri-teness of classification. For the 16 items coded, the overallaw intercoder reliability was 93.8%. The coders resolved anyisagreements by discussing the terms until the team arrivedt a consensus. The three categories of determinants serve tosolate individual, product, and seller differences and reducehe large number of independent variables to a more suc-
inct hierarchical model. Note that it is beyond the scope ofhis study to build a holistic theory of retail patronage. Werganize the variables around these three categories and testelevant hypotheses using meta-analytical integration.
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tailing 82 (3, 2006) 229–243

elationship between product-relevant factors and retailatronage

roduct qualityConsumers perceive the quality of a product differently

epending on the store type from which the purchase is madeDarden and Schwinghammer 1985). Furthermore, a con-umer’s perception of the quality of a store’s merchandiseelates to the patronage of that store (Darley and Lim 1993;acoby and Mazursky 1985; Olshavsky 1985). As an impor-ant component of store evaluation, merchandise quality has

positive link to merchandise value (Grewal et al. 2003).n brief, merchandise determines a retailer’s reputation andnfluences consumers’ choice at stores.

1. Perceived product quality is positively related to retailatronage.

riceShopping channels are significantly different in terms of

he general price levels for products sold. A higher price rep-esents a monetary measure of immediate costs, which leadso a reduced willingness to buy (Dodds et al. 1991; Waltersnd Rinne 1986). Low prices, in the form of either price pro-otions or general price levels, can create store traffic and

ncrease category sales. However, consumers’ responsive-ess to low prices may be heterogeneous. Consumers mayse under uncertainty another choice strategy, what Tellisnd Gaeth (1990) call “price-seeking,” in which consumershoose the highest-priced brand to maximize their expecteduality. This theory posits a positive relationship betweenonetary price and perceptions of product quality (cf. Dodds

t al. 1991; Kerin et al. 1992; Rao and Monroe 1989). How-ver, the extent to which consumers use price as an indicatorf quality depends on the availability of alternative diag-ostic information (Rao and Monroe 1988), in that whenore extrinsic cues (e.g., brand, store name) are available,

he price–quality relationship weakens (Dodds et al. 1991).lthough price has a positive effect on perceived quality, itas a negative effect on perceived value and willingness touy (Dodds et al. 1991).

2. The general price level in a store is negatively relatedo retail patronage.

roduct selection/assortmentProduct selection (or assortment) is defined as “the number

f different items in a merchandise category” (Levy and Weitz995, p. 30). As a major retailer descriptor, product selec-ion contributes significantly to the explanation of patronagef alternative retail centers (Arnold et al. 1983; Craig et al.984; Koelemeijer and Oppewal 1999; Louviere and Gaeth987). The breadth (number of brands) and depth (number
f stockkeeping units) of an assortment offered in a shop-ing center helps retailers cater to the heterogeneous tastesf their patrons (Dhar et al. 2001). Not only can greater vari-ty help a retailer attract more consumers, it also can entice

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Y. Pan, G.M. Zinkhan / Journa

hem to make purchases while in the retail center. A wideelection of products also can minimize the perceived costse.g., travel time, effort) associated with each shopping tripnd ease the shopping task (e.g., by enhancing comparisonhopping); in other words, a retailer that offers greater vari-ty in product categories can improve shopping conveniencend make it easier for consumers to combine their visits toifferent stores (Dellaert et al. 1998). An empirical study bytassen et al. (1999) shows that the assortment decision in atore is as important, if not more important, than other keyariables such as price.

3. There is a positive correlation between product assort-ent and retail patronage.

elationship between market-relevant factors and retailatronage

onvenienceA convenience orientation is a key benefit that shoppers

eek in the modern environment. In this sense, consumers’erceptions of convenience (e.g., opening hours, location,arking) will have a positive influence on their satisfactionith the service (Berry et al. 2002). Consumers’ perceived

xpenditure of time and effort interacts to influence theirerceptions of service convenience (Berry et al. 2002), andetail facilities can be designed to affect those time and efforterceptions. For example, a central location can reduce theransaction costs associated with shopping (e.g., transporta-ion cost, time spent). The law of retail gravitation (Reilly931) suggests that the potential attraction of a shoppingenter should be assumed to be inversely proportional to theriving time from a shopper’s home to the center. The moreecent central place theory (Craig et al. 1984) suggests thatentral business districts and regional shopping centers thatffer a large agglomeration of goods and services attract cus-omers from greater distances than neighborhood centers thatffer fewer goods and services. Empirical evidence supportshese theories by showing that easy accessibility has a highorrelation with shopping center selection (Bellenger et al.977). In addition to a convenient location, other conveniencencentives provided by retailers, such as longer operatingours or ample parking, can draw patrons to a store (Hansennd Deutscher 1977–1978).

4. Shopping convenience (opening hours, location, andarking) provided by a retailer increases retail patronage.

ervice qualityPrevious studies have found a direct link between ser-

ice quality and patronage intentions (e.g., Baker et al. 2002;irohi and McLaughlin 1998; Zeithaml and Berry 1996). For
xample, Finn and Louviere (1990) find that different apparelhopper segments tend to choose shopping centers that theyssociate with different combinations of features. Shoppingenters that provide good service and a wide selection, but
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tailing 82 (3, 2006) 229–243 231

ess emphasis on low prices, are more likely to fall into shop-ers’ consideration set (i.e., retail alternatives a consumers aware of and evaluates positively). In Malhotra (1983)hreshold model of store choice, service is one of the fivedentified salient characteristics (along with variety and selec-ion, acceptable prices, convenience of location, and physicalacilities).

5. Service quality is positively related to retail patronage.n particular, higher service quality is associated with higheretail patronage intentions.

riendliness of salespeopleRetail stores offer a chance for human interactions. Some

onsumers enjoy talking to and socializing with others dur-ng a shopping visit and seek a social experience outside theome (Tauber 1972). These people generally experience atrong motivation to associate themselves meaningfully withroups of “kindred spirits” to reduce feelings of boredom andoneliness. To cope with and alleviate such feelings of lone-iness, people pursue various strategies, including shoppingRubenstein and Shaver 1980). The desire for human inter-ction thus may drive some shoppers to stores in which theynd salespeople friendly and communicative.

6. There is a positive correlation between the friendlinessf salespeople and shoppers’ retail patronage.

tore imageHere, store image is defined as “the way in which the store

s perceived by shoppers.” Image formations result in predis-ositions that generally guide patronage (Darley and Lim993), including shopping trips, expenditure behavior, andtore loyalty (Arnold et al. 1983; Sirgy and Samli 1985). Thempressions shoppers form of stores have a significant impactn their store patronage. The retail store environment offers aultitude of stimuli that can serve as cues to consumers look-

ng for information-processing heuristics (Baker et al. 1994).hese sensory search attributes, particularly visual cues about

he store, have a significant impact on consumers’ patron-ge behavior because consumers tend to make judgmentsbout stores on the basis of their subjective impressions (e.g.,mbient design, social factors; Baker et al. 1994). Empiricalvidence suggests that image perceptions account for a veryigh proportion of the variance in retail patronage (Finn andouviere 1996; Kasulis and Lusch 1981).

7. Store image is positively related to retail patronage.

tore atmosphereUnlike store image, which involves shoppers’ percep-

ions, store atmospherics deal strictly with the physical store
ttributes. Research on mall shopping has revealed that manyonsumers are prone to make a decision about where tohop on the basis of their attitude toward the shopping cen-er environment (Finn and Louviere 1990, 1996; Gentry

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nd Burns 1977). For example, recreational shoppers whonjoy shopping as a leisure activity may shop impulsivelynd place higher importance on store decor (Bellenger andorgaonkar 1980). Lambert (1979) similarly suggests stores

hould provide rest areas and an appropriate store tempera-ure. Arousal induced by the store environment intensifiesoth pleasure and displeasure, such that time and spend-ng behavior increase in pleasant environments and decreasen unpleasant environments (Donovan et al. 1994). More-ver, shoppers’ evaluations of the store’s atmosphere affectheir perceptions of value and their store patronage intentionsGrewal et al. 2003).

8. Store atmosphere relates to retail patronage behavior,uch that shoppers’ perceptions of pleasant store atmospher-cs lead to greater retail patronage intentions.

ast checkoutThe time pressures that many people experience are hav-

ng a major effect on consumer behavior; they perceiveheir discretionary time available as insufficient to accom-

odate all their desired uses of it. The results are con-inual choices among various activities and the pursuit offficiency-producing behaviors. Retail stores are devotingore resources to time-saving services, such as fast checkout

Lambert 1979). Time savings for consumers are readily rec-gnized and therefore likely to influence their retail choice.

9. Checkout speed is positively related to retail patronage.

elationship between personal factors and retailatronage

emographic variablesA considerable body of empirical research on shopping

ehavior suggests that consumer demographic variables maye related to retail store patronage (e.g., Bellenger et al.976–1977; Korgaonkar et al. 1985; Samli 1975). However,o consensus exists about the relationship between shop-ers’ demographic profiles and their patronage behavior.or example, in their study of department store shoppers,rask and Reynolds (1978) find that frequent patrons tend

o be slightly younger, better educated shoppers with higherncomes. However, Roy (1994) argues that young people,acing greater constraints on their time, may be restrainedrom frequently visiting a retailer. Empirical studies of shop-er motivations (Westbrook and Black 1985) also identifypredominantly older age segment that derives satisfaction

rom aspects of shopping, such as negotiation with salespeo-le and an affiliation with other shoppers. Older shoppers mayhop more frequently if they view shopping as a recreationalctivity.

Conflicting views also emerge with respect to income.ccording to Goldman (1977–1978), low-income consumers

end to have lower marginal opportunity costs for their time,n that the potential benefits of comparison shopping are

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tailing 82 (3, 2006) 229–243

ikely to be of greater importance to them. Therefore, theyight be motivated to shop around and thus be less store

oyal than high-income consumers. Levy (1966), however,tates that low-income women may like to go shopping justo have a reason to get out of the house. Because higher-ncome consumers are more likely to visit a mall only whenhey have to and lower-income people are more likely to shopor recreation, there might be a negative correlation betweenncome and retail frequency of visit.

10a. Women tend to visit a retail outlet more frequentlyhan men.

10b. Frequent patrons tend to be older than less frequentatrons.

10c. Frequent patrons tend to have lower incomes thaness frequent patrons.

tore/store-type attitudeAccording to the theory of planned behavior (Ajzen 1985),

behavioral intention (i.e., decision) is partially determinedy the person’s attitudes, which means that consumers’ atti-udes toward retail stores likely play a key role in their choicef shopping modes. When a consumer holds a general attitudeoward a store (or store type), that attitude is readily acces-ible and probably will have a direct effect on the person’store-specific quality perceptions (Bauer and Greyser 1968;

acKenzie and Lutz 1989), as well as spillover effects ontore patronage through the process of affect transfer (Darleynd Lim 1993; Lutz 1985). Several empirical investigationsrovide support for the positive relationship between attitudend patronage (e.g., Eastlick and Liu 1997; Korgaonkar et al.985).

11. The general attitude toward a store or store type isositively related to retail patronage.

otential moderating effects

Sultan et al. (1990) indicate that four broad categoriesf characteristics often account for systematic differencescross correlations: measurement method, research context,stimation procedure, and model specification. Because ournits of analysis are bivariate correlations that are unaf-ected by the estimation procedure or model specification,e seek systematic differences in the study characteristics.esearch on retail patronage has been conducted in a varietyf contexts with different measurement instruments. Differ-nces in method and research context also could contribute

n our investigation, we examine five potential moderators –ype of scale used, type of sample, study design, shopping

ode, and product type – to assess their impact on sampleomogeneity.

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ulti- versus single-item scaleSingle treatments with low reliability can drastically atten-

ate effect-size estimates and decrease precision (Fern andonroe 1996). The use of multiple-item scales, in contrast,

nhances measurement reliability. Therefore, this practicehould provide stronger relationships than single-item mea-ures can (Peter and Churchill 1986).

tudent versus nonstudent sampleThe composition of samples also has the potential to affect

he relationships studied. The use of student samples in rela-ional research can lead to restricted ranges and attenuatedffect sizes (Pedhazur and Schmelkin 1991) because a homo-eneous sample likely will respond similarly to questionnairetems, which limits the response variation across the range ofcale values (Fern and Monroe 1996). This limitation resultsn a bias toward stronger effects than would be found amongonstudent samples. The heterogeneity of the nonstudentample, in contrast, increases error variance, which attenu-tes the magnitude of effect and possibly underestimates theffect size (Fern and Monroe 1996). Moreover, the impact ofntecedent variables on retail patronage may differ betweentudent and nonstudent samples because of inherent differ-nces in shopping behavior between the two samples.

xperimental versus nonexperimentalThe use of experiments also may represent a poten-

ial moderator variable. A carefully conceived experimentalesign enables a researcher to assign subjects randomly toonditions and exercise more control over the variables underonsideration, which in turn generates less error variance inhe denominator of the effect size and produces larger effectizes. In addition, the exclusion of potential confounds mayead to seemingly stronger relationships in an experimentaletting compared with a nonexperimental one.

tore versus nonstore shopping modeThe shopping mode likely will moderate the relation-

hip between retail patronage and its correlates. That is,tore patronage in a traditional bricks-and-mortar store for-at might differ from nonstore retailing in various ways.or example, a consumer who chooses a traditional retailerver an e-tailer (or catalog) may attach more importance toustomer service, and customer service is more accessiblen a store (e.g., helpful salespeople, easy return of defec-ive products). Moreover, the physical facilities (e.g., parkingacilities, convenient location, hours of operation) providedy a bricks-and-mortar retailer should not carry equal weightor a nonstore retailer.

ype of goodShopping patterns may vary for different product types.

or example, for specialty goods that demand a considerable
mount of shopping effort, consumers are more likely to relyn salespeople’s expertise to help them compare among alter-atives, which would increase the effect of service quality onetail patronage. In contrast, for convenience goods that peo-
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tailing 82 (3, 2006) 229–243 233

le buy frequently with minimal shopping effort, purchases often characterized by inertia, which may lead shoppers tognore or overlook the service quality provided. The relation-hip between price and patronage also might be moderated byroduct type; consumers may choose a high-priced retailero enhance the expected quality of a product when they knowhe price better than they do the quality (Tellis and Gaeth990), such as when they shop for specialty goods.

Method and results

ampling frame

We chose those articles that shared the same criterionariable: retail patronage. Our review of empirical workn this area reveals that this construct is often operational-zed as either shoppers’ store choice or shopping frequency.or example, some authors focus on shoppers’ store patron-ge (intention or behavior) choice (e.g., Grewal et al. 2003;enhove et al. 1999; Woodside and Trappey 1992), whereasthers examine shoppers’ repeated patronage in a store overperiod of time (i.e., shopping frequency) (e.g., Darley

nd Lim 1993; Korgaonkar et al. 1985). Therefore, oureta-analysis concentrates on these two dimensions of retail

atronage; we define store choice as the likelihood that ahopper will patronize a retailer and shopping frequency ashe number of times a shopper patronizes a retailer during aiven period of time.

We identified the studies through the following searchrocedure: (1) an initial keyword search in business and non-usiness databases, including ABI/Inform, PsycInfo, Socio-ogical Abstracts, Social Science Citation Index, and Socialciences Abstracts; (2) a search through listings of con-erence proceedings in the “Papers First” database and thedvances in Consumer Research online proceedings; (3) an

nteractive search of the references from relevant articlesdentified from the keyword search until no new referencesould be identified; and (4) letters sent to 165 authors knownor their work on retail patronage, in which we requested theirublished or unpublished studies on this topic.

Our search did not include those studies that examinedatronage intentions for a specific product (e.g., product priceffects a shopper’s intention to buy the product; Dodds et al.991; Grewal et al. 1998), because our dependent variable ishe patronage choice/intentions toward a retailer. Of the 165uthors we contacted, 31 responded to our inquiry, and theseesponses resulted in one usable study. In total, we identi-ed 80 studies that reported one or more antecedents of retailatronage. Among the 80 studies, 35 examine idiosyncraticelationships that pertain to a specific shopping mode (e.g.,nternet experience as an antecedent to online shopping inten-

urther discretion. The remaining 45 studies, which we reportn Appendix, represent empirical work in business, psycho-ogical, and sociological literature and, we believe, a fairlyell rounded set of studies on this topic.

234 Y. Pan, G.M. Zinkhan / Journal of Retailing 82 (3, 2006) 229–243

Table 1Effects reported in the studies

Predictor Number of effects reported Range of reported effects (r) Cumulative n

Product-relevant factorsLow pricea 14b/19c −.01 to .702 4302Qualitya 16/18 −.05 to .90 4443Selectiona 14/14 .102 to .92 3272

Market-relevant factorsConvenient parking facilitiesa 8/9 .01 to .53 449Convenient locationa 21/22 −.05 to .76 934Convenient opening hoursa 8/9 .06 to .56 449Friendliness of salespeoplea 11/13 −.02 to .62 1206Servicea 14/17 .045 to .95 3802Fast checkouta 9/11 .09 to .712 643Store atmospherea 3/5 .016 to .55 970Store imaged 12/20 .04 to .468 1445

Personal factorsStore/store-type attituded 13/13 .111 to .45 1271Genderd 4/4 .139 to .19 1393Incomed 2/11 −.13 to .198 2514Aged 5/11 −.104 to .12 2275

a Predictor variables for store choice.b Number of statistically significant (at α = .05) effects reported.c

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In Table 1, we provide a complete taxonomy of predictorsf retail patronage and the range of effects (r) reported in theriginal studies. It reveals marked differences in the direction,agnitude, and statistical significance of the effect sizes for

he same pairwise relationships across studies. Take “storerice level” as an example. The range of reported effects forhis relationship across all studies spans −.01 to .702. Amonghe 19 studies that examine this relationship, 14 (74%) showtatistically significant results. Only 2 (10.5%) report rela-ively large effect sizes (r > .5), whereas a few more (5) reportelatively small effect sizes (r < .1). We note that these appar-nt inconsistencies might be due to nonsubstantive factorse.g., sampling error, measurement error, deviation from con-truct validity).

omputation and coding

We define the effect-size estimate as the degree to whichhe predictor–/criterion–variable relationship appears in theopulation of retail patronage research. Although correla-ions are the most common metrics used in our studies,

any also report F, t, and chi-square statistics. To exam-ne the strength of the relationship, we convert variousummary statistics (e.g., F, t, Z, chi-square) to the com-on correlation coefficient metric, r, following the formu-

as suggested by Hays (1973), Kendall and Stuart (1967),osenthal (1991), and Wolf (1986). Furthermore, some stud-

es report several effect-size estimates for one predictor usinghe same subjects. To obtain a single result for the mul-iple correlated results from a single study, Wolf (1986)uggests using the average of the statistics that examine

mF(r

he same relationship. For correlational relationships, thisrocess typically involves transforming the raw Pearson cor-elation coefficients (r) into an associated Z statistic andhen transforming it back to r. In the studies examined here,99 raw test statistics yielded 150r that we coded into ouratabase.

To avoid the possibility that smaller or less representa-ive samples are overrepresented in the analysis, we weighthe effect size r using the relevant sample size information.n addition to considering sampling errors, we also correcthe effect sizes for attenuation due to measurement errorsf scale reliability measures are available for the predictornd/or criterion variables. File drawer N indicates the num-er of studies that confirm the null hypothesis that woulde needed to reverse a conclusion that a significant rela-ionship exists, which we estimated for a significance levelf .05. For computational details, see Hunter and Schmidt1990).

In Tables 2 and 3, we summarize the number of studies,eighted mean observed correlation (sample-size adjustedean), weighted mean correlation corrected for attenuation

rom measurement error (reliability adjusted mean), totalariance, sampling error variance, reliability variation vari-nce, remaining variance, 95% confidence interval for eachairwise relationship represented by multiple study effects,le drawer N, and results of homogeneity tests. Conven-

ional, appropriate magnitudes of r that correspond to small,
edium, and large effect sizes are .1, .3, and .5, respectively.ollowing this guideline, we find that a group of predictorsproduct quality, service quality, wide selection) provideselatively large effect sizes for explaining consumers’ retail

Y.Pan,G.M

.Zinkhan

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Table 2Main effects of retail choice

Independent variables Ka Weighted r

(observed)Weighted r

(corrected)95% Confidence interval(observed)

95% Confidence interval(corrected)

Totalvariance

Samplingerrorvariance

Reliabilityvariationvariance

Remainingvarianceb

File drawerN (p = .05)

Q (df)

Convenient parking facilities 9 .266 n.a.c .0055 .5265 n.a. .015 .018 n.a.d 39 7.53 (8)Friendliness of salespeople 12 .273 .286 .0912 .4548 .0946 .4774 .027 .008 .00002 .019 (70.6) 67 37.80e (11)Service quality 15 .637 .649 .5637 .7103 .5736 .7244 .228 .003 .00003 .225 (98.7) 75 1278.75e (14)Low price 17 .364 .389 .2569 .4711 .2773 .5007 .044 .003 .00003 .04 (92.7) 94 234.25e (16)Good quality 17 .579 .595 .4983 .6597 .5117 .6783 .092 .003 .00055 .089 (96.7) 138 710.66e (16)Store atmosphere 4 .395 .419 .2886 .5014 .3003 .5377 .052 .003 .00011 .049 (94.4) 35 63.2e (3)Fast checkout 11 .382 n.a. .1612 .6028 n.a. .042 .014 n.a. .028 (67.1) 61 37.66e (10)Wide selection 12 .696 n.a. .6347 .7573 n.a. .171 .003 n.a. .168 (98.5) 86 950.92e (11)Convenient location 12 .386 n.a. .1957 .5763 n.a. .037 .009 n.a. .028 (75.7) 83 54.51e (11)Convenient opening hours 9 .363 n.a. .1196 .6064 n.a. .027 .017 n.a. .01 (37.4) 45 13.11 (8)

a The number of effect sizes combined.b The percentage of total variance remaining is in parentheses.c n.a. indicates insufficient reliability information is available to correct the study effects for measurement error.d Reliability variation variances are not calculated as predictor or criterion reliability estimates are not available to adjust the mean correlation for the differences in scale reliabilities.e Significant at p < .05.

Table 3Main effects of frequency of visit of a particular store

Independent variables Ka Weighted r


(corrected)95% Confidence interval(observed)

95% Confidence interval(corrected)

Totalvariance

Samplingerrorvariance

Reliabilityvariationvariance

Remainingvarianceb

File drawerN (p = .05)

Q (df)

Age 6 .073 n.a.c −.0273 .1733 n.a. .0052 .0026 n.c.d .0026 (49.6) n.c.e 11.53f (5)Store/store-type attitude 5 .292 .334 .1793 .4047 .2021 .4659 .0112 .0033 .00032 .0076 (67.6) 30 16.94f (4)Store image 9 .157 .170 .0057 .3083 .0096 .3304 .0094 .0058 .00001 .0035 (37.9) 26 12.64 (8)Genderg 4 .154 n.a. .0513 .2567 n.a. .0004 .0027 n.c. 9 .45 (3)Income 6 .057 n.a. −.0386 .1526 n.a. .0099 .0024 n.c. .008 (75.9) n.c. 23.22f (5)

a The number of effect sizes combined.b The percentage of total variance remaining is in parentheses.c n.a. indicates insufficient reliability information is available to correct the study effects for measurement error.d Reliability variation variances are not calculated as predictor or criterion reliability estimates are not available to adjust the mean correlation for the differences in scale reliabilities.e n.c. means the file drawer N was not calculated, because the 95% confidence interval contained 0.f Significant at p < .05.g Coded as (1) male, (2) female.

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hoices. Other predictors (store attitude) are important pre-ictors of the frequency of patrons’ visits to a particular store.n this sense, we find evidence that retailing literature gener-tes robust results and has been successful in identifying keyariables that managers should emphasize in their planningrocess.

Tables 2 and 3 also show that the corrected mean corre-ations for 13 of the antecedents are significant at the .05evel. The high file drawer numbers for publication bias alsondicate that these results are significant beyond chance. Forxample, to bring the significant effect of service on storehoice down to the level of just significant at α = .05, 75 nullesults would need to be uncovered and included in our analy-is. The effect sizes for most demographic variables, however,re relatively small. The mean correlations for age (r = .073)nd income (r = .057) are not statistically significant at con-entional probability levels (p > .05), which means that theumulative evidence indicates their effects on retail patron-ge do not generalize across studies. As Tables 2 and 3 alsouggest, reliability differences and sampling error accountor only a small proportion of the variation across stud-es, which leaves much room for moderator variables toperate.

Our bivariate analysis confirms all our main effects pre-ictions except for two hypotheses: H10b (age) and H10cincome). Shoppers’ retail choice is influenced by the fol-owing factors (in order of importance): assortment, service,roduct quality, store atmosphere, store location, price level,heckout speed, hours of operation, friendliness of salespeo-le, and parking facilities. The frequency of visits to a stores subject to the influence of store attitude, store image, andender.

Note that for two variables (i.e., convenient parking facil-
ties and gender), the total variances are smaller than theampling error variances, a situation for which Hunter andchmidt (1990) provide an explanation. We compute the esti-ated variance of population correlations as the difference
able 4ultiple regression models for selected predictors of retail patronage

redictor Standardized coeffic(standard error): MoDependent variable:retail choice

riendliness of salespeople .106 (.02)c

ow price .44 (.019)c

ood quality .502 (.02)c

tore atmosphere .281 (.02)c

ge –tore/store-type attitude –ender –

ncome –2 (adjusted R2) .618 (.617)(p value) 424.23 (<.001)a Statistical significance is based on the median sample size of 1054, on which thb Statistical significance is based on the median sample size of 812, on which thec p < .001.

ts(e

tailing 82 (3, 2006) 229–243

etween the given variance of the observed correlations andhe statistically given sampling error variance, in which theariance of the observed correlations is a sample estimate.herefore, unless the number of studies is infinite, there wille some error in the empirical estimate. In our case, samplingrror caused the variance of the observed correlations to dif-er slightly from the expected value, and that error caused themaller estimated total variance.

ultivariate analysis of the antecedents of retailatronage

In addition to a bivariate analysis of the correlations, aultivariate analysis can further our understanding becausee simultaneously estimate the relative impact of the inde-endent variables on the dependent variables. We estimaten ordinary least squares regression model of retail patron-ge, whose matrix of corrected correlations we constructedrom the available data. Because few studies report correla-ional data about the interrelationships among the predictorariables, our correlation matrix contains data for only a sub-et of the predictors. Table 4 contains the findings from ourstimation of the regression models for retail choice and fre-uency of visit.

As we show in Table 4, the relatively parsimonious modelModel 1) accounts for a majority of the variance (61.8%)n retail choice. Product quality (β = .502, p < .001) and lowrice level (β = .44, p < .001) have the largest effects on shop-ers’ intentions to patronize a retailer. Moreover, the effects ofhe friendliness of salespeople and store atmosphere, thoughelatively small in magnitude, are significant in the regressionodel.In Model 2, we identify store attitude as a dominant predic-

ientdel 1a

Standardized coefficient(standard error): Model 2b

Dependent variable:frequency of visit

––––.019 (.033).377 (.034)c

.130 (.033)c

.188 (.035)c

.158 (.154)37.86 (<.001)

e individual correlations are based.individual correlations are based.

or of shopping frequency (β = .377, p < .001), and the regres-ion coefficients for gender (β = .13, p < .001) and incomeβ = .188, p < .001), though relatively modest in size, alsomerge as important drivers of shopping frequency. Age is

Y. Pan, G.M. Zinkhan / Journal of Retailing 82 (3, 2006) 229–243 237

Table 5Comparison of complete set and homogeneous subsets

Relationship Complete set Homogeneous subset

K Cumulative N Weighted r


(corrected)K Cumulative N Weighted r


(corrected)

Friendliness of salespeople 12 1206 .273 .286 9 968 .212 .221Service 15 3802 .637 .649 12 1114 .175 n.a.Low price 17 4302 .364 .389 9 1287 .073 .089Good quality 17 4443 .579 .595 11 1194 .401 .428Fast checkout 11 643 .382 n.a.a 9 449 .271 n.a.Wide selection 12 3272 .696 n.a. 10 759 .344 n.a.Convenient location 12 934 .386 n.a. 9 599 .271 n.a.Store atmosphere 4 970 .395 .419 3 679 .543 .640Store/store-type attitude 5 1271 .292 .334 3 682 .216 .237Age 6 2275 .073 n.a. 5 1995 .098 n.a.I n.

e study

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he only predictor variable that does not capture much of theariance in the bivariate and multivariate analyses.

omogeneity tests

For each pairwise relationship, we conduct a homogeneityest in which we successively delete the study effects to iden-ify any outliers, following the procedure recommended byedges and Olkin (1985). We compute the test statistic, Q,

or each pairwise relationship on Fisher’s z-transformationsf the correlation coefficients according to the formula

=k∑

i=1

(ni − 3)(zi − z+)2, (1)

here z+ is the mean of the weighted z-transformed corre-ations (weights are the reciprocals of the variances, to givereater weight to more precise estimates), and ni is the sampleize of a study. This statistic has an approximate chi-squareistribution with k − 1 degrees of freedom, where k is theumber of included studies.

Table 2 includes the results of our test for homogeneityf effects across all studies. If the results are heterogeneous,oderator variables exist. The homogeneity tests reveal the

verall consistency of results for four relationships (i.e., con-enient parking facilities → store choice, convenient openingours → store choice, store image → frequency of visit, gen-er → frequency of visit), which signals the robustness andeneralizability of these relationships across study contexts.e also find significant heterogeneity among the effect sizes

btained from the independent studies for the other relation-hips.

To identify outliers, we deleted that study with the largesteighted deviation from the mean effect size, recalculated

he mean effect size, and repeated the homogeneity test. We
ontinued until we arrived at a reduced set of homogeneoustudies, the results of which we report in Table 5. For mostairwise relationships, we must delete a small number oftudy effects (n ≤ 3) to achieve overall homogeneity. These
wnpn

a. 5 1933 .015 n.a.

effects for measurement error.

ata indicate significant variation in the pairwise relation-hips.

Because the heterogeneity of study effects suggests theotential presence of moderator variables, we conducted sep-rate meta-analytic syntheses for different subsets of studiesy examining a series of potential moderators. Then, we con-ucted moderating analyses only for those relationships forhich at least 10 study effects were available.

otential moderating effects of study characteristics:ests of moderators

We partitioned the study effects into subgroups on theasis of the values of their moderator variables, then com-ared the weighted mean correlations of the subgroups. Inable 6, we report the results of the moderator analyses,

ncluding the means and significance of each moderator foroth the corrected and uncorrected correlations. The aggre-ated data provide some interesting insights regarding theelative strength of the effect sizes in specific study con-itions. The five potential moderators (i.e., study design,ample type, scale type, shopping mode, product type) appearo have a pervasive influence for all but one pairwise relation-hip (fast checkout).

As we reveal in Table 6, measurement and contextualactors can account for a statistically significant amount ofhe variance. As we expected, shopping mode moderates theffects of several variables. For example, the effects of ser-ice, quality, and selection on retail choice are more manifestor store than for nonstore patronage. Product type also mod-rates the effect of various variables on retail choice, suchhat the impact of service, quality, and selection on patronagentention is significantly greater for specialty goods than foronvenience goods. This unsurprising result suggests that,
hen purchasing a specialty good (as opposed to a conve-ience good), shoppers are more highly involved with theurchase and willing to spend more time comparing alter-ative stores on criteria such as service and quality. A few

238 Y. Pan, G.M. Zinkhan / Journal of Retailing 82 (3, 2006) 229–243

Table 6Subgroup means by moderator variables

Experimental vs.nonexperimental

Single vs. multiplescales

Student vs.nonstudent subjects

Store vs. nonstoreshopping mode

Convenience vs.nonconveniencegoods

Observed r, corrected r,N


Observed r, correctedr, N


Observed r, correctedr, N

Friendliness ofsalespeople

.251 vs. .286, .284 vs.

.286, 2 vs. 10.386 vs. .205a, .386 vs..217a, 9 vs. 3

.251 vs. .286, .284 vs.

.286, 2 vs. 10n.a., n.a., 12 vs. 0 0.251 vs. 0.286, 0.284

vs. 0.286, 2 vs. 10Service .464 vs. .665a, .526 vs.

.665a, 3 vs. 12.705 vs. .360a, .705 vs..378a, 12 vs. 3

.503 vs. .655a, .591vs. .655a, 2 vs. 13

.683 vs. .210a, .699 vs.

.210a, 13 vs. 20.158 vs. 0.752a,0.158 vs. 0.774a, 10vs. 5

Low price .031 vs. .450a, .037 vs..450a, 4 vs. 13

.483 vs. .048a, .483 vs.

.058a, 12 vs. 5.028 vs. .427a, .036vs. .429a, 3 vs. 14

.365 vs. .348, .392 vs.

.348, 16 vs. 10.358 vs. 0.365, 0.358vs. 0.397, 11 vs. 6

Good quality .444 vs. .604a, .528 vs..604a, 3 vs. 14

.604 vs. .444a, .604 vs.

.528a, 14 vs. 3.444 vs. .604a, .528vs. .604a, 3 vs. 14

.674 vs. .206a, .700 vs.

.206a, 15 vs. 20.487 vs. 0.595a,0.487 vs. 0.615a, 11vs. 6

Fast checkout n.a.b, n.a., 0 vs. 11 n.a., n.a., 11 vs. 0 n.a., n.a., 0 vs. 11 n.a., n.a., 0 vs. 11 n.a., n.a., 11 vs. 0Wide selection n.a., n.a., 0 vs. 12 .754 vs. .103a, n.a., 11

vs. 1n.a., n.a., 0 vs. 12 .737 vs. 307a, n.a., 11

vs. 10.265 vs. 0.822a, n.a.,10 vs. 2

Convenient location n.a., n.a., 0 vs. 12 .464 vs. .216a, n.a., 11vs. 1

n.a., n.a., 0 vs. 12 n.a., n.a., 12 vs. 0 n.a., n.a., 12 vs. 0

a

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ther patterns are evident across the data, including the differ-nce in effect sizes for experimental versus nonexperimentalesigns. In contrast to our expectation, correlations are lowerith experiments. Furthermore, the impact of sample type on

he effect sizes is directionally inconsistent with our expecta-ions; student subjects appear to create a downward bias in thetrength of the studied relationships. We also expected rela-ionships to be weaker in studies that used single scales, buthis expectation is not supported. We discuss the implicationsf these findings subsequently.

Discussion and conclusion

Using a meta-analytical approach, our study providesnsights into retail patronage, synthesizes empirical worksrom retailing literature, and provides a quantitative summaryf the cumulative findings. As we show in Tables 2 and 3, wend a relatively strong relationship between shoppers’ storehoice and several important predictors. Of the three cate-ories of predictor variables, selection has the highest averageorrelation with store choice, followed by service, quality,tore atmosphere, low price levels, convenient location, fastheckout, convenient opening hours, friendliness of sales-eople, and convenient parking facilities. Other antecedentariables (e.g., store image, store attitude, gender) are impor-ant predictors of shopping frequencies, though the effectizes of some variables (e.g., age, income) are not significant.ender is the only successful demographic variable, which

uggests that women tend to be more frequent shoppers thanen.A second insight relates to the three categories of predictor

ariables that explain patronage. The two dimensions of retail

tiir

his category or the attenuation factor is not available.

atronage are explained by very different sets of predictors.f the three categories, personal factors (e.g., demographics,

ttitude toward a store) seem to be the dominant predictors ofhopping frequencies, whereas market- and product-relevantariables are more likely to influence shoppers’ decisions toatronize a particular store, given that a variety of stores arevailable. These findings suggest that retailers have variousools at hand (e.g., greater assortment, low prices) to influencehoppers’ intention to patronize their stores. However, shop-ing frequency, over which retailers have much less control,argely depends on a consumer’s will. This conclusion shoulde interpreted with caution though. In our analysis of shop-ing frequencies, some factors (e.g., assortment, quality) thatccur in some studies have not been investigated frequentlynough to be integrated into our meta-analysis. Therefore,e cannot rule out the possibility that some other product-

nd market-relevant factors may have significant influencen shopping frequencies.

Our findings also reveal the existence of methodologicalrtifacts. As we indicate in Table 6, the study designs andesearch methods used can influence the effects of some keyetailing predictors. Take location as an example. Theoret-cally, location is a key variable for predicting the successf traditional retailers, but our results only detect a mediumffect (r = .386). A post hoc analysis reveals that most retail-ng studies are not really designed to allow location to displayts key effect (e.g., much of the location effect may occurelow the conscious level). In Table 6, we also reveal that in anrtificial research context (e.g., student sample in experimen-
al settings), the impact of predictors on patronage behaviors less obvious than in nonexperimental settings. In an exper-mental setting, researchers often ask subjects to evaluate aetailer on the basis of a brief description and then indicate

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heir behavioral intentions. These behavioral accounts areften speculative and perhaps constructed on the spot ratherhan being true indicators of the relevant behavior. The usef these student samples also presents a major challenge tohe generalizability of retailing research findings. Studentseem less interested in retailers’ strategies to attract patronse.g., good service, high product quality) than general con-umers, and their shopping behavior may be more influencedy factors such as peer recommendation and brand.

In addition, single scales produce higher correlations thanultiple scales in many cases. Our post hoc review reveals

aried and sometimes weak measures of key variables andhereby highlights the need to identify superior measurementpproaches in future studies. The way in which retail patron-ge is measured can lead to different estimates of relationshiptrength; for example, research often measures patronage asecalled behavior (e.g., “Name the regular store in which youoncentrate the majority of your purchases”) or anticipateduture behavior (e.g., “The likelihood that I would shop in thistore is very high”). Neither of these approaches provides aood estimate of shoppers’ actual patronage behavior. Annderstanding of these effects can shed more light on theobustness of findings in retailing research.

The findings of our study thus call for different theoriesor studying retail patronage. For traditional retailers, fac-ors such as physical location, parking facilities, checkoutpeed, and store atmosphere can make or break a store. How-ver, with the evolution of nonstore retailing formats (e.g.,-tailing), these traditionally important predictors of retailatronage may become less crucial or even obsolete. Our find-ngs provide some indication of this trend; we find that theffects of various factors on patronage (e.g., service, quality,election) are less obvious in nonstore than in store shoppingodes. Shoppers may attach more importance to other fac-

ors (e.g., return policy, company reputation) in their decisiono patronize a nonstore retailer, so the evolution of nonstoreetailing may signal the need to develop an updated set ofetailing theories.

We observe a pattern of strong relationships in our meta-nalysis, but their strength may be inflated by a potentialublication bias. Despite our effort to locate unpublishedtudies, we did not find many, which could influence theffect-size estimates. The file drawer Ns calculated in ourtudy provide some estimates of the number of studies withonsignificant results that would be required to nullify the sig-ificant results, such as the 94 nonsignificant results studieseeded to nullify the “low price” result at the .05 signifi-ance level. Given the relatively limited number of studiese found that report this variable (17), the possibility of find-

ng an additional 94 studies is rather low. All the significantelationships (except that between gender and frequency ofisit) require many zero-effect studies to lead to conclusions
f nonsignificance. Therefore, we have confidence in theseignificant results.
Measurement error in the variables reduces effect-sizestimates, and variations in reliability across studies cause

ctpp

tailing 82 (3, 2006) 229–243 239

ariations in the observed effect sizes beyond that producedy sampling error. If the true effect size is homogeneouscross studies, the variation in reliability would produce aalse impression of heterogeneity (Hunter and Schmidt 1990).n our meta-analysis, we corrected for this attenuation byeighting each study with reliability measures, but many

tudies (especially those published a long time ago) did noteport reliability measures, which could lead to systematiceductions in the mean effect sizes.

Homogeneity tests reveal that the effects of convenientarking facilities and hours of operation on retail choice andhe effects of gender and store image on shopping frequencyre relatively robust across study contexts. For other rela-ionships (e.g., fast checkout, selection, store atmosphere),e must delete one or two outliers to achieve overall homo-eneity. Homogeneity tests, however, indicate substantialariation in the relationships between shoppers’ perceptionsf low price and good product quality and their retail choice.hese study effects are significantly affected by the method-logical choices and study contexts.

Implications and directions for further research

Our study has implications for both academicians andetail managers. We gauge the current level of knowledgebout retail patronage with our critical review of the empiri-al studies on this topic and a meta-analysis of these studies.y synthesizing the traditional retailing literature in a formalay, our study offers greater understanding of the general

trength and variability of the relationships and the condi-ions that moderate those relationships. It also tests a broadheoretical typology and a wide range of variables that mayxplain retail patronage behavior.

We therefore tap into areas that are of great interest to retailanagers. Retailers have tracked, scanned, monitored, and

ollowed consumers’ shopping behavior for decades. Ques-ions such as how shoppers choose a particular store, howften they visit, why they visit, and who visits have beenopular inquiries. However, inconsistent research findingsake it problematic to communicate with retailers who want

o apply this knowledge. We thus provide basic answers toome key questions. A retailer can enhance consumer patron-ge behavior by identifying and implementing an appropriatearketing strategy, which must start with a good under-

tanding of the many factors and dimensions that influencehoppers’ choice behavior. For example, to increase initialatronage, consumer promotions should focus on store- androduct-specific elements (e.g., wide assortment, premiumervice, pleasant in-store decor). Managers also must rec-gnize that shopping frequencies tend to be associated withhopper characteristics, so they should tailor their marketing
ommunications to frequent shoppers, which will increasehe likelihood that they experience positive returns from theirromotional efforts. Examining the determinants of storeatronage thus enables managers to evaluate and understand

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he extent to which their own perceptions of the store and itsfferings are congruent with shoppers’ perceptions.

We conclude that differences in study and sample charac-eristics contribute to the variances in store patronage foundcross prior studies. For example, the use of student sub-ects remains a somewhat controversial issue in marketingnd retailing research. It is argued that student subjects maye appropriate for tests of theory. Here, however, we find thathe use of such subjects can deflate estimated effect sizes. Inhe same way, experimental designs may introduce an ele-

ent of artificiality that can distort results. On the one hand,uch method effects are well known; on the other, researchersometimes forget about these crucial differences when tryingo make sense out of cumulative findings (e.g., in researcheports and academic literature).

In terms of main effects, we find that the following vari-bles are especially important for explaining retail choice:ide selection, service, and product quality. Store attitude

s a more important predictor than image or demographicariables for explaining shopping frequencies. But all of theariables in Tables 2 and 3 have been shown to be importantn some studies, so our analysis reveals a way to rank-orderhese predictors.

The study of demographic variables represents pioneer-ng work on store patronage; we find that the majority ofemographic variables do not have a strong effect, except forender, which remains a key predictor of shopping frequency.ith respect to the effects of age and income, our null results
ay be due to two factors: First, only a small proportion of the
tudies finds a significant effect, and second, there appears toe a bimodal distribution around 0, in that some studies reportpositive effect and others report a negative one. Given the

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tailing 82 (3, 2006) 229–243

mall number of studies (n < 10) available, we cannot under-ake a moderator analysis to uncover why such a disparateattern occurs. Nonetheless, we take a closer look at the stud-es’ characteristics to pinpoint some potential explanations.f the two studies that show significant effects for income,ne investigates discount stores and finds a negative relationetween income and shopping frequency (Korgaonkar et al.985), and the other studies the frequency of catalog ordersnd finds a positive relationship (Lumpkin and Hawes 1985).or age, the only negative effect reported appears in a studynalyzing convenience food stores (Darden and Lumpkin984). We suggest that additional research should explorehe effect of income and age on retail patronage across var-ous store types and product types (e.g., convenience versusonconvenience goods).

Because main effects can be relatively uninteresting inarketing research, we must go beyond those main effects

nd explore the interaction effects. We provide evidence thats yet unnamed moderators affect key retailing relationshipse.g., product quality → retail choice). Future researchersherefore must explore if and to what extent those moder-tors account for heterogeneity in studies, which could leado greater confidence regarding the generalizability of theelationships.

Our study includes only those antecedent factors that haveeen frequently (n ≥ 4) investigated in retailing literature.mong the predictors that did not meet this threshold are

ask definition (i.e., the purpose of a shopping trip), intendedurchase quantities, and personality traits (e.g., risk percep-ion, self-confidence, locus of control). These factors there-ore may have not received sufficient attention from retailingesearchers.

l of Retailing 82 (3, 2006) 229–243 241
Appendix A

Selected empirical studies on retail patronage

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Documents

Determinants of retail patronage: A meta-analytical perspective