Journal of Retailing and Consumer Services 19 (2012) 150–158

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Journal of Retailing and Consumer Services

0969-69

doi:10.1

n Corr

E-m

ssheng@1 Te2 Te

journal homepage: www.elsevier.com/locate/jretconser

Antecedents of customer loyalty: An empirical synthesis and reexamination

Yue Pan a,n, Simon Sheng b,1, Frank T. Xie c,2

a Department of Management & Marketing, University of Dayton, 812 Miriam Hall, Dayton, OH 45469-2271, United Statesb School of Business, University of Alabama, Birmingham, AL 35294, United Statesc School of Business Administration, University of South Carolina, 471 University Parkway, Aiken, SC 29801, United States

a r t i c l e i n f o

Available online 5 December 2011

Keywords:

Customer loyalty

Meta-analysis

Predictors

Moderators

89/$ - see front matter & 2011 Elsevier Ltd. A

016/j.jretconser.2011.11.004

esponding author. Tel.: þ1 937 229 1773; fa

ail addresses: Yue.Pan@notes.udayton.edu (Y.

uab.edu (S. Sheng), frankx@usca.edu (F.T. Xi

l.: þ1 205 934 8840; fax: þ1 205 934 0058.

l.: þ1 803 641 3242; fax: þ1 803 641 3445.

a b s t r a c t

Despite the importance of customer loyalty, no comprehensive, empirical work has attempted to assess

the general findings across academic studies. The study intends to fill that void by conducting a meta-

analysis of empirical findings on the predictors of customer loyalty. Although findings of this study

support all the hypothesized main effects, they indicate stronger effect size for trust than for other

determinants of loyalty. The study also tests the robustness of previous findings across various research

and measurement contexts. The analysis of moderating effects reveals several interesting findings. For

instance, attitudinal loyalty measures seem to be a plausible surrogate for behavioral loyalty measures.

The effects of customer satisfaction and trust on loyalty are less prominent when products are

purchased on a regular and relatively short (as opposed to an irregular and relatively long) purchase

cycle. Factors that largely relate to product performance (e.g., satisfaction, quality) have a weaker

impact on loyalty in B2B than in B2C settings. Some relationships (e.g., the effect of quality on loyalty)

become stronger over time. Furthermore, our results detect consistently weaker effects from studies

using single-item (relative to multi-item) loyalty measures.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Customer loyalty is a company’s most enduring assets. Bycreating and maintaining customer loyalty, a company develops along-term, mutually beneficial relationship with the customers.Corporate executives are interested in fundamental questionsconcerning the concept of customer loyalty, e.g., the drivingforces of customer loyal behavior. The practical and conceptualimportance of this topic has been underscored by the substantialvolume of studies published in leading academic journals. Despitethe myriad studies published, there are a number of factors thatlimit a comprehensive understanding of customer loyalty andprevent the generalization of research findings. First, consensus israrely found in the accumulated empirical research. For instance,although most studies presume that buyer satisfaction with abrand/seller leads to future patronage intention (e.g., Jones andReynolds, 2006; Meuter et al., 2000), many fail to provide a stronglinkage between customer satisfaction and loyalty (e.g., Khatibiet al., 2002; Stoel et al., 2004)).

ll rights reserved.

x: þ1 937 229 3788.

Pan),

e).

Second, the inconsistency in findings is confounded by the factthat previous empirical studies have been conducted in variousresearch contexts. Because of heterogeneous findings and diversestudy conditions in the extant literature, the relationship betweenvarious correlates and loyalty cannot be determined a priori.These disparate findings complicate academic researchers’ effortsto develop a clear and comprehensive understanding of customerloyalty.

Third, there seems to be no agreement on conceptualizing andoperationalizing the loyalty construct. A review of the literaturereveals that the choice of loyalty measurement instruments issomewhat arbitrary, which makes it difficult to generalizeresearch findings across studies. Some authors view ‘‘share ofrequirements’’ (i.e., the proportion of volume accounted for by abrand, within its base of buyers) as the most appropriate measureof loyalty (cf. Baldinger and Rubinson, 1997), whereas others relyon survey-based attitudinal measures (e.g., brand preference,willingness to provide positive word of mouth) to study loyalty.Although many researchers concur that a conceptualization ofloyalty should incorporate both behavioral and attitudinal com-ponents (e.g., Dick and Basu, 1994; Rundle-Thiele, 2005), theextent to which attitudinal measures are ample replacement for,or good supplement to behavioral measures is still largelyuntested. Significant opportunities exist to extend and refineappropriate loyalty measures. Without a widely accepted con-ceptual and operational definition, the analysis of loyalty is at

Y. Pan et al. / Journal of Retailing and Consumer Services 19 (2012) 150–158 151

best piecemeal. By integrating and comparing previous studiesutilizing attitudinal and behavioral measures of loyalty, we assessif, and to what extent attitudinal measures are good substitutesfor behavioral measures.

Despite the importance of customer loyalty, no comprehensivework has been advanced to assess the general findings acrossacademic studies. We seek to fill that void by conducting a meta-analysis of empirical findings on the predictors of customerloyalty. Through this study, we aim to make several contributionsto the loyalty literature. First, we add to the contemporary state ofknowledge by establishing the generalizability of the relation-ships between customer loyalty and its important correlates.A systematic review and integration of the empirical evidencecan document the statistical significance, direction, and magni-tude of the study effects. More importantly, it can shed light onthe extent to which the effect sizes in individual studies arecaused by the true value (i.e., population value) or study artifacts(e.g., measurement errors, publication bias). Second, built uponthe findings of this meta-analysis, we assess the relative strengthsof the studied relationships, and identify the variables that havemore (or less) predictive and explanatory power relative toothers. Finally, research on customer loyalty has been conductedin various methodological and study contexts, yet no attempt hasbeen made to evaluate the robustness of effects across studyconditions. The study advances our understanding of customerloyalty by exploring some contextual factors that may contributeto the divergence in individual findings.

2. Conceptual framework and research hypotheses

In this study, we adopt a wide perspective of loyalty. Theconceptualization of customer loyalty is not restricted to loyaltywith respect to tangible goods, it also includes loyalty toward aservice or service provider. Although there is no universallyaccepted definition of loyalty in the literature, it is typicallybelieved to consist of an attitudinal and a behavioral component(e.g., Chaudhuri and Holbrook, 2001; Rundle-Thiele, 2005;Russell-Bennett et al., 2007). Therefore, we define loyalty as thestrength of a customer’s dispositional attachment to a brand (or aservice provider) and his/her intent to rebuy the brand (orrepatronize the service provider) consistently in the future. Here,we integrate findings from prior research and model the ante-cedents of loyalty. Fig. 1 depicts the theoretical framework of this

Customer satisfaction (H1)

Trust (H2)

Psychological commitment (H3)

LP membership (H4)

Perceived value (H5)

Product quality (H6)

Perceived fairness (H7)

Switching costs (H8)

Brand reputation (H9)

Prof

Lom(H

Bu

Ti

Customer-related factors

Product-related factors

Fig. 1. Conceptua

study. Implicit in our theoretical framework is the recognitionthat individual and product characteristics interact and combineto shape one’s loyalty toward a product. Further, we identifyseveral variables that are crucial in moderating the relationshipsbetween loyalty and its correlates. In what follows, we develophypotheses regarding the antecedents and moderating effects ofcustomer loyalty.

2.1. Antecedents to customer loyalty: customer-related factors

Customer satisfaction. Customer satisfaction has often beenregarded as a major determinant of loyalty (Dick and Basu, 1994).Yet empirical evidence is somewhat mixed. For instance, somestudies fail to provide a strong linkage between customer satis-faction and loyalty (e.g., Khatibi et al., 2002; Stoel et al., 2004).Others indicate that the satisfaction-loyalty relationship is indir-ect and complex (e.g., Anderson and Mittal, 2000; Magi, 2003).Despite the mixed findings, in general, the literature anticipates alinear and positive effect of satisfaction on loyalty (cf. Jones andReynolds, 2006; Seiders et al., 2005).

H1. Customer satisfaction with a product has a significant andpositive effect on customer loyalty.

Trust. Trust has been identified as a major driver of loyalty(e.g., Chaudhuri and Holbrook, 2001; Garbarino and Johnson,1999). A consumer who trusts in a product is more likely todevelop favorable attitudes toward it, to pay a premium price forit, to remain loyal to it, and to spread positive word-of-mouth(Chaudhuri and Holbrook, 2001). The impact of trust on customerloyalty becomes especially relevant when confronted withswitching decisions with a high level of perceived risk anduncertainty (Lewis, 2002). Based on the afore-mentioned argu-ment, we propose that:

H2. A customer’s trust in a product has a significant and positiveeffect on his/her loyalty toward that product.

Psychological commitment. Commitment may be understoodas symbolic attachment or identification with a product. It is anecessary condition for loyalty to occur (Bloemer and de Ruyterk,1998). Commitment is at the core of the value that a strong brandprovides to its customers. It is the highest level of relationalbonding and is essential for successful long-term relationships(Garbarino and Johnson, 1999; Johnson et al., 2006; Morgan and

Loyalty

oduct type: Intangibility (H10), purchase cycle the product (H11)

yalty measurement: Behavioral vs. attitudinal easures (H12), multi- vs. single-item measures 13)

siness vs. consumer market (H14)

me variable (H15)

l framework.

Y. Pan et al. / Journal of Retailing and Consumer Services 19 (2012) 150–158152

Hunt, 1994). Committed customers tend to invest more heavily intheir relationship with the seller. They will perceive greaterbenefits to loyalty and greater risks to switching brands(Evanschitzky et al., 2006). Such findings have led us to thefollowing hypothesis:

H3. A customer’s psychological commitment toward a productfosters his/her loyalty.

Loyalty program (LP) membership. Loyalty programs aredesigned to cultivate customer loyalty by rewarding repeatpurchases. Members of a loyalty program reap a wide variety of‘‘hard’’ (e.g., discounts, coupons, rebates for past purchases) and‘‘soft’’ benefits (e.g., special invitations, shopping convenience),and therefore are likely to become dedicated patrons of a store(Gable et al., 2008; Lowenstein, 1995). Customers drawn by suchbenefits will regularly return for additional purchases, resulting ina long-term, mutually beneficial relationship with the company(Dixon et al., 2005). Hence, we offer the following hypothesis:

H4. LP memberships tend to enhance customer loyalty. That is,members of a company’s loyalty program tend to show higherloyalty toward the company’s products.

2.2. Antecedents to customer loyalty: product-related factors

Perceived value. The cost in combination with the benefitof using a product determines overall perceived value of theproduct, which will influence customers’ purchase intention andbehavior (Lai et al., 2009). Customers compare benefits receivedwith investment put in, and choose the product that offers thebest value compared to other alternatives. When perceived valueof a product meets or exceeds their expectation, customers viewthe product a worthy buy. When the perceived value is low,customers would be more inclined to switch to competing brandsin order to increase perceived value, thus resulting in a decline inloyalty (Anderson and Srinivasan, 2003). This theoretical reason-ing is largely supported by empirical studies (e.g., Johnson et al.,2006; Brodie et al., 2009).

H5. Perceived value of a product is positively related to customerloyalty.

Product quality. Previous research suggests either a direct(e.g., Boulding et al., 1993) or indirect (e.g., Woodruff, 1997) effectof product quality on loyalty. A high level of product quality oftenengenders feelings of pleasure, contentment, excitement, andsatisfaction. It may foster customer confidence and trust in thebrand (or service). Particularly, when a customer’s evaluation ofthe perceived performance of specific attributes of a product isbetter than his/her prior expectations, this will result in unwaver-ing customer loyalty (Parasuraman et al., 1988).

H6. Product quality is positively related to customer loyalty.

Perceived fairness/justice. The effect of perceived fairness/justice on loyalty is particularly manifest in a service recoverycontext. Perceived justice is the main determinant of complai-nants’ repatronage intentions (Blodgett et al., 1993). Customersevaluate fairness by comparing their perceptions of the experi-ence received to what they believe it should be (Seiders and Berry,1998). When they encounter conflicts with their fairness stan-dards, they may experience a sense of injustice, which in turnleads to dissatisfaction and eventually termination of theexchange relationship. Firms that fail to project an image offairness cannot develop the level of customer confidence neededto establish loyalty (Seiders and Berry, 1998).

H7. Perceived justice has a positive effect on customer loyalty.

Switching costs. As in Fornell’s (1992) typology, switchingcosts include both economic (e.g., transaction costs, search costs)and psychological (e.g., emotional costs, cognitive effort) values.Switching costs are often recognized as a means for keepingcustomers in relationships, regardless of their satisfaction withthe provider. Customers may remain loyal when high switchingbarriers make it costly to switch to another supplier. The morecustomers have to give up or sacrifice to switch to a substitutableproduct, the more they become dependent on the current one inuse, and the less their intention to switch. Switching costs areused as a corporate strategy to increase customer loyalty (Dickand Basu, 1994). The varying extent to which firms controlswitching costs may explain variations in customer retentionlevels. Therefore, we propose that:

H8. A positive relationship exists between switching costs andcustomer loyalty.

Brand reputation. Reputation is often seen as a mechanism ofassuring trustworthy behavior of a firm. In business and servicemarkets, the company’s name is often the brand name across arange of product classes. Under such circumstances, the companyreputation acts as the umbrella brand for these product categories(Cretu and Brodie, 2007). The associations customers have aboutthe reputation of a retailer affect the value of what they purchasefrom that retailer (Brown and Dacin, 1997). Customers are likelyto perceive a brand with a good reputation as trustworthy asopposed to one with a poor reputation. Furthermore, brandreputation is often used as a proxy for product quality whenintrinsic cues or attributes are difficult to employ (Kirmani andRao, 2000). When exposed to complex market cues, customersmay not engage in elaborate information processing. To avoidinformation overload and any resulting dysfunctional conse-quences, they may simplify the buying decision by only attendingto a striking evaluative criterion such as brand reputation.A product with a good reputation will reduce the perceived riskassociated with performance ambiguity and information asym-metry and lead to favorable purchase and repurchase intent.

H9. Brand reputation has a significant and positive influence oncustomer loyalty.

2.3. Potential moderators

Sultan et al. (1990) advise that four broad categories ofcharacteristics often account for systematic differences acrosscorrelations. They are measurement method, research context,estimation procedure, and model specification. Because ourunits of analysis are bivariate correlations that are unaffected byestimation procedure and model specification, we seek forsystematic differences in study characteristics. Research on cus-tomer loyalty has been conducted in various methodological andresearch contexts. Heterogeneous study characteristics can con-tribute to variance in effect sizes. In our investigation, we identifyseveral potential moderators (i.e., product type, loyalty measure-ment, market setting, and time variable) and assess their impacton sample homogeneity.

Product type: tangible vs. intangible product. Many arguethat findings in the area of product loyalty cannot be generalized toservice loyalty (Keaveney, 1995). For instance, satisfaction has agreater effect on loyalty for services than it does for products, asintangible products have fewer differentiating characteristics com-pared to tangibles (Edvardsson et al., 2000). The intrinsic serviceattributes (e.g., intangibility, variability) make it harder for custo-mers to assess value obtained before purchase and consumption.The social nature of services makes satisfaction more salient forcustomers, with consequent effects on evaluative and relational

Y. Pan et al. / Journal of Retailing and Consumer Services 19 (2012) 150–158 153

elements of service loyalty. Furthermore, service loyalty is moredependent on the development of interpersonal relationships asopposed to loyalty with tangible products (Macintosh andLockshin, 1998). Intangible attributes such as reliability and con-fidence may play a more important role in building or maintainingloyalty in a service context than in a product context (Dick andBasu, 1994). These well-established differences between tangibleand intangible products lead to a generalized expectation thatreasons for remaining loyal in a service setting might differ fromthose in a goods setting. Therefore, we propose that:

H10. The relationships between loyalty and its predictors arestronger (weaker) in a service (goods) setting.

Product type: regular vs. irregular purchase cycle. Manyproducts are purchased on a regular and relatively short purchasecycle. Buyers are in the market at predictable intervals during theyear. Most fast-moving consumer goods (FMCG) sold throughsupermarkets, drugstores, convenience outlets fall into this cate-gory, as do many services such as hair care and dry cleaning.Other products have a long and irregular purchase cycle, such aslife insurance, management consultancy, and luxury items. Theseproducts are typically characterized by long interpurchase times.On every purchase cycle, the buyer essentially becomes a newprospect again and is functionally a new individual to be reachedby sellers’ marketing effort (Rossiter and Danaher, 1998).

Buyers of frequently purchased products are characterized bya high rate of brand switching, and low involvement and risk(Rundle-Thiele and Bennett, 2001). Most customers buy severalbrands in a product category and relatively few are sole buyers ofeach brand (Oliveira-Castro et al., 2005). One reason for therelatively low loyalty is that people tend to buy the cheapestbrand in their consideration set (Foxall and James, 2003). They aremore sensitive to competitive advertising or sales promotions,and more likely to try a new brand. Hence, they tend to displayless loyal behavior on successive shopping occasions. For infre-quently purchased products, buyers generally exhibit less switch-ing behavior (Rundle-Thiele and Bennett, 2001). Such productstend to be higher in value than those people buy on a regularbasis, which increase the level of involvement. Therefore, shop-pers are inclined to base their choice decision on an overallassessment of the product, using a more elaborate set of evalua-tive criteria (e.g., overall satisfaction, trust, product quality).Based on this discussion, we propose that:

H11. The relationships between loyalty and its predictors arestronger (weaker) for products with an irregular (regular) pur-chase cycle.

Loyalty measurement: behavioral vs. attitudinal measuresof loyalty. One of the hurdles in studying customer loyalty is theabsence of a consensus on the definition and measurement of thisconstruct. In the extant literature, there are two schools ofthought when it comes to defining and operationalizing brandloyalty – researchers who approach brand loyalty strictly from abehavioral perspective (e.g., customers’ share of spending with abrand, repurchase intentions) and those who insist that inaddition to behavioral aspects, a favorable attitude toward thebrand (e.g., brand preference) is also required to define loyalty.Behavioral loyalty can be measured by observing the return visitsover time. Most empirical studies, based on cross-sectionalsurveys, often use attitudes as a surrogate measure for behavioralloyalty. Despite the popularity of this practice, no study hasassessed the substitutability of attitudinal and behavioral loyaltymeasures.

We suspect that the use of attitudinal and behavioral mea-sures may lead to varied strengths of the studied relationships.

Psychological models of individual behavior predict that attitudesare likely to precede behavior (Ajzen and Fishbein, 1991). Follow-ing this logic, Russell-Bennett et al. (2007) propose that attitu-dinal loyalty mediates the effect of satisfaction on behavioralloyalty. Such mediation should make the impact of satisfaction onbehavioral loyalty weaker. Furthermore, display of behavioralloyalty takes more of a customer’s commitment than formationof attitudinal loyalty, making it less likely to occur. Hence, theeffects of loyalty predictors will be more prominent on attitudinalthan behavioral loyalty.

H12. The strength of the relationship between loyalty and itspredictors is weaker (stronger) for studies using behavioral(attitudinal) loyalty measures.

Loyalty measurement: single- vs. multi-item measures ofloyalty. The use of multiple-item scales enhances reliability inmeasurement of abstract constructs. Therefore, this practiceshould provide stronger relationships than the use of single-itemmeasures (Peter and Churchill, 1986). Rundle-Thiele (2005)demonstrates the superiority of a multidimensional over aunidimensional model of consumer loyalty. Given the multidimen-sional nature of the loyalty construct, we believe that a multi-itemmeasure can better capture the many facets of customer loyaltyand produce stronger effects. Therefore, we offer the followinghypothesis:

H13. The relationships between loyalty and its predictors arestronger (weaker) for studies using multi-item (single-item)loyalty measures.

Business vs. consumer market. A key distinction betweenorganizational and consumer buying behavior lies in the nature ofthe relationship between buyers and sellers. Organizational buy-ing is more complex than consumer buying behavior. The pur-chase decision is often made by a group (buying center), and mustsatisfy differing needs or objectives of participants (Moriarty,1983). Reciprocal arrangements (i.e., an industrial buying practicein which two firms agree to buy each other’s products) and long-term purchase contracts often exist in organizational buying.Based on the characteristics of industrial buying, we suspect thatfactors other than product performance-related attributes mayplay a role in a business buyer’s repeat purchase decision. Forinstance, long-term contracts, plus the uncertainty in supplyavailability at the expected prices may well prevent businessbuyers from switching suppliers (Valuckaite and Snieska, 2007).In fact, the link between satisfaction and loyalty often appearsweak or even absent in business markets, because maturecustomer–supplier relationships are often characterized by iner-tia where partners tend to maintain the status quo (Narayandas,2005). Due to the unique nature of the business market, weexpect product-performance attributes have a weaker impact oncustomer loyalty in business than in consumer markets. Hence,we propose that:

H14. The relationships between loyalty and its predictors areweaker (stronger) in a business (consumer) market.

Time variable. Many studies have suggested that customersare less loyal now than in the past (e.g., Bennett and Rundle-Thiele, 2005; Taher et al., 1996). Compared with the moreexclusive loyalty in the past, customers increasingly hold poly-gamous loyalty to brands (i.e., they divide their purchases amongmultiple brands in a category) (Bennett and Rundle-Thiele,2005). Postmodern buyers become more critical and skepticalabout influences from marketers (Elliot et al., 1993). Whencontemplating their next purchase, satisfied customers nowadaysdo not automatically buy the same brand (Taher et al., 1996).

Table 1Effects reported in the original studies.

Loyalty correlates No. of effectsreported

Range of thereported effects (r)

Cumulativen

Antecedents to loyalty:

customer-related factors

Customer satisfaction 198a/208b

.03938 50,432

Trust 61/62 � .08.86 15,289

Psychological

commitment

26/32 .0788 12,860

LP membershipc 8/9 .03755 3,052

Product-related factors

Perceived value 41/42 .05980 14,074

Product quality 134/139 .0295 16,194

Perceived fairness 24/25 .081755 6,840

Switching costs 13/15 � .1168 6,873

Brand reputation 22/23 .258 3,788

a Number of statistically significant (at a¼ .05) effects reported.b Total number of effects reported.c Loyalty program membership.

Y. Pan et al. / Journal of Retailing and Consumer Services 19 (2012) 150–158154

Previous empirical research examining the loyalty relationshipshas largely disregarded temporal effects. An analysis of temporalpatterns of study effects will provide initial insights into the ever-changing nature of the loyalty construct. Here, we apply the timevariable as a possible explanation for the variability of effect sizesacross studies. Because research is limited in this area, we do notoffer strong, directional hypotheses.

H15. The relationships between loyalty and its predictors changeover time.

3. Method

3.1. Sampling frame

Empirical studies were selected if they reported at least onerelationship specified in Fig. 1. The studies were identified via thefollowing search procedure: (1) we did computerized searchesusing EBSCOhost and PsycInfo databases, followed by (2) aninteractive search of the references from relevant articles identi-fied, until no new references could be identified, (3) we sent arequest through ELMAR listserv, asking for published and unpub-lished studies on this topic, and (4) we identified studies throughissue-by-issue searches of the Journal of Consumer Satisfaction,

Dissatisfaction and Complaining Behavior, and the proceedings ofAMA and ACR. To be included in our meta-analysis, studies have to

Table 2Main effects of loyalty correlates.

Loyalty correlates Ka Weighted r

(observed)Weighted r

(corrected)95%confidenceinterval(observed)

95%coninte(cor

Customer

satisfaction

118 .519 .613 .4492 .5880 .531

Trust 33 .591 .694 .5320 .6506 .624

Psychological

commitment

21 .511 .595 .4525 .5696 .526

LP membership 4 .321 .349 .2569 .3843 .279

Perceived value 27 .519 .619 .4566 .5821 .544

Product quality 38 .524 .624 .4547 .5927 .541

Perceived fairness 17 .545 .613 .4766 .6141 .535

Switching costs 9 .367 .452 .3055 .4283 .376

Brand reputation 10 .505 .631 .4303 .5805 .536

a The number of effect sizes combined.b Significant at po .05.

meet four criteria: they have to (1) report sample size, (2) analyzethe bivariate relationship between loyalty and its determinants,(3) report either Pearson correlation coefficients or statistics thatcan be transformed into correlations, and (4) examine relation-ships that were frequently reported (n43) in previous studies.

In total, 139 empirical studies with usable test statistics wereuncovered, reporting a total of 555 raw effect sizes. We believethat we provide an extensive and rather exhaustive search of thepublished as well as unpublished literature. The studies identifiedrepresent a fairly well-rounded set of empirical work in thebusiness and psychological literatures. Table 1 provides an over-view of effects reported in the studies. As is shown in the table,marked differences exist in the direction, magnitude, and statis-tical significance of the effect sizes for the same pairwise relation-ships across studies.

3.2. Procedure

For each study identified, the sample size, correlations betweenvariables of interest, reliabilities measures (if reported), and studycharacteristics were recorded. To assess the potential temporaleffect, we also traced the year of the data collected. If suchinformation was not available, then the year the study was acceptedor published was used as a surrogate. A product is coded as having a‘‘regular purchase cycle’’ if buyers purchase the brand at least onceduring a year (e.g., FMCG, Baldinger and Rubinson, 1997).

For studies that did not report correlations, summary statistics(e.g., F, t, Z) were converted to the correlation coefficient, follow-ing the formulas provided by Hedges and Olkin (1985). Somestudies reported several effect size estimates for one relationshipusing the same sample. To avoid multiple correlated results froma single study, we used the Fisher Z-transformation to obtain theaverage of the statistics. We weighted each effect size using therelevant sample size and reliability information. The effect sizeswere corrected for attenuation due to measurement errors byincorporating reliability estimates for the correlated variables(i.e., divided by the product of the square roots of the tworeliabilities) whenever such information is available. For studiesthat did not report reliabilities, the sample-size-weighted meanreliability across all studies that did report it was used (Hunterand Schmidt, 1990).

Rosenthal and Rubin (1982) propose that statistical tests beused as an aid in deciding whether study outcomes are morevariable than would be expected from sampling error alone. Ifthey are not, then there is no basis for searching for moderators.Hedges and Olkin (1985) provide statistical tests to assess the

fidencervalrected)

Totalvariance

Sampling errorvariance

File drawer N(p¼0.05)

Q (d.f.)

1 .6941 .052 .0017 1090 4084.25b (117)

4 .7638 .046 .0013 317 978.93b (32)

2 .6633 .089 .0012 203 1674.01b (20)

4 .4186 .013 .0013 21 38.30b (3)

1 .6941 .033 .0015 244 576.02b (26)

9 .7052 .047 .0017 307 1887.13b (37)

7 .6899 .029 .0015 164 276.09b (16)

0 .5290 .071 .0015 36 439.56b (8)

4 .7246 .055 .0023 78 251.69b (9)

Table 3The effects of moderator variables.

Goods vs.service loy.

Behavioral vs.attitudinal loy.

Single vs. multi-itemmeasuresa

Business vs. consumermarket

Regular vs. irregularpurchase cycle

Year study wasconducted

Observed r Observed r Observed r Observed r Observed r Std. b (observed r)

Corrected r Corrected r Corrected r Corrected r (Corrected r)

z z z z (z)

N N N N N

Customer

satisfaction–

loyalty

.470 vs. .535 .459 vs. .541 .413 vs. .566n .422 vs. .517nn .415 vs. .531n .099

.564 vs. .620 .536 vs. .623 41 vs. 90 .489 vs. .604nn .493 vs. .618nn .068

.697 vs. .848 .669 vs. .844 .573 vs. .801nn .614 vs. .848nn .084

13 vs. 80 57 vs. 20 18 vs. 120 31 vs. 77

Trust – loyalty n.a. .498 vs. .581 n.a. b .440 vs. .553 .442 vs. .614nn .193

.561 vs. .661 .532 vs. .642 .537 vs. .707 .160

.676 vs. .812 .701 vs. .810 .535 vs. .968n .059

15 vs. 6 5 vs. 34 9 vs. 19

Product quality –

loyalty

n.a. .403 vs. .458 .309 vs. .529n .342 vs. .481nn .401 vs. .476 .330nn

.465 vs. .531 17 vs. 29 .415 vs. .569nn .490 vs. .555 .349nn

.543 vs. .657 .510 vs. .735 .763 vs. .695 .252

21 vs. 9 11 vs. 35 9 vs. 24

n Significant at po0.01.nn Significant at po0.05.a For this moderator, we only look at the observed mean correlations, since the reliability adjustment account for the differences in the corrected means (or Z-

transforms) that are due to single- vs. multiple-item scales.b n.a. means it has not been calculated because the moderator does not exist for this category or small sample size exists for at least one subgroup (no5).

Y. Pan et al. / Journal of Retailing and Consumer Services 19 (2012) 150–158 155

homogeneity of effect sizes across studies. The test statistic, Q, iscomputed for each pairwise relationship on Fisher’s Z-transformsof the correlation coefficients. This statistic has an approxi-mate chi-square distribution with k-1 degrees of freedom, wherek is the number of studies included in the analysis (Hedges andOlkin 1985).

4. Findings from the meta-analysis

4.1. Descriptive Findings

Table 2 presents the descriptive findings. As is shown inTable 2, the integrated study results support relatively strongrelationships between loyalty and its correlates, with meancorrected correlations ranging from .349 to .694. Furthermore,the file drawer N’s suggest that the synthesized effects are strong.File drawer N indicates the number of studies that confirm thenull hypothesis that would be needed to reverse a conclusion thata significant relationship exists, which we estimate for a signifi-cance level of 0.05. For instance, to bring the significant effect ofswitching costs on loyalty down to the level of just significant ata¼ .05, it would require 36 null-effect studies to be added to ouranalysis. Given the few studies uncovered (n¼9), the odds offinding additional 36 null-effect studies are low. Therefore,H1–H9 are supported.

Despite the significant effects at the integrative level, markedvariation in effects across studies is apparent for each pairwiserelationship. Homogeneity tests reveal overall inconsistency ofresults for all the relationships considered, suggesting that theserelationships cannot be generalized across study contexts. Theexistence of moderating effects, therefore, is indicated.

4.2. Moderator findings

To determine whether potential moderators account for varia-tions in the study effects, we compare the mean correlations andFisher’s Z-transformed values of corrected correlations by sub-groups based on levels of the coded study characteristics. We testeach moderator separately to maximize the number of usableobservations. In addition, individual study correlations and Fisher’s

Z-transforms are regressed on the year the study was conducted totest if study effects exhibit a temporal pattern. In this study, thesample sizes are not large enough to effectively test the moderatingeffects for all pairwise relationships. Therefore, we focus on a fewrelationships for which we have sufficient data. Table 3 reports theresults of the moderator analyses.

Several noteworthy insights emerge from a close examinationof Table 3. Consistent with H13, the factor ‘‘single- vs. multi-itemmeasure’’ is statistically significant in the two models examined,suggesting that using single-item scales can substantially deflateeffect sizes. As expected, irregularity of purchase cycle seems tobe a factor that contributes to the divergence in findings acrossstudies. In particular, the effects of satisfaction and trust onloyalty are stronger when customers buy products with irregularpurchase cycles, confirming H11. Despite an observed largereffect of product quality on loyalty for products with an irregularpurchase cycle, the difference is not statistically significant. Thefactor ‘‘business vs. consumer market’’ can at times have asignificant impact on the relationship between loyalty and itscorrelates. Satisfaction and quality have a significantly largerimpact on loyalty in consumer markets than in business markets.In the case of ‘‘trust-loyalty’’, although the moderating effect is inthe hypothesized direction (i.e., the relationship is weaker in abusiness than a consumer market), it doesn’t demonstrate statis-tical significance. We also observe a general temporal pattern inthe studied relationships, as the reported effect sizes becomestronger with the passage of time, lending support for H15.Contrary to our expectation, we do not spot any strong moderat-ing effect of ‘‘goods vs. service loyalty’’ and ‘‘attitudinal vs.behavioral measures’’. The impact of satisfaction on loyaltyremains relatively stable across service and goods settings.Attitudinal measures may be considered a good supplement ofand an ample substitute for behavioral measures of loyalty, as noapparent differences are detected between studies using thesemeasures.

4.3. Regression findings

As a final analysis, the two categories of loyalty predictors areentered in a regression model to determine which variables havemore (or less) predictive and explanatory power in relation to

Table 4Correlations of selected loyalty correlates reported in individual studies.

Customersatisfaction

Trust Psychologicalcommitment

Productquality

Switchingcosts

Customersatisfaction

.87

Trust .764 a .87520 b

9997 c

Psychologicalcommitment

.6 .614 .87712 12

7573 5552

Productquality

.67 .696 .778 .85323 8 2

9490 4998 689

Switchingcosts

.278 .206 .401 .457 .85 2 3 3

4577 2461 1304 3453

Note: Entries on the diagonal reflect mean reliabilities.

a Weighted average correlation (corrected) values.b Number of correlations obtained for each analysis.c Total sample size used for each analysis.

Table 5Multiple regression and relative importance analysis results for selected predictor

variables.

Antecedents tocustomer loyalty

Standardizedcoefficient (standarderror)a

Raw relative weights (relativeweights as percentage of R2)

Customer

satisfaction

.079 (.013)b .111 (18.7%)

Trust .499 (.013)b .198 (33.2%)

Psychological

commitment

.138 (.013)b .097 (16.2%)

Product quality � .01 (.014) .093 (15.6%)

Switching costs .276 (.009)b .097 (16.3%)

R2 (adjusted R2) .596 (.595)

F (p value) 2022.16b

a Statistical significance is based on the median sample size of 6873 on which

the individual correlations are based.b po0.001.

Y. Pan et al. / Journal of Retailing and Consumer Services 19 (2012) 150–158156

others. A matrix of corrected correlations is constructed from theavailable data and used as input for estimating a multivariateregression model of customer loyalty:

Loyalty¼ b1X1þb2X2þ � � � þb5X5þe

where Xi(i¼1, 2, y, 5) are antecedents to loyalty, and bi(i¼1, 2, y, 5)

are corresponding parameter estimates. The regression resultsand the meta-analytic correlations used to generate the model arereported in Tables 4 and 5. Since few studies report correlationaldata on the interrelationships among the determinants of loyalty,the correlation matrix contains data for only a subset of thevariables (i.e., we only include interrelationships with multiplestudy effects that relate one construct to every other construct inthe model, Brown and Peterson, 1993). Nonetheless, in therelatively parsimonious model, the predictors in combinationaccount for a large portion of the variance (R2

¼ .596) in thecriterion variable.

For the sake of substantive interpretation, we are interested inthe extent to which each variable contributes to the prediction ofthe criterion variable. The relative magnitude of the regressioncoefficients does not permit comparisons among the regressors interms of which are more (or less) important, when the predictorsare non-orthogonal (i.e., when predictors are correlated).A relative importance analysis supplements traditional multiplelinear regression analysis by providing information about the

relative contributions predictors make to the overall explanatorypower of the regression model. Relative importance refers to theproportionate contribution each predictor makes to R2 consider-ing both its individual effect and its effect when combined withthe other variables in a regression equation (Johnson andLeBreton, 2004). As multicollinearity is apparent in this study,we assess relative importance by calculating relative weights,following Johnson (2000). In essence, relative weights (or EpsilonWeights) are computed by creating a new set of uncorrelatedpredictors (ZK) that are maximally related to the original set ofcorrelated predictors (XJ). Results of the relative importanceanalysis are reported in Table 5.

The relative importance analysis shows that ‘‘trust’’ appears tobe one of the most important predictors of consumer loyalty,contributing 33.2% of explained variance in the dependent vari-able in the regression model.

5. Discussion

Customer loyalty is a complex multi-dimensional constructwith both attitudinal and behavioral components. For instance, incase of absence of store commitment, a patron to a store is merelyspuriously loyal (e.g., repatronage directed by inertia). In light ofthis argument, Dick and Basu (1994) suggest a theoretical frame-work that envisages the loyalty construct as being composed ofrelative attitude and patronage behavior. In terms of operationa-lization, ideally, the only way to reveal behavioral loyalty is toobserve the proportion of purchases devoted to a certain brand.However, collecting purely behavioral data is a painstaking effort.In the loyalty literature, a frequently used proxy is to measurecustomers’ attitudes toward the brand in question. Despite thepopularity of this practice, research on the substitutability ofattitudinal and behavioral loyalty measures is scant. In this study,the distinction between these two measures does not lead to anysignificant difference in all the relationships examined, suggestingthat affective loyalty proves to be a plausible surrogate forbehavioral loyalty.

Patterns of loyalty seem to be a consequence of product andmarket characteristics. As expected, irregularity of purchase cycleemerges as an important moderator. The effects of customersatisfaction and trust on loyalty are less prominent when pro-ducts are purchased on a regular and relatively short (as opposedto an irregular and relatively long) purchase cycle. A low level oftime interval during successive purchases allows buyers toexperiment with various brands, because if they are not satisfiedwith a brand, they can quickly and effortlessly switch to anotheralternative. Conversely, buyers of an infrequently purchasedproduct tend to choose more cautiously. They dread a wrongproduct choice, because the impact will be very forceful (Theymight be stuck with the product for a long time!). To avoid theadverse effects of selecting a wrong product (e.g., financial loss,frustration), buyers are likely to resort to their past experience toguide their future purchase decisions. The more satisfied they arewith the product in the past (or the more trust they place in theproduct), the more they would return.

The effects of loyalty antecedents display different patterns inB2B and B2C setting. Our results indicate that factors that largelyrelate to product performance (e.g., satisfaction, quality) have aweaker impact on loyalty in business than in consumer markets.A possible explanation is that other factors might interfere withan industrial buyer’s repeat purchase decision. For instance, B2Btransactions typically involve higher switching costs than B2Ctransactions. This constitutes a ‘‘lock-in’’ strategy to retain custo-mers. Furthermore, the need to attend to different decisionparticipants’ objectives, the long-term contract that locks the

Y. Pan et al. / Journal of Retailing and Consumer Services 19 (2012) 150–158 157

buyer-supplier relationship, the uncertainty in supply availability,the inertia of the purchase system, may all influence buyers’intentions to remain loyal. Note that we do not deny the influenceof rational and technical aspects of product benefits on loyalty(e.g., high quality, good reputation), nor do we intend to overlookthe power of customers’ past purchase and consumption experi-ence (e.g., customer satisfaction, trust) in affecting their decisionto repatronize a supplier. Rather, we argue that the effect of suchfactors is more likely to be eroded in a B2B setting, as compared toa B2C setting. Therefore, for business markets, it is necessary tohave a broader conceptual framework than has traditionally beenused to investigate loyalty in consumer markets.

Interestingly, the effect of quality on loyalty becomes moresalient over time. In an era of declining loyalty, it’s particularlyimportant to manage a customer’s experience with a product. Ourresults suggest that creating a high-quality image will becomeincreasingly crucial in building and maintaining customer loyalty.In the 2000s, value marketing becomes a serious strategy in thehighly competitive business world. An appreciation of addedvalue may be associated with bundled benefits that come withthe purchase (e.g., good quality).

Our results detect consistently weaker effects from studiesusing single-item loyalty measures. Single treatments with lowreliability can drastically attenuate effect-size estimates anddecrease precision. Conversely, the use of multi-item scalesenhances measurement reliability and hence should providestronger relationships than single-item measures can (Pan andZinkhan, 2006; Peter and Churchill, 1986). Here, we suggest thatresearch on customer loyalty employ multi-item measures thatreflect both attitudinal and behavioral elements.

In our analysis, the distinction of ‘‘goods vs. service loyalty’’does not lead to different estimates of relationship strengths.With the increasing emphasis on services in all markets, thedifferences in business practices in goods and service marketsmight be diminishing. Therefore, commonalities may exist incustomers’ repatronage behavior. Customer loyalty may be drivenby the same factors in goods and service purchase contexts.

Although findings of this study support all the hypothesizedmain effects, they indicate stronger effect size for trust than forother determinants of loyalty (See Tables 2 and 5). This resultgives support to the findings of authors such as Garbarino andJohnson (1999), and Ibanez et al. (2006), who suggest a strongerinfluence of trust on loyalty than of other studied variables. Thisevidence, in combination with prior research, provides an impor-tant addition to conventional understanding regarding the ante-cedents of loyalty. The importance of building consumer trust tocreate an emotional, lasting, and loyal relationship with thecustomer is well-documented. Social Exchange Theory (SET)provides theoretical foundations for the role of trust in transac-tional relationships. SET, from a sociopolitical perspective, servesas one explanation for justifying relational exchanges (Luo andDonthu, 2007). SET posits that trusted relationships are likely toreduce the risk of opportunistic behavior (Moorman et al., 1993),and increase the likelihood of a long-term orientation in exchange(Luo and Donthu, 2007). Accordingly, loyalty programs should beimplemented to build such strong, favorable and unique associa-tions with the brand.

6. Implications and directions for future research

Research on consumer loyalty has been conducted in a numberof methodological contexts. Moreover, effect sizes are inconsis-tent in terms of the significance, magnitude, and, in some cases,valence. Despite the inconsistencies, no attempt has been made toassess the robustness of effects across conditions. The study

provides a critical review and formal synthesis of the loyaltyliterature. Such assessment is useful for understanding the gen-eral strength and variability of the relationships under diversestudy conditions.

Our study has implications for researchers who use surveydata to explain loyalty behavior. Attitudinal measures prove to bea good supplement to, and in some cases, ample replacement forbehavioral measures. Future empirical research could gain furtherinsights if they incorporate both attitudinal and behavioraldimensions in loyalty measurement. The methodological limita-tions of relying on only one dimension are stressed by our study.

The study also has managerial implications. Customer retentionhas long been proven a cost-efficient strategy. Companies with aloyal customer base can not only survive, but also thrive in today’shighly competitive market. Managers, therefore, would like toknow what factors exactly lead to customer loyalty. By synthesiz-ing the previous empirical studies and pinpointing the key vari-ables that relate to consumer loyalty, the current study investigatesissues that are of great interest to corporate executives.

One major limitation of our study results from data availabil-ity. For instance, correlations between some key variables areabsent from previous studies. Still, empirical findings of someimportant loyalty antecedents (e.g., length of relationship, custo-mer characteristics) are not reported frequently enough to allow ameta-analytical integration. Due to the nature of our data, someintuitively appealing linkages cannot be established, which inhi-bits us from exploring how these variables interact and mediateeach other to influence customer loyalty.

Another limitation of the study pertains to the extent ofliterature search. Despite our attempt to conduct an exhaustivesearch of published and unpublished studies on customer loyalty,we well acknowledge the fact that we may still have excludedsome potentially important information sources such as confer-ence proceedings (e.g., EIRASS proceedings), due to our limitedaccess to such sources. Nonetheless, we don’t think this limitationwill significantly affect our study results, mainly for three reasons.First, the relatively extensive search of the published literaturemay uncover some of the studies that initially appeared in someconference proceedings. The interactive search of the referencesfrom relevant articles also helps identify important conferencepapers. Second, the request sent through ELMAR listserv invitedquite a few responses from authors who published in the field ofcustomer loyalty. We believe this request should have reachedmost, if not all, scholars who worked and published in this area.Last but not the least, file drawer N’s suggest that the synthesizedeffects in our study are strong, and that the odds of findingadditional studies that can offset such effects are low.

Our study is subject to some important caveats. Conclusionsfrom a meta-analysis are strengthened when the results are basedon large numbers of studies and large pools of subjects. Somefindings in this study should be robust (e.g., the ‘‘perceived value–loyalty’’ relationship is based on 27 independent samples of14074 subjects). Others are more susceptible to change as newevidence emerges (e.g., the ‘‘loyalty program membership–loyalty’’ relationship is based on 4 samples of 3052 subjects).The moderator tests are also affected by the number of studiesavailable for analysis. As in many meta-analyses, our moderatortests suffer from a second-order sampling problem, because thereal sample size in searches for moderators is the number ofstudies. The lack of strong evidence of some moderating effectscould be attributed to low statistical power.

It is worthy of further elaboration to develop a richer modelthat includes other constructs beyond the ones examined in thisstudy. For instance, the impact of customers’ involvement level ontheir loyalty may prove to be an interesting area of study. A highlevel of involvement may imply more dependence on a certain

Y. Pan et al. / Journal of Retailing and Consumer Services 19 (2012) 150–158158

brand/firm, whereas a low involvement level may lead to lessstrong customer loyalty. Further analysis may find productcategory could also make a difference in the dyadic relationshipsstudied here. For instance, we may see stronger correlations injewelry and fashion products, than more standardized productssuch as CDs and books. Yet another issue worthy of examinationis the online versus offline shopping context, where loyalty maybe more prevalent in one compared to the other.

Acknowledgment

The first author acknowledges the SBA summer researchsupport from the University of Dayton.

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