Antecedents of customer loyalty: An empirical synthesis and reexamination

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

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Journal of Retailing and Consumer Services 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 b c

Department of Management & Marketing, University of Dayton, 812 Miriam Hall, Dayton, OH 45469-2271, United States School of Business, University of Alabama, Birmingham, AL 35294, United States 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

a b s t r a c t

Available online 5 December 2011

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 metaanalysis 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.

Keywords: Customer loyalty Meta-analysis Predictors Moderators

1. Introduction Customer loyalty is a company’s most enduring assets. By creating and maintaining customer loyalty, a company develops a long-term, mutually beneficial relationship with the customers. Corporate executives are interested in fundamental questions concerning the concept of customer loyalty, e.g., the driving forces of customer loyal behavior. The practical and conceptual importance of this topic has been underscored by the substantial volume of studies published in leading academic journals. Despite the myriad studies published, there are a number of factors that limit a comprehensive understanding of customer loyalty and prevent the generalization of research findings. First, consensus is rarely found in the accumulated empirical research. For instance, although most studies presume that buyer satisfaction with a brand/seller leads to future patronage intention (e.g., Jones and Reynolds, 2006; Meuter et al., 2000), many fail to provide a strong linkage between customer satisfaction and loyalty (e.g., Khatibi et al., 2002; Stoel et al., 2004)).

n

Corresponding author. Tel.: þ1 937 229 1773; fax: þ 1 937 229 3788. E-mail addresses: [email protected] (Y. Pan), [email protected] (S. Sheng), [email protected] (F.T. Xie). 1 Tel.: þ1 205 934 8840; fax: þ1 205 934 0058. 2 Tel.: þ1 803 641 3242; fax: þ1 803 641 3445. 0969-6989/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jretconser.2011.11.004

Second, the inconsistency in findings is confounded by the fact that previous empirical studies have been conducted in various research contexts. Because of heterogeneous findings and diverse study conditions in the extant literature, the relationship between various correlates and loyalty cannot be determined a priori. These disparate findings complicate academic researchers’ efforts to develop a clear and comprehensive understanding of customer loyalty. Third, there seems to be no agreement on conceptualizing and operationalizing the loyalty construct. A review of the literature reveals that the choice of loyalty measurement instruments is somewhat arbitrary, which makes it difficult to generalize research findings across studies. Some authors view ‘‘share of requirements’’ (i.e., the proportion of volume accounted for by a brand, within its base of buyers) as the most appropriate measure of loyalty (cf. Baldinger and Rubinson, 1997), whereas others rely on 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 of loyalty should incorporate both behavioral and attitudinal components (e.g., Dick and Basu, 1994; Rundle-Thiele, 2005), the extent to which attitudinal measures are ample replacement for, or good supplement to behavioral measures is still largely untested. Significant opportunities exist to extend and refine appropriate loyalty measures. Without a widely accepted conceptual and operational definition, the analysis of loyalty is at

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best piecemeal. By integrating and comparing previous studies utilizing attitudinal and behavioral measures of loyalty, we assess if, and to what extent attitudinal measures are good substitutes for behavioral measures. Despite the importance of customer loyalty, no comprehensive work has been advanced to assess the general findings across academic studies. We seek to fill that void by conducting a metaanalysis of empirical findings on the predictors of customer loyalty. Through this study, we aim to make several contributions to the loyalty literature. First, we add to the contemporary state of knowledge by establishing the generalizability of the relationships between customer loyalty and its important correlates. A systematic review and integration of the empirical evidence can document the statistical significance, direction, and magnitude of the study effects. More importantly, it can shed light on the extent to which the effect sizes in individual studies are caused by the true value (i.e., population value) or study artifacts (e.g., measurement errors, publication bias). Second, built upon the findings of this meta-analysis, we assess the relative strengths of the studied relationships, and identify the variables that have more (or less) predictive and explanatory power relative to others. Finally, research on customer loyalty has been conducted in various methodological and study contexts, yet no attempt has been made to evaluate the robustness of effects across study conditions. The study advances our understanding of customer loyalty by exploring some contextual factors that may contribute to the divergence in individual findings.

2. Conceptual framework and research hypotheses In this study, we adopt a wide perspective of loyalty. The conceptualization of customer loyalty is not restricted to loyalty with respect to tangible goods, it also includes loyalty toward a service or service provider. Although there is no universally accepted definition of loyalty in the literature, it is typically believed 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 the strength of a customer’s dispositional attachment to a brand (or a service provider) and his/her intent to rebuy the brand (or repatronize the service provider) consistently in the future. Here, we integrate findings from prior research and model the antecedents of loyalty. Fig. 1 depicts the theoretical framework of this

151

study. Implicit in our theoretical framework is the recognition that individual and product characteristics interact and combine to shape one’s loyalty toward a product. Further, we identify several variables that are crucial in moderating the relationships between loyalty and its correlates. In what follows, we develop hypotheses regarding the antecedents and moderating effects of customer loyalty. 2.1. Antecedents to customer loyalty: customer-related factors Customer satisfaction. Customer satisfaction has often been regarded as a major determinant of loyalty (Dick and Basu, 1994). Yet empirical evidence is somewhat mixed. For instance, some studies fail to provide a strong linkage between customer satisfaction and loyalty (e.g., Khatibi et al., 2002; Stoel et al., 2004). Others indicate that the satisfaction-loyalty relationship is indirect and complex (e.g., Anderson and Mittal, 2000; Magi, 2003). Despite the mixed findings, in general, the literature anticipates a linear and positive effect of satisfaction on loyalty (cf. Jones and Reynolds, 2006; Seiders et al., 2005). H1. Customer satisfaction with a product has a significant and positive 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 to develop favorable attitudes toward it, to pay a premium price for it, to remain loyal to it, and to spread positive word-of-mouth (Chaudhuri and Holbrook, 2001). The impact of trust on customer loyalty becomes especially relevant when confronted with switching decisions with a high level of perceived risk and uncertainty (Lewis, 2002). Based on the afore-mentioned argument, we propose that: H2. A customer’s trust in a product has a significant and positive effect on his/her loyalty toward that product. Psychological commitment. Commitment may be understood as symbolic attachment or identification with a product. It is a necessary condition for loyalty to occur (Bloemer and de Ruyterk, 1998). Commitment is at the core of the value that a strong brand provides to its customers. It is the highest level of relational bonding and is essential for successful long-term relationships (Garbarino and Johnson, 1999; Johnson et al., 2006; Morgan and

Customer satisfaction (H1)

Customer -related factors

Trust (H2) Psychological commitment (H3) Loyalty LP membership (H4) Perceived value (H5)

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

Product quality (H6)

Product -related factors

Perceived fairness (H7) Switching costs (H8) Brand reputation (H9)

Loyalty measurement: Behavioral vs. attitudinal measures (H12), multi- vs. single-item measures (H13) Business vs. consumer market (H14) Time variable (H15)

Fig. 1. Conceptual framework.

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Hunt, 1994). Committed customers tend to invest more heavily in their relationship with the seller. They will perceive greater benefits to loyalty and greater risks to switching brands (Evanschitzky et al., 2006). Such findings have led us to the following hypothesis: H3. A customer’s psychological commitment toward a product fosters his/her loyalty. Loyalty program (LP) membership. Loyalty programs are designed to cultivate customer loyalty by rewarding repeat purchases. 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 such benefits will regularly return for additional purchases, resulting in a 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 higher loyalty toward the company’s products. 2.2. Antecedents to customer loyalty: product-related factors Perceived value. The cost in combination with the benefit of using a product determines overall perceived value of the product, which will influence customers’ purchase intention and behavior (Lai et al., 2009). Customers compare benefits received with investment put in, and choose the product that offers the best value compared to other alternatives. When perceived value of a product meets or exceeds their expectation, customers view the product a worthy buy. When the perceived value is low, customers would be more inclined to switch to competing brands in order to increase perceived value, thus resulting in a decline in loyalty (Anderson and Srinivasan, 2003). This theoretical reasoning 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 customer loyalty. Product quality. Previous research suggests either a direct (e.g., Boulding et al., 1993) or indirect (e.g., Woodruff, 1997) effect of product quality on loyalty. A high level of product quality often engenders feelings of pleasure, contentment, excitement, and satisfaction. It may foster customer confidence and trust in the brand (or service). Particularly, when a customer’s evaluation of the perceived performance of specific attributes of a product is better than his/her prior expectations, this will result in unwavering 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 recovery context. Perceived justice is the main determinant of complainants’ repatronage intentions (Blodgett et al., 1993). Customers evaluate fairness by comparing their perceptions of the experience received to what they believe it should be (Seiders and Berry, 1998). When they encounter conflicts with their fairness standards, they may experience a sense of injustice, which in turn leads to dissatisfaction and eventually termination of the exchange relationship. Firms that fail to project an image of fairness cannot develop the level of customer confidence needed to establish loyalty (Seiders and Berry, 1998). H7. Perceived justice has a positive effect on customer loyalty.

Switching costs. As in Fornell’s (1992) typology, switching costs 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 keeping customers in relationships, regardless of their satisfaction with the provider. Customers may remain loyal when high switching barriers make it costly to switch to another supplier. The more customers have to give up or sacrifice to switch to a substitutable product, the more they become dependent on the current one in use, and the less their intention to switch. Switching costs are used as a corporate strategy to increase customer loyalty (Dick and Basu, 1994). The varying extent to which firms control switching costs may explain variations in customer retention levels. Therefore, we propose that: H8. A positive relationship exists between switching costs and customer loyalty. Brand reputation. Reputation is often seen as a mechanism of assuring trustworthy behavior of a firm. In business and service markets, the company’s name is often the brand name across a range of product classes. Under such circumstances, the company reputation acts as the umbrella brand for these product categories (Cretu and Brodie, 2007). The associations customers have about the reputation of a retailer affect the value of what they purchase from that retailer (Brown and Dacin, 1997). Customers are likely to perceive a brand with a good reputation as trustworthy as opposed to one with a poor reputation. Furthermore, brand reputation is often used as a proxy for product quality when intrinsic cues or attributes are difficult to employ (Kirmani and Rao, 2000). When exposed to complex market cues, customers may not engage in elaborate information processing. To avoid information overload and any resulting dysfunctional consequences, they may simplify the buying decision by only attending to a striking evaluative criterion such as brand reputation. A product with a good reputation will reduce the perceived risk associated with performance ambiguity and information asymmetry and lead to favorable purchase and repurchase intent. H9. Brand reputation has a significant and positive influence on customer loyalty. 2.3. Potential moderators Sultan et al. (1990) advise that four broad categories of characteristics often account for systematic differences across correlations. They are measurement method, research context, estimation procedure, and model specification. Because our units of analysis are bivariate correlations that are unaffected by estimation procedure and model specification, we seek for systematic differences in study characteristics. Research on customer loyalty has been conducted in various methodological and research contexts. Heterogeneous study characteristics can contribute to variance in effect sizes. In our investigation, we identify several potential moderators (i.e., product type, loyalty measurement, market setting, and time variable) and assess their impact on sample homogeneity. Product type: tangible vs. intangible product. Many argue that findings in the area of product loyalty cannot be generalized to service loyalty (Keaveney, 1995). For instance, satisfaction has a greater effect on loyalty for services than it does for products, as intangible products have fewer differentiating characteristics compared to tangibles (Edvardsson et al., 2000). The intrinsic service attributes (e.g., intangibility, variability) make it harder for customers to assess value obtained before purchase and consumption. The social nature of services makes satisfaction more salient for customers, with consequent effects on evaluative and relational

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elements of service loyalty. Furthermore, service loyalty is more dependent on the development of interpersonal relationships as opposed to loyalty with tangible products (Macintosh and Lockshin, 1998). Intangible attributes such as reliability and confidence may play a more important role in building or maintaining loyalty in a service context than in a product context (Dick and Basu, 1994). These well-established differences between tangible and intangible products lead to a generalized expectation that reasons for remaining loyal in a service setting might differ from those in a goods setting. Therefore, we propose that:

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

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

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

Product type: regular vs. irregular purchase cycle. Many products are purchased on a regular and relatively short purchase cycle. Buyers are in the market at predictable intervals during the year. Most fast-moving consumer goods (FMCG) sold through supermarkets, drugstores, convenience outlets fall into this category, as do many services such as hair care and dry cleaning. Other products have a long and irregular purchase cycle, such as life insurance, management consultancy, and luxury items. These products are typically characterized by long interpurchase times. On every purchase cycle, the buyer essentially becomes a new prospect again and is functionally a new individual to be reached by sellers’ marketing effort (Rossiter and Danaher, 1998). Buyers of frequently purchased products are characterized by a high rate of brand switching, and low involvement and risk (Rundle-Thiele and Bennett, 2001). Most customers buy several brands in a product category and relatively few are sole buyers of each brand (Oliveira-Castro et al., 2005). One reason for the relatively low loyalty is that people tend to buy the cheapest brand in their consideration set (Foxall and James, 2003). They are more sensitive to competitive advertising or sales promotions, and more likely to try a new brand. Hence, they tend to display less loyal behavior on successive shopping occasions. For infrequently purchased products, buyers generally exhibit less switching behavior (Rundle-Thiele and Bennett, 2001). Such products tend to be higher in value than those people buy on a regular basis, which increase the level of involvement. Therefore, shoppers are inclined to base their choice decision on an overall assessment of the product, using a more elaborate set of evaluative criteria (e.g., overall satisfaction, trust, product quality). Based on this discussion, we propose that: H11. The relationships between loyalty and its predictors are stronger (weaker) for products with an irregular (regular) purchase cycle. Loyalty measurement: behavioral vs. attitudinal measures of loyalty. One of the hurdles in studying customer loyalty is the absence of a consensus on the definition and measurement of this construct. In the extant literature, there are two schools of thought when it comes to defining and operationalizing brand loyalty – researchers who approach brand loyalty strictly from a behavioral perspective (e.g., customers’ share of spending with a brand, repurchase intentions) and those who insist that in addition to behavioral aspects, a favorable attitude toward the brand (e.g., brand preference) is also required to define loyalty. Behavioral loyalty can be measured by observing the return visits over time. Most empirical studies, based on cross-sectional surveys, often use attitudes as a surrogate measure for behavioral loyalty. Despite the popularity of this practice, no study has assessed the substitutability of attitudinal and behavioral loyalty measures. We suspect that the use of attitudinal and behavioral measures may lead to varied strengths of the studied relationships.

Loyalty measurement: single- vs. multi-item measures of loyalty. The use of multiple-item scales enhances reliability in measurement of abstract constructs. Therefore, this practice should provide stronger relationships than the use of single-item measures (Peter and Churchill, 1986). Rundle-Thiele (2005) demonstrates the superiority of a multidimensional over a unidimensional model of consumer loyalty. Given the multidimensional nature of the loyalty construct, we believe that a multi-item measure can better capture the many facets of customer loyalty and produce stronger effects. Therefore, we offer the following hypothesis: H13. The relationships between loyalty and its predictors are stronger (weaker) for studies using multi-item (single-item) loyalty measures. Business vs. consumer market. A key distinction between organizational and consumer buying behavior lies in the nature of the relationship between buyers and sellers. Organizational buying is more complex than consumer buying behavior. The purchase decision is often made by a group (buying center), and must satisfy differing needs or objectives of participants (Moriarty, 1983). Reciprocal arrangements (i.e., an industrial buying practice in which two firms agree to buy each other’s products) and longterm purchase contracts often exist in organizational buying. Based on the characteristics of industrial buying, we suspect that factors other than product performance-related attributes may play a role in a business buyer’s repeat purchase decision. For instance, long-term contracts, plus the uncertainty in supply availability at the expected prices may well prevent business buyers from switching suppliers (Valuckaite and Snieska, 2007). In fact, the link between satisfaction and loyalty often appears weak or even absent in business markets, because mature customer–supplier relationships are often characterized by inertia where partners tend to maintain the status quo (Narayandas, 2005). Due to the unique nature of the business market, we expect product-performance attributes have a weaker impact on customer loyalty in business than in consumer markets. Hence, we propose that: H14. The relationships between loyalty and its predictors are weaker (stronger) in a business (consumer) market. Time variable. Many studies have suggested that customers are less loyal now than in the past (e.g., Bennett and RundleThiele, 2005; Taher et al., 1996). Compared with the more exclusive loyalty in the past, customers increasingly hold polygamous loyalty to brands (i.e., they divide their purchases among multiple brands in a category) (Bennett and Rundle-Thiele, 2005). Postmodern buyers become more critical and skeptical about influences from marketers (Elliot et al., 1993). When contemplating their next purchase, satisfied customers nowadays do not automatically buy the same brand (Taher et al., 1996).

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Table 1 Effects reported in the original studies. Loyalty correlates

No. of effects Range of the Cumulative reported reported effects (r) n

Antecedents to loyalty: customer-related factors Customer satisfaction Trust Psychological commitment LP membershipc Product-related factors Perceived value Product quality Perceived fairness Switching costs Brand reputation a b c

b

198a/208 61/62 26/32 8/9

41/42 134/139 24/25 13/15 22/23

.03938  .08.86 .0788

50,432 15,289 12,860

.03755 .05980 .0295 .081755  .1168 .258

3,052 14,074 16,194 6,840 6,873 3,788

Number of statistically significant (at a ¼.05) effects reported. Total number of effects reported. Loyalty program membership.

meet four criteria: they have to (1) report sample size, (2) analyze the bivariate relationship between loyalty and its determinants, (3) report either Pearson correlation coefficients or statistics that can be transformed into correlations, and (4) examine relationships that were frequently reported (n 43) in previous studies. In total, 139 empirical studies with usable test statistics were uncovered, reporting a total of 555 raw effect sizes. We believe that we provide an extensive and rather exhaustive search of the published as well as unpublished literature. The studies identified represent a fairly well-rounded set of empirical work in the business and psychological literatures. Table 1 provides an overview of effects reported in the studies. As is shown in the table, marked differences exist in the direction, magnitude, and statistical significance of the effect sizes for the same pairwise relationships across studies.

3.2. Procedure

Previous empirical research examining the loyalty relationships has largely disregarded temporal effects. An analysis of temporal patterns of study effects will provide initial insights into the everchanging nature of the loyalty construct. Here, we apply the time variable as a possible explanation for the variability of effect sizes across studies. Because research is limited in this area, we do not offer strong, directional hypotheses. H15. The relationships between loyalty and its predictors change over time. 3. Method 3.1. Sampling frame Empirical studies were selected if they reported at least one relationship specified in Fig. 1. The studies were identified via the following search procedure: (1) we did computerized searches using EBSCOhost and PsycInfo databases, followed by (2) an interactive search of the references from relevant articles identified, until no new references could be identified, (3) we sent a request through ELMAR listserv, asking for published and unpublished studies on this topic, and (4) we identified studies through issue-by-issue searches of the Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, and the proceedings of AMA and ACR. To be included in our meta-analysis, studies have to

For each study identified, the sample size, correlations between variables of interest, reliabilities measures (if reported), and study characteristics were recorded. To assess the potential temporal effect, we also traced the year of the data collected. If such information was not available, then the year the study was accepted or published was used as a surrogate. A product is coded as having a ‘‘regular purchase cycle’’ if buyers purchase the brand at least once during 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, following the formulas provided by Hedges and Olkin (1985). Some studies reported several effect size estimates for one relationship using the same sample. To avoid multiple correlated results from a single study, we used the Fisher Z-transformation to obtain the average of the statistics. We weighted each effect size using the relevant sample size and reliability information. The effect sizes were corrected for attenuation due to measurement errors by incorporating reliability estimates for the correlated variables (i.e., divided by the product of the square roots of the two reliabilities) whenever such information is available. For studies that did not report reliabilities, the sample-size-weighted mean reliability across all studies that did report it was used (Hunter and Schmidt, 1990). Rosenthal and Rubin (1982) propose that statistical tests be used as an aid in deciding whether study outcomes are more variable than would be expected from sampling error alone. If they are not, then there is no basis for searching for moderators. Hedges and Olkin (1985) provide statistical tests to assess the

Table 2 Main effects of loyalty correlates. Loyalty correlates

Ka

Customer satisfaction Trust Psychological commitment LP membership Perceived value Product quality Perceived fairness Switching costs Brand reputation a b

Weighted r (observed)

Sampling error variance

File drawer N (p ¼0.05)

Q (d.f.)

.4492 .5880 .5311 .6941 .052

.0017

1090

4084.25b (117)

.694 .595

.5320 .6506 .6244 .7638 .046 .4525 .5696 .5262 .6633 .089

.0013 .0012

317 203

978.93b (32) 1674.01b (20)

.349 .619 .624 .613 .452 .631

.2569 .4566 .4547 .4766 .3055 .4303

.0013 .0015 .0017 .0015 .0015 .0023

21 244 307 164 36 78

38.30b 576.02b 1887.13b 276.09b 439.56b 251.69b

Weighted r (corrected)

95% confidence interval (observed)

118 .519

.613

33 .591 21 .511 4 27 38 17 9 10

.321 .519 .524 .545 .367 .505

The number of effect sizes combined. Significant at p o.05.

.3843 .5821 .5927 .6141 .4283 .5805

95% confidence interval (corrected)

.2794 .5441 .5419 .5357 .3760 .5364

.4186 .6941 .7052 .6899 .5290 .7246

Total variance

.013 .033 .047 .029 .071 .055

(3) (26) (37) (16) (8) (9)

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Table 3 The effects of moderator variables.

Customer satisfaction– loyalty Trust – loyalty

Product quality – loyalty

Goods vs. service loy. Observed r Corrected r z N

Behavioral vs. attitudinal loy. Observed r Corrected r z N

Single vs. multi-item measuresa Observed r

.470 vs. .535 .564 vs. .620 .697 vs. .848 13 vs. 80 n.a.

.459 vs. .541 .536 vs. .623 .669 vs. .844 57 vs. 20 .498 vs. .581 .561 vs. .661 .676 vs. .812 15 vs. 6 .403 vs. .458 .465 vs. .531 .543 vs. .657 21 vs. 9

.413 vs. .566n 41 vs. 90

n.a.

N

n.a.

b

.309 vs. .529n 17 vs. 29

Business vs. consumer market Observed r Corrected r z N

Regular vs. irregular purchase cycle Observed r Corrected r z N

Year study was conducted Std. b (observed r) (Corrected r) (z)

.422 vs. .517nn .489 vs. .604nn .573 vs. .801nn 18 vs. 120 .440 vs. .553 .532 vs. .642 .701 vs. .810 5 vs. 34 .342 vs. .481nn .415 vs. .569nn .510 vs. .735 11 vs. 35

.415 vs. .531n .493 vs. .618nn .614 vs. .848nn 31 vs. 77 .442 vs. .614nn .537 vs. .707 .535 vs. .968n 9 vs. 19 .401 vs. .476 .490 vs. .555 .763 vs. .695 9 vs. 24

.099 .068 .084 .193 .160 .059 .330nn .349nn .252

n

Significant at po 0.01. Significant at p o 0.05. 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 Ztransforms) 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 (n o5). nn

homogeneity of effect sizes across studies. The test statistic, Q, is computed for each pairwise relationship on Fisher’s Z-transforms of the correlation coefficients. This statistic has an approximate chi-square distribution with k-1 degrees of freedom, where k is the number of studies included in the analysis (Hedges and Olkin 1985).

4. Findings from the meta-analysis 4.1. Descriptive Findings Table 2 presents the descriptive findings. As is shown in Table 2, the integrated study results support relatively strong relationships between loyalty and its correlates, with mean corrected 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 the null hypothesis that would be needed to reverse a conclusion that a significant relationship exists, which we estimate for a significance level of 0.05. For instance, to bring the significant effect of switching costs on loyalty down to the level of just significant at a ¼.05, it would require 36 null-effect studies to be added to our analysis. Given the few studies uncovered (n¼ 9), the odds of finding additional 36 null-effect studies are low. Therefore, H1–H9 are supported. Despite the significant effects at the integrative level, marked variation in effects across studies is apparent for each pairwise relationship. Homogeneity tests reveal overall inconsistency of results for all the relationships considered, suggesting that these relationships cannot be generalized across study contexts. The existence of moderating effects, therefore, is indicated. 4.2. Moderator findings To determine whether potential moderators account for variations in the study effects, we compare the mean correlations and Fisher’s Z-transformed values of corrected correlations by subgroups based on levels of the coded study characteristics. We test each moderator separately to maximize the number of usable observations. In addition, individual study correlations and Fisher’s

Z-transforms are regressed on the year the study was conducted to test if study effects exhibit a temporal pattern. In this study, the sample sizes are not large enough to effectively test the moderating effects for all pairwise relationships. Therefore, we focus on a few relationships for which we have sufficient data. Table 3 reports the results of the moderator analyses. Several noteworthy insights emerge from a close examination of Table 3. Consistent with H13, the factor ‘‘single- vs. multi-item measure’’ is statistically significant in the two models examined, suggesting that using single-item scales can substantially deflate effect sizes. As expected, irregularity of purchase cycle seems to be a factor that contributes to the divergence in findings across studies. In particular, the effects of satisfaction and trust on loyalty are stronger when customers buy products with irregular purchase cycles, confirming H11. Despite an observed larger effect of product quality on loyalty for products with an irregular purchase cycle, the difference is not statistically significant. The factor ‘‘business vs. consumer market’’ can at times have a significant impact on the relationship between loyalty and its correlates. Satisfaction and quality have a significantly larger impact on loyalty in consumer markets than in business markets. In the case of ‘‘trust-loyalty’’, although the moderating effect is in the hypothesized direction (i.e., the relationship is weaker in a business than a consumer market), it doesn’t demonstrate statistical significance. We also observe a general temporal pattern in the studied relationships, as the reported effect sizes become stronger with the passage of time, lending support for H15. Contrary to our expectation, we do not spot any strong moderating effect of ‘‘goods vs. service loyalty’’ and ‘‘attitudinal vs. behavioral measures’’. The impact of satisfaction on loyalty remains relatively stable across service and goods settings. Attitudinal measures may be considered a good supplement of and an ample substitute for behavioral measures of loyalty, as no apparent differences are detected between studies using these measures. 4.3. Regression findings As a final analysis, the two categories of loyalty predictors are entered in a regression model to determine which variables have more (or less) predictive and explanatory power in relation to

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

Trust Psychological commitment

Product quality

Switching costs

.87

.764 a 20 b 9997 c Psychological .6 commitment 12 7573 Product .67 quality 23 9490 Switching .278 costs 5 4577

.875

.614 12 5552 .696 8 4998 .206 2 2461

.877

.778 2 689 .401 3 1304

.853

.457 3 3453

.8

relative contributions predictors make to the overall explanatory power of the regression model. Relative importance refers to the proportionate contribution each predictor makes to R2 considering both its individual effect and its effect when combined with the other variables in a regression equation (Johnson and LeBreton, 2004). As multicollinearity is apparent in this study, we assess relative importance by calculating relative weights, following Johnson (2000). In essence, relative weights (or Epsilon Weights) are computed by creating a new set of uncorrelated predictors (ZK) that are maximally related to the original set of correlated predictors (XJ). Results of the relative importance analysis are reported in Table 5. The relative importance analysis shows that ‘‘trust’’ appears to be one of the most important predictors of consumer loyalty, contributing 33.2% of explained variance in the dependent variable in the regression model.

Note: Entries on the diagonal reflect mean reliabilities. a b c

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

Table 5 Multiple regression and relative importance analysis results for selected predictor variables. Antecedents to Standardized customer loyalty coefficient (standard error)a Customer satisfaction Trust Psychological commitment Product quality Switching costs R2 (adjusted R2) F (p value)

Raw relative weights (relative weights as percentage of R2)

.079 (.013)b

.111 (18.7%)

b

.499 (.013) .138 (.013)b

.198 (33.2%) .097 (16.2%)

 .01 (.014) .276 (.009)b .596 (.595) 2022.16b

.093 (15.6%) .097 (16.3%)

a Statistical significance is based on the median sample size of 6873 on which the individual correlations are based. b po 0.001.

others. A matrix of corrected correlations is constructed from the available data and used as input for estimating a multivariate regression model of customer loyalty: Loyalty ¼ b1 X 1 þ b2 X 2 þ    þ b5 X 5 þ e where Xi(i ¼ 1, 2, y, 5) are antecedents to loyalty, and bi(i ¼ 1, 2, y, 5) are corresponding parameter estimates. The regression results and the meta-analytic correlations used to generate the model are reported in Tables 4 and 5. Since few studies report correlational data on the interrelationships among the determinants of loyalty, the correlation matrix contains data for only a subset of the variables (i.e., we only include interrelationships with multiple study effects that relate one construct to every other construct in the model, Brown and Peterson, 1993). Nonetheless, in the relatively parsimonious model, the predictors in combination account for a large portion of the variance (R2 ¼.596) in the criterion variable. For the sake of substantive interpretation, we are interested in the extent to which each variable contributes to the prediction of the criterion variable. The relative magnitude of the regression coefficients does not permit comparisons among the regressors in terms of which are more (or less) important, when the predictors are non-orthogonal (i.e., when predictors are correlated). A relative importance analysis supplements traditional multiple linear regression analysis by providing information about the

5. Discussion Customer loyalty is a complex multi-dimensional construct with both attitudinal and behavioral components. For instance, in case of absence of store commitment, a patron to a store is merely spuriously loyal (e.g., repatronage directed by inertia). In light of this argument, Dick and Basu (1994) suggest a theoretical framework that envisages the loyalty construct as being composed of relative attitude and patronage behavior. In terms of operationalization, ideally, the only way to reveal behavioral loyalty is to observe 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 measure customers’ attitudes toward the brand in question. Despite the popularity of this practice, research on the substitutability of attitudinal and behavioral loyalty measures is scant. In this study, the distinction between these two measures does not lead to any significant difference in all the relationships examined, suggesting that affective loyalty proves to be a plausible surrogate for behavioral loyalty. Patterns of loyalty seem to be a consequence of product and market characteristics. As expected, irregularity of purchase cycle emerges as an important moderator. 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. A low level of time interval during successive purchases allows buyers to experiment with various brands, because if they are not satisfied with a brand, they can quickly and effortlessly switch to another alternative. Conversely, buyers of an infrequently purchased product tend to choose more cautiously. They dread a wrong product choice, because the impact will be very forceful (They might be stuck with the product for a long time!). To avoid the adverse effects of selecting a wrong product (e.g., financial loss, frustration), buyers are likely to resort to their past experience to guide their future purchase decisions. The more satisfied they are with the product in the past (or the more trust they place in the product), the more they would return. The effects of loyalty antecedents display different patterns in B2B and B2C setting. Our results indicate that factors that largely relate to product performance (e.g., satisfaction, quality) have a weaker impact on loyalty in business than in consumer markets. A possible explanation is that other factors might interfere with an industrial buyer’s repeat purchase decision. For instance, B2B transactions typically involve higher switching costs than B2C transactions. This constitutes a ‘‘lock-in’’ strategy to retain customers. Furthermore, the need to attend to different decision participants’ objectives, the long-term contract that locks the

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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 influence of rational and technical aspects of product benefits on loyalty (e.g., high quality, good reputation), nor do we intend to overlook the power of customers’ past purchase and consumption experience (e.g., customer satisfaction, trust) in affecting their decision to repatronize a supplier. Rather, we argue that the effect of such factors is more likely to be eroded in a B2B setting, as compared to a B2C setting. Therefore, for business markets, it is necessary to have a broader conceptual framework than has traditionally been used to investigate loyalty in consumer markets. Interestingly, the effect of quality on loyalty becomes more salient over time. In an era of declining loyalty, it’s particularly important to manage a customer’s experience with a product. Our results suggest that creating a high-quality image will become increasingly crucial in building and maintaining customer loyalty. In the 2000s, value marketing becomes a serious strategy in the highly competitive business world. An appreciation of added value may be associated with bundled benefits that come with the purchase (e.g., good quality). Our results detect consistently weaker effects from studies using single-item loyalty measures. Single treatments with low reliability can drastically attenuate effect-size estimates and decrease precision. Conversely, the use of multi-item scales enhances measurement reliability and hence should provide stronger relationships than single-item measures can (Pan and Zinkhan, 2006; Peter and Churchill, 1986). Here, we suggest that research on customer loyalty employ multi-item measures that reflect 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, the differences in business practices in goods and service markets might be diminishing. Therefore, commonalities may exist in customers’ repatronage behavior. Customer loyalty may be driven by the same factors in goods and service purchase contexts. Although findings of this study support all the hypothesized main effects, they indicate stronger effect size for trust than for other determinants of loyalty (See Tables 2 and 5). This result gives support to the findings of authors such as Garbarino and Johnson (1999), and Ibanez et al. (2006), who suggest a stronger influence of trust on loyalty than of other studied variables. This evidence, in combination with prior research, provides an important addition to conventional understanding regarding the antecedents of loyalty. The importance of building consumer trust to create an emotional, lasting, and loyal relationship with the customer is well-documented. Social Exchange Theory (SET) provides theoretical foundations for the role of trust in transactional relationships. SET, from a sociopolitical perspective, serves as one explanation for justifying relational exchanges (Luo and Donthu, 2007). SET posits that trusted relationships are likely to reduce 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 be implemented to build such strong, favorable and unique associations with the brand.

6. Implications and directions for future research Research on consumer loyalty has been conducted in a number of methodological contexts. Moreover, effect sizes are inconsistent in terms of the significance, magnitude, and, in some cases, valence. Despite the inconsistencies, no attempt has been made to assess the robustness of effects across conditions. The study

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provides a critical review and formal synthesis of the loyalty literature. Such assessment is useful for understanding the general strength and variability of the relationships under diverse study conditions. Our study has implications for researchers who use survey data to explain loyalty behavior. Attitudinal measures prove to be a good supplement to, and in some cases, ample replacement for behavioral measures. Future empirical research could gain further insights if they incorporate both attitudinal and behavioral dimensions in loyalty measurement. The methodological limitations of relying on only one dimension are stressed by our study. The study also has managerial implications. Customer retention has long been proven a cost-efficient strategy. Companies with a loyal customer base can not only survive, but also thrive in today’s highly competitive market. Managers, therefore, would like to know what factors exactly lead to customer loyalty. By synthesizing the previous empirical studies and pinpointing the key variables that relate to consumer loyalty, the current study investigates issues that are of great interest to corporate executives. One major limitation of our study results from data availability. For instance, correlations between some key variables are absent from previous studies. Still, empirical findings of some important loyalty antecedents (e.g., length of relationship, customer characteristics) are not reported frequently enough to allow a meta-analytical integration. Due to the nature of our data, some intuitively appealing linkages cannot be established, which inhibits us from exploring how these variables interact and mediate each other to influence customer loyalty. Another limitation of the study pertains to the extent of literature search. Despite our attempt to conduct an exhaustive search of published and unpublished studies on customer loyalty, we well acknowledge the fact that we may still have excluded some potentially important information sources such as conference proceedings (e.g., EIRASS proceedings), due to our limited access to such sources. Nonetheless, we don’t think this limitation will significantly affect our study results, mainly for three reasons. First, the relatively extensive search of the published literature may uncover some of the studies that initially appeared in some conference proceedings. The interactive search of the references from relevant articles also helps identify important conference papers. Second, the request sent through ELMAR listserv invited quite a few responses from authors who published in the field of customer loyalty. We believe this request should have reached most, if not all, scholars who worked and published in this area. Last but not the least, file drawer N’s suggest that the synthesized effects in our study are strong, and that the odds of finding additional studies that can offset such effects are low. Our study is subject to some important caveats. Conclusions from a meta-analysis are strengthened when the results are based on large numbers of studies and large pools of subjects. Some findings in this study should be robust (e.g., the ‘‘perceived value– loyalty’’ relationship is based on 27 independent samples of 14074 subjects). Others are more susceptible to change as new evidence 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 studies available for analysis. As in many meta-analyses, our moderator tests suffer from a second-order sampling problem, because the real sample size in searches for moderators is the number of studies. The lack of strong evidence of some moderating effects could be attributed to low statistical power. It is worthy of further elaboration to develop a richer model that includes other constructs beyond the ones examined in this study. For instance, the impact of customers’ involvement level on their loyalty may prove to be an interesting area of study. A high level of involvement may imply more dependence on a certain

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brand/firm, whereas a low involvement level may lead to less strong customer loyalty. Further analysis may find product category could also make a difference in the dyadic relationships studied here. For instance, we may see stronger correlations in jewelry and fashion products, than more standardized products such as CDs and books. Yet another issue worthy of examination is the online versus offline shopping context, where loyalty may be more prevalent in one compared to the other.

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