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A study, investigating retailer-consumer relationships, has 3 distinct intended contributions: 1. to show that different relationship marketing tactics have a different impact on consumer perceptions of a retailer's relationship investment; 2. to demonstrate that perceived relationship investment affects relationship quality, ultimately leading to behavioral loyalty; and 3. to reveal that the effect of perceived relationship investment on relationship quality is contingent on a consumer product category involvement and proneness to engage in retail relationships. The authors empirically cross-validate the underlying conceptual model by studying 6 consumer samples in a 3-country, transatlantic, comparative study that investigates 2 industries.

In the current retail environment, relationship marketing tactics play a predominant role because of the increased importance consumers attach to relational properties of their interactions with retailers (Crosby, Evans, and Cowles 1990; Dorsch, Swanson, and Kelley 1998). In comparison with manufacturers, retailers have an advantage in building enduring relationships with consumers because they are in a better position to detect consumer purchase patterns and apply this knowledge in a cost-efficient way (Sweeney, Soutar, and Johnson 1999). Examples of relationship marketing practices in retailing are widespread. Ritz-Carlton is well known for its personalized welcome and farewell of guests, using the guest's name when possible. Loyalty programs initiated by airlines consist of not only rewarding the most valuable customers in the form of mileage prizes but also showing recognition and providing special privileges.

Although academics recognize the importance of relationship marketing practices (Berry 1995; Goff et al. 1997), empirical evidence on the nature and extent of the impact of relationship marketing tactics on relationship quality is scarce (Gwinner, Gremler, and Bitner 1998). Specifically, although relationship marketing has a strong theoretical base in industrial and channel marketing (e.g., Doney and Cannon 1997), systematic research on relationship marketing in a consumer environment is lacking (Beatty et al. 1996). Yet several authors agree with Dwyer, Schurr, and Oh (1987), who note that relational bonds create benefits in business as well as in consumer environments (Christy, Oliver, and Penn 1996; Sheth and Parvatiyar 1995). In particular, collecting information from the consumer's side of the retailer-consumer dyad is considered an important future research avenue (Gwinner, Gremler, and Bitner 1998; Sheth and Parvatiyar 1995).

With that in mind, our objectives are threefold. First, we want to determine whether different relationship marketing tactics have a differential impact on consumer perceptions of relationship investment by the retailer. We consider this important because retailers are often surrounded by uncertainty and incorrect beliefs about what matters to customers, which results in relationship marketing programs that are ineffectively implemented. Given the observation that retailers largely make use of traditional, defensive strategies, it is especially relevant to collect information on consumer perceptions of alternative, relationship-focused strategies (Beatty et al. 1996; Bolton 1998; Dorsch, Swanson, and Kelley 1998; Sirohi, McLaughlin, and Wittink 1998). Yet few efforts have been made to delineate different relationship marketing tactics (Christy, Oliver, and Penn 1996). Furthermore, hardly any systematic empirical investigation has been published that examines the reactions of consumers to relational strategies (Gwinner, Gremler, and Bitner 1998).

Second, we want to provide empirical evidence for the impact of perceived relationship investment on relationship quality, and ultimately on behavioral loyalty. Based on the reciprocity principle, this effect has been examined extensively in business markets (e.g., Anderson,and Weitz 1992; Ganesan 1994; Huppertz, Arenson, and Evans 1978), but to our knowledge, it has not been included yet as a topic of empirical investigation in consumer research.

Third, this research is one of the first empirical studies designed to analyze whether the effect of perceived relationship investment on relationship quality is contingent on consumer characteristics. Several authors stress that relationship marketing practices are not considered effective in every situation or context (Day 2000; Kalwani and Narayandas 1995). Yet few empirical efforts have been made to assess the moderating role of consumer characteristics on relationship marketing effectiveness (Beatty et al. 1996; Bendapudi and Berry 1997).

In addressing these issues, we hope to contribute to the aforementioned existing gaps in the relationship marketing research. Attempts to validate relationship marketing studies across settings are still exceptional (Geyskens et al. 1996), so we conduct a fairly comprehensive and rigorous test of our research hypotheses by empirically cross-validating our conceptual model in a multi-country and multi-industry context. Steenkamp and Baumgartner ( 1998) stress the need to validate models developed in one country, mostly the United States, in other countries as well.

Theoretical Background and Hypotheses

Although an all-encompassing theory of relationship marketing is still lacking (Bagozzi 1995), the principle of reciprocity is considered a useful framework for investigating exchange relationships (Huppertz, Arenson, and Evans 1978). Reciprocity is identified as a key feature explaining the duration and stability of exchange relationships (Larson 1992). Moreover, it is often considered one of the most robust effects found in psychological literature (Moon 2000). Gouldner (1960, p. 168) states that the generalized norm of reciprocity "evokes obligation toward others on the basis of their past behavior." The principle of reciprocity states that people should return good for good, in proportion to what they receive (Bagozzi 1995). According to the reciprocal action theory, actions taken by one party in an exchange relationship will be reciprocated in kind by the other party, because each party anticipates the feelings of guilt it would have if it violated the norm of reciprocity (Li and Dant 1997).

Reciprocity has regularly been used as a framework of thought or a key variable of interest in research on channel relationships. For example, reciprocity is apparent from the willingness of a firm to give preference to a supplier that is also a customer of the firm's products (Bergen, Dutta, and Walker 1992). Compaq refused to sell directly because doing so would constitute competing with its own dealers. Compaq's dealers considered this refusal a sign of Compaq's commitment to them, and the dealers reciprocated by providing the brand greater support and shelf space (Day 1990). In general, reciprocation of behavior will foster a positive atmosphere, remove barriers of risk, and enable channel relationships to move forward (Smith and Barclay 1997).

Bagozzi (1995) indicates that the phenomenon of reciprocity is also present in consumer-firm relationships, and he stresses that further research on relationship marketing should investigate the psychological manifestations of reciprocity and the way it functions in everyday consumer exchanges. Also, Huppertz, Arenson, and Evans (1978) indicate that the principle of reciprocity could be used for understanding consumer behavior in general. Nevertheless, Moon (2000) recently has questioned whether the norm of reciprocity is compatible with the realities of consumer research, since engaging in a reciprocal interaction between a consumer and a company would require a one-to-one interaction with every consumer. Given the recognized importance of the reciprocity principle in consumer relationships and given our focus on relationship marketing tactics that are targeted at individual consumers, we regard the concept of reciprocity as an appropriate framework of thought for building our conceptual model as depicted in Figure I.

The idea behind our model is consistent with the work of Blau (1964), who recognizes that an investment of time, effort, and other irrecoverable resources in a relationship creates psychological ties that motivate parties to maintain the relationship and sets an expectation of reciprocation. We apply this principle in a consumer context, representing irrecoverable resources by the construct of perceived relationship investment. The resulting constructs of relationship quality and behavioral loyalty, embodying consumers' reciprocation of a retailer's investments, reflect the extent to which consumers want to maintain their relationship. This is similar to Bagozzi's (1995) argument that consumers demonstrate loyalty to certain sellers in reciprocation of these sellers' investments in the relationship. In addition, Kang and Ridgway (1996) argue that consumers feel obligated to pay back the marketer's "friendliness." Moreover, to detect the extent to which relationship marketing tactics contribute to perceptions of relationship investment, we assess the relationship between four relationship marketing tactics (direct mail, preferential treatment, interpersonal communication, and tangible rewards) and perceived relationship investment. Finally, we incorporate consumer relationship proneness and product category involvement as moderators between perceived relationship investment and relationship quality. In the sections that follow, we define each of the constructs and describe their expected effects.

Perceived Relationship Investment

When a supplier makes a relationship investment of any kind on behalf of a customer, this customer ought to be favorably impressed (Hart and Johnson 1999). Investing time, effort, and other irrecoverable resources in a relationship creates psychological bonds that encourage customers to stay in that relationship and sets an expectation of reciprocation (Smith and Barclay 1997). Although the predominant approach regarding the construct of specific investment in a businessto-business or channel context has been to examine unrecoverable investments in a specific A-to-B relationship (e.g., Anderson and Weitz 1992; Smith and Barclay 1997), we examine investments that are unrecoverable only in the context of "one A to many B's," that is, a retailer to its set of regular customers rather than a retailer to one specific regular customer. The underlying rationale for this choice is that relationship marketing tactics directed at consumers are most often part of an overall relationship marketing strategy that is applied similarly to all regular customers rather than developed on a case-by-case basis as is common practice in business-to-business settings. Therefore, we define perceived relationship investment as a consumer's perception of the extent to which a retailer devotes resources, efforts, and attention aimed at maintaining or enhancing relationships with regular customers that do not have outside value and cannot be recovered if these relationships are terminated (Smith 1998).

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We investigate the mediating role of perceived relationship investment, accounting for the connection between relationship marketing tactics and relationship quality. In line with our theoretical perspective of reciprocation, the measurement items of relationship investment emphasize an aim for reciprocation by consumers that is based on retention efforts made by a retailer (e.g., "This store makes efforts to increase regular customers' loyalty"). We position relationship marketing tactics applied by the retailer as antecedents of relationship investment to provide managerial guidelines as to what affects perceptions of relationship investment. Relationship quality, ultimately influencing behavioral loyalty, is positioned as a consequence of relationship investment. A positive path between relationship investment and relationship quality implies that the consumer reciprocates a retailer's actions.

Relationship Marketing Tactics

Few efforts have been made to define what relationship marketing tactics really are and how valuable consumers perceive them to be (Dorsch, Swanson, and Kelley 1998; Gwinner, Gremler, and Bitner 1998). Nevertheless, the successful establishment of commercial relationships is considered to depend largely on fine-tuning such tactics (Christy, Oliver, and Penn 1996; Dwyer, Schurr, and Oh 1987). In general, the literature distinguishes among three levels of relationship marketing (Berry 1995). A first level relies on pricing incentives to secure customer loyalty and is often referred to as level one relationship marketing. It is considered the weakest level of relationship marketing because competitors can easily imitate price. A second level of relationship marketing focuses on the social aspects of a relationship, which are exemplified by regularly communicating with consumers or referring to their names during encounters. These socially inspired tactics are usually bundled into what is called level two relationship marketing. Level three relationship marketing, offering structural solutions to customer problems, is not investigated in this study. The reason for this choice is that level three relationship marketing does not involve true relationship marketing tactics or skills, as Berry (1995, p. 241) argues: "At level three, the solution to the customer's problem is designed into the service-delivery system rather than depending upon the relationship-building skills." Consequently, we distinguish among four types of relationship marketing tactics distributed across level one relationship marketing (tangible rewards) and level two relationship marketing (direct mail, preferential treatment, and interpersonal communication).

Direct mail. We define direct mail as a consumer's perception of the extent to which a retailer keeps its regular customers informed through direct mail (e.g., Anderson and Narus 1990; Dwyer, Schurr, and Oh 1987; Morgan and Hunt 1994). In general, it is recognized that buyer-seller communication increases the probability of discovering behaviors that generate rewards; enhances the prediction of behavior of the other party and clarifies each other's roles (Doney and Cannon 1997; Smith and Barclay 1997); leads to the discovery of similarities; and encourages feelings of trust, special status, and closeness (Anderson and Narus 1990). By conveying interest in the customer, communication is often considered a necessary condition for the existence of a relationship (Duncan and Moriarty 1998). In our study, we limit communication media to direct communication media, because mass media communication does not allow for targeting specific groups such as regular versus nonregular customers. Moreover, the underlying reason for limiting direct communication media to direct mail is that in the research contexts investigated, other types of direct media communication are only occasionally used. As a result, we seek to establish that direct mail, as a way of communicating with customers, should be a strong precursor for consumer perceptions of relationship investment. Therefore,

H1: A higher perceived level of direct mail leads to a higher perceived level of relationship investment.

Preferential treatment. We define preferential treatment as a consumer's perception of the extent to which a retailer treats and serves its regular customers better than its nonregular customers (e.g., Gwinner, Gremler, and Bitner 1998). For example, account holders at major shops are sometimes offered special shopping evenings or preferential access to certain products for sale. Sheth and Parvatiyar (1995, p. 264) recognize that "implicit in the idea of relationship marketing is consumer focus and consumer selectivity-that is, all consumers do not need to be served in the same way." O'Brien and Jones (1995) criticize companies for inadvertently treating all customers as equal; by not differentiating, companies waste resources in oversatisfying less profitable customers while undersatisfying more valuable, loyal customers. Peterson (1995) argues that distincfive treatment enables a seller to address a person's basic human need to feel important. Thus, we expect to demonstrate that a stronger perception of preferential treatment leads to a higher perceived level of relationship investment made by the retailer. Accordingly,

H2: A higher perceived level of preferential treatment leads to a higher perceived level of relationship investment.

Interpersonal communication. We define interpersonal communication as a consumer's perception of the extent to which a retailer interacts with its regular customers in a warm and personal way (e.g., Metcalf, Frear, and Krishnan 1992). Interpersonal communication differs from preferential treatment in that the former refers to the personal touch in communication between a store and its customers and the latter emphasizes that regular customers receive a higher service level than nonregular customers. The importance of personal exchanges between consumers and retailers in influencing relationship outcomes should not be surprising given that relationships are inherently social processes (Beatty et al. 1996). For example, almost five decades ago, Stone (1954) highlighted the importance of social exchange in recognizing the existence of shoppers who appreciate personal contact in the store. Evans, Christiansen, and Gill (1996, p. 208) state that the social interaction afforded by shopping has been suggested to be "the prime motivator for some consumers to visit retail establishments." Examples of social relationship benefits are feelings of familiarity, friendship, and social support (Berry 1995); personal recognition and use of a customer's name (Howard, Gengler, and Jain 1995); knowing the customer as a person; engaging in friendly conversations; and exhibiting personal warmth (Crosby, Evans, and Cowles 1990). This theorizing is summarized in the following hypothesis:

H3: A higher perceived level of interpersonal communication leads to a higher perceived level of relationship investment.

Tangible rewards. We describe tangible rewards as a consumer's perception of the extent to which a retailer offers tangible benefits such as pricing or gift incentives to its regular customers in return for their loyalty. Babin, Darden, and Griffin (1994) refer to a duality of rewards for many human behaviors, the distinction between performing an act to "get something" versus doing so because "you love it." Many marketers focus on the former, providing rewards that rely primarily on pricing incentives and money savings to secure customers' loyalty (Berry 1995; Peterson 1995). Similarly, our construct of tangible rewards implies that customers receive something tangible in return for their loyalty. Examples of tangible rewards marketers provide as a means of appreciating customers' patronage are frequent flyer miles, customer loyalty bonuses, free gifts, and personalized centsoff coupons (Peterson 1995). Also, trying to earn points-on such things as hotel stays, movie tickets, and car washeshelps customers remain loyal, regardless of service enhancement or price promotions of competitors (Sharp and Sharp 1997). Therefore, we formulate the following:

H4: A higher perceived level of tangible rewards leads to a higher perceived level of relationship investment.

Relationship Quality

The choice of relationship quality as a relationship outcome in our study is consistent with previous studies on relationship marketing (e.g., Kumar, Scheer, and Steenkamp 1995). Relationship quality can be considered an overall assessment of the strength of a relationship (Garbarino and Johnson 1999; Smith 1998). Previous research conceptualizes relationship quality as a higher-order construct consisting of several distinct, though related, dimensions (e.g., Dorsch, Swanson, and Kelley 1998; Kumar, Scheer, and Steenkamp 1995). Although there still exists discussion on which dimensions make up relationship quality, prior conceptualizations mainly emphasize the critical importance of relationship satisfaction, trust, and relationship commitment as indicators of relationship quality. For example, Crosby, Evans, and Cowles (1990) and Dwyer, Schurr, and Oh ( 1987) consider relationship satisfaction and trust to be indicators of the higher-order construct of relationship quality. Hennig-Thurau and Klee (1997), Leuthesser (1997), and Dorsch, Swanson, and Kelley (1998) further argue to add relationship commitment as a dimension of relationship quality. Therefore, we assume that a better-quality relationship is accompanied by a greater satisfaction, trust, and commitment. We prefer the abstract relationship quality construct over its more specific dimensions because, even though these various forms of attitude may be conceptually distinct, consumers have difficulty making fine distinctions between them and tend to lump them together (Crosby, Evans, and Cowles 1990). Next, we briefly elaborate on the dimensions of relationship quality.

Relationship satisfaction. Satisfaction with the relationship is regarded as an important outcome of buyer-seller relationships (Smith and Barclay 1997). We define relationship satisfaction as a consumer's affective state resulting from an overall appraisal of his or her relationship with a retailer (Anderson and Narus 1990). Thus, we conceptualize relationship satisfaction as an affective state (Smith and Barclay 1997) in contrast with more rational outcomes (Anderson and Narus 1990). In addition, we view it as a cumulative effect over the course of a relationship compared with satisfaction that is specific to each transaction (Anderson, Fornell, and Rust 1997).

Trust. The development of trust is thought to be an important result of investing in dyadic buyer-seller relationships (e.g., Gundlach, Achrol, and Mentzer 1995). Drawing on the existing literature (e.g., Morgan and Hunt 1994), we define trust as a consumer's confidence in a retailer's reliability and integrity. Several scholars consider perceived trustworthiness and trusting behaviors as two distinct, though related, aspects of trust. Whereas trustworthiness refers to a belief or confidence, trusting behaviors are related to the willingness to engage in risk-taking behavior, reflecting a reliance on a partner (Smith and Barclay 1997). Although some scholars merge both aspects into one definition of trust (e.g., Moorman, Desphande, and Zaltman 1993), others claim that trustworthiness is a necessary and sufficient condition for trust to exist (e.g., Anderson and Narus 1990). In line with the latter group, our definition encompasses only the notion of trustworthiness.

Relationship commitment. Commitment is generally regarded to be an important result of good relational interactions (Dwyer, Schurr, and Oh 1987). In our study, we define relationship commitment as a consumer's enduring desire to continue a relationship with a retailer accompanied by this consumer's willingness to make efforts at maintaining it (e.g., Morgan and Hunt 1994). Note that the definition implies the presence and consistency over time of both the desire to continue a relationship and the willingness to make efforts directed at sustaining this relationship (Macintosh and Lockshin 1997). We believe that the desire for continuity is a necessary but insufficient condition for relationship commitment because, for example, it might be driven simply by habitual cues or marketplace constraints. As a result, our measures of commitment incorporate both aspects.

The association between relationship investment and relationship quality has rarely been investigated empirically. A notable exception is the strong support Crosby, Evans, and Cowles (1990) find for a positive path from relational selling behavior to relationship quality. Furthermore, Wray, Palmer, and Bejou (1994) find evidence for a positive relationship between a salesperson's customer orientation and relationship quality. Finally, Lagace, Dahlstrom, and Gassenheimer (1991) find a positive path from ethical salesperson behavior to relationship quality. Although these constructs are not completely similar to our construct of relationship investment, they provide an initial basis for our next hypothesis.

Stronger evidence can be found for the impact of relationship investment on the dimensions of relationship quality. Relationship investment has been shown to predict satisfaction in business marketing relationships (e.g., Anderson and Narus 1990; Ganesan 1994; Smith and Barclay 1997). Customers tend to be more satisfied with sellers who make deliberate efforts toward them (Baker, Simpson, and Siguaw 1999). Also, trust has been shown to be resulting from relationship investment. For example, Ganesan (1994) finds that specific investments made by one partner result in increased trust. With respect to commitment, Dwyer, Schurr, and Oh 1987, p. 19) suggest that commitment is "fueled by the ongoing benefits accruing to each partner." In line with this, Bennett (1996) argues that the strength of customers' commitment depends on their perceptions of efforts made by the seller. Furthermore, several authors have empirically investigated the relationship between relational performance, a construct that shows similarities to relationship investment, and relationship commitment (Anderson and Weitz 1992; Baker, Simpson, and Siguaw 1999; Morgan and Hunt 1994). Therefore, we suggest the following:

H5: A higher perceived level of relationship investment leads to a higher level of relationship quality.

Behavioral Loyalty

Models that theorize attitudinal as well as behavioral relationship outcomes have strong precedence in relationship studies (e.g., Bolton 1998; Macintosh and Lockshin 1997). Accordingly, we build on existing literature, which states that the effectiveness of relationship marketing tactics should also be evaluated in terms of the behavioral changes they create (Sharp and Sharp 1997). As a result, we included the construct of behavioral loyalty, defined as a composite measure based on a consumer's purchasing frequency and amount spent at a retailer compared with the amount spent at other retailers from which the consumer buys. In other words, behavioral loyalty is measured as a unique combination of behavioral indicators, concordant with suggestions made by Sirohi, McLaughlin, and Wittink (1998) and Pritchard, Havitz, and Howard (1999).

Hennig-Thurau and Klee (1997) argue that relationship quality is an antecedent of repeat purchase behavior. Furthermore, some empirical evidence has been found for relationships between dimensions of relationship quality and behavioral loyalty. With respect to satisfaction as a dimension of relationship quality, Bolton (1998) and Macintosh and Lockshin (1997) find positive paths from relationship satisfaction to both relationship duration and purchase intentions, which can be considered behavioral indicators of loyalty. Regarding trust as a relationship quality dimension, Smith and Barclay ( 1997), for example, report a positive effect of trust on forbearance from opportunism. Moorman, Desphande, and Zaltman (1993) suggest that customers who are committed to a relationship might have a greater propensity to act because of their need to remain consistent with their commitment. Morgan and Hunt (1994) find empirical support for the relationship between a customer's commitment and acquiescence, propensity to leave, and cooperation, all of which can be regarded as behavioral outcomes of relationships. Derived from these findings, we investigate the following:

H6: A higher level of relationship quality leads to a higher level of behavioral loyalty.

Factors Moderating the Effect of Perceived Relationship Investment

In addition to testing for the effects we have described thus far, this article also takes an initial step toward assessing the role of moderators that influence the effectiveness of perceived relationship investment. An examination of such moderators enables marketers to understand when investing in relationships is expected to be more effective or less effective. Not all consumers search for more than the timely exchange of a product or service with a minimum of hassles, so making resourceintensive relationship investments is considered neither appropriate nor necessary for every consumer (Bendapudi and Berry 1997; Christy, Oliver, and Penn 1996; Day 2000). Given our focus on and general interest in the consumer, we investigate whether the effects of perceived relationship investment are contingent on either of two consumer characteristics: product category involvement and consumer relationship proneness.

Product category involvement. In line with Mittal (1995), we define product category involvement as a consumer's enduring perceptions of the importance of the product category based on the consumer's inherent needs, values, and interests. Researchers have suggested that people who are highly involved with a product category reveal a tendency to be more loyal (Dick and Basu 1994; King and Ring 1980). They reason that a relationship can add value only for customers who are already interested in the product. Solomon and colleagues (1985) claim that in low-involvement situations, the treatment of customers as individuals would probably not pay off, whereas in high-involvement situations, customers desire more personal treatment. Gordon, McKeage, and Fox (1998) state that involved buyers are more likely to participate in marketing relationships and to derive value from these relationships. Such relationships may be perceived as invasive or annoying when directed at consumers with lower levels of involvement. Consequently, approaches by the seller, however well-intentioned, could be regarded by the customer as undesirable when the customer's involvement is low (Christy, Oliver, and Penn 1996). We expect the effects of perceived relationship investment to be strengthened in the case of high levels of product category involvement:

H7: A higher level of product category involvement strengthens the impact of perceived relationship investment on relationship quality.

Consumer relationship proneness. Gwinner, Gremler, and Bitner ( 1998) argue that relationship marketing success may depend not only on its strategy or implementation but also on the preferences of the individual customer. Christy, Oliver, and Penn (1996) use the term "psychologically predisposed" to express the idea that some customers are intrinsically inclined to engage in relationships. However, despite the recognized importance of customers' proneness to engage in relationships with sellers, no study has yet investigated its impact on relationship effectiveness (Sheth and Parvatiyar 1995). In this study, we define consumer relationship proneness as a consumer's relatively stable and conscious tendency to engage in relationships with retailers of a particular product category. Several authors stress that a buyer's proneness to engage in relationships may vary across groups of sellers (Bendapudi and Berry 1997; Christy, Oliver, and Penn 1996) (e.g., apparel stores versus supermarkets), so we postulate that consumer relationship proneness must be defined within a particular product category. In addition, we emphasize consumers' conscious tendency to engage in relationships as opposed to a tendency based more on inertia or convenience (e.g., Dick and Basu 1994). From a seller's perspective, investing in relationships with buyers is not always considered a preferable strategy, because not all types of buyers are prone to engage in relationships with sellers (Berry 1995; Crosby, Evans, and Cowles 1990; Sheth and Parvatiyar 1995). We assume that relationship-prone consumers should reciprocate a retailer's efforts more strongly, because by definition, relationshipprone consumers are most likely to develop relationships. Consequently, we test the following:

H8: A higher level of consumer relationship proneness strengthens the impact of perceived relationship investment on relationship quality.

Method

Setting

An externally valid, fuller understanding of consumer relationships requires that the validity of conceptual models developed in one setting be examined in other settings as well. Our study is conducted in the food and apparel industries, covering a wide variety of retailers, including discount stores, mass merchandisers, traditional department stores, and prestige stores. We consider these industries similar with respect to the competitiveness of their industry environment and the opportunities for consumers to switch. However, the industries differ on many other dimensions. For example, social features of a relationship might be expected to be more important in an apparel context that is characterized by a high degree of personal contact and advice. Conversely, economic features might play a more important role in relationships between food retailers and consumers who have a strong emphasis on discounts and anonymous self-service. In addition to studying various industries, in response to recent calls for cross-cultural research on relationships (Iacobucci and Ostrom 1996), our study is of a transatlantic nature; it includes respondents not only from the United States but also from two highly developed western European countries, the Netherlands and (the Flemish part of) Belgium. The selection of both European countries was a matter of convenience. To the best of our knowledge, this is the first study on consumer relationships that compares survey data from three different countries. According to Hofstede's (1980) classification of countries according to cultural dimensions (power distance, uncertainty avoidance, individualism, and masculinity), largescale differences exist among these dimensions across the three countries. The power distance scores for the United States, the Netherlands, and Belgium are, respectively, 40, 38, and 65; uncertainty avoidance: 46, 53, and 94; individualism: 91, 80, and 75; and masculinity: 62, 14, and 54. In addition, significant variations in competitive conditions and legal environments among the three countries are prevalent. In conclusion, the settings incorporated in our study differ greatly from one another, which should provide a fertile environment for conducting a true cross-validation.

Measure Development

Measures for some of the constructs we are examining were available in the literature, though most were adapted to suit a retail environment. For the four relationship marketing tactics, relationship investment, and consumer relationship proneness, scales applicable to a retail context were not available and were developed for the purpose of this study. First, focus groups were used to examine how consumers described relationship investment, relationship marketing tactics, and relationship proneness. Four focus groups were organized in which participants were asked open-ended questions about their own behavior with respect to shopping for clothing. Then, direct questions were posed to acquire knowledge on relationship investment, relationship marketing tactics, and relationship proneness. Finally, projective techniques were used during the remainder of the discussions (i.e., depth descriptions, photosorts). Participants received a monetary incentive in return for their cooperation. The results were helpful in generating items.

Second, in an effort to enhance face validity, a group of Dutch and Belgian expert judges (four academics and three practitioners) qualitatively tested an initial pool of items intended to measure various relationship marketing tactics. Experts were provided with the definitions of the relationship marketing tactics and were asked to classify each item to the most appropriate tactic. Items that were improperly classified were reformulated or deleted. Third, equivalence for all items was sought by conducting back-translation. A U.S.-born American citizen who was fluent in Dutch first translated the original Dutch version of the questionnaire into American English, and a native Dutch speaker who was fluent in American English then retranslated the questionnaire into Dutch. The quality of the English translation was evaluated by a monolinguistic, U.S.-born American citizen on clarity and comprehensiveness of the translated questionnaire. The Dutch questionnaire was used in the Dutch as well as in the Belgian sample (covering the Flemish part of Belgium).

Finally, 12 graduate students in marketing research (4 in each country) were instructed to pretest a questionnaire that included all constructs on a total sample of 60 consumers through personal in-home interviews. Items measuring the various constructs were mixed in the questionnaire to reduce halo effects. To ensure that respondents were distributed across age, sex, and country, students were assigned to particular combinations of quota criteria and were allowed to select respondents who matched these criteria (e.g., friends, family, neighbors). They asked respondents to complete the questionnaire and then describe the meaning of each question, explain their answers, and state any problems they encountered while answering questions. Small revisions to the U.S. and Dutch/Belgian version of the questionnaire were made on basis of the pretest.

Final Measures

Final attempts at measure purification were conducted on a sample (n = 371) drawn to resemble the eventual multi-country, multi-industry sample. We factored the items to investigate whether they correctly measured their intended constructs. Theoretically, it was likely that the latent constructs would be correlated, so we applied an oblique rotation. We only retained items that minimally loaded .65 on the proper latent factor and maximally loaded .30 on the others to enhance the distinctiveness of the intended constructs. The resulting measurement appeared to be clean across scales, countries, and industries. The Appendix contains all (seven-point Likert) scales, organized by construct. Moreover, Table I provides an overview of construct means, standard deviations, and correlations.

With respect to relationship satisfaction, trust, and relationship commitment, we first factor-analyzed these multiitem scales for each construct separately; across all samples, a single factor emerged in each case. As Cronbach's alpha values ranged between .70 and .93, reliability was uniformly high in all samples for all three constructs. Then we assessed the second-order factor model with the first-order factors (relationship satisfaction, trust, and relationship commitment) that originated from the higher-order factor relationship quality.2 These measurement results were acceptable in each sample (comparative fit index [CFI] and nonnormed fit index [NNFI] ranged from .93 to .97 for CFI and from .89 to .96 for NNFI). All first-order and second-order factor loadings were significant, demonstrating convergent validity. This provided us with enough confidence to calculate averages for relationship satisfaction, trust, and relationship commitment based on the three items of each construct and use these averages as indicators of the construct relationship quality (see Crosby, Evans, and Cowles 1990; Posdakoff and Mackenzie 1994).

Samples

Information was collected from real consumers as opposed to student samples. Mall-intercept personal interviews were administered in the United States (food: n = 231, apparel: n = 230), the Netherlands (food: n = 337, apparel: n = 338), and Belgium (food: n = 289, apparel: n = 302). Samples were drawn from shopping mail visitors to obtain variance in age ( 18 to 25 years, 26 to 40 years, 41 to 55 years, and 55 years and over), sex, and allocated share of wallet for the store reported on (0%-20%, 21%-40%, 41%-60%, 61%-80%, and 81%-100%). We also sought even coverage over interviewing time of day and interviewing day of week to reduce possible shopping pattern biases. Across our samples, an average of 37% of the visitors who were approached participated.

Procedure

Participants were first asked whether they had ever made a purchase in the particular product category. If so, they were asked to indicate the names of five stores at which they usually bought food or apparel. Next, respondents indicated their approximate share of wallet for each store listed (measured on a continuous scale from 0% to 100%) and the extent to which they believed they were regular customers of each store (measured on a scale from I to 7). Finally, the interviewers selected a specific store to focus on for the remaining questions on the basis of the reported share of wallet figures. Care was taken that respondents reporting low, medium, and high levels of share of wallet were represented in each sample. By definition, a relationship is of extended duration and composed of multiple interactions, so many of the costs and benefits from buyer-seller relationships cannot be assessed a priori (Dwyer, Schurr, and Oh 1987; Parasuraman 1997). Gwinner, Gremler, and Bitner ( 1998) state that though customers may receive relationship benefits and believe that these benefits are important, they may not always be aware of these benefits' existence in the early stages of a relationship and may not have assessed their value yet. Therefore, only those stores were included for which respondents indicated at least a 4 on the 7-point scale that measured "being a regular customer of the store." To enhance interrater reliability, the cover letter attached to the questionnaire explained the term "regular customer" to respondents as "a customer who regularly buys clothes/food in a store and not simply visits the store to look around." Results

Examination of Data Pooling

To decide whether we needed to estimate separate models for each sample, we investigated the possibility of pooling data across countries and/or industries by means of several multigroup LISREL analyses. To assess pooling of industry samples, we evaluated two nested models for each country: (1) a model in which all structural paths were set equal across the two industry samples (equal model) and (2) a model in which all structural paths were set free across the two industry samples (free model). We followed the same procedure to assess pooling of country samples. With respect to pooling across industries, the free model in the Dutch sample obtained a significantly better fit than the equal model, which indicates that not all of the paths were equal across apparel and food. With respect to pooling across countries, the differences between the equal and free models were statistically significant for four of six country comparisons. Therefore, we decided not to pool the data across countries or industries.

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Overall Model Evaluation

In Table 2, we report the values of the fit statistics. The chisquares are all significant (p < .05; Bollen 1989), a finding not unusual with large sample sizes (Doney and Cannon 1997). The ratios of chi-square to degrees of freedom (d.f.) are between 2.01 and 2.59, all within the acceptable range of 2 to 5 (Marsh and Hovecar 1985). The values for CFI, NNFI, root mean square error of approximation (RMSEA), and standardized root mean residual (SRMR) are acceptably close to the standards suggested by Hu and Bentley (1999): .95 for CFI and NNFI, .06 for RMSEA, and .08 for SRMR. Given that these batteries of overall goodness-of-fit indices were accurate and that the model was developed on theoretical bases, and given the high level of consistency across samples, no respecifications of the model were made. This enables us to proceed in evaluating the measurement and structural models.

Measurement Model Evaluation

In Table 3, we report the results of the measurement models. We assessed the quality of our measurement efforts by investigating unidimensionality, convergent validity, reliability, discriminant validity, and metric equivalence. Evidence for the unidimensionality of each construct included appropriate items that loaded at least .65 on their respective hypothesized component and loaded no larger than .30 on other components in a factor analysis. In addition, the overall goodness of fit supports unidimensionality (Steenkamp and van Trijp 1991). Convergent validity was supported by all loadings being significant (p < .01) and nearly all R2 exceeding .50 (Hildebrandt 1987). We assessed reliability jointly for all items of a construct by computing the composite reliability and average variance extracted (Baumgartner and Homburg 1996; Steenkamp and van Trijp 1991). For a construct to possess good reliability, composite reliability should be between .60 and .80, and the average variance extracted should at least be .50 (Bagozzi and Yi 1988). All scales demonstrate good reliabilities.

We tested discriminant validity by means of several subsequent procedures. First, as a basic test of discriminant validity, we checked whether correlations among the latent constructs were significantly less than 1. In all samples, construct correlations indeed met this criterion. Second, we compared a series of nested confirmatory factor models in which correlations between latent constructs were constrained to I (each of the 21 off-diagonal elements was constrained and the model reestimated in turn), and indeed chi-square differences were significant for all model comparisons (p <.01) in all samples, again in support of discriminant validity. Third, we performed a stronger test for discriminant validity provided by Fornell and Larcker (1981 ). This test suggests that a scale possesses discriminant validity if the average variance extracted by the underlying construct is larger than the shared variance (i.e. the squared intercorrelation) with other latent constructs. On the basis of this most restrictive test, we found strong evidence for discriminant validity between each possible pair of latent constructs in all samples (i.e., all pairs of seven factors in all three countries in both industries). Only two exceptions were found. In the U.S. food sample, the squared intercorrelation between preferential treatment and tangible rewards (.79) was larger than the shared variance extracted by both constructs (.76 and .69, respectively). In the Dutch apparel sample, the squared intercorrelation between relationship investment and relationship quality (.67) was larger than the shared variance extracted by relationship quality (.63). However, given that neither problem occured in the other five samples, we do not consider this a major problem.

Finally, to cross-nationally investigate the interrelationships between constructs in a nomological net, Steenkamp and Baumgartner (1998) indicate that full or partial metric invariance must be satisfied because the scale intervals of the latent constructs must be comparable across countries. We assessed metric invariance by comparing two nested models for each construct separately in terms of the difference in chi-square relative to degrees of freedom, RMSEA, NNFI, and CFI.3 In the first model (base model), all error variances and all factor loadings were allowed to be free across samples. (One marker item was selected, and the same marker item was used in each sample.) Only the factor variance of the latent construct was constrained to be equal across samples. (We measured each latent construct on basis of three indicators, so at least one parameter should be fixed across samples to generate a nonsaturated model.) In the second model (equal loadings model), we additionally constrained the remaining two factor loadings (apart from the marker item) to be equal across the six samples. While metric invariance is "a reasonable ideal.... a condition to be striven for, not one expected to be fully realized" (Horn 1991, p. 125), our measurement model supported full metric invariance for three of seven constructs incorporated. For constructs not revealing full metric invariance (direct mail, interpersonal communication, tangible rewards, and relationship commitment), we sequentially relaxed constraints on parameters to test for partial metric invariance. Partial metric invariance was supported for all remaining constructs.

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In summary, the measurement models are clean, with evidence for unidimensionality, convergent validity, reliability, discriminant validity, and metric invariance, which enabled us to proceed to the structural model evaluation.

Structural Model Evaluation

Table 4 indicates that in each sample, all significant relationships between latent constructs are in the hypothesized direction, which provides initial evidence for our conceptual model and supports the nomological validity of the constructs. An important finding is that the relationship between perceived relationship investment and relationship quality and the positive path from relationship quality to behavioral loyalty are confirmed across all samples. This result provides strong empirical evidence for the cross-validation of this part of our conceptual model, which is especially noteworthy given that the countries examined differ considerably on demographic, economic, and cultural dimensions. Consequently, there was strong and uniform support for HS and H6.

In examining H1-H4, which explicate the associations between relationship marketing tactics and perceived relationship investment, only in the United States is there a consistent pattern of effects across the two industries. In addition, only for preferential treatment in the food industry and for interpersonal communication in the apparel industry is there a consistent pattern of effects across the three countries. Apart from these effects, the data provided mixed evidence. Specifically, direct mail had a positive impact on perceived relationship investment (H1) in three of four European samples as opposed to the U.S. samples, in which no significant paths were detected. Preferential treatment revealed a nonsignificant relationship with perceived relationship investment (H2) in all samples except for the Belgian apparel sample. Interpersonal communication had the strongest impact on perceived relationship investment (H3), being cross-validated in all samples except for the Belgian food sample. Finally, the data support a positive path from tangible rewards to perceived relationship investment (H4) in three of four European samples but do not provide evidence for this path in the U.S. samples.

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We now turn to two model modifications: First, we test a rival structural model to enhance our confidence in the focal model further, and second, we introduce the potential moderators of product category involvement and consumer relationship proneness, in accordance with our prior theorizing.

A Rival Model

It is generally agreed that researchers should compare rival models and not just test the performance of a proposed model (Bagozzi and Yi 1988). In discussing the construct of perceived relationship investment previously, we provided a theoretical basis for positioning perceived relationship investment as a mediating variable. Because our parsimonious hypothesized model allows no direct paths from any of the four relationship marketing tactics to relationship quality or to behavioral loyalty, it implies a central nomological status for relationship investment. A nonparsimonious rival model would hypothesize only direct paths from each of the precursors to the outcomes relationship quality and behavioral loyalty. This model makes relationship investment nomologically similar to the four relationship marketing tactics. The tested rival model (see Figure 2) therefore permits no indirect effects, implying that relationship investment is not allowed to mediate any of the relationships.

On the basis of Morgan and Hunt (1994), we compared our hypothesized model with the rival model on the following criteria:4 overall fit, parsimony, percentage of either model's parameters that were statistically significant, and R2s for the endogenous constructs. With respect to overall fit, the average CFI of the rival model was slightly higher than that of the hypothesized model (.947 versus .938), and the rival model's mean ratio of chi-square to degrees of freedom was slightly lower than that of the hypothesized model (2.24 versus 2.32). Note, however, that to achieve this slight increase in fit, we needed to estimate four additional paths in the rival model, which reduced the rival model's parsimony and partially offset the incremental improvement in fit. In addition, only 47% of the paths in the rival model were significant as opposed to 67% in the hypothesized model, which suggested that the additional paths were not meaningful theoretically or empirically. Finally, the average explained variance of relationship quality was .56 in the rival model as opposed to .47 in the hypothesized model. This is not surprising because in addition to relationship investment, as a precursor of relationship quality, four extra antecedents were modeled to explain relationship quality in the rival model. In contrast, the average explained variance of behavioral loyalty was only .12 in the rival model as opposed to .14 in the hypothesized model. This means that the explanatory power of relationship quality as a single antecedent of behavioral loyalty is stronger than the combined explanatory power of the four relationship marketing tactics plus relationship investment.

On the basis of these findings, we believe that the exercise of fitting a rival model has strengthened the support we found for the meaningfulness and robustness of our hypothesized model. In addition to the conceptual support found for positioning perceived relationship investment as a mediating variable in the hypothesized model, the rival model empirically demonstrates its added value. Neglecting the mediating role of this construct reduces its parsimony and results in a lower percentage of significant path coefficients.

Moderating Influences

We tested moderating effects through multigroup analyses, splitting the samples into subsamples according to whether consumers scored high or low on the moderating variables to ensure within-group homogeneity and between-group heterogeneity. The subgroup method is a commonly preferred technique for detecting moderating effects (Stone and Hollenbeck 1989). For each moderator, Table 5 displays the results for 12 separate structural model estimations in terms of chi-square and degrees of freedom.

Moderating influence of product category involvement. Considering product category involvement as a moderator, in the equal models, we set all paths of the structural model equal across high- and low-product category involvement subsamples. In the free models, we constrained all paths to be equal across high- and low-product category involvement subsamples, except for the link that was potentially affected by the moderator variable. Differences in chisquare values between models determine whether product category involvement acts as a moderating variable; that is, a significant decrease in chi-square from the equal model to a model in which one relationship is set free implies that the moderator variable has a significant influence on that relationship. Table 5 reveals that the level of product category involvement significantly moderates the impact of perceived relationship investment on relationship quality in three samples (U.S. food, U.S. apparel, and Dutch apparel). For relationships that were moderated, the within-group path coefficients were consistently lower in the low-involvement than the high-involvement subsample. The following differences in path coefficients were found for the link from perceived relationship investment to relationship quality: U.S. food +.19, U.S. apparel +.23, and Dutch apparel +.09. In conclusion, for some industry-country combinations, our data suggest that investing in a relationship generates a higher payoff in terms of increased relationship quality when customers are more involved with the product category. These findings tentatively support H7.

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Moderating influence of consumer relationship proneness. We used the same procedure to assess the moderating impact of consumer relationship proneness. The results show that consumer relationship proneness significantly moderates the impact of perceived relationship investment on relationship quality in three samples (U.S. food, U.S. apparel, and Belgian food). For relationships that were moderated, within-group path coefficients were consistently lower in the low-relationship proneness than the highrelationship proneness subsample. The following differences in path coefficients were found for the link from perceived relationship investment to relationship quality: U.S. food +.30, U.S. apparel +. 15, and Belgian food +.30. These findings suggest that the impact of perceived relationship investment may be stronger when customers are more prone to engage in relationships with sellers. These results provide preliminary support for H8.

Discussion and Implications

The development and sustainability of loyalty is increasingly difficult to achieve and is still surrounded with ambiguity regarding its underlying determinants, so we believe that our research makes a significant contribution to relationship marketing theory in three different ways. First, our model contributes to the existing literature by specifying how retailers can guide consumer perceptions of relationship investment by applying four different relationship marketing tactics. Prior studies have rarely investigated the role of such tactics in shaping consumer relationships. Second; our study demonstrates why retailers benefit from investing in consumer relationships by assessing the impact of perceived relationship investment on relationship quality and ultimately on behavioral loyalty. Third, this study is a first attempt to provide insights into the role of contingency factors in determining relationship quality by emphasizing the moderating impact of a newly introduced construct, consumer relationship proneness, and product category involvement. We tested these three research questions comprehensively and rigorously by replicating the study across three countries and two industries.

With respect to our first research question, relationship marketing tactics were found to play a differential yet consistently positive role in affecting perceived relationship investment. Today's retailers increasingly offer comparable merchandise, copy competitors' price promotions, share common distribution systems, and treat customers well in terms of services offered, so there are increased opportunities for directing greater attention to developing and implementing relationship marketing tactics. With respect to the direct mail tactic, we found mixed evidence for its positive effect on perceived relationship investment. Most strikingly, no empirical support was found for positive effects of direct mail in the United States. A likely explanation for this finding is that the longer tradition of sending direct mail to regular customers in affluent U.S. markets has worn out its effect on perceived relationship investment. Whereas in the United States, direct marketing expenditures constituted 57.8% of total advertising expenditures in 1997 (DMA/WEFA 1998), these percentages were significantly lower in the Netherlands and Belgium during the same period: 47.4% and 38.9%, respectively (FEDMA 1998). This is illustrated by the difference across countries in the number of direct mail pieces received per capita. The average number of U.S. direct mail pieces received over the past 50 years has risen from approximately 145 pieces per year to more than 700 per year (James and Li 1993). In 1997, Dutch consumers received an average of only 81.7 pieces of addressed direct mail, and Belgian consumers found an average of 110.1 pieces of addressed mail in their mailbox (FEDMA 1998).

Interpersonal communication proved to be a dominant determinant of perceived relationship investment, being replicated in five out of six samples, an observation that is sensible given that relationships are inherently social. It demonstrates the crucial role of retail employees who are in direct contact with customers. Retailers capable of training and motivating their employees to show warm and personal feelings toward customers can reap the resulting benefits in terms of improved perceptions of relationship investment. Also, when hiring store personnel, store management needs to focus on candidates' social abilities that facilitate social interactions with target consumers (Weitz and Bradford 1999). This is especially important, because the emergence of automated retailing has gradually reduced opportunities for social interaction in the store. Retailers should investigate whether consumers are willing to trade off the loss of social contact for the benefits of automation.

Preferential treatment revealed a nonsignificant relationship with perceived relationship investment in all samples except one, and this contradicts the common opinion that regular buyers should be treated and served differently than nonregular buyers should. A potential explanation for this finding might be that customers do not appreciate being openly favored above other customers. If this is true, it would hold important implications for retailers, because it emphasizes that efforts directed at customers should be made delicately to avoid putting customers in an uncomfortable position. Alternatively, perhaps preferential treatment is simply not as powerful as the other antecedents of perceived relationship investment, and in the presence of the other tactics, preferential treatment is less valued by the consumer.

Finally, mixed evidence was detected for the positive effects of tangible rewards on perceived relationship investment. Again, this was true in the U.S. samples in which no significant paths were found. In U.S. markets, the longer tradition of providing customers with tangible rewards for their loyalty might decrease the impact of such offers. The natural appeal of tangible rewards can be assumed to decrease if more sellers start offering them. As tangible rewards become widespread, their absence may disappoint consumers, whereas their presence would not necessarily boost customer retention. Competitors can easily imitate tangible rewards such as frequent flyer programs, customer loyalty bonuses, and free gifts. Perhaps such "wear-out" effects have simply occurred less in the European markets.

A second key research objective of this study was to assess the effect of perceived relationship investment on relationship quality and ultimately behavioral loyalty. We expected perceived relationship investment to play an important role in determining relationship quality, which was confirmed in all six samples. The path from relationship quality to behavioral loyalty was also demonstrated across samples. These results support the findings of Bagozzi (1995) and Kang and Ridgway (1996), who argue that consumers feel obligated to reciprocate a retailer's investments in the retailer-consumer relationship by increasing their loyalty to this retailer. This finding implies that it pays off for retailers to invest in consumer relationships, because it results in increased loyalty.

Finally, we found initial support for our third research question. We collected empirical evidence for what previously have been only assumptions suggesting that customer characteristics can influence the effectiveness of relationship marketing investments (e.g., Ganesan 1994). The results show that consumer relationship proneness repeatedly acts as a moderator of the effectiveness of perceived relationship investment, perhaps operating as a heightened sensitivity to a seller's efforts directed at buyers (see Dwyer, Schurr, and Oh 1987). In addition, product category involvement moderated the effect of perceived relationship investment in some cases. Paths that are significantly moderated suggest that consumers with a lower degree of product category involvement are less influenced by a retailer's investment in the relationship (e.g., consistent with Solomon et al. 1985). Leuthesser (1997) points out that a buyer's stake in a relationship with a seller tends to be higher with greater involvement in the product category. Our data then might be reasonably interpreted as higher stakes in a relationship, which cause consumers to appreciate a retailer's investments more strongly.

These observations emphasize that retailers should not lose sight of the importance of consumer-related factors in shaping relationship quality. No matter how much trouble the retailer goes to in order to increase relationship quality, the effects of those efforts and resources can be tempered or strengthened by the consumer's level of relationship proneness and product category involvement. Consequently, retailers should not only invest more in consumer relationships but also pay equal attention to finding consumers who are most receptive to such investments. In addition to the more traditional criteria of product-market segmentation such as market size, market growth, and expected market share, segmenting consumers according to levels of consumer relationship proneness or product category involvement could affect expected share of market and share of customer. For example, a practical approach toward accomplishing this objective might be to add a few questions to the registration form of a store's customer loyalty card that measure consumer relationship proneness and product category involvement.

Limitations and Directions for Further Research

Some limitations might be related to collecting our data and interpreting our results. A first limitation might be the omission of important variables. For example, additional tangible elements in the retail mix, such as pricing and promotion, product quality and assortment, and service quality, could be added as antecedents of relationship investment. This is evidenced by the fact that the percentage of explained variance of perceived relationship investment could still be improved. Relationship marketing theory not only should have eyes for typical relationship marketing constructs but also could examine the value of existing instruments such as SERVQUAL in affecting relationships. Although the SERVQUAL measures (Parasuraman, Zeithaml, and Berry 1988) can be applied to a broad spectrum of contexts, no previous research of which we are aware has examined their effects on the relationship outcomes examined in this study. Moreover, it is likely that the relative importance of product, service, and relationship marketing tactics in determining relationship investment varies according to the length of a relationship. We could assume that the longer a relationship exists, the stronger is the relative impact of relationship marketing tactics on perceived relationship investment compared with product and service tactics. Consequently, it could be fruitful to compare research models incorporating all these components across buyer segments that exhibit different levels of relationship length.

Second, this study focused on consumer-specific moderators of perceived relationship investment, but a challenging research avenue would be to assess the role of other contingency factors. For example, it might be interesting to study the differences between large store chains and small, independent neighborhood stores. It could be argued that small stores would demonstrate more relationship-friendly characteristics than large store chains, given that the degree of social exchange and the possibilities for interpersonal communication are generally greater in smaller stores. Whereas larger store chains generally operate on the basis of anonymous self-service, the survival of small, independent stores is often dependent on personal service and knowledge of consumer preferences. A third potential shortcoming in the study is common method bias. We used one questionnaire to measure all constructs included, so perhaps the strength of the relationships among these constructs may be somewhat inflated. A fourth potential limitation is related to the measurement of behavioral loyalty. The true meaning of behavioral loyalty may be only partially captured given that its measure was based on self-reports. Database information could be used as input for measuring actual purchasing behavior. The confidence in our results could be strengthened with access to behavioral data on customer purchase histories that are not subject to potential recall loss. It would then be possible to examine longer strings of purchases and perhaps to incorporate contextual information. These recognized shortcomings could inspire researchers to define their future research agendas.

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Berry, Leonard L. (1995), "Relationship Marketing of ServicesGrowing Interest, Emerging Perspectives," Journal of the Academy of Marketing Science, 23 (4), 236-45.

Blau, Peter (1964), Exchange and Power in Social Life. New York: John Wiley & Sons.

Bollen, Kenneth A. (1989), Structural Equations with Latent Variables. New York: John Wiley & Sons.

Bolton, Ruth N. (1998), "A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction," Marketing Science, 17 (1), 45-65.

Christy, Richard, Gordon Oliver, and Joe Penn (1996), "Relationship Marketing in Consumer Markets," Journal of Marketing Management, 12 (1-3), 175-87.

Crosby, Lawrence A., Kenneth R. Evans, and Deborah Cowles (1990), "Relationship Quality in Services Selling: An Interpersonal Influence Perspective," Journal of Marketing, 54 (3), 68-81.

Day, George S. (1990), Market Driven Strategy. New York: The Free Press.

[Author Affiliation]

Kristof De Wulf is Assistant Professor, Vlerick Leuven Gent Management School and Faculty of Economics and Business Administration, Ghent University. Gaby Odekerken-Schroder is Assistant Professor, Faculty of Economics and Business Administration, Maastricht University. Dawn lacobucci is Professor, University of Arizona.