Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and...

Spreading the Word: Investigating Antecedents of Consumers' Positive Word-of-Mouth Intentions and Behaviors in a Retailing Context

Tom J. Brown Oklahoma State University

Thomas E. Barry Southern Methodist University

Peter A. Dacin Queen's University, Canada

Richard F. Gunst Southern Methodist University

Empirical studies investigating the antecedents of positive word of mouth (WOM) typically focus on the direct effects of consumers' satisfaction and dissatisfaction with previous purchasing experiences. The authors develop and test a more comprehensive model of the antecedents ofpositive .WOM (both intentions and behaviors), including consumer identification and commitment. Specifically, they hypothesize and test commitment as a mediator and moderator of satisfaction on positive WOM and commitment as a mediator of identification on WOM. Using data obtained from customers of a retailer offering both products and services, they find support for all hypothesized relationships with WOM intentions and~or WOM behaviors as the dependent variable. The authors conclude with a discussion of their findings and implications for both marketing theory and practice.

Journal of the Academy of Marketing Science. Volume 33, No. 2, pages 123-138. DOI: 10.1177/0092070304268417 Copyright �9 2005 by Academy of Marketing Science.

Keywords: ~vord of mouth; identification; consumer commitment

The recent focus in the literature on relationship marketing highlights potential responses that can emerge from efforts directed at forming relationships with consumers (e.g., Bettencourt 1997; Crosby, Evans, and Cowles 1990; Garbarino and Johnson 1999; Nijssen, Singh, Sirdeshmukh, and Holzmueller 2003; Sheth and Parvatiyar 1995; Verhoef, Franses, and Hoekstra 2002). Of all these responses, some scholars and practitioners suggest that word of mouth (hereafter WOM) may be among the most important (Christopher, Payne, and B allantyne 1991; Reichheld 2003; White and Schneider 2000). In services marketing, for example, authors describe WOM as "a dominant force in the marketplace" (Mangold, Miller, and Brockway 1999:73) and the "ulti- mate test of the customer's relationship" (Bendapudi and Berry 1997:30).

124 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE SPRING 2005

Given its assigned importance, it is surprising to find relatively few studies directed at understanding factors that influence WOM. In 1967, Arndt noted that "the process and causal mechanisms of word-of-mouth advertising have not really been given much attention" (p. 291). More than 30 years later, Anderson (1998), commenting on the field, once again noted that "the antecedents of word of mouth have seldom received direct attention" (p. 6).

Existing empirical studies investigating the antecedents of WOM typically focus on the direct effects of consumers' satisfaction and dissatisfaction with previous purchasing experiences on WOM. The findings of these studies are generally equivocal, however. Some find a direct positive influence of satisfaction on WOM (e.g., Blodgett, Granbois, and Waiters 1993; Heckman and Guskey 1998; Mittal, Kumar, and Tsiros 1999; Richins 1983; Swan and Oliver 1989); others find no direct relationship between the two constructs (e.g., Arnett, German, and Hunt 2003; Bettencourt 1997; Reynolds and Beatty 1999).

In other studies, researchers have also examined the influence of opinion leadership on WOM (Richins and Root-Shaffer 1988) and the structure of networks through which WOM is transferred (e.g., Brown and Reingen 1987; Money, Gilly, and Graham 1998). Still, despite this research, our understanding of the antecedents of WOM remains incomplete and would benefit from more direct attention.

The purpose of this article is to address this gap in our understanding of the antecedents of consumers' WOM activities. In particular, we develop and test a model of factors influencing positive WOM in the context of customers of an automobile .dealership offering both products and services on those products. Our context offers the opportunity for both single- and multi-interaction buyer-seller transactions. That is, some customers may have engaged in a single transaction with the retailer, while others may have had multiple interactions over time.

In developing our model, we begin by discussing the relationships between satisfaction and positive WOM and commitment and positive WOM that typically appear in the literature. We then extend the existing ideas and suggest that satisfaction and commitment act interactively. Specifically, we suggest that commitment both moderates and mediates the effects of satisfaction on consumer WOM and that this can account for some of the equivocal findings in the literature. Furthermore, we examine the effects of an additional antecedent of positive WOM-- customer identification with the organization. While never empirically tested, the literature suggests a direct link between this antecedent and positive WOM. However, rather than a direct effect, we propose that the effects of identification on positive WOM are mediated by commitment. Figure 1 presents our proposed model.

FIGURE 1 Proposed Model

dealership o ,. tion r - - - - - K

commitment

consumer identification

Panel A. Model of Antecedents of Positive Word-of-Mouth Intentions

dealership .ction

consumer commitment

consumer identification

Panel B. Model of Antecedents of Positive Word-of-Mouth Behaviors

This research is important for a variety of reasons. First, it addresses an extremely important topic in marketing. WOM is an effective influence on consumer judgments and behavior (John 1994; Herr, Kardes, and Kim 1991; Sultan, Farley, and Lehmann 1990) and an important source of consumer expectations (Zeithaml, Berry, and Parasuraman 1993). David Aaker (1991), for example, noted that the real value of those customers most loyal to an entity is not so much the business that they personally generate but rather their impact on others in the marketplace. Similarly, in their treatment of relationship marketing, Christopher et al. (1991) placed customer advocates--a concept similar to Jones and Sasser's (1995) notion of apostles--on the highest rung on the ladder of customer loyalty. Furthermore, the growing presence of the Internet is expanding the availability and importance of WOM in the marketplace (Kwak, Zinkhan, and Pitt 2001; Pitt, Berthon, Watson, and Zinkhan 2002; Zinkhan, Kwak, Morrison, and Peters 2003).

Second, we contribute conceptually to current literature investigating antecedents of WOM by developing and testing a more comprehensive model of the simultaneous effects of several key antecedents. Unlike previous con- ceptualizations, our approach suggests critical moderating and mediating roles for commitment and includes identifi-

Brown et al. / POSITIVE WORD OF MOUTH 125

cation, another potential antecedent of WOM suggested in the literature.

Finally, we collect data at two points in time from a sample drawn from the existing customer base of an organization. With this design we can assess both consumer WOM intentions (measured at one point in time) and actual behaviors (measured at a later point in time). Thus, this extends existing work on commitment and satisfaction beyond its typical focus on behavioral intentions only and allows us to suggest ways in which marketers can benefit from this understanding.

Next, we discuss key concepts and develop an argu- ment for why satisfaction and commitment may interact in the prediction of WOM, and we discuss the influence of identification as an antecedent of WOM that is mediated by the degree of commitment consumers report toward a marketing organization. Following proposition development, we discuss the method and results of a two-stage empirical study. Our analysis entails both structural equation modeling, in which we introduce a confirmatory factor analysis using asymptotic covariance matrices, and hierarchical regression analysis. We end with a discussion of the implications of our findings for marketing managers and researchers and suggest directions for future research.

WORD OF MOUTH

The popular press contains many examples of companies attempting to harness the reputed power of WOM advertising. Numerous managerial-oriented articles and books suggest ways of encouraging positive WOM, an indication of the importance of WOM to practitioners. Among the various approaches, one finds "grassroots marketing" (Deal and Abel 2001), "viral marketing" (Kelly 2000), or simply "buzz marketing" (Rosen 2000). The basic idea behind WOM is that information about products, services, stores, companies, and so on can spread from one consumer to another. In its broadest sense, WOM communication includes any information about a target object (e.g., company, brand) transferred from one individual to another either in person or via some communication medium.

While WOM can be positive or negative, marketers are naturally interested in promoting positive WOM, such as recommendations to others, and that is the focus of our research. Harrison-Walker (2001) defined WOM as "in- formal, person-to-person communication between a perceived noncommercial communicator and a receiver re- garding a brand, a product, an organization, or a service" (p. 63). Adopting this perspective, positive WOM might include making others aware that one does business with a company or store, making positive recommendations to others about a company, extolling a company's quality orientation, and so on.

Aspects of positive WOM communication have been studied empirically from a variety of perspectives. Diffu- sion researchers identify WOM as the primary driver in the diffusion of new innovations (Arndt 1967; Bass 1969; Mahajan, Muller, and Bass 1990; Sultan et al. 1990). Feick and Price (1987; Higie, Feick and Price 1987) detail the characteristics of "market mavens" who gather and dis- seminate marketplace information (see also Gelb and Johnson 1995 in a health care context). Richins and Root- Shaffer (1988) discussed the influence of opinion leadership on WOM, and Bone (1995) investigated the influence of WOM on product judgments. Summers (1972) noted WOM in his examination of the characteristics of consumer product innovators. Other researchers investigated the structure of networks through which the transmission of WOM information occurs (e.g., Brown and Reingen 1987) and conditions influencing the degree to which individuals transfer and use WOM-based information, including characteristics of the information source and/or seeker (Duhan, Johnson, Wilcox, and Harrell 1997; Gilly, Graham, Wilfinbarger, and Yale 1998), national culture (Money et al. 1998), and cooperation among information exchange partners (Frenzen and Nakamoto 1993).

In sum, much of the existing literature on WOM- related concepts focuses on (1) identifying the type of individual who is prone to spread WOM, (2) detailing the social networks through which WOM spreads, or (3) ex- amining influences on the interpersonal information search process. Despite the extensiveness of WOM research, only a few studies seek to explicitly relate consumer positive WOM behaviors to its antecedents, and these studies typically focus on the direct relationship between underlying consumer postpurchase responses such as satisfaction and commitment and WOM.

SATISFACTION AND COMMITMENT AS ANTECEDENTS OF POSITIVE WOM

Satisfaction with products, services, retailers, salespeople, and so on is an important postpurchase response often associated with consumer outcomes such as loyalty and retention (e.g., Anderson and Sullivan 1993; Bolton and Lemon 1999; Heskett, Jones, Loveman, Sasser, and Schlesinger 1994; Oliver 1997; Rust and Zahorik 1993) and, to a lesser extent, positive WOM (e.g., Heckman and Guskey 1998; Mittal et al. 1999; Reynolds and Beatty 1999; Swan and Oliver 1989). Satisfaction is the response to pleasurable fulfillment of needs, desires, and so on (Oliver 1997).

When a marketer delivers high satisfaction to consumers, the expectation is that the consumers will spread positive WOM. Yet, the empirical evidence about the relationship between satisfaction and WOM intentions or behavior is equivocal. Some researchers provide evidence


of a significant positive influence of satisfaction on WOM (e.g., Heckman and Guskey 1998; Mittal et al. 1999; Swan and Oliver 1989). Others, however, do not find evidence of a direct relationship between the constructs (e.g., Arnett et al. 2003; Bettencourt 1997; Reynolds and Beatty 1999). For instance, Reynolds and B eatty (1999) did not find support for their proposition that satisfaction with a retailer leads to greater positive WOM about that retailer. In their sample of university alumni, Arnett et al. (2003) found no relationship between satisfaction with the university and efforts of alumni to promote the university to others.

One explanation for the equivocal results is that existing research has ignored the possibility that the influence of satisfaction on WOM may differ depending on other characteristics of consumers such as level of commitment to the focal entity. As we discuss later, considering other potential antecedents of WOM and their interactive effects with satisfaction offers a possible explanation for the con- flicting findings.

The definition of commitment in the relationship literature is that it is "an enduring desire to maintain a valued relationship" (Moorman, Zaltman, and Deshpande 1992:316). Borrowing directly from this definition, we define commitment as it relates to WOM as an enduring desire to maintain a relationship with a specific entity, which in our case is a retailer. While some marketing researchers adopt a multidimensional conceptualization of commitment (e.g., Gruen, Summers, and Acito 2000), others prefer a unidimensional approach (e.g., Morgan and Hunt 1994). We adopt the latter perspective because it allows for the possibility of causal relationships among what the former perspective labels the "dimensions" of commitment. As we demonstrate via theory and empirical evidence, identification with the organization is better conceptualized as an antecedent than a dimension of commitment in the investigation of its effects on positive WOM. Identification is conceptually similar to "affective commitment" in Gruen et al. (2000) and Harrison-Walker (2001), and the "identification processes" of Pritchard, Havitz, and Howard (1999).

Although several researchers examine the influence of consumer commitment on various types of consumer responses (e.g., purchase intentions, Garbarino and Johnson 1999; service recovery expectations, Kelley and Davis 1994; resistance to change to another provider, Pritchard et al. 1999), we are aware of only two studies that address the effect of commitment on positive WOM. Bettencourt (1997) found a positive relationship between consumer commitment to a grocery store and a measure of loyalty that largely represented positive WOM and further suggests that commitment could possibly mediate the effect of customer satisfaction on positive WOM. Harrison- Walker (2001) examined the relationship between affective commitment and WOM activity, but as we note in a later section, the notion of affective commitment is con-

ceptually similar to our notion of identif icat ion. Following, we discuss the possible relationships between satisfaction and commitment and their effect on positive WOM.

Commitment as a Mediator of the Effects of Satisfaction on WOM

The rationale for suggesting that commitment mediates the relationship between satisfaction and positive WOM is that existing research and theory is almost uniform in suggesting that commitment to an entity is built largely on a foundation of satisfactory interactions between exchange partners (e.g., Dwyer, Schurr, and Oh 1987; Morgan and Hunt 1994; Oliver 1999).

As noted previously, several empirical studies provide evidence of an association between satisfaction and commitment. Thus, we also expect that a customer's cumulative satisfaction with an organization will positively affect the degree of relationship commitment. Thus, consistent with the literature, we expect the following:

Hypothesis 1: Consumer commitment mediates the relationship between satisfaction and positive WOM intentions and behaviors.

Commitment as a Moderator of the Effects of Satisfaction on WOM

While the previous discussion and the literature suggest that commitment mediates the relationship between satisfaction and positive WOM, we believe that at any point in time, commitment may also moderate this relationship. Although unusual, there is precedent for a single variable serving as both mediator and moderator for the relationship between two other variables. For instance, in the organizational behavior domain, Ganzach (1998) demonstrated that job complexity both mediated and moderated the influence of intelligence on job satisfaction.

We base our expectation for a moderating effect of commitment on Garbarino and Johnson (1999), who demonstrated that satisfaction and commitment have differen- tial effects on future intentions depending on the current state of a customer's level of commitment. They found that satisfaction is the primary driver of future intentions toward an entity for consumers with low relational orientation (i.e., transaction-oriented consumers with lower levels of commitment), while commitment takes on a much more prominent role as a driver for consumers with high relational orientation (i.e., relationship-oriented consumers with higher levels of commitment). Consequently, while the direct relationships between satisfaction and WOM and commitment and WOM continue to underlie the model, we expect the nature of these relationships to be dependent on the degree of commitment to the organiza-


tion. That is, when commitment to the selling organization is low (i.e., transaction-oriented consumers), the direct influence of satisfaction on WOM is likely to be stronger than when commitment to the organization is high (i.e., relationship-oriented consumers). Thus, when both transaction- and relationship-oriented customers are present, the effects of satisfaction and commitment interact in predicting customer responses such as positive WOM, such that the influence of satisfaction would be lower for higher-commitment customers (relationship- oriented customers) than for lower-commitment customers (transaction-oriented customers).

While there is no direct evidence of the interactive relationship of commitment and satisfaction in the literature, we believe that there is indirect evidence for this relationship. For example, Ahluwalia, Burnkrant, and Unnava (2000) found that attitudes of lower-commitment respondents are more influenced by negative information than are attitudes of higher-commitment respondents. In our context, this would suggest that as a whole, lower- commitment customers (transaction-oriented customers) may be more likely to demonstrate more sensitivity to variances in satisfaction than would higher-committed customers (relationship-oriented customers).

We believe that it is important to consider this interactive relationship between satisfaction and commitment on positive WOM for both theoretical and practical reasons. Although theorists have made great strides in understanding satisfaction and commitment and their influence on consumer responses, most of the extant work focuses on the "main effects" of these constructs on responses. Less attention is paid to the establishment of boundary conditions or "interactive effects" Furthermore, the possibility that satisfaction and commitment might interact in affecting positive WOM holds an important implication for understanding the nature of a retailer's customer base. At any single point in time, some customers will have deep levels of relationship commitment, based in part on satisfaction derived from the retailer. Other customers have yet to develop much commitment to the retailer at all. For highly committed customers, their degree of satisfaction may be less relevant to positive WOM; their level of commitment to the organization would sustain positive WOM. In contrast, for customers with lower levels of commitment, the degree of satisfaction provided by the retailer is likely to be very important in influencing positive WOM.

An interactive perspective on the influence of satisfaction and commitment may also help to explain inconsis- tent findings in prior research. For studies with samples composed of consumers in contexts conducive to higher levels of commitment to the marketing organization, the results suggested no relationship between satisfaction and positive WOM. For example, Bettencourt (1997) asked his respondents to respond with respect to the grocery store at which they did the most shopping; Reynolds and Beatty

(1999:18) used a sample of "relationship customers," described as customers with an ongoing relationship with a salesperson.' For studies composed of samples drawn in such a manner as to represent all potential levels of commitment, the results often suggested a positive relationship between satisfaction and WOM (i.e., Mittal et al. 1999; Swan and Oliver 1989). While no direct assessment of consumer commitment appears in these studies, we be- Iieve that the positive relationship between satisfaction and WOM was likely driven by the increased presence of less committed consumers in the samples. Thus, although prior research is consistent with our proposed interaction between satisfaction and commitment, further research is warranted to investigate the following hypothesis.

Hypothesis 2: The relationship between satisfaction and positive WOM intentions and behaviors is more positive when consumer commitment to the organization is lower than when consumer commitment is higher.

In sum, the first two propositions suggest that consumer commitment both mediates and moderates the effects of satisfaction on positive WOM. As a mediator variable, consumer commitment highlights one process through which satisfaction exerts an influence on WOM-- higher levels of satisfaction lead to higher levels of commitment, which in turn lead to greater WOM activity on the part of consumers. As a moderator variable, commitment serves to place a boundary on the direct effect of satisfaction on positive WOM.

IDENTIFICATION AS AN ANTECEDENT OF POSITIVE WOM

An entity's identity has been described as a schema or perception of what is central and distinctive about the entity (Dutton, Dukerich, and Harquail 1994). Organiza- tional identification has been defined as the degree to which an individual perceives a oneness with an organization (Ashforth and Mael 1989; Bhattacharya, Rao, and Glynn I995), the degree of overlap of self-schema and organization schema (Bergami and Bagozzi 2000; Bhattacharya and Sen 2003), and the degree to which the self is defined by the same attributes the individual believes define the organization (Dutton et al. 1994). Fol- lowing from the work of Bergami and Bagozzi (2000), we define identification as the degree of overlap of self- schema and organization schema.

Most empirical research on identification is set in organizational settings in which respondents are members of an identifiable organization (e.g., members/supporters of an art museum, Bhattacharya et al. 1995; company employees, Bergami and Bagozzi 2000; alumna of a particu-


lar school, Mael and Ashforth 1992). In this research, we address the degree to which consumers may identify with a particular marketing firm (see Bhattacharya and Sen 2003).

There are several conceptually similar concepts to consumer identification in the marketing literature, including consumer "self-connection" to a brand (Foumier 1998), the "congruence" between a consumer's character and that of a company (Sen and Bhattacharya 2001), the "shared values" between individuals and organizations (Heckman and Guskey 1998) and between organizations (Morgan and Hunt 1994), the "self-image congruence" between consumers and brands (Sirgy et al. 1997) and stores (Sirgy and Samli 1985), a consumer's "position involvement" with a brand (Pritchard et al. 1999), and "affective commitment" toward an organization (Gruen et al. 2000; Harrison-Walker 2001). Each of these concepts has been theoretically and empirically associated with positive consumer responses, including WOM.

Conceptually, this makes sense because for some consumers, saying positive things about a marketing organization to others may provide a means of expressing their own self-identity (Arnett et al. 2003), and the greater the degree of overlap between the organization and the self, the more likely that the individual will say positive things about the organization to others. As Bhattacharya and Sen (2003) noted, "Consumers become champions of the companies with whom they identify" (p. 76-77). However, for reasons highlighted below, we believe that rather than a direct effect on positive WOM, the effect of identification on positive WOM will be mediated by consumer commitment.

Commitment as a Mediator of the Effects of Identification on WOM

The organizational behavior literature contains ample empirical evidence that identification and commitment are distinct concepts (e.g., Ashforth and Saks 1996; Bergami and Bagozzi 2000; Mael and Tetrick 1992). Although some marketing researchers conceptualize commitment as containing an identification component or dimension (e.g., Garbarino and Johnson 1999; Gruen et al. 2000; Pritchard et al. 1999), Bhattacharya et al. (1995) and Pratt (1998) argued persuasively for the need for the conceptual delineation of the constructs. As we define them in this article, commitment is an enduring desire to maintain a valued relationship, while identification is a cognitive assessment of the degree of overlap between the self and some organization or group. We posit that rather than directly affecting WOM, increased identification with a firm leads to greater commitment to a relationship with that firm, which in turn affects positive WOM.

Research on how individuals perceive and interact with organizations provides the rationale for our proposed

causal influence of identification on commitment. For example, Ashforth and Mael (1989) noted that "individuals tend to choose activities congruent with salient aspects of their identities, and they support the institutions em- bodying those identit ies. . , it is likely that identification with an organization enhances support for and commitment to it" (pp. 25-26). Similarly, Wiener (1982) proposed that identification is a determinant of employee commitment, and Pratt (1998) argued that organizations seek to control employees by fostering identification, which in turn leads to greater commitment. The marketing literature also provides some support for our conjecture. In a distri- bution setting, Morgan and Hunt (1994) demonstrated that retailer perceptions of shared values with their suppliers positively influence relationship commitment toward the suppliers. In a consumer context, increased perceived identity overlap with a marketing organization would cause the individual to desire to maintain a relationship with the organization. In effect, to reject the relationship would be to reject one's own identity, when identification is high. As a result, we hypothesize that consumer identification will lead to commitment, which in turn leads to positive WOM.

Hypothesis 3: Consumer commitment mediates the relationship between identification and positive WOM intentions and behaviors.

METHOD

We tested our hypotheses in a field study with an automobile dealership's customer base. Because this context included buyer-seller contacts ranging from single interactions to multiple interactions over time, it provided sufficient opportunity for all study constructs to be meaning- ful and relevant. Furthermore, although existing studies use a durable goods context such as automobiles to study important marketing phenomena (e.g., Mittal et al. 1999; Oliver 1993), this context has been largely neglected in studies of consumer commitment.

The automobile dealership that supplied the context for our study had operated in a major metropolitan area in the southwestern United States for 10 years at the time of data collection and is considered quite successful. The dealership sold and serviced one brand of automobile ranging in price from about $30,000 to $60,000. The dealership com- monly serviced vehicles purchased at other dealerships.

We collected data using two successive mail surveys. In the first survey, we sent a four-page questionnaire to 3,000 dealership customers selected via systematic random sam- piing of the dealer's computerized customer database (i.e., every kth element after a random start). In this survey, we measured all study variables (including positive WOM intentions) except positive WOM behaviors. Respondents


returned the survey in a postage-paid envelope addressed to one of the authors. We also asked respondents whether they would agree to participate in a follow-up survey. Respondents who agreed to participate in the follow-up survey received an additional two-page survey 3 months later that included measures of positive WOM behaviors.

We received 447 first-round surveys; after adjusting for incorrect addresses, the response rate was 16 percent. The response rate for the follow-up survey was 50 percent of the 297 respondents who had agreed to participate. We deleted cases with excessive missing data (more than half the items on any multiple-item scales or any single-item scales) as well as two cases identified as problematic in casewise diagnostics (Cook and Weisberg 1982). We used mean substitution to replace all remaining missing values (111 substitutions of 9,925 possible values, or 1%). This process resulted in 397 cases for the first survey and 147 cases for the follow-up survey. The sample was 58 percent male and median age 50, with 34 percent 44 years of age or younger, 30 percent 55 or older. Because neither gender (t = 1.495, p >. 10) nor age (r = .06, p >. I0) were related to WOM intentions, we did not include these variables in the analyses.

We tested for nonresponse bias by comparing respondents' demographic statistics with known population demographics for the particular brand of automobile, which we obtained from the manufacturer through the dealership. Chi-square tests detected no significant differences between our sample and buyer demographics on age (• 5 df= 3.41, p > .10). There was a significant difference detected for gender, however; our sample had more women (42%) than the overall population of drivers of this brand (33%; X 2, 1 df= 16.08,p < .01). Follow-up analyses, however, revealed no differences between men and women on any study variables except automobile satisfaction (women were more satisfied than men; mean = 6.33 versus 6.15, p < .01) and identification (women expressed lower levels of identification with the dealership; mean = .48 versus .52, p < .10). In light of this, it is unlikely that nonresponse bias had any material effect on our results.

Predictor, Control, and Dependent Variables

Predictor variables. We assessed cumulative (as opposed to transaction-specific) dealership satisfaction on 10 attributes of the dealership (e.g., overall quality of dealership, trustworthiness of dealership) using 7-point dissatisfied-satisfied scales. Oliver (1993) referred to measures of this type as measures of attribute satisfaction. Coefficient alpha for the measure was .94. The appendix presents all scales used in our analyses and reports coefficient alpha, where applicable.

We assessed consumers' commitment to the dealership using four items based on Morgan and Hunt (1994) and Moorman et al. (1992; e.g.~ "The relationship that I have

with [dealership] is something I am very committed to"). Respondents used 7-point Likert-type strongly disagree- strongly agree scales to indicate their degree of commitment to the dealership. Coefficient alpha for the measure was .94.

Bergami and Bagozzi (2000) argued that measures of identification, specifically the perceived degree of overlap between organization and individual identities, should be as direct as possible. In their research, they developed and validated a measure of identification that we adapt for our study. The measure has two parts, a visual scale and a verbal scale. The visual scale consists of two sets of circles, one representing the organization's identity and one the individual's identity. These circles gradually overlap until there is complete overlap (only one circle). On this visual scale, respondents indicate which of eight levels of overlap best represents the level of overlap between their identity and the identity of the dealership. The verbal scale for identification instructs respondents to "please indicate to what degree your self-image overlaps with the image of [dealership]" on a 7-point bipolar scale anchored not at all and very much. We combined the visual and verbal scales by dividing each scale response by the respective number of scale positions in the measure (eight for the visual scale and seven for the verbal scale) and computing the average. The correlation between the two measures was .76.

Control variables. To control for the possibility that positive WOM for the dealership might be due more to the ownership of a certain auto brand rather than to the characteristics of the dealership itself, we included measures of ownership and automobile satisfaction. Some members of the customer database may have purchased their cars else- where and taken them to the particular dealership for service only, while others may have purchased or leased their cars fi'om the dealership. Still others may have obtained a car from the dealership but sold it prior to the time of the survey. Because currently driving a car obtained from the dealership may affect the degree of positive WOM, we included this variable as a potential covariate. We measured it as a dummy variable representing whether the respondent or a family member was currently driving a car obtained from the dealership. The measure of automobile satisfaction included seven specific attributes of automobiles in general (appearance, safety, price, quality, power, features, durability) assessed on 7-point dissatisfied- satisfied scales. We asked respondents, "How satisfied are you with the following aspects of [brand] automobiles?" Cronbach's alpha for the scale was .85.

Dependent variables. Marketing researchers often use measures of behavioral intentions, although recent research by Mittal and Kamakura (2001) suggests that they are at best imperfect representations of actual behavior. To address this issue, we collected both WOM intentions (in the first survey) and WOM behaviors (in the follow-up


TABLE 1 Measurement Model in SEM (asymptotic covariance matrix; N = 397)

Phi Values

Number Composite Construct of ltems Reliability 1 2 3 4

1. Identification 2 .86 1.00 2. Automobile satisfaction 7 .86 .21 (.05) a 1.00 3. Dealership satisfaction 10 .94 .38 (.05) .37 (.07) 1.00 4. Commitment 4 .95 .63 (.04) .27 (.05) .57 (.03) 1.00 5. Word-of-mouth intentions 2 .98 .41 (.04) .25 (.07) .78 (.04) .61 (.03)

Overall model fit statistics Satorra-Bentler scaled • 493.54 (p < .01) Degrees of freedom = 265 Root mean square error of approximation (RMSEA) = .05 Comparative Fit Index (CFI) = .95

1.00

NOTE: SEM = structural equation modeling. a. Robust standard error of the estimate shown in parentheses. All phi-values are statistically significant atp < .01.

survey). Note, however, that we used intentions measures only as a proxy for behaviors and not as a determinant of behavior as in the theory of reasoned action (Fishbein and Ajzen 1975). We measured consumers' positive WOM intentions via two 7-point items (e.g., "If a friend were shopping for [automobile brand], how likely is it that you would recommend [dealership]?" anchored by definitely would not and definitely would). The correlation between the items was .97.

We assessed positive WOM behaviors by asking respondents the frequency with which they performed seven behaviors (e.g., "Recommended dealership to close personal friends") during the 3-month period prior to com- pleting the follow-up questionnaire. We developed this list of behaviors from a list of behaviors submitted to dealership executives for review and input. For each item, respondents provided the frequency of performing the behaviors on 7-point scales anchored by never and frequently. Coefficient alpha for the measure was .95.

ANALYSIS AND RESULTS

We performed measure validation through confirmatory factor analysis (CFA) in LISREL 8.3 (J6reskog and S6rbom 1999). Preliminary analyses indicated several highly skewed measures, in particular those assessing dealership satisfaction, automobile satisfaction, and WOM intentions. Traditional structural equation modeling (SEM) approaches with highly nonnormal data result in unreliable standard errors and chi-square values (J6reskog and S6rbom 1996). Therefore, we used the asymptotic covariance matrix as input and maximum likelihood as method of estimation (Satorra 1992). We also used the Satorra-Bentler (SB; 2001) scaled chi-square statistic, which applies a correction to address nonnormality

of data, in establishing overall model fit. Because of the need for large sample sizes when using the asymptotic covariance matrix, we worked with the initial WOM- intentions data set (N = 397), which included all study variables except WOM behavior.

Table 1 provides a summary of the measure validation results. The overall model fit statistics for the CFA are good (SB scaled • 265 df= 493.54, p < .01, root mean square error of approximation [RMSEA] = .05, comparative fit index [CFI] = .95), a general indication that the individual indicants are behaving as expected.

Each of the measures exhibits strong composite reliability, from a low of .86 for the identification and automobile satisfaction indicators to a high of .98 for the positive WOM intentions items. Together with the reported values of Cronbach's alpha (see appendix), this suggests that our measures were reliable. Furthermore, all indicators had statistically significant loadings on their respective latent constructs. The robust standard errors resulting from the use of the asymptotic covariance matrix were substantially larger (and the t-values smaller) than those produced by a model using the standard covariance matrix as input, vali- dating the need for revised SEM procedures in the face of strong nonnormality in the data set.

We also have evidence of discriminant validity. First, the phi matrix and associated robust standard errors presented in Table 1 ensured that unit correlation among latent variables is extremely unlikely. Second, for 9 of the 10 pairwise relationships in the phi matrix, the average variance extracted (AVE) for each latent variable exceeded the square of the correlation between the variables. The correlation between dealership satisfaction and WOM intentions was high enough (phi = .78) to cause the squared correlation to be slightly higher than the AVE for dealership satisfaction (.61versus .60, respectively). To provide a further check on discriminant validity for these


TABLE 2 Descriptive Statistics and Pairwise Correlations for Hierarchical Regression Analyses

Variable M SD 1 2 3 4 5

A. Analyses on word-of-mouth intentions (n = 397) a 1. Identification 2. Automobile satisfaction 3. Dealership satisfaction 4. Commitment 5. Word-of-mouth intentions

B. Analyses on word-of-mouth behaviors (n = 147) b 1. Identification 2. Automobile satisfaction 3. Dealership satisfaction 4. Commitment 5. Word-of-mouth behavior

0.50 0.26 1.00 6.22 0.69 ,19 1.00 6.15 0.96 .34 .36 1.00 4.14 1.72 .56 .28 .53 1.00 6.06 1.59 .37 .25 .74 .59

0.54 0.25 1.00 6.24 0.65 .28 1.00 6.31 0.69 .29 .52 1.00 4.36 1.70 .58 .32 .47 1.00 3.93 1.94 .42 .27 .35 .48

1.00

1.00

a. Product usage: 72 percent drive vehicles obtained through the dealership. b. Product usage: 76 percent drive vehicles obtained through the dealership.

two latent variables, we compared the scaled difference chi-square statistic of the hypothesized measurement model to a second model that constrained the correlation between the two latent variables to unity. The corrected chi-square difference test using the SB scaled chi-square values (Satorra and Bentler 2001) indicated that the hypothesized measurement model was superior to the constrained model (corrected A • = 12.04, 1 df, p < .01). As a result, we are confident that each of the latent variables in our model exhibits discriminant validity with all other latent variables.

After measure validation, we computed index scores for use in hierarchical OLS regression analyses to test our hypotheses, using appropriate product terms to test interaction effects. This approach was appropriate because we did not have sufficient sample size with WOM behavior as the dependent variable to support asymptotic analyses in SEM, and it was important to use a consistent data analysis technique across both primary dependent variables. Given the difficulties of obtaining significant interaction effects in field studies (McClelland and Judd 1993), our approach might be considered conservative.

Results

Descriptive statistics and pairwise correlations for the measures used in analyses on WOM intentions (n = 397) and WOM behaviors (n = 147) appear in Table 2. The results of the hierarchical regression analyses appear in Table 3. We note that although the WOM intentions dependent variable was highly skewed, the residuals from the regression were symmetric, thereby lowering concerns about nonnormality. The overall models were significant (/'6, 39o = 119.59, p < .001 for the WOM intentions model; F6, 140 = 10.89, p < .001 for the WOM behaviors model), explaining 64 percent of the variance in WOM intentions

and 29 percent of the variance in WOM behaviors. The first control variable, car ownership, was a significant predictor of WOM intentions (t = 2.78); driving a car obtained from the dealership was associated with higher levels of WOM intentions. Current car ownership, however, was not a significant predictor of WOM behavior. Automobile satisfaction was not a significant predictor in either model.

The Mediating Role of Consumer Commitment

In Hypotheses 1 and 3, we suggest that consumer commitment serves as a mediator of the effects of satisfaction and identification on WOM. The results in Table 3 for the overall model indicate support for these hypotheses. As noted in Model A, dealership satisfaction (t = 8.64) and identification with the dealership (t = 10.32) were each significant predictors of commitment. In Models B and C (with WOM intentions and WOM behaviors as dependent variables, respectively), both satisfaction and identification were initially significantly related to WOM intentions and WOM behavior. When commitment was added to the models in Stage 2, the effect of satisfaction on WOM intentions decreased somewhat yet remained statistically significant, consistent with partial mediation, and the effect of satisfaction on WOM behavior became nonsignificant, indicating full mediation (Baron and Kenny 1986). Furthermore, with commitment in the model at Stage 2, the effect of identification on WOM intentions became nonsignificant, indicating full mediation through commitment and confirming that identification preceded commitment and not vice versa. The effect of identification on WOM behavior was reduced, although not eliminated, with the introduction of commitment to the model, indicating partial mediation through commitment. 2 Identi- fication continued to maintain a direct effect on WOM


TABLE 3 Moderated Hierarchical Regression Results

Predictor

Model A Model B Model C

D V = Commitment D V = W O M Intentions D V = W O M Behavior

(n = 397) (n = 397) (n = 147)

B t B t B t

Stage 1 Constant Dealership satisfaction (DS) Identification Auto satisfaction Ownership

Stage 2 Constant Dealership satisfaction Identification Auto satisfaction Ownership Commitment (C)

Stage 3 Constant Dealership satisfaction Identification Auto satisfaction Ownership Commitment

Interaction: C x DS

-2.40 -3.81" -1.21 -2.37 ' -2.66 -1.71 0.65 8.64* 1.16 19.13* 0.59 2.42* 2.76 10.32" 0.78 3.61" 2.53 4. t3" 0.16 1.54 -0.08 -0.99 0.21 0.81 0.27 1.90 0.34 2.92* 0.26 0.75

-0.66 -1.33 -1.79 -1.16 1.01 15.93" 0.33 1.32 0.15 0.64 1.47 2.12"

-0.12 - 1.48 0.20 0.78 0.28 2.48* 0.24 0.71 0.23 5.83* 0.33 2.99*

-4.26 -5.73" -9.33 -2.92" 1.48 15.48" 1.37 2.99* 0.09 0.41 1.66 2.44*

-0.00 -0.04 0.37 1.45 0.30 2.78* 0.08 0.23 1.51 7.29* 2.54 3.05 *

-0.20 --6.29* -0.35 -2.68*

F4,392 -- 81.15 F6, 39o = 119.59 F6, 140 = 10.89 p < .05 p < .05 p < .05

R2adj. = .45 R2adj. = .64 R2adj. = .29

NOTE: DV = dependent variable; WOM = word of mouth. *p < .05.

behavior (t = 2.44, p < .05) in addition to its effect mediated through commitment.

The Moderating Role of Consumer Commitment

The second hypothesis suggested that satisfaction and consumer commitment interact in predicting consumer WOM activity, such that the relative effect of satisfaction would decrease at higher levels of commitment. The results presented in Table 3 for the WOM intentions model and the WOM behavior model supported this hypothesis (t = -6.29 and t = -2.68, respectively). In both models, the higher the level of commitment to the dealership reported by consumers, the less positive the slope representing the effect of global satisfaction with the dealership on WOM intentions and WOM behavior. Simple slope analysis for the effect of satisfaction on WOM intentions for low commitment (one standard deviation below the mean; slope = .99), mean commitment (slope = .65), and high commitment (one standard deviation above the mean; slope = .30)

reflected the negative interaction. The corresponding simple slopes for the effect of satisfaction on WOM behavior (low commitment, .45; mean commitment, -. 14; high commitment, -.72) also reflected decreasing slopes associated with increasing levels of commitment, with one difference--when commitment was high, the influence of satisfaction on WOM behavior turned negative. We discuss this result in our Discussion section.

Wave Analysis

Finally, we performed a wave analysis to compare the responses of those who responded to both surveys (n = 147) with those who only responded to the first survey (n = 250). The groups did not differ on demographic variables (sex, age, length of time doing business with dealership, number of cars purchased from dealership, current ownership of vehicle obtained from dealership; all p > .05). There were differences on some attitudinal variables (identification, dealership satisfaction, commitment, intentions; all p < .05); mean responses are higher for those


who responded to both waves. However, the relationships that were found to be significant for those who responded to the first survey only (n = 250) were also significant and in the same direction for those who responded to both surveys (n = 147). Thus, we find these relationships to be robust across the waves.

DISCUSSION

The objective of this research was to address a gap in our understanding of the antecedents of WOM activities. We developed and tested a model of factors influencing positive WOM that included satisfaction, commitment, and identification. The results of our analyses demonstrate support for all our hypothesized relationships. Our research illustrates that satisfaction, commitment, and identification exert significant influences on positive WOM intentions and behaviors, with the effects of satisfaction and identification at least partially mediated through commitment. At the same time, consumer commitment inter- acts with satisfaction such that the influence of satisfaction on positive WOM becomes less positive at higher levels of commitment to the marketing organization.

At a theoretical level, these findings add to our understanding of factors that influence positive WOM. The conceptual model we develop in this article challenges current thinking about the antecedents of positive WOM by intro- ducing important moderating and mediating relationships of antecedents to positive WOM. Several important contributions merit discussion.

Our model and findings suggest that commitment both mediates and moderates the effect of satisfaction on positive WOM. Existing research demonstrates that higher satisfaction leads to greater levels of commitment and WOM intentions and that commitment leads to increased WOM behavior, but this research goes further. Our research provides further insight into the possible nature of the relationship between satisfaction and commitment in the prediction of both positive WOM intentions and actual WOM behavior. The results suggest that consumer commitment to the retailer partially mediates the effect of satisfaction on positive WOM. This is consistent with Bettencourt (1997), with Oliver's (1999) treatise on the development of consumer loyalty, and with suggestions in the relationship marketing literature (e.g., Dwyer et al. 1987; Morgan and Hunt 1994). Indeed, it is difficult to imagine a consumer developing a committed relationship to a retailer or saying positive things about the retailer to others without having experienced satisfaction from exchanges with that retailer. However, for consumers with higher levels of commitment to a relationship with the marketer, the overall level of satisfaction exerts less influence on positive WOM.

In addition to mediating the influence of satisfaction on positive WOM, our research suggests that commitment is also a significant mediator of (i.e., is affected by) the effect of consumer identification. Interestingly, identification also has a direct effect on positive WOM behavior inde- pendent of its effect mediated through commitment. Thus, as individuals perceive greater overlap between dealership identity and their own identity, two important things hap- pen: actual positive WOM behavior increases (a direct effect) and commitment to the marketer increases, which in turn also increases positive WOM behaviors (an indirect effect).

Together, the findings that commitment mediates the effects of both satisfaction and identification on positive WOM are important. In essence, they provide insights to developing and maintaining commitment over a longer term. The mediating role of c .ommitment is not likely one that will last forever, just as Oliver (1999) noted that higher forms of loyalty are susceptible to erosion if the underlying foundation (i.e., satisfactory performance) crumbles. Consequently, the mediating effect of consumer commitment may depend largely on the degree to which it can be developed based on factors other than satisfaction alone.

The finding that satisfaction may be inversely related to WOM behavior when commitment is high is intriguing. It may be that a highly committed consumer who has experienced lower levels of satisfaction may "talk up" a company as a defense mechanism, based on a need to reinforce that he or she made a good decision to enter into a relationship with the company in the first place. Similarly, people may overcompensate in their behaviors toward a company to regain balance or cognitive consistency when satisfaction is low but commitment is high. Whatever causes the effect, it appears to occur below the level of conscious- ness; a similar effect was not obtained for intended WOM behaviors.

The results of this study have important contributions for the practice of marketing. In particular, we address the question, "How might a company engender higher levels of positive WOM among its customer base?" Although consumers may talk about companies for a variety of reasons, our research demonstrates the key roles played by consumer commitment as a mediator of both satisfaction and identification. Our results suggest that consumer commitment can be influenced by factors other than satisfaction, such as identification. Furthermore, we believe it is important for a company to develop commitment based on satisfaction and these other factors. On the basis of our research, consumers who are highly committed to the retailer but report less-than-perfect satisfaction can maintain high levels of both positive WOM intentions and behavior. The development of consumer commitment may be especially important for organizations offering a large services component, perhaps as part of a consumption system (Mittal et al. 1999), given the variable nature of ser-


vice delivery and the importance of WOM for services (Murray 1991).

The development of committed customers is a key objective for marketing managers. In addition to offering products and services that deliver customer satisfaction, we suggest that managers can promote commitment directly and indirectly through the identities that they help create for their organizations. If a consumer is to feel committed to a relationship with a particular retailer, he or she will need to know something about the other party in the relationship. To this end, managers must consciously and consistently define their organizations through all types of interaction and communication with consumers. The management of identity (i.e., putting a "face" on an organization) provides consumers with an entity that they can embrace and to which they can become committed. Con- sumers' perceptions of the organization's identity also serve an important role in the formation of identification, the overlap of self-schema with the organizational schema. Identification, in tum, serves as a determinant of commitment, as evidenced by the results of this research.

Limitations and Future Research

Although we do not take measures on related concepts, we speculate that consumer identification may subsume several other constructs used in marketing including "self- connection" (Fournier 1998), "character congruence" (Sen and Bhattacharya 2001), "self-image congruence" (Sirgy et al. 1997), and "position involvement" (Pritchard et al. 1999). More research is needed to investigate the relationships between these constructs and positive WOM intentions and behaviors.

While we suspect that our results are generalizable to other contexts, our research deals exclusively with a marketing firm in a product category best characterized by higher levels of involvemont for most consumers. In addition, we conduct the research using the customer base of a single retailer that offers both products and services. Con- sequently, future research that examines consumer positive WOM for different entities (e.g., brands, salespeople) and product categories across multiple consumer samples is warranted.

Another limitation of our research is its relatively short time frame. Even though we collected two-stage data, the time frame during which we assess WOM behaviors is only 3 months. It would be informative to determine how long consumers' sense of commitment to a marketing organization can continue to offset any possible decreases in satisfaction. Future research should examine both the duration of the interactive effect as well as whether the mediating effect of commitment on positive WOM as a result of identification and other variables is more long- lived than the mediating effect of commitment based on satisfaction alone,

We suggest a significant role for consumer identification with retailers in our model. Considerable research remains, however, to understand fully the influence of identification on positive WOM. For example, researchers might investigate the degree to which consumers use particular types of corporate associations (Brown and Dacin 1997) when evaluating the degree of overlap between their own self-schema and the corporate schema. At this point, little is known about which specific corporate associations--for example, brand, location, employees, other store attributes, associations created through advertising--consumers consider when assessing the degree of identification with the marketing entity. In addition, because consumer cognitive identification involves a comparison of self-schema and company schema, further research is needed to investigate the role of the self in the identification process. As others have noted (e.g., J. Aaker 1999; Krishnamurthy and Sujan 1999), the self is a malleable concept, not a one-dimensional one. It is surprising to find almost no extant research on how (potentially multiple) self-schemas are combined with company schemas to produce identification within individuals.

Finally, in using a survey to collect data, we encoun- tered several limitations with respect to our data, most notably skewness. These limitations are common with survey data and often serve to restrict the types of analyses researchers can conduct, especially CFA and construct validation of scales. Fortunately, in this study, we were able to address these limitations by basing our psycho- metric validation on asymptotic correlations and maximum likelihood as method of estimation (Satorra 1992) and the SB scaled chi-square statistic. We found this approach very promising for survey research and investigating construct validity and encourage others to incorporate it in their analyses of survey data.

Despite these limitations, our research provides evidence that the relationship between satisfaction and WOM is more complex than previous research results suggest, with satisfaction having a more positive effect when commitment to the organization is low. In addition, respondent identification with the organization appears to have both direct and indirect effects (mediated through commitment) on WOM behavior.

APPENDIX Measures Used in Analyses

(A) Auto Satisfaction (~ = .85)

"How satisfied are you with the following aspects of [automobile brand]? Circle a number on each scale." (7-point, dissatisfied- satisfied)

Appearance

Safety

Price


A P P E N D I X (continued)

Quality

Power

Features

Durability

(B) Identification With Marketing Firm (r = .76)

Visual Identification

"Some people suggest that customers want to be associated with companies that reflect the attributes and values of the customers themselves. Imagine that one of the circles at the left in each row represents your own personal identity and the other circle at the right represents [dealership's] identity. Please indicate which one case (A, B, C, D, E, F, G, or H) best describes the level of overlap between your and [dealership's] identities. Circle only one letter on the following scale:"

Verbal Identification

"Please indicate to what degree your self-image overlaps with [dealership's] image." (7-point, not at all-very much)

(C) Dealership Satisfaction ((x = .94)

"Please indicate your satisfaction with the following aspects of the [dealership] by circling a number on each scale:" (7-point, dissatisfied-satisfied)

Overall quality of dealership

Effectiveness of salespeople

Trustworthiness of dealership

Dealership management

0 O 0

c O 0 ~ CD

(if:)

0 " 0

Far Apart

Close Together but Separate

Very Small Overlap

Small Overlap

Moderate Overlap

Large Overlap

Very Large Overlap

Complete Overlap

Service department honesty

Quality of service performed

Appearance of dealership

Customer treatment by employees

Customer amenities

Ethics of business practices

(D) Dealership Relationship Commitment (c~ = .94)

"How much do you agree or disagree with each of the following statements?" (7-point, strongly disagree-strongly agree)

I am committed to my relationship with [dealership].

I really care about my ongoing relationship with [dealership].

The relationship that I have with [dealership] is something I am very committed to.

The relationship that I have with [dealership] deserves my maximum effort to maintain.

(E) Word-of-Mouth Intentions (r = .97)

(7-point, definitely would not-definitely would)

"If a friend were shopping for a [brand], how likely is it that you would recommend [dealership]?"

"If you were helping your son, daughter, or other close relative make a decision on what dealership to shop for a [brand], how likely is it that you would recommend [dealership]?"

(F) Word-of-Mouth Behaviors (~ = .95)

(7-point, never-frequently)

Mentioned to others that you do business with dealership.

Made sure that others know that you do business with dealership.

Spoke positively about dealership employee(s) to others.

Recommended dealership to family members.

Spoke positively of the dealership to others.

Recommended dealership to acquaintances.

Recommended dealership to close personal friends.

(G) Car Ownership

"Do you (or close family members) currently drive a vehicle purchased or leased from [dealership]?"

A C K N O W L E D G M E N T S

We thank the automobile dealership that sponsored this research and John C. Mowen, Albert Satorra, Kevin E. Voss, and Joshua L. Wiener for advice and comments on the article.


NOTES

1. Reynolds and Beatty (1999) found no influence of satisfaction with a company on customer WOM about the company. They did, however, obtain a positive relationship between salesperson satisfaction and WOM about the salesperson. If our interactive perspective is correct, the obtained relationship would have been even stronger had less-committed consumers comprised the sample.

2. By reversing the order of entry of commitment and identification in the hierarchical model, we examined the relative decrease in the beta coefficient of commitment when identification was added to the model versus the decrease in the coefficient for identification when commitment was added to the regression for the word-of-mouth behavior model. The results indicated that a larger portion of the effect of identification on WOM behavior is mediated through commitment than vice versa, supporting the proposed causal linkage.

REFERENCES

Aaker, David A. 1991. Managing Brand Equity. New York: Free Press. Aaker, Jennifer. 1999. "The Malleable Self: The Role of Self-Expression

in Persuasion." Journal of Marketing Research 36 (February): 45-57. Ahluwalia, Rohini, Robert E. Burnkrant, and H. Rao Unnava. 2000.

"Consumer Response to Negative Publicity: The Moderating Role of Commitment?' Journal of Marketing Research 37 (May): 203-214.

Anderson, Eugene W. 1998. "Customer Satisfaction and Word of Mouth." Journal of Service Research 1 (August): 5-17.

- - and Mary W. Sullivan. 1993. "The Antecedents and Conse- quences of Customer Satisfaction for Firms." Marketing Science 12 (Spring): 125-143.

Arndt, Johan. 1967. "The Role of Product-Related Conversation in the Diffusion of a New Product." Journal of Marketing Research 4 (August): 291-295.

Arnett, Dennis B., Steve D. German, and Shelby D. Hunt. 2003. "The Identity Salience Model of Relationship Marketing Success: The Case of Nonprofit Marketing." Journal of Marketing 67 (April): 89- 105.

Ashforth, Blake E. and Fred Mael. 1989. "Social Identity Theory and the Organization." Academy of Management Review 14 (1): 20-39.

- - and Alan M. Saks. 1996. "Socialization Tactics: Longitudinal Effects on Newcomer Adjustment?' Academy of Management Jour- nal 39 (1): 149-178.

Baron, Reuben M. and David A. Kenny. 1986. "The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations." Journal of Personality and Social Psychology 51 (December): 1173-1182.

Bass, Frank. 1969. "A New Product Growth Model for Consumer Durables" Management Science 15 (January): 215-227.

Bendapudi, Neeli and Leonard L. Berry. 1997. "Customers' Motivations for Maintaining Relationships With Service Providers." Journal of Retailing 73 (1): 15-37.

Bergami, Massimo and Richard P. Bagozzi. 2000. "Self-Categorization, Affective Commitment and Group Self-Esteem as Distinct Aspects of Social Identity in the Organization." British Journal of Social Psy- chology 39 (4): 555-577.

Bettencourt, Lance A. 1997 "Customer Voluntary Performance: Custom- ers as Partners in Service Delivery." Journal of Retailing 73 (3): 383- 406.

Bhattacharya, C. B., Hayagreeva Rao, and Mary Ann Glynn. 1995. "Understanding the Bond of Identification: An Investigation of Its Correlates Among Art Museum Members." Journal of Marketing 59 (October): 46-57.

- - and Sankar Sen. 2003. "Consumer-Company Identification: A Framework for Understanding Consumers' Relationships With Com- panies." Journal of Marketing 67 (April): 76-88.

Blodgett, Jeffrey G., Donald H. Granbois, and Rockney G. Waiters. 1993. "The Effects of Perceived Justice on Complainants' Negative Word-

of-Mouth Behavior and Repatronage Intentions." Journal of Retail- ing 69 (Winter): 399-428.

Bolton, Ruth N. and Katherine N. Lemon. 1999. "A Dynamic Model of Customers' Usage of Services: Usage as an Antecedent and Conse- quence of Satisfaction" Journal of Marketing Research 36 (May): 171-186.

Bone, Panla Fitzgerald. 1995. "Word-of-Mouth Effects on Short-Term and Long-Term Product Judgments?' Journal of Business Research 32 (Marc~): 213-223.

Brown, Jacqueline Johnson and Peter H. Reingen. 1987. "Social "ties and Word-of-Mouth Referral Behavior." Journal of Consumer Research 14 (December): 350-362.

Brown, Tom J. and Peter A. Dacin. 1997. "The Company and the Product: Corporate Associations and Consumer Product Responses." Journal of Marketing 61 (January): 68-84.

Christopher, Martin, Adrian Payne, and David Ballantyne. 1991. Rela- tionship Marketing. Oxford, UK: Butterworth-Heinemann.

Cook, R. Dennis and Sandford Weisberg. 1982. Residuals and Influence in Regression. New York: Chapman and Hall.

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

Deal, Marianna and Pete Abel. 2001. "Grass Roots: The Exponential Power of One" Brandweek, February 26, 30.

Duhan, Dale E, Scott D. Johnson, James B. Wilcox, and Gilbert D. Harrell. 1997. "Influences on Consumer Use of Word-of-Mouth Rec- ommendation Sources?' Journal of the Academy of Marketing Sci- ence 25 (Fall): 283-295.

Dutton, Jane E., Janet M. Dukerich, and Celia V. Harquail. 1994. "Orga- nizational Images and Member Identification." Administrative Sci- ences Quarterly 39 (June): 239-263.

Dwyer, E Robert, Paul H. Schurr, and Sejo Oh. 1987. "Developing Buyer-Seller RelationshipsY Journal of Marketing 51 (April): 11-27.

Feick, Lawrence E and Linda L. Price. 1987. "The Market Maven: A Dif- fuser of Marketplace Information?' Journal of Marketing 51 (Janu- ary): 83-97.

Fishbein, Martin and Icek Ajzen. 1975. Belief Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

Founaier, Susan. 1998. "Consumers and Their Brands: Developing Rela- tionship Theory in Consumer Research." Journal of Consumer Research 24 (March): 343-373.

Frenzen, Jonathan and Kent Nakamoto. 1993. "Structure, Cooperation, and the Flow of Market Information?' Journal of Consumer Research 20 (December): 360-375.

Ganzach, Yoav. 1998. "Intelligence and Job Satisfaction?' Academy of Management Journal 41 (5): 526-539.

Garbarino, Ellen and Mark S. Johnson. 1999. "The Different Roles of Satisfaction, Trust, and Commitment in Customer Relationships." Journal of Marketing 63 (April): 70-87.

Gelb, Betsy and Madeline Johnson. 1995. "Word-of-Mouth Communica- tion: Causes and Consequences." Journal of Health Care Marketing 15 (Fall): 54-58.

Gilly, Mary C., John L. Graham, Mary Finley Wolfinbarger, and Laura J. Yale. 1998. "A Dyadic Study of Interpersonal Information Search." Journal of the Academy of Marketing Science 26 (Spring): 83-100.

Gruen, Thomas W., John O. Summers, and Frank Acito. 2000. "Relation- ship Marketing Activities, Commitment, and Membership Behaviors in Professional Associations." Journal of Marketing 64 (July): 34-49.

Harrison-Walker, L. Jean. 2001. "The Measurement of Word-of-Mouth Communication and an Investigation of Service Quality and Cus- tomer Commitment as Potential Antecedents." Journal of Service Research 4 (August): 60-75.

Heckman, Robert and Audrey Guskey. 1998. "The Relationship Between Alumni and University: Toward a Theory of Discretionary Collabora- tive Behavior." Journal of Marketing Theory and Practice 6 (Spring): 97-112.

Herr, Paul M., Frank R. Kardes, and John Kim. 1991. "Effects of Word- of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective." Journal of Consumer Research 17 (March): 454-462.

Heskett, James L., Thomas O. Jones, Gary W. Loveman, W. Earl Sasser Jr., and Leonard A. Schlesinger. 1994. "Putting the Service-Profit


Chain to Work." Harvard Business Review 72 (March-April): 164- 174.

Higie, Robin A., Lawrence E Feick, and Linda L. Price. 1987. "Types and Amount of Word-of-Mouth Communications About Retailers." Jour- nal of Retailing 63 (Fall): 260-278.

John, Joby. 1994. "Referent Opinion and Health Care Satisfaction." Jour- nal of Health Care Marketing 14 (Summer): 24-30.

Jones, Thomas O. and W. Earl Sasser Jr. I995. "Why Satisfied Custom- ers Defect." Harvard Business Review 73 (November-December): 88-89.

J6reskog, Karl G. and Dag S6rbom. 1996. L1SREL 8: Structural Equation Modeling With the SIMPLIS Command Language. Chicago: Scien- tific Software International.

- - and . 1999. LISREL 8.30. Chicago: Scientific Software International.

Kelley, Scott W. and Mark A. Davis. 1994. "Antecedents to Customer Expectations for Service Recovery." Journal of the Academy of Mar- keting Science 22 (1): 52-61.

Kelly, Erin. 2000. "This is One Virus You Want to Spread." Fortune, November 27,297-300.

Krishnamurthy, Parthasarathy and Mita Sujan. 1999. "Retrospection Ver- sus Anticipation: The Role of the Ad Under Retrospective and Antici- patory Self-Referencing." Journal of Consumer Research 26 (June): 55-69.

Kwak, Hyokjin, George M. Zinkhan, and Leyland E Pitt. 2001. "Global and Cultural Perspectives on Web-Based Chatting: An Exploratory Study." Asia Pacific Advances in Consumer Research, Vol. 4, Paula M. Tidwell and Thomas E. Muller, eds. Provo, UT: Association for Consumer Research, 243-250.

Mael, Fred and Blake E. Ashforth. 1992. "Alumni and Their Alma Mater: A Partial Test of the Reformulated Model of Organizational Identifi- cation." Journal of Organizational Behavior 13 (March): 103-123.

- - a n d Lois E. Tetrick. 1992. "Identifying Organizational Identifica- tion." Educational and Psychological Measurement 52 (4): 813-824.

Mahajan, Vijay, Eitan Muller, and Frank M. Bass. 1990. "New Product Diffusion Models in Marketing: A Review and Directions for Research." Journal of Marketing 54 (January): 1-26.

Mangold, W. Glynn, Fred Miller, and Gary R. Brockway. 1999. "Word- of-Mouth Communication in the Service Marketplace." Journal of Services Marketing 13 (1): 73-89.

McClelland, Gary H. and Charles M. Judd. 1993. "Statistical Difficulties of Detecting Interactions and Moderator Effects." Psychological Bul- letin t 14 (2): 376-390.

Mittal, Vikas and Wagner A. Kamakura. 2001. "Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics." Journal of Marketing Research 38 (February): 131-142.

, Pankaj Kumar, and Michael Tsiros. 1999. "Attribute-Level Per- formance, Satisfaction, and Behavioral Intentions Over Time: A Con- sumption-System Approach." Journal of Marketing 63 (April): 88- 101.

Money, R. Bruce, Mary C. Gilly, and John L. Graham. 1998. "Explora- tions of National Culture and Word-of-Mouth Referral Behavior in the Purchase of Industrial Services in the United States and Japan." Journal of Marketing 62 (October): 76-87.

Moorman, Christine, Gerald Zaltman, and Rohit Deshpande. 1992. "Relationships Between Providers and Users of Market Research: The Dynamics of Trust Within and Between Organizations." Journal of Marketing Research 29 (August): 314-328.

Morgan, Robert M. and Shelby D. Hunt. 1994. "The Commitment-Trust Theory of Relationship Marketing." Journal of Marketing 58 (July): 20-38.

Murray, Keith B. 1991. "A Test of Services Marketing Theory: Consumer Information Acquisition Activities." JoulTzal of Marketing 55 (Janu- ary): 10-25.

Nijssen, Edwin, Jagdip Singh, Deepak Sirdeshmukh, and Hartmut Holzmueller. 2003. "Investigating Industry Context Effects in Consumer-Firm Relationships: Preliminary Results From a Disposi- tional Approach." Journal of the Academy of Marketing Science 31 (Winter): 46-60.

Oliver, Richard L. 1993. "Cognitive, Affective and Attribute Bases of the Satisfaction Response." Journal of Consumer Research 20 (Decem- ber): 418-430.

. 1997. Satisfaction: A Behavioral Perspective on the Consumer. Boston: McGraw-Hill.

. 1999. "Whence Consumer Loyalty?" Journal of Marketing 63 (Special Issue): 33-44.

Pitt, Leyland E, Pierre R. Bertbon, Richard T. Watson, and George M. Zinkhan. 2002. "'The Internet and the Birth of Real Consumer Poweff' Business Horizons 45 (July-August): 7-I4.

Pratt, Michael G. 1998. "To Be or Not to Be? Central Questions in Orga- nizational Identification." In Identity in Organizations: Building The- ory Through Conversations. Ed. D. A. Whetten and E C. Godfrey. Thousand Oaks, CA: Sage, 171-207.

Pritchard, Mark E, Mark E. Havitz, and Dennis R. Howard. 1999. "Ana- lyzing the Commitment-Loyalty Link in Service Contexts." Journal of the Academy of Marketing Science 27 (3): 333-348.

Reichheld, Frederick E 2003. "The One Number You Need to Know." Harvard Business Review 8 l (12): 46-54.

Reynolds, Kristy E. and Sharon E. Beatty. 1999. "Customer Benefits and Company Consequences of Customer-Salesperson Relationships in Retailing." Journal of Retailing 75 (1): 11-32.

Richins, Marsha L. 1983. Negative Word-of-Mouth by Dissatisfied Con- sumers: A Pilot Study." Journal of Marketing 47 (Winter): 68-78.

- - and Teri Root-Shaffer. 1988. "The Role of Involvement and Opinion Leadership in Consumer Word-of-Mouth: An Implicit Model Made Explicit?' In Advances in Consumer Research, Vol. 15. Ed. Michael J. Houston. Provo, UT: Association for Consumer Research, 32-36.

Rosen, Emanuel. 2000. The Anatomy of Buzz: How to Create Word-of- Mouth Marketing. New York: Doubleday/Currency.

Rust, Roland T. and Anthony J. Zahorik. 1993. "Customer Satisfaction, Customer Retention, and Market Share." Journal of Retailing 69 (Summer): 193-215.

Satorra, Albert. 1992. "Asymptotic Robust Inferences in the Analysis of Mean and Covariance Structures." Sociological Methodology 22 (1): 249-278.

- - and Peter M. Bentler. 2001, "A Scaled Difference Chi-Aquare Test Statistic for Moment Structure Analysis." Psychometrika 66 (December): 507-514.

Sen, Sankar and C. B. Bhattacharya. 2001. "Does Doing Good Always Lead to Doing Better? Consumer Reactions to Corporate Social Responsibility." Journal ofMarketing Research 38 (May): 225-243.

Sheth, Jagdish N. and Atul Parvatiyar. 1995. "Relationship Marketing in Consumer Markets: Antecedents and Consequences" Journal of the Academy of Marketing Science 23 (Fall): 255-271.

Sirgy, M. Joseph, Dhruv Grewal, Tamara E Mangleburg, Jae-ok Park, Kye-Sung Chon, C. B. Claiborne, J. S. Johar, and Harold Berkman. 1997. "Assessing the Predictive Validity of Two Methods of Measur- ing Self-Image Congruence." Journal of the Academy of Marketing Science 25 (Summer): 229-241.

- - and A. Coskun Samli. 1985. "A Path Analytic Model of Store Loyalty Involving Self-Concept, Store Image, Geographic Loyalty, and Socioeconomic Status." Journal of the Academy of Marketing Science 13 (Summer): 265-291.

Sultan, Fareena, John U. Farley, and Donald R. Lehmann. 1990. "A Meta- Analysis of Applications of Diffusion Models."JournalofMarketing Research 27 (February): 70-77.

Summers, John O. 1972. "Media Exposure Patterns of Consumer Innova- tors." Journal of Marketing 36 (January): 43-49.

Swan, John E. and Richard L. Oliver. 1989. "Postpurchase Communica- tions by Consumers." Journal of Retailing 65 (Winter): 516-533.

Verhoef, Peter C., Philip Hans Franses, and Janny C. Hoekstra. 2002. "The Effect of Relational Constructs on Customer Referrals and Number of Services Purchased From a Multiservice Provider: Does Age of Relationship Matter?" Journal of the Academy of Marketing Science 30 (Summer): 202-216.

White, Susan S. and Benjamin Schneider. 2000. "Climbing the Commit- ment Ladder: The Role of Expectations Disconfirmation on Custom- ers' Behavioral Intentions." Journal of Service Research 2 (February): 240-253.

Wiener, Yoash. 1982. "Commitment in Organizations: A Normative View." Academy of Management Review 7 (3): 418-428.

Zeithaml, Valarie A., Leonard L. Beny, and A. Parasuraman. 1993. "The Nature and Determinants of Customer Expectations of Service." Journal of the Academy of Marketing Science 21 (Winter): 1-12.


Zinkhan, George M., Hyokjin Kwak, Michelle Morrison, and Cara Okleshen Peters. 2003. "Web-Based Chatting: Consumer Commu- nication in Cyberspace." Journal of Consumer Psychology 13 (1-2): 17-27.

ABOUT THE AUTHORS

Tom J. Brown ([email protected]) is Ardmore Professor of Business Administration and an associate professor of marketing at Oklahoma State University. He received his Ph.D. from the University of Wisconsin-Madison. His articles have appeared in leading marketing journals including the Journal of Marketing Research, the Journal of Marketing, the Journal of Consumer Research, and the Journal of the Academy of Marketing Science. His current research interests include causes and effects of corporate reputation and the customer orientation of service work- ers. He is cofounder of the Corporate Identity/Associations Research Group. Teaching interests include marketing research, services marketing, and corporate communications. He is coauthor (with Gilbert A. Churchill Jr.) of Basic Marketing Research (5th ed.).

Thomas E. Barry ([email protected]) is a professor of marketing and vice president for executive affairs at Southern Meth- odist University in Dallas, Texas. He received his Ph.D. from the University of North Texas. His primary teaching and research interests are in the areas of integrated marketing communications, marketing management, brand equity, loyalty, and advertising effectiveness. His research has appeared in numerous journals including the Journal of Marketing Research, the Jour- nal of Advertising Research, the Journal of Advertising, the Jour- nal of Consumer Psychology, and the Journal of the Academy of Marketing Science. He is the author or coauthor of two books in marketing and advertising management. He has consulted for a

variety of firms and is a director on four boards. In 1995, he received the Outstanding Contributions in Advertising Research Award from the American Academy of Advertising.

Peter A. Daein (pdacin @business.queens.ca) is a professor of marketing at Queen's University in Kingston, Ontario, Canada. He received his Ph.D. from the University of Toronto. His primary teaching and research interests lie in consumer/managerial judgment formation, brand equity/dilution, corporate reputation, and research methods and design. He has also published in the area of sales force management. His research has appeared in several leading journals including the Journal of Marketing, the Journal of Marketing Research, and the Journal of Consumer Research. In addition, he has published in numerous conference proceedings. He is currently the chair of the American Marketing Association's Consumer Behavior Special Interest Group, serves on the Academic Council of the American Marketing Associa- tion, and is cofounder of the Corporate Identity/Associations Research Group.

Richard E Gunst ([email protected]) is a professor and chair of the Department of Statistical Science at Southern Meth- odist University (SMU) in Dallas, Texas. He received his Ph.D. from SMU. His primary teaching and research interests are in the areas of linear and nonlinear modeling and regression analysis, with an emphasis on spatial statistical modeling. He has co- authored three books on regression analysis and the statistical design and analysis of experiments, in addition to publishing scholarly articles in the Journal of the American Statistical Asso- ciation, Biometrika, Biometrics, and Technometrics. He has received the W. J, Youden (1974, 1985) and Frank Wilcoxon (1994) Publication Awards from Technometrics, and the Ameri- can Statistical Association's Award for Outstanding Statistical Application Award (1994). He is a fellow of the American Statis- tical Association and received its Founders Award in 1999.

Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and...

Documents

Transcript of Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and...