Drivers of consumer-brand identification Nicola Stokburger-Sauera, S. Ratneshwarb, Sankar Senc Forthcoming, International Journal of Research in Marketing a Nicola Stokburger-Sauer is Professor of Service Management and Marketing, Department for Strategic Management, Marketing and Tourism, University of Innsbruck, Universitaetsstr. 15, A6020 Innsbruck, Austria. Email nicola.stokburger-sauer@uibk.ac.at. Tel: (0043) 512-507-7080. b S. Ratneshwar is Bailey K. Howard World Book Chair of Marketing, Trulaske College of Business, University of Missouri, Columbia, MO 65203. Email ratneshwar@missouri.edu. Tel: (573) 882-3748. c Sankar Sen is Professor of Marketing and International Business, Zicklin School of Business, Baruch College/CUNY, One Bernard Baruch Way, New York, NY 10010. Email sankar.sen@baruch.cuny.edu. Tel: (646) 312-3302. 2 DRIVERS OF CONSUMER-BRAND IDENTIFICATION The concept of consumer-brand identification (CBI) is central to our understanding of how, when, and why brands help consumers articulate their identities. This paper proposes and tests an integrative theoretical framework of the antecedents of CBI. Six drivers of CBI, a moderator, and two consequences are posited and tested with survey data from a large sample of German household consumers. The results confirm the influence of five of the six drivers, namely, brandself similarity, brand distinctiveness, brand social benefits, brand warmth, and memorable brand experiences. Further, we find that all five of these antecedents have stronger causal relationships with CBI when consumers have higher involvement with the brand‘s product category. Finally, CBI is tied to two important pro-company consequences, brand loyalty and brand advocacy. Theoretical and managerial significance of the findings are discussed. Keywords: Consumer-brand identification, Consumer self-identity, Brand relationships, Product category involvement 3 ―Choices are made more easily—either more routinely or more impulsively, seemingly— because one object is symbolically more harmonious with our goals, feelings, and selfdefinitions than another.‖ Sidney J. Levy (1959, p. 120) ―Why has the Toyota Prius enjoyed such success…when most other hybrid models struggle to find buyers? One answer may be that buyers of the Prius want everyone to know they are driving a hybrid…In fact, more than half the Prius buyers surveyed this spring…said the main reason they purchased their car was that ‗it makes a statement about me.‘‖ Micheline Maynard (2007) 1. Introduction Striving for a sense of self (i.e., answering the question ―Who am I?‖) is a fundamental aspect of the human condition (Belk, 1988; Berger & Heath, 2007; Brewer, 1991; Freud, 1922; Kleine, Kleine, & Kernan, 1993; Tajfel & Turner, 1985). Further, as succinctly put by Belk (1988, p. 160), ―we are what we have‖—what we buy, own, and consume, define us to others as well as ourselves. In this context, it is widely recognized that brands have the ability to embody, inform, and communicate desirable consumer identities (Bhattacharya & Sen, 2003; Escalas, 2004; Escalas & Bettman, 2003, 2009; Fournier, 1998, 2009; Lam, Ahearne, Hu, & Schillewaert, 2010; Levy, 1959; Strizhakova, Coulter, & Price, 2008; Tsai, 2005). Not surprisingly then, a growing body of research has focused on what it means for consumers to identify with brands and the implications of such consumer-brand identification (CBI) for both consumer behavior and effective brand management (e.g., Chernev, Hamilton, & Gal, 2011; Escalas & Bettman, 2003, 2009; Lam et al., 2010). Much less is understood, however, about the drivers of CBI—what factors cause it, when, and why. While a comprehensive sense for what produces CBI is of considerable importance to both marketing academics and practitioners, these issues have been examined from many diverse 4 perspectives, causing our understanding to be rather fragmented and piecemeal. For instance, in their work on consumer-company identification, Bhattacharya and Sen (2003) draw on social identity theory to proffer consumers‘ self-defining and enhancing motives as the main drivers of identification (see also Ahearne, Bhattacharya, & Gruen, 2005; Bhattacharya, Rao, & Glynn, 1995). The work of Esacalas and Bettman (2003, 2004, 2009), on the other hand, locates such identity-based bonds in the broader social context, suggesting that consumers bond with brands whose identities are consonant with desirable reference groups and celebrity endorsers. The communal consumption of brands and its role in the construction of identity narratives by consumers is stressed in the work of Fournier (2009), McAlexander, Schouten, and Koenig (2002), Muniz and O‘ Guinn (2001), and O‘ Guinn and Muniz (2009). Sociocultural factors such as the circulation of brand stories and myths among consumers are highlighted by Brown, Kozinets, and Sherry (2003), Diamond, Sherry, Muniz, McGrath, Kozinets, and Borghini (2009), Holt (2005), and Thompson, Rindfleisch, and Arsel (2006). Thomson, MacInnis, and Park (2005) and Park, MacInnis, Priester, Eisingerich, and Iacobucci (2010), on the other hand, emphasize the role of emotional reactions to the brand in the formation of consumer-brand connections. It is also worth noting that while it is assumed that CBI can occur in a wide range of categories, empirical research in this domain has usually been restricted to single category studies (e.g., Lam et al., 2010), thereby precluding a deeper understanding of the categoryspecific determinants, if any, of CBI. Given this backdrop of fragmented insights into the drivers of CBI, the present paper makes three key contributions. First, it synthesizes a wide range of ideas pertaining to identity construction, identification, and brand relationships to provide a comprehensive conceptual framework for the determinants of CBI. The outcome of our conceptual synthesis is a set of six 5 antecedents of CBI that includes three primarily cognitive variables (brand-self similarity, brand distinctiveness, and brand prestige) as well as three affectively rich brand-related factors (brand social benefits, brand warmth, and memorable brand experiences). Second, to strengthen the validity of our conceptual framework as well as go beyond extant single-category examinations of CBI (e.g., Lam et. al. 2010), we implicate an important category-level variable—product category involvement— as a moderator of the relationships between CBI and its various drivers. As well, we relate CBI to two key consequences: brand loyalty and brand advocacy. Finally, in the process of establishing this nomological network for CBI, we develop a valid, parsimonious measure of this focal construct that attempts to assess the state of CBI more independently of its antecedents and consequences (e.g., social rewards, negative affective states produced by discontinued brand usage) than do extant measures (e.g., Lam et al. 2010). Next, we develop our conceptual framework culminating in a set of predictions pertaining to the antecedents and consequences of CBI. We then test our hypotheses with survey data from a large sample of German household consumers. The paper ends with a discussion of the theoretical and managerial significance of our findings. 2. Conceptual framework 2.1. The concept of consumer-brand identification Brands, as carriers of symbolic meanings (Levy, 1959), can help consumers achieve their fundamental identity goals and projects (Belk, 1988; Escalas and Bettman, 2009; Fournier, 20009; Huffman, Ratneshwar, and Mick 2000; Holt 2005). Therefore, consumer-brand identification, defined here as a consumer‘s perceived state of oneness with a brand, is a valid and potent expression of our quest for identity-fulfilling meaning in the marketplace of brands. This definition is consistent with the organizational behavior literature, wherein identification 6 typically has been defined as a perception of oneness with or belongingness to some human aggregate, such as employees with companies and students with their alma maters (Ashforth & Mael, 1989; Bergami & Bagozzi, 2000; Bhattacharya et al., 1995; Mael & Ashforth, 1992; Stuart, 2002). Note that consistent with the theorizing in this domain (Bergami and Bagozzi 2000; Bhattacharya and Sen 2003), we assume that the state of CBI is distinct from the process of comparison of self traits with brand traits that might contribute to CBI. Table 1 present an overview of constructs related to CBI and their definitions in prior literature. While our conceptualization of CBI is rooted in organizational identity, it is related to the construct of self-brand connections proposed by Escalas and Bettman (2003, 2009), defined as the extent to which an individual has incorporated a brand into his/her self-concept. However, CBI is, notably, narrower in that it excludes the potential motivations guiding such self-brand connections, such as communicating one‘s identity to others and achieving one‘s desired self (both part of Escalas and Bettman‘s self-brand connection measure). CBI is similarly akin to the broader notions of brand-self connection in the work of Park et al. (2010) and the component called self-connection in Fournier‘s (2009) Brand Relationship Quality (BRQ) Scale. As in the case of self-brand connections, we regard CBI as narrower than, but potentially overlapping with, constructs such as BRQ (Fournier, 2009) and brand attachment (Park et al., 2010). Lam et al. (2010) take a somewhat different approach by defining CBI as ―a customer‘s psychological state of perceiving, feeling, and valuing his or her belongingness with a brand‖ (p. 130). In doing so, these authors view CBI as a formative construct comprised of three dimensions. The cognitive dimension of their construct is very similar to the notion of cognitive organizational identification in the work of Bergami and Bagozzi (2000). The emotional consequences of brand usage serve as the second dimension of Lam et al.‘s CBI construct and 7 ―evaluative CBI‖ is its final dimension, defined as ―whether the consumer thinks the psychological oneness with the brand is valuable to him or her individually and socially‖ (p. 137). Unlike Lam et al. (2010), we do not regard brand partner value as part of the construct of identification. Indeed, as we argue later, the social benefits of a brand can actually influence brand identification, thereby serving as an antecedent to the construct rather than being an integral part of it. We further depart from Lam et al.‘s (2010) conceptualization in that, consistent with the theorizing of Park et al. (2010), we view CBI primarily as a cognitive representation, albeit one that can have an abundance of emotional associations. Our view is in accord with Bergami & Bagozzi (2000), who argue that the emotional consequences of identification need to be kept separate from the state of identification. More generally, we regard CBI as different from the pure emotional bond that is embodied in the concepts of emotional brand attachment (Malär et al., 2011) and brand love (Batra, Ahuvia, & Bagozzi, 2012; Carroll & Ahuvia, 2006). Batra, Ahuvia, and Bagozzi (2012), in particular, include not only the dimensions of positive emotional connection and self-brand integration in their construct of brand love, but also positive brand evaluations. In contrast, we regard positive brand evaluations (i.e., brand attitudes) as conceptually different from CBI because while the former references just the brand as a target of evaluation, CBI hinges on both the perceived brand identity and the self-identity of the consumer. In that, brand evaluations are likely to be either inputs or outputs of CBI rather than a part of it. Similarly, Allen and Meyer‘s (1990) measure of affective commitment goes beyond individuals‘ perceived psychological state to include certain consequences of that state (e.g., ―I enjoy discussing my organization with people outside it‖). < Insert Table 1 about here > 8 2.2. Antecedents of CBI The need for identification is thought to be motivated by one or more higher-order selfdefinitional needs (Brewer, 1991; Kunda, 1999; Tajfel & Turner, 1985). Specifically, we need to (1) know ourselves, (2) feel relatively unique, and (3) feel good about ourselves. Thus, three key needs that are likely to drive identification in the consumption domain are that for self-continuity or self-verification, self-distinctiveness, and self-enhancement (Berger & Heath, 2007; Bhattacharya & Sen, 2003; Chernev et al., 2011). Based on these need drivers, we argue that identification with a brand is likely to be related to the extent to which a person perceives the brand (1) to have a personality that is similar to his or her own (i.e., brand-self similarity), (2) to be distinctive, and (3) and to be prestigious. Note however that many theories of social identity and identification based on selfdefinitional needs (e.g., those in the social cognition and organizational behavior domains) mainly focus on the cognitive construction of the self. As such, they do not account fully for the nature of the ties that bind consumers to brands. Most fundamentally, brands are things we consume, often over time and repeatedly—thereby implicating aspects of the consumption experience itself as integral to why we identify with some brands and not many others (Escalas, 2004; Escalas & Bettman, 2003; Fournier, 1998; Holt, 2005; McAlexander, Schouten, & Koenig, 2002; Thompson, Rindfleisch, & Arsel, 2006; Thomson, MacInnis, & Park, 2005). Accordingly, we consider the phenomenology of consumers‘ interactions with brands and proffer three additional antecedent factors as predictive of consumer-brand identification. These include the extent to which consumers (1) feel that their interactions with a brand helps them connect with important social others, (2) perceive a brand in warm, emotional terms rather than cold, rational ones, and (3) have fond memories of brand consumption experiences (see Figure 1). Notably, 9 these three antecedents are, by virtue of their very experiential nature, more affect-laden than the three previously described cognitively-driven antecedents of identification. < Insert Figure 1 about here > 2.2.1. Brand-self similarity Much research (e.g., Kunda, 1999) attests to self-continuity or self-verification as a key motive people have in their efforts to maintain a clear and functional sense of who they are. Moreover, this need for a stable and consistent sense of self is increasingly met in today‘s consumer culture through assessments of congruity or similarity between one‘s sense of self and one‘s sense of commercial entities such as companies or brands (see Bhattacharya & Sen, 2003; Escalas & Bettman, 2003; Kleine et al., 1993). In the brand domain, several researchers have pointed to the important role of the perceived congruity between brand and self personalities in consumers‘ affiliations with brands (Aaker, 1997; Grubb & Grathwohl, 1967; Levy, 1959; Sirgy, 1982). Based on this prior literature, we formally define brand-self similarity as the degree of overlap between a consumer‘s perception of his/her own personality traits and that of the brand, and we propose that this construct is a determinant of CBI. H1: The more the brand-self similarity, the more the consumer will identify with that brand. 2.2.2. Brand distinctiveness It has long been recognized that people strive to distinguish themselves from others in social contexts (e.g., Tajfel & Turner, 1985); Snyder and Fromkin‘s (1977) theory of uniqueness positions this need as a key component of people‘s drive to feel good about themselves (i.e., selfesteem). This theme is developed further in Brewer‘s (1991) theory of optimal distinctiveness, which suggests that people attempt to resolve the fundamental tension between their need to be similar to others and their need to be unique by identifying with groups that satisfy both needs. 10 The expression of such distinctiveness motives in the consumption realm is perhaps best reflected in the construct labeled as consumer‘s need for uniqueness (Tepper Tian, Bearden, & Hunter, 2001), defined as ―an individual‘s pursuit of differentness relative to others that is achieved through the acquisition, utilization, and disposition of consumer goods for the purpose of developing and enhancing one‘s personal and social identity‖ (p. 50). We therefore posit that the distinctiveness of a brand is a key precursor to a consumer‘s desire to identify with that brand (see also Berger & Heath, 2007). Further support for this argument comes from research which has documented that consumers often seek to affirm their identities via consumption of brands that are perceived as being the polar opposites of massproduction, mass-consumption brands (Thompson et al., 2006). All else being equal, brands with images or identities that set them apart from their competitors will be more likely to be identified with, provided, of course, that the basis of this distinctiveness is not perceived as entirely undesirable or negative. Formally, we define brand distinctiveness as the perceived uniqueness of a brand‘s identity in relation to its competitors, and we implicate it as a driver of CBI. H2: The more a consumer perceives a brand to be distinctive, the more the person will identify with that brand. 2.2.3. Brand prestige People like to see themselves in a positive light. Self-concept research (Kunda, 1999) indicates that people‘s need for self-continuity goes hand in hand with their need for selfenhancement, which entails the maintenance and affirmation of positive self-views that lead to greater self-esteem. Thus, it is not surprising that this identity-related need is also met, in part, through people‘s identification with prestigious social entities such as organizations (Bhattacharya & Sen, 2003; Dutton, Dukerich, & Harquail, 1994; Mael & Ashforth, 1992). This 11 aspect is paralleled in the notion of the extended self in the domain of consumer behavior, which refers to the incorporation of products and services that reflect positively on the owner into the person‘s sense of self (Belk, 1988; Kleine et al., 1993). More broadly, much consumer research attests to the driving role of self-enhancement in consumers‘ affinities towards brands (Escalas & Bettman, 2003; Fournier, 1998; Thomson et al., 2005; Rindfleisch, Burroughs, & Wong, 2009). Drawing on this research, we hypothesize that brand prestige, defined as the status or esteem associated with a brand, is a driver of CBI. H3: The more a consumer perceives a brand to be prestigious, the more the person will identify with that brand. 2.2.4. Brand social benefits Self-definition is per se a social endeavor—it involves locating oneself in reference to one‘s social environment (Berger & Heath, 2007; Brewer, 1991; Laverie, Kleine, & Kleine, 2002; Solomon, 1983; Tajfel & Turner, 1985). Further, several studies suggest that brands are major carriers of social and cultural meaning (Diamond et al., 2009; Holt, 2005; Thompson et al. 2006). Three broad streams of research point to the social benefits provided by certain brands. First, research on reference groups, defined as social groups that are important to a consumer, suggests that people often consume brands used by their reference groups to gain or strengthen their membership in such groups (see Escalas & Bettman, 2003 for a review). Second, the growing literature on brand communities portrays the brand as an essential device for connecting people to one another (Muniz & O‘Guinn, 2001; O‘Guinn & Muniz, 2009; Stokburger-Sauer, 2010). A brand community can be conceptualized as a specialized, nongeographically bound community that is based on a structured set of social relationships among the admirers of a brand (Muniz & O‘Guinn, 2001). Membership in such communities can result 12 not only in identification with the community, but also with the brand that is its raison d‘etre (Bagozzi & Dholakia, 2006; Bagozzi, Bergami, Marzocchi, & Morandin, 2008; O‘Guinn & Muniz, 2009). Finally, and most broadly, scholars who have investigated subcultures of consumption suggest that consumers sometimes coalesce into distinct subgroups of society on the basis of a shared commitment to a brand (Schouten & McAlexander, 1995; Thompson et al., 2006). As in the case of brand communities, such groups are identifiable; they can have a hierarchical social structure, a set of shared beliefs and values, and a unique set of rituals and jargon. Based on these various streams of research, we argue that consumers are more likely to identify with brands which help them to connect with important others, groups, communities, or subcultures. Formally defined as the social interaction opportunities and gains afforded by a brand, we expect brand social benefits to be a driver of CBI. H4: The more social benefits a consumer perceives in a brand, the more the person will identify with that brand. 2.2.5. Brand warmth Drawing on Hirschman and Holbrook‘s (1982) notion of hedonic consumption, a large and growing body of research distinguishes between hedonic and utilitarian benefits, where the former refer to intensely experiential or emotional benefits (e.g., fun, pleasure, and excitement) and the latter to instrumental or functional benefits (e.g., Dhar & Wertenbroch, 2000; Okada, 2005). Interestingly, research in the person perception area provides a similar dichotomy by theorizing that the content of peoples‘ stereotypes can be organized with two key perceptual dimensions, ―warmth‖ and ―competence‖ (Fiske, Cuddy, Glick, & Xu, 2002). Thus, a social or ethnic category in which a person belongs may be seen by others in relatively warm or cold terms, independent of perceptions of effectiveness, capability, or competence. 13 We suggest that a similar warm versus cold distinction can be applied to brands. Based on a brand‘s product category (e.g., clothing vs. coat hangers), its salient or differentiating attributes (e.g., visually pleasing vs. boring aesthetics in the brand‘s product designs), and its positioning via marketing communications (e.g., Apple vs. Dell; Rathnayake, 2008), its personality can come across as relatively warm or cold (Aaker, 1997; Fournier, 1998; Keller 2004). Further, as in the case of person perception (Fiske et al., 2002), we posit that the warmcold distinction as applied to brands is relatively independent of perceptions of brand quality, reliability, and functionality. We further propose that the extent to which a brand is perceived in warm, emotional terms rather than in a cold, rational manner is a key determinant of CBI. Given that identity construction and maintenance are inherently affective processes, warm, lovable brands are likely to be viewed as more suitable candidates for such important life-projects than cold ones. This notion is underscored by research on brand love—the passionate attachments that some consumers form with brands—that locates it primarily in hedonic brands (Carroll & Ahuvia, 2006). Indeed, much research points to the integral role of emotions in the construction of consumption-based identities (see Laverie et al., 2002 for a recent review), implicating warm, emotional brands as stronger candidates for identification rather than their cold counterparts. More specifically, warm brand are more likely, ceterus paribus, to carry more abstract, higherorder, identity-related brand meanings, pertaining for instance to its values and ethics, rather than concrete, lower-order, meanings, pertaining to its concrete features. In that, warm brands are likely to be stronger and more meaningful candidates for identification than cold ones, with consumers feeling more intensely about the former and their role in their lives than the latter (Fournier, 1998; Park et al., 2010). 14 H5: The more a consumer perceives a brand to have a warm (vs. cold) personality, the more the person will identify with that brand. 2.2.6. Memorable brand experiences Brands vary in the extent to which they provide their consumers with memorable experiences. Some brands do not occupy a salient position in memory in spite of frequent usage (Park et al., 2010). Other brands, even when infrequently used, can leave an indelible, affectively-charged mark on consumers‘ consciousness, to which they return periodically to relive their experiences. Arnould and Price (1993) document the nature of such emotional as well as memorable brand experiences stemming from an extraordinary consumption activity (i.e., river-rafting), but suggest that such experiences may also be tied, under certain conditions, to more mundane consumption activities involving everyday brands. The role of memorable brand experiences is further supported by consumer research on autobiographical memories and narrative processing (Escalas, 2004; Sujan, Bettman, & Baumgartner, 1993). This line of research suggests that the consumption of certain brands is associated with greater self-referencing and the construction of brand-related stories or narratives. Such a self-referencing process in turn produces more affect-laden as well as easily retrievable traces in memory (Escalas, 2004). Over time, such memories can even become imbued with strong feelings of nostalgia (Brown et al., 2003; Holbrook, 1993; Holt, 2005; Moore & Wilkie, 2005; Muehling & Sprott, 2004). Drawing on these ideas, we propose that the final antecedent of CBI is a construct we term memorable brand experiences, defined as the extent to which consumers have positive, affectively-charged memories of prior brand experiences. Such brands are more likely to play a defining role in a person‘s sense of self on account of a greater commingling of brand-related 15 thoughts with self-related thoughts (Davis, 1979; Moore & Wilkie, 2005). As well, such experiences are often likely to result from narrative rather than discursive processing, since the former have been demonstrated to build stronger connections between the consumer and the brand (Escalas, 2004). H6: The more memorable brand experiences a consumer has, the more the person will identify with that brand. 2.3. Product category involvement as a moderator Knowing who they are (i.e., self-definition) and feeling good about themselves are twin goals of utmost importance to most individuals. Thus, the efforts they make to meet these objectives are likely to be both considered and considerable. In particular, to the extent that these goals are met, in part, through consumers‘ identification with certain brands, these brands have to belong, almost by definition, to product categories that they actually care about (Reimann & Aron, 2009; Malär et al. 2011). It is only in such categories that individuals are likely to find brands that can actually meet their self-definitional needs, that is, are eligible and worthy of identification. Product category involvement (PI) is generally understood as the perceived relevance of a product category to an individual consumer based on his or her inherent values, needs, and interests (Zaichkowsky, 1985). We expect PI to moderate the relationship between the various antecedents and CBI for at least two reasons. First, as the definition suggests, categories are more involving to people when they associate them with important higher-order goals such as value satisfaction (Bloch & Richins, 1983). In turn, such categories are likely to be more closely associated with individuals‘ self-concepts and viewed as self-defining, making them stronger candidates for CBI (Reimann & Aron, 2009). Second, people are more motivated to 16 systematically process information pertaining to categories with which they are more involved (Chen & Chaiken, 1999). Thus, all else being equal, consumers‘ knowledge structures regarding high (vs. low) involvement product categories are more likely to contain deeply processed and highly elaborated beliefs regarding brands‘ abilities to meet self-definitional needs. To the extent that judgments of a brand‘s standing with regard to each of the six posited antecedent variables, and an assessment of the implications of these perceptions for identification, require cognitive resources and effort, these influences are more likely to materialize in product categories where the consumer is highly involved. That is, high (vs. low) PI should enhance not only a brand‘s perceived ability to meet a consumer‘s self-definitional needs, but also a person‘s motivation to process relevant information in that regard. H7: The higher a consumer‘s involvement in the product category in which a brand belongs, the stronger the relationship between (a) brand-self similarity and CBI, (b) brand distinctiveness and CBI, (c) brand prestige and CBI, (d) brand social value and CBI, (e) brand warmth and CBI, and (f) memorable brand experiences and CBI. 2.4. Consequences of CBI 2.4.1. Brand loyalty The conceptual and practical significance of examining the antecedents of CBI is predicated on the extent to which CBI, in turn, produces valuable pro-brand outcomes. Drawing on prior research, we relate CBI to two valuable consequences – brand loyalty and brand advocacy – that contribute to a company‘s financial performance and, ultimately, long term success (for a review, see Gupta & Zeithaml, 2006). The marketing literature provides ample support for the notion that identification is linked to a sustained, long-term preference for the identified-with company‘s products (Bhattacharya and 17 Sen 2003). Homburg, Wieseke, and Hoyer, (2009), for instance, report a strong influence of customer-company identification on customer loyalty. Additionally, Lam et al. (2010) show that CBI inhibits consumers from switching brands. Finally, Park et al. (2010) report a positive relationship between a construct related to CBI, namely brand attachment, and actual purchase behavior (i.e., total purchases in a set time frame). It can thus be argued that CBI is a predictor of loyal brand behavior, which we define as a deeply held intent to rebuy or re-patronize a preferred brand in the future (Oliver 1999). H8: The more a consumer identifies with a brand, the more loyal the person will be to that brand. 2.4.2. Brand advocacy One of the key consequences of identification is the promotion of the identified-with organization or company (e.g., Ashforth and Mael 1989; Bhattacharya and Sen 2003). Such promotion or advocacy can take place both socially and physically. Socially, advocacy includes the recommendation of the company (and its offerings, or as an employer) to others or the defense of the company when it is attacked by others. Physically, advocacy can involve buying and using company merchandise that displays the company logo or name, collecting memorabilia, apparel, or even acquiring tattoos (Katz 1994). In organizational research, Mael and Ashforth (1992), for instance, have found that there is a strong positive relationship between identification of alumni with their alma mater and both social (e.g., advising others to attend school) and physical promotion (e.g., attending college banquet). Accordingly, we suggest that CBI will produce brand advocacy, at least in the social sense of the brand‘s promotion to social others. This is consistent with the research of Park et al. (2010), who report a strong influence of brand attachment on such promotion behavior. Additionally, Ahearne, Bhattacharya, and Gruen 18 (2005) show that customer-company identification affects customer extra-role behaviors, which was measured in part through company recommendation. All in all, then, we expect CBI to be positively related to brand advocacy. H9: The more a consumer identifies with a brand, the more the person will advocate that brand. Next, we describe three studies, two pilot and one main, that together provide tests of the nomological network for CBI laid out above. 3. Pilot studies 3.1. Pilot study 1 Our goals in this study were to develop a better understanding of the phenomenon of CBI, generate items to measure CBI, and conduct a reality check on whether we were generally on the right path with the variables we had identified as the drivers of CBI. One-on-one depth interviews lasting between 30 and 80 minutes were conducted with eight (three female and five male) German consumers ranging in age from 26 to 57. The interviews were conducted in German by the first author and a research assistant. A semi-structured approach was used in these interviews. The interviews commenced with a very broad question on the topic of identification, ―Is there anything you identify with?‖ Subsequently, more focused questions were asked, e.g., ―Which people do you identify with? Which companies? Which brands?‖ The interview questions then built on the participants‘ initial responses by exploring the salient attributes and characteristics of particular companies and brands named by the participants and their links to the person‘s sense of self. Several of the follow-up questions also probed the types of thoughts and feelings that were prompted by the brands mentioned by the participants. 19 The data from these depth interviews furthered our phenomenological understanding of the concept of CBI and the specific role of brands in identity construction. The comments of the participants regarding what identification meant to them also enabled us to generate 16 potential items for our measure of CBI. Participants‘ comments and responses additionally provided us some initial validation for the antecedent variables postulated in our framework. For example, many of the comments related to similarity between what a brand personified to a participant and the latter‘s construal of his/her own personality. A second group of responses delved into unique or distinguishing characteristics of particular brands, including aspects such as brand quality and prestige, and tied them to self-image and the projection of identity. Another set of responses involved explicit references to friends, significant others, and other consumers who used the same brand. Finally, a sense of deep intimacy with a brand, emotional connections to a brand, and vivid brand consumption experiences were also frequently brought up by the participants. 3.2. Pilot study 2 Our goal in this study was to develop a parsimonious, valid measure of CBI that captured the psychological state of identification, distinguishing it from its antecedents and consequences. We started with the 16 items from pilot study 1 and added to it 16 other items that were adapted from prior literature on identification (e.g., Bergami & Bagozzi, 2000; Mael & Ashforth, 1992; Smidts, Pruyn, & van Riel, 2001). The study was conducted online with German consumers, and with a very large mail order retailer as the target brand. We chose this retailer as the target brand for two reasons. First, due to the brand‘s long history and tradition, not only is virtually every respondent likely to be aware of it but also many are likely to have or have had a relationship with it. Relatedly, because of its market presence and full-range of products, this retailer has a very large customer base, making it more likely that our respondents are quite knowledgeable it. 20 Indeed, we found that of the total sample of 382 participants, as many as 148 participants (39%) were customers; the average age of the respondents was 36.7 years and 43% were female. As in the main study, participants were asked to rate their level of agreement or disagreement (7-point scales) with the statements pertaining to the various items. Analytical methods such as exploratory factor analysis (EFA), reliability analyses (Cronbach alpha and item to total correlations, ITTC), and confirmatory factor analysis (CFA) were employed. Statistical criteria for item retention were an average factor loading above .70 and an average corrected ITTC above .50. The normal distribution assumption associated with the maximum likelihood (ML) method of estimation in CFA did not hold for the responses to the scale. The ML procedure, however, is known to be fairly robust against moderate violations of normality (e.g., Browne 1984; McDonald & Ho 2002). The global fit criteria we applied in CFA were Chi-square test (2), Comparative Fit Index (CFI), Root Mean Squared Error of Approximation (RMSEA), and Tucker-Lewis Index (TLI). Additionally, we used the following local fit measures: indicator reliability (IR), factor loading (FL), t-value of factor loading (tvalue), composite reliability (CR), as well as average variance explained (AVE). The MPLUS program was employed for CFA (Muthén & Muthén, 2006). Results of a multitude of iterative analyses showed that 15 of the 32 items were the best candidates for assessing CBI. Of these, ten emerged from the first pilot study, while five were based on measures used in prior research. Next, we describe our main study, the goals of which were two-fold: to (a) develop our final measure of CBI based on the fifteen items obtained in this pilot study, and (b) use this measure to test our predictions regarding the antecedents and consequences of CBI. 21 4. Main study 4.1. Method 4.1.1. Participant demographics and selected product categories We tested the hypothesized model of CBI with data from a panel of German household consumers (796 participants). A few of the participants either did not follow the instructions or provided incomplete responses and thus were dropped from the data set (final N = 781). The participants had an average age of 40.6 years and nearly half (46.9%) were female. Table 2 provides a demographic profile of the survey participants broken down by product category. The study involved four product categories, that is, athletic shoes, mobile phones, soft drinks, and grocery stores. We selected product categories that vary considerably in the needs that they serve; yet all are widely consumed, and often in public. Further, to ensure that the data would have sufficient variance with regard to key variables such as CBI and product category involvement, we avoided products where people might have responded uniformly at the upper end of the scales for those variables (e.g., automobiles). Note that the four product categories included one frequently purchased consumable (soft drinks), two shopping goods (athletic shoes and mobile phones), and one service-oriented category (grocery stores). < Insert Table 2 about here > 4.1.2. Data collection procedure Data for this study were collected through an online survey of a German household panel maintained by a commercial market-research firm. Panel members were contacted via email and provided the URL of a password-protected web site for participating anonymously in the online survey. A lottery-based monetary incentive system was used to motivate participation. Given the length of the instrument, each respondent was asked to take the survey with respect to only one 22 product category, which the survey software randomly assigned to each respondent. In effect, the final sample sizes did not vary much across the target product categories and ranged from 198 to 212 in each case. Participants were informed that they would be participating in a short consumer behavior survey. The survey first assessed whether a respondent was a user in the assigned product category. If the answer was in the negative, a different product category was randomly assigned. Next, depending on the assigned product category, the respondent was asked to name the brand that he or she (a) had last consumed (soft drinks), or (b) had last visited (grocery stores), or (c) currently used most often (athletic shoes and mobile phones). After eliciting the target brand in this manner, the survey then presented the respondent with items designed to measure the variables in the theorized model. The sequence of the items was intentionally designed to be reverse to the causal direction of the hypotheses in order to minimize any possible demand effects. Specifically, the instrument first measured brand loyalty and brand advocacy. Next, the survey had items to assess CBI (described in section 4.1.4., below), and then the hypothesized antecedents and moderating variable. Note that in order to better evoke the thoughts and feelings associated with the target brand, the survey software automatically inserted the respondentspecific brand name in each and every one of the brand-related scale items that were presented to the respondents. The survey closed with a few demographic questions. 4.1.3. Overview of study measures All items used in the survey involved 7-point agree-disagree Likert scales. These items were initially developed in English by the research team, then translated into German, and finally translated back into English to check for vocabulary, idiomatic, grammatical, and syntactical equivalence (Steenkamp, Hofstede, & Wedel, 1999). The questionnaire was pre-tested with a 23 small sample of respondents (N = 12), and based on their feedback, some minor changes were made in the wording of the items. For developing the items used in our measures, we drew on existing scales wherever possible and adapted them as necessary to fit the context of our research. The details are discussed in subsequent sections and the full scale items are presented along with reliability and validity statistics in the Appendix. 4.1.4. CBI measure As discussed earlier, prior literature and our two pilot studies yielded a battery of 15 potential items to assess CBI. Results of a multitude of iterative analyses (including alpha, ITTC, EFA, and CFA) revealed that of these 15, five items best represented the state of CBI (see Table 3). This scale is highly reliable with an alpha of .94 and ITTCs ranging from .82 to .89. EFA explains 82% of variance. Secondgeneration fit indices were satisfactory as well. The study, for instance, produced the following goodness-of-fit-measures: 2 (5) = 54.12, RMSEA = .10, SRMR = .03, CFI = .99 and TLI = .98. < Insert Table 3 about here > We tested the robustness of the scale in four additional ways. First, we assessed the model fit separately for each of the four product categories and found it to be satisfactory in every case. Second, using a bootstrapping procedure (Fitzgerald Bone, Sharma, and Shimp 1989), we serially drew 100 bootstrap samples (with replacement) of the total sample with a 50% fraction of the base sample and then tested the measurement model on the basis of the covariance matrices produced by these samples. Results showed that the scale was stable with respect to all fit indices. Third, in an effort to further evaluate the candidate scale, three alternative factor 24 structures (with 15, 8, and 6 items, respectively) were estimated. These scales were compared to the five-item scale using 2-tests in CFA. In order to ensure that no important content was lost in developing a parsimonious scale, a measure containing all 15 items was used as null model. The five-item scale strongly outperformed the null model (∆ 2 (85) = 911.6, p < .01) and the sixitem scale (∆ 2 (4) = 19.61, p < .01). Additionally, the five-item scale slightly outperformed the six-item scale with respect to global fit (CFI = .990 vs. .989; TLI = .985 vs. .979; SRMR = .029 vs. .031). Overall, the five-item scale was found to be the best-fitting CBI measure (see Table 4). < Insert Table 4 about here > Finally, in order to examine the robustness of the CBI scale, we conducted a validation study in another country and with respondents who varied considerably in their demographics compared to the main study respondents. This study involved a survey of a sample of U.S. college students from a large mid-western university (415 participants; final N = 414) and included two of the four product categories in the main study, namely, athletic shoes (N = 212) and soft drinks (N = 203). The study was conducted with a computerized survey and administered in a university laboratory setting. Students signed up for participation in order to earn extra class credit. The average age of the student participants was 20.6 years and 53.9% were female. Again, with this sample, the scale was found to be highly reliable, with an alpha of .93 and ITTCs ranging from .78 to .84. EFA explained 78.3 % of variance. CFA global and local fit indices were satisfactory as well. A test of discriminant validity of all study constructs including CBI is described in section 4.2.2. However, we also assessed discriminant validity for the CBI measure with regard to another related construct, namely, brand commitment. As evident in the definition of Allen and Meyer (1996, p. 253), who view affective commitment as ―identification with, involvement in, 25 and emotional attachment to the organization,‖ identification and commitment are closely related constructs. Notwithstanding, in our conceptualization we do not view identification as part of commitment or, conversely, commitment as part of identification. Instead we assume that brand commitment is a psychological consequence of CBI. Evidence of discriminant validity for our CBI measure vis a vis brand commitment thus helps establish the credentials of the former. To operationalize brand commitment, we adapted items from the commitment scales of Coulter, Price, and Feick (2003) and Beatty, Kahle, and Homer (1988). The resulting 3-item scale showed acceptable reliability and fit measures in our data with an alpha value of .68 and 62.3% of variance extracted by EFA. Discriminant validity for CBI versus commitment was analyzed with the help of the Fornell and Larcker (1981) test. Pairwise correlations between factors obtained from a two-factor model were compared with the variance extracted estimates (AVE) for the two constructs. Discriminant validity exists when both variance extracted estimates exceed the square of the correlation between the factors. The AVE for CBI was .78 and for commitment it was .43; in contrast, the squared correlation between CBI and commitment was .42. Thus, even though there is a positive relationship between the two constructs, there is evidence of discriminant validity between them. 4.1.5. Other study measures The measure for brand-self similarity was derived from separate assessments of the brand‘s personality and the respondent‘s own personality, using identical scales (Sirgy & Danes, 1982). These scales drew on Aaker‘s (1997) work on brand personality, which shows that it can be conceptualized as having five different dimensions, which in turn can be represented via a total of 15 ―facets‖ (Aaker, 1997, pp. 351-352). We therefore assessed both brand personality and the respondent‘s own personality with 15-items on 7-point agree-disagree scales, with one item for 26 each of the facets proposed by Aaker (1997). The brand-self similarity measure was then constructed as follows. First, the Euclidean distance D between the two personality assessments was computed. Next, this distance measure was rescaled to a similarity measure S with a theoretical range of 1 (minimum) to 7 (maximum) by applying an appropriate linear transformation (S = 7 - .2582D). The three items used to measure brand distinctiveness were taken from existing scales (e.g., Bhattacharya & Sen, 2003). Brand prestige was also measured with three items drawn from prior literature (e.g., Bhattacharya & Sen, 2003; Mael & Ashforth, 1992). The four items used for brand social benefits were based on the literature on brand communities (e.g., McAlexander et al., 2002). Brand warmth was operationalized with three items adapted from Moore, Ratneshwar, and Moore (2012). Memorable brand experiences was measured with three items adapted from Gladden and Funk (2001). Product category involvement was assessed with four items drawn from prior research (e.g., Mittal & Lee, 1988). Brand loyalty was measured with three items taken from the literature (Chaudhuri & Holbrook, 2001; Coulter, Price, & Feick, 2003). Finally, brand advocacy was assessed with three items (e.g., Brown, Barry, Dacin, & Gunst, 2005). 4.2. Results 4.2.1. Preliminary analyses All measures were found to be highly reliable and valid. The psychometric properties of the scales were assessed through Cronbach‘s α, CR, ITTC‘s, EFA, and CFA. Descriptive statistics and the correlations between the various measures are shown in Table 5 with the data pooled across the four product categories. Before pooling the data, tests of measurement invariance were conducted using MPLUS. Chi-square difference tests in CFA (Steenkamp & Baumgartner, 1998) confirmed that the measures were equivalent across the four product categories. 27 < Insert Table 5 about here > 4.2.2. Test for discriminant validity We assessed discriminant validity with the help of the Fornell and Larcker (1981) test. Table 5 shows the correlations between all constructs. The squared correlations are less than the AVE of each and every construct. Thus, there is evidence of discriminant validity. 4.2.3. Tests for common method bias Because we measured all constructs using respondents‘ self-reports, it is important to rule out common method variance as a source of bias in the results (Podsakoff et al., 2003). Accordingly, we conducted the following tests. First, we included a common method factor (CMF) into the structural equation model (SEM). Similar to Homburg, Mueller, and Klarmann (2011) who included a common method factor in the SEM to test H1 only, the CMF in our SEM was constructed to load on a selection of items used to measure the antecedents and CBI. Inclusion of such a factor helps to control for common method bias in our tests of hypotheses. We specified the loadings of the CMF to be of the same size in order to reflect the notion that common method variance influences all items equally and in order to achieve model convergence (e.g., Homburg, Mueller, & Klarmann, 2011; Rindfleisch et al., 2008). This procedure of including a method factor did not affect the findings regarding our hypotheses (see section 4.2.4.), which indicates that common method variance did not bias the results. Second, we relied on the findings of simulation studies which show that the risk of common method variance is strongly reduced in models involving non-linear relationships (e.g., Evans, 1985; Siemsen, Roth, & Oliveira, 2010). Multiple group analyses of our data (see section 4.2.5) showed that the relationships between the antecedent variables and CBI differ in different 28 subgroups of the sample. In addition to multiple group analysis, we tested the nonlinearity in our model for selected variables using latent interaction terms (e.g., Homburg, Klarmann, & Schmitt, 2010). The analyses, for instance, showed that a latent interaction model produces significant (p < .01) and stable results for the interactions of product category involvement (PI) with brand-self similarity and brand distinctiveness, respectively. 4.2.4. Tests of main effects H1 to H6 and H8 to H9 were tested with structural equation modeling using MPLUS (Muthén & Muthén, 2006). In addition to the hypothesized antecedents of brand-self similarity, brand distinctiveness, brand prestige, brand social benefits, brand warmth, and memorable brand experiences, we added three dummy variables as independent variables to account for product category main effects on CBI. This set of predictor variables explained over half of the variance in CBI (squared multiple correlation, SMC = .59). Likewise we achieved a good SMC for brand advocacy (.47) and a satisfying SMC for brand loyalty (.16). The fit statistics indicate a good fit of our model: χ2 (389) = 2,206, RMSEA = .08, SRMR = .07, CFI = .91, TLI = .90. The standardized parameter estimates for the main effects model are shown in the second column of Table 6. The results supported the predictions for brand-self similarity (H1, γ = .05, p < .10), brand distinctiveness (H2, γ = .08, p < .05), brand social benefits (H4, γ = .34, p < .01), brand warmth (H5, γ = .30, p < .01), and memorable brand experiences (H6, γ = .15, p < .01). Regarding brand prestige (H3), our study did not yield a statistically significant result. With respect to the downstream relationships between CBI and brand loyalty (H8) and brand advocacy (H9), respectively, the model showed strong and significant results (β = .55 and .68, both p < .01). 29 The main effects model was further validated with the U.S. sample mentioned earlier. The results were generally replicated in that sample, except that the relationship between brand distinctiveness and CBI was statistically non-significant, whereas the path from brand prestige to CBI was marginally significant (p < .10). < Insert Table 6 about here > 4.2.5. Tests of moderated relationships In order to test the moderated relationships implied by H7a to H7f for product category involvement (PI), we employed multiple group structural equation modeling. We first performed a median split for PI in each of the four product categories to create two subgroups, one with low PI and one with high PI. Next, we tested the model for CBI (excluding loyalty and advocacy) simultaneously in both subgroups using MPLUS. The model fit was satisfactory, χ2 (498) = 1,433, RMSEA = .07, SRMR = .05, CFI = .93, TLI = .92. The standardized parameter estimates for the two subgroups are shown in columns three and four of Table 6. The relationship between brand prestige and CBI was statistically nonsignificant in both subgroups. The coefficients for the paths between brand-self similarity and CBI, and brand distinctiveness and CBI, were statistically significant only in the high PI subgroup. The coefficients for the paths between the other three antecedents and CBI were statistically significant in both PI groups. However, in accord with our prediction, in all three cases the coefficient values were larger in the high PI subgroup when compared to the low PI subgroup. As a follow up to the above pattern of results, a χ2-difference test was used to assess the statistical significance of the difference in the path coefficients between the subgroups. Here, an unconstrained model was compared with a constrained model wherein the two path coefficients 30 were set to be equal. The results confirmed that the higher the PI, the stronger the relationship between brand-self similarity and CBI (H7a: Δ χ2 (1) = 10.73, p < .01), brand distinctiveness and CBI (H7b: Δ χ2 (1) = 17.53, p < .01), brand social benefits and CBI (H7d: Δ χ2 (1) = 15.24, p < .01), brand warmth and CBI (H7e: Δ χ2 (1) = 18.69, p < .01), and memorable brand experiences and CBI (H7f: Δ χ2 (1) = 13.69, p < .01).1 4.2.6 Test of an alternative model Given that our hypotheses tests entail causal modeling of survey data, the question naturally arises as to whether an alternative model might fit the data equally well. One way of examining this issue is to test an alternative model where the roles of the antecedent variables and the focal dependent variable (CBI) are reversed. Our approach here follows the path of Morgan and Hunt (1994) and Hennig-Thurau, Gwinner, and Gremler (2002). Specifically, we earlier offered theoretical arguments as to why brand-self similarity, brand distinctiveness, brand prestige, brand social benefits, brand warmth, and memorable brand experiences should influence CBI, which in turn should influence brand loyalty and brand advocacy. It is far more difficult to argue that CBI could play a similar causal role in driving the hypothesized antecedent variables. Accordingly, if our theorizing has merit, the causal model we employed earlier for H1 to H6 and H8 to H9 should fare much better empirically than an alternative model wherein CBI is treated as an independent (i.e., exogenous) variable and brandself similarity, brand distinctiveness, brand prestige, brand social benefits, brand warmth, and 1 As in the case of the main effects model, we attempted to validate the tests of the moderated relationships with the U.S. data. However, on account of the much smaller sample size and the complex nature of the multi-group model, we were unable to obtain any plausible results. 31 memorable brand experiences become mediating variables in the relationship between CBI and the two downstream variables of brand loyalty and brand advocacy. Following this logic, we tested the aforementioned alternative model with SEM. The model included the dummy variables for product category and the nine variables mentioned above. Note that the alternative model has ten more paths than the hypothesized model (i.e., 18 vs. 8 hypothesized paths, i.e., more paths by a factor of 2.25). We first compared the overall fit of the two models. The results showed that the global fit of the alternative model was much worse than that of the hypothesized model: CFIAlternative Model = .86 vs. CFI = .91; SRMRAlternative Model = .11 vs. SRMR = .07; and χ2 (413)Alternative Model = 3,195 vs. χ2 (389) = 2,206. Next, since the two models are not nested in one another, it is appropriate to rely on Akaike‘s Information Criterion (AIC; Akaike 1987) for model comparison (Rust, Lee, & Valente, 1995). Smaller values of AIC indicate a better model fit. Again, the alternative model fared worse with respect to this fit statistic, AICAlternative Model = 48,645 vs. AIC =47,694. Finally, it is worth comparing the proportions of the two models‘ hypothesized parameters that turned out to be statistically significant. Once again the alternative model did worse than our hypothesized model. As noted earlier, as many as seven out of eight hypothesized paths (88%) in the hypothesized model were found to be statistically significant. In contrast, in the alternative model, only 12 out of 18 paths between the variables (67%) were statistically significant. The paths that were not supported by the data in the alternative model were those from brand-self similarity, brand warmth, and memorable brand experiences to brand loyalty; and the paths from brand-self similarity, brand distinctiveness, and memorable brand experiences to brand advocacy. Therefore, all of the comparisons confirm that the hypothesized model is indeed superior to the alternative model. 32 5. General discussion The idea that brands can play a crucial role in the construction and maintenance of consumers‘ identities is not new (Levy, 1959; Keller, 1993). Nonetheless, it is only recently that the concept of consumer-brand identification is finally getting the conceptual and empirical attention it deserves (Escalas & Bettman, 2003, 2009; Fournier, 2009; Lam et al., 2010). This research adds to the growing body of knowledge on this topic by proposing and testing an integrated framework for the drivers, moderators, and consequences of consumer-brand identification. Based on a synthesis of a variety of literatures, the framework includes six antecedent factors for CBI, of which three are mainly cognitive in nature (brand-self similarity, brand distinctiveness, and brand prestige), while the other three are more affect-based (brand social benefits, brand warmth, and memorable brand experiences). We also theorize that the drivers will display stronger relationships with CBI when a consumer‘s involvement with a brand‘s product category is relatively high. The results of two pilot studies and a main survey study provided convincing empirical support for our framework. The six antecedents together accounted for over half of the variance in CBI in the main study, pointing to their collective efficacy as determinants of our focal construct. Interestingly, very little support was obtained for the predictive role of brand prestige. Aside from the most obvious possibility that brand prestige does not, in general, influence CBI, it is plausible, given that there was sufficient variance on the brand prestige measure, that CBI is less sensitive to brand prestige in categories such as supermarkets, soft drinks and even athletic shoes than in the more conventionally status or luxury product categories. Clearly further research is needed to better understand the precise role of brand prestige. The results also provided reliable and consistent support for the hypothesized moderating role of product 33 category involvement. Additional analyses revealed no evidence of a common method bias or the plausibility of alternative nomological networks (e.g., with reversed causality). In providing these insights, this paper makes several contributions, both theoretical and managerial, which are discussed next. 5.1. Contributions to branding and consumer behavior theory This paper advances our understanding of the relationship between brands and consumer identity in several key ways. First, this research provides an integrative understanding of the antecedents of CBI, bringing together drivers that so far have been examined in isolation. In particular, this paper is the first to propose and test an overarching framework for CBI, one that contains both cognitive and experiential (i.e., affect-rich) drivers of identification. Second, by providing evidence for the role of multiple drivers of CBI in a single model, we are able to demonstrate that each of these drivers has an influence even when controlling for the effects of the others. Third, in contrast to the prominence bestowed on the cognitive drivers of identification in the company domain (e.g., Bhattacharya & Sen, 2003), our findings highlight a generally stronger role of the affective drivers (see, e.g., Table 6). It may be the case that a consumer‘s idiosyncratic experiences with the object of identification matter more than reflective, mindful factors when the object is concrete and palpable, such as a brand, than when it is more abstract and amorphous, such as a company. Given that we did not contrast in our studies consumer identification with a brand versus a company, future research that does so would help establish more definitively whether and to what extent the drivers of the two differ. Further, this research is the first, to the best of our knowledge, to empirically examine the category-specific moderators of the relationship between CBI and a number of diverse drivers. By documenting the moderating effect of product category involvement, the present research is 34 able to provide a more nuanced, contingent theoretical picture of the forces underlying CBI (see also Escalas & Bettman, 2009; Reimann & Aron, 2009). Of course, future research may be able to expand our nomological network even further, providing a fuller explication of the various contingencies that regulate the influence of the key drivers of CBI. Such research may also be able to clarify whether the same drivers and moderating variables are at work in determining the intensity and durability of consumer-brand relationships (Fournier, 2009; Park et al., 2010). For instance, while we conceptualized brand-self similarity as the perceived congruence between consumers‘ sense of their current or actual self and that of the brand, it is possible that under certain contingencies, such as low category involvement, low self-esteem or high selfconsciousness (Malär et al., 2011), it is consumers‘ sense of who they ideally would be (i.e., ideal self) that may be more relevant in driving CBI. 5.2. Managerial implications Our research corroborates the positive link between CBI and its pro-brand consequences such as loyalty and advocacy. Thus, it would seem that marketers would want to maximize CBI. At the same time, however, some recent research points to the potential limits, and indeed drawbacks, of encouraging CBI (Chernev et al. 2011) suggesting that doing so may, in fact, make the consumer-brand relationship more rather than less susceptible, at least in the short-run, to competing forces that satiate consumers‘ need for identity-expression. While marketers would be wise to take this eventuality into account in planning their brand strategies, its potential to mitigate the positive effects of CBI is contingent on future research that establishes the longerterm, rather than the short-term, effect of identity saturation on brand preferences. That said, this research provides brand managers with some actionable insights into the why of CBI in their specific contexts. Managers of all product categories, regardless of consumer 35 involvement levels, need to focus on, and better understand, the more idiosyncratic and affectrich experiences their consumers have with their brands to be able to harness these in the service of greater CBI. Additionally, managers who serve products/services to highly involved consumers should also pay special attention to consumers‘ perceptions of a brand‘s personality and distinctiveness. More specifically, our research suggests that to enhance CBI, managers need to ensure that their brands have high social value, and thus to serve consumers‘ interpersonal goals (see also Muniz & O‘Guinn, 2001; O‘Guinn & Muniz, 2009; Schouten & McAlexander, 1995). This can occur not only through the fostering of interactions between the brand and the consumer through a myriad of approaches, from event marketing to product co-creation, but also interactions among consumers around a brand, through brand communities, both physical and virtual. As well, marketers can try to enhance or at least highlight the emotional appeal of their brand, albeit within the constraints imposed by the brand‘s functionality and overall positioning, with the objective of getting consumers to think of the brand in a warm, emotional way rather than as a cold entity (see also Aaker, 1997; Diamond et al., 2009; Fournier, 1998; Park et al., 2010). This can be achieved not just through conventional communication strategies but also through a coherent, well thought-out CSR (i.e., corporate social responsibility) platform that, done correctly and genuinely, might contribute greatly to a brand‘s perceived warmth. Our findings also suggest that marketers can increase CBI by creating distinctive and memorable out-of-the-ordinary brand experiences (e.g., cruise ship gala banquets, Napa Valley winery wine tastings, extraordinary personal touches at Kimpton Hotels). Clearly, this is easier said than done, since affectively-charged memories reside ultimately in the consumer‘s mind and are, thus, not under complete control of the marketer. At a minimum, then, marketers can try and 36 provide the types of unique and vivid experiences that engender indelible memories and then assess the extent to which they impact CBI. 5.3. Limitations and future research directions While this paper represents a significant advance in our understanding of the antecedents of CBI, it is not without limitations. The most important of these is the fact that to test our framework, we had to infer causal relationships from cross-sectional survey data. Given the lengthy gestation period that is likely involved in a consumer identifying with a brand, experimental and longitudinal study designs seem a little unrealistic. Nonetheless, future research may be able to test at least parts of our framework with alternative methodologies. As well, our results reveal a great deal of consonance between the European respondents of our main survey and the U. S. respondents of the validating survey, in terms of the key relationships in our model. However an important question remains: Might the nomological network for CBI be different, particularly in radically different cultures in, for instance, parts of Asia, Africa, and the Middle-East? Future tests of the generalizability of the relationships to a broader cultural context are needed. In particular, given the rising economic, political, and cultural clout of the BRIC nations, it would be important to validate our model with populations from these countries. Similarly, while our framework was tested with research participants spanning a wide range of demographics, there is room for greater generalizability through tests among demographic groups that we did not examine. More specifically, some secondary analyses suggest that the relationships between CBI and its antecedents are directionally stronger for the men in our sample than for the women. A deeper investigation of such gender differences might be of both conceptual and practical value. 37 Further, our study categories were restricted largely to products rather than services, with supermarkets perhaps being the only exception. One could argue that this comprises a particularly stringent test of our theory since the posited relationships, particularly between CBI and its experience-based determinants, are likely to be stronger for services than for products. At the same time, we obtained no meaningful differences across the four categories we examined in terms of the predicted relationships. Regardless, it is essential that our model be examined in a wider set of categories and settings, including services that have experiences as their core offering. Such research and other of its kind may be able to provide many more valuable insights on the fascinating topic of why consumers identify with some brands and not others. 38 Appendix Measures for variables Unless otherwise noted, the measures were assessed on 7-point Likert scales where 1 = ―completely disagree‖ and 7 = ―completely agree.‖ Cronbach‘s α values and explained variance from exploratory factor analysis (EFA) are noted for each construct. Consumer-brand identification (α = .94, EFA explained variance = 82.0%) 1. I feel a strong sense of belonging to brand X. 2. I identify strongly with brand X. 3. Brand X embodies what I believe in. 4. Brand X is like a part of me. 5. Brand X has a great deal of personal meaning for me. Brand-self similarity (α = .94, EFA explained variance = 72.3%; the measure, however, entered in aggregated form into the SEM, see section 4.4. for more details of this measure) Brand X is … I am … 1. down-to-earth. 9. reliable. 2. honest. 10. intelligent. 3. wholesome. 11. successful. 4. cheerful. 12. upper class. 5. daring. 13. charming. 6. spirited. 14. outdoorsy. 7. imaginative. 15. tough. 8. up-to-date. Brand distinctiveness (α = .91, EFA explained variance = 84.6%) 1. Brand X has a distinctive identity. 2. Brand X is unique. 3. Brand X stands out from its competitors. Brand prestige (α = .92, EFA explained variance = 86.6%) 1. Brand X is very prestigious. 2. Brand X is one of the best brands of [athletic shoes]. 3. Brand X is a first-class, high quality brand. Brand social benefits (α = .93, EFA explained variance = 83.0%) 1. Brand X offers me the opportunity to socialize. 2. I feel a sense of kinship with other people who use brand X. 3. I gain a lot from interactions with other customers/users of brand X. 4. Being a customer of brand X makes me feel like I belong to a special group. Brand warmth (α = .85, EFA explained variance = 77.4%) 1. Brand X creates warm feelings among its users. 2. Brand X is very loveable. 39 3. Brand X is emotional rather than rational. Memorable brand experiences (α = .93, EFA explained variance = 87.3%) 1. I have had a lot of memorable experiences with brand X. 2. Thinking of brand X brings back good memories. 3. I have fond memories of brand X. Product category involvement (α = .84, EFA explained variance = 86.2%) 1. I am very interested in anything related to [product category, e.g., athletic shoes]. 2. Which brand of [athletic shoes] I buy matters a lot. 3. I value [athletic shoes] as an important part of my life. 4. [Athletic shoes] mean a lot to me. Brand loyalty (α = .80, EFA explained variance = 72.0%) 1. 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Ashforth & Mael (1989), Mael & Ashforth (1992) Cognitive connection between the definition of an organization and the definition a person applies to himself or herself, thereby viewing identification as a process of self-definition. Dutton, Dukerich, & Harquail (1994), Dukerich, Golden, & Shortell (2002) Perceived oneness or belongingness to an organization of which the person is a member. Bhattacharya, Rao, & Glynn (1995) Cognitive organizational identification Cognitive state of self-categorization that reflects selfawareness of one‘s membership in an organization. Bergami & Bagozzi (2000) Consumercompany identification Identification with a company as an active, selective, and volitional act motivated by the satisfaction of one or more self-definitional needs. Bhattacharya & Sen (2003) Customer-brand identification Customer‘s psychological state of perceiving, feeling, and valuing his or her belongingness with a brand. Lam et al. (2010) Brand attitude Individual‘s judgment of the extent to which a brand is good or bad. Park et al. (2010) Brand love Degree of passionate emotional attachment a satisfied consumer has for a particular trade name. Carroll & Ahuvia (2006) Different cognitions (e.g., about self-identity), feelings, sense of connectedness and fit, and behaviors (e.g., frequent interactions, resource investments). Batra, Ahuvia, & Bagozzi (2012) Strength of the bond connecting a brand with the self. Park et al. (2010) Brand attachment Emotional brand Bond that connects a consumer with a specific brand and attachment involves feelings (i.e., affection, passion, connection) toward the brand. Malär et al. (2011) Brand prominence Salience of the cognitive and affective bond that connects a brand to the self. Park et al. (2010) Self-brand connection, Brand-self connection Extent to which an individual has incorporated a brand into his or her self-concept. Escalas & Bettman (2003, 2009), Fournier (2009), Park et al. (2010) Consumerbrand identification Consumer‘s perceived state of oneness with a brand. Present Research 53 Table 2 Main study: product categories and profile of respondents Pooled Sample Cell Phones Athletic shoes Soft drinks Product category Involvement* mean (s.d.) Number of test persons Age Gender (female) Profession (pooled sample): 2.81 (1.51) 3.06 (1.57) 2.92 (1.55) 2.90 (1.61) 796 198 200 199 40.6 39.9 38.7 40.1 46.9% 44.9% 43.7% 52.3% high school student: .6% homemaker: 4.6% student: 11.1% retiree: 5.9% worker: 5.8% unemployed: 4.3% (state) employee: 55.5% other: 2.8% self-employed: 14.7% * Product category involvement was measured on a 1-7 scale.. Grocery stores 2.36 (1.17) 199 43.8 45.2% Table 3 Test statistics for the 5-item CBI scale Construct/Item Consumer-brand identification scale I feel a strong sense of belonging to brand X. I identify strongly with brand X. Brand X embodies what I believe in. Brand X is like a part of me. Brand X has a great deal of personal meaning for me. ITTC Alpha Factor loading EFA .94 AVE EFA CR CFA .82 .88 Indicator reliability CFA AVE CFA .61 .89 .93 .86 .82 .86 .83 .88 .91 .90 .71 .78 .76 .85 .91 .77 AVE = Average Variance Explained, CFA = Confirmatory Factor Analysis, CR = Composite Reliability, EFA = Exploratory Factor Analysis, ITTC = Item-to-Total-Correlation,. 54 Table 4 Test of model fit of competing CBI models 2 965.72 243.73 73.73 54.12 Model Null model (15 items, 1 factor) Model one (8 items, 1 factor) Model two (6 items, 1 factor) Model three (5 items, 1 factor) 2-difference1 df 90 20 9 5 721.99 (df = 70)*** 170.00 (df = 11)*** 19.61 (df = 4)*** The 2-differences represent comparisons of model one versus the null model, model two versus model one, and model three versus model two. *** significant at p < .01 1 Table 5 Descriptive statistics and correlations between measures Variable 1. Consumer-brand identification 2. Brand-self similarity 3. Brand distinctiveness 4. Brand prestige 5. Brand social benefits 6. Brand warmth 7. Memorable brand experiences 8. Product category involvement 9. Brand loyalty 10. Brand advocacy Mean SD CR AVE 1 2 3 4 5 6 7 1.98 1.32 .88 .61 4.50 1.07 N.A. N.A. .46 3.36 1.78 .91 .78 .48 .32 4.32 1.66 .92 .80 .41 .35 .68 1.82 1.22 .93 .78 .68 .43 .43 .31 2.43 1.44 .85 .66 .69 .55 .60 .58 .69 2.75 1.72 .93 .81 .59 .45 .53 .52 .56 .68 2.81 1.51 .85 .59 .47 .30 .40 .37 .53 .57 .51 4.08 3.03 1.45 1.60 .82 .84 .61 .63 .42 .68 .25 .31 .27 .33 .23 .28 .37 .46 .38 .47 .32 .40 8 9 .20 .32 .60 The theoretical scale range is 1-7 for all variables. For more details of the measures, see section 4.1 and the Appendix. The statistics reported above are based on data pooled across the product categories in the main study. AVE = Average Variance Extracted based on CFA, CR = Composite Reliability 55 Table 6 Results of the structural equation models: main model and moderated model Relationships H1: Brand-Self Similarity → CBI H2: Brand Distinctiveness → CBI H3: Brand Prestige → CBI H4: Brand Social Benefits → CBI H5: Brand Warmth → CBI H6: Memorable Brand Experiences → CBI Dummy variable #1 Dummy variable #2 Dummy variable #3 H8: CBI → Brand Loyalty H9: CBI → Brand Advocacy Brand Loyalty  Brand Advocacy Main Model (N=796) .05* .08** .01 .34*** .30*** .15*** -.05* -.05 -.05 .55*** .68*** .48*** Moderated Model Product Involvement 2 Low High difference (N=391) (N=405) .04 .07* 10.73*** -.04 .11* 17.53*** -.10 -.03 -.19** .35*** 15.24*** .32*** .37*** 18.69*** .14** .15*** 13.69*** -.06 -.01 --.07 -.10** --.03 .00 ----------- Standardized path coefficients from structural equation models are reported in columns 2, 3, and 4. The 2differences for comparisons of the unconstrained versus the constrained model are shown in column 5. The dummy variables represent main effects for the four product categories included in the study. *** significant at p < .01, ** significant at p < .05, * significant at p < .10