In Search of the “Meta-Maven”: An Examination of Market Maven Behavior across Real-Life, Web, and Virtual World Marketing Channels

ABSTRACT

Recently, a new set of channels for consumer and business interaction have emerged—three- dimensional “virtual” worlds. This study attempts to better understand the nature of market maven behavior (diffusers of general marketplace and shopping information) across three different channels—virtual worlds, the Web, and real-life—and to examine the extent to which market maven behavior is transferable across channel context (i.e., “fluid”) or channel dependent. Using data from two surveys (one in the virtual world “Second Life” and a follow-up Web survey for the same respondents), this paper explores differences and determinants of maven behavior. Employing partial least squares analysis, the findings indicate that market maven propensity is transferable across channels (i.e., high-scoring market mavens retain this across channel). However, while there may be the transferability of market maven behavior across channels, the findings demonstrate that maven propensity is influenced by the channel context. Consequently, individuals with high maven propensity tend to exhibit channels in which this behavior is more prominent. Therefore, market maven behavior might not only span general product categories, but also the channel itself (i.e., maven behavior remains fairly constant—or fluid—across channel). The findings also point to possible characteristics that may be used in the identification of market mavens: market mavens typically have greater cognizance of other mavens, are technology-savvy and individualistic, are of either gender and tend to be older and more intensive and experienced users of Web platforms and also intensive users of virtual worlds than those with low maven propensity. The findings of the study contribute to understanding market maven behavior, and provide an insight into the practices of mavens in a multichannel context, particularly in the case of the emerging channels that are virtual worlds.

The market maven concept, first introduced by Feick and Price (1987), has attracted considerable scholarly interest. This group of consumers possesses generalized marketplace information and takes a keen interest in disseminating this to others, thus acting as a useful means of distributing product information often with greater credibility than many traditional marketing communications sources. The existence of the market maven has received widespread report in physical channels (i.e., real-world) (Abratt, Nel, & Nezer, 1995; Clark & Goldsmith, 2005; Feick & Price, 1987; Goldsmith, Clark, & Goldsmith, 2006; Walsh, Gwinner, & Swanson, 2004; Williams & Slama, 1995) as well as Web-based channels (Belch, Krentler, & Willis-Flurry, 2005). A notable absence from these studies is an understanding of market maven behavior in alternative communication channels. With the recent emergence and growth of social networking technologies—including the likes of MySpace, Facebook, YouTube, LinkedIn, Flickr, Bebo, Hi5, Friendster, 47 Things, and many more—a new set of channels for market maven behavior has emerged. One such channel is that of the virtual world (e.g., Second Life, developed by Linden Lab).

Virtual worlds have been recognized as potentially one of the more important channels for marketing and marketplace information (Hemp, 2006), as testified by the large number of diverse key brands that have created a presence there, including Toyota, Reuters, Nokia, and Dell. Little is known, however, concerning how these emergent channels differ from existing channels and the factors that might contribute toward market maven behavior. For example, how do market mavens behave across multiple channels—including, for example, physical channels, the Web, and virtual worlds—and to what extent is market maven propensity retained by individuals across marketing channels (i.e., the degree to which individuals who have a high market maven propensity transfer this across channel setting)? If it were possible to establish an individual's market maven propensity that exists independent of channel, the implications for marketers would be significant; such “meta-mavens,” if they can be identified, would provide enormous value to marketers across many channels in terms of disseminating positive word-of-mouth and the personal influence they exert on other consumers. Based on the foregoing, the aim of this study is threefold:

1. To identify the extent to which market maven behavior is retained across physical, Web, and virtual world channels (i.e., whether a maven is always a maven regardless of channel context);
2. To identify the degree to which market maven behavior is constant across physical, Web, and virtual world marketing channels (i.e., whether mavenism is greatest in a particular channel); and
3. To examine the personal characteristics of market mavens across channel.

This study is timely for a number of reasons. First, market mavens are an important group of consumers as they are effective at spreading word-of-mouth through their often extensive social networks making them a useful target for companies, particularly concerning new goods and services (Slama & Williams, 1990; Sundaram, Mitra, & Webster, 1998; Williams & Slama, 1995). Second, understanding the transferability of maven behavior across channels affords an understanding of the degree to which maven behavior might be universal (i.e., not channel dependent). Third, studies grounded in virtual worlds provide a context in which to examine established marketing tenets (Hemp, 2006) and to question assertions concerning consumer behavior based on “real-world” actions that may have to be reconsidered within the context of virtual worlds. As such, this study represents one of the first attempts to understand marketing phenomena within the context of virtual worlds. Fourth, if enough studies of this sort can be done, perhaps broad generalizations can be derived that can be integrated into the evidence from psychology to deepen the understanding of consumer behavior in general. To this end, the authors hope that this study encourages others to embark on research in this area of investigation.

THE MARKET MAVEN CONCEPT

Defined by Feick and Price (1987) as “Individuals who have information about many kinds of products, places to shop, and other facets of markets, and initiate discussions with consumers and respond to requests from consumers for market information” (p. 85), the market maven construct has been a catalyst for numerous confirmatory studies in the United States as well as in other country contexts (e.g., Slama & Williams, 1990; Abratt, Nel, & Nezer, 1995; Wiedmann, Walsh & Mitchell, 2001; Chelminski & Coulter, 2002, 2007; Steenkamp & Gielens, 2003; Goldsmith, Clark, & Goldsmith, 2006, Clark, Goldsmith, and Goldsmith, 2008; Ruvio & Shoham, 2007; Goodey & East, 2008). The involvement or interest demonstrated by the market maven is not restricted to a particular product category but rather is linked to general marketplace and shopping interest. As such, market mavens are considered to be distinct to other influencers such as opinion leaders, innovators, and early adopters as their activities encompass general market knowledge and activities rather than relating to a particular product category (Feick & Price, 1987; Goldsmith, 1996). This is an important distinction as innovators and opinion leaders have limited function for “broad-based retailers” as researchers have generally been unable to “….generalize their findings away from specific products or product categories” (Abratt, Nel, & Nezer, 1995 p. 32).

Influencers use their networks and communities to spread word-of-mouth to reach greater numbers of social contacts than a typical consumer. In the case of mavens, their personal influence is largely based on altruistic motives—for the pleasure of sharing information and to reinforce their image in their community (Sundaram, Mitra, & Webster, 1998)—spanning multiple product categories making them a useful target for companies (Slama & Williams, 1990). For example, the social integration of the maven affords them their influence, with individuals who know them being more likely to act on their information as they tend to have greater confidence in word-of-mouth than commercial sources such as advertising due to perceived credibility (Gelb & Johnson, 1995; Keller & Berry, 2003; Williams & Slama, 1995). Word-of-mouth can also influence product choice (Price & Feick, 1984; Kiel & Layton, 1981) making it a useful managerial issue to examine. Consequently, mavens can be helpful to other consumers in advertising saturated markets by becoming “competent information providers and advisors” (Walsh, Gwinner, & Swanson, 2004 p. 100), particularly as they are generally “smarter” shoppers (Slama, Nataraajan, & Williams, 1992).

Research examining market mavens is pervasive in the marketing literature. For example, studies have considered the demographic profiles (Abratt, Nel, & Nezer, 1995), personality characteristics (Clark, Goldsmith, & Goldsmith, 2008; Geissler & Edison, 2005; Goldsmith, Flynn, & Goldsmith, 2003; Ruvio & Shoham, 2007), purchasing alternatives and criteria (Williams & Slama, 1995), and motivations for mavenism (Chelminski & Coulter, 2007; Clark & Goldsmith, 2005; Inman, McAlister, & Hoyer, 1990; Steenkamp & Gielens, 2003; Walsh, Gwinner, & Swanson, 2004) as well as the notion of industrial mavens in a business-to-business context that display “…broad-based information on industrial markets” (Nataraajan & Angur, 1997 p. 354). A notable absence from this list is an understanding of the degree to which market maven tendencies are constant or dynamic across setting and context. Market mavenism is thought to be continuous in that it captures market maven propensity rather than to identify mavens versus nonmavens (Feick & Price, 1987). In this sense, just as market maven propensity may feasibly be higher for one individual as compared to another and may also increase or decrease over the lifetime of a consumer, contextual factors in terms of channel may equally play a role in determining market maven tendencies.

The rise in social networking technologies and virtual worlds also potentially facilitates the ease with which mavens can disseminate product and market information. For some individuals, the extension of maven behavior across channel may be associated with their personality characteristics. For example, a maven with an affinity for technology and high propensity to adopt new technologies will be likely to extend their maven behavior across channel. Further, other demographic, personality, or technology use characteristics of mavens may impact on the extent to which maven behavior in a real-world setting might extend to the Web and virtual worlds.

Against the foregoing, identifying the transferability or fluidity of market maven behavior across channels (i.e., the extent to which market mavenism is constant or changeable across channel) as well as identifying the presence of mavens in multiple marketing channels would seem valuable. As Geissler and Edison (2005 p. 74) note: “…finding new ways of identifying and profiling market mavens and targeting marketing communications to them may be increasingly important in the twenty-first century.” This takes on particular importance against a background of growing product choice, media saturation, and multiple channels (e.g., physical channels, the Web, and virtual worlds) in which consumers can interact with one another as well as with organizations. In the following section, the existence of markets mavens in three-dimensional (3-D) virtual worlds is considered.

VIRTUAL WORLDS DEFINED

Investment in online and digital technologies by business is remarkable with billions of dollars being expended in recent years (Arnold, 2004). The rapid rise of these technologies affords consumers new venues for interaction with other consumers as well as with businesses (Chelminski & Coulter, 2007; Geissler & Edison, 2005). As a consequence, in addition to traditional real-world settings where consumers might disseminate information (e.g., via the telephone, in retail outlets, or through face-to-face interactions with friends and family), there are broadly six virtual/online means by which consumers can interact (cf. Kozinets, 2002) including:

1. Chat rooms (often organized around special interests such as consumer and lifestyle issues);
2. Web rings (related home pages where individuals can share information based on a single mailing list);
3. Electronic bulletin boards (allowing participants to post-group relevant information);
4. Lists (or listservs) (theme-based e-mail mailing lists);
5. Social networking tools (such as MySpace, Facebook, and YouTube); and
6. Virtual worlds, which can be broadly classified as either massively multiplayer online role-playing games (such as World of Warcraft and EverQuest) or virtual economies where participants create businesses and communities (such as Second Life, IMVU, and Active Worlds).

These new channels provide the means for consumers to exert greater interpersonal influence, thus making it important that marketers better understand the role of influencers in these settings. In comparison to physical and Web channels, virtual worlds are a relatively new concept and hence require explanation.

Three-dimensional “virtual worlds” (sometimes referred to as “experience worlds”) are increasingly becoming an important channel for companies to communicate with current and potential customers. These “fast-growing Internet-based simulated environments where users can not only interact with each other, but with products and services provided by businesses and individuals” (Lui, Piccoli, & Ives, 2007 p. 77) provide a platform for interactivity that can positively influence product knowledge, attitudes toward brands, telepresence, and purchase intention (Klein, 2003; Li, Daugherty, & Biocca, 2002; Steuer, 1992; Suh & Lee, 2005). While the Web introduced a new highly interactive medium that altered the parameters of mass and personal communication (Hoffman & Novak, 1996; Rogers, 1986), virtual worlds stand to make an equally important impact on individual's daily lives and shopping behavior. As Drew Stein, CEO of Infinite Vision Media (an interactive marketing agency that worked with Dell Island in the virtual world Second Life), notes: “as people get more familiar with 3-D experiences, the flat Web page is going to seem like a thing of the past” (reported in Lui, Piccoli, & Ives, 2007).

As the distinction between virtual worlds (such as Second Life) and social networking sites (such as Facebook) has not been well established in the literature, it is worth delineating the differences between these two computer-mediated environments. Virtual worlds can be thought of as a distinct form of social networking site in terms of the extent of the individual's interaction with the medium. For example, the “optimal experience” achieved in terms of “playfulness” or “flow” experienced in a particular environment (cf. Csikszentmihalyi, 1977) is demonstrably different between both types of site. It is proposed that there is a heightened sense of “flow” in virtual worlds—the sense of fun attained in structured activities (Hoffman & Novak, 1996). For flow to occur, two key antecedents are necessary: the level of focused attention possible (the extent to which an individual is subsumed within the activity) and the balance between the skills and challenges involved in the interaction (Hoffman & Novak, 1996). In this sense, virtual worlds provide greater scope of focused attention and tasks/challenges than social networking sites. There are numerous tasks in virtual worlds such as Second Life not necessarily available via social networking sites that represent challenges ranging from shopping (at in-world stores), commerce (e.g., real estate), ability to engage in interactive games and leisure pursuits (including museums and art galleries), and networking opportunities (e.g., via special interest groups including those affiliated to religious groups and educational institutions). If the challenge presented by a particular activity is perceived as being incongruent with the individual's level of skill then they can switch tasks or create their own. Focused attention is predicated on the capacity of the individual to become immersed in the environment. In this sense, virtual worlds provide an ideal context for focused attention as they are highly detailed, 3-D-rendered, interactive environments, consistent with the optimal conditions for focused attention where the individual “…is in control of his actions, and in which there is little distinction between self and environment, between stimulus and response, or between past, present and future” (Csikszentmihalyi, 1977 p. 36). Virtual worlds also provide greater “telepresence”: “…the extent to which one feels present in the mediated environment, rather than in the immediate physical environment,” by creating “…an almost ‘being there’ experience” (Steuer, 1995 p. 36). Therefore, virtual worlds constitute a rich visual medium with a depth of engagement and high level of interactivity not afforded by most social networking sites.

The emergence of virtual worlds encourages marketers to reflect on the body of knowledge amassed on customers based on their real-world and Internet-based shopping behavior and to question some of these tenets or widely held axioms. As Fortin (2000) notes, “a common question that generally arises when a new technology is introduced is: How does this affect what we already know about a phenomenon?” (p. 524). Virtual worlds—such as Second Life and World of Warcraft—capitalize on an individual's desire to inhabit alternative personalities and to occupy an anonymous “alternative self” in a virtual environment, thus potentially changing one's identity and even behavior via an avatar—comprising not only “complex beings created for use in a shared virtual reality but any visual representation of a user in an online community” (Hemp, 2006 p. 50). Avatars act as proxies for the real-world self—offering the possibility for their human controller to explore “…hidden aspects of their identities” that “…differ substantially from one another and from the creator's public self” (Hemp, 2006 p. 50). As a consequence, consumers may behave differently in such environments than they would in real-world interactions and encounters thus providing a group of potentially new, or at least different, consumers and marketing opportunities.

In the case of Second Life—a 3-D virtual world that is free to join and is created by its residents (one of the channel contexts examined in the present study) —approximately 14 million residents (as of August, 2008) engage in leisure pursuits and trade using the in-world unit-of-trade (the Linden Dollar), which can be converted into U.S. dollars via online currency exchanges. Consumers can disseminate information in virtual worlds through a variety of means including dialogue (via text chat, instant messaging, and Voice-over-IP) with other avatars in public and through engagement in special interest groups. A number of major brands have a presence in Second Life including high-tech firms (Sun Microsystems, Dell), car manufacturers (Nissan, BMW), luxury items (Armani, Hublot), service providers (IBM, Playboy), consumer goods (Sony-Ericsson, Nokia), as well as diverse others (Stanford University, the Swedish Embassy, Visit Mexico, and the Weather Channel). On the basis of these attributes, Second Life has been identified as an environment in which to examine consumers’ behavior in a virtual world context and the implications this might have for marketing (Hemp, 2006).

Against the abovementioned, many assertions concerning consumer behavior based on “real-world” actions may have to be reconsidered within the context of virtual worlds, particularly as individuals’ avatars may behave differently to their real-world alter egos. The transferability of market mavenism across channels as well as the personal characteristics of mavens is now considered.

HYPOTHESIS DEVELOPMENT

Market Maven Propensity across Channel

The market maven measure and concept was predicated on real-life interaction between consumers (Feick & Price, 1987) (i.e., a single channel), at a point in time when both the Web and virtual worlds were at an embryonic stage in their development. The present study considers maven behavior within channels, hence channel refers to the context or setting in which consumers interact and disseminate information about products and services. In a review of the extant literature, no study could be identified that considers market maven behavior across multiple channel settings. In their investigation of Internet teen-mavens, however, Belch, Krentler, and Willis-Flurry, (2005) assert that teen-mavens (who take particular pleasure in surfing the Internet), relative to others online, can be relied upon to provide useful information and in so doing influence the family decision-making process to a greater extent than nonmarket mavens. Geissler and Edison (2005 p. 87) also allude to the notion of market maven transferability when they propose that “Today's mavens probably communicate with more consumers than ever before. Indeed, more and more consumers are going online each year and mavens may be quite influential towards a growing number of online consumers, along with their traditional circle of friends, neighbors and other associates.” Therefore, one might act as a market maven across multiple channels.

Although the preceding does not afford an understanding of the transferability of maven behavior in different settings, it does suggest that mavens can display such behavior in situations other than a physical, or “real-world,” context. Further examination of the maven concept and the nature of personality itself can help better understand the transferability of market mavenism.

Market mavenism is “a role individuals can adopt” (Feick & Price, 1987 p. 85), predicated on the assumption that by collating and disseminating marketplace information they can increase their perceived power in society as they become more useful in their interactions with others (Sieber, 1974). Feick and Price (1987) suggest that this compels certain individuals to disseminate information in the expectation that they will become the recipient of “rewards” (i.e., that they will be the recipient of informed information on topics from others in return). Therefore, Web-based channels and virtual worlds offer platforms ideally suited to these individuals through facilitating social interaction via avatars (in the case of virtual worlds) and the dissemination of marketplace information.

Some individuals feel that it is their duty to become well-informed consumers thus acting as information seekers (Feick & Price, 1987; Kassarjian, 1981). What drives one individual to become better informed than another is attributed in part to differences in attitudes and behavior (Feick & Price, 1987)—hence a construct of personality. Specifically, this is a question of the stability of personality insofar as it can be transferred across setting or context.

Personality is thought to include “objective and observable traits” as well as “roles, attitudes, goals, and behavioral tendencies” (Tickle, Heatherton, & Wittenberg, 2001 p. 243). The extent to which an individual's personality can change (other than via neuro-biological injury such as through major brain damage) has represented one of the most significant issues in personality psychology (Heatherton & Weinberger, 1994). Although opinion is to some extent divided on the topic, with some early views proposing that personality might be dynamic (Allport, 1937; Gendlin, 1964), most contemporary views hold that personality is relatively predictable and stable across time, situation, and over the adult lifespan (McCrae & Costa, 1990; Tickle, Heatherton, & Wittenberg, 2001). In support, Steuer (1995 p. 36) suggests that in certain computer-mediated environments, “…people respond to mediated stimuli in ways similar to their real-life counterparts.” Evidence also supports the notion that computers can provoke social responses that are analogous to human interactions (Nass & Steuer, 1993; Nass, Steuer, Henrikson, & Dryer, 1994; Wang, Baker, Wanger, & Wakefield, 2007). Thus, even when presented with new channels such as virtual worlds and the opportunities they provide to change appearance as well as to interact with human-controlled and possibly computer-controlled avatars, important underlying personality traits may be expected to stay the same. Therefore, it is asserted that:

H 1. Individuals with high market maven propensity retain this characteristic across channel.

A pertinent question arises from the foregoing discussion. If market mavens do retain their maven behavior across channel then is this behavior equally strong in all channels? Put another way, might a market maven have a particular channel in which he or she exhibits their strongest maven behavior? As this is an exploratory study, and as no compelling evidence exists to suggest the contrary, the null hypothesis is offered:

H 2. Market maven behavior remains constant for an individual across physical, Web, and virtual world channels.

Market Maven Propensity and Personal Characteristics

The second area of investigation relates to the personal characteristics of market mavens. Although understanding the characteristics of market mavens (and particularly identifying e-mavens) is an area of intuitive importance surprisingly little guidance is offered as to what characteristics might be most revelatory (Geissler & Edison, 2005; Walsh & Mitchell, 2001). As demographic factors have not proved particularly useful in classifying market mavens (discussed briefly next), additional variables to explore maven characteristics were sought. Therefore, in addition to key demographics (age and gender), variables were selected related to personality characteristics (individualism, affinity for technology, and knowledge of others as mavens) and interaction with the channel (knowledge of channel and intensity of channel usage). Some of the hypotheses proposed are new whereas others have been tested previously. These variables are employed as they either represent demographic indicators or else relate to the study theme of virtual worlds. These are now briefly examined.

Demographic Factors

No clear or consistent demographical profile has been proposed of market mavens with considerable variability of views evident in the literature; indeed, Geissler and Edison (2005 p. 85) even go as far as to note the “lack of success of demographically profiling mavens,” a view largely shared by the originators of the concept (Feick & Price, 1987). Equivocal (or at least inconsistent) findings have been offered in prior studies (e.g., Clark & Goldsmith, 2005; Clark, Goldsmith, & Goldsmith, 2008; Geissler & Edison, 2005; Goldsmith, Clark, & Goldsmith, 2006) consequently making targeting mavens challenging. As no compelling argument or evidence has been offered to propose that mavens are of a particular gender or age group, the following hypothesis is offered:

H 3. There will be no differences in gender and market maven propensity, and

H 4. There will be no differences in age and market maven propensity.

Personality Characteristics

The first area of market maven personality characteristics concerns the ability of consumers to recognize others as market mavens and their influence—an important means of validating the market maven construct (Feick & Price, 1987). This said, however, subsequent market maven enquiries have rarely employed this measure. Feick and Price (1987) found that a sizeable proportion of respondents had knowledge of other mavens, were assisted by them in raising awareness of goods and services, and received their help in evaluating goods. These measures were adopted for much the same reason as Feick and Price (i.e., as a means of validating the existence of market mavens and their recognition by others). It is proposed that individuals with high market maven propensity will have a greater likelihood of recognizing this characteristic in others that they interact with. This is based on the premise that mavens seek out other mavens in order to exchange information, as already noted (Feick & Price, 1987; Sieber, 1974). Hence,

H 5. Individuals with high market maven propensity will be more likely to: (i) have knowledge of other market mavens; (ii) be assisted by them in raising awareness of goods and services; and (iii) use their help in evaluating goods and services than individuals with low market maven propensity.

Individualism is a well-established concept that has its roots in personality traits attributed to the cultural differences between societies and refers to the uniqueness of each individual and concerns related to their own well-being in contrast to collectivistic individuals (Triandis, 1994, 1995). Although individualism is primarily a means to examine consumers from different societies (e.g., see Kim, 1994; Maheswaran & Shavitt, 2000), it can also serve to understand the psychological differences between individuals within a society.

Individualists have a need for achievement, are competitive (Triandis, 1994; Triandis, Bontempo, & Villereal, 1988) and tend to maintain largely superficial relationships, instead placing greater emphasis on disseminating and receiving market information (Markus & Kitayama, 1991). Individualists are also more inclined to be confident in their decision making and proactive in disseminating their opinions and ideas (Chelminski & Coulter, 2007). Therefore, individualism would appear to be a trait consistent with high market maven propensity. Indeed, as Chelminski and Coulter (2007 p. 87) note: “where people are more individualistic, and thus more confident in their marketplace endeavors, market mavenism is likely to be more prevalent.” In addition, Clark, Goldsmith, and Goldsmith (2008) found a relationship between aspects of consumer self-confidence and mavenism; conceptually not unrelated to individualism (Bearden, Hardesty, & Rose, 2001). Therefore, it is proposed that individuals with high market maven propensity are also more likely to exhibit strong individualism traits. Stated in the formal manner:

H 6. Individuals with high market maven propensity will display higher individualism than individuals with low market maven propensity.

Affinity for technology can be defined as “the degree to which an individual likes or looks forward to learning about and being involved with new technology” (Geissler & Edison, 2005 p. 77). Consumers differ in their attraction to technology with some eager to adopt new technology and others anxious of such changes (Rogers, 2003). Hence, some consumers (based on their personal innovativeness) are more likely to adopt new technology than others (Agarwal & Karahanna, 2000; Agarwal & Prasad, 1998; Wolfradt & Doll, 2001). As mavens are considered innovative and with an attraction to new products (Feick & Price, 1987; Slama & Williams, 1990), it seems likely that they will have a greater affinity to trial and adopt new technology than individuals with low maven propensity (Geissler & Edison, 2005). Therefore, it is proposed that market mavenism is related to affinity for technology:

H 7. Individuals with high market maven propensity will have a greater affinity for technology than individuals with low market maven propensity.

Channel Interaction

Market mavens enjoy learning about products and services, collecting coupons from newspapers and magazines and generally engaging with shopping behavior to a greater extent than other individuals (Feick & Price, 1987). Virtual worlds potentially afford mavens a new channel in which to interact. Clearly, users of virtual channels can engage with a particular channel as much as he or she wishes from light to heavy usage. In addition, an individual will also vary in the length of time they have been using a particular channel.

It is proposed that if, as is supposed, mavens have a greater affinity for technology than nonmavens and proclivity for shopping interest then these characteristics will be extended to the Web and virtual world channels. Hence, market mavens’ user interaction and involvement with both the Web and virtual world channels in terms of user experience (the length of time the individual has been using each medium) and intensity of usage (measured in terms of the time spent in each medium) will be greater, in general, than those with low market maven propensity as they search for and disseminate product information. Therefore, it is proposed:

H 8. Individuals with high market maven propensity will have greater: (i) channel experience; and (ii) intensity of usage of both Web and virtual worlds than individuals with low maven propensity.

METHODOLOGY

Research Design and Measurement

The research design adopted by the study involved a cross-sectional, convenience sample using two self-report surveys across three marketing channels: two online channels (Second Life and the Web) and real-life. Second Life is arguably one of the most advanced of virtual worlds and was deemed an ideal context in which to collect data.

In order to be consistent with previous research examining market mavens, only minor modifications were made to Feick and Prices’ (1987) original measure to reflect the different channels under examination (see Appendices for all measures and items). The market maven scale measures an individual's tendency to disseminate useful market information across a variety of products and brands and is a unidimensional, 7-point, six-item, summated scale. The nomological validity of the market maven measure has been generally supported in comparison to the broadly conceptually similar measures of opinion leadership and innovators/early adopters (Feick & Price, 1987; Ruvio & Shoham, 2007).

Although the market maven measure has not been without its critics (see, e.g., Goodey & East, 2008; Voss, Stem, & Fotopoulos, 2000; Williams & Slama, 1995), it has proved broadly robust across different cultural settings including the United States (e.g., Goldsmith, Clark, & Goldsmith, 2006; Slama & Williams, 1990), Germany (Wiedmann, Walsh, & Mittchell, 2001), South Africa (Abratt, Nel, & Nezer, 1995), The Netherlands (Steenkamp & Gielens, 2003), Israel (Ruvio & Shoham, 2007), South Korea (Chelminski & Coulter, 2007), and Poland (Chelminski & Coulter, 2002), as well as for studies of Web mavens (Belch, Krentler, & Willis-Flurry, 2005) and industrial mavens (Nataraajan and Angur, 1997), thus demonstrating the robust nature of the concept and its broad applicability.

In addition to capturing market maven behavior, data were captured for level of user interaction and involvement with both virtual world and Web in terms of user experience (i.e., the length of time using each medium) and intensity of usage (measured in time spent in each medium) as well as respondent demographics (age and gender).

Finally, three personality indicators were employed, which included “maven knowledge and importance” (consumers’ identification of others as market mavens, their usefulness in assisting awareness and in aiding the evaluation of goods and services) (Feick & Price, 1987), “individualism” (Chelminski & Coulter, 2007, based on Singelis, 1994 and Triandis & Gelfand, 1998), and affinity for technology (using “personal innovativeness in information technology” or PIIT) (Agarwal & Prasad, 1998).

Data Collection and Sample

Of the two surveys employed in the study, the first survey was administered by means of two avatar “survey bots” operating in Second Life for 10 days (n = 240). Each bot is essentially an avatar automated to deliver the survey items in text form and to collect responses in a database. Each bot had an advertisement for the survey in its group name, above the avatar. Details of the survey were also provided in the profile of the avatar and respondents were requested to instant message (IM) the bot. Respondents initiate contact and are given details of the survey and how to begin the questionnaire by sending an IM (with the word “SURVEY”). The survey then begins, with the respondent prompted to answer the questions in numerical format (e.g., “What is your gender? 1 = Male, 2 = Female”). One bot was male, another was female; both had formal attire. To collect sufficient responses, each bot was placed at a high-traffic location selected from Second Life's “popular locations” list. The two locations were chosen to be as generic as possible (to appeal to both genders, different ages, and nationalities) and each focused on providing both free and paid-for digital content and on generating traffic through paid “camping” activities (where individuals are paid small amounts of money for time spent “sitting” at a particular location).

Respondents to the Second Life survey were then invited to complete an online survey (via QuestionPro.com) measuring market maven behavior on the Web and in real-life (n = 102). The second survey was sent only to individuals who had completed the Second Life survey to ensure a matched sample of respondents. This was sent four weeks after the first survey was closed in order to clean the initial response list. A small monetary incentive (in Linden Dollars) of just under US$2 was provided to respondents for each completed survey (approximately 1 US$ = 265 Linden$, as of May, 2008). Two reminder messages were sent to Second Life participants who had completed the first survey to encourage them to complete the second survey. All responses were collected within 10 days (see Appendices Appendix and Appendix for all items and measures for both surveys).

The demographic profile of respondents to each survey (see Table 1) suggests gender profiles were split broadly equally between males and females, although slightly more females replied to the second survey. In terms of age, data on respondents for all age categories (18–65+) were captured. Respondents’ age profiles were skewed toward the 18–34 group; the age profile of the second survey, however, was spread slightly more evenly. As no comparative demographic profile (e.g., age and gender) for Second Life or virtual world usage could be identified it is not possible to be certain as to how the study sample aligns with the actual demographic profile of virtual world users.

Table 1. Respondents’ Age and Gender Profiles

	Virtual World (n = 240)	Real-Life/Web (n = 102)
Gender
Male	125 (52.1%)	47 (46.1%)
Female	115 (47.9%)	55 (53.9%)
Age
18–24	98 (40.8%)	28 (27.5%)
25–34	66 (27.5%)	36 (35.3%)
35–44	43 (17.9%)	20 (19.6%)
45–54	23 (9.6%)	13 (12.7%)
55–64	6 (2.5%)	2 (2%)
65+	4 (1.7%)	3 (2.9%)

Data Analysis

In order to model and test the assumptions made and to assess the dimensionality of the scales, partial least squares path modeling (PLS-PM) was used with reflective indicators (Centroid Weighting Scheme) in Smart-PLS where appropriate (Ringle, Wende, & Will, 2005). PLS has the advantage of being effective on small samples, and does not require distributional assumptions of the sample. Further, PLS is a useful statistical tool for building and testing more inclusive models that examine more complex sets of relationships for each of the various channels under study.

In order to gauge the adequacy of the sample for PLS, a post hoc power analysis using G*Power 3 was conducted (Faul, Erdfelder, Lang, & Buchner, 2007). The analysis (α = 0.05, 1 − β = 0.8) revealed that the sample sizes were adequate for the four separate PLS models generated for small-to-moderate population effects (f ≥ 0.10, Models I and II; f ≥ 0.13, Model III; f ≥ 0.23, Model IV) (Faul et al., 2007).

The associated reliability and validity statistics for the surveys and models are provided in the relevant tables associated with the PLS analysis (see Appendix Appendix for factor loadings and associated statistics). All items load significantly on their factors with one exception—the third PIIT item did not load significantly in PLS Models III and IV and was omitted from further analysis. The factors display acceptable levels of reliability (Cronbach's α > 0.7 and composite reliability, ρc > 0.7, cf. Nunnally, 1978) and validity (AVE > 0.5, cf. Fornell & Larcker, 1981). Discriminant validity is also demonstrated; the square root of AVE (average variance extracted) is larger than the intercorrelations. Furthermore, the intercorrelation between any pair of constructs does not exceed 0.9 (Model I, 0.393–0.746; Model II, 0.056–0.512; Model III, 0.063–0.659; and Model IV, 0.055–0.663), suggesting that the multicollinearity problem can be ignored (Hair, Anderson, Tatham, & Black, 1998).

RESULTS

Market Maven Propensity across Channel

Initially, the relationship between the three contexts of maven behavior measured was examined (namely, real-life, Web, and virtual world mavenism). The results indicated a strong association between the channels examined for market maven behavior (see Figure 1), particularly from real-life to the Web (p < 0.001) and from the Web to virtual worlds (p < 0.01). Hence, market mavenism would appear to be a generally constant phenomenon across channels. As such, a maven in real-life is more likely to display maven tendencies in other channels than a nonmaven, thus supporting the findings received for Hypothesis 1. Notably though, the R² for virtual world market mavenism is only 0.2, indicating that there are other factors that, in the context of a richer nomological net, are likely to better explain mavenism in the virtual world context. It is also interesting to note the weak direct maven relationship from real-life to virtual worlds (p = 0.10); it is posited that this is likely to be due to the quite different perceptions, attitudes, and behaviors between these channels (cf. Hemp, 2006). There is a stronger intuitive relationship between the Web channel and virtual worlds than between virtual worlds and real-life. Both of the former channels are information technology based and share some common characteristics, including the importance of human–computer interaction in behavioral outcomes and the use of electronic communications and social networking technologies. These findings also offer support for Hypothesis 2 concerning the variability of market mavenism between channels.

Figure 1. PLS Model I—Maven behavior across all channels.

Note: Significance levels denoted by †(10%), *(5%), **(1%), and ***(0.1%).

Virtual world mavenism, n = 240; Web mavenism/real-life mavenism, n = 102.

Market Maven Propensity and Personal Characteristics

This section considers market maven propensity and personal characteristics. All models tested (see Figures 2, 3, 4) employed gender and age as moderating variables but did not identify any significant results. Therefore, these findings offer no support to reject both null Hypotheses 3 and 4 (which posited that there would be no difference between age, gender, and market maven propensity).

Figure 2. PLS Model II—Maven behavior in real-life.

Note: Significance levels denoted by †(10%), *(5%), **(1%), and ***(0.1%);

n = 102.

Figure 3. PLS Model III—Maven behavior on the Web.

Note: Significance levels denoted by †(10%), *(5%), **(1%), and ***(0.1%);

n = 102.

Figure 4. PLS Model IV—Maven behavior in virtual worlds.

Note: Significance levels denoted by †(10%), *(5%), **(1%), and ***(0.1%);

n = 240.

The next series of tests examined the relationships between market mavenism and (i) individualism and knowledge of others as mavens for real-life settings, and also (ii) individualism, knowledge of others as mavens, personal innovativeness in information technology, and channel interaction (experience and intensity) for both Web and virtual world channels (see Figures 2, 3, 4). Each of these constructs is now considered in turn.

Across the three channels examined, the direct relationship between market mavenism and knowledge of others as mavens (a composite of having knowledge of others as mavens, receiving assistance from them, and using their help in the evaluation of goods and services) was modeled. The findings suggest that market mavenism leads individuals to seek and receive assistance from other mavens across all three channels and that mavens rely strongly on their network of other mavens for market information, thus providing support for Hypothesis 5. Interestingly, however, this behavior is strongest in real-life (p < 0.001), followed by the Web (p < 0.001), and finally, virtual worlds (p < 0.01), suggesting that mavens are less likely to be aware of other mavens and draw on their assistance on the Web and in virtual worlds compared to real-life, which may be attributed to mavens being more easily identifiable in real-life than in electronic channels.

Next, the relationship between individualism and market mavenism was considered for the three channels. The results obtained from the three PLS models indicate that individualism is a determinant of mavenism for all channels, and these effects are stronger in real-life (p < 0.001) than on the Web or in virtual worlds (p < 0.01). This implies that market mavens are highly individualistic and provides support for Hypotheses 6. Further, these results suggest that individuals may be marginally less individualistic when online, which might be attributed to the nature of social networking technologies such as aspects of the Web and virtual worlds.

Another further personality construct included in the assessment of the two technology channels, personal innovativeness in information technology (PIIT), proved significant in both contexts (i.e., Web and virtual world channels). In particular, PIIT was more significant for determining maven behavior on the Web (p < 0.01) than in virtual worlds (p < 0.05). Thus, innovative users who are more likely to adopt a technology, such as the Web or virtual worlds, are more likely to use it for market maven purposes, thus offering further support for Hypothesis 7.

Finally, the impact of the individual's interaction with the channel (in terms of experience and intensity of use) on maven behavior was examined. For this, the impact of Web experience (WEBEX) and Web use intensity (WEBINT) on Web maven behavior was modeled as was the relationship between virtual world experience (VWEX) and use intensity (VWINT) on virtual world maven behavior. The results for interaction variables on Web mavens were not significant, suggesting that use experience and use intensity did not significantly encourage users to become mavens in the Web channel. The results for virtual world mavens, however, suggest a significant relationship between virtual world use intensity and virtual world maven behavior (p < 0.001). This appears to imply that those frequently engaged in virtual world activities are more likely to engage in maven behavior. However, another interesting finding is the significant negative relationship between Web use intensity (WEBINT) and virtual world maven behavior (p < 0.001). Thus, it appears that a higher intensity of Web use detracts from virtual world use and thus the ability to develop maven characteristics, suggesting that there is a degree of trade-off between the use of these different electronic channels. Therefore, only partial support can be offered for Hypothesis 8, which stipulated that individuals with high market maven propensity will have greater (i) channel experience and (ii) intensity of usage of both Web and virtual worlds than individuals with low maven propensity.

Based on the tests conducted, the results of the hypothesis tests are summarized below (see Table 2). Evidence is offered to support four of the hypotheses and provide partial support for one, while no evidence was found to reject the two null hypotheses. One hypothesis was rejected.

Table 2. Summary of Hypothesis Tests

Hypothesis	Accept/Reject
H1: Individuals with high market maven propensity retain this characteristic across channel.	Accept
H2: Market maven behavior remains constant across physical, Web, and virtual marketing channels.	Reject
H3: There will be no difference in gender and market maven propensity.	Failed to reject
H4: There will be no differences in age and market maven propensity.	Failed to reject
H5: Individuals with high market maven propensity will be more likely to have (i) knowledge of other market mavens, (ii) be assisted by them in raising awareness of goods and services, and (iii) use their help in evaluating goods and services than individuals with low market maven propensity.	Accept
H6: Individuals with high market maven propensity will display higher individualism than individuals with low market maven propensity.	Accept
H7: Individuals with high market maven propensity will have a greater affinity for technology than individuals with low market maven propensity.	Accept
H8: Individuals with high market maven propensity will have greater (i) channel experience and (ii) intensity of usage of both Web and virtual worlds than individuals with low maven propensity.	Partially accept

CONCLUSIONS AND IMPLICATIONS

Virtual worlds are fast becoming recognized as an important channel for companies to communicate and interact with current and potential customers. They afford companies the opportunity to simulate customers’ experiences in physical stores as well as enhancing product knowledge and influencing customer attitudes and purchase intentions (Lui, Piccoli, & Ives, 2007). This study represents one of the first attempts to better understand how consumer behavior might differ in virtual world channels, and, by so doing, better inform the understanding of an important group of consumers, namely market mavens. This group of consumers is of particular value to marketers as they are important agents in disseminating positive (and negative) word-of-mouth and exerting personal influence over other consumers.

In the title to this study, a question was posed which suggested the possible existence of the “meta-maven”—individuals whose market maven behavior transcends channel context. By providing empirical support for market mavenism across channel and by exploring the existence of mavens in virtual worlds, this study contributes and extends Feick and Price's (1987) seminal work. Consequently, market maven behavior might not only span general product categories and market information, but also the channel itself (i.e., maven behavior remains fairly constant—or fluid—across channel).

The second area of inquiry was to understand the degree to which market maven behavior was seen to be constant across physical, Web and virtual world channels or whether maven behavior varied by channel context. The results suggest that market maven behavior is influenced by channel. Specifically, individuals with strong real-life market maven behavior perceive this to be at its strongest in this context, followed by the Web and lastly in virtual worlds. Therefore, while there may be the transferability of market maven behavior by channel (i.e., high-scoring market mavens retain this across channel), the findings indicate that mavenism propensity is influenced by the channel context.

The significance of the research for marketers is clear. Given that online communities are useful channels for consumers to engage in discussions, inform, and potentially influence others consumers (Kozinets, 1999; Muniz & O'Guinn, 2001) targeting mavens via online channels would appear beneficial. This has important implications for online advertising and viral marketing. Marketing communications and strategies targeted at market mavens deployed through online channels (including using advertising, viral messaging, and cybercoupons) not only provides significant value in a single channel through word-of-mouth and other viral behavior, but should also prove useful across multiple channels (e.g., real-life, the Web, and virtual worlds) through transferable maven behavior. Therefore, the comprehension that a market maven might display such behavior across channel context reinforces the view that mavens are fairly universal in their behavior. Indeed, the relative anonymity afforded to individuals in channels such as the Web and virtual worlds may even encourage maven behavior and its transferability across channel. As such, disseminating information in such relatively anonymous channels may be perceived by mavens to be relatively low risk in comparison to interactions with individuals in real-life settings.

In the multichannel environment, market mavens take on greater importance as useful agents to disseminate marketplace information across channel context. The findings also point to possible characteristics that may be used in the identification of market mavens by marketers: market mavens will typically have greater cognizance of other mavens, be technology-savvy and individualistic, will be of either gender and tend to be older, will be more intensive and experienced users of Web platforms and intensive users of virtual worlds. Virtual worlds therefore offer a further means of targeting mavens.

As the understanding of market mavens—in terms of their demographics and behavior—is growing, it is worthwhile reflecting on the findings of this study in comparison with this earlier body of literature. In terms of gender, while Feick and Price (1987) and Goldsmith, Clark, and Goldsmith (2006) found that mavens were more likely to be female, this study found no gender-based differences (in line with the findings of Walsh, Gwinner, & Swanson, 2004; Clark & Goldsmith, 2005; Geissler & Edison, 2005, and Clark, Goldsmith, & Goldsmith, 2008). This study also found no evidence that mavenism is linked to age, which is in keeping with the general consensus (Clark & Goldsmith, 2005; Clark, Goldsmith, & Goldsmith, 2008; Feick and Price, 1987; Goldsmith, Clark, & Goldsmith, 2006; Walsh, Gwinner, & Swanson, 2004).

The findings suggest that market mavens were more likely to be individualistic than nonmarket mavens. This is consistent with the findings of Chelminski and Coulter (2007), and also offers similarities with Clark, Goldsmith, and Goldsmith (2008)—who found a relationship between aspects of consumer self-confidence and mavenism—and Clark and Goldsmith (2005), who offered evidence to suggest that market mavenism is associated with higher self-esteem and a need for uniqueness expressed through brand choice.

In terms of affinity for new technology, the findings support Geissler and Edison (2005), who found that mavens were likely to have a greater affinity toward new technology than nonmavens, and broadly with Andrews and Benedicktus (2006) who found that mavenism was associated with innovativeness and negatively related to resistance to change. For channel experience and intensity of usage, the results for Web mavens were not significant, although the data did show a relationship between virtual world usage intensity and market mavenism. Given their affinity for new technology, however, it is perhaps not surprising that mavens seek further conduits to diffuse their knowledge.

Often overlooked by researchers of the market maven concept is the notion that mavens are cognizant of other mavens. Feick and Price (1987) found that a sizeable proportion of respondents had knowledge of other mavens, were assisted by them in raising awareness of goods and services, and received their help in evaluating goods. The results concur with Feick and Price's (1987) original findings, and also extend them as they show that this behavior occurs across all three channels examined, suggesting that there is something pervasive about this aspect of a maven's personality.

Finally, and central to the study, it was asserted that market mavens retain this characteristic across channel—an assertion that has no base of comparison in previous studies as it has not been the subject of empirical attention. While some variability across channel was found, market mavenism would appear to be a generally constant phenomenon across physical, Web, and virtual world channels. In this regard, this finding offers support for the pervasive nature of Feick and Price's (1987) market maven concept.

This study, like all others, has several limitations that need mention. Initially, a convenience sample was employed. More precise sampling techniques may aid generalizability. Although Second Life is arguably the most popular virtual world, it is not the only virtual world; studies of different virtual worlds may lead to different results. Finally, while this study has added to the understanding of the demographic profiles of market mavens and personal characteristics, other measures could be used and would be valuable in shedding further light on this important group of consumers.

Virtual worlds have added new channels to typical business models. Such channels are unlikely to displace other more “traditional” channels and shopping modes but rather to offer greater consumer choice and access (cf. Goldsmith & Flynn, 2005). Greater channel choice open to consumers merits additional study chiefly in terms of the channel decisions made by consumers as well as those of marketers. Understanding the transferability of key concepts (such as market mavenism) and the generalizability of “real-world” findings to virtual worlds will further extend knowledge of this increasingly important medium.

Appendix

Table A1. Virtual World Maven Survey

Measure	Item
Gender	Are you:
	• Male
	• Female
Age	• 18–24 years
	• 25–34 years
	• 35–44 years
	• 45–54 years
	• 55–64 years
	• 65+ years
Virtual world knowledge	How long have you been using Second Life?
	• less than one month
	• more than one and less than three months
	• more than three and less than six months
	• more than six and less than 12 months
	• more than one year and less than two years
	• more than two years
Virtual world intensity	In an average week, how much time would you say you spend on Second Life?
	• less than one hour
	• between one and four hours
	• between four and 10 hours
	• between 10 and 30 hours
	• between 30 and 60 hours
	• more than 60 hours
Virtual world market mavenism	Please answer the following statements on a scale from 1 = strongly disagree to 7 = strongly agree, and where 4 = neutral (neither agree nor disagree). The statements will ask you about your opinions and behavior in Second Life.
	• I like using information collected from Second Life to introduce new brands and products to my friends.
	• I like helping people by using Second Life to provide them with information about many kinds of products.
	• People ask me for information on Second Life about products, locations to shop, or sales.
	• If someone wanted to know which Second Life locations had the best bargains on several types of products, I could tell him or her where to shop.
	• My friends think of me as a good source of information on Second Life when it comes to new products or sales.
	• Think about a person who gets information from Second Life about a variety of products, and likes to share this information with others. This person knows about how to use Second Life, how to find information on Second Life, what the best locations are, and so on, but does not necessarily feel he or she is an expert on the products he/she gathers information on. How well does this description fit you?

Appendix

Table B1. Web and Real-Life Maven Survey

Measure	Item
Gender	Are you:
	• Male
	• Female
Age	• 18–24 years
	• 25–34 years
	• 35–44 years
	• 45–54 years
	• 55–64 years
	• 65+ years
Web knowledge	How long have you been using the Web?
	• less than a year
	• more than a year and less than two years
	• more than two years and less than three years
	• more than three years and less than five years
	• more than five years and less than 10 years
	• more than 10 years
Web intensity	In an average week, how much time would you say you spend using the Web? · less than one hour
	• between one and four hours
	• between four and 10 hours
	• between 10 and 30 hours
	• between 30 and 60 hours
	• more than 60 hours
Web market mavenism	The following statements will ask you about your opinions and behavior regarding your use of the World Wide Web (NOT Second Life). Please answer the statements on a scale from 1 = strongly disagree to 7 = strongly agree, and where 4 = neutral (neither agree nor disagree).
	• I like helping people by using the Web to provide them with information about many kinds of products.
	• I like using information collected from the Web to introduce new brands and products to my friends.
	• People ask me for information on the Web about products, locations to shop, or sales.
	• If someone wanted to know which Web sites had the best bargains on several types of products, I could tell him or her where to shop.
	• My friends think of me as a good source of information on the Web when it comes to new products or sales.
	• Think about a person who gets information from the Web about a variety of products, and likes to share this information with others. This person knows about how to use the Web, how to find information on the Web, what the best locations are, and so on, but does not necessarily feel he or she is an expert on the products he/she gathers information on. How well does this description fit you?
Real-life market mavenism	The following statements will ask you about your opinions and behavior in real-life (i.e., physical channels), including shopping in physical stores. Please answer the statements on a scale from 1 = strongly disagree to 7 = strongly agree, and where 4 = neutral (neither agree nor disagree).
	• I like using information collected from real-life to introduce new brands and products to my friends.
	• I like helping people by using real-life channels to provide them with information about many kinds of products.
	• People ask me for information from real-life channels about products, locations to shop, or sales.
	• If someone wanted to know which locations in real-life had the best bargains on several types of products, I could tell him or her where to shop.
	• My friends think of me as a good source of information in real-life when it comes to new products or sales.
	• Think about a person who gets information from real-life about a variety of products, and likes to share this information with others. This person knows about how to use real-life channels, how to find information in real-life, what the best locations are, and so on, but does not necessarily feel he or she is an expert on the products he/she gathers information on. How well does this description fit you?
Influence of others as mavens
Knowledge	Do you know someone, other than yourself, who has information about a variety of products, stores, sales, etc. and likes to share this general information with others?
	Yes/No
Assistance	How important is this person in helping you to find out about new brands and products?
Evaluation	How important is this person in helping you to evaluate different brands and products?
	• Extremely important
	• Important
	• Somewhat important
	• Neutral
	• Somewhat unimportant
	• Unimportant
	• Extremely unimportant
	• Not applicable
Individualism	Please answer the following statements on a scale from 1 = strongly disagree to 7 = strongly agree, and where 4 = neutral (neither agree nor disagree).
	• I'd rather depend on myself than others.
	• I rely on myself most of the time.
	• My personal identity, independent of others, is very important to me.
	• Being able to take care of myself is a primary concern for me.
	• I enjoy being unique and different from others.
	• It is important to me that I do my job better than others.
PIIT	Please answer the following statements on a scale from 1 = strongly disagree to 7 = strongly agree, and where 4 = neutral (neither agree nor disagree).
	• If I heard about a new information technology, I would look for ways to experiment with it.
	• Among my peers, I am usually the first to try out new information technologies.
	• I like to experiment with new information technologies.
	• In general, I am hesitant to try out new information technologies.

Appendix

Factor Loadings and Associated Statistics for PLS Models

Table C1. Model I—Results of PLS Analysis

Items	Web Maven (Loadings)	Real-Life Maven (Loadings)	Virtual World Maven (Loadings)
Note Significance level denoted by ***(0.1%). AVE = average variance extracted; CR = composite reliability.
Web maven1	0.890***
Web maven2	0.874***
Web maven3	0.788***
Web maven4	0.857***
Web maven5	0.875***
Web maven6	0.784***
Real-life maven1		0.789***
Real-life maven2		0.889***
Real-life maven3		0.879***
Real-life maven4		0.895***
Real-life maven5		0.906***
Real-life maven6		0.828***
VW maven1			0.752***
VW maven2			0.771***
VW maven3			0.595***
VW maven4			0.712***
VW maven5			0.762***
VW maven6			0.713***
Reliability and average variance extracted
AVE	0.716	0.749	0.518
Cronbach's α	0.920	0.933	0.812
CR	0.938	0.947	0.865

Table C2. Model II—Results of PLS Analysis

	Individualism (Loadings)	Influence of Others as Mavens (Loadings)	Real-Life Maven (Loadings)
Note Significance level denoted by ***(0.1%); intercorrelations for discriminant validity are shown in italics on diagonal; n = 102. AVE = average variance extracted; CR = composite reliability.
Individualism1	0.881***
Individualism2	0.863***
Individualism3	0.839***
Individualism4	0.868***
Individualism5	0.893***
Individuailism6	0.831***
Influence of others as mavens1		0.946***
Influence of others as mavens2		0.955***
Influence of others as mavens3		0.931***
Real-life maven1			0.779***
Real-life maven2			0.885***
Real-life maven3			0.876***
Real-life maven4			0.898***
Real-life maven5			0.908***
Real-life maven6			0.842***
Individualism →			0.512***
real-life maven
Real-life maven →			0.297***
influence of others
as mavens
Reliability and average variance extracted
AVE	0.744	0.891	0.750
Cronbach's α	0.931	0.941	0.933
CR	0.946	0.961	0.947
R^2		0.088	0.262
Discriminant validity
Individualism	0.863
Influence of others as mavens	0.056	0.944
Real-life maven	0.512	0.297	0.886

Table C3. Model III—Results of PLS Analysis

	Individualism (Loadings)	Influence of Others as Mavens (Loadings)	Affinity for Technology (Loadings)	Web Maven (Loadings)
Note Significance levels denoted by **(1%), and ***(0.1%); intercorrelations for discriminant validity are shown in italics on diagonal; n = 102. ^aItem omitted from structural model. AVE = average variance extracted; CR = composite reliability.
Individualism1	0.882***
Individualism2	0.854***
Individualism3	0.841***
Individualism4	0.872***
Individualism5	0.899***
Individualism6	0.826***
Influence of others as mavens1		0.941***
Influence of others as mavens2		0.947***
Influence of others as mavens3		0.938***
PIIT1			0.899***
PIIT2			0.822***
PIIT3			0.050^a
PIIT4			0.901***
Web maven1				0.886***
Web maven2				0.875***
Web maven3				0.775***
Web maven4				0.856***
Web maven5				0.882***
Web maven6				0.795***
Individualism → Web maven				0.246**
PIIT → Web maven				0.356***
WEBEX → Web maven				0.066
WEBINT → Web maven				0.089
Web maven → influence of others as mavens				0.261***
Reliability and average variance extracted
AVE	0.744	0.888	0.765	0.716
Cronbach's α	0.931	0.941	0.846	0.920
CR	0.946	0.959	0.907	0.938
R^2		0.068		0.398
Discriminant validity
Individualism	0.863
Influence of others as mavens	0.063	0.942
PIIT	0.658	0.143	0.875
Web maven	0.543	0.261	0.583	0.846

Table C4. Model IV—Results of PLS Analysis

	Individualism (Loadings)	Influence of Others as Mavens (Loadings)	Affinity for Technology (Loadings)	Virtual World Maven (Loadings)
Note Significance level denoted by **(1%), and ***(0.1%); intercorrelations for discriminant validity are shown in italics on diagonal; n = 240. ^aItem omitted from structural model. AVE = average variance extracted; CR = composite reliability.
Individualism1	0.880***
Individualism2	0.843***
Individualism3	0.841***
Individualism4	0.876***
Individualism5	0.896***
Individualism6	0.838***
Influence of others as mavens1		0.942***
Influence of others as mavens2		0.951***
Influence of others as mavens3		0.935***
PIIT1			0.923***
PIIT2			0.756***
PIIT3			0.082^a
PIIT4			0.914***
Virtual world maven1				0.720***
Virtual world maven2				0.749***
Virtual world maven3				0.618***
Virtual world maven4				0.697***
Virtual world maven5				0.715***
Virtual world maven6				0.776***
Individualism → virtual world maven				0.288***
PIIT → virtual world maven				0.199***
WEBEX → virtual world maven				0.055
WEBINT → virtual world maven				−0.339***
VWEX → virtual world maven				0.057
VWINT → virtual world maven				0.428***
Virtual world maven → influence of others as mavens				0.183**
Reliability and average variance extracted
AVE	0.744	0.889	0.753	0.510
Cronbach's α	0.931	0.941	0.846	0.812
CR	0.946	0.960	0.901	0.861
R^2		0.034		0.359
Discriminant validity
Individualism	0.863
Influence of others as mavens	0.055	0.943
PIIT	0.663	0.134	0.868
Virtual world maven	0.447	0.183	0.386	0.714