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Journal of Relationship Marketing
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How to Make Brand Communities Work: Antecedents and Consequences
of Consumer Participation
David M. Woisetschlägera; Vivian Hartlebb; Markus Bluta
University of Dortmund, b University of Muenster,
To cite this Article Woisetschläger, David M. , Hartleb, Vivian and Blut, Markus(2008) 'How to Make Brand Communities
Work: Antecedents and Consequences of Consumer Participation', Journal of Relationship Marketing, 7: 3, 237 — 256
To link to this Article: DOI: 10.1080/15332660802409605
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How to Make Brand Communities Work:
Antecedents and Consequences of Consumer
David M. Woisetschl¨ager
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University of Dortmund
Vivian Hartleb
University of Muenster
Markus Blut
University of Dortmund
ABSTRACT. The majority of brand community literature deals with the
exploration of the nature of brand communities and the measurement of
community effects. However, existing literature on how to implement and
to manage company-run brand communities is rare. In the present article,
we conceptualize drivers and consequences of consumer brand community
participation and empirically test our model with a data set of 1,025 members of a virtual brand community. Results indicate that identification with
community, satisfaction with community, and degree of influence explain
most of the variance in consumer participation. Moreover, positive influences of participation on recommendation behavior, brand image of the
David M. Woisetschl¨ager is Assistant Professor of Services Management, University of Dortmund, Dortmund, Germany.
Vivian Hartleb is Research Assistant, Marketing Center Muenster, University
of Muenster, Germany.
Markus Blut is Assistant Professor of Marketing, University of Dortmund,
Address correspondence to: David M. Woisetschl¨ager, University of Dortmund,
Otto-Hahn-Str. 6, Dortmund, Germany. (E-mail: [email protected])
Journal of Relationship Marketing, Vol. 7(3), 2008
Available online at
C 2008 by The Haworth Press. All rights reserved.
doi: 10.1080/15332660802409605
community sponsor, and intention to continue community membership can
be confirmed.
KEYWORDS. Brand community, consumer participation, brand image,
word–of–mouth, community loyalty
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Within the past decade the Internet has become one of the most important
and most commonly used communication media (Lagrosen, 2005). It offers boundless possibilities for information and communication exchange
without any geographical restrictions. Especially over the past few years,
groups with common interests or problems have increasingly been organizing themselves into blogs or virtual communities (e.g., Apple, Harley
Davidson, Saab, Tom Petty, Xena)—a phenomenon that is of interest to
both academic research and marketing practice (Carlson, Suter, & Brown,
2008). According to Boorstin (1973), consumers with similar norms, values, and habits (e.g., the consumption of the same brand or product) form
groups called communities of consumption. A special kind of community
of consumption is a brand community, which is defined as “a specialized,
non-geographically bound community, based on a structured set of social relationships among admirers of a brand” (Muniz & O’Guinn, 2001,
p. 412). Unlike the much broader concept of communities of consumption,
the center of a brand community is the brand itself; therefore, consumers
join these communities to exchange experiences with other like-minded
people fascinated by a specific brand.
Two main streams of research can be identified from the existing brand
community literature: first, studies in which the nature of brand communities is explored and, second, studies that deal with measurement of the outcomes of customers’ brand community engagement. Muniz and O’Guinn
(2002) as well as Schau and Muniz (2002)are among the earliest proponents
contributing to the first stream of research. Whereas Muniz and O’Guinn
assessed the three core components of brand communities as (a) “consciousness of kind,” (b) “shared rituals and traditions,” and (c) “a sense
of moral responsibility” and identified brands as a social phenomenon,
Schau and Muniz (2002) analyzed several brand communities and revealed four types of relationships between individual identity and community membership: (a) subsumed identity, (b) super member, (c) community
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Woisetschl¨ager, Hartleb, and Blut
membership as identity component, and (d) multiple memberships. Following the customer-centric model of brand community of McAlexander,
Schouten, and Koenig (2002), Ouwersloot and Odekerken-Schr¨oder (2008)
segmented brand community members into four categories based on consumption motivation: (a) enthusiasts, (b) users, (c) behind-the-scenes, and
(d) not-me. Other starting points to explore the nature of brand communities have been personal self-exhibition in front of other consumers in
the context of a convenience product brand community (Cova & Pace,
2006), anti-brand communities (Hollenbeck & Zinkhan, 2006), the consumer quest for authenticity (Leigh, Peters, & Shelton, 2006), the role
of social environments (Luedicke, 2006), rumor in brand communities
(Muniz, O’Guinn, & Fine, 2006), and social versus psychological brand
communities (Carlson et al., 2008).
In contrast to these studies, the second stream of research examines
the outcomes of customers’ engagement within a brand community. For
instance, analyses of brandfests (McAlexander et al., 2002; Schouten,
McAlexander, & Koenig, 2007) have revealed that greater integration in
a brand community increases customer loyalty and that transcendent customer experiences lead to stronger relationships with the brand, the product, the hosting company, and other customers. In the automobile context,
Algesheimer, Dholakia, and Herrmann (2005) showed that among other
facets, brand relationship quality leads to greater brand loyalty intentions
and community engagement to greater membership continuance intentions. Furthermore, McAlexander, Kim, and Roberts (2003) analyzed the
impact of satisfaction, brand community integration, and consumer experiences on customer loyalty in a casino. McAlexander, Koenig, and Schouten
(2004) examined how the nature of relationships among students affects
students’ long-term loyalty to their university. In a study about antecedents
and purchase consequences of customer participation in small-group brand
communities, Bagozzi and Dholakia (2006) suggested that social identity
and group behavior are key explanatory variables of brand behavior, and
in another study 1 year later Hickman and Ward (2007) analyzed the influence of social identification on group behavior in the context of internal
and external trash talk. Casal´o, Flavi´an, and Guinal´ıu (2007) detected, for
example, that participation has a positive influence on consumer commitment to the brand, and satisfaction with previous interactions increases the
level of trust.
Although consequences for marketing management can be derived
from the findings of most of the studies described above, little is written
about how to successfully implement and manage a company-run brand
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community (Carlson et al., 2008). From a managerial point of view, companies are confronted with the increasing popularity of (virtual) brand
communities so that both management of a brand community implemented
by the consumers and the implementation of a brand community by the
company itself are now more important. It seems surprising that a lot of
companies are not aware of the phenomenon of brand communities, as
only a few of them have integrated these into their marketing mix (Cova
& Pace, 2006; McAlexander et al., 2002). If community platforms are set
up, they are mostly driven by the ideas of advertising agencies and are
generally seen as part of a short-term campaign, not as strategic long-term
investment. It seems, up to now, that the potential of brand communities
has been underestimated in marketing practice.
In this article, we focus on a company that is aware of the potential
of brand communities. The company bought the naming rights to one of
the biggest football stadiums in Germany and implemented its own virtual
brand community on the Internet as “virtual football stadium,” especially
for fans of the local football team. In this article, we (a) conceptualize
factors that contribute to successful consumer participation in the brand
community; (b) investigate the impacts of consumer participation on community loyalty, word of mouth, and brand image perception; and (c) examine moderator variables influencing the relationship between consumer
participation and the associated consequences.
The theoretical framework of our study is based primarily on social
identity theory (SIT) and the concept of psychological sense of community (PSOC). Both are frequently employed in the existing literature on
(virtual) communities in general as well as on brand communities in particular (Bhattacharya, Rao, & Glynn, 1995; Carlson et al., 2008; Obst,
Zinkiewicz, & Smith, 2002a, 2002b, 2002c). In the following sections, we
introduce the theoretical basis of our study and then derive our hypotheses
based on theory and existing (brand) community literature. Finally, we
test our empirical model using a data set of 1,025 members of this virtual brand community and discuss our findings and directions for further
Within the discipline of community psychology, the concept of PSOC
has become one of the major bases for analyzing interpersonal relationships
Woisetschl¨ager, Hartleb, and Blut
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(Carlson et al., 2008; Chavis & Pretty, 1999). Obst et al. (2002a, 2002b,
2002c) extended this concept with aspects of social identity based on SIT.
Besides social identity, PSOC comprises other elements such as the building of a corporate feeling, friendship, trust, support, and the satisfaction
of needs. In this context, a brand can thus be seen as linking consumers
with similar passions. Communities distinguish themselves from other
communities by their passion for a particular brand. Following Sarason
(1977), sense of community is the
perception of similarity with others, an acknowledged interdependence with others, a willingness to maintain this interdependence by
giving to or doing for others what one expects from them, the feeling
that one is part of a larger dependable and stable structure. (p. 157 )
McMillan and Chavis (1986) conducted the most influential study of PSOC
(Obst et al., 2002a). They argued that PSOC consists of four elements:
membership, influence, integration and fulfillment of needs, and shared
emotional connections (McMillan & Chavis, 1986). Building on this study
and on a revised version of PSOC by McMillan (1996), Obst et al. (2002a,
2002b, 2002c) combined this concept with SIT. They pointed out that
identification with the community is an essential element of PSOC and
referred to SIT as a suitable theoretical background (Obst et al., 2002a). SIT
argues that the self-concept of a person is made up of two different aspects
of identity: personal identity and social identity (Lantz & Loeb, 1998;
Tajfel & Turner, 1986; Turner & Oaks, 1989). Personal identity consists of
specific characteristics, such as talents and interests. Social identity “is the
perception of belonging to a group with the result that a person identifies
with that group (i.e., I am a member)” (Bhattacharya et al., 1995, p. 47).
More relevant to this study, consumers who identify themselves with a
brand and a brand community, respectively, classify themselves and other
consumers as being a group member (in-group) or as not being a group
member (out-group; Hickman & Ward, 2007). The phenomenon described
by SIT has been observed in brand communities, for example in terms of
“oppositional brand loyalty” as described by Muniz and O’Guinn (2001)
and “internal and external trash talk and Schadenfreude” as analyzed by
Hickman and Ward.
In line with PSOC and SIT, we assume that establishing a successful brand community depends on participation of community members
(Casal´o et al., 2007). Especially in virtual communities, there are many
ways of participating and interacting with other community members,
FIGURE 1. Conceptual Model.
Word of
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Degree of
such as through chats, boards, or news groups. These forms of interaction
enable consumers to share information about the brand and the product.
Furthermore, consumers also lend support to one another when faced with
problems and thereby develop social bonds. Hence, if companies intend
to establish a brand community, they have to ensure participation of community members. Against this background, our conceptual model captures
antecedents of consumer participation, consequences of consumer participation, and moderator variables influencing the relationship between
consumer participation and the associated consequences (see Figure 1).
Antecedents of Consumer Participation
We posit that a community member’s participation is affected by identification with the brand community, satisfaction with the brand community,
and the perceived degree of influence opportunities. There are three major
reasons why we study these potential antecedents. First, these variables are
recognized as being powerful drivers for ensuring a brand community’s
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Woisetschl¨ager, Hartleb, and Blut
customer participation (Algesheimer et al., 2005; Nambisan & Baron,
2007; von Loewenfeld, 2006). Second, management can influence these
three variables, and, therefore, the variables could be used to establish a
successful brand community. Finally, although these variables are considered to be crucial for brand community success, the consequences for
brand community management have not been studied extensively before.
Influence of community identification on consumer participation. The
first important antecedent of brand community participation is social identity. It is defined as “the perception of belonging to a group with the result
that a person identifies with that group” (Bhattacharya et al., 1995, p. 47).
Social identity primarily comprises the feeling of belonging to a group
(Bhattacharya et al., 1995) and is closely connected to one of the most
important elements of communities: consciousness of kind. “Consciousness of kind is the intrinsic connection that members feel towards one
another and collective sense of difference from others not in the community” (Muniz & O’Guinn, 2001, p. 413). Carlson et al. (2008) argued that
identification is important for developing a psychological sense of brand
community. The consequence of self-categorization to a particular virtual brand community is a positive distinction of the community’s values,
norms, and behaviors toward other communities, which thereby results in
an increase in group members’ self-esteem (Turner, 1987). Therefore, we
assume that community identification is inextricably linked with consumer
Further studies support this assumption: Algesheimer et al. (2005)
showed that brand community identification has a positive impact on
community engagement. In their study about antecedents and purchase
consequences of customer participation in small-group brand communities, Bagozzi and Dholakia (2006) found that social identity has a positive
impact on desire to participate in both Harley riding groups and non-Harley
riding groups. Hence, we propose that identification with a group results
in active community interaction and hypothesize the following:
H1 : Community identification has a positive influence on consumer
Influence of community satisfaction on consumer participation. The
second important antecedent of brand community participation is satisfaction with the community. Satisfaction is an overall evaluation of performance, and it is based on prior experiences (Anderson & Fornell, 1994).
Two conceptualizations of customer satisfaction exist in the literature:
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(a) transaction-specific and (b) overall satisfaction (Cronin & Taylor, 1994).
In our research, we focus on overall satisfaction, because it is a better predictor of a community’s past, current, and future performance. Based on
PSOC, community members strive for fulfillment of their needs. As a prerequisite of being actively participating members, community members
have to evaluate their membership positively (McMillan & Chavis, 1986).
Existing findings in the virtual community literature confirm the impact of satisfaction with the community on community participation. For
example, Casal´o et al. (2007) showed that in virtual brand communities,
satisfaction with the community has an indirect effect on community participation. Furthermore, de Valck, Langerak, Verhoef, and Verlegh (2007)
distinguished between four dimensions of community members’ satisfaction: satisfaction with (a) member-to-member interaction, (b) organizerto-member interaction, (c) organizer-to-community interaction, and (d) the
community site. These dimensions have been found to influence members’
visit frequency. Nambisan and Baron (2007) and Lin (2006) found a positive effect between satisfaction and customer participation in a community
context. Hence, we propose that identification with a group results in active
community interaction and hypothesize the following:
H2 : Community satisfaction has a positive influence on consumer
Influence of consumers’ perceived degree of influence on consumer
participation. The third important factor that contributes to community
participation is the perceived degree of influence on the community. This
is defined as an individual’s need to have some control and influence
(Obst et al., 2002a). As noted before, degree of influence is one of the
four dimensions of PSOC (McMillan & Chavis, 1986). It is closely related to the concept of self-efficacy; therefore, people perceiving a high
degree of influence should be more willing to engage in the community
(Bandura, 1986). Yet this linkage was investigated empirically only by von
Loewenfeld (2006), who supported our assumption. Hence, we propose
that the degree of influence with a group results in active community
participation and hypothesize the following:
H3 : The degree of influence in the community has a positive influence
on consumer participation.
Woisetschl¨ager, Hartleb, and Blut
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Consequences of Consumer Participation
We posit that community member participation affects community loyalty, word of mouth, and the image of the community presenter’s brand.
Again, we have three reasons for studying these consequences. First,
these consequences are outcomes of community member engagement
(Algesheimer et al., 2005; Kim & Jung, 2007; Muniz & Schau, 2007).
Second, they are important outcomes for companies intending to establish
new brand communities. Finally, although these variables are considered
to be crucial for brand community success, empirical studies testing the
impact of consumer participation on these consequences and, in particular,
on the sponsor’s brand image are largely lacking.
Influence of consumer participation on brand image. One important
outcome of community participation represents the community presenter’s
brand image, which is defined as the perceptions about a brand as reflected
by the brand associations held in consumer memory (Keller, 1993). In
studies analyzing brand communities, it has been argued that customer
empowerment and consumer-generated content may be of high relevance
for increasing brand image (Cova & Pace, 2006; Muniz & Schau, 2007).
These community activities may be community member integration, when
developing innovations together with the company (F¨uller, Jawecki, &
M¨uhlbacher, 2007), or cooperative development of advertising campaigns
(Muniz & Schau, 2006, 2007). Muniz and Schau (2006) argued that members of the community act as marketing consultants, brand managers,
salespeople, copywriters, product engineers, service technicians, and pricing specialists. Most of this research assuming a positive effect of customer
interaction on brand image is qualitative in nature, and quantitative studies
analyzing the effect are still lacking (Muniz & Schau, 2007). According
to PSOC, customer engagement within the community results in positive
emotions toward the brand. Hence, we hypothesize the following:
H4 : Consumer participation has a positive influence on brand image.
Influence of consumer participation on community loyalty and word of
mouth. Further potential outcomes of community participation are community loyalty and word of mouth (Kim & Jung, 2007). Loyalty is an
overall attachment or deep commitment to a product, service, brand, or
organization (Oliver, 1999). The more common manifestations of loyalty
are recommendation behavior and patronage intention (Dwyer, Schurr,
& Oh, 1987; Fornell, 1992; Zeithaml, Berry, & Parasuraman, 1996). In a
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brand community context, Algesheimer et al. (2005) showed that a stronger
community engagement leads to stronger membership continuance and
community recommendation intentions. Bagozzi and Dholakia (2006) assessed a significant indirect effect of desire to participate on brand loyalty.
Jang, Ko, and Koh (2007) as well as Casal´o et al. (2007) found that participation contributes positively to the community commitment of members.
In the context of brandfests, Schouten et al. (2007) showed that transcendent customer experiences can strengthen a person’s ties to a community
and therefore lead to greater loyalty. According to SIT, interaction with
other members leads to a strengthening of in-group consciousness. Hence,
we propose that community participation has a positive effect on community members’ loyalty intentions and on their word-of-mouth behavior and
hypothesize the following:
H5–6 : Consumer participation has a positive influence on community
loyalty (H5 ) and word-of-mouth behavior (H6 ).
Moderating Effect of Interaction Preference
In the existing brand community literature, little attention is paid to factors determining the effectiveness (i.e., consequences) of customer participation. It is likely that the strength of the relationships between community
participation and consequences of community participation is moderated
by other variables. In marketing research, existence of moderators has been
discussed as the rule and not the exception (Ping, 1995). A customer’s interaction preference might be one of these factors influencing the strength
and/or the direction of customer participation effectiveness. Interaction
preference is defined as the prevailing tendency of an individual to interact with relative strangers (i.e., people they have never met offline) in an
online environment (Wiertz & de Ruyter, 2007). When customers have a
high preference for interacting within the community, we assume customer
participation to have a stronger effect on word-of-mouth behavior, brand
image, and community loyalty. If one compares two participating community members, one having a high preference for interaction with other
people in general and the other having no such preference, the first person
is much more likely to recommend the brand community (perceive a favorable brand image/stay loyal) because he or she receives more benefits
from participating in the community. Hence,
Woisetschl¨ager, Hartleb, and Blut
H7–9 : The higher the interaction preference, the greater the positive
effect of consumer participation on word-of-mouth behavior (H7 ),
brand image (H8 ), and community loyalty (H9 ).
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Questionnaire Development and Pretesting
We tested our hypotheses in an online study using a Web survey design. The online questionnaire was pretested by 20 German undergraduate
marketing students. The pretest was intended to refine the questionnaire,
especially with regard to question content, wording, format, and layout.
We measured attitude-related variables, except for community loyalty,
with multi-item scales. Community loyalty was measured by using one
single item adapted from Algesheimer et al. (2005). Although multi-item
scales are generally preferred, the latest studies support the use of singleitem scales for loyalty-related research (LaBarbera & Mazursky, 1983;
Lemon, White, & Winer, 2002; Mittal, Ross, & Baldasare, 1998; Rossiter,
2002). Furthermore, we took five items that are regularly used in the
literature (Woisetschl¨ager, 2007; Woisetschl¨ager, Michaelis, & Backhaus,
in press) to measure brand image. Word of mouth was measured by using a
scale from Zeithaml et al. (1996). Interaction preference was measured by
using an established scale from Wiertz and de Ruyter (2007). Participation,
degree of influence, as well as identification and satisfaction with the virtual
community were adapted from von Loewenfeld (2006). Table 1 provides
a description of the items used to measure the constructs.
All items were measured by using 7-point Likert-type scales with anchors of 1 = strongly agree and 7 = strongly disagree. Measurement
reliability of the reflective constructs was examined through a confirmatory factor analysis. It can be noted that composite reliabilities for all
constructs exceeded. 6, the generally recommended threshold (Bagozzi &
Yi, 1988). Moreover, discriminant validity between the constructs is given,
because none of the squared correlation coefficients between any of the
constructs exceeded the average variance extracted for a construct (Fornell
& Larcker, 1981). Results of correlation analysis are depicted in Table 2.
Participation (von Loewenfeld, 2006)a
1. Members of the [Name] community help each other.
2. When I seek for advice, I am likely to find someone supportive in the [Name] community.
3. I found new friends as a result of joining the [Name] community.
4. Friendships in the [Name] community are important to me.
5. Social contacts and friendships are supported by the [Name] community’s offers for interaction.
Identification With Virtual Community (von Loewenfeld, 2006)a
1. I see myself as belonging to the [Name] community.
2. The [Name] community plays a part in my everyday life.
3. I see myself as a typical and representative member of the [Name] community.
4. The virtual [Name] community confirms in many aspects my view of who I am.
5. I can identify with the [Name] community.
6. I have strong feelings for the [Name] community.
7. I feel like I belong in the [Name] community.
Satisfaction With Virtual Community (von Loewenfeld, 2006)a
1. Overall, the [Name] community meets my expectations.
2. The content of the [Name] community matches exactly with my interests.
3. The [Name] community fulfills my needs.
Degree of Influence (von Loewenfeld, 2006)a
1. As a member of [Name] community, I can influence the community as a whole.
2. I am satisfied with the degree of influence to shape the [Name] community.
3. A single member has the chance to be an active part in the [Name] community.
Word of Mouth (Zeithaml et al., 1996)a
1. I have said positive things about [Name] community to other people.
2. I have recommended [Name] community to people who seek my advice.
3. I have encouraged other people to join [Name] community.
TABLE 1. Constructs and Measures
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Measured using 7-point Likert-type scales anchored by 1 = strongly agree, 7 = strongly disagree.
Cronbach’s Alpha; 2Composite Reliability; 3Average Variance Extracted.
Brand Image (Woisetschlager,
2006; Woisetschlager
et al., 2007)a
1. [Brand] is trustworthy.
2. [Brand] is reliable.
3. [Brand] is likeable.
4. [Brand] is a very good brand.
5. [Brand] is a very attractive brand.
Community Loyalty (Algesheimer et al., 2005)a
1. It would be very difficult for me to leave the [Name] community.
Interaction Preference (Wiertz & Ruyter, 2007)a
1. I am someone who enjoys interacting with other community members.
2. I am someone who likes actively participating in discussions with other community members.
3. In general, I am someone who, given the chance, seeks contact with other community members.
4. In general, I thoroughly enjoy exchanging ideas with other community members.
TABLE 1. Constructs and Measures (Continued)
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TABLE 2. Correlations Between Constructs
1. Participation
2. Identification with
Virtual Community
3. Satisfaction with
Virtual Community
4. Degree of Influence
5. Word of Mouth
6. Brand Image
7. Interaction Preference
Sample and Data Collection
Data were collected via the Internet. The questionnaire was e-mailed to
all registered members of the virtual brand community. At the time when
the survey started, a total of 8,361 users were registered. Of the users, 21.7%
were female and 78.3% were male. Registered members were on average
26.32 years old (SD = 11.42) and had been members for 4.66 months
(SD = 2.94). A total of 1,025 members participated in the survey, equaling a response rate of 12.26%. Of the participants, 24.1% were female
and 75.9% were male. The respondents were on average 26.03 years old
(SD = 11.84) and had been members for 4.72 months (SD = 3.24). According to these descriptive statistics, the sample was comparable to the
population of all registered members.
We used structural equation modeling to test the hypothesized direct and
moderating effects. The model fits the data well. The comparative fit index
was .925, the Tucker–Lewis index was .917, the root mean square error
of approximation was .073, and the standardized root-mean-square residual was .083. Table 3 shows the parameter estimates of our model. Path
coefficients between participation and the three independent constructs
identification with virtual community, satisfaction with virtual community, and degree of influence were positive and statistically significant at
the .01 level. Regarding the consequences, the path coefficients between
Woisetschl¨ager, Hartleb, and Blut
TABLE 3. Results of the Basic Model With Direct Effects
Direct Effect
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Participation (r 2)
Identification with virtual community
Satisfaction with virtual community
Degree of influence
Word of Mouth (r 2)
Brand Image (r 2)
Community Loyalty (r 2)
Goodness-of-fit statistics
Comparative fit index
Tucker–Lewis index
Root mean square error of approximation
Standardized root-mean-square residual
p < .01.
participation and word of mouth, brand image, and community loyalty
were positive and significant at the .01 level.
Identification with virtual community had the strongest effect on participation (λ = .567, p < .01), followed by perceived degree of influence (λ = .254, p < .01) and satisfaction with virtual community (λ =
.095, p < .01). Hence, hypotheses H1 through H3 were supported by our
findings. Our model explained 72.6% of the variance in the participation
construct. Investigating the consequences, we have to sum up that participation had the strongest effect on word-of-mouth behavior (λ = .729, p <
.01), followed by community loyalty (λ = .687, p < .01) and brand image
(λ = .480, p < .01). Thus, hypotheses H4 through H6 were supported by
our findings. The derived model explained about 53.1% of the variance in
word of mouth (brand loyalty = 47.1%; brand image = 23.0%).
In order to test the moderating influences proposed in hypotheses H7
through H9 , we used multigroup structural equation modeling. Median
splits based on the values of the moderator variables were used to create
the groups. A chi-square difference test was conducted for the possible
moderator effects by comparing a restricted and a nonrestricted model.
Chi-square differences with three degrees of freedom (critical chi-square
TABLE 4. Results of Multigroup Analysis
Interaction Preference
χ2 (df = 1)
Participation → Word of mouth
Participation → Brand image
Participation → Community loyalty
χ2 (df = 3)
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p < .05 ∗ ∗ ∗ p < .01.
value [df = 3, p = .05] = 7.81) were assessed. After confirming a general moderating effect, we compared two models that differed only in
one effect as suggested by our hypotheses. A moderating effect would be
present when the improvement in chi-square moving from the restricted to
the nonrestricted model is significant, meaning the chi-square difference
between the two models (and one degree of freedom) is larger than 3.84
(p = .05). Results in Table 4 show that interaction preference had a significant impact on the participation–word-of-mouth linkage (at the .01 level).
For respondents with high interaction preference the link was stronger, giving support for H7 . Regarding the link between participation and brand image, a significant positive moderating effect of interaction preference was
found (at the .01 level), supporting H8 . Contrary to our expectations, interaction preference was not found to moderate the participation–community
loyalty linkage; hence, H9 had to be rejected.
Results of our empirical analysis reveal that most of the variance in
participation can be explained by three factors: identification, satisfaction,
and degree of influence. Marketing management has to keep these factors
in mind when new features for the community are developed. First, when
a company sets up a company-driven community, homogeneous members
should be grouped into subcommunities to increase the identification of
community members with the community. This could be accomplished by
sending automated introductions of members to other members, depending
on their profile match. Second, when implementing a virtual brand community, companies should focus on providing interaction elements to the
users. In fact, successful online communities (e.g., business communities
such as and, or communities for social exchanges such as and provide many valuable
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Woisetschl¨ager, Hartleb, and Blut
and adaptable examples of how to stimulate interaction between members
of the community. The more satisfied the community members are, the
more willing they will be to keep the community alive through interacting
with other members. Third, the more open the platform is to user-generated
content, the more community members will contribute to the community.
From a management perspective, online communities are implemented
for several reasons. A company’s brand image is enhanced by having
customers exchange with like-minded people in a setting in which the
brand is embedded. Our results demonstrate that active members of the
community tend to evaluate the community presenter’s brand image more
favorably. This fact should be an incentive to marketing managers to invest
in a virtual community. Furthermore, when marketing managers intend to
increase brand community size, they can acquire new members through
recommendations of existing members. To ensure the long-term success of
the community, companies can increase community members’ willingness
to stay loyal to the community by engaging members.
Regarding the moderating effect of interaction preference, community
management can use this personal characteristic to cluster community
members and design specific actions to increase word-of-mouth behavior
of groups, being sensitive for interacting with others. Similarly, positive
effects of community participation on brand image should be realized.
Besides these managerial implications, future research should consider
analyzing the effects of implementing features on the change in evaluation
of the drivers of community interaction over time. Longitudinal research
designs would allow for the monitoring of attitudes and behavior of users.
As a result, membership exits could be forecasted and member clusters
based on attitudes and usage could be built. Further personal characteristics such as age, income, and gender should be tested on the consumer
participation–outcome linkage. How to generate business with a content
and loyal virtual community provides another fruitful avenue for research.
Finally, future research should focus on the question of how business offers could be communicated to the community without leading to adverse
reactions from users.
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