Customer-to-customer interaction in brand communities
A Research Agenda
Customer-to-customer interaction in online brand communities
A value-based approach
This paper aims to provide a model to examine the value perceptions of customers who engage
with other customers in sharing information, views and personal feelings in online brand
communities. The proposed model adopts a value-based approach that lays out the steps to
measure the perceived values and benefits of customers who participate in customer-to-customer
(C2C) interactions. Drawing on the Motivation, Ability and Opportunity (MOA) framework, the
proposed model looks at C2C interactions as an information source and uses the MOA theory to
examine the antecedents and outcomes of C2C interactions. The perceived values of C2C
interactions is derived from a value model that compares the functional and social benefits
perceived by a customer against the sacrifices that the customer needs to make in order to realize
the benefits. The effect of customer’s perception of values on loyalty intentions such as
repurchasing decisions is proposed, whilst taking into consideration the overall value of a firm’s
offering as a mediating variable.
Online brand communities, MOA, customer-to-customer interactions, C2C interactions, value
model, loyalty intentions
1.1 Online Communities
Online or ‘virtual’ communities can be defined as “a social aggregation of people carrying out
public discussions long enough, with sufficient human feeling, to form webs of personal
relationships in the cyberspace” (Rheingold, 1993). Rheingold has drawn out a significant aspect
of online communities which is serving the need for people to communicate and interact with
From the marketing perspective, companies have recognized the potential benefits of online
brand communities - a platform to build strong relationship with their customers, partners,
channels and other stakeholders. Despite a continuing frenzy in social media and an escalating
interest in creating brand communities around websites, we know little how customers behave in
brand communities. The research of online communities has been scarce and under-developed in
this area (Bagozzi and Dholakia, 2002), which underlies the importance to explore customer
behaviors in online brand communities.
Like any communities, brand communities are “marked by a shared consciousness, rituals and
traditions (Muniz and O’Guinn, 2001)”, which are held together by people who share a set of
common goals and values. The definition of brand community as “a specialized, non-
geographically bound community, based on a structured set of social relationships among users
of a brand (Muniz and O’Guinn, 2001)” has been widely accepted by scholars (Algesheimer et
al., 2005; Bagozzi and Dholakia, 2006). So far, research has been conducted on brand
communities of cars (Muniz and O’Guinn, 2001; McAlexander et al. 2002; Algesheimer et al.
2005; Bagozzi and Dholakia, 2006), motorcycles (McAlexander et al. 2002), computers (Muniz
and O’Guinn, 2001; Muniz and Schau, 2005), watches and game tournaments (Ouwersloot et al.,
Drawing from their research, Muniz and O’Guinn (2001) propose that brand communities should
be studied as a ‘customer-customer-brand’ triad, rather than as a ‘customer-brand’ dyad, where
“brand communities are social entities that reflect the situated embeddedness of brands in the
day-to-day lives of consumers and the ways in which brands connect consumer to brand, and
consumer to consumer.”
Based on their research on the user communities of Harley-Davidson and Jeep owners,
McAlexander et al. (2002) suggest a ‘customer-centric’ model (Figure 1) for brand
communities, where “the existence and meaningfulness of the community inhere in customer
experience rather than in the brand around which that experience revolves.” The ‘customer-
centric’ model (McAlexander et al., 2002) is composed of four interactions: customer-to-
customer, customer-to-brand, customer-to-product and customer-to-firm.
Figure 1: Customer-centric Model of Brand Community
(McAlexander et al., 2002)
The customer-centric model has since been used by various researchers (Algesheimer et al.,
2005; Ouwersloot, 2008; Sicilia, 2008) to study brand communities. Though McAlexander et al.
have provided empirical evidence to support their ‘customer-centric’ model, they have not
provided details on any of the four interactions. This research will focus on customer-to-
1.2 Customer-to-customer Interactions
The servuction framework (Langeard et al., 1981) draws attention to the presence of other
customers in a service encounter, the so-called customer A (the customer receiving the service)
and customer B (others who are present in the service encounter and have an influence on
customer A’s perception of service experience). Martin and Pranter (1989) distinguish direct and
indirect customer-to-customer interactions, where direct refers to service encounters where there
are interpersonal interactions between customers and indirect refers to those that there are no
interactions between customers.
Since the seminal paper on customer-to-customer interactions by Martin and Pranter (1989),
research on customer-to-customer (or ‘C2C’) interactions has branched out into various fields
such as service marketing and consumer behavior (Baron and Harris, 2007; Grove and Fisk,
1997; Pranter and Martin, 1991), brand communities (McAlexander, 2002; McWilliam, 2000),
professional associations (Gruen et al., 2000 & 2007) and online brand communities (Bagozzi
and Dholakia, 2002; Hagel and Armstrong, 1997; Kozinets, 2002; Sawhney and Prandelli, 2000).
Bagozzi and Dholakia (2002) view social interactions as the objective of online communities,
and the goals of an online community can be either functional or hedonic, where the community
act as “an important reference group for its individuals and participants”, where “interactions
between consumers” are as important as those between the marketers and the consumers. Barron
and Harris (2007) develop the Consumer Experience Relationship Model (CERM) that puts
customers at the focal point in a network of consumer experience. The influence of customer-to-
customer interactions has been supported by empirical evidence in retail area, tourist attractions,
hairdressing and professional associations (Gruen et al., 2007; Grove et al., 1998; Harris et al.,
1997; Moore et al., 2005). Unlike ‘customer-to-provider’ interactions, customer-to-customer
interactions add values to the traditional marketing relationship between customers and a firm
from a different perspective (Zeithaml & Bitner, 1996).
The purpose of this research is to develop a model to understand online brand communities from
the perspective of ‘customer-to-customer’ (or ‘C2C’) interactions. It will explore the benefits
and values derived from C2C interactions, and their impact on loyalty intentions.
As most online brand communities are publicly accessible, their participants might include
visitors who have not made any purchases of the brand. A majority of these visitors lands at the
brand website by accident or for no apparent reasons and do not intend to engage in interactions
with others in the community website.
As the goal of this research is to focus on interactions between customers of a brand, it will only
include participants of an online community who are also customers of the brand.
2. Conceptual Framework
In general, online communities are perceived to offer functional values such as information and
advice; social values such as self-esteem, friendship and social status; and entertainment value
(Burnett, 2000; Muniz and O’Guinn, 2001; Sicilia and Palazon, 2008). Apart from a common
perception that values are created through interactions between customers in brand communities
from sharing of social, economic and knowledge resources (McAlexander, 2002), and
experiences (Arnould and Price, 1993), there has been few research that examine what values are
created through C2C interactions in brand communities.
Gruen et al. (2005) have developed a model (Figure 2) that explores the factors that affect “the
degree to which customers enter into and engage in know-how exchanges with other customers”
in a brand community. These factors (or antecedents) of C2C interactions are the Motivation,
Opportunity and Ability (MOA) of a customer. The MOA approach is initially developed by
MacInnis and Jaworski (1991) in a study of the communication effectiveness of advertising
messages. Gruen et al. (2007) adopts the approach as an “explanatory framework” for “C2C
interactions from the perspective that C2C exchanges function as a source of information to
customers.” However, their research has been confined to the functional (or know-how) aspect
of C2C interactions with no regard to the social aspects of C2C interactions.
To utilize the MOA approach, it is important to check whether it can be used for other types of
C2C interactions. Upon review of the conceptual approaches by Gruen et al. (2007) and
MacInnis & Jaworski (1991), there are no special constraints that will confine the use of MOA
approach to functional C2C interactions.
Thus, a hypothesized model is proposed to examine the perceived functional and social benefits
by customers as a result of their interactions in online brand communities. The proposed model
(Figure 3) will be tested by Structural Equation Modeling (SEM) to determine the extent to
which it is consistent with the empirical data.
Figure 2: MOA model (Gruen et al., 2007)
2.1 Motivation, opportunity and ability
Motivation directs individuals to engage in goal-oriented behaviors and make decisions (Hoyer
and MacInnis, 1997; MacInnis and Jaworski, 1989). In our context, it means the desire (Petty&
Cacioppo, 1986) or readiness (Burnkrant & Sawyer, 1983; Moorman, 1990) of a customer who
engages in C2C interactions in brand communities. Motivation for knowledge exchange
(McLure et al., 2000) can be self-interest or moral obligation depending on whether one
perceives knowledge as an object, as embedded in individuals or as embedded in society.
However, hedonic and utilitarian motivations (Batra and Ahtola, 1991) need not be mutually
exclusive. Hence, it is important to develop the appropriate measurement scales for motivation
at the empirical stage.
Figure 3: Proposed Framework
Opportunity refers to the degree in which a situation is conducive to the accomplishment of
goals, such as the extent to which distractions or limitations will affect one’s attention to process
information. A high opportunity means the amount of attention given to process the information
is not much inhibited. In our context, opportunity to engage in C2C interactions is not defined as
a lack of opportunity in relation to the disruption of cognitive responses (MacInnis et al, 1991)
but rather a distraction that diverts attention from one stimulus to another, not affecting the
outcomes of allocated attention.
Opportunity can be considered (MacInnis et al., 1991) from “a positive view of availability” or
“a negative perspective of impediments”, which affects the extent to which customers are
involved in C2C interactions. According to Gruen et al., (2005), it “may be more a function of
the restrictions an individual faces (e.g. time, connection availability, and organizational
policies) on participating in the community”.
Ability is concerned with the resources of a customer that influence the outcome of an event
(Hoyer and MacInnis, 1997), and it initially refers to the skills or proficiencies in interpreting
brand information in an advertisement (MacInnis et al. 1991). In the context of online brand
communities, ability to engage in C2C interactions is (Gruen et al., 2005) “the competency in the
process driving know-how exchanges, as opposed to competency in the content of the know-how
that is being exchanged.” Thus, Gruen et al. view that a customer’s competence in the subject
matter has no impact on the level of his or her interactions with others. Their consideration of
ability from a process perspective might be a narrowed one.
Level of one’s expertise has been cited as a barrier to participate in online communities (McLure
Wasko & Faraj, 2000). In fact, MacInnis et al. (1991) cites others’ views (Alba and Hutchinson,
1987; Sujan, 1985) that “high ability implies that prior knowledge necessary to interpret brand
information is present and is accessed”. As a result, it is important to distinguish between the
two variants of ability – competence in subject matter expertise versus competence in the C2C
interaction process – to find out whether both, or either one, of them are dominant factors, and
under what conditions. An example would be to consider whether one’s knowledge of computer
programming is a dominant factor to promote or inhibit participate in a computer forum.
Gruen et al. (2007) have provided empirical evidence to show that motivation, opportunity and
ability are three levers that influence the level of ‘customer-to-customer exchange’. Each of the
MOA factors – Motivation, Opportunity and Ability - will exert an influence on C2C interactions
but each will not act entirely in an independent manner. These factors will operate in an additive
or compensatory mode only if each factor has achieved its minimum threshold and certain
conditions are met. Under the extreme condition that the value of any one of the factors is zero
(i.e. complete absence), there will be no C2C interactions regardless of the magnitude of the
other two factors. A low value of either opportunity or ability is likely to inhibit the effect of
motivation on C2C interactions, whereas a high value of opportunity may not be able to
compensate for a low value of ability or an opportunity. Motivation is the primary factor that
influences C2C interactions, whereas opportunity and ability exerts relatively lesser effect on
C2C interactions. As Gruen et al. (2006, 2007) has found that the influence of opportunity on
the overall value of the firm’s offering is non-significant (H3b in Figure 2), this link will not be
considered in our hypotheses.
As discussed previously, we will extend the MOA approach from know-how C2C interactions to
functional and social C2C interactions. Thus, the following hypotheses are put forward.
H1a-c Levels of (a) Motivation, (b) Opportunity, and (c) Ability have a positive effect on the level C2C
H2a-b The relationship between Motivation and C2C Interactions will be (a) positive and significant when
Ability is high and Opportunity is high; and (b) non-significant when Ability is low or Opportunity is
2.2 Functional and social benefits
Based on the empirical evidence in their MOA approach, Gruen et al. (2007) have found that: (i)
know-how C2C interactions are a source of values to customers; and (ii) know-how C2C
exchange has an impact on the overall value of a firm’s offering and customer loyalty intentions.
However, their model has not covered two key aspects of C2C interactions:
(i) It has not included the social aspect of C2C interactions, which are potentially a
significance source of value creation (Burnett, 2000; Muniz and O’Guinn, 2001;
Sicilia and Palazon, 2008);
(ii) It has not included economic or non-economic costs (or sacrifices) incurred by
customers in the consideration of perceived values (refer ‘Section 2.4 Value
Model‘ for a detailed explanation);
Thus, the proposed model will address these two points by:
(i) Including the considerations of both perceived functional and social benefits in
(ii) Including a value component that takes into consideration perceived functional
and social benefits against sacrifices;
A survey of research literature has found that customers generally perceive both functional and
social benefits in online communities. Functional benefits include access to information
(Furlong, 1989;; Hagel & Armstrong, 1997; Wellman et al., 1996), member generated content
(Hagel & Armstrong, 1997), a valued resource of knowledge and information (Hiltz & Wellman,
1997; Rheingold, 1993; Sproull & Faraj, 1997) and the presence of ‘weak ties’ (Constant,
Sproull & Kiesler, 1996). Social benefits include social support (Thoits, 1982), sense of
belonging and affiliation (Watson & Johnson, 1972), self-identity (Hogg, 1996), emotional
support, encouragement, companionship, sense of belonging and reciprocity (Furlong, 1989;
Hiltz, 1984; Hiltz & Wellman, 1997; Korenman & Whatt, 1996; Wellman & Gulia, 1999).
Though there is an overall appreciation of the benefits created by online communities, there has
been little research done on how those benefits are created. Hence, the development of ideas in
this area will be a major part of this research.
A key question to ask would be under what conditions are those benefits attributable to C2C
interactions in an online community. This is an important question because C2C interactions
imply an aggregate level effect of activities to individual agents that is much larger than just the
sum of individual-level effects (Hartmann, 2008).
There are instances where values can be generated in a brand community with little or no C2C
interactions. For example, a customer might only be interested to look up some tips in a Q&A
forum. He or she thinks that it is beneficial because he or she perceives it as a knowledge
repository, and learns a great deal from it. The customer has no interactions with others but
values are created for him or her. On the other hand, not all C2C interactions can create values.
Negative values might result instead. For example, a customer might have an altercation with
others in a discussion room and feel upset about it. It might be caused by a difference in
opinions or unfriendly remarks by the other parties during an encounter.
Values are often generated through C2C interactions in post-purchase situations. A customer
involved in C2C interactions can continue to accumulate skills or expertise by sharing of
knowledge, experience or ‘know-how’ (von Hippel, 1988) of the products or services he or she
has purchased. Additional values are then created for the customer as he or she will increase his
or her core competency and knowledge of the products or services (Gruen, et al. 2005).
As a result, C2C interactions can produce or destroy values despite the fact that they might have
nothing else to do with the products sold or services provided. In order to explore the major
scenarios where values are created or destroyed, it is important, during the empirical stage, to
ascertain the validity of this construct by various methods such as focus groups and in-depth
It also raises important managerial issues such as mediation policy, supervision and operational
procedures of online brand communities, as well as collaboration between various functions in
an organization such as marketing, customer service and public relations.
Another key question is what would be the major kinds of C2C interactions that lead to
perceived benefits. Word-of-mouths have been favoured by many organizations to acquire new
customers because they tend to overcome consumer resistance with lower costs; however, there
is scant empirical evidence of their relative effectiveness (Trusov et al., 2009). In a brand
community where a majority of C2C interactions are amongst customers of the same brand, the
usefulness of word-of-mouths as a customer acquisition tool is lessened. However, they are
commonly used in product recommendations and referrals, brand and product reputations and
discussions of customer experiences.
One possible way to categorize C2C interactions is by the context of the interactions that take
place in a brand community such as blogs, technical forums, product review forums, chat rooms
and general discussion forums. For example, functional benefits are likely to be created through
C2C interactions in a technical forum or a product review forum, whereas social benefits are
likely to be created through interactions in a chat room or a general discussion forum. Though it
is probable that a combination of functional and social benefits can be generated in a single
context, the categorization provides a systematic way to examine the major kinds of C2C
interactions and the benefits they are mostly likely to create.
Regarding social benefits arising out of C2C interactions, friendship, support and personal
recognition are amongst the common social experiences (Berry, 1995). These experiences are
found to be very important benefits to those involved (Gwinner et., 1998). Amongst the list of
potential social benefits, as mentioned at the beginning of Section 2.3, it is important to cull the
list down to the relevant ones applicable in a brand community. To explore the major kinds of
social values, it is important to ensure the validity of the constructs at the empirical stage.
Thus, the following hypotheses are put forward.
H3a C2C Interactions has a positive impact on Perceived Functional Benefits.
H3b C2C Interactions has a positive impact on Perceived Social Benefits.
2.4 Value model
It is generally accepted that customers do not always purchase service of the highest quality
(Olshavsky, 1985) nor do they purchase the lowest cost service (Onkvist and Shaw, 1987). In
marketing, value is typically defined from the customer’s perspective and it usually refers to a
ratio or trade-off of total benefits to total sacrifices (Buzzell & Gale, 1987; Monroe, 1990;
Monroe and Krishnan, 1985). Value is considered as a tradeoff in consumer’s decision making
between the relevant ‘gives’ and ‘gets’ and, in a service context, quality is analogous to the ‘gets’
whereas sacrifices, of which monetary cost is a component, is analogous to the ‘gives’ (Bolton
and Drew, 1988; Heskett et al, 1990; Zeithaml, 1988).
Consumer’s value perception includes social, emotional and epistemic components (Sheth et al,
1991), which lends support to a value-based approach that examines social benefits. Patterson et
al. (1997) discuss value as a “key linkage between the cognitive elements of perceived quality or
performance, perceived monetary sacrifice and behavioral intentions.” A schematic is
illustrated (Figure 4) by Cronin (Cronin et al. (1997).
Figure 4: Value Model (Cronin et al., 1997)
Thus, the functional and social benefits that result from C2C interactions can be considered as
‘gets’, a measure of the outcomes or performances, of the interactions. The ‘gives’ or sacrifice is
a broader construct that includes “non-pecuniary costs such as the time, effort, and risk
assumption”. In our context, sacrifices are context-dependent where customers may assign
different importance to the same sacrifice attribute. For example, a customer who looks for
technical information may consider spending ten minutes in a discussion forum a small sacrifice
compared to spending the same amount of time in a chat room.
Though Gruen et al. (2007) recognize value as “the perception of benefits received by the
customer from the offering provided by the marketing organization in relation to the sacrifice
made to obtain those benefit”, they have not considered sacrifice in their model.
In the proposed model, perceived functional values are considered as the trade-offs between
perceived functional benefits and sacrifices, and perceived social values the trade-offs between
perceived social benefits and sacrifices. Thus, the following hypotheses are put forward.
H4 Perceived Function Benefits have a positive effect on Perceived Functional Value.
H5 Perceived Social Benefits have a positive effect on Perceived Social Value.
H6a Sacrifice has a negative effect on Perceived Functional Value.
H6b Sacrifice has a negative effect on Perceived Social Value.
2.5 Loyalty intentions and overall value of firm’s offering
Patterson et al. (1997) cites from other researchers that a consequence of perceived values is
purchase intentions or willingness to buy (Baker, 1990; Dodds & Monroe, 1985; Dodds et al,
1991). Another consequence of value perception is repurchasing intentions (Bolton & Drew,
Gruen et al. (2007) define loyalty intentions as repurchase intentions of the firm’s offering as
well as word-of-mouth that “encourage others to purchase the firm’s offering”. However, they
hypothesize loyalty intentions as a direct effect of C2C (know-how) interactions (Figure 2) by
omitting the value approach. They have assumed sacrifice as negligible (Gruen et al., 2007) and
treated the performance of C2C interactions simply as a surrogate of value perception.
Reciprocity is used as their argument (Gruen et al., 2007) to account for the direct relationship
between C2C interactions and loyalty intentions. They claim that “when know-how is received
by one individual, an obligation becomes formed with the other party.. (von Hippel, 1988)…
these obligations between members create the need to remain a member in order to have the
ability to repay the debt of the obligation.”
Despite their claim might be supported under some conditions, the relationship between
reciprocity and loyalty intentions is not widely supported in the literature and it has not the rigor
as the broader supported relationship between perceived value and loyalty intentions. As a
result, the proposed model hypothesizes a linkage between perceived value and loyalty
intentions. Thus, the following hypotheses are put forward.
H7 Perceived Functional Value has a positive impact on Loyalty Intentions.
H8 Perceived Social Value has a positive impact on Loyalty Intentions.
Gruen et al. (2007) examines the extent to which the overall value of the firm’s offering will
mediate the relationship between C2C interactions and loyalty intentions (Figure 2). In our
proposed model, the impact of perceived functional and social values on the overall value of the
firm’s offering is hypothesized. In addition, the mediating influence of the overall value of the
firm’s offering on loyalty intentions is also hypothesized. Thus, the following hypotheses are put
H9 Perceived Functional Value has a positive impact on Perceived Overall Value of the Firm’s
H10 Perceived Social Value has a positive impact on Perceived Overall Value of the Firm’s Offering.
H11 The higher the level of the Perceived Overall Value of the Firm’s Offering, the greater the level of
3. Summary of Research Design
The research design is a cross-sectional explanatory study that comprises of a combination of
qualitative and quantitative methods. The main objective of the qualitative method is to increase
the internal validity of the constructs by developing suitable measurement scales for them, to
refine theory building and testing of the value-based model. It will consist of in-depth interviews
and focus groups with selective participants of the online brand community. The quantitative
method will use questionnaires.
An electronic survey will be used. One of the advantages of using questionnaire is that it is an
inexpensive method and can be designed to ensure privacy and anonymity of the respondents,
which is suitable for an online community as most its members prefers anonymity. As the
respondents will not be guided to answer the questionnaire, it is important that the questions are
clear, easy to understand and not subject to misinterpretation. As a result, there will be multiple
trial runs and pretests of questionnaire. To increase the response rates, it is important to consider
factors such as (i) a good layout and reasonable length of the questionnaire; (ii) a well-planned
method to engage the respondents; (iii) an incentive for the respondents to complete and return
As Structural Equation Modeling needs a large sample size relative to other multivariate
methods, the factors that might affect the sample size needs to be considered (Hair et al., 2010):
(i) multivariate normality of the data; (ii) estimation technique; (iii) model complexity; (iv)
amount of missing data; and (v) average error variance among the reflective indicator.
The constructs need to be accurately defined as they form the basis of designing the individual
indicators for each construct. Four kinds of construct validity need to be considered: convergent
validity, discriminant validity, nomological validity and face validity. Though the measurement
scales of some of the constructs have been developed in previous research, they need to be
reviewed to ensure construct validity.
Reliability is the extent to which a set of indicators of a latent construct is internally consistent.
Coefficient alpha estimate will be used to the establish reliability of the constructs.
Structural Equation Modeling (SEM) will be used to analyze the model. The first step is to assess
the validity of the measurement model by examining the overall model fit and the validity of the
constructs. The overall fit of the model will be estimated by Confirmatory Factor Analysis
(CFA) and the indicators used will be chi-square statistic, the CFI and the RMSEA. The second
step is to assess the validity of the structural model in terms of the SEM model fit and the extent
to which the structural relationships are consistent with theoretical expectations. Path
coefficients and loading estimates are examined to ensure that they are not changed substantially
from the CFA model. Finally, standardized residuals and modification indices will be estimated.
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