Customer-to-customer interaction in brand communities


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Customer-to-customer interaction in brand communities

  1. 1. A Research Agenda Customer-to-customer interaction in online brand communities A value-based approach Michael Ling July 2014
  2. 2. Abstract 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. Keywords Online brand communities, MOA, customer-to-customer interactions, C2C interactions, value model, loyalty intentions
  3. 3. 1. Introduction 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 others. 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
  4. 4. and O’Guinn, 2001; Muniz and Schau, 2005), watches and game tournaments (Ouwersloot et al., 2008). 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. FirmBrand Focal Custo Custo mer Produ ct Figure 1: Customer-centric Model of Brand Community (McAlexander et al., 2002)
  5. 5. 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- customer interactions. 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
  6. 6. 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). 1.3 Purpose 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.
  7. 7. 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.
  8. 8. 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. Ability C-to-C Know-how Exchange Motivation Opportunit y H1 a H1b H3b Figure 2: MOA model (Gruen et al., 2007) H4 H5 H3a H1 c H2a,b H2a,b Loyalty Intentions Overall Value of the Firm’s Offering
  9. 9. 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. Ability to Engage in C2C Interaction s C-to-C Interaction s Sacrifices Perceived Functional Benefits Perceived Functional Value H1a H1b H1c H5 H9 H6b H9 Figure 3: Proposed Framework Perceived Social Value H2a,b H2a,b H4 H3b H8 Perceived Social Benefits H3a Perceived Overall Value of Firm’s Offering Loyalty Intentions H6a H7 H1 0 H1 1 Opportunit y to Engage in C2C Interaction s Motivation to Engage in C2C Interaction s
  10. 10. 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,
  11. 11. 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.
  12. 12. H1a-c Levels of (a) Motivation, (b) Opportunity, and (c) Ability have a positive effect on the level C2C Interactions. 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 low. 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 C2C interactions; (ii) Including a value component that takes into consideration perceived functional and social benefits against sacrifices;
  13. 13. 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
  14. 14. 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 interviews. 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
  15. 15. 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
  16. 16. 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) Purchase Intentions Service Quality Value Sacrifice
  17. 17. 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,
  18. 18. 1991). Another consequence of value perception is repurchasing intentions (Bolton & Drew, 1991). 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
  19. 19. 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 forward. H9 Perceived Functional Value has a positive impact on Perceived Overall Value of the Firm’s Offering. 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 Loyalty Intentions.
  20. 20. 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. Data Collection 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 the questionnaire. Sampling 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.
  21. 21. Construct Validity 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 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. Data analysis 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.
  22. 22. 4. References Alba, J. W, and Hutchinson, J. W. (1987), Dimensions of Consumer Expertise. Journal of Consumer Research, 13 (March), 411-54. Algesheimer, R., Dholakia, U.M. and Hermann, A. (2005). The Social Influence of Brand Community: Evidence from European Car Clubs, Journal of Marketing, 69(3), 19-34. Arnould, E.J. and Price, L.L. (1993). River Magic: Extraordinary Experience and the Extended Service Encounter, Journal of Consumer Research, 20(June). 24-45 Bagozzi, R.P. and Dholakia, U.M. (2002). Intentional Social Action in Virtual Communities. Journal of Interactive Marketing, 16(2), 2-21. Bagozzi, R.P. and Dholakia, U.M. (2006). Antecedents and Purchase Consequences of Customer Participation in Small Group Brand Communities. International Journal of Research Marketing, 23(March). 45–61. Baker, J.A. (1990). The effect of retail store environments of consumer perceptions of quality, price and value. An unpublished doctoral dissertation. Texas A&M University. Baron, S. and Harris, K. (2007). Interactions and Relationships From the Consumer Experience Perspective. Proceedings of the QUIS 10 Conference: Orlando, FL. Batra, R. and Ahtola, O. (1990). Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes. Marketing Letters, 2(2), 159-170. Berry, L.L. (1995). Relationship marketing of services – Growing Interest, Emerging Perspectives. Journal of the Academy of Marketing Science, 23 (Fall), 236–245. Bolton, R.N. & Drew, J.H. (1988). A model of perceived service value. In Frazier, G., Ingene, C., Aaker, D., Ghosh, A., Kinnear, T., Levy, S., Staelin, R. and Summers, J. (Eds). Efficiency and Effectiveness in Marketing: The 1988 AMA Educator’s Proceedings (p. 213). American Marketing Association Series, 54. Bolton, R.N. and Drew, J.H. (1991). A multistage model of customers’ assessments of service quality and value. Journal of Consumer Research, 17(March), 375-84. Burnett, G. (2000). Information Exchange In Virtual Communities: A Typology. Information Research, 5(4). Burkrant, R. E. and Sawyer, A. G. (1983). Effects of Involvement and Message Content on Information Pro-cessing Intensity. In Harris, R. J. (Eds). Information Processing Research in Advertising (p. 43-64). Hillsdale, NJ: Lawrence Erlbaum Associates. Buzzell, R. and Gale, B. (1987). The PIMS Principles, The Free Press, New York, NY.
  23. 23. Claricini, O. and Scarpi, D. (2007). The Influence of Brand Community Integration, Word of Mouth and Community Loyalty on Brand Loyalty. Paper presented at 36th EMAC Conference, Reykjavik, 22-24 May. Constant, D., Sproull, L., and Kiesler, S. (1996). The Kindness of Strangers: The Usefulness of Electronic Weak Ties for Technical Advice. Organization Science, 7(2), 119–135. Cronin, J.J., Brady, M.K., Brand R.R., Hightower, R. & Shemwell, D.J. (1997). A Cross- sectional Test of the Effect and Conceptualization of Service Value. Journal of Services Marketing, 11(6). 375-91. Dodds, W.B. and Monroe, K.B. (1985). The effect of brand and price information on product evaluations. In Hirschman, E. and Holbrook, M.B. (Eds). Advances in Consumer Research (p. 85-90),12. Dodds, W.B., Monroe, K.B. and Grewal, D. (1991). The Effect of Price, Brand and Store Information on Buyers’ Product Evaluations. Journal of Marketing Research, 28(August), 307- 19. Furlong, M. S. (1989). An electronic community for older adults: The SeniorNet Network. Journal of Communication, 39 (3), 145–153. Grove, S.J. & Fisk, R.P. (1997). The Impact Of Other Customers On Service Experiences: A Critical Incident Examination Of ‘Getting Along’. Journal of Retailing, 73(1). 63-85. Grove, S.J., Fisk, R.P. and Dorsch, M.J. (1998). Assessing the Theatrical Components of The Service Encounter: A Cluster Analysis Examination. The Service Industries Journal, 18(3), 116- 34. Gruen, T. W., Summers J. O., & Acito F. (2000). Relationship Marketing Activities, Commitment and Membership Behavior in Professional Associations. Journal of Marketing, 64(3). 34–49. Gruen, T. W., Osmonbekov T., & Czaplewski A. J. (2005). How E-communities Extend The Concept Of Exchange In Marketing: An Application Of The Motivation, Opportunity, and Ability (MOA) Theory. Marketing Theory, 5(1). 33-49. Gruen, T. W., Osmonbekov, T., & Czaplewski, A. J. (2006). eWOM: The Impact Of Customer- to-customer Online Know-how Exchange On Customer Value And Loyalty. Journal of Business Research, 59, 449–456. Gruen, T. W., Osmonbekov T., & Czaplewski A. J. (2007). Customer-to-customer Exchange: Its MOA Antecedents and Its Impact on Value Creation and Loyalty, Journal of the Academy of Marketing Science, 35, 537-549.
  24. 24. Gwinner, K.P., Gremler, D.D., & Bitner, M.J. (1998). Relational benefits in services industries: The customer’s perspective. Journal of the Academy of Marketing Science, 26 (2), 101–114. Hagel, J., & Armstrong, A.G. (1997). Net Gain: Expanding Markets Through Virtual Communities: Harvard Business School Press. Hair J. F. Jr., Black W. C., Babin B. J., & Anderson R. E. (2010). Multivariate Data Analysis – A Global Perspective (7th ed.): Pearson. Harris, K., Davies, B. and Baron, S. (1997). Conversations During Purchase Consideration: Sales Assistants and Customers. International Review of Retail Distribution and Consumer Research, 7 (3), 173-90. Hartmann, W. R., Manchanda, P., Nair, H., Bothner, M., Dodds, P., Godes, D., Hosanagar, K., & Tucker, C. (2008). Modeling Social Interactions: Identification, Empirical Methods and Policy Implications. Marketing Letters, 19, 287-304. Heskett, J. L., Sasser, W. E. Jr. & Hart, C. W. L. (1990). Service Breakthroughs: Changing the Rules of the Game: The Free Press. Hiltz, S. R. (1984). Online Communities: A Case Study of the Office of the Future: Ablex Publishing Corporation. Hiltz, S. R., Wellman, B. (1997). Asynchronous Learning Networks as a Virtual Classroom. Communications of the ACM, 40 (9), 44–49 Hogg, M. A. (1996). Group Structure and Social Identity. In W. P. Robinson (Eds). Social Groups and Identities: Developing the Legacy of Henri Tajfel (p.65–94). UK : Butterworth- Heinemann. Hoyer, W.D. and MacInnis, D. (1997). Consumer Behavior. Boston, MA: Houghton Mifflin. Korenman, J., Wyatt, N. (1996). Group Dynamics in an e-mail Forum. In S. C. Herring (Eds). Computer-mediated Communication: Linguistic, Social and Cross-cultural Perspectives (pp. 225–242). Philadelphia : John Benjamins. Kozinets, R.V. (2002). The Field Behind The Screen: Using Netnography for Marketing Research in Online Communities, Journal of Marketing Research, 39, 61-72. Langeard, E., Bateson, J. E. G., Lovelock, C. H. and Eiglier, P. (1981). Services Marketing: New Insights from Consumers and Managers: Marketing Science Institute. MacInnis, D.J., Moorman, C. & Jaworski, B.J. (1991). Enhancing and Measuring Consumers’ Motivation, Opportunity, and Ability to Process Brand Information from Ads, Journal of Marketing, 55 (October). 32–53.
  25. 25. McAlexander, J. H., Schouten J. W., & Koenig H. F. (2002). Building Brand Community, Journal of Marketing, 66 (January). 38–54. McLure Wasko M., & Faraj S. (2000). It is What One Does: Why People Participate and Help Others in Electronic Communities of Practice, Journal of Strategic Information Systems, 9, 155- 173. McWilliam, G. (2000). Building Stronger Brands through Online Communities, Sloan Management Review, Spring, 43-54. Martin, C.L. & Pranter, C.A. (1989). Compatibility Management: Customer-to-customer Relationships in Service Environments, Journal of Services Marketing, 3(Summer), 5-15. Monroe, K.B. (1990). Pricing: Making Profitable Decisions, 2nd ed., McGraw-Hill Book Company, New York, NY. Monroe, K.B. and Krishnan, R. (1985). The effect of price on subjective product evaluations. In Jacoby, J. and Olson, J. (Eds), Perceived Quality: How Consumers View Stores and Merchandise (p. 209-32). Lexington Books, Lexington, MA, Moore, R., Moore, M.L. and Capella, M. (2005). The Impact of Customer-to-customer Interaction in a High Personal Contact Service Setting. Journal of Services Marketing, 19 (7), 482-91. Muniz, A.M. Jr & O’Guinn, T.C. (2001). Brand Community, Journal of Consumer Research, 27, pp. 412-32. Muniz, A.M. Jr & Schau, H.P. (2005). Religiosity in the Abandoned Apple Newton Brand Community, Journal of Consumer Research, 31, 737-47. Olshavsky, R. W. (1985). Perceived Quality in Consumer Decision Making: An Integrated Theoretical Perspective (pp. 3-29). In J. Jacoby & J. Olson (Ed.), Perceived Quality. Lexington, MA: Lexington. Onkvist, S. & Shaw, J.J. (1987). The measurement of service value: some methodological issues and models. In C. Suprenant (Ed.), Add Value to Your Service: 6th Annual Services Marketing Proceedings. American Marketing Association, Chicago, IL. Ouwersloot H., & Odekerken-Schroder G. (2008). Who’s Who in Brand Communities – and Why? European Journal of Marketing, 42 (5/6), 571-585. Patterson, P. G., & Spreng, R. A. (1997). Modelling the Relationship between Perceived Value, Satisfaction and Repurchase Intentions in a Business-to-business, Services Context: An Empirical Examination. International Journal of Service Industry Management, 8(5), 414-434.
  26. 26. Petty, R. E, & Cacioppo, J. T. Communication and Per-suasion: Central and Peripheral Routes to Attitude Change. New York: Springer-Verlag. Pranter, C.A. & Martin, C.L. (1991). Compatibility Management: Roles in Service Performances. Journal of Services Marketing, 5(2), 43-53. Rheingold, B. T. (1993). Virtual Community: Homesteading on the Electronic Frontier. Cambridge: The MIT Press. Sawhney, M., & Prandelli, E. (2000). Communities of Creation: Managing Distributed Innovation in Turbulent Markets. California Management Review, 42(4), 24-55. Sheth, J.N., Newman, B.I. and Gross, B.L. (1991). Why we buy what we buy: a theory of consumption values. Journal of Business Research, 22, 159-70. Sproull, L., and Faraj, S. (1997). Atheism, Sex and Databases: The Net as a Social Technology. In S. Kiesler (Eds). Culture of the Internet (pp. 35–51). Mahwah , NJ : Lawrence Erlbaum Associates. Sicilia, M., & Palazón, M. (2008). Brand communities on the internet: A Case Study of Coca- Cola's Spanish Virtual Community. Corporate Communications, 13(3), 255-270. Thoits, P. A. (1982). Conceptual, Methodological, and Theoretical Problems in Studying Social Support as a Buffer Against Life Stress. Journal of Health and Social Behavior, 23, 145–159. Trusov, M., Bucklin R. E., & Pauwels, K. (2009). Effects of Word-of-Mouth versus Traditional Marketing: Findings from an Internet Social Networking Site, Journal of Marketing, 73(September), 90-102. von Hippel, E. (1988). The Sources of Innovation: Oxford University Press. Watson, G., Johnson, D. (1972). Social Psychology: Issues and Insights: J. B. Lippincott. Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., Haythornthwaite, C. (1996). Computer Networks as Social Networks: Collaborative work, Telework, and Virtual Community. Annual Review of Sociology, 22, 213–238. Wellman, B., Gulia, M. (1999). Virtual Communities as Communities. In Smith M. A. & P. Kollock (Eds). Communities in Cyberspace (pp. 167–194). New York : Routledge. Zeithaml, V.A. (1988). Consumer Perceptions of Price, Quality, and Value: A Means-end Model and Synthesis of Evidence. Journal of Marketing, 52(April), 35-48. Zeithaml, V.A., & Bitner, M.J. (1996). Service marketing. New York: McGraw Hill.