Understanding knowledge contribution in online knowledge communities: A model of community support and forum leader support

Electronic Commerce Research and Applications 14 (2015) 34–45

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Understanding knowledge contribution in online knowledge communities: A model of community support and forum leader support Hua Jonathan Ye a, Yuanyue Feng b,⇑, Ben C.F. Choi c,d a

University of Auckland Business School, The University of Auckland, New Zealand College of Management, Shenzhen University, China c Australian School of Business, University of New South Wales, Australia d School of Computing, National University of Singapore, Singapore b

a r t i c l e

i n f o

Article history: Received 19 October 2013 Received in revised form 3 November 2014 Accepted 6 November 2014 Available online 18 November 2014 Keywords: Knowledge contribution Online knowledge communities Perceived community support Perceived leader support Organizational support theory Social exchange theory

a b s t r a c t No research has quantitatively investigated knowledge contribution from the perspective of community support and leader support. Drawing on social exchange theory and organizational support theory, this study develops a model of perceived community support and leader support for knowledge contribution in online knowledge communities. The research model was tested using survey data collected from 169 online knowledge community users. The result shows that perceived community support and perceived leader support positively affect users’ knowledge contribution. Additionally, we identified the antecedents of perceived community support and leader support. Theoretical and practical implications are discussed. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction Nowadays, online knowledge communities are becoming popular and increasingly adopted by individuals (Chiu et al. 2006, Faraj et al. 2011, Ma and Agarwal 2007). It has been reported by TheBiggestBoards.com1 that as of May 2014, there are in total 2550 online knowledge communities, among which 139 communities have more than 100,000 registered users. The biggest online knowledge community listed in the report – the UK Gaia Online2 contains 26.5 million registered users with more than 2.08 billion posts in total. These communities are revolutionizing lives by offering a space for social interactions (Phang et al. 2009), where individuals obtain knowledge and feedback from others and to express opinions (Faraj et al. 2011, Ma and Agarwal 2007). In a recent survey, for example, 70% of Americans are found to acquire knowledge ⇑ Corresponding author at: Department of Management Science, College of Management, Shenzhen University, Nanhai Ave 3688, Shenzhen, Guangdong 518060, PR China. Tel.: +86 755 26536121; fax: +86 755 26534451. E-mail addresses: [email protected] (H.J. Ye), [email protected] (Y. Feng), [email protected] (B.C.F. Choi). 1 The reported data was retrieved from http://www.thebiggestboards.com on May 31st, 2014. TheBiggestBoards.com is a Web directory that lists most active online communities ranked by their post count, user count, category, and software used to build the online community. 2 http://www.gaiaonline.com/forum/index.php. http://dx.doi.org/10.1016/j.elerap.2014.11.002 1567-4223/Ó 2014 Elsevier B.V. All rights reserved.

through online knowledge sharing platforms, such as bulletin boards and online forums, and 62% reported to have spent more than 30 min online every week to acquire new knowledge (Zalesne 2009). Knowledge contribution is considered a critical element for the sustainability of online knowledge community as individuals share and combine knowledge for their own benefits, while enhancing the value of the community (Faraj et al. 2011, Jeppesen and Frederiksen 2006). However, the lack of knowledge contribution has been identified as an alarming issue faced by online knowledge communities (Fang and Chiu 2010). For example, a survey of 1650 Americans has revealed that less than 1-out-of-10 (6%) have engaged in knowledge contribution behaviors, such as posting of comments (PEW 2009). Past research has revealed similar evidence. For instance, Cummings et al. (2002) have examined 1066 listservs for a 130-day period and reported that less than 50% of members contributed messages. Likewise, Preece et al. (2004) have revealed that contributing individuals made up 54.5% of health support communities and only 18% of software support communities. Much research to date has focused on understanding what motivates knowledge contributions in online knowledge communities from different perspective (e.g., Chiu et al. 2006, Ma and Agarwal 2007, Sun et al. 2012, Wasko and Faraj 2005). However, limited research has investigated the influences of online knowledge community itself (e.g., providing support to individual

H.J. Ye et al. / Electronic Commerce Research and Applications 14 (2015) 34–45

members) on individuals’ knowledge contribution. Researchers note that the online knowledge community per se, e.g., community size (Butler 2001), roles and rules (Preece et al. 2004), and community governance (Murray and Mahony 2007), could affect members’ knowledge contribution behaviors. Although past literature has postulated or marginally informed the impacts of online community support on members’ knowledge contribution (Andrews 2002, Coulson 2005, Turner et al. 2001), few have quantitatively scrutinized the relationship. Online community support here refers to members’ general belief that the online knowledge community, as a collective whole, is a source of knowledge and social support. Further, there is a lack of research examining the influences of forum leader support on members’ knowledge contribution. Typically, forum leader is defined as individuals who can influence other members or play leading roles in online communities (Koh et al. 2007). The enthusiasm and involvement of forum leaders are essential to the building of community membership (Figallo 1998, Koh et al. 2003) and foster community members’ care and attention on the community (Kim 2000, Preece 1998, 1999), regardless of whether the forum leader is officially assigned and titled by the online community provider or is the self-proclaimed proprietor of an online community. In online knowledge communities, forum leaders may include active members, opinion leaders, and moderators (Lu et al. 2011). These forum leaders contribute to online knowledge communities by either serving as sources of useful knowledge or, helping encourage and guide members’ productive contributions and collaboration (Preece 2002, Williams and Cothrel 2000, Zhang and Watts 2008). Extant literature notices that the interpersonal interaction between members and the forum leaders may affect members’ knowledge contribution (Figallo 1998, Koh et al. 2007, Preece and Shneiderman 2009). The forum leaders may support member’s knowledge contribution through developing necessary social climate to elicit interactions (Koh et al. 2007), synthesizing discussions and arguments to articulate ideas for other members (Cassell et al. 2006), or re-organizing and combining postings for easier retrieval and better understanding (Faraj et al. 2011). The existence of the forum leaders helps enhance the sustainability of online knowledge communities (Preece and Shneiderman 2009). Although past literature has postulated the potential influences of forum leader support, the effectiveness of forum leader support on members’ knowledge contribution has not been empirically examined. Forum leader support here refers to members’ general belief that the forum leader, as a specific person, is a source of knowledge and social support. In addition, given the potential importance of community support and forum leader support, it is critical to know what drives member’s perception of such supports. However, no study has examined the antecedents of community support and forum leader support. In this paper, we ask two important research questions: (1) how do community support and forum leader support affect member’s knowledge contribution and (2) what are the antecedents of community support and forum leader support? To develop our research model, we integrate social exchange theory (Homans 1958) and organizational support theory (Eisenberger et al. 1986). Specifically, the model maintains that perceived community support and perceived leader support enhance knowledge contribution in online knowledge communities. Since online knowledge communities represent a virtual form of organization with rules and norms resembling the regulations and cultures of mundane organizations (Faraj et al. 2011), organizational support theory could be applied to the context of our study (Andrews 2002, Coulson 2005, Turner et al. 2001). To identify the antecedents of perceived community support and leader support, we draw on social exchange theory as suggested by previous studies (e.g., Wasko and Faraj 2005). However, it is important to note at the

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up front that the focus of this paper is on knowledge-based type of online communities such as www.codeproject.com and stackoverflow.com, rather than other types of online communities such as networking-based type of online communities (e.g., facebook.com, twitter.com). The paper proceeds as follows. In the next section, we introduce the theoretical foundation of this paper, i.e., social exchange theory and organizational support theory. Based on the theoretical foundation, we develop our rationale for each of the hypotheses accordingly. Subsequently, we introduce the data collection method and the operationalization of constructs. We then conduct data analysis and present the results. Ultimately, we conclude this paper with a discussion of the contributions and implications of the findings. 2. Theoretical background 2.1. Social exchange theory Social exchange theory explains human behaviors in social exchanges (Blau 1964). It posits that individuals behave in ways that maximize their benefits (Molm 1997) and that they take part in an exchange only when they expect benefits from it (Gefen and Ridings 2002). Social exchange differs from economic exchange in the sense that the exchange is not governed by explicit rules or agreements. In such exchanges, people do others a favor with a general expectation of some future returns but no clear expectation of exact future returns. This belief of future returns (or reciprocity) is the central to a social exchange because the lack of explicit rules and regulations means that people have to rely on this belief to justify their expected benefits from the exchange. Therefore, social exchange theory assumes the existence of relatively long-term relationships of interest as opposed to one-off exchanges (Molm 1997). Social exchange theory has been used to understand the knowledge exchange phenomenon in organizations (Kankanhalli et al. 2005) and online communities (Chiu et al. 2006, Wasko and Faraj 2005). In the context of online communities, members engage in a social exchange with the online community as a whole as well as with a particular forum leader. According to the theory, a member will contribute to an organization or community as long as they obtain benefits from their contributions such as reputation and recognition (Jeppesen and Frederiksen 2006, Kankanhalli et al. 2005). Furthermore, past research adopting social exchange theory suggests that knowledge contribution can be derived by expectation of fair exchange, e.g., obtaining information or knowledge from others (Wasko and Faraj 2005) as well as norms for reciprocal exchange (Ye et al. 2010), e.g., pro-sharing norms (Kankanhalli et al. 2005). 2.2. Organizational support and knowledge contribution As a critical extension of the social exchange theory, the organizational support theory posit that there are two types of perceptions, i.e. organizational support and supervisor support, that predict individuals’ behaviors within an organization (Eisenberger et al. 2002, Rhoades et al. 2001). Organizational support refers to individuals’ general beliefs concerning the extent to which the organization values their contributions and cares about their well-beings (Eisenberger et al. 1986). Supervisor support represents individuals’ general beliefs that the supervisor is appreciative to their contributions and cares about their well-beings (Eisenberger et al. 2002). Previous research has examined the outcomes of support perceptions. Prior organizational studies suggest that communal exchange and dyadic interaction both motivate organizational citizenship behavior (Eisenberger et al. 2002, Shanock and

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H.J. Ye et al. / Electronic Commerce Research and Applications 14 (2015) 34–45

Eisenberger 2006), which is defined as ‘‘on the job behaviors which are discretionally, not formally or directly recognized by the organizational reward system, yet promote the effectiveness of the organization’’ (Moorman and Blakely 1995, Organ 1997). For example, Eisenberger et al. (2001) empirically found that perceived organizational support of employees will motivate them to reciprocate the organization with affective commitment and organizational spontaneity. Meanwhile, Schyns and Felfe (2006) found that the relationship between leader and members is influential on group consensus, which in turn, will induce organizational citizenship behaviors. Similarly, Wayne et al. (1997) noted that the perception of supervisor supportiveness leads to organizational citizenship behaviors (i.e., favor-doing behavior). In the context of online knowledge communities, individuals voluntarily engage in knowledge contribution, which refers to the degree of individual’s knowledge contribution to the online knowledge community, with the aim of helping other members who need help/information or develop new insights (Ma and Agarwal 2007). Knowledge contribution has been viewed as a critical form of organizational citizenship behavior (e.g., Lin et al. 2009, Watson and Hewett 2006, Yu and Chu 2007). Similarly, extant research has suggested that community members that perceive supportiveness from the online community itself or from the forum leaders will reciprocate by contributing their knowledge to the online community (Anderson 2004, Iriberri and Leroy 2009, Turner et al. 2001, Wiertz and Ruyter 2007, Yu and Chu 2007). However, there is a lack of studies that theoretically and empirically examine the influence of perceived community support and perceived leader support on knowledge contribution in online knowledge communities. To address the knowledge gap, this study draws on social exchange theory and organizational support theory to examine the influence of perceived community support and perceived leader support on knowledge contribution in online knowledge communities. Additionally, this study investigates the antecedents of community support and leader support based on social exchange theory, which is discussed in next section. In terms of organizational support, previous literature has investigated its antecedents. Researchers have found that organizational support perception is determined by expectation fulfillment and incentives (Coyle-Shapiro and Conway 2005, Rhoades and Eisenberger 2002). For instance, Rhoades and Eisenberger (2002) observed that organizations that recognize and fulfill employees’ expectations signal their appreciation of employees’ contributions. This will lead employees to perceive organizational support. Aselage and Eisenberger (2003) also suggested that individuals perceive organizational support when the organization fulfills their expectations. In a similar vein, Coyle-Shapiro and Conway (2005) reported that organizational incentives are important to individuals’ perception of organizational supportiveness. In the context of online communities, when the communities can fulfill their members’ needs for information or knowledge by, for instance, providing various tools for knowledge sharing and searching (Andrews et al. 2001), their members’ will perceive the community as being supportive for their welfare, thereby engendering high perception of community support (Iriberri and Leroy 2009, Wiertz and Ruyter 2007). In terms of supervisor support, past research suggested that respects from supervisors will increase individual’s perception of supervisor support (e.g., Dholakia et al. 2004, Schyns and Felfe 2006). Through receiving cues like being recognized and cared by supervisors, individuals could perceive a high level of support from supervisors (Eisenberger et al. 2002). In the context of online communities, for instance, when the members perceive the forum leaders recognizing their participations and contributions, they are more likely to develop high perception of leader support (Kankanhalli et al. 2005).

From the above discussion, it can be seen that community support is based on a communal exchange between community members and the online community as a whole, while leader support is based on a dyadic interaction between community members and the forum leaders. More specifically, community members will exchange with the community as well as their forum leader for knowledge aids and social support in online knowledge communities (Chiu et al. 2006). They will perceive support from the community by browsing the postings and getting solutions, and perceive support from the forum leader by receiving recognitions and attentions from the forum leader (Koh et al. 2007, Preece and Shneiderman 2009). Following the above discussion, we expect that information need fulfillment affects perceived community support while perceived recognition from leader affects perceived leader support. Furthermore, according to social exchange theory, the norm of reciprocal exchange (e.g., pro-sharing norm) is critical to the sustainability of knowledge contribution (Kankanhalli et al. 2005, Ye et al. 2010) while perceived co-presence of forum leader engenders the members’ feelings of companionship (Ma and Agarwal 2007). Members feeling a norm of pro-sharing in the community will perceive the community as a supportive environment promoting knowledge sharing and contribution, which, in turn, motivates them to engage in knowledge contribution behaviors. Similarly, members obtain psychological support through companionship, which is an incentive for members to participate in online knowledge communities. Therefore, we also expect that pro-sharing norm affects perceived community support while perceived copresence of leader affects perceived leader support. 3. Research model and hypotheses On the basis of social exchange theory and organizational support theory, we synthesize past empirical findings and propose that knowledge contribution can be predicted by support perceptions (i.e., perceived community support and perceived leader support). Specifically, we expect that perceived community support and perceived leader support positively affect knowledge contribution. Furthermore, drawing on social exchange theory, we identify the antecedents of perceived community support and perceived leader support. In particular, we expect that pro-sharing norm and information need fulfillment positively affect perceived community support while perceived recognition from leader and perceived co-presence of leader positively affect perceived leader support. The model is shown in Fig. 1. 3.1. Perceived community support From a social exchange perspective, when benefits directed at individuals are honored by the organization, a feeling of obligation is created (Wayne et al. 1997). This obligation, in turn, propels individuals to reciprocate in ways that are beneficial to the organization (Eisenberger et al. 1986). In a similar vein, individuals who perceive community support experience a sense of indebtedness (Ye et al. 2010), and hence are motivated to address such aversive feeling by making contributions to the organization (Moorman et al. 1998). In online knowledge contribution, individuals exert explicit efforts on contributing their knowledge and making their perspectives and opinions transferrable to the online community. The knowledge contribution exemplifies individuals’ reciprocity to the support they have obtained from the online knowledge community (Ye et al. 2010). Moreover, as per organizational support theory, individuals who perceive support from the organization (i.e., online knowledge communities) intend to engage in organizational citizenship behaviors (i.e., knowledge contribution in online

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Fig. 1. Research model.

knowledge communities). Thus, we expect that perceived community support will enhance knowledge contribution. H1. Perceived community support is positively related to knowledge contribution. 3.2. Perceived leader support A number of organizational studies applies social exchange theory to suggest that when individuals perceive support from their supervisors (i.e., leaders), they will reciprocate such supervisor support by engaging in behaviors that benefit the organization (Jordan et al. 2002, Kim and Taylor 2001, Liden and Maslyn 1998). For example, in a study on perceived organizational support and leader–member exchange, Wayne et al. (1997) have reported that individuals who experience high quality leader–member relationship are more willing to reciprocate through organizational citizenship behaviors. Likewise, Settoon et al. (1996) have found that better relationship between supervisors and subordinates enhances work quality and induces mutual helping among coworkers. These findings imply that individuals’ positive beliefs about the forum leader may induce beneficial behavior towards the organization. In the context of online knowledge communities, forum leader usually answer questions of community members, and engage in synthesizing and combining useful postings for members’ convenience and ease of future retrieval (Koh et al. 2007, Preece and Shneiderman 2009). Perception of such leader support will stimulate members to behave reciprocally toward the forum, e.g., knowledge contribution (Yu and Chu 2007). Moreover, from the organizational support theory, individuals who perceive support from the forum leaders who act as agents of the online communities are more willing to engage in behaviors that benefit the online knowledge communities (i.e., knowledge contribution). Therefore, we expect that perceived leader support will enhance knowledge contribution through individuals’ positive beliefs on the forum leader. H2. Perceived leader support is positively related to knowledge contribution. 3.3. Pro-sharing norm Pro-sharing norm refers to the prevalence of norms that are intended to facilitate knowledge sharing in online knowledge

communities (Kankanhalli et al. 2005). As per social exchange theory, pro-sharing norm induces a feeling of reciprocity in which individuals expect other community members to contribute knowledge and provide social support, which in turn, may induce their perceptions of community support (Chiu et al. 2006). Past studies have found that individuals use pro-sharing norm as a proxy to evaluate normative influence of the online knowledge community (Yee et al. 2007). Individuals who perceive a strong pro-sharing norm are likely to regard the online knowledge community as committed and supportive to its members (Ye et al. 2010). Pro-sharing norm can ensure that members will be reciprocated by the community in future exchanges (Kankanhalli et al. 2005, Yee et al. 2007). The perception of future reciprocity could increase members’ belief that the community is enacting a healthy environment for development and providing better services for members. Thus, we expect that pro-sharing norm will enhance perceived community support perception: H3. Pro-sharing norm is positively related to perceived community support.

3.4. Information need fulfillment Based on social exchange theory, individuals are motivated by benefits to participate in a social exchange (Kankanhalli et al. 2005). Under such expectations, individuals are motivated to pursue their extrinsic and intrinsic benefits (Wasko and Faraj 2005). Past research has examined extrinsic benefit through information need fulfillment (Dholakia et al. 2004) and intrinsic benefits in terms of perceived recognition from the forum leader (Schyns and Felfe 2006). Information need is defined as individuals’ desire to locate and obtain information to satisfy their conscious or unconscious needs (Dholakia et al. 2004). In online knowledge communities, fulfillment of information need is not limited to the acquisition of knowledge but also the understanding of others members’ opinions and perspectives (Flanagin and Metzger 2001). From a social exchange perspective, individuals’ positive regard toward an online knowledge community is influenced by the amount of useful information they obtain through participations (Ma and Agarwal 2007, Ridings and Gefen 2004). When individuals’ information need is fulfilled, they are satisfied with the online knowledge community and hence perceive strong support from the online knowledge community. Thus, we expect that information need fulfillment will enhance perceived community support.

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H4. Information need fulfillment is positively related to perceived community support. 3.5. Perceived recognition from leader Whereas extrinsic benefit is derived from information need fulfillment, intrinsic benefit is obtained through perceived recognition from the forum leader. Perceived recognition from leader refers to the extent to which the forum leader appreciates and acknowledges individuals’ contributions to the online knowledge community (Kottke and Sahrainski 1988). Past leadership research borrows logics from the social exchange theory to suggest that individuals respond more positively to leaders who recognize them on the basis of their contributions (Podsakoff et al. 2003, Wayne et al. 2002). For instance, Tangirala et al. (2007) have investigated the relationship between supervisors and subordinates and found that recognition from the supervisors results in better employees’ attitude towards the supervisors. Similarly, in a study on leader– member exchange, Wat and Shaffer (2005) have found that when the leader recognizes a member’s contribution to the organization, the member would form more positive perception on the leader. In the context of online knowledge communities, recognition is also shown to induce reciprocal respect and commitment between members (Jeppesen and Frederiksen 2006, Kankanhalli et al. 2005). When individuals perceive that the forum leaders recognize their participations, they develop strong feelings of leader support. Thus, we hypothesize that perceived recognition from leader will enhance perceived leader support. H5. Perceived recognition from leader is positively related to perceived leader support. 3.6. Perceived co-presence of leader Perceived co-presence of leader refers to a psychological connection to and with the forum leader (Nowak and Biocca 2003). Past research has considered the effect of co-presence, as an IT-artifact, on the quality of dyadic exchange. For example, Matheson (1991) has found that perceived co-presence has a positive relationship with interpersonal awareness. In a study of virtual education, Richardson and Swan (2003) have found that perceived co-presence significantly influences students’ relationship with the instructor. In essence, these studies hold that perceived co-presence induces interpersonal closeness, which in turn, influences the quality of a dyadic exchange. As per social exchange theory, the quality of a social exchange will enhance individual expectation for future exchanges (Molm 1997). Considering that perceived leader support is known to be derived from social exchange, it is safe to argue that perceived co-presence of the forum leader will enhance perceived leader support. Hence, we propose that perceived co-presence will enhance perceived leader support. H6. Perceived co-presence of leader is positively related to perceived leader support. We also include age, gender, Internet experience, experience with the online knowledge community, experience with leader, and community dummy in our model as control variables that might affect knowledge contribution. 4. Research methodology We collect data from the target population (i.e., online knowledge community users) through an online survey. Most of the constructs in our theoretical model are latent variables, which are best

studied using the survey approach (Nunnally and Bernstein 1994). It is worth noting that the online knowledge community that this study focuses on corresponds to the type of online community of common interest or information exchange categorized by Armstrong and Hagel (1996), as knowledge contribution is a key theme of this type of online community.3 Table 1 provides formal definitions of the constructs. Where available, the constructs were measured using instruments adapted from past studies to enhance validity (Stone 1978). Otherwise, new instruments were developed based on a review of the previous knowledge management and information systems literature. All the instruments were measured using seven-point scales anchored from ‘‘strongly disagree’’ to ‘‘strongly agree’’ (see Table 2 for the instruments). Before the main study, the online survey was pilot tested using respondents from online knowledge communities4 with the instruments’ wording, content, format, and procedures. For this pilot test, surveys were distributed to 60 active users. Pilot participants completed the instruments and provided comments about length, wording, and instructions. Three of the participants were interviewed to gain a richer understanding of the feedback.5 Based on the results of the pilot test, minor modifications were made to the survey design. We used university students with online knowledge community experience as our samples. The rationale for doing so was to exemplify those who are typically active in online forums (Synovate 2008). An email invitation was sent to 1300 students who had been randomly selected from the email directory of a university. The e-mail invitation included a short description of the study and a hyperlink to the online survey questionnaire. The survey was divided into three parts. The first part included the clear definitions of online knowledge community and forum leader.6 Based on these definitions, respondents were instructed to indicate the online knowledge community which they have used, their user names, and identify a forum leader with his or her user name. In the second part, measurements for all research variables were presented (Table 2). Finally, the third part included measurements for the control variables (Table 3). The online survey ran for 2 weeks. A total of 214 completed surveys were received, yielding a response rate of 16.5%. Communities with less than 20 respondents were discarded from further analysis. As a result, 169 respondents from 6 communities remained (see Table 4). All reported online knowledge communities had a strong focus on knowledge-sharing (i.e., product reviews and immigration tips). The largest response from a single community was 44 (26%) and the smallest was 20

3 The other three types of online communities classified by Armstrong and Hagel (1996) are communities of relationship, communities of fantasy, and communities for transaction. People participate in communities of relationship for social interaction, where they can extend their social networks and seek for emotional support. Such communities are relationship oriented and emphasize less on the knowledge contribution of individual members. Communities of fantasy are places where people participate for role playing. Such communities are game oriented and also rely less on knowledge contribution. Communities of transaction such as Amazon.com and eBay.com enable business transactions and delivery. They likewise rely less on members’ knowledge contribution to succeed. As the focus of this study is on knowledge contribution, we did not study these three types of online communities. 4 Samples of the pilot study were recruited from the same online knowledge communities in the main study. 5 The interviews were used to know the context and operations of online knowledge communities and revise our instruments, rather than doing a mix-method study. 6 Definitions of online knowledge community and forum leader are provided as follows: Online knowledge community refers to a group of people with common interests, goals, or practices, who participate to share knowledge and engage in social interactions over the Internet (i.e., online forums and message boards). Forum Leader refers to a member of the online community who can influence other members or play leading roles in the online community (e.g., active member, opinion leader, moderator).

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H.J. Ye et al. / Electronic Commerce Research and Applications 14 (2015) 34–45 Table 1 Formal definitions of constructs. Construct (abbreviation)

Definition

Pro-sharing norm (PsM)

The prevalence of norms that are intended to facilitate knowledge sharing in the online knowledge community (Kankanhalli et al. 2005) The extent to which the online knowledge community helps individual members to get information, to learn how to do things, and to generate ideas (Dholakia et al. 2004, Ma and Agarwal 2007) The perception of increase in image and reputation to the forum leader due to contributing knowledge to the online knowledge community (Kankanhalli et al. 2005) A psychological connection to and with the forum leader (Nowak and Biocca 2003) Individual members’ general beliefs that the forum leader is appreciative to their contributions and cares about their wellbeings (Eisenberger et al. 2002) Individual members’ general beliefs concerning the extent to which the online knowledge community values their contributions and cares about their well-beings (Eisenberger et al. 1986) The degree of individual’s knowledge contribution to the online knowledge community, with the aim of helping other members who need help/information or develop new insights (Ma and Agarwal 2007)

Information need fulfillment (INF) Perceived recognition from leader (PRfL) Perceived co-presence of leader (PCoPL) Perceived leader support (PLS) Perceived community support (PCS) Knowledge contribution (KC)

Table 2 Operationalization of constructs. Constructs

Items

Source

Pro-sharing norm (PsM)

PsM1 PsM2 PsM3

There is a norm of cooperation in this online community There is a norm of collaboration in this online community There is a norm of helping others in this online community

Adapted from Kankanhalli et al. (2005)

Information need fulfillment (INF)

INF1 INF2 INF3

I can obtain necessary information from this online community I can learn how to do things from this online community I can generate ideas with the help of this online community

Adapted from Dholakia et al. (2004), Ma and Agarwal (2007)

Perceived recognition from leader (PRfL)

PRfL1 PRfL2

Adapted from Kankanhalli et al. (2005)

PRfL3

Sharing my knowledge improves my image to this forum leader Users in this community who share their knowledge receive more prestige than those who do not from this forum leader Sharing my knowledge improves this forum leader’s recognition of me

Perceived co-presence of leader (PCoPL)

PCoPL1 PCoPL2 PCoPL3

I believe this forum leader find our interaction stimulating I believe this forum leader communicates warmth rather than coldness I believe this forum leader creates a sense of closeness between us

Adapted from Nowak and Biocca (2003)

Perceived leader support (PLS)

PLS1 PLS2 PLS3

In general, this forum leader strongly considers my goals and values In general, this forum leader is willing to help me when I need a special favor In general, this forum leader cares about my opinions

Adapted from Eisenberger et al. (2002)

Perceived community support (PCS)

PCS1 PCS2 PCS3

In general, this online community strongly considers my goals and values In general, this online community is willing to help me when I need a special favor In general, this online community cares about my opinions

Adapted from Eisenberger et al. (1986)

Knowledge contribution (KC)

KC1

I help other people in this online community who need help/information from other members I take an active part in helping others in this online community I contribute knowledge to this online community I contribute knowledge to other members that may result in their development of new insights

Adapted from Ma and Agarwal (2007)

I daydream a lot When I go to the movies I find it easy to lose myself in the film I often think of what might have been

Adapted from Malhotra et al. (2004)

KC2 KC3 KC4 Fantasizing (FAN)

FAN1 FAN2 FAN3

Note: All items are based on 7-point Likert scale (1 = strongly disagree to 7 = strongly agree).

Table 3 Control variables. Age

Please provide your age (in years).

Gender Internet experience Online community Community experience Forum leader experience

Male/female How long have you been using the World Wide Web (in years)? Please indicate an online knowledge community you have experience with How long have you been using the online knowledge community (in years)? How long have you known this forum leader (in years)?

(12%). The average age of the respondents was 21.6 years, and 45.6% are female. The respondents also reported that they had an average of 9.53 years of internet experience and 3.04 years of online knowledge community experience. As recommended by

Armstrong and Overton (1997), non-response bias was assessed by comparing early and late respondents. No significant differences between the first third and last third of all respondents were found on the variables in the research model. This suggests that nonresponse bias was not a serious concern in this study. We compared the demographics of our sample with the information provided by an analytic website, i.e., Alexa.com and found that the demographics of our sample at large corresponded with those of average participants in the six communities. We have run a set of Chow’s (1960) tests to assess whether it is possible to combine the data from the six communities. The resulting F-statistics were not significant, suggesting that the data could be pooled together. 5. Data analysis and results The survey data was analyzed using Partial Least Square (PLS). PLS is a suitable choice for analyzing the multi-stage model with

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Table 4 Online knowledge communities surveyed. Online knowledge community

Community topic

No. of respondents (% of total)

Flowerpod Hardwarezone Vrzone SgForum TalkBack SgChinese

Skincare, cosmetic and hair IT gadgets Computer modifications National service Campus life Immigration rules and regulations

44 32 27 24 22 20

(26) (19) (16) (14) (13) (12)

multiple relationships (Chin 1998, Ma and Agarwal 2007). Besides, PLS places minimal demands on variable distributions. This will reduce the bias caused by relying on factor-based covariance approaches using software such as LISREL and AMOS to analyze variables that are not normally distributed (Chin 1998, Ma and Agarwal 2007). Following Chin (1998), bootstrapping was performed to test the statistical significance of path coefficients. In the model tested, all constructs were modeled as reflective, because their measurement items are manifestations of these constructs and because these items covary (Chin et al. 2003). SmartPLS (version 2.0.M3) was used for data analysis. 5.1. The measurement model Convergent validity is assessed by (1) reliability of items, (2) composite reliability of constructs (>0.7), (3) average variance extracted (AVE) (>0.5) (Hu et al. 2004), and (4) factor analysis results. Examining each item’s loading on its corresponding construct assesses reliability of items (Loading > 0.7). In this study, the loading of each item meets this criterion (Table 4). Regarding internal consistency (reliability), composite reliability scores and Cronbach’s alpha scores for every construct (as shown in Table 5) are well above 0.70, which is the suggested benchmark for acceptable reliability (e.g., Barclay et al. 1995, Chin 1998, Fornell and

Table 5 Correlations between measures and latent variables. INF

KC

PRfL

PCS

PCoPL

PLS

PsM

INF1 INF2 INF3

0.91 0.94 0.89

0.44 0.47 0.49

0.27 0.23 0.31

0.53 0.57 0.56

0.26 0.38 0.40

0.42 0.49 0.47

0.36 0.40 0.42

KC1 KC2 KC3 KC4

0.47 0.47 0.45 0.52

0.96 0.96 0.96 0.89

0.27 0.28 0.28 0.32

0.55 0.51 0.54 0.55

0.29 0.28 0.29 0.35

0.48 0.52 0.51 0.49

0.49 0.45 0.50 0.41

PRfL1 PRfL2 PRfL3

0.20 0.23 0.36

0.20 0.23 0.37

0.93 0.90 0.95

0.19 0.25 0.31

0.29 0.27 0.36

0.37 0.32 0.49

0.24 0.20 0.38

PCS1 PCS2 PCS3

0.57 0.55 0.57

0.47 0.58 0.53

0.27 0.26 0.24

0.91 0.94 0.91

0.21 0.24 0.29

0.38 0.50 0.43

0.43 0.45 0.43

PCoPL1 PCoPL2 PCoPL3

0.38 0.34 0.36

0.36 0.30 0.25

0.32 0.34 0.29

0.29 0.22 0.24

0.95 0.95 0.94

0.37 0.36 0.39

0.33 0.27 0.18

PLS1 PLS3 PLS4

0.41 0.38 0.51

0.44 0.43 0.51

0.44 0.25 0.44

0.36 0.38 0.50

0.27 0.32 0.42

0.88 0.83 0.90

0.36 0.36 0.36

PsM1 PsM2 PsM3

0.41 0.35 0.35

0.36 0.44 0.44

0.24 0.29 0.26

0.35 0.46 0.38

0.25 0.23 0.23

0.32 0.40 0.33

0.82 0.84 0.90

Notes: PsM, Pro-sharing norm; INF, information need fulfillment; PRfL, perceived recognition from leader; PCoPL, perceived co-presence of leader; PCS, perceived community support; PLS, perceived leader support; KC, knowledge contribution.

Larcker 1981, Hair et al. 1995). The adoption of 0.7 as the threshold for both the composite reliability (ICR) and Cronbach’s alpha has been used by a number of papers in premier IS journals (e.g., Kankanhalli et al. 2005, Ma and Agarwal 2007, Wasko and Faraj 2005). AVE measures the amount of variance that a construct captures from its indicators relative to the amount due to measurement error (Chin et al. 2003). It is recommended to exceed 0.50 (Hu et al. 2004). Table 5 shows that the AVE score for every construct, ranging from 0.72 to 0.90, satisfies this requirement. In addition, to show good convergent validity in factor analysis results, all of the items should load highly on their own latent variables (Hair et al. 2006, Tabachnick and Fidell 2000). The factor analysis results in this study (see Table 5) are satisfactory according to these criteria. Discriminant validity is assessed by examining the indicatorconstruct loadings and inter-construct correlations (Chin et al. 2003). As shown by Table 5, that all indicators load more strongly on their corresponding constructs than on other constructs in the model. Table 6 shows the square roots of the average variance extracted (AVE) are larger than the inter-construct correlations. Overall, the constructs demonstrate strong discriminant validity. Finally, we assessed the extent of common method variance (CMV) using the marker-variable technique (Malhotra et al. 2004). The marker variable utilized was fantasizing. Results from confirmatory factor analysis showed that the smallest correlation with fantasizing was 0.06 (p = n.s.), indicating that CMV was not substantial in our study (Lindell and Whitney 2001). 5.2. The structural model The path coefficients and explained variances for the structural model are shown in Fig. 2. Demographic variables (i.e., gender, age, internet experience, community experience, and leader experience) and the online community dummy variable were included in the analysis as controls for knowledge contribution. None of the control variables were significant. All the six hypotheses were supported. Consistent with our prediction, perceived community support exhibits a positive influence on knowledge contribution (b = 0.41, p < 0.01), hence supporting H1. Perceived leader support shows positive influence on knowledge contribution (b = 0.34, p < 0.01), thereby supporting H2. Consistent with our prediction, pro-sharing norm is positively related to perceived community support (b = 0.22, p < 0.01), thereby supporting H3. Information need fulfillment is found to be positively related to perceived community support (b = 0.43, p < 0.01) whereas perceived recognition from leader is found to be positively related to perceived leader support (b = 0.35, p < 0.01), therefore H4 and H5 are supported. As anticipated, H6 is supported as perceived co-presence exhibits a positive relationship with perceived leader support (b = 0.28, p < 0.01). Table 7 summarizes the results of the hypothesis tests. 5.3. Post hoc mediation analysis Multiple regression analyses were conducted to assess each component of the proposed mediation model. Results are shown in Table 8. First, it was found that pro-sharing norm, information need fulfillment, perceived recognition from leader and perceive co-presence of leader are positively related to knowledge contribution. Second perceived community support and perceived leader support were found to be positively related to knowledge contribution. Third, pro-sharing norm and information need fulfillment are positively related to perceived community support while perceived recognition from leader and perceived co-presence of leader are positively related to perceived forum leader support.

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H.J. Ye et al. / Electronic Commerce Research and Applications 14 (2015) 34–45 Table 6 Means, standard deviations, scale reliabilities, and intercorrelations. Variable

Mean

SD

CA

CR

AVE

PsM

INF

PRfL

PCoPL

PCS

PLS

KC

PsM INF PRfL PCoPL PCS PLS KC

5.13 4.83 4.69 4.79 5.67 4.48 5.09

1.11 1.18 1.07 1.15 1.24 1.29 1.18

0.98 0.96 0.91 0.91 0.94 0.84 0.81

0.94 0.97 0.95 0.94 0.96 0.9 0.89

0.83 0.88 0.84 0.84 0.90 0.75 0.72

0.91 0.51 0.30 0.61 0.38 0.51 0.43

0.94 0.30 0.57 0.32 0.53 0.49

0.92 0.27 0.34 0.44 0.31

0.92 0.27 0.48 0.47

0.95 0.39 0.27

0.87 0.42

0.85

Notes: Diagonal elements are the average variance extracted (AVE). SD, standard deviation; CA, Cronbach’s alpha; CR, composite reliability.

Fig. 2. Results of PLS analysis.

6. Discussion

Table 7 Tests of research hypotheses. Proposed paths H1 H2 H3 H4 H5 H6

PCS PLS PsM INF PRfL PCoPL

? ? ? ? ? ?

KC KC PCS PCS PLS PLS

Path estimates

p-Levels

S.E.

Hypothesis tests

0.41 0.34 0.22 0.43 0.35 0.28

<0.01 <0.01 <0.01 <0.01 <0.01 <0.01

0.09 0.10 0.08 0.08 0.09 0.09

Supported Supported Supported Supported Supported Supported

Because both the a-path and b-path were significant, mediation analysis were tested using the Bootstrapping method with bias-corrected confidence estimates (MacKinnon et al. 2002, Preacher and Hayes 2008). In the present study, the 95% confidence interval of the indirect effects was obtained with 5000 bootstrap re-samples (Preacher and Hayes 2008). Results of the mediation analysis confirmed the mediating role of perceived community support in the relation between pro-sharing norm and knowledge contribution (Beta = 0.15, CI = 0.05–0.27) and between information need fulfillment and knowledge contribution (Beta = 0.26, CI = 0.26–0.41), and perceived forum leader support in the relation between perceived recognition from leader and knowledge contribution (Beta = 0.37, CI = 0.22–0.52) and between perceive co-presence of leader and knowledge contribution (Beta = 0.18, CI = 0.10–0.29). In addition, results indicate the direct effects of perceived recognition from leader on knowledge contribution (Beta = 0.03, p > 0.05) and perceive co-presence of leader on knowledge contribution (Beta = 0.13, p > 0.05) became non-significant when controlling for perceived forum leader support, thus suggesting full mediations.

Nowadays, people are increasingly referring to online communities for information and knowledge (Chiu et al. 2006, Faraj et al. 2011, Ma and Agarwal 2007). Information and knowledge offered are the key to the sustainability of these communities and encouraging individuals to contribute knowledge in them is always an important topic that intrigues both practitioners and researchers (Faraj et al. 2011, Ma and Agarwal 2007). Considering this, we seek to enrich the understanding of individuals’ knowledge contribution in the context of online knowledge communities through the perceived community support and perceived leader support. Results support our hypotheses that perceived community support and perceived leader support have unique contributions to the explanation of knowledge contribution in online knowledge communities. We also expect to achieve a more comprehensive understanding of the antecedents of support perceptions. Our findings show that both pro-sharing norm and information need fulfillment affect perceived community support while both perceived recognition from leader and perceived copresence affect perceived leader support. 6.1. Theoretical implications This study makes several important theoretical contributions. First, past research (e.g., Chiu et al. 2006, Ma and Agarwal 2007) has paid little attention to the effects of the support from the online knowledge community itself and forum leader on knowledge contribution. Drawing on the organizational support literature, we explicitly differentiated between perceived community support and perceived leader support and tested their influences on

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H.J. Ye et al. / Electronic Commerce Research and Applications 14 (2015) 34–45

Table 8 Bootstrapping test for indirect effects (sample size = 169). Relations

** ***

Coefficient

t-Statistics

Independent variable ? mediator (a path)

PsM ? PCS INF ? PCS PRfL ? PLS PCoPL ? PLS

0.42 0.57 0.67 0.35

6.20*** 9.92*** 11.53*** 5.50***

Mediator ? dependent variable (b path)

PCS ? KC PLS ? KC

0.46 0.56

5.35*** 5.84***

Independent variable ? dependent variable (c path)

PsM ? KC INF ? KC PRfL ? KC PCoPL ? KC

0.53 0.53 0.41 0.32

7.58*** 7.57*** 5.15*** 4.37***

Independent variable ? dependent variable (c0 path)

PsM ? KC INF ? KC PRfL ? KC PCoPL ? KC

0.38 0.26 0.03 0.13

5.22*** 3.22** 0.31 1.87

Mediating effects

PsM ? PCS ? KC INF ? PCS ? KC PRfL ? PLS ? KC PCoPL ? PLS ? KC

Bootstrapping b

0.15 0.26 0.37 0.18

Confidence interval (95%) Lower

Upper

0.05 0.15 0.22 0.10

0.27 0.41 0.52 0.29

p < 0.01. p < 0.001.

individuals’ knowledge contribution in online knowledge communities. Our paper contributes to past online communities literature (e.g., Chiu et al. 2006, Ma and Agarwal 2007, Faraj et al. 2011) by finding that perceived community support is indeed distinct from perceived leader support, and these support beliefs strongly affect individuals’ knowledge contribution. Further, this study extends the applicability of organizational support theory (Eisenberger et al. 2002, Rhoades et al. 2001) to the context of online knowledge communities. The explanatory power and support of our model indicate that organizational support theory is an appropriate theory for explaining knowledge contribution in online communities. Taken as a whole, our study offers concrete account of how perceived community support and perceived leader support shape knowledge contribution in online knowledge communities. Second, we contribute to the literature (Eisenberger et al. 2002, Rhoades et al. 2001) by identifying the antecedents of perceived community support and perceived leader support in the context of online knowledge communities. Based on the social exchange theory, this study synthesizes past research to propose four antecedents of support perceptions and hypothesizes their distinct effects on two support perceptions. Our paper extends previous literature (e.g., Kankanhalli et al. 2005, Ma and Agarwal 2007) by finding the effects of four antecedents on support perceptions. Overall, the four antecedents offer a coherent perspective for future knowledge contribution research. Third, previous literature found that the four antecedents were directly linked to knowledge contribution (e.g., Kankanhalli et al. 2005, Ma and Agarwal 2007). This study contributes to previous knowledge contribution studies by identifying the mediation effects of perceived community support and perceived leader support on knowledge contribution. Specifically, our study identifies the underlying mechanism through which pro-sharing norm, information need fulfillment, perceived recognition from the leader, and perceived co-presence of the leader affect individual’s knowledge contribution. This extends our knowledge on how individuals are driven to contribute knowledge in online communities. 6.2. Practical implications Our findings have important implications to application designers and online service providers. Application designers of online

knowledge communities often provide features that reduce efforts for knowledge codification and discovery (Chiu et al. 2006). While features facilitating the fulfillment of knowledge needs are somewhat common, little design efforts have been made on enhancing the exchange of societal benefits in online knowledge communities. To this end, we advocate a design strategy which improves societal benefits. As predicted by the proposed model, individuals’ perceived leader support is found to be enhanced by recognition from the forum leader. While this result is largely consistent with conventional wisdom, a more interesting finding of this study is probably that individuals perceive leader support when they experience high co-presence of the forum leader. This finding suggests that if the online knowledge community incorporates virtual copresence features, the quality of interpersonal interactions is enhanced. This is because perceived co-presence augments the exchange of relational messages, such as personal or social information, thereby enhancing individuals’ social perceptions. Thus, it is important that application designers consider features that facilitate recognitions and co-presence in online knowledge communities. Likewise, online service providers may help motivate knowledge contribution. We found that perceived leader support has a significant influence on knowledge contribution. For instance, when co-presence of the forum leader is high, individuals are more likely to perceive leader support. Likewise, when individuals are recognized by the forum leader, they perceive stronger leader support. Because perceived leader support is a powerful determinant of knowledge contribution, one recommendation is that online service providers should ensure the presence of a forum leader to be prominent. For example, through status notifications, the forum leader’s online status can be made available to individuals. Furthermore, online service providers should make it convenient for individuals’ contributions to be recognized. For instance, individuals’ knowledge contribution can be recognized through peers’ ratings or testimonials. Meanwhile, it is worthwhile noting that pro-sharing norm is found to have a significant influence on perceived community support. This finding implies that a strong norm of knowledge sharing is an important antecedent to individuals’ positive beliefs towards the online knowledge community. In this regard, we encourage online service providers to emphatically stipulate that knowledge

H.J. Ye et al. / Electronic Commerce Research and Applications 14 (2015) 34–45

contribution is vital to the sustainability of the online knowledge community. Individuals should also be made aware that non-contribution is not helpful to the sustainability of the online knowledge community. Last but not the least, the findings of our paper on antecedents of knowledge contribution in online knowledge communities can also shed light to the operations and management of online knowledge sharing forums of e-commerce websites, as knowledge sharing and contribution is critical for the forum users to better evaluate and use the e-commerce products/services in such websites. 6.3. Limitations and future directions We acknowledge some limitations in this study. First, we examined individuals’ knowledge contribution behaviors in online knowledge communities which they had actual experience. This approach is expected to be realistic and consistent with past studies (Ma and Agarwal 2007, Wasko and Faraj 2005). As a result, our findings are generally expected to be comparable with those of prior studies. However, this study employed a recall method to activate respondents’ memory about the forum leader. Though such recall bias is somewhat unavoidable, it nevertheless introduces some potential covariates (i.e., community tenure and leader tenure). As noted earlier, we controlled the effects of community experience and leader experience and found that these covariates had no significant influence on the dependent variable. Nevertheless, the findings of this study should be viewed with this bias in mind. Second, our findings can be best generalized to those knowledge-based online communities which have a strong focus on knowledge-sharing. We acknowledge that community support and leader support could be less relevant in other types of online communities. For example, in online social network communities (i.e. Facebook and MySpace), individuals might focus on socially attractive interactants and disregard knowledge leaders. Third, despite our best effort to incorporate all the relevant factors into the model, we cannot exclude a possibility of omitted variables which could influence the study’s results. For example, our model did not consider perceived co-presence of other forum users, which is considered one of the important determinants of knowledge contribution (Ma and Agarwal 2007). However, past studies suggest that the perception of general community support mediates the impact of perceived co-presence of others on individuals’ knowledge contribution (Koh et al. 2007). Given that support perceptions are already accounted for in our model (i.e., perceived community support), we believe that the impact of perceived copresence of others on knowledge contribution will be minimal. This study opens up a number of exciting avenues for further research. This study shows the significance of perceived leader support in knowledge contribution. Yet, recognition of a forum leader remains largely unknown. We encourage researchers to identify factors that may be important to the recognition of a forum leader and examine how such factors affect knowledge contribution. 7. Conclusion Despite various measures taken by online service providers, knowledge contribution continues to be inadequate in online knowledge communities (Faraj et al. 2011, PEW 2009). Given the importance of knowledge contribution, practitioners have expressed substantial concerns on encouraging such behaviors. To that end, we offer a theory-driven approach to evaluate the importance of community support and leader support in helping practitioners to enhance the sustainability of online knowledge

43

communities. Our findings clearly indicate that the integration of social exchange and organizational support theories is essential for a better understanding of knowledge contribution in the context of online knowledge communities. We believe that the model proposed in this study can serve as a solid foundation for future work in this important area. Acknowledgements We would like to thank Professor Xudong Lin from the Department of Management Science, College of Management, Shenzhen University for his insightful comments and great help. The authors thank for financial supports from the National Nature Science Foundation of China (71371127), the National Soft Science Research Plan (2013GXS4D138), and the Foundation Project for Humanities and Social Sciences Research of Ministry of Education of China (13YJA630050). This research is also partially supported by the Project to Aid Young Teachers in Humanities and Social Sciences of Shenzhen University, China (14QNFC31). References Anderson, B., 2004. Dimensions of learning and support in an online community. Open Learning: The Journal of Open, Distance and e-Learning 19 (2), 183–190. Andrews, D.C., 2002. Audience-specific online community design. Communications of the ACM 45 (4), 64–68. Andrews, D.C., Preece, J., Turoff, M., 2001. A conceptual framework for demographic groups resistant to online community interaction. In: Proceedings of the 34th Hawaii International Conference on System Sciences. IEEE Computer Society, Washington, DC, USA. Armstrong, A.G., Hagel, J., 1996. The real value of on-line communities. Harvard Business Review 74 (3), 134–141. Armstrong, J.S., Overton, T.S., 1997. Estimating non-response bias in mail surveys. Journal of Marketing Research 14 (3), 396–402. Aselage, J., Eisenberger, R., 2003. Perceived organizational support and psychological contracts: A theoretical integration. Journal of Organizational Behavior 24 (5), 491–510. Barclay, D., Higgins, C., Thompson, R., 1995. The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technology Studies 2 (2), 285–324. Blau, P.M., 1964. Exchange and Power in Social Life. John Wiley, New York. Butler, B.S., 2001. Membership size, communication activity, and sustainability: A resource-based model of online social structures. Information Systems Research 12 (4), 346–362. Cassell, J.D., Huffaker, D., Tversky, D., Ferriman, K., 2006. The language of online leadership: Gender and young engagement on the internet. Developmental Psychology 42 (3), 436–449. Chin, W.W., 1998. The partial least squares approach for structural equation modeling. In: Marcoulides, G.A. (Ed.), Modern Methods for Business Research. Methodology for Business and Management. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, US, pp. 295–336. Chin, W.W., Marcolin, B.L., Newsted, P.R., 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and electronic mail emotion/adoption study. Information Systems Research 14 (2), 189–217. Chiu, C.-M., Hsu, M.-H., Wang, T.G., 2006. Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems 42 (3), 1872–1888. Chow, G.C., 1960. Tests of equality between sets of coefficients in two linear regressions. Econometrica 28 (3), 591–605. Coulson, N.S., 2005. Receiving social support online: An analysis of a computermediated support group for individuals living with irritable bowel syndrome. CyberPsychology & Behavior 8 (6), 580–584. Coyle-Shapiro, J.A.-M., Conway, N., 2005. Exchange relationships: Examining psychological contracts and perceived organizational support. Journal of Applied Psychology 90 (4), 774–781. Cummings, J.N., Butler, B., Kraut, R., 2002. The quality of online social relationships. Communications of the ACM 45 (7), 103–108. Dholakia, U.M., Bagozzi, R.P., Pearo, L.K., 2004. A social influence model of consumer participation in network- and small group-based virtual communities. International Journal of Research in Marketing 21 (3), 241–263. Eisenberger, R., Armeli, S., Rexwinkel, B., Lynch, P.D., Rhoades, L., 2001. Reciprocation of perceived organizational support. Journal of Applied Psychology 86 (1), 42–51. Eisenberger, R., Huntington, R., Hutchison, S., Sowa, D., 1986. Perceived organizational support. Journal of Applied Psychology 71, 500–507. Eisenberger, R., Stinglhamber, F., Vandenberghe, C., Sucharski, I.L., Rhoades, L., 2002. Perceived supervisor support: Contributions to perceived organizational support and employee retention. Journal of Applied Psychology 87, 565–573.

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