Understanding the continuance intention of knowledge sharing in ...

Understanding the Continuance Intention of Knowledge Sharing in Online Communities of Practice Through the Post-Knowledge-Sharing Evaluation Processes

Christy M.K. Cheung Department of Finance and Decision Sciences, School of Business, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR. E-mail: [email protected] Matthew K.O. Lee Department of Information Systems, College of Business, City University of Hong Kong, Kowloon Tong, Hong Kong SAR. E-mail: [email protected] Zach W.Y. Lee Department of Finance and Decision Sciences, School of Business, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR. E-mail: [email protected]

Web 2.0 creates a new world of collaboration. Many online communities of practice have provided a virtual Internet platform for members to create, collaborate, and contribute their expertise and knowledge. To date, we still do not fully understand how members evaluate their knowledge-sharing experiences, and how these evaluations affect their decisions to continue sharing knowledge in online communities of practice. In this study, we examined why members continue to share knowledge in online communities of practice, through theorizing and empirically validating the factors and emergent mechanisms (post-knowledge-sharing evaluation processes) that drive continuance. Specifically, we theorized that members make judgments about their knowledgesharing behaviors by comparing their normative expectations of reciprocity and capability of helping other members with their actual experiences. We empirically tested our research model using an online survey of members of an online community of practice. Our results showed that when members found that they receive the reciprocity they expected, they will feel satisfied. Likewise, when they found that they can help other members as they expected, they will feel satisfied and their knowledge self-efficacy will also be enhanced. Both satisfaction and knowledge self-efficacy further affect their intention to continue sharing knowledge in an online community of practice. We expect this study will generate interest among researchers in this important area of research, and that the model proposed in Received March 28, 2012; revised September 10, 2012; accepted September 29, 2012 © 2013 ASIS&T • Published online 28 May 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.22854

this article will serve as a starting point for furthering our limited understanding of continuance behaviors in online communities of practice.

Introduction Traditional literature on knowledge management focuses mostly on knowledge creation and dissemination within organizations (Alavi & Leidner, 2001; Grover & Davenport, 2001; Sambamurthy & Subramani, 2005). However, with the widespread diffusion of high-speed Internet, considerable attention is being focused on the role of online social spaces (e.g., online communities of practice) in knowledge management. Online communities provide useful platforms for knowledge extraction, exchange, and creation, both within and across organizational boundaries (Baker-Eveleth, Sarker, & Eveleth, 2005; Brown & Duguid, 2001; Wasko & Teigland, 2004; Wenger, 1998). Members from diverse organizational, national, and cultural backgrounds can contribute, discuss, and share their knowledge with other members. As a result, knowledge has become more collaborative and integrated (Cho, Chen, & Chung, 2010). In recent years, information systems (IS) researchers largely sought to explore how and why individuals adopt and come to share knowledge in online communities (Gu & Jarvenpaa, 2003; Kankanhalli, Tan, & Wei, 2005; Wasko & Faraj, 2005). Social exchange theory (Blau, 1964) is the most prevalent theory applied to the explanation of knowledge sharing in online communities. Researchers assume that people are ultimately directed by self-interest, and they

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 64(7):1357–1374, 2013

evaluate the costs and benefits before they share knowledge in online communities. A variety of factors associated with knowledge sharing in online environments have also been identified in prior literature, including individual cognition (e.g., status and reputation, enjoyment, reciprocity), interpersonal interaction (e.g., social interaction and trust), and organizational context (e.g., organizational support and reward systems) (Liang, Liu, & Wu, 2008). However, the value of an online community of practice depends largely on the ongoing participation of its members (Chen, 2007; Fang & Chiu, 2010). In a general sense, members with similar experiences can freely exchange their observations and knowledge in these online social platforms. Specifically, members in an online community of practice can share their knowledge by helping each other to solve problems, telling stories of personal incidents, and debating issues based on shared interests. If there are numerous members who are willing to stay and contribute their knowledge, this will improve the likelihood of connecting with individuals who are able and willing to help. In addition, previous studies have found that many initially active online communities have failed to retain their members and became “cyber ghost towns” (Phang, Kankanhalli, & Sabherwal, 2009, p. 722). Thus, it is important to understand how to sustain an online community of practice by motivating members to continue to share knowledge in the community. Many organizations, ranging from educational organizations, to governmental departments to business sectors, have also realized the value and opportunities of creating and maintaining online communities of practice. For instance, TAcommunities.org provides a platform for members to learn, share, and coalesce around issues that impact students and children with disabilities, and their families. PoliceOne.com is an online community of practice providing police officers with information and resources that help them to protect the general public and stay safe while carrying out their duties. Utilizing strict member-verification procedures, the website provides the police force with a trusted and reliable online environment for knowledge exchange between officers and departments from across the United States and around the world. The website Developer Force, developer.force.com, is a free community-based online portal of saleforce.com. It allows developers to learn, access key resources, and discuss a diverse set of topics, such as web service, database construction, and application development, with colleagues and other professionals in the field. In a sense, a well-established online community provides an ideal platform for sharing and collaborating knowledge on specific topics of interest. To date, we still do not fully understand how members evaluate their knowledge exchange experiences within online communities of practice, and how these evaluations affect their decision to continue sharing knowledge. This study enriches existing literature on knowledge sharing in online communities of practice by addressing a previously unexplored issue, namely the factors that shape the continuance of knowledge-sharing behavior. In this study, the 1358

emergent mechanisms (post-knowledge-sharing evaluation processes) that drive continuance behavior are introduced and empirically validated. Specifically, we theorize that members make judgments about their knowledge-sharing behaviors by comparing their normative expectations of reciprocity and their ability to help other members, with their actual experiences. These judgments affect members’ satisfaction and knowledge self-efficacy, and thus influence their intentions to continue sharing knowledge in online communities of practice. By focusing on the role of satisfaction and knowledge self-efficacy, this study integrates diverse research streams into a unified model that explains knowledge sharing in online communities of practice. This study identifies key drivers that compel members to continue sharing knowledge in online communities of practice. We expect that the results of this study will provide community moderators with guidelines for maintaining long-term relationships with members and for encouraging them to continue sharing knowledge in the communities. The rest of this article is organized as follows. First, we provide a review of the literature related to knowledgesharing behaviors, as well as the theoretical foundations of the post-knowledge-sharing evaluation processes. Second, we integrate the key factors from expectation disconfirmation theory and social cognitive theory, and theorize a research model for the continuance of knowledge sharing in virtual communities. Next, we describe the research methodology used to empirically test the research model, and present the results of data analysis. Finally, we summarize the findings and discuss the implications for both research and practice.

Theoretical Background Prior literature provides us with a rich foundation on which to build a research model to examine the continuance of knowledge sharing in online communities of practice. In this section, we first review the relevant literature on knowledge sharing. We then address the theoretical foundations of the emergent post-knowledge-sharing evaluation processes, and the significance of two relevant concepts, satisfaction and knowledge self-efficacy, in the continuance of knowledge sharing in online communities.

Knowledge Sharing Knowledge sharing or knowledge contribution in online communities is increasingly acknowledged as an important research topic (Choi, Lee, & Yoo, 2010; Lin, Hung, & Chen, 2009; Ma & Agarwal, 2007; Teng & Song, 2011; Yu, Lu, & Liu, 2010). Within an online community, individuals with common interests, backgrounds, and goals can share knowledge by posting questions, providing answers, and debating issues based on shared interests, using social media such as online discussion forums, e-mail listservers, electronic

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—July 2013 DOI: 10.1002/asi

bulletin boards, blogs, and wikis (Chai, Das, & Rao, 2011; Yang & Lai, 2011; Yates, Wagner, & Majchrzak, 2010). However, the sustainability of an online community, particularly an online community of practice, depends largely on whether members are willing to initially and continually share knowledge. One of the major challenges is that knowledge in online communities of practice is commonly conceived as a public good (Wasko & Teigland, 2004). A public good is characterized as “a shared resource from which every member of a group may benefit, regardless of whether or not they personally contribute to its provision, and whose availability does not diminish with use” (Cabrera & Cabrera, 2002, p. 693). The fundamental problem of a public good is its imbalance. Individuals are more likely to consume without making a reciprocal contribution, resulting in a social dilemma situation. Social dilemmas occur whenever an individual attempts to maximize his/her self-interest and makes a rational decision. In other words, it is very likely that most members within an online community only maximize their personal gains and consume available knowledge content without contributing to the community themselves. In recent years, more and more researchers have become interested in exploring the factors that drive members to share knowledge. Liang et al. (2008) have further classified those knowledge-sharing factors into three dimensions, namely individual cognition, interpersonal interaction, and organizational efforts. Individual cognition refers to the perceived benefits and commitment. Perceived benefits essentially include extrinsic returns such as status, reciprocity, and reputation (Wasko & Faraj, 2005), and intrinsic returns such as enjoyment and social interaction with other community members (Cho et al., 2010; Hew & Hara, 2007). Commitment refers to the psychological attachment to an online community. Wasko and Faraj (2005) found that commitment is an important motivational factor that drives members to provide more helpful responses to others. Interpersonal interaction refers to the actions of individuals in dyadic relations, including factors such as social interactions with other community members, trust among members, and social network centrality (Chai et al., 2011; Usoro, Sharratt, Tsui, & Shekhar, 2007). Organizational efforts include organizational support and the reward system (Davenport & Prusak, 1998; van Knippenberg & Sleebos, 2006). These are factors specific to knowledge sharing in an organizational context. In this review of prior literature, we identified and summarized 34 studies related to knowledge-sharing behavior in online communities (see the summary in Appendix A). Post-Knowledge-Sharing Evaluation Processes As mentioned before, existing IS studies mostly focus on the motivations underlying knowledge-sharing behaviors. The current study goes beyond the initial usage behavior and investigates continuance behavior in virtual knowledge communities. Particularly, we emphasize the post-knowledge-sharing evaluation processes (explained

through the expectation disconfirmation theory and social cognitive theory), and identify the underlying reasons that keep members sharing knowledge in an online community of practice. Expectation Disconfirmation Theory In recent years, considerable attention has focused on IS continuance (post-adoption behavior) (e.g., Bhattacherjee, 2001; Bhattacherjee & Premkumar, 2004; Jasperson, Carter, & Zmud, 2005; Limayem & Cheung, 2011; Limayem, Hirt, & Cheung, 2007). According to the IS continuance model (Bhattacherjee, 2001), users are able to compare their initial expectations with their actual experiences in the postadoption (continuance) stage. The underlying theoretical foundation of the IS continuance model is built on expectation disconfirmation theory (Oliver, 1977). This expectation confirmation paradigm is conceptually well-explicated and appears to be robust enough to apply to a broad range of human expectations (Fisk & Young, 1985). Oliver (1977) pioneered adaptation-level theory in consumer satisfaction research, and explained the formation of satisfaction in terms of expectation, perceived performance, and disconfirmation. Expectations create a frame of reference as a comparative judgment, where a cognitive comparison of prepurchase expectation level with product or service performance is then executed. If the perceived performance exceeds expectation (positive disconfirmation), a consumer becomes satisfied. In contrast, if the perceived performance falls below expectation (negative disconfirmation), a consumer becomes dissatisfied. A satisfactory purchase experience is necessary if ongoing relationships are to be maintained and future relationships are to be facilitated (Oliver, 1993). Satisfied customers tend to have a stronger repurchase intention and positive word-of-mouth (Yang & Peterson, 2004). A similar argument has been proposed in the IS continuance model (Bhattacherjee, 2001), where satisfactory usage experience with an information system is a requirement for continuance intention. Several IS studies have already demonstrated the importance of satisfaction in determining continuance behavior (Bhattacherjee, 2001; Cheung & Lee, 2009; Devaraj, Fan, & Kohli, 2002; McKinney, Yoon, & Zahedi, 2002; Susarla, Barua, & Andrew, 2003). Social Cognitive Theory Social cognitive theory also tells us that human behaviors function through outcome expectations (Bandura, 1986), that is, people perform a certain behavior based on their judgment on the likely consequences (outcome expectations) such performance will produce. In other words, people form beliefs about what they can do, predict likely outcomes of prospective actions, and set goals for themselves to accomplish valued futures. Self-efficacy is one of the most important concepts derived from social cognitive theory. Bandura (1986) defines perceived self-efficacy as


1359

Post-Knowledge-Sharing Evaluation in Online Communities of Practice

FIG. 1.

Research Model.

“people’s beliefs about their capabilities to produce designated levels of performance that exercise influence over events that affect their lives” (p. 71). Self-efficacy is created through mastery experience. Successes build a strong belief in one’s self-efficacy and motivate an individual to continue to pursue the activities that were successful. There has been considerable attention given to the concept of self-efficacy in IS research (Hasan, 2006; Hsu, Ju, Yen, & Chang, 2007; Marakas, Yi, & Johnson, 1998; Torkzadeh, Chang, & Demirhan, 2006). A lot of studies have focused on the role of self-efficacy in technology acceptance, implementation, and use. Recently, the concept of self-efficacy has been applied in the context of knowledge management. Kankanhalli et al. (2005) built on prior studies and suggested that knowledge self-efficacy relates to the beliefs that one’s knowledge can help to solve job-related problems, improve work efficiency, or make a difference to his/her organization. People with stronger knowledge selfefficacy are expected to exert more effort and tend to be more persistent in their efforts. Research Model and Hypotheses This study aims to examine how members evaluate their knowledge exchange experiences with an online community of practice, and how these evaluations affect their intentions to continue sharing knowledge. Our research model is depicted in Figure 1. Intention to Continue Sharing Knowledge in Online Communities of Practice Fostering knowledge sharing is a necessary component of knowledge management. It embeds the notion of “willingness to share” or “voluntary act of making information available to others” (Davenport & Prusak, 1997, p. 87). This study adopts a similar conceptualization of IS continuance intention and defines “intention to continue sharing knowledge” as “the likelihood a member will continue sharing knowledge in an online community of practice.” In the current study, we define knowledge as personalized or subjective information related to facts, procedures, concepts, interpretations, ideas, observations, and judgments (Alavi & Leidner, 2001). 1360

Online communities open up new possibilities for individuals to generate value and share knowledge with other people. We believe that after several interactions with other users in an online community of practice, members are able to compare their expectations about their knowledge-sharing behaviors with their actual usage experiences. Enjoyment of helping others has been frequently cited as an important factor that determines willingness to contribute knowledge in online communities of practice (Wasko & Faraj, 2005). People are willing to help others to solve challenging problems because answering questions provides them with feelings of pleasure (Lakhani & von Hippel, 2003). Reciprocity is another important factor that affects people’s willingness to share knowledge. Reciprocity is conceived as a benefit for individuals to engage in social exchange (Oh, 2012). Previous research showed that most members in online communities of practice have an expectation that their contributions will result in future returns (Chiu, Hsu, & Wang, 2006). Building on social cognitive theory and expectation disconfirmation theory, we theorize that members of an online community of practice form outcome expectations based on reciprocity (whether they can receive help in return for their contributions), and their ability to help other members (whether they can help other members through their contributions) (Bandura, 1986). They then compare their expectations with the actual experiences in an online community of practice and make judgments about their knowledge-sharing behaviors. When they find that their contributions can actually help other members in the community, their knowledge self-efficacy will be enhanced and their intentions to continue sharing knowledge will be increased. In addition, when they find their expectations are fulfilled, they will be satisfied and their intentions to continue sharing knowledge will be increased. Therefore, the hypotheses are: H1: A member will have a higher level of knowledge selfefficacy, when he/she finds that his/her contributions are more helpful than they expected. H2: A member will be more satisfied with the knowledgesharing experience when he/she finds that his/her contributions are more helpful than his/her normative expectations. H3: A member will be more satisfied with the knowledge sharing experience when he/she receives more reciprocity (receiving the help in return) than his/her normative expectations. H4: A member will be more likely to continue sharing knowledge in an online community when he/she is more satisfied.

Compeau and Higgins (1995) further suggested that selfefficacy judgment is related to the emotional response of an individual. Studies in psychology have also demonstrated that self-efficacy is significantly related to affect (or emotional responses) because people prefer and enjoy behaviors


that they feel they are capable of performing. Applying this argument in the context of online communities of practice, when a member has a higher degree of knowledge selfefficacy, he/she will be more satisfied about his/her sharing experience within an online community of practice. Thus, the hypothesis is: H5: A member will be more satisfied with the knowledgesharing experience when he/she has a higher level of knowledge self-efficacy.

Previous studies have illustrated the importance of knowledge self-efficacy on people’s intention to share knowledge (Bock, Zmud, Kim, & Lee, 2005; Cabrera & Cabrera, 2002; Kankanhalli et al., 2005). Lee, Cheung, Lim, and Sia (2006) also found that when a person feels that he/she cannot contribute useful information to others (lack of knowledge selfefficacy), he/she will be less likely to share information in the community. Thus, we believe that knowledge selfefficacy will be an important factor that drives an individual to continue sharing knowledge in an online community of practice. The hypothesis is: H6: A member will be more likely to continue sharing knowledge in an online community when he/she has a higher level of knowledge self-efficacy.

Research Method Our research model was tested empirically using an online survey in a well-established online community of practice. Hong Kong Education City (HKed [hkedcity.net]), a leading one-stop education portal with a vision to build Hong Kong into a learning city, was officially launched in 2000 with the aim to take a leading role in promoting quality education to schools, teachers, students, parents, and the public (HKedCity, 2012). In the current study, the unit of analysis is the teacher or educator who uses the “Teachers’ Channel” of the HKed City. Teachers’ Channel is a virtual, professional community that provides teachers and educators with resources for professional development and updated news on education related issues. There are eight main sections in the Teachers’ Channel: (a) Recommended Teaching Material, where teachers can contribute or retrieve teaching resources; (b) Professional Development, which contains resources that help teachers broaden their teaching horizons, facilitate them to teach, and provide them with strategies and techniques to handle school cases; (c) Teacher TV, an online TV channel for teachers to learn and observe teaching strategies from peers; (d) Teacher Master Calendar, a public calendar which records all teaching developments or enhancement courses, workshops, and seminars; (e) News about Education, where hot news and current issues of education are posted; (f) HKedCity Forum (Teacher), an online discussion forum that allows teachers to post and discuss any educational related issues; (g) Edblog, a public blog for teachers to discuss current teaching related issues; and (h) Subject iWorld, another platform for teachers to

collaborate and create useful teaching resources on a subject or issue basis (HKedCity, 2012). Data Collection Because we were not able to contact members of HKed City directly, we compiled an e-mail list of primary and secondary school teachers in Hong Kong. Though not all school websites would release their teachers’ e-mails, the e-mail addresses that we found covered schools from all 18 districts in Hong Kong. An invitation and description e-mail containing the URL link to the online questionnaire was sent to teachers on the e-mail list. To increase the response rate, incentives of three USB flash drives and 30 book coupons were offered as lucky draw prizes. Four-hundred and eight completed online questionnaires were collected. We examined nonresponse bias by using the comparison of differences between the early and late respondents. Specifically, we compared the first 30 responses with the last 30 responses in this study (Sivo, Saunders, Chang, & Jiang, 2006). Our results showed that there is no statistical difference between the early and late respondents in our measures. Thus, nonresponse error was assumed not to exist in this study. Among our respondents, 124 individuals had shared knowledge in the Teachers’ Channel. Of these, 66% were male and 34% were female. Approximately 25% were aged 21–30, about 60% were aged 31–50, and only 8% were aged 51 or above: 71% were secondary school teachers and 29% were primary school teachers; 21% had more than 20 years teaching experience. About 38% had less than 2 years experience with the virtual professional community, over 40% of them used the forum weekly, and all of them had been involved at one point in knowledge-sharing experiences (i.e., 78% had posted questions, 85% had posted comments, and 85% had answered other posts) within the community. Measures The five constructs of interest to this study were confirmation/disconfirmation of reciprocity, confirmation/ disconfirmation of helping, satisfaction with knowledgesharing experience, knowledge self-efficacy, and intention to continue sharing knowledge. All the measures used in the study were adapted from the prior literature, with minor modifications to fit the context of the current study. The measures of the constructs in this study are listed in the Appendix B. All constructs were measured using multi-item perceptual scales; each construct was measured by a few items for construct validity and reliability. Common method variance is defined as “systematic error variance shared among variables measured with and introduced as a function of the same method and/or source” (Richardson, Simmering, & Sturman, 2009, p. 763). Because our data were collected from a single source (i.e., self-report questionnaire), there is a potential for the occurrence of method variance. As suggested by Podsakoff et al.


1361

FIG. 2.

TABLE 1.

The Slider Scale.

Psychometric Table of Measurement.

Construct Continuance intention CR = 0.99, AVE = 0.97 Disconfirmation of reciprocity CR = 0.94, AVE = 0.83 Disconfirmation of helping CR = 0.97, AVE = 0.90 Satisfaction CR = 0.94, AVE = 0.79

Knowledge self-efficacy CR = 0.97, AVE = 0.94

Item

Mean

Standard deviation

Loading

t statistic

CI 1 CI 2 DRECIP 1 DRECIP 2 DRECIP 3 DHELP 1 DHELP 2 DHELP 3 SAT 1 SAT 2 SAT 3 SAT 4 SE 1 SE 2

65.98 66.29 18.44 14.97 12.74 14.02 14.19 11.77 18.52 17.13 15.12 16.12 17.07 17.29

17.72 19.43 16.34 15.59 15.59 12.82 14.08 14.46 13.94 18.52 17.28 17.81 15.86 17.03

0.99 0.99 0.90 0.93 0.90 0.96 0.96 0.93 0.81 0.93 0.91 0.91 0.97 0.97

333.87 321.52 38.45 39.56 29.30 98.64 107.63 60.25 18.84 86.79 37.11 35.21 133.38 172.06

Note. CR = composite reliability; AVE = average variance extracted.

(2003), the problem of common method bias can be minimized by using different scaling endpoints and formats for the measures of dependent and independent constructs. In the current study, a slider scale with different scaling endpoints was used in this study and provided a continuous scale from 0 to 100 or -50 to 50 (see Figure 2). Respondents either clicked or dragged the slider to indicate their preference point. Data Analysis and Results We used PLS-Graph (partial least squares) version 3.00 (Chin, 1994) to perform the statistical analysis in this study. The analysis proceeded in two stages: We first examined the measurement model by assessing psychometric properties of our measures; then we evaluated the structural model by testing our proposed hypotheses. The results of the two-step PLS analysis are described here. Measurement Model Convergent validity, which indicates the extent to which the items of a scale that are theoretically related to each other should be related in reality, was examined using the following three criteria suggested by Fornell and Larcker (1981): (a) composite reliability (CR) for each construct should exceed 0.80, (b) average variance extracted (AVE) for each construct should exceed 0.50, and (c) all item 1362

loadings should be significant and larger than 0.70. The result of our analysis is shown in Table 1. All scale items for our reflective scales had path loadings exceeding 0.70, and all CR and AVE values met the recommended thresholds. Discriminant validity is the extent to which the measure is not a reflection of some other variables. It is indicated by low correlations between the measure of interest and the measures of other constructs (Fornell & Larcker, 1981). We examined discriminant validity using Fornell and Larcker’s recommendation that the square root of the average variance extracted for each construct should be higher than the correlations between it and all other constructs. Table 2 shows that the squared root of average variance extracted for each construct was greater than the correlations between the constructs and all other constructs. The results suggested an adequate discriminant validity of the measures. We further conducted cross-loading analysis to assess the discriminant validity of the scales used in the current study. Table 3 reports the loading and cross-loading of all reflective measures in the model. Note that the item loadings in the corresponding columns were all higher than the loadings of the items used to measure the other constructs. The same was true across the rows. The item loadings were higher for their corresponding constructs than for others. Therefore, our measures satisfy the criteria for discriminant validity as suggested by Chin (1998).


TABLE 2.

Correlations Between Constructs with Reflective Measures.

Continuance intention (CI) Disconfirmation of reciprocity (DRECIP) Disconfirmation helping (DHELP) Satisfaction (SAT) Knowledge self-efficacy (SE)

CI

DRECIP

DHELP

0.98 0.76

0.91

0.58

0.64

0.95

0.66 0.73

0.66 0.73

0.60 0.69

SAT

SE

0.89 0.69

0.97

Note. Bold diagonal elements are the square root of AVE for each construct. Off-diagonal elements are the correlations between constructs.

Structural Model Figure 3 presents the overall explanatory power, estimated path coefficients (significant paths are indicated with asterisks), and associated t values of the paths of the research model. Tests of significance of all paths were performed using the bootstrap resampling procedure. The results show that the exogenous variables explain 59% of the variance in “intention to continue sharing knowledge in an online community of practice,” 54% of the variance in “satisfaction” and 48% of the variance in “knowledge self-efficacy.” All the hypothesized paths were significant in the PLS structural model, lending overall support to the impact of the post-knowledge-sharing evaluation processes on the expected continuance of knowledge sharing in online communities of practice. Disconfirmation of helping others exhibited a strong and significant effect on knowledge self-efficacy (b = 0.69, p < 0.01), and knowledge selfefficacy in turn determined satisfaction with an online community of practice (b = 0.35, p < 0.01). Both disconfirmation of reciprocity (b = 0.30, p < 0.01) and disconfirmation of helping others (b = 0.17, p < 0.10) had significant effects on satisfaction with an online community of practice. Intention to continue sharing knowledge in an online community of practice was then predicted by both satisfaction (b = 0.30, p < 0.01), and knowledge self-efficacy (b = 0.53, p < 0.01). Discussion and Implications The objective of this study was to address a gap in our understanding of the continuance of knowledge sharing in online communities of practice. Specifically, we developed and empirically tested a rich theoretical model to explain how members evaluate their knowledge-sharing experience and how the evaluations affect their continuance decision. Our results, based on an online survey with members of an online community of practice, provide support for the theoretical model and hypotheses, and add to the existing IS literature on the validation of knowledge-sharing behavior in online communities of practice.

Discussion of Key Findings Post-knowledge-sharing evaluation processes. According to social cognitive theory (Bandura, 1986), human behavior functions through outcome expectation and self-efficacy. People perform certain behavior based on their judgment of the likely consequence (outcome expectation) such behaviors will produce. Existing knowledge-sharing literature has found that reciprocity and the enjoyment of helping are two key factors that drive people to share in online communities (Cho et al., 2010; Oh, 2012; Usoro & Majewski, 2011; Wasko & Faraj, 2005). By integrating the expectation disconfirmation paradigm (Oliver, 1977) into the current investigation, our study has provided empirical support to existing theories on how members evaluate their knowledge-sharing experiences in online communities of practice. As hypothesized, our results show that when members’ expectations of reciprocity and capability of helping others were fulfilled, they were satisfied. In addition, when members found that their contributions could successfully help other members in the community, their knowledge self-efficacy was enhanced. This in turn increased their level of satisfaction with the knowledge-sharing experience. The post-knowledge-sharing evaluation processes have been validated in the context of online communities of practice. The role of satisfaction and knowledge self-efficacy in continuance decision. This study confirmed the salience of both satisfaction and knowledge self-efficacy in continuance decision of knowledge sharing in online communities of practice. Consistent with traditional IS continuance literature (Bhattacherjee, 2001), user satisfaction exhibited a significant impact on members’ intention to continue sharing knowledge in an online community of practice. Our study has further highlighted the role of knowledge selfefficacy in the context of online communities for professional groups. For instance, the impact of knowledge self-efficacy on members’ intention to continue sharing knowledge in online communities of practice was found to be greater than that of user satisfaction, a commonly investigated factor in IS continuance study. Indeed, the results of this study were contradicted with some studies that focused on customer knowledge sharing in online product review platforms (Cheung & Lee, 2012), in which the authors did not find any significant relationship between knowledge self-efficacy and customer knowledge-sharing intention. One possible explanation is that communities of practice involve knowledge exchange among professional groups who are usually dealing with high-level concepts that require more insights than product-oriented experience sharing. According to Ardichvili, Page, and Wentling (2003), communities of practice tend to be formed by a specific group of subject matter experts or professionals. Individuals with diverse professional backgrounds and a thorough understanding of their own subject areas, help to promote professional activities and sustain the communities of practice (Olebe, 2005). Hew and Hara (2006) also


1363

TABLE 3.

CI1 CI2 DRECIP1 DRECIP2 DRECIP3 DHELP1 DHELP2 DHELP3 SAT1 SAT2 SAT3 SAT4 SE1 SE2

Loadings and Cross-Loadings for Reflective Measures.

Continuance intention

Disconfirmation of reciprocity

Disconfirmation of helping

Satisfaction

Knowledge self-efficacy

0.99 0.99 0.78 0.68 0.59 0.56 0.53 0.56 0.75 0.55 0.50 0.47 0.70 0.73

0.77 0.72 0.90 0.93 0.90 0.63 0.64 0.55 0.69 0.56 0.49 0.54 0.69 0.72

0.62 0.53 0.48 0.65 0.62 0.96 0.96 0.93 0.64 0.45 0.53 0.46 0.65 0.68

0.66 0.64 0.66 0.60 0.53 0.65 0.52 0.53 0.81 0.93 0.91 0.91 0.64 0.69

0.72 0.73 0.65 0.69 0.65 0.68 0.62 0.65 0.73 0.59 0.54 0.50 0.97 0.97

Note. Bold elements are the loadings for reflective measures.

FIG. 3.

Result of Research Model.

indicated that among all types of knowledge, knowledge that required greater insight was shared most frequently in communities of practice, such as book knowledge (i.e., facts, general regulations, status, or published work), and practical knowledge (i.e., knowledge related to actual practice). Compared with knowledge shared in communities of practice, the knowledge shared over customer review websites, such as in Amazon.com, tends to be more product driven and product oriented, and under less knowledge self-efficacy. Limitations of the Study In interpreting the results of this study, one must pay attention to a number of limitations. First, to keep the proposed research model parsimonious, it only considered disconfirmations of reciprocity and the capability of helping other members. As shown in Table 1, there are a number of key motivators of knowledge sharing (i.e., reputation, reward, moral obligation, commitment) that researchers can explore with respect to the evaluation processes and their impacts on members’ intention to continue sharing knowledge in an online community of practice. Second, the study respondents were the users of an education portal. The study 1364

represented one type of professional group where members usually share some common interests, background, and goals to participate and collectively contribute their professional knowledge. Care must be taken when extrapolating the findings to other types of online communities. Third, because of the cross-sectional nature of the study, spurious cause–effect inferences could have been presented. A longitudinal design is needed for valid cause–effect inferences. Finally, this study only used one single questionnaire to measure all constructs included, so common-method bias may be present in the measurements. However, we believe that our careful attention to questionnaire design (e.g., using different scaling methods for both dependent and independent variables), and the test of psychometric properties of our measurements instill confidence in our findings. Implications for Research and Practice This research addressed an important area of user behavior in online communities of practice. The potential of the Internet as a platform for online social activities has been widely appreciated. However, a number of outstanding and perplexing issues still need to be resolved. The sustainability


of these online communities is one of the yet-to-be resolved issues most commonly encountered. This study contributes to the conceptual and empirical understanding of continuance behaviors in online communities of practice. Implications of this study are noteworthy for both researchers and practitioners. Implications for Researchers The current study is one of very few studies of online communities that goes beyond adoption and initial usage by examining members’ continuance behavior. Existing studies investigate individual perceptions such as reciprocity, enjoyment of helping others, sense of belonging, moral obligation and the like to understand knowledge-sharing behavior, but this research went further. We integrated social cognitive theory and expectation disconfirmation theory, and proposed a theoretical model to explain how members evaluated their knowledge-sharing experiences, and how these evaluations affected their decisions to continue sharing knowledge in online communities of practice. Our research model explained over half (59%) of the variance in intention to continue sharing in online communities of practice, providing strong empirical support of the significance of the integrated research model. We believe that the post-knowledgesharing evaluation processes are important mechanisms in explaining members’ continuance in knowledge sharing in online communities of practice. In addition, the current study confirmed the salient effects of both satisfaction and knowledge self-efficacy in the decision to continue sharing knowledge in online communities of practice. Most IS continuance studies have focused on how user satisfaction affects continuance behavior (e.g., Bhattacherjee, 2001). In line with the specific context of the current investigation, this study confirmed the role of knowledge self-efficacy in the continuance of knowledge sharing in online communities of practice. It also provided support to Compeau and Higgins’s (1995) argument that self-efficacy judgment triggers emotional responses in an individual. This study has further validated the role of knowledge self-efficacy in the continuance of knowledge sharing. Implications for Practitioners To ensure the sustainability of an online community of practice, community administrators need to develop systematic processes to promote knowledge sharing within the community. The results showed that the fulfillment of members’ expectations regarding reciprocity and their ability to help other members have significant impacts on members’ satisfaction, knowledge self-efficacy, and intention to continue to share knowledge. Community designers should provide a mechanism where members who have provided useful suggestions to other members are identified. For instance, designers may implement a rating system where members can review and rate other comments. In this

case, contributors can easily find out whether their contributions make a significant difference to the community. This design feature has been used in some educational portals (e.g., NSTA Learning Center). The rating system should also connect knowledge contributors and knowledge seekers so that the knowledge seekers can show their appreciation for the knowledge received. Cho et al. (2010) further suggested that community administrators can send e-mails to members to assure them that their knowledge sharing would be valuable and useful, which would promote their knowledge self-efficacy. In conclusion, this study integrated social cognitive theory and expectation disconfirmation theory to explain the postknowledge-sharing evaluation processes. All the hypothesized paths were significant in our research model, lending overall support to the impact of the post-knowledge-sharing evaluation processes on the continuance of knowledge sharing in online communities of practice. We expect this study will generate researchers’ interest in this important area of research and that the model proposed in this article will serve as a starting point for furthering our limited understanding of continuance behaviors in online communities. Acknowledgments The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKBU 240609).

References Alavi, M., & Leidner, D.E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136. Ardichvili, A., Maurer, M., Li, W., Wentling, T., & Reed, S. (2006). Cultural influences on knowledge sharing through online communities of practice. Journal of Knowledge Management, 10(1), 94–107. Ardichvili, A., Page, V., & Wentling, T. (2003). Motivation and barriers to participation in virtual knowledge-sharing communities of practice. Journal of Knowledge Management, 7(1), 64–77. Bagozzi, R.P., & Dholakia, U.M. (2002). Intentional social action in virtual communities. Journal of Interactive Marketing, 16(2), 2–21. Baker-Eveleth, L., Sarker, S., & Eveleth, D.M. (2005, January). In formation of an online community of practice: An inductive study unearthing key elements. Paper presented at the 38th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood NJ: Prentice Hall. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation confirmation model. MIS Quarterly, 25(3), 351–370. Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 351–370. Blau, P.M. (1964). Exchange and power in social life. New York: Wiley. Bock, G.-W., Zmud, R.W., Kim, T.-G., & Lee, J.-N. (2005). Behavioural intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87–111. Brown, J.S., & Duguid, P. (2001). Knowledge and organization: A socialpractice perspective. Organization Science, 12(2), 198–213.


1365

Cabrera, A., & Cabrera, E.F. (2002). Knowledge-sharing dilemmas. Organizational Studies, 23(5), 687–710. Chai, S., Das, S., & Rao, R. (2011). Factors affecting bloggers’ knowledge sharing: An investigation across gender. Journal of Management Information Systems, 28(3), 309–341. Chang, H.H., & Chuang, S.-S. (2011). Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information & Management, 48(1), 9–18. Chen, C.-J., & Hung, S.-W. (2010). To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities. Information & Management, 47(4), 226–236. Chen, Y.L. (2007). The factors influencing members’ continuance intentions in professional virtual communities—a longitudinal study. Journal of Information Science, 33(4), 451–467. Cheung, C.M.K., & Lee, M.K.O. (2009). User Satisfaction with an Internetbased portal: An asymmetric and nonlinear approach. Journal of the American Society for Information Science and Technology, 60(1), 111– 122. Cheung, C.M.K., & Lee, M.K.O. (2012). What drives consumers to spread electronic word of mouth on online consumer-opinion platforms. Decision Support Systems, 53(1), 218–225. Chin, W.W. (1994). PLS graph manual. Unpublished manuscript, University of Calgary, Canada. Chin, W.W. (1998). The partial least squares approach to structural equation modeling. In G.A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Erlbaum. Chiu, C.-M., Hsu, M.-H., & Wang, E.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. Chiu, C.-M., Wang, E.T.G., Shih, F.-J., & Fan, Y.-W. (2011). Understanding knowledge sharing in virtual communities. Online Information Review, 35(1), 134–153. Cho, H., Chen, M., & Chung, S. (2010). Testing an integrative theoretical model of knowledge-sharing behavior in the context of Wikipedia. Journal of the American Society for Information Science and Technology, 61(6), 1198–1212. Choi, S.Y., Lee, H., & Yoo, Y. (2010). The impact of information technology and transactive memory systems on knowledge sharing, application, and team performance: A field study. MIS Quarterly, 34(4), 855–870. Chou, S.-W. (2010). Why do members contribute knowledge to online communities? Online Information Review, 34(6), 829–854. Compeau, D.R., & Higgins, C.A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. Davenport, T.H., & Prusak, L. (1997). Information ecology: Mastering the information and knowledge environmen. New York: Oxford University Press. Davenport, T.H., & Prusak, L. (1998). Working knowledge: how organisations manage what they know. Boston, MA: Harvard Business School Press. Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C channel satisfaction and preference: Validating e-commerce metrics. Information Systems Research, 13(3), 316–333. Ensign, P.C., & Hébert, L. (2010). How reputation affects knowledge sharing among colleagues. MIT Sloan Management Review, 51(2), 78–81. Fang, Y.-H., & Chiu, C.-M. (2010). In justice we trust: Exploring knowledge-sharing continuance intentions in virtual communities of practice. Computers in Human Behavior, 26(2), 235–246. Fisk, R.P., & Young, C.E. (1985). Disconfirmation of equity expectation: Effects on consumer satisfaction with services. Advances in Consumer Research, 12, 340–345. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Grover, V., & Davenport, T.H. (2001). General perspectives on knowledge management: Fostering a research agenda. Journal of Management Information Systems, 18(1), 5–21.

1366

Gu, B., & Jarvenpaa, S.L. (2003, December). In Online discussion boards for technical support: The effect of token recognition on customer contributions (pp. 110–120). Paper presented at the 24th International Conference on Information System, Seattle, WA. Hara, N., & Hew, K.F. (2007). Knowledge-sharing in an online community of health-care professionals. Information Technology & People, 20(3), 235–261. Hasan, B. (2006). Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance. Information & Management, 43(5), 565–571. Hersberger, J.A., Murray, A.L., & Rioux, K.S. (2007). Examining information exchange and virtual communities: an emergent framework. Online Information Review, 31(2), 135–147. Hew, K.F., & Hara, N. (2006). Identifying factors that encourage and hinder knowledge sharing in a longstanding online community of practice. Journal of Interactive Online Learning, 5(3), (293–316). Hew, K.F., & Hara, N. (2007). Empirical study of motivators and barriers of teacher online knowledge sharing. Educational Technology Research and Development, 55(6), 573–595. HKedCity. (2012). Hong Kong Education City. Retrieved from http:// www.hkedcity.net/ Hsu, M.-H., Chang, C.-M., & Yen, C.-H. (2011). Exploring the antecedents of trust in virtual communities. Behaviour & Information Technology, 30(5), 587–601. Hsu, M.-H., Ju, T.L., Yen, C.-H., & Chang, C.-M. (2007). Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations. International Journal of HumanComputer Studies, 65(2), 153–169. Jasperson, J., Carter, P.E., & Zmud, R.W. (2005). A comprehensive conceptualization of postadoptive behavior associated with information technology enabled work systems. MIS Quarterly, 29(3), 525–557. Jeon, S., Kim, Y.-G., & Koh, J. (2011). An integrative model for knowledge sharing in communities-of-practice. Journal of Knowledge Management, 15(2), 251–269. Kankanhalli, A., Tan, B.C.Y., & Wei, K.-K. (2005). Contributing knowledge to electronic knowledge repositories: An empirical investigation? MIS Quarterly, 29(1), 113–143. Koh, J., & Kim, Y.-G. (2004). Knowledge sharing in virtual communities: An e-business perspective. Expert Systems with Applications, 26, 155– 166. Lakhani, K., & von Hippel, E. (2003). How Open Source Software Works: “Free” User-to-User Assistance. Research Policy, 32(7), 923– 943. Lee, M.K.O., Cheung, C.M.K., Lim, K.H., & Sia, C.L. (2006). Understanding customer knowledge sharing in web-based discussion boards: An exploratory study. Internet Research, 16(3), 289–303. Liang, T.P., Liu, C.C., & Wu, C.H. (2008). In Can Social Exchange Theory Explain Individual Knowledge-Sharing Behavior? A Meta-Analysis (pp. 171). Paper presented at the 29th International Conference on Information Systems (ICIS), Paris, France. Limayem, M., & Cheung, C.M.K. (2011). Predicting the Continued Use of Internet-Based Learning Technologies: The Role of Habit. Behaviour & Information Technology, 30(1), 91–99. Limayem, M., Hirt, S.G., & Cheung, C.M.K. (2007). How habit limits the predictive power of intention: the case of information systems continuance. MIS Quarterly, 31(4), 705–737. Lin, F.-L., & Chiou, G.-F. (2010). Prestige as an indicator of knowledge exchange in the community of school technology coordinators. Online Information Review, 34(1), 5–20. Lin, F.-r., Lin, S.-c., & Huang, T.-p. (2008). Knowledge sharing and creation in a teachers’ professional virtual community. Computers & Education, 50(3), 742–756. Lin, M.-J.J., Hung, S.-W., & Chen, C.-J. (2009). Fostering the determinants of knowledge sharing in professional virtual communities. Computers in Human Behavior, 25(4), 929–939. Ma, M., & Agarwal, R. (2007). Through a glass darkly: Information technology design, identity verification, and knowledge contribution in online communities. Information Systems Research, 18(1), 42–67.


Majewski, G., Usoro, A., & Khan, I. (2011). Knowledge sharing in immersive virtual communities of practice. VINE, 41(1), 41–62. Marakas, G.M., Yi, M.Y., & Johnson, R. (1998). The multilevel and multifaceted character of computer self-efficacy: Toward a clarification of the construct and an integrative framework for research. Information Systems Research, 9(2), 126–163. McKinney, V., Yoon, K., & Zahedi, F.M. (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296–315. Oh, S. (2012). The Characteristics and Motivations of Health Answerers for Sharing Information, Knowledge, and Experiences in Online Environments. Journal of the American Society for Information Science and Technology, 63(3), 543–557. Olebe, M. (2005). Helping New Teachers Enter and Stay in the Profession. The Clearing House, 78(4), 158–163. Oliver, R.L. (1977). Effect of expectation and disconfirmation on postexposure product evaluations: An alternative interpretation. Journal of Applied Psychology, 62(4), 480–486. Oliver, R.L. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20(3), 418–430. Phang, C.W., Kankanhalli, A., & Sabherwal, R. (2009). Usability and sociability in electronic communities: A comparative study of knowledge seekers and contributors. Journal of AIS, 10(10), 721–747. Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y., & Podsakoff, N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. Richardson, H.A., Simmering, M.J., & Sturman, M.C. (2009). A tale of three paradigms: Examining post hoc statistical corrections for common method variance from three divergent perspectives. Organizational Research Methods, 12, 762–800. Sambamurthy, V., & Subramani, M. (2005). Special issue on information technologies and knowledge management. MIS Quarterly, 29(2), 193– 195. Shen, K.N., Yu, A.Y., & Khalifa, M. (2010). Knowledge contribution in virtual communities: Accounting for multiple dimensions of social presence through social identity. Behaviour & Information Technology, 29(4), 337–348. Siau, K., Erickson, J., & Nah, F.F.-H. (2010). Effects of national culture on types of knowledge sharing in virtual communities. IEEE Transactions on Professional Communication, 53(3), 278–292. Sivo, S.A., Saunders, C., Chang, Q., & Jiang, J.J. (2006). How Low Should You Go? Low Response Rates and the Validity of Inference in IS Questionnaire Research. Journal of the Association for Information Systems, 7(6), 351–414. Sun, Y., Fang, Y., & Lim, K.H. (2012). Understanding sustained participation in transactional virtual communities. Decision Support Systems, 53(1), 12. Susarla, A., Barua, A., & Andrew B, W. (2003). Understanding the service component of application service provision: An empirical analysis of satisfaction with ASP services. MIS Quarterly, 27(1), 91–123.

Teng, J.T.C., & Song, S. (2011). An exploratory examination of knowledgesharing behaviors: Solicited and voluntary. Journal of Knowledge Management, 15(1), 104–117. Torkzadeh, G., Chang, J.C.-J., & Demirhan, D. (2006). A contingency model of computer and Internet self-efficacy. Information & Management, 43, 541–550. Usoro, A., & Majewski, G. (2011). Intensive knowledge sharing: Finnish Laurea lab case study. VINE, 41(1), 7–25. Usoro, A., Sharratt, M.W., Tsui, E., & Shekhar, S. (2007). Trust as an antecedent to knowledge sharing in virtual communities of practice. Knowledge Management Research and Practice, 5(3), 199–212. van Knippenberg, D., & Sleebos, E. (2006). Organizational Identification versus organizational commitment: Self-definition, social exchange, and job attitudes. Journal of Organizational Behavior, 27(5), 571–584. Voelpel, S., Eckhoff, R., & Förster, J. (2008). David against Goliath? Group size and bystander effects in virtual knowledge sharing. Human Relations, 61(2), 273–297. Wasko, M.M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice? MIS Quarterly, 29(1), 35–37. Wasko, M.M., & Teigland, R. (2004). Public goods or virtual commons? Applying theories of public goods, social dilemmas, and collective action to electrnoic networks of practice. Journal of Information Technology Theory and Application, 6(1), 25–42. Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge, UK: Cambridge University Press. Wolf, P., Späth, S., & Haefliger, S. (2011). Participation in intra-firm communities of practice: A case study from the automotive industry. Journal of Knowledge Management, 15(1), 22–39. Yang, H.-L., & Lai, C.-Y. (2011). Understanding knowledge-sharing behaviour in Wikipedia. Behaviour & Information Technology, 30(1), 131–142. Yang, Z., & Peterson, R.T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology and Marketing, 21(10), 799–822. Yates, D., Wagner, C., & Majchrzak, A. (2010). Factors affecting shapers of organizational wikis. Journal of the American Society for Information Science and Technology, 61(3), 543. Yoon, C., & Wang, Z.-W. (2011). The role of citizenship behaviors and social capital in virtual communities. The Journal of Computer Information Systems, 52(1), 106–115. Yu, C.-P., & Chu, T.-H. (2007). Exploring knowledge contribution from an OCB perspective. Information & Management, 44(3), 321–331. Yu, J., Jiang, Z.J., & Chan, H.C. (2011). The influence of sociotechnological mechanisms on individual motivation toward knowledge contribution in problem-solving virtual communities. IEEE Transactions on Professional Communication, 54(2), 152–167. Yu, T.-K., Lu, L.-C., & Liu, T.-F. (2010). Exploring factors that influence knowledge sharing behavior via weblogs. Computers in Human Behavior, 26(1), 32–41. Zboralski, K. (2009). Antecedents of knowledge sharing in communities of practice. Journal of Knowledge Management, 13(3), 90–101.


1367

1368


Knowledge sharing requires the effort of the individuals who do the sharing and are involved in the social process.

Knowledge sharing involves members contributing knowledge and seeking knowledge for reuse.

N/A

N/A

Chang & Chuang (2011)

Chen & Hung (2010)

Chen (2007)

Chiu, Hsu, & Wang, (2006)

Chai, Das, & Rao (2011)

Knowledge management is a complex socio-technical system that encompasses various forms of knowledge generation, storage, representation, and sharing. N/A

Definition of knowledge contribution (sharing)

Ardichvili, Maurer, Li, Wentling, & Stuedemann (2006)

Author (year)

Summary of Key Studies.

Appendix A

Virtual community

Professional virtual communities


Virtual community

Online social network (blogs)

Virtual communities of practice

Technology

Social capital theory Social role theory

Social capital theory

Social cognitive theory Social exchange theory

Expectation confirmation theory

Social cognitive theory Social capital theory

䊏

䊏

䊏

䊏

䊏䊏

䊏

䊏

Communities of practice theory

䊏

Theoretical background Saving face Modesty competitiveness Authority, seniority, and hierarchy Preferred modes of communication In-group and out-group orientation

Information privacy concerns Trust 䊏 Reciprocity 䊏 Social ties 䊏 Gender 䊏 Social interaction 䊏 Trust 䊏 Identification 䊏 Reciprocity 䊏 Shared language 䊏 Participant involvement 䊏 Reputation 䊏 Altruism 䊏 Norm of reciprocity 䊏 Interpersonal trust 䊏 Knowledge-sharing self-efficacy 䊏 Perceived relative advantage 䊏 Perceived compatibility Contextual factors: 䊏 Social interaction ties expectation 䊏 Social interaction ties confirmation 䊏 Post-usage social interaction ties Technological factors: 䊏 Knowledge quality expectation 䊏 Knowledge quality confirmation 䊏 System quality expectation 䊏 System quality confirmation 䊏 Website use satisfaction 䊏 Personal outcome expectations 䊏 Community-related outcome expectations Structural dimension: 䊏 Social interaction ties Relational dimension: 䊏 Trust 䊏 Norm of reciprocity 䊏 Identification Cognitive dimension: 䊏 Shared language 䊏 Shared vision 䊏

䊏

䊏

䊏

䊏

䊏

䊏

Factors

Survey

Survey

Survey

Survey

Survey

Interviews; Analysis of activity logs

Data collection


1369

N/A

N/A

N/A

N/A

Knowledge sharing is a process by which knowledge is transferred from the members who possess it to those who seek it. It is generally voluntary without shared norms and routines to guide members’ knowledge-sharing behavior.

Ensign & Hébert (2010)

Hara & Hew (2007)

Hersberger, Murray, & Rioux (2007)

Hsu, Chang, & Yen (2011)

N/A

Chou (2010)

Chiu, Wang, Shih, & Fan (2011)

Virtual community

Online virtual communities

Online community of practice

Social network

Online communities

Professional virtual community

Motivational model Social cognitive theory

Expectance disconfirmation theory Justice theory

䊏

䊏

Social capital theory Theory of planned behavior

N/A

N/A

N/A

䊏

䊏

䊏

䊏

Positive knowledge quality disconfirmation Positive self-worth disconfirmation 䊏 Positive social interaction disconfirmation 䊏 Distributive justice 䊏 Procedural justice 䊏 Interactional justice 䊏 Playfulness (affect) 䊏 Performance expectancy 䊏 Satisfaction 䊏 Perceived identity verification 䊏 Computer self-efficacy 䊏 Computer anxiety 䊏 Personal innovativeness in IT 䊏 Past behavior 䊏 Duration of interaction 䊏 Frequency of interaction 䊏 Personal and professional interaction 䊏 Cowork and colocation interaction 䊏 Predictability 䊏 Reciprocity 䊏 Obligation 䊏 Self-selection 䊏 Validation with practice 䊏 Knowledge of practice 䊏 Non-competitive environment 䊏 Asynchronous medium 䊏 Role of listserv moderator Tier 1: 䊏 Foundational building blocks 䊏 Membership 䊏 Influence 䊏 Integration and fulfillment of needs 䊏 Shared emotional connection Tier 2: 䊏 Social networks as information networks Tier 3: 䊏 Information exchange Tier 4: 䊏 Information acquiring and sharing Trust in member: 䊏 Knowledge growth 䊏 Perceived responsiveness 䊏 Social interaction ties 䊏 Shared vision Trust in system: 䊏 System quality 䊏 Knowledge quality 䊏

䊏

Survey

Case study

Case study

Survey

Survey

Survey

1370


N/A

Knowledge contribution is a kind of social contribution that combines gift-giving attitudes and beliefs. Knowledge sharing involves a process of communication whereby two or more parties are involved in the transfer of knowledge.

Koh & Kim (2004)

Lin & Chiou (2010)

Lin, Hung, & Chen (2009)

N/A

Jeon, Kim, & Koh (2011)


Knowledge sharing is the behavior when an individual disseminates his/her acquired knowledge to other individuals within an organization.

(Continued)

Hsu, Ju, Yen, & Chang (2007)

Author (year)

APPENDIX A


Online community of practice

Virtual communities (Freechal.com)

Community of practice

Virtual communities

Technology

Theory of planned behavior Motivation model Triandis model

䊏

Social cognitive theory Social exchange theory Motivation theory

䊏䊏

䊏

Social network theory

䊏

N/A

䊏

䊏

Social cognitive theory

䊏

Theoretical background

Contextual factors: Norm of reciprocity 䊏 Trust Personal perceptions: 䊏 Knowledge-sharing self-efficacy 䊏 Perceived relative advantage 䊏 Perceived compatibility 䊏

Person: Knowledge-sharing self-efficacy 䊏 Personal outcome expectations 䊏 Community-related outcome expectations Environment: 䊏 Economy-based trust 䊏 Information-based trust 䊏 Identification-based trust 䊏 Facilitating conditions 䊏 Attitude 䊏 Intention 䊏 Subjective norm 䊏 Perceived behavioral control 䊏 Type of cop Intrinsic motivation: 䊏 Enjoyment in helping 䊏 Need for affiliation Extrinsic motivation: 䊏 Image 䊏 Reciprocity Virtual community activity: 䊏 Knowledge-sharing activity Virtual community’s outcomes: 䊏 Community participation 䊏 Community promotion Community service provider’s outcome: 䊏 Loyalty toward the virtual community provider 䊏 Network size of knowledge exchange 䊏 Network density of knowledge exchange 䊏 Tie strength of knowledge exchange 䊏

Factors

Survey

Interviews

Survey

Survey

Survey

Data collection


1371

N/A

N/A

Knowledge sharing enables participants to provide online knowledge to other peer participants. N/A

Oh (2012)

Shen, Yu, & Khalifa (2010)

Siau, Erickson, & Nah (2010)

Usoro, Sharratt, Tsui, & Shekhar (2007)

Knowledge sharing is a process of communication between two or more participants involving the provision and acquisition of knowledge.

N/A

Majewski, Usoro, & Khan (2011)

Sun, Fang, & Lim (2012)

N/A

N/A

Ma & Agarwal (2007)

Lin, Lin, & Huang (2008)

Virtual community of practice

Virtual community

Virtual community

Virtual communities

Online communities (Yahoo! Answers)

Virtual community of practice

Online community

Teachers’ professional virtual community (SCTNet)

Social psychology theory

Social exchange theory Weak tie theory

Social presence theory Social identity theory

Hofstede’s theory

Expectancy-value theory Social learning process Trust

䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

N/A

䊏

N/A

䊏

䊏

䊏

䊏

䊏

䊏

䊏

Extrinsic motivation Intrinsic motivation Task complexity Self-efficacy Integrity-based trust Competence-based trust Benevolence-based trust

Contexts: Environmental context 䊏 IT context 䊏 Project context 䊏 Organizational context 䊏 Group context 䊏 Individual context Causal condition: 䊏 Individual level 䊏 Group level Strategies: 䊏 Collaboration strategies 䊏 Using IT strategies 䊏 Knowledge sharing and creation strategies Perceived identity verification: 䊏 Virtual copresence 䊏 Persistent labeling 䊏 Self-presentation 䊏 Deep profiling Satisfaction 䊏 Norms of reciprocity 䊏 Trust 䊏 Perception of community 䊏 Enjoyment 䊏 Efficacy 䊏 Learning 䊏 Personal gain 䊏 Altruism 䊏 Community interest 䊏 Social engagement 䊏 Empathy 䊏 Reputation 䊏 Reciprocity 䊏 Awareness 䊏 Affective social presence 䊏 Cognitive social presence 䊏 Social identity 䊏 National culture 䊏

Survey

Survey

Survey

Survey

Survey

Survey

Survey

Analysis of activity logs; Interviews; Survey

1372


N/A

Knowledge-sharing intention refers to the degree to which one believes that he/she will engage in sharing knowledge in Wikipedia. Knowledge-sharing behavior refers to the frequency of knowledge sharing in Wikipedia.

Wolf, Späth, & Haefliger (2011)

Yang & Lai (2011)

Wasko & Faraj (2005)

Knowledge sharing refers to various practices that may include relatively trivial issues, for instance, sharing corporate procedures, as well as branching out to include more value-adding contents such as the sharing of best practices or forums that allow interaction among a firm’s employees. N/A

Voelpel, Eckhoff, & Forster (2008)


N/A

(Continued)

Usoro & Majewski (2011)

Author (Year)

APPENDIX A

Wikipedia

Community of practice

Electronic networks of practice (computer-mediated discussion forum)

Virtual knowledge-sharing group (Yahoo! Group)

Virtual communities of practice

Technology

General theory of social inhibition

Social exchange theory

䊏

䊏

䊏

䊏

Theory of planned behavior Technology acceptance model

N/A

Prisoner’s dilemma

䊏

Theoretical background Knowledge provision Knowledge reception Perception of a community Norm of reciprocity Perceived benefits Perceived cost Trust Group size Bystander effect Social inhibition Diffusion of responsibility Social influence Audience inhibition Forms of interaction

Individual motivations: Reputation 䊏 Enjoy helping Structural capital: 䊏 Centrality Cognitive capital: 䊏 Self-rated expertise 䊏 Tenure in the field Relational capital: 䊏 Commitment 䊏 Reciprocity 䊏 Perceived benefits 䊏 Perceived barriers to cop 䊏 Invested effort 䊏 Internal self-concept motivation 䊏 External self-concept motivation 䊏 Information quality 䊏 System quality 䊏 Individual attitudes 䊏 Individual intention 䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

䊏

Factors

Survey

Case study

Survey

Survey

Survey

Data collection


1373

N/A

A process by which an individual imparts his/her expertise, insight, or understanding to another individual so that the recipient may potentially acquire and use the knowledge to perform his/her task(s) better, is an essential part of effective knowledge management. N/A

Yu, Jiang, & Chan (2011)

Lu, & Liu (2010)

Note. N/A = Not appropriate.

Zboralski (2009)

N/A

N/A

Yu & Chu (2007)

Yoon & Wang (2011)

Communities of practice

Weblogs

Problem-solving virtual communities

Online gaming communities

Virtual community

Expectancy-value theory

䊏

䊏

Social theory of learning

N/A

Organizational citizenship behavior

Social capital theory

䊏

䊏

䊏

䊏

䊏

Member motivation Community leader Management support

Social interaction ties Trust 䊏 Norm of reciprocity 䊏 Identification 䊏 Shared goals Cohesiveness: 䊏 Individual (cohesiveness-I) 䊏 Group (cohesiveness –G) Affection similarity: 䊏 Positive (similarity-PA) 䊏 Negative (similarity-NA) Leader-member exchange (LMX) Technological: 䊏 Perceived effectiveness of knowledge repository 䊏 Perceived effectiveness of reputation system Social: 䊏 Perceived pro-sharing norms 䊏 Perceived salience of social identity Altruistic motive: 䊏 Moral obligation motive 䊏 Motive to advance Egoistic motive: 䊏 Image motive 䊏 Enjoyment motive 䊏 Self-enhancement motive 䊏 Reciprocity motive 䊏 Enjoy helping 䊏 Sharing culture 䊏 Usefulness/ relevancy 䊏 Fairness 䊏 Identification 䊏 Openness 䊏

䊏

Survey

Survey

Survey

Survey

Survey

Appendix B Continuance intention (modified from Bagozzi & Dholakia, 2002) CI 1

Please express the degree to which you might intend to continue sharing in the Teachers’ Channel in the next few weeks. (Extremely unlikely/ extremely likely) I intend to continue sharing in the Teachers’ Channel in the next few weeks. (Extremely disagree/ extremely agree)

CI 2

Disconfirmation of reciprocity (modified from Kankanhalli et al., 2005) DRECIP 1 DRECIP 2

Compared to my initial expectations, the level of reciprocity (i.e., get back help when I need) in the Teachers’ Channel is (much worse than expected/ much better than expected) When you share knowledge in the Teachers’ Channel, how big is the difference between what you expected and what the reciprocity actually occurred in the Teachers’ Channel? (Far below my expectation/ far above my expectation)

Disconfirmation of helping (modified from Kankanhalli et al., 2005) DHELP 1 DHELP 2 DHELP 3

Compared to my initial expectations, the helpfulness of my answers in the Teachers’ Channel is (much worse than expected/ much better than expected) Compared to my initial expectations, the helpfulness of my response (i.e., helping other people to solve problems) in the Teachers’ Channel is (far below my expectation/ far above my expectation) How big is the difference between what you perceived the helpfulness of your answers to be and how they actually helped others in the Teachers’ Channel? (Far below my expectation/ far above my expectation)

Satisfaction (Bhattacherjee, 2001)

SAT1 SAT2 SAT3 SAT4

How do you feel about your sharing experience in the Teachers’ Channel? Extremely dissatisfied /extremely satisfied Extremely displeased/extremely pleased Extremely frustrated/extremely contented Absolutely terrible /absolutely delighted

Knowledge self-efficacy (Modified from Kankanhalli et al., 2005) SE 1 SE 2

1374

I have confidence in my ability to provide knowledge that others in the Teachers’ Channel consider valuable. (Extremely disagree/ extremely agree) I have the expertise needed to provide valuable knowledge for Teachers’ Channel. (Extremely disagree/ extremely agree)
