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上海翻譯公司完成人力資源介紹英文翻譯

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上海翻譯公司完成人力資源介紹英文翻譯

The success of knowledge management initiatives depends on knowledge sharing. This paper reviews qualitative and quantitative studies of individual-level knowledge sharing. Based on the literature review we developed a framework for understanding knowledge sharing research. The framework identiﬁes ﬁve areas of emphasis of knowledge sharing research: organizational context, interpersonal and team characteristics, cultural characteristics, individual characteristics, and motivational factors. For each emphasis area the paper discusses the theoretical frameworks used and summarizes the empirical research results. The paper concludes with a discussion of emerging issues, new research directions, and practical implications of knowledge sharing research.

Knowledge is a critical organizational resource that provides a sustainable competitive advantage in a competitive and dynamic economy (e.g., Davenport & Prusak, 1998; Foss & Pedersen, 2002; Grant, 1996; Spender & Grant, 1996). To gain a competitive advantage it is necessary but insufﬁcient for organizations to rely on stafﬁng and training systems that focus on selecting employees who have speciﬁc knowledge, skills, abilities, or competencies or helping employees acquire them (e.g., Brown & Duguid, 1991). Organizations must also consider how to transfer expertise and knowledge from experts who have it to novices who need to know (Hinds, Patterson, & Pfeffer, 2001). That is, organizations need to emphasize and more effectively exploit knowledge-based resources that already exist within the organization (Damodaran & Olphert, 2000; Davenport & Prusak, 1998; Spender & Grant, 1996).

As one knowledge-centered activity, knowledge sharing is the fundamental means through which employees can contribute to knowledge application, innovation, and ultimately the competitive advantage of the organization (Jackson, Chuang, Harden, Jiang, & Joseph, 2006). Knowledge sharing between employees and within and across teams allows organizations to exploit and capitalize on knowledge-based resources (Cabrera & Cabrera, 2005; Damodaran & Olphert, 2000; Davenport & Prusak, 1998). Research has shown that knowledge sharing and combination is positively related to reductions in production costs, faster completion of new product development projects, team performance, ﬁrm innovation capabilities, and ﬁrm performance including sales growth and revenue from new products and services (e.g., Arthur & Huntley, 2005; Collins & Smith, 2006; Cummings, 2004; Hansen, 2002; Lin, 2007d; Mesmer-Magnus & DeChurch, 2009).

Because of the potential beneﬁts that can be realized from knowledge sharing, many organizations have invested considerable time and money into knowledge management (KM) initiatives including the development of knowledge management systems (KMS) which use state-of-the-art technology to facilitate the collection, storage, and distribution of knowledge. However, despite these investments it has been estimated that at least $31.5 billion are lost per year by Fortune 500

companies as a result of failing to share knowledge (Babcock, 2004). An important reason for the failure of KMS to facilitate knowledge sharing is the lack of consideration of how the organizational and interpersonal context as well as individual characteristics inﬂuence knowledge sharing (Carter & Scarbrough, 2001; Voelpel, Dous, & Davenport, 2005).

This paper contributes to our understanding of knowledge sharing in several ways. First, we review and integrate the literature from several different disciplines investigating how organizational, team, and individual characteristics inﬂuence individual-level knowledge sharing. Studies of individual-level knowledge sharing have been conducted in information systems (e.g., Wasko & Faraj, 2005), organizational behavior (e.g., Bordia, Irmer, & Abusah, 2006), strategic management (e.g., Reagans & McEvily, 2003), and psychology (e.g., Lin, 2007a) but no systematic review has been conducted to date. Prior reviews have focused on technological issues involved in knowledge sharing or knowledge transfer across units, organizations, or within interorganization networks (see Alavi & Leidner, 2001; Argote, 1999; Argote, McEvily, & Reagans, 2003 for reviews). This review focuses on understanding the factors that inﬂuence knowledge sharing between employees. This is important because team and organizational level knowledge is inﬂuenced by the extent to which knowledge sharing occurs between employees (e.g., Cabrera & Cabrera, 2005; Gupta & Govindarajan, 2000; Nonaka, 1994; Polanyi, 1966; Tsoukas & Vladimirous, 2001).

Second, this review provides an organizing framework for previous knowledge sharing research and identiﬁes emerging theoretical and methodological issues and future research needs. The framework shown in Fig. 1 was based on the review of the literature and provides a structure for the paper. Fig. 1 shows ﬁve emphasis areas of knowledge sharing research, the topics within each area of emphasis that have been investigated, and the relationships between each area of emphasis and knowledge sharing. As shown in Fig. 1, the topics studied within each area of emphasis have been shown to directly or indirectly inﬂuence knowledge sharing through motivational factors. The right hand of Fig. 1 shows the common dependent variables examined in the literature (knowledge sharing intention, intention to encourage knowledge sharing, and knowledge sharing behaviors). Also, in Fig. 1, the topics shown in the shaded boxes with solid lines have been examined in the existing literature. The topics shown in the boxes with dotted lines need future research attention. The topics shown in the overlapping areas represent those that have been examined in prior studies but still warrant further research attention. Third, this review contributes to human resource management practice by discussing the implications of knowledge sharing research for the implementation, support, and effectiveness of knowledge sharing initiatives in organizations.

The paper begins by discussing how we identiﬁed the studies included in the review and deﬁnes important concepts found in knowledge sharing research (e.g., knowledge, knowledge sharing). Next, for each area of emphasis the paper identiﬁes its theoretical foundations and reviews research results. The last two sections of the paper discuss emerging issues and future research questions that need to be addressed to advance our understanding of individual-level knowledge sharing and the practical implications of knowledge sharing research.

1. Identiﬁcation of studies

We conducted a narrative review of the literature rather than a meta-analysis because of the wide variety of disciplines contributing to individual-level knowledge sharing research, the small number of empirical studies investigating any one of the factors in each emphasis area, the lack of use of common measures of knowledge sharing, and our interest in understanding the different theories that have been used as the basis for knowledge sharing research. The articles included in this review were primarily identiﬁed using ABI-Inform and Business Source Premier. Articles published in academically refereed journals in management, organizational behavior, human resource development, applied psychology, and information systems were included in the review. Work published in books, conferences, or working papers was excluded. Reference sections of articles found were also searched. Knowledge sharing, knowledge exchange, and their variations were used as search terms.

We found seventy-six qualitative and quantitative studies that were published since Argote (1999) through early 2008. The review also includes three studies published prior to 1999 (Constant, Kiesler, & Sproull, 1994; Constant, Sproull, & Kiesler, 1996; Lam, 1996) because they have been cited quite often in the literature but were not included in previous reviews. The areas of emphasis of knowledge sharing research shown in Fig. 1 and the theories or theoretical frameworks used were identiﬁed based on the authors' review of the studies. Any disagreements between the two authors were discussed and consensus was reached.

2. Deﬁnitions

Researchers have not reached consensus on the distinctions, if any, between knowledge and information. For example, Nonaka (1994) considers information to be just “a ﬂow of messages” whereas knowledge is based on information and justiﬁed by one's belief. Other researchers believe that all information is considered knowledge but knowledge is more than just information, i.e., knowledge includes information and know-how (e.g., Kogut & Zander, 1992; Machlup, 1980; Zander & Kogut, 1995). Management information systems' researchers tend to use “knowledge” to suggest that there is value and uniqueness in examining KMS compared to the traditional information systems (Alavi & Leidner, 2001).

Many researchers use the terms knowledge and information interchangeably, emphasizing that there is not much practical utility in distinguishing knowledge from information in knowledge sharing research (see Bartol and Srivastava, 2002; Huber, 1991; Makhija and Ganesh, 1997). We adopt this perspective by considering knowledge as information processed by individuals including ideas, facts, expertise, and judgments relevant for individual, team, and organizational performance (e.g., Alavi & Leidner, 2001; Bartol & Srivastava, 2002).

Knowledge sharing refers to the provision of task information and know-how to help others and to collaborate with others to solve problems, develop new ideas, or implement policies or procedures (Cummings, 2004; Pulakos, Dorsey, & Borman, 2003). Knowledge sharing can occur via written correspondence or face-to-face communications through networking with other experts, or documenting, organizing and capturing knowledge for others (Cummings, 2004; Pulakos et al., 2003). Although the term knowledge sharing is generally used more often than information sharing, researchers tend to use the term “information sharing” to refer to sharing with others that occurs in experimental studies in which participants are given lists of information, manuals, or programs.

Knowledge sharing differs from knowledge transfer and knowledge exchange. Knowledge transfer involves both the sharing of knowledge by the knowledge source and the acquisition and application of knowledge by the recipient. “Knowledge transfer” typically has been used to describe the movement of knowledge between different units, divisions, or organizations rather than individuals (e.g., Szulanski, Cappetta, & Jensen, 2004). Although “knowledge exchange” has been used interchangeably with “knowledge sharing” (e.g., Cabrera, Collins, & Salgado, 2006), knowledge exchange includes both knowledge sharing (or employees providing knowledge to others) and knowledge seeking (or employees searching for knowledge from others). In this review, we use the term “knowledge exchange” when discussing studies that measured knowledge sharing using scales that assessed both knowledge sharing and seeking.

3. Areas of emphasis in knowledge sharing research

The framework presented in Fig. 1 organizes knowledge sharing research based on several areas of emphasis including organizational context, interpersonal and team characteristics, cultural characteristics, individual characteristics, and motivational factors. Each area of emphasis consists of related topics that we identiﬁed in our review of knowledge sharing research.

3.1. Organizational context

3.1.1. Organizational culture and climate Many studies have examined the effect of organizational culture on knowledge sharing. Based on a qualitative study of ﬁfty companies, De Long and Fahey (2000) found that the beneﬁts of a new technology infrastructure were limited if long-standing organizational values and practices were not supportive of knowledge sharing across units.

A number of cultural dimensions that likely inﬂuence knowledge sharing have been identiﬁed, but trust has attracted the most research attention. A culture that emphasizes trust has been found to help alleviate the negative effect of perceived costs on sharing (Kankanhalli, Tan, & Wei, 2005). It has also been linked with the implementation of intranet-based KMS, individual knowledge sharing, and ﬁrms' capability of knowledge exchange and combination (Chiu, Hsu, & Wang, 2006; Collins & Smith, 2006; Liao, 2006; Ruppel & Harrington, 2001; Willem & Scarbrough, 2006). Similarly, an organizational climate that emphasizes individual competition may pose a barrier to knowledge sharing whereas cooperative team perceptions help create trust, a necessary condition for knowledge sharing (Schepers & Van den Berg, 2007; Wang, 2004; Willem & Scarbrough, 2006). In addition to trust, research has also shown that organizations with cultures emphasizing innovation are more likely to implement intranet KMS (Ruppel & Harrington, 2001) and facilitate information sharing through subjective norms that encourage sharing (Bock, Zmud, Kim, & Lee, 2005; McKinnon, Harrison, Chow, & Wu, 2003). Lin and Lee (2006) found that executives' perceptions of the relative advantage of knowledge sharing for the business, compatibility to existing business process, and complexity to encourage knowledge sharing served as mediators between organizational climate and an organization's intention to encourage knowledge sharing.

Mixed results have been found in studies examining the relationship between learning culture and knowledge sharing. Taylor and Wright (2004) found that a climate that encouraged new ideas and focused on learning from failure was positively related to effective knowledge sharing. Hsu's (2006) case study also advocated continuous learning initiatives. Lee, Kim, and Kim (2006), however, failed to ﬁnd a signiﬁcant relationship between knowledge sharing and a learning orientation, i.e., a climate focusing on learning and trying new approaches.

The relationship between norm of reciprocity and knowledge sharing (based on social capital theory) has been examined in the context of communities of practice. A community of practice is a work-related group of individuals who share common interests or problems, and learn from each other through on-going interactions (Lave & Wenger, 1991) and this may exist within one organization or in the form of a professional network that transcends the boundaries of organizations (Brown & Duguid, 1991, 2001). Individuals' knowledge sharing in communities of practice is reciprocated by a third party rather than the recipient (i.e., generalized reciprocation occurs, see Ekeh, 1974). Norm of reciprocity, one dimension of social capital (Nahapiet & Ghoshal, 1998), refers to knowledge exchanges that are mutual and perceived as fair by both parties. Two studies that examined the norm of reciprocity within an electronic professional community showed inconsistent results. Chiu et al. (2006) found norm of reciprocity to be positively associated with individuals' sharing knowledge while Wasko and Faraj (2005) found a negative relationship. The inconsistent results suggest that the relationship may be contingent on other factors such as participants' personality and perceived usefulness of the community. For example, Kankanhalli et al. (2005) found perceived reciprocity to be positively related to participants' likelihood to contribute knowledge to the community under weak rather than strong pro- sharing norms. This suggests that strong pro-sharing norms may compensate for the low level of reciprocity in the community.

3.1.2. Management support

Management support for knowledge sharing has been shown to be positively associated with employees' perceptions of a knowledge sharing culture (e.g., employee trust, willingness of experts to help others) and willingness to share knowledge (Connelly & Kelloway, 2003; Lin, 2007d). Lee et al. (2006) found that top management support affected both the level and quality of knowledge sharing through inﬂuencing employee commitment to KM. Perceived supervisor and coworkers support and their encouragement of knowledge sharing also increase employees' knowledge exchange and their perceptions of usefulness of knowledge sharing (Cabrera et al., 2006; Kulkarni, Ravindran, & Freeze, 2006).

King and Marks (2008), however, failed to ﬁnd a signiﬁcant effect for perceived organizational support after controlling for ease of use and usefulness of KMS. It appears that management support speciﬁc to knowledge sharing is a better predictor of employee knowledge sharing. They found supervisory control (i.e., perceived supervisor inﬂuence over utilizing the KMS in the organization appropriately) was a signiﬁcant predictor of individual effort which was related to the frequency of knowledge sharing. Similarly, based on French and Raven's (1959) typology of social power, Liao (2008) found that a manager's control of rewards for desired behavior (i.e., reward power) and the employees' belief that the manager had knowledge and expertise in the area (i.e., expert power) were positively related to employees' self-reported knowledge sharing. Both social exchange theory and agency theory have been used in studies examining the management support–knowledge sharing relationship. Overall, these studies show that management support likely inﬂuences knowledge sharing.

3.1.3. Rewards and incentives

A lack of incentives has been suggested to be a major barrier to knowledge sharing across cultures (Yao, Kam, & Chan, 2007). Incentives including recognition and rewards have been recommended as interventions to facilitate knowledge sharing and help build a supportive culture (e.g., Hansen, Nohria, & Tierney, 1999; Liebowitz, 2003; Nelson, Sabatier, & Nelson, 2006). Despite the anticipated positive inﬂuence of incentives on knowledge sharing the empirical results of studies examining the effects of extrinsic rewards have been mixed.

Based on both social exchange and social capital theories, organizational rewards such as promotion, bonus, and higher salary have been shown to be positively related to the frequency of knowledge contribution made to KMSs especially when employees identify with the organization (Kankanhalli et al., 2005). Similarly, employees who perceive a higher level of incentives to share and use knowledge are more likely to report that the content of KMS is useful (Cabrera et al., 2006; Kulkarni et al., 2006). Based on a sample from Korea, Kim and Lee (2006) also found that an organizational emphasis on performance-based pay system contributed to knowledge sharing.

Contrary to the expected positive effect of rewards, Bock et al. (Bock & Kim, 2002; Bock et al., 2005) found that anticipated extrinsic rewards had a negative effect on attitudes toward knowledge sharing. Several studies found no relationship between extrinsic motivation and knowledge sharing intentions or attitudes toward knowledge sharing (Kwok & Gao, 2005; Lin, 2007c,d). Chang, Yeh, and Yeh (2007) also showed that outcome-based rewards and sufﬁcient rewards for effort did not foster knowledge sharing among product development team members.

It is important to note that the internal validity of the research on the rewards–knowledge sharing relationship may be suspect

because in these studies all measured variables were collected on the same survey making it impossible to rule out alternative causal directions for the observed signiﬁcant relationships or the results attributable to common method variance. The inconsistent ﬁndings also suggest the possibility of moderators such as personality or contextual conditions.

Researchers have also examined how different types of rewards (rather than the presence or absence of rewards) inﬂuence knowledge sharing. In a lab experiment using a dyadic decision-making scenario, Ferrin and Dirks (2003) found that a cooperative reward system positively affected information sharing between partners whereas a competitive system had the opposite effect. Similarly, studies that have examined the inﬂuence of group-based incentives generally found positive results compared to those that examined individual incentives, piece-rate and tournament incentives (e.g., Quigley, Tesluk, Locke, & Bartol, 2007; Taylor, 2006). Siemsen, Balasubramanian, and Roth (2007) found an interactive effect between individual- and group-based incentives such that the positive relationship between group reward and perceived reward for knowledge sharing was stronger when individual-based rewards were increased. L. Weiss (1999) emphasized the need to align incentives and knowledge sharing. Weiss explained that the billable hour system used for many professional jobs such as consultants or lawyers is a disincentive for knowledge sharing. Consultants or lawyers do not bill clients for time devoted to knowledge sharing because clients are unwilling to pay for services from which they do not receive an exclusive beneﬁt. Therefore, the incentives support serving clients and not sharing knowledge.

Because of the difﬁculties in manipulating reward systems in ﬁeld studies it is not surprising that most of the studies have been conducted using student samples or experiments in which scenarios or narratives were used to create different incentive conditions. Arthur and Aiman-Smith (2001) was one exception. They examined a gainsharing plan designed to increase employees' suggestions. The volume of suggestions increased rapidly following implementation of the plan, but then leveled off and started to decline over time. However, over time the proportion of suggestions representing second-order learning which challenges existing routines and thoughts became larger than suggestions representing ﬁrst-order learning (e.g., material saving suggestions).

3.1.4. Organizational structure

A functionally segmented structure likely inhibits knowledge sharing across functions and communities of practices (Lam, 1996; Tagliaventi & Mattarelli, 2006). Researchers have shown that knowledge sharing may be facilitated by having a less centralized organizational structure (Kim & Lee, 2006), creating a work environment that encourages interaction among employees such as through the use of open workspace (Jones, 2005), use of ﬂuid job descriptions and job rotation (Kubo, Saka, & Pam, 2001), and encouraging communication across departments and informal meetings (Liebowitz, 2003; Liebowitz & Megbolugbe, 2003; Yang & Chen, 2007). Overall, the results of these studies suggest that organizations should create opportunities for employee interactions to occur and employees' rank, position in the organizational hierarchy, and seniority should be de- emphasized to facilitate knowledge sharing.

3.2. Interpersonal and team characteristics

3.2.1. Team characteristics and processes

Only a few studies have investigated a small number of team characteristics and processes in relation to knowledge sharing. The results of these studies suggest that team characteristics and processes inﬂuence knowledge sharing among team members. For example, the longer a team has been formed and the higher the level of team cohesiveness the more likely team members are to share knowledge (Bakker et al., 2006; Sawng, Kim, & Han, 2006). De Vries, van den Hooff, and de Ridder (2006) examined team communication styles, agreeable and extravert styles, and found that they were positively associated with knowledge sharing willingness and behaviors. Srivastava, Bartol, and Locke (2006) studied management teams in hotel properties. They found that empowering leadership fostered knowledge sharing among team members.

3.2.2. Diversity

Research has investigated how the minority status or diversity of team members relates to knowledge sharing. Based on the similarity-attraction paradigm, Ojha (2005) showed that team members who considered themselves a minority based on gender, marital status, or education were less likely to share knowledge with team members. Sawng et al. (2006) found that R&D teams in large organizations with higher female–male ratios were more likely to engage in knowledge sharing. A few studies have examined the role of social connections with other group members in knowledge sharing (Phillips, Mannix, Neale, & Gruenfeld, 2004; Thomas-Hunt, Ogden, & Neale, 2003). These studies suggest that socially isolated members are more likely to disagree with others and contribute their unique knowledge within a heterogeneous team. The acknowledgement of team members' expertise also helps increase participation in knowledge sharing within a functionally diversiﬁed team (Thomas-Hunt et al., 2003).

It is important to note that there is a large body of research focusing on information sampling and how unshared information is pooled to facilitate group decision-making that might be useful for studying knowledge sharing in teams (e.g., Larson & Harmon, 2007; Stasser & Titus, 1987; see Argote, 1999; Stasser & Titus, 2003 for reviews). Studies of information sampling and

information pooling use experiments with student participants. Each participant is given both shared and unique information and asked to participate in a group decision. A hidden proﬁle exists in each group which leads to the optimal decision. In our discussion above we have only included a few recent studies that directly examined information/knowledge sharing.

3.2.3. Social networks

Knowledge sharing may also be embedded in broader organizational networks such as communities of practice. The ties among individuals within social networks can facilitate knowledge transfer and enhance the quality of information received (e.g., Cross & Cummings, 2004; Hansen, 1999; Reagans & McEvily, 2003). In virtual communities both the number of direct ties and personal relationships an individual has with other members have been shown to be positively related to the quantity and the perceived helpfulness of knowledge shared (Chiu et al., 2006; Wasko & Faraj, 2005). Individuals' expectation of maintaining and strengthening their social ties by frequently participating in a web-based professional community has been found to positively affect their intention to continue participating in the community (Chen, 2007).

The concept of tie strength suggests that strong ties involve higher emotional closeness whereas weak ties are more likely to be nonredundant connections and thus be associated with nonredundant information (Granovetter, 1973; Perry-Smith, 2006). Reagans and McEvily (2003) found tie strength and social cohesion to be positively related to the ease of knowledge transfer as perceived by the knowledge source, suggesting that the connections with knowledge recipients may motivate providers to share knowledge. Levin and Cross (2004) found that controlling for trustworthiness, knowledge recipients with weak ties reported more beneﬁts compared to those with strong ties.

These studies have focused more on relationships rather than individuals. The ﬁndings suggest that the existence of network connections and the associated social capital can facilitate knowledge sharing within a community of practice (e.g., Kankanhalli et al., 2005; Nahapiet & Ghoshal, 1998).

3.3. Cultural characteristics

Multinational organizations and international subsidiaries involving employees with different national cultures and languages can pose challenges for knowledge sharing (Ford & Chan, 2003; Minbaeva, 2007). To deal with these challenges, Siemens modiﬁed the reward system for knowledge sharing in their Indian and Chinese subunits to adapt to local income levels (Voelpel et al., 2005). In two related studies, Chow et al. compared Chinese and Anglo-American culture (Chow, Deng, & Ho, 2000; Chow, Harrison, McKinnon, & Wu, 1999). Both studies suggest that participants from the Chinese culture tended to share information for the good of the organization even when sharing was potentially personally disadvantageous (e.g., sharing past mistakes on the job). Chow et al. (2000) also found that Chinese participants were less likely than American participants to share their own “lessons” with someone considered an “out-group” member. Hwang and Kim (2007) measured one cultural dimension, collectivism, and found that one's collectivism was positively related to their attitude toward using the group email function in an online classroom management system to share knowledge. This relationship was fully mediated by their identiﬁcation with the group and the congruence of such behaviors with their values.

3.4. Individual characteristics

Despite studies suggesting that individuals are predisposed to certain work attitudes and behaviors (e.g., Judge & Bono, 2001), only a few studies have empirically examined the role of individual personality or dispositions in knowledge sharing. Lin (2007a) examined the moderating role of exchange ideology which is a dispositional orientation that deﬁnes the relationship between what one gives and receives from an organization. Cabrera et al. (2006) examined openness to experience and found it to be positively related to individuals' self-report of knowledge exchange. They suggest that individuals high in openness to experience tend to have a high level of curiosity resulting in a pique interest to seek others' ideas and insights. Research has also shown that employees' comfort level and ability to use computers likely inﬂuence the usage of collaborative electronic media for information sharing (Jarvenpaa & Staples, 2000) and employees with a higher level of education and longer work experience are more likely to share their expertise and have positive attitudes toward sharing (Constant et al., 1994).

The two studies that investigated the expertise–knowledge sharing relationship found mixed results. Constant et al. (1996)

found that individuals with higher expertise were more likely to share useful knowledge when other employees asked questions using a company KMS. However, Wasko and Faraj (2005) did not ﬁnd individuals' self-rated expertise to be related to knowledge sharing. Knowledge sharing does, however, appear to be contingent on individuals' conﬁdence of sharing useful knowledge with others. Several studies have shown that individuals who are more conﬁdent in their ability to share useful knowledge are more likely to express intentions to share knowledge and report higher levels of engagement in knowledge sharing (e.g., Cabrera et al., 2006; Lin, 2007c,d). On the other hand, evaluation apprehension, anxiety based on fear of negative evaluations, has been found to be negatively related to knowledge sharing (Bordia et al., 2006).

3.5. Motivational factors

3.5.1. Beliefs of knowledge ownership

Only a few studies have considered individuals' beliefs regarding knowledge ownership, i.e., whether the organization or employees own knowledge (e.g., Constant et al., 1994; Kolekofski & Heminger, 2003). Research has shown that when employees

believed they owned information (rather than the organization) they were more likely to report that they would engage in knowledge sharing (Constant et al., 1994; Jarvenpaa & Staples, 2000). This result can be attributed to employees' internal satisfaction derived from sharing their knowledge with others. Jarvenpaa and Staples (2001) later found that dimensions of organizational culture such as solidarity and need for achievement were related to ownership beliefs. Constant et al. (1994) and Jarvenpaa and Staples (2000, 2001) manipulated participants' perceptions of ownership by providing them with different vignettes (e.g., describing a scenario where the participants were asked to share presentation slides and background notes or their own expertise).

3.5.2. Perceived beneﬁts and costs

Perceived beneﬁts/costs have been one of the most studied antecedents of knowledge sharing. Social exchange theory suggests that individuals evaluate the perceived ratio of beneﬁts to costs and base their action decisions on the expectation that it will lead to rewards such as respect, reputation, and tangible incentives (Blau, 1964; Emerson, 1981). Consistent with this theory, research shows that perceived beneﬁts are positively associated with knowledge sharing while perceived costs have a negative inﬂuence on knowledge sharing. Most of the studies of perceived beneﬁts/cost were conducted in the context of professional communities.

Participating in knowledge sharing in an online community of practice has been found to be related to increased internal satisfaction, perceived obligation to reciprocate the knowledge gains from the forum, enhanced professional reputations, and helping advance the community (e.g., Lin, 2007c; Hew & Hara, 2007; Wasko & Faraj, 2000, 2005). Interestingly, Bordia et al. (2006) found a positive inﬂuence of beneﬁts on knowledge sharing only for technology-aided sharing but not in a face-to-face context. In general, prior research seems to suggest that knowledge sharing is more strongly related to employees' beliefs that their shared knowledge is useful to others than the personal beneﬁts they gain, especially in a professional network (Chiu et al., 2006; Siemsen et al., 2007; Wasko & Faraj, 2000).

Hew and Hara's (2007) qualitative study of three online professional communities examining the perceived costs that might inhibit knowledge sharing found lack of time and unfamiliarity with the subject to be the two most frequently cited reasons for not sharing knowledge. Similarly, Kankanhalli et al. (2005) found that the more time and effort employees perceived as necessary to codify knowledge in order to share knowledge the less likely they would use electronic knowledge repositories for knowledge sharing especially when there was a weak trust of other employees contributing and reusing knowledge.

3.5.3. Interpersonal trust and justice

Researchers have used social exchange theory to examine how trust and justice, two key components in interpersonal relationships (Organ, 1990; Robinson, 1996), relate to knowledge sharing. Examining trust and justice is important because knowledge sharing involves providing knowledge to another person or a collective such as a team or community of practice with expectations for reciprocity (e.g., Wu, Hsu, & Yeh, 2007).

Based on interviews conducted in 20 organizations Abrams, Cross, Lesser, and Levin (2003) identiﬁed ten behaviors and practices that promote interpersonal trust in a knowledge sharing context. They suggested that the effectiveness of these “trust builders” (e.g., engage in collaborative communication and disclose one's own expertise and limitations) depends on characteristics of the organization. Trust has also been examined as an antecedent or mediator of knowledge sharing (e.g., Butler, 1999; Lin, 2007b). Research has shown that affect- and cognition-based trust have positive inﬂuence on knowledge sharing at the dyadic and team levels (Chowdhury, 2005; Mooradian, Renzl, & Matzler, 2006; Wu et al., 2007). Further, Bakker et al. (2006) examined three dimensions of trustworthiness: capability, integrity, and benevolence. They found that individuals tended to share less knowledge with team members whom were perceived to be very capable (capability) and share more knowledge when they believed other team members were honest, fair and followed principles (integrity). Whether a trustee was believed to have good will to the trustor (benevolence), however, was not signiﬁcantly related to knowledge sharing.

Although this body of research generally has shown a positive interpersonal trust–knowledge sharing relationship,

Sondergaard, Kerr, and Clegg (2007) insightfully pointed out that trust could be a double-edged sword. Unjustiﬁed trust may cause a potential user to refrain from questioning the usefulness of the knowledge and its context for application, leading to misapplication or misuse of the knowledge. Two studies that have focused on employees' trust in management rather than trust of other employees found mixed results (Mooradian et al., 2006; Renzl, 2008).

The justice–knowledge sharing relationship has received little research attention although the role of justice in affecting the

quality of social exchange relationships between employers and their employees is well-established (e.g., Rupp & Cropanzano, 2002). Schepers and van den Berg (2007) found procedural justice to be positively related to perception of knowledge sharing among employees. Using part-time business administration students in Taiwan, Lin (2007b) found that both distributive and procedural justice had positive indirect effects on tacit knowledge sharing via organizational commitment while distributive justice also inﬂuenced knowledge sharing through trust in coworkers.

3.5.4. Individual attitudes

This line of research is heavily grounded in the theory of reasoned action and the subsequent adapted technology acceptance model which describe how individual behaviors are inﬂuenced by beliefs and attitudes (Davis, 1989; Fishbein & Ajzen, 1975). Individuals' expectations of the usefulness of their knowledge and that through sharing they can improve relationships with others have been shown to be related to positive knowledge sharing attitudes which in turn were related to knowledge sharing intentions and behaviors (Bock & Kim, 2002). Similarly, a study of hospital physicians in Korea found that attitudes partially mediated the relationship between subjective norms and physicians' intention to share knowledge (Ryu, Ho, & Han, 2003). Lin and Lee (2004) investigated senior managers' perceptions of encouraging knowledge sharing among employees rather than those of

the individual sharers. They found that managers' intention of encouragement was positively related to employee sharing behaviors. In addition, studies have found that organizational attitudes including job satisfaction and organizational commitment also foster knowledge sharing (de Vries, van den Hooff, & de Ridder, 2006; Lin, 2007a,b).

Overall, it appears that job and organizational attitudes have a signiﬁcant inﬂuence on knowledge sharing. Attitudes toward knowledge sharing have been shown to not only have a direct effect on knowledge sharing but also have an indirect effect on self- reported sharing behavior through positively inﬂuencing intentions to share (e.g., Bock et al., 2005; Lin, 2007c).

4. Knowledge sharing research: emerging issues and future research directions

4.1. Expanding the theoretical perspectives used in studying knowledge sharing

Research on knowledge sharing has drawn upon a wide range of theories. The criterion we used for identifying the theoretical foundation for the articles included in this review was quite simple: Did the article mention any theoretical perspective as the basis for the study? Based on this criterion the theory of reasoned action, social exchange theory, and social capital and network theories were the most commonly used theoretical perspectives used to study knowledge sharing (approximately one-third of the studies used one of these theories).2 However, over 20% of the studies we reviewed did not explicitly ground their research in any

theory.

Social exchange theory has been used to investigate perceived beneﬁts and costs as well as the effects of organizational justice and trust on knowledge sharing. Future research should continue to examine knowledge sharing from a social exchange

perspective which can provide insights that have yet to be examined. More research is needed to identify and investigate the unconditional trust may have different relationships with knowledge sharing (cf. Jones & George, 1998).

Future studies using generalized social exchange perspective and the theory of social dilemmas may help increase our understanding of the conditions under which knowledge sharing is likely to occur. Knowledge sharing using a KMS that facilitates

a community of practice likely creates a public goods social dilemma, i.e., individuals' rational action is to maximize personal Teng, 2002).

Several studies included in the review used social capital and network theories. Many of these studies rely on Nahapiet and Ghoshal's (1998) social capital framework (i.e., structural, relational, cognitive dimensions). However, other perspectives of social

network theories such as structural holes and closeness of network theories are relatively underutilized and may improve our social connections.

How does the “boundary-spanning” community facilitate knowledge sharing? In a social network some employees may be more critical than others depending on their network positions. Speciﬁcally, if employees bridge structural holes between

otherwise disconnected individuals or groups they allow knowledge and information to be exchanged more effectively within a 2 A wide range of other theories have been used in knowledge sharing research including expectancy theory, agency theory, knowledge-based view of the

ﬁrm, equity theory, Kelley and Thibaut's (1978) interdependence theory, Hofstede's cultural framework, theory of absorptive capacity, social power theory,

innovation diffusion theory, the similarity-attraction paradigm, social cognitive theory, economic exchange theory, Zand's (1972) model of the dynamic of trust,

job characteristics model, expectation–conﬁrmation theory, social categorization theory, the Big Five personality theory, attribution theory, balance theory, social

inﬂuence theory, Detert et al.'s (2000) framework of culture, Constant et al.'s (1994) theory of information sharing, McAllister's (1995) classiﬁcation of trust,

empowering leadership, Swan's (1999) community model, mechanistic versus organic organizational models, theory of planned action, social interdependence

theory, socio-technical perspective, Quinn and Rohrbaugh's (1981) framework for organizational effectiveness, socially-situated view of knowledge and learning,

organizational learning perspective, social categorization theory, and resource-based view of the ﬁrm.

the network make them more or less likely to share knowledge in order to maintain their position? Does the status of the

knowledge seeker make a difference?

Moreover, the results of the studies that have examined knowledge transfer from strong/weak tie perspectives suggest potential research questions on knowledge sharing. For example, Hansen (1999) showed that weak ties helped transfer less

complex knowledge across divisions in less time but hindered the transfer of more complex knowledge. This suggests that subordinate) and personal friends versus colleagues.

The inﬂuence of attitudes toward knowledge sharing on knowledge sharing intentions and behavior has been investigated rather extensively using the theory of reasoned action. However, few studies have examined their antecedents. For example, Kwok

and Gao (2005) showed that the richness of channel for knowledge sharing and one's absorptive capability to learn from others sharing.

Furthermore, although the role of motivation has been recognized and emphasized in the knowledge sharing literature (e.g., motivation theories such as expectancy theory and social cognitive theory (e.g., Chiu et al., 2006; Quigley et al., 2007) have not participation in training and development (Maurer & Tarulli, 1994; Noe & Wilk, 1993).

In some organizations employees consider knowledge sharing an extra-role behavior, i.e., it is not included in formal job descriptions, while in others it is considered an in-role behavior because knowledge sharing is expected and is evaluated and/or

rewarded (e.g., Ewing & Keenan, 2001; Stevens, 2000). Future research needs to investigate whether there are differences in the considered an extra-role behavior.

Finally, more research drawing upon the team composition literature is needed to increase our understanding of how to engage team members to enhance knowledge sharing and positively affect team and organizational performance. For example, surface- level and deep-level diversity (i.e., demographic differences and attitudinal differences) (Harrison, Price, & Bell, 1998) within a

when teams are managing multiple tasks (Marks, Mathieu, & Zaccaro, 2001).

4.2. Reasons for sharing or not sharing knowledge

It is important to recognize that employees may decide to share (or not share) knowledge for various reasons. For example, as we reviewed earlier, research has shown that individuals may share knowledge because they enjoy helping others (or altruism) or

as a result of reciprocation (e.g., Kankanhalli et al., 2005). While reciprocation arguably has attracted most attention we believe there are other reasons that deserve further research attention.

4.2.1. Impression management and attribution

Employees may choose to share knowledge as a way to help develop personal relationships with peers or to simply manage their impression on others. These different intentions may inﬂuence with whom knowledge is shared (e.g., supervisors, coworkers within

the same unit, or managers across units whom they do not know at a personal level). Employees' personal characteristics may also likely to be viewed less favorably and the recipient is less likely to reciprocate by sharing knowledge.

4.2.2. Power perspective

One major inhibitor of knowledge sharing is that knowledge can be considered a source of power and superiority (e.g., Gupta &

evaluations from human resource systems (e.g., performance appraisal, stafﬁng, etc.) and personal gains such as cash bonuses, employees' perceptions of how knowledge may serve as a source of referent, expert, and reward power are needed.

Although individuals may refrain from sharing knowledge for fear of losing power it is also feasible that individuals can increase their expert and referent power by sharing knowledge. For example, high self-monitors may be more likely to identify

circumstances when they could gain expert power through knowledge sharing. As a result, high self-monitors might be more of receiving personal recognition.

4.2.3. Issues derived from evaluation apprehension

Evaluation apprehension inhibits knowledge sharing (Bordia et al., 2006). Evaluation apprehension may result from self- perceptions that shared knowledge is inaccurate, not valued, and likely to result in unfavorable criticism from others. How can

evaluation apprehension be reduced? From a situational perspective, research has shown that organizational culture that emphasizes trust and innovation is conducive to knowledge sharing. Future research is needed to examine whether such cultures

help reduce evaluation apprehension by reducing the likelihood that knowledge shared will be critically judged.

Bordia et al. (2006) directly examined the evaluation apprehension–knowledge sharing relationship. However, the fear associated with possible negative evaluation also relates to one's self-evaluation. Although we found several studies have examined individuals'

knowledge self-efﬁcacy, research on related but unique concepts such as organization-based self-esteem (OBSE) is necessary to better

understand the role of self-evaluation in knowledge sharing.

OBSE, a core component of self-evaluation and a speciﬁc form of self-esteem, has been deﬁned as “the degree to which an individual believes him/herself to be capable, signiﬁcant, and worthy as an organizational member” (Pierce & Gardner, 2004, p. 593). Self-

consistency theory suggests that individuals tend to behave in a way that is consistent with their current views of self-worth (Korman, more affected by their level of trust with the knowledge recipient.

Similarly, recent research on a broad personality concept, core self-evaluations, which consists of global self-esteem, generalized self-efﬁcacy, locus of control, and emotional stability (Judge, Bono, & Locke, 2000), may also contribute to our

understanding of knowledge sharing. It would be interesting to investigate if core self-evaluations inﬂuence knowledge sharing

through inﬂuencing perception of the usefulness of knowledge sharing and reducing evaluation apprehension.

Furthermore, research investigating different types of interventions designed to help enhance one's knowledge sharing-related self-efﬁcacy is needed. For example, receiving organizational recognition, positive feedback on the knowledge shared, or feedback

on how the knowledge shared has helped coworkers or the company may facilitate knowledge sharing self-efﬁcacy. When the value of one's knowledge is recognized by others, individuals may gain an enhanced self-perception of competency, credibility,

and conﬁdence (cf. Stasser & Titus, 2003) which increases the likelihood they will share their knowledge with others.

4.2.4. Social costs

Research on hidden proﬁles focuses on how information sampling affects team decision-making (Stasser & Titus, 2003). The issue of social costs associated with unique information may help us understand why certain information/knowledge is less likely

to be shared. Speciﬁcally, it is important for future research to examine when individuals are likely to share knowledge that might

boss or an inﬂuential peer.

4.2.5. Knowledge sharing as a learning experience for the sharer One reason employees seek knowledge in an online community is to learn (Wasko & Faraj, 2000). However, there are also circumstances when knowledge sharing may be considered a learning process for the sharer. For example, employees high in learning goal orientation may perceive knowledge sharing as a learning opportunity because they will not be able to successfully explain something well to their peers unless they fully understand it themselves. If employees are motivated to share knowledge

with their peers but they are not sure if they are able to communicate the knowledge in a manner in which it will be understood,

they are more likely to use knowledge sharing as an opportunity to deepen their own understanding and ﬁnd a better way to be less likely to share knowledge.

Moreover, in an online organizational community of practice, knowledge sharers may learn others' perspectives on the same issue or problem being discussed. Additionally, employees may share their ideas with others to further develop them and to

facilitate creativity (cf. Oldham, 2003).

4.3. Examining knowledge sharing from interactional and process perspectives

Researchers have suggested that personality characteristics likely affect how individuals interpret and respond to work environment stimuli because of predispositions to perceive stimuli in a certain way (e.g., Shoda & Mischel, 1993; Weiss & Adler,

1984). We found in our review of the literature that researchers have tended to investigate the direct relationship between knowledge sharing.

Future research is also needed to examine how personality affects individuals' responses to organizational work practices designed to motivate knowledge sharing. For example, given the mixed results we found in the literature, more research on the extrinsic

rewards–knowledge sharing relationship is needed. It is possible that the effectiveness of extrinsic rewards for motivating knowledge that reward knowledge sharing.

More research is also needed to help us understand the mechanisms underlying the observed relationships found in the literature. For example, few studies have examined the relationship between team characteristics and knowledge sharing,

particularly the process through which team characteristics affect knowledge sharing. Team-level trust and cohesiveness may 2007). Also, leader–member exchanges may mediate the justice–knowledge sharing relationship.

4.4. Understanding differences between interpersonal and technology-aided knowledge sharing

Few studies have examined the differences between knowledge sharing via KMS and face-to-face interactions (Bordia et al., 2006 is an exception). This is important because the factors inﬂuencing the decision to share knowledge in face-to-face versus

technology-aided interactions are likely different, e.g., employees who are high in extraversion may be more likely to share experience of sharing knowledge in a team or a KMS.

Almost all the studies of knowledge sharing communities were conducted using electronic knowledge systems (for an exception see Lin, 2007c). Using electronic systems is only one way of sharing knowledge. Future research needs to investigate

how perceived beneﬁts and costs may differ in face-to-face knowledge sharing communities compared to an electronic KMS. Despite the increasing use of technology to facilitate knowledge sharing within organizations, face-to-face interactions are still an

indispensible mechanism for knowledge sharing especially when more “sticky” knowledge is involved (Szulanski, 2000).

4.5. The inﬂuence of organizational and national culture on knowledge sharing

changing of organizational culture and regulate employees' behaviors (e.g., Swart & Kinnie, 2003), future research needs to investigate how the culture/norms in professional communities of practices are established.

The majority of studies that have examined non-Western cultural inﬂuences on knowledge sharing have been conducted in Chinese cultures. More studies on how cultural differences affect knowledge sharing in emerging economies in countries in Africa,

the Middle East, and South America are needed.

Also, more research is needed to investigate how in-group/out-group membership inﬂuences knowledge sharing. For example, how can the inﬂuence of in-group/out-group membership on knowledge sharing be reduced? Would providing training to

individuals on how to present their lessons or negative experiences in a more neutral way help alleviate concerns over sharing them with an “out-group” member? This issue might be more complex in a multinational organization where the inﬂuence of in-

group/out-group differences may vary across cultures.

4.6. Methodological issues in knowledge sharing research

Approximately one-third of the studies included in this review were qualitative studies which have used interviews, observation, and/or archival documents analysis to answer their research questions. Only a small number of the qualitative studies

also collected quantitative data for analysis. An important strength of the studies reviewed was that the majority were conducted organizations can inﬂuence employee perceptions of knowledge ownership to increase knowledge sharing.

It is important to recognize that the quantitative studies of knowledge sharing included in this review suffer from several signiﬁcant limitations. First, the majority of studies measured knowledge sharing using either willingness (or intention) to share

knowledge or self-reported knowledge sharing behaviors. Also, several measures combined knowledge sharing with utilizing interest separately from the measures of knowledge sharing.

Few studies we reviewed measured objective knowledge sharing and over half of them were experiments using a student sample (for exceptions see Arthur & Aiman-Smith, 2005; Wasko & Faraj, 2005). This observation might be attributed to the

difﬁculty in collecting third-party data and archival data in a ﬁeld study. In experiments using student samples it is relatively easy 2003).

More research that uses objective measures of knowledge sharing is needed but objectively measuring knowledge sharing especially in ﬁeld studies poses some challenges. However, researchers can take several steps to increase the internal and external

validities of knowledge sharing research. First, because measures of knowledge sharing are not readily available in the literature sharing).

Second, researchers interested in examining knowledge sharing in an organizational online community of practice or other electronic KMS may obtain a record of knowledge sharing. Following the process used by Wasko and Faraj (2005) a coding scheme

can be developed for subject matter experts (raters) to use to evaluate the knowledge sharing dimensions of interest in a measures.

In studies of knowledge sharing in teams it would be beneﬁcial for team members to provide ratings of knowledge sharing. However, these ratings will not likely capture an individual sharing knowledge across teams. Ideally, to comprehensively capture an individual's overall knowledge sharing performance, the employee's knowledge recipients within a speciﬁed period of time can be identiﬁed and asked to evaluate each case of knowledge sharing all of which will then be combined to be used as the indicator.

Because this is almost impossible to accomplish proxies will have to be used (e.g., the average ratings of a group of peers and/or the

rating from managers who have the opportunity to observe and obtain feedback from others on the employee's knowledge

sharing).

Third, more empirical studies involving ﬁeld experiments and using longitudinal research designs are needed because such

designs can help establish the causal relationship between individual, team, and organizational factors and knowledge sharing.

Studies are needed to evaluate interventions which alter organizational work practices such as rewards or performance

management systems to facilitate knowledge sharing. For example, when a company plans to pilot an intervention before

implementing a KM initiative company-wide, ideally, two comparable units of an organization may be carefully chosen with one

unit ﬁrst serving as the control condition and then implementing the same intervention. This design allows researchers to

compare overall differences in knowledge sharing between the two units as well as differences that occur across time.

Researchers should also consider conducting longitudinal studies that measure knowledge sharing before and after an

intervention. A longitudinal design with repeated measures can also help us better understand the reciprocal reinforcing effect.

For example, trust and organizational culture may help enhance knowledge sharing which may in turn reinforce the trust and the

culture.

Finally, phenomena such as knowledge sharing do not reside within one level of analysis but rather are hierarchical, which

necessitates an examination across levels to capture their complexity (Klein & Kozlowski, 2000). For example, Quigley et al. (2007)

used both individual and dyadic levels to examine dyadic knowledge sharing. More work using multilevel analysis is needed to

appropriately examine knowledge sharing dynamics. It is possible that some team- or community-level factors such as team size

and autonomy and individual-level factors may jointly inﬂuence knowledge sharing of the team or community members.

5. Practical implications of knowledge sharing research

There are several implications for human resource management practices we can draw from the consistent ﬁndings in the existing knowledge sharing literature. First, a culture emphasizing trust and innovation is conducive to knowledge sharing. It appears that the importance of organizational culture lies in its ability to have a direct effect on employees' knowledge sharing behavior as well as an indirect effect through inﬂuencing managers' attitudes toward knowledge sharing. Human resource practices including fairness in decision-making and open communication likely promote an organizational culture that supports knowledge sharing (Cabrera & Cabrera, 2005). An important caveat is that a positive culture alone may be insufﬁcient to facilitate knowledge sharing. Research suggests it is important to design KM initiatives that are aligned with existing working habits and routines and link knowledge sharing to company goals and values (Hickins, 1999; McDermott & O'Dell, 2001). Because the implementation of a KMS or a new strategic emphasis on knowledge sharing involves asking managers and employees to adopt new attitudes and behaviors related to knowledge sharing, a change management strategy needs to be considered. This strategy needs to create a need to change the status quo, and include activities designed to insure that employees are satisﬁed with the change process (e.g., reduce the stress level of company employees during change) (M.C. Jones, Cline, & Ryan, 2006; Taylor & Wright, 2004).

Second, research has shown that management and supervisor support is critical for the success of KM and knowledge sharing initiatives. Organizations should require and reward managers for providing the support necessary for encouraging knowledge sharing among employees. Management support for knowledge sharing may be demonstrated by emphasizing sharing “lessons learned” instead of “mistakes made” (Teo, 2005).

Third, prior research appears to suggest the importance of increasing individuals' conﬁdence in sharing useful knowledge with others. Bryant's (2005) study suggests that knowledge sharing can be enhanced by increasing employees' self-efﬁcacy through training. It may also be important for organizations to help shape and facilitate employee perceptions of knowledge ownership which have been found to enhance their knowledge sharing because of internal satisfaction.

Finally, although there has been only a small number of cross-cultural studies conducted to date, the results suggest that organizations need to pay close attention to cultural characteristics in developing human resource practices that will facilitate knowledge sharing, i.e., there is not one universal set of practices that can be used to facilitate knowledge sharing in global and multinational organizations. For example, organizations may need to make adjustments to the type of incentives provided to ﬁt the cultural contexts (Voelpel et al. 2005).

6. Conclusion

This review provides an organizing framework for current research, discusses emerging issues, and identiﬁes future research needs and practical implications of knowledge sharing research. Our review highlights that although there is a growing multidisciplinary literature on knowledge sharing, much remains to be studied.

Acknowledgement

We wish to thank the Associate Editor David G. Allen and the anonymous reviewers for their helpful comments on the manuscript.

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