In many previous studies related to knowledge sharing (KS) the focus has been on exploring the attributes of individuals that are useful in encouraging those who possess knowledge to share it. For example, Wu, Lin, Hsu, and Yeh (2009) pointed out that interpersonal trust is an important determinant of KS. Lin (2007) identified extrinsic and intrinsic motivations as important to KS intentions. However, employees do not work in isolation; each of them is embedded in a formal or informal social network with relational connections to coworkers (Gargiulo, Ertug, & Galunic, 2009)

Hansen (1999) defined knowledge sharing as the provision or receipt of task information, know-how, feedback, and other pertinent issues. KS involves the exchange of useful information and experience between two or more people in their day-to-day interactions. Hence, KS is related to the interactions among different employees. In this study, we considered the sharing of knowledge among different employees within a company as a KS network and the employees as actors and nodes within the KS network. Each of the employees is embedded in the KS network. Recently, the role of network connections in organizational learning and knowledge management has gained much attention (e.g., Cross & Cummings, 2004; Gargiulo et al., 2009; Tsai, 2001). Therefore, the study of personal networks and information accessibility will enrich understanding of KS.

Through networking, individuals can gain access to valuable information and knowledge. Networking promotes knowledge sharing and transfer among members, providing employees with opportunities for learning and cooperation. KS networking is a social interaction process and, through socialization and interaction, individuals share relevant information, ideas, and expertise with one another (Cross & Cummings, 2004). Within a KS network, each employee plays the part of a network node and occupies a different network position that provides different opportunities for access to new knowledge that is pertinent to his or her work. A central position in the KS network provides greater access to knowledge and innovative ideas for employees (Tsai, 2001).

Based on a network perspective on KS, in this study we examined two important concepts of network centrality – in-degree centrality, and in-closeness centrality – within a KS network. In-degree centrality of a KS network presents the total number of employees from whom a focal employee has directly received knowledge. In-closeness centrality of a KS network describes the sum of the shortest distances from all other employees to a focal employee; it is a measure of a focal employee’s opportunity to receive knowledge from coworkers, both directly and indirectly. The smaller the sum of the shortest distances a focal employee has, the greater the in-closeness centrality he or she has and the more quickly he or she can access knowledge through direct and indirect connections to coworkers. In other words, the greater an employee’s in-degree and in-closeness centralities, the more knowledge sources the employee has.

Knowledge is considered to be a strategically important resource (Barney, 1991). The ability to absorb and share information becomes a competitive advantage, particularly in a knowledge-intensive economy (Grant, 1996). KS creates opportunities to maximize the ability to assimilate and apply new ideas that are widely considered to contribute substantially to the effectiveness of an organization and personal work performance. Therefore, the effort and attention paid to improving KS has been increasing. Because a central network position is associated with more knowledge sources we argued that employees at the center of networks have more resources than others in the network to achieve better work performance.

In addition to exploring the consequences of KS network centrality, our aim in this study was to identify the determinants of KS network centrality. An individual’s attitude toward KS is driven by an anticipated reciprocal relationship (Wu et al., 2009). Generally, trust is the essential component of a social exchange relationship; the higher the degree of trust between the trusted and the trustee, the stronger a social exchange relationship may be between them (Blau, 1964; Wasko & Faraj, 2005). Trust is the positive psychological expectation that another will not act opportunistically when an individual agrees to make himself or herself vulnerable to another (Rousseau, Sitkin, Burt, & Camerer, 1998). Thus, if employees are perceived as very trustworthy by their coworkers, those coworkers will be more willing to share their knowledge without worrying that they are being taken advantage of. Therefore, employees with higher levels of perceived trust by coworkers might be more likely to become the central point within a KS network. Furthermore, because the perception of trust goes directly from coworkers to a focal employee, trust has a stronger influence on in-degree centrality than it does on in-closeness centrality, which is involved with direct and indirect relational connections. As a result, drawing on a network perspective, in this study we examined the relationships between perceived trust, KS network centrality, and work performance.

Method

Participants and Procedure

This study was conducted in four companies in Taiwan; two manufacturing firms, one insurance company, and one sales agency. There were two reasons for the selection of these four companies. First, we wanted to have a sample set consisting of different types of industries. In this way, we could ensure that the results of this study are more generalizable. Second, because data collection in a network structure is difficult, it was important to have people employed in each of these four companies to help us collect the data. A network consisted of one sector or several sectors in each company.

The participants from each network were contacted and given a sociometric questionnaire by an employee working in the same company. We collected 170 completed questionnaires from employees in the four companies. The size of the networks varied from 32 to 64 employees; being 32, 33, 41, and 64, respectively. Male employees accounted for 57.6% of all participants, with the majority of participants being married and aged between 26 and 45. Most of them had received at least a high school education, had between 1 and 10 years of working experience, and held a nonmanagerial position.

Three steps were taken for analyzing the data collected. First, the social network analysis software, Ucinet, was used to calculate the scores of network centrality. Second, we conducted a correlation analysis in order to understand the correlations among the focused variables. Third, regression analyses were conducted to test the predictions.

Measures

Work performance. An employee’s work performance was rated by his or her sectional or departmental supervisors or managers on a 5-point Likert scale ranging from not very good (1) to excellent (5). This not only allowed us to get a more objective assessment of work performance but also helped to reduce common method bias by separating our sampling sources.

KS network positions. There were two KS network centralities in this study: in-degree centrality and in-closeness centrality and both were measured at the individual level. To identify an employee’s centrality, in this study we asked the respondents, “To whom do you usually talk about work information, knowledge, and experience?” A list of coworkers was provided from which respondents selected their answers. According to these answers, we made a relational matrix of interindividual links for each of the companies. Drawing on these relational matrices, we then calculated the in-degree and in-closeness centralities for each employee.

Trust. We explored the interindividual trusting relationships in the companies with the following question: “To whom do you chat about your personal thoughts and private affairs?” Using the data collected from the responses to this question, we created a relational matrix measuring interindividual trust. In-degree centrality was used as a measure of trust. The greater an employee’s in-degree centrality of trust, the more perceived trust the employee had.

Control variables. Gender (coded 1 for male, 0 for female), age (classified into nine groups, ranging from younger than 20 to older than 56), education (four categories: high school, college, university undergraduate, and university postgraduate), and seniority (eight groups ranging from less than one year to over 19 years) were used as control variables.

Results

Correlations among Trust, KS Network Centrality, and Work Performance

The correlations among trust, KS network centrality, and work performance are shown in Table 1. In-degree, in-closeness of KS network centrality, and trust were all found to be strongly positively correlated with work performance. Trust was strongly positively correlated with the in-degree of KS network centrality, but because there was no correlation with the in-closeness of KS network centrality we did not carry out any further tests on the influence of trust on in-closeness of KS network centrality in the following regression analysis.

Predictions of KS Network Centrality and Trust

Hierarchical regression analyses were conducted in order to examine the effects of KS network centrality on work performance and the influence of trust on in-degree of KS network centrality. Table 2 and Figure 1 contain the results of the hierarchical regression analyses. Model 1 was used to test the main effects of KS network centrality and it was found that both the in-degree and in-closeness of KS network centrality had significantly positive effects on work performance; when both kinds of KS network centrality increased this enhanced the possibility that an employee performed better in their work. Model 2 was used to examine the influence of trust on the in-degree of KS network centrality and it was found that the positive effect of trust on the in-degree of KS network centrality was significant. An employee with a higher level of KS in-degree centrality than his or her coworkers is likely to have a higher level of perceived trust by coworkers.

Discussion

In this study we focused on the relationships among trust, KS network centrality, and work performance. The empirical results provide valuable support for the established inferences. First, the empirical results demonstrate that higher centrality within a KS network, either in terms of in-degree or in-closeness, can lead to a better work performance. Within a KS network, how much access an employee has to external knowledge is dependent on his or her network centrality. By occupying a central position in a network either in terms of in-degree or in-closeness, an employee can more easily gain useful knowledge from coworkers and thereby achieve better work performance. This result not only adds to the generalization of the effect of KS on performance, but also demonstrates the importance of accessing new knowledge through the network. Taking a network perspective on the KS issue could be a promising approach for future studies of KS.

We were also concerned with the perspective taken in previous studies, that trust plays an important role in knowledge-sharing activities (Currie & Kerrin, 2003; Wu et al., 2009; Zárraga & Bonache, 2003). Our empirical results show that the higher the perceived trust of an employee, the higher the KS in-degree centrality the employee can occupy. If an employee has a high degree of perceived trust, the employee is more likely to be able to access external knowledge in comparison to an employee who is not considered trustworthy. According to the social capital perspective (Nahapiet & Ghoshal, 1998), people who have more social capital will have advantages over those with less social capital. Trust is an important factor of social capital. Our results in this study support the idea that trust can help employees to improve their network position and access more knowledge to achieve better work performance. Thus, results gained in this study also contribute to empirical evidence relating to social capital theory.

With regard to the research limitations, first, the sample size was too small to allow us to draw broad conclusions. However, it is worth noting that network studies generally have smaller sample sizes because of the difficulties and challenges associated with data collection in the network. The boundaries and completeness of a network affect the quality of measures for network characteristics. It is possible that we may not have mapped a perfect network and therefore missed some information. Another limitation may come from the fact that although occupying a central position brings benefits to employees, it also has the opposite effect. A very central position may lead to knowledge overload, which can be detrimental for employees who occupy this position (Perry-Smith, 2008). However, we did not discuss this effect. In future research, this issue could be better addressed by taking both positive and negative angles. We limited our discussion of KS network centrality to the aspect of receiving knowledge without considering the given knowledge. Future researchers could take into account both kinds of knowledge-sharing activity (e.g., KS in-degree centrality vs. KS out-degree centrality). Finally, KS network centrality predominantly relates to the quantity of knowledge received. We used KS network centrality to present the construct of KS, and this has only a limited ability to capture the quality of receiving knowledge.

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This research was supported financially by grants 96-2416-H-155-028-MY2 and 99-2410-H-231-002 from the National Science Council of Taiwan.

Wei-Li Wu, Department of International Business, Chien Hsin University of Science and Technology, No. 229 Jiansing Rd., Jhongli City, Taoyuan County 320, Taiwan, ROC. Email: [email protected]