Can social networking improve individual competitiveness? Exploring the effects of social network centralities on knowledge acquisition and work efficiency in organizational work teams.
Recent years have witnessed the increasing popularity of using social networking websites such as Facebook and Twitter among people from all walks of life (Ellison, Steinfield, & Lampe, 2007; Thelwall, Buckley, & Paltoglou, 2011). Meanwhile, organizational scholars and practitioners have started to recognize and embrace the hidden power of social networking in organizational practices (Contractor, Monge, & Leonardi, 2011). It is found that the traditional hierarchical organizational chart has become inadequate in capturing how information flows and how work actually gets done in today's organizations (Cross & Borgatti, 2004). Instead, organizational employees are increasingly communicating, collaborating, and sharing critical information through informal and emergent social networks (Monge & Contractor, 2003). Despite the bourgeoning popularity of studying organizational employees' social networks, a crucial question remains unanswered in extant literature: can social networking really improve individual competitiveness in the workplace? To answer this question, this study seeks to utilize a social network theoretical perspective to examine the effects of organizational work team members' social networking structures on three crucial facets of individual competiveness: knowledge acquisition (the quantity and quality of information acquired) and work efficiency. Specifically, this study focuses on testing the influence of two prominent properties of social networks, degree and betweenness centralities, on team members' competitive behaviors. The findings from this study will not only enrich current research on the relationship between social networking and individual competitiveness enhancement, but also provide insights for practitioners who hope to unearth the hidden power of social networking in today's workplaces.
LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
Theoretical Perspective of Social Networks
A thriving approach to studying how organizational work team members communicate and collaborate with each other is the social network theoretical perspective (Kilduff & Tsai, 2003; Monge & Contractor, 2003). A social network is defined as of a set of actors (nodes) and the relationships (ties) among these actors (Scott, 2000). The nodes of a social network can be individuals, groups, organizations, communities, and even nations. The ties of a social network represent certain types of interrelationships among the nodes. Traditionally, a relational tie in social network research is limited to "social" relationships such as friendships and interpersonal communication relationships. In recent years, more research attention has been paid to expanding the scope of "social" networks to a broader horizon, which includes "non-social" relationships such as work collaboration, information sharing, strategic alliances, partnerships, etc. (Monge & Contractor, 2003). The social network theoretical perspective entails two fundamental characteristics that distinguish it from other theoretical perspectives in social science research (Katz, Lazer, Arrow, & Contractor, 2004). First, the social network perspective focuses more on the relationships and interactions amongst nodes in the network (e.g. communication and collaboration relationships between individuals) rather than the intrinsic attributes of nodes themselves (e.g. gender, age, income, education level of individuals). Second, a specific type of relationship between two nodes in a social network is influenced by not only existing relationships between the two, but also by their relationships with other nodes in the same network (Monge & Contractor, 2003). For example, in an organizational work team, team member A's advice seeking from member B depends on not only A's trust of B, but on A's trust, or lack thereof, of other team members as well.
The social network theoretical perspective has been applied to examining individual and group behaviors in organizational settings in a variety of ways (Nohria & Eccles, 1992). Previous research has studied how organizational knowledge learning and transfer could be influenced by different configurations of network structures such as nodal centralities (Tsai, 2001), structural holes (Burt, 1992, 2005), and network centralization (Rulke & Galaskiewicz, 2000). A series of research has examined the effects of organizational members' social networks on information seeking and knowledge sharing (Palazzolo, 2005; Su & Contractor, 2011). Other research has studied how employees' social network structures could influence their job satisfaction (Su, Huang, & Contractor, 2010), organizational innovation (Burt, 1987; Monge, Cozzens, & Contractor, 1992), and turnover (Feeley, Hwang, & Barnett, 2008). A more recent study even applied the social network perspective to understanding the influence of leadership in online communities based on users' central positions in the online networks (Huffaker, 2010).
Despite the wealth of literature that has documented the importance and benefits of leveraging people's social networks, little is known about the direct relationship between organizational work team members' social networks and their competitiveness enhancement. Although previous research suggests that a central position in the social network would positively influence individual competitiveness (Brandon & Hollingshead, 2004), there is scant empirical evidence that provides adequate support for such a prediction. Thus, this study will utilize the social network theoretical perspective to predict the potential influence of organizational work team members' social networking structures on their competitiveness, and then use empirical data to test the hypotheses. Specifically, this study focuses on studying three crucial facets of individual competiveness in the workplace: knowledge acquisition (the quantity and quality of information acquired) and work efficiency.
Effects of Social Networking on Knowledge Acquisition
In the emerging knowledge economy, individual knowledge has become a critical driving force of employees' performance and organizational profits (Badaracco, 1991). Faced with increasing task interdependence in today's work teams, individuals often find themselves in need of external knowledge to facilitate their task accomplishments. Thus, the ability to find and acquire knowledge from their team members with the expertise they need creates a new type of competitive advantage for team members (Argote & Ingram, 2000). Members who are able to efficiently and effectively acquire knowledge from others would garner a greater level of competitiveness in the workplace. There are two critical aspects of knowledge acquisition that would potentially lead to an enhanced level of individual competitiveness: the quantity and quality of knowledge acquisition. The more information and the higher quality information members are able to retrieve from others, the more likely they will utilize accurate and valuable information to support decision making and problem solving, and thereby improve their competiveness within the team. As the social network has increasingly become a prevailing vehicle for seeking and sharing work-related information (Cross, Rice, & Parker, 2001), it is imperative to understand how the quantity and quality of knowledge acquisition can be influenced by team members' social networking structures.
A prominent property of an individual's social networking structures is degree centrality, which measures the number and strength of a node's direct relational ties with others in the social network (Wasserman & Faust, 1994). When applying the concept of degree centrality to understanding team members' social networking structures, a member who has a high degree centrality in the social network is the one who communicates directly and frequently with a large number of coworkers in the same work team. This type of networking structure puts this member in a central position in the social network such that this member is actively playing a "hub" role by communicating directly with many others. Previous research suggests that an individual with a high degree centrality in the social network is more likely to develop an accurate recognition of others' knowledge (Brandon & Hollingshead, 2004). Other research suggests that a strong communication relationship has a positive influence on transferring complex knowledge across departmental boundaries within the organizations (Hansen, 1999). Thus, when a team member has a high degree centrality in the social network, s/he garners a greater level of advantage in accurately identifying "who knows what" and accessing expertise resources within the team. Consequently, s/he is able to acquire a large volume of information, as well as high quality information, from the social network in which s/he plays a central role. Therefore, this study proposes the following hypotheses.
Hypothesis 1a: The higher degree centrality team members have in the social network, the more information they will acquire from other members.
Hypothesis 1b: The higher degree centrality team members have in the social network, the higher quality information they will acquire from other members.
In addition to degree centrality, another centrality measure of the social network, betweenness centrality, also exerts a noteworthy influence on individual competitiveness in organizational work teams. Depending on how and with whom team members are connected in the social network, they vary in the level of betweenness centrality, which measures the extent to which an individual communicates directly with those who do not communicate amongst themselves (Freeman, 1979). Therefore, instead of playing the "hub" role enabled by high degree centrality, individuals who have high betweenness centralities undertake a "brokerage" role by connecting those members who are mutually disconnected. Previous research asserts that people with high betweenness centralities are filling the structural holes in the social network and are able to harvest a greater level of social capital in the network (Burt, 1992, 2005). Other research suggests that individuals who bridge those mutually disconnected members tend to develop a more accurate perception of each other's knowledge and make a more effective utilization of individually possessed expertise (Moreland, 1999). Thus, team members with high betweenness centralities are situated in a unique and advantageous position in the social network, which allow them to efficiently identify and access isolated information sources within the team (Burt, 1992). As a result, these members would gain a greater level of competitiveness in the organization by virtue of acquiring knowledge of greater quantity and quality. Therefore, this study proposes the following hypotheses.
Hypothesis 2a: The higher betweenness centrality team members have in the social network, the more information they will acquire from other members.
Hypothesis 2b: The higher betweenness centrality team members have in the social network, the higher quality information they will acquire from other members.
Effects of Social Networking on Work Efficiency
Knowledge acquisition alone does not guarantee improved performance in the workplace. The ability to attain information of high quantity and quality is at best a necessary but not sufficient condition for improved individual performance in the work team. Thus, it is imperative to examine the effects of social networking centralities on individuals' work efficiency, as well. First, members with high degree centralities in the social network have a large number of direct communication relationships with others in the team; thus, they are expected to have the advantage in efficiently requesting and acquiring resources needed to accomplish their assigned tasks. In addition, when problems arise and solutions are needed, they can tap into their social networks for expertise and help. Therefore, a high degree centrality in the social network would be associated with high work efficiency. Second, for team members with high betweenness centralities, although they may not have as many direct communication relationships with others as members with high degree centralities, their "brokerage" roles allow them to access resources that are isolated and hidden from the rest of the network. Due to their strategic positions in the network, they have the privilege and advantage in maneuvering communication and information flow between those disconnected members, which allows them to efficiently garner information and resources they need to support and facilitate their own work processes. Therefore, this study proposes the following hypotheses.
Hypothesis 3a: The higher degree centrality team members have in the social network, the greater efficiency they will display at work.
Hypothesis 3b: The higher betweenness centrality team members have in the social network, the greater efficiency they will display at work.
METHOD AND ANALYSIS
To empirically test the hypotheses proposed above, this study collected survey data from 110 employees from 9 work teams of two multi-national consulting firms. All teams were intact, real-world work groups in which team members had been working together for over 6 months at the time of this study. Team members had an average age of 32 (SD = 3.96) and an average tenure of 2.76 years (SD = 2.11) in the team. Teams ranged in size from 8 to 20 members. Data were collected through an online survey that was customized and distributed to each member via email or in person. The response rate was 100%, although a few members chose not to answer a few questions.
The survey asked respondents to report the frequency of their social communication with every other member in the team. The response set was measured on a 7-point scale from never to once per day. This data was converted into a matrix where each cell represents the mean value of the social communication frequency reported by the corresponding pair of team members. Then this matrix data was imported into UCINET 6.0 (Borgatti, Everett, & Freeman, 2002), a computer program that is specifically designed for social network analysis. By using this program, this study computed the normalized degree centrality and betweenness centrality scores for each team member. The range of a normalized network centrality is between 0 and 100. This study examines two aspects of work team members' knowledge acquisition: the quantity and quality of information acquired from other members in the same team. The quantity of knowledge acquisition was measured by a 3item scale in the survey that asked respondents to report the amount of information they had acquired from other members in the team. The quality of knowledge acquisition was measured by a 3-item scale as well. These three questions asked respondents to rate the quality of information they had acquired from others in the team. The responses to all 6 question items were on a 5-point scale ranging from strongly disagree to strongly agree. The inter-item reliability measure (Cronbach's alpha coefficient) was .83 for the quantity measure and .86 for the quality measure. Work efficiency was measured by a 3-item scale in the survey that asked each respondent to rate the efficiency of her/his own work. The responses to each item were on a 5-point scale ranging from strongly disagree to strongly agree. The inter-item reliability measure was .87.
This study used multiple regression analysis to test each set of hypotheses. As this study focuses on testing the effects of social network centralities on different facets of work team members' competitiveness, the two predictor variables entered in each regression equation remained the same: team members' degree and betweenness centralities. The dependent variable predicted in each equation was the quantity of knowledge acquisition, the quality of knowledge acquisition, and work efficiency respectively. Given the nested nature of the data (i.e. 110 individuals nested within 9 work teams which are also nested within 2 consulting firms), this study followed experts' recommendations (Raudenbush & Bryk, 2002; Snijders & Bosker, 1999) and removed the grouping effects by group-mean-centering the raw data prior to regression analyses.
Table 1 reports the descriptive statistics and zero-order correlations among all variables examined in this study. Table 2 summarizes the results of multiple regression analyses used to test each hypothesis. The first set of hypotheses (H1a and H1b) predicted a positive effect of team members' degree centralities in the social network on the quantity and quality of their knowledge acquisition. H1a was supported, because degree centrality had a significant positive influence on the quantity of knowledge acquisition in the regression equation ([beta] = .244, p < .05; [R.sup.2] = .073, F = 4.220, p < .05). However, H1b was not supported, as there was no significant influence of degree centrality on the quality of knowledge acquisition ([beta] = -.055, p > .05; [R.sup.2] = .078, F = 4.531, p < .05). The second set of hypotheses (H2a and H2b) predicted that team members' betweenness centralities would have a positive effect on the quantity and quality of their knowledge acquisition. The results showed no support for H2a, because degree centrality had no significant influence on the quantity of knowledge acquisition ([beta] = .050, p >.05; [R.sup.2] = .073, F = 4.220, p < .05). In contrast, betweenness centrality had a significant positive influence on the quality of knowledge acquisition ([beta] = .300, p < .01; [R.sup.2] = .078, F = 4.531, p < .05). Finally, H3a and H3b posited that both degree and betweenness centralities would positively predict team members' work efficiency. The results showed that while H3a was supported ([beta] = .242, p < .05; [R.sup.2] = .102, F = 6.077, p < .01), H3b was not supported ([beta] = .126, p > .05; [R.sup.2] = .102, F = 6.077, p < .01). These findings suggest that team members' work efficiency could be enhanced by their degree centralities but not by their betweenness centralities in the social network.
This study speaks to a pressing theoretical and empirical concern in organizational communication and management studies: can social networking really improve individual competiveness in organizational work teams? To answer this question, this study utilizes a social network theoretical perspective to explain how two prominent properties of social networks, degree and betweenness centralities, can influence three crucial facets of individual competitiveness in organizational work settings: the quantity and quality of knowledge acquisition, and work efficiency. By analyzing survey data collected from 110 individuals from 9 intact organizational work teams of two multi-national consulting firms, this study found that team members' degree and betweenness centralities in the social networks had contrasting effects on the enhancement of their competitiveness. Specifically, the results showed that team members with higher degree centralities were likely to acquire a greater quantity rather than quality of information from other members, whereas team members with higher betweenness centralities were likely to acquire a greater quality rather than quantity of information from their team members. Further, this study found that members' work efficiency could be enhanced by an increased level of degree centrality in the social network, but not by an increased level of betweenness centrality in the social network (see Table 3 for a summary of findings).
Over the past decades, increasing research attention has been paid to unraveling the relationship between social capital and knowledge capital to enhance the competitive advantage of individuals, as well as organizations (Nahapiet & Ghoshal, 1998). This study contributes to this line of research by examining the potential effects of social network centralities on the quantity and quality of knowledge acquisition. Interestingly, this study found that while both degree and betweenness centralities significantly influenced team members' knowledge acquisition, there were contrasting effects on the quantity and quality of the information acquired. On the one hand, a high degree centrality is associated with high quantity but not high quality of knowledge acquisition. This finding suggests that as degree centrality is determined by the number and strength of direct communication relationships team members have in the social network, this type of central position would help them garner a greater volume of information from others. However, the large quantity of information might have been acquired at the price of sacrificing the high quality of knowledge acquisition. Given the cognitive limits of individuals' capability to retrieve, process, and absorb external knowledge, the more communication ties team members have in the social network, the less likely they are able to concentrate on filtering out irrelevant or inaccurate information so as to ensure the high quality of knowledge acquisition.
On the other hand, this study found that a high betweenness centrality was positively associated with high quality but not high quantity of knowledge acquisition. A possible explanation to this finding is that as betweenness centrality is primarily defined and measured by the extent to which a team member directly communicates with those who do not communicate among themselves, a member with high betweenness centrality may not have as many social communication relationships as a member with high degree centrality in the social network. Thus, the "brokerage" role enabled by high betweenness centrality gives the brokers advantages and privilege in retrieving information from a local segment of the entire social network, which limits their access to a large quantity of knowledge sources. However, the benefits of dealing with information acquired from only a small group of members include the ability to scrutinize the knowledge sources and the easiness to verify the quality of information acquired. Thus, this helps explain the positive effect of betweenness centrality on the quality of knowledge acquisition. To sum up, both degree and betweenness centralities of team members' social networks contribute to their competitiveness enhancement, but the actual mechanism and target of influence varies between these two prominent properties of social networking structures.
Finally, this study found a contrasting effect of social network centralities on team members' work efficiency. While members with high degree centralities reported a greater level of efficiency at work, members with high betweenness centralities showed no enhanced work efficiency in this study. This finding concurs with previous research that social capital accumulated from extensive and strong social communication relationships with others would help reduce the time and efforts members have to invest in seeking information and resources on their own (Cross & Borgatti, 2004; Nahapiet & Ghoshal, 1998), which consequently leads to greater work efficiency. Members with high betweenness centralities, however, may not enjoy such benefits because they have limited connections in the social network. Although the "brokerage" role they play in the social network is strategically important, they may not be as extensively and widely connected as members with high degree centralities. As a result, their work efficiency could not be considerably improved by filling the "structural holes" and bridging those disconnected members in the social network.
This study contributes to current literature by utilizing a social network theoretical perspective to examine how social network centralities could contribute to the enhancement of organizational work team members' competitiveness. In addition to proposing a conceptual framework of the relationship between social network centralities and individual competitiveness, this study collected empirical social network data from real world organizational work teams to test the theoretically deduced hypotheses. An important implication of this study is that different types of central positions in the social network are likely to exert different influences on various facets of individual competitiveness in the workplace. Future research should take a more nuanced approach to further explore how degree and betweenness centralities could influence the improvement of individual competitiveness. Indeed, this study demonstrates the importance and viability of utilizing a social network theoretical perspective in understanding how work team members' competitiveness can be enhanced by where they are situated and how they are connected in the social network.
This research studies only a limited number of facets of competitiveness in organizational work teams. There are other aspects of individual competitiveness that may potentially be influenced by team members' social network centralities. For example, team members' competitiveness can be conceived and measured by their work productivity, enhancement of expertise and skills, prospectus of promotion, innovations, and physical and psychosocial well-being in the workplace. Further, this study focuses on individual competiveness rather than team or organizational competitiveness. Thus, future research should examine a broader range of facets of competitiveness improvement as a result of social networking at various levels, including individuals, teams, departments, and organizations. Finally, this study uses subjective, self- reported measures of competitiveness by virtue of survey data collection. In the survey, team members were asked to report their own perceptions of knowledge acquisition and work efficiency. As individuals may not always be accurate in self-reporting, future research should obtain a more objective measurement of the key variables examined in this study.
Can social networking really improve individual competitiveness in organizational work teams? A quick and brief answer is yes, based on the findings of the current research. However, it is important to note that not all social networking endeavors will automatically translate into improved competitiveness. For individual employees, this study suggests that they should seek to place themselves in strategically important positions in the social network. In other words, employees are advised to engage in social networking deliberately rather than arbitrarily, because different roles in the social network would lead to different levels and facets of competitiveness improvement. For example, for those employees whose interests are focused on obtaining a large quantity of information and achieving work efficiency with a minimal investment of time and effort, they should strive to increase their degree centralities in the social network by directly communicating with as many colleagues as possible. In contrast, for employees whose work requires high quality rather than high quantity of information, they are encouraged to boost their betweenness centralities by communicating with those colleagues who are mutually disconnected in the social network.
An important practical implication of this study is that organizational managers should support and encourage employees to engage in constructive social networking with their colleagues in the workplace, so that employees can strategically use their emerging roles in the social network to enhance their knowledge acquisition and work efficiency. On the one hand, the management can sponsor a variety of social events wherein employees can interact with their existing friends and make new friends within the organization. On the other hand, organizations can set up virtual social interaction space on the organizational intranet so that employees can maintain and expand their social networks electronically. However, the management should be mindful of the potentially negative impact of excessive social networking on employees' productivity and commitment to work. The organization-sponsored social networking venues should be appropriately monitored and regulated so that social networking is not interfering with employees' task accomplishments. This study invites a more cautious and nuanced approach to understanding and examining the constructive influence of social networking on organizational employees' competitiveness enhancement. As today's workplace has become increasingly interdependent and connected (Easley & Kleinberg, 2010), how to leverage people's social networks to improve their competitiveness is crucial to the success of individual employees, organizations, and the society at large.
Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behavior & Human Decision Processes, 82(1), 150-169.
Badaracco, J. L. J. (1991). The knowledge link: How firms compete through strategic alliances. Boston, MA: Harvard Business Press.
Borgatti, S., Everett, M. G., & Freeman, L. C. (2002). Ucinet 6 for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies.
Brandon, D. P., & Hollingshead, A. B. (2004). Transactive memory systems in organizations: Matching tasks, expertise, and people. Organization Science, 15(6), 633-645.
Burt, R. S. (1987). Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology, 92(6), 1287-1335.
Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.
Burt, R. S. (2005). Brokerage and closure: An introduction to social capital. Oxford, England: Oxford University Press.
Contractor, N., Monge, P., & Leonardi, P. (2011). Multidimensional networks and the dynamics of sociomateriality: Bringing technology inside the network. International Journal of Communication, 5, 682-720.
Cross, R., & Borgatti, S. (2004). The ties that share: Relational characteristics that facilitate information seeking. In M. H. Huysman & V. Wulf (Eds.), Social capital and information technology (pp. 137-161). Boston, MA: MIT Press.
Cross, R., Rice, R., & Parker, A. (2001). Information seeking in social context: Structural influences and receipt of informational benefits. IEEE Transactions, 31(4), 438-448.
Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. New York: Cambridge University Press.
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook "friends:" Social capital and college students' use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143-1168.
Feeley, T. H., Hwang, J., & Barnett, G. A. (2008). Predicting employee turnover from friendship networks. Journal of Applied Communication Research, 36(1), 56-73.
Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215-239.
Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82-111.
Huffaker, D. (2010). Dimensions of leadership and social influence in online communities. Human Communication Research, 36(4), 593-617.
Katz, N., Lazer, D., Arrow, H., & Contractor, N. (2004). Network theory and small groups. Small Group Research, 35(3), 307-332.
Kilduff, M., & Tsai, W. (2003). Social networks and organizations. London: Sage.
Monge, P., & Contractor, N. (2003). Theories of communication networks. New York: Oxford University Press.
Monge, P., Cozzens, M. D., & Contractor, N. (1992). Communication and motivational predictors of the dynamics of organizational innovation. Organization Science, 3, 250-274.
Moreland, R. (1999). Transactive memory: Learning who knows what in work groups and organizations. In L. Thompson, D. Messick & J. Levine (Eds.), Shared cognition in organizations: The management of knowledge (pp. 3-31). Hillsdale, NJ: Lawrence Erlbaum.
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 242-266.
Nohria, N., & Eccles, R. (Eds.). (1992). Networks and organizations: Structures, Form and Action. Boston, MA: Harvard Business Press.
Palazzolo, E. (2005). Organizing for information retrieval in transactive memory systems. Communication Research, 32(6), 726-761.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Rulke, D. L., & Galaskiewicz, J. (2000). Distribution of knowledge, group network structure, and group performance. Management Science, 46(5), 612-625.
Scott, J. (2000). Social network analysis: A handbook (2nd ed.). Newbury Park, CA: Sage.
Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage.
Su, C., & Contractor, N. (2011). A multidimensional network approach to studying team members' information seeking from human and digital knowledge sources in consulting firms. Journal of the American Society for Information Science and Technology, 62(7), 1257-1275.
Su, C., Huang, M., & Contractor, N. (2010). Understanding the structures, antecedents and outcomes of organisational learning and knowledge transfer: A multi-theoretical and multilevel network analysis. European Journal of International Management, 4(6), 576-601.
Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418.
Tsai, W. (2001). Knowledge transfer in intraorganizational networks: Effects of network position and absorptive capacity on business unit innovation and performance. Academy of Management Journal, 44(5), 996-1004.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.
Chunke Su, University of Texas at Arlington
TABLE 1 Variables 1 2 3 4 5 Degree centrality -- .456 ** .267 ** .082 .299 ** Betweenness centrality -- .161 .275 ** .236 * Quantity of knowledge acquisition -- .325 ** .315 ** Quality of knowledge acquisition -- .204 * Work efficiency -- Mean 68.093 5.112 3.579 3.321 2.943 SD 14.285 10.750 .837 .897 .708 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). TABLE 2 Standardized coefficient F Independent variables [beta] R-Squared Statistics Predicting Quantity of Knowledge Acquisition H1a: Degree centrality .244 * H2a: Betweenness centrality .073 4.220 * .050 Predicting Quality of Knowledge Acquisition H1b: Degree centrality -.055 H2b: Betweenness centrality .078 4.531 * .300 ** Predicting Work efficiency H3a: Degree centrality .242 * H3b: Betweenness centrality .102 6.077 ** .126 * p < .05. ** p < .01. TABLE 3 Facets of individual Influence of degree Influence of competitiveness centrality betweenness centrality Quantity of knowledge acquisition Positive Insignificant Quality of knowledge acquisition Insignificant Positive Work efficiency Positive Insignificant
|Printer friendly Cite/link Email Feedback|
|Date:||Jun 1, 2011|
|Previous Article:||Stress, task, and relationship orientations of Vietnamese: an examination of gender, age, and government work experience in the Asian culture.|
|Next Article:||Managing generational diversity in the 21st century.|