Skip to main content
SpringerLink
Log in

The Effect of Social Network Centrality on Knowledge Sharing

  • Research Papers
  • Published:
Journal of Service Science Research

Abstract

Many companies have been carrying out various knowledge management attempts, hoping that employees share their knowledge voluntary with other members and make synergy. From this point of view, many previous studies have explored the factors that affect individuals’ intention to share knowledge. In this study, we tried to discover the factors affecting from the roles and positions of individuals within the social network. To identify the roles and positions, we used three centrality measures (degree/closeness/betweenness) that can be calculated using Social Network Analysis (SNA). The research findings showed that the network roles and positions of individuals significantly affect their knowledge sharing intentions within and outside the teams. Since the high degree centrality provides a member with the position as a leader or a hub, one tries to actively participate in knowledge sharing within and outside the team in order to maintain the network position. A member who can quickly interact with many other members within a team (high closeness centrality) is more interested in knowledge sharing within the team than knowledge sharing outside the team. Since betweenness centrality offers a member various resources outside the team, a member who has high betweenness centrality plays a crucial role in disseminating and regulating knowledge among multiple teams. The members who play important roles in the network want to engage in knowledge sharing activities more actively than other members to maintain the benefits they can have in the network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Argote L, McEvily B, & Reagans R (2003) Managing knowledge in organizations: An integrative framework and review of emerging themes. Management Science 49: 571–582.

    Article  Google Scholar 

  • Ansari AH & Malik S (2017) Ability-based emotional intelligence and knowledge sharing: The moderating role of trust in co-workers. VINE Journal of Information and Knowledge Management Systems 47(2): 211–227.

    Article  Google Scholar 

  • Baek SI & Kim YM (2015) Longitudinal analysis of online community dynamics. Industrial Management & Data Systems 115(4): 661–677.

    Article  Google Scholar 

  • Bock G, Zmud RW, Kim Y, & Lee J (2005) Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly 29(1): 87–111.

    Article  Google Scholar 

  • Brass DJ & Burkhardt ME (1992) Centrality and Power in Organizations. In N. Nohria & R. Eccles (Eds.) Networks and Organizations: Structure, Form, and Action. MA: Harvard Business School Press: 191–215.

    Google Scholar 

  • Butler BS (2001) Membership size, communication activity, and sustainability: A resource-based model of online social structures. Information Systems Research 12(4): 346–362.

    Article  Google Scholar 

  • Butler B, Sproull L, Kiesler S, & Kraut R (2002) Community effort in online groups: Who does the work and why. Leadership at a Distance: Research in Technologically Supported Work: 171–194.

  • Chai S & Kim M (2012) A socio-technical approach to knowledge contribution behavior: An empirical investigation of social networking sites users. International Journal of Information Management 32(2): 118–126.

    Article  Google Scholar 

  • Chiu CM, Hsu MH, & Wang ET (2006) Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems 42(3): 1872–1888.

    Article  Google Scholar 

  • Cho H, Gay G, Davidson B, & Ingraffea A (2007) Social networks, communication styles, and learning performance in a CSCL community. Computers & Education 49(2): 309–329.

    Article  Google Scholar 

  • Cross R & Cummings JN (2004) Tie and network correlates of individual performance in knowledge-intensive work. Academy of Management Journal 47(6): 928–937.

    Google Scholar 

  • De Toni AF & Nonino F (2010) The key roles in the informal organization: A network analysis perspective. The Learning Organization 17(1): 86–103.

    Article  Google Scholar 

  • De Vries RE, Van den Hooff B, & de Ridder JA (2006) Explaining knowledge sharing the role of team communication styles, job satisfaction, and performance beliefs. Communication Research 33(2): 115–135.

    Article  Google Scholar 

  • Dogan SZ, Arditi D, Gunhan S, & Erbasaranoglu B (2013) Assessing coordination performance based on centrality in an e-mail communication network. Journal of Management in Engineering 31(3): 04014047.

    Article  Google Scholar 

  • Freeman LC (1979) Centrality in social networks-conceptual clarification. Social Networks 1(3): 215–239.

    Article  Google Scholar 

  • Gallivan MJ, Spitler VK, & Koufaris M (2005) Does information technology training really matter? A social information processing analysis of coworkers’ influence on it usage in the workplace. Journal of Management Information Systems 22(1): 153–192.

    Article  Google Scholar 

  • Gruenfeld DH, Mannix EA, Williams KY, & Neale MA (1996) Group composition and decision making: How member familiarity and information distribution affect process and performance. Organizational Behavior and Human Decision Processes 67(1): 1–15.

    Article  Google Scholar 

  • Hendriks P (1999) Why share knowledge? The influence of ICT on the motivation for knowledge sharing. Knowledge and Process Management 6(2): 91–100.

    Article  Google Scholar 

  • Hooff B & Huysman M (2009) Managing knowledge sharing: Emergent and engineering approaches. Information & Management 46: 1–8.

    Article  Google Scholar 

  • Huffaker D (2010) Dimensions of leadership and social influence in online communities. Human Communication Research 36(4): 593–617.

    Article  Google Scholar 

  • Huysman M & Wulf V (2006) IT to support knowledge sharing in communities, towards a social capital analysis. Journal of Information Technology 21(1): 40–51.

    Article  Google Scholar 

  • Kankanhalli A, Tan BC, & Wei KK (2005) Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Quarterly 29(1): 113–143.

    Article  Google Scholar 

  • Krackhardt D (1990) Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly 35: 342–369.

    Article  Google Scholar 

  • Song S, Nerur S, & Teng JT (2007) An exploratory study on the roles of network structure and knowledge processing orientation in work unit knowledge management. ACM SIGMIS Database 38(2): 8–26.

    Article  Google Scholar 

  • Sung K, Kim T, Jahng J, & Ahn J (2009) The effect of personal characteristics and user involvement on knowledge sharing in the knowledge-exchange website context. Journal of Society for e-Business Studies 14(4): 229–253.

    Google Scholar 

  • Tortoriello M, Reagans R, & McEvily B (2012) Bridging the knowledge gap: The influence of strong ties, network cohesion, and network range on the transfer of knowledge between organizational units. Organization Science 23(4): 1024–1039.

    Article  Google Scholar 

  • Wang S & Noe RA (2010) Knowledge sharing: A review and directions for future research. Human Resource Management Review 20(2): 115–131.

    Article  Google Scholar 

  • Wang S, Noe RA, & Wang ZM (2014) Motivating knowledge sharing in knowledge management systems: A quasi-field experiment. Journal of Management 40(4): 978–1009.

    Article  Google Scholar 

  • Wasko MM & Faraj S (2005) Why should i share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly 29(1): 35–57.

    Article  Google Scholar 

  • Wasserman S & Faust K (1994) Social Network Analysis: Methods and Applications. MA: Cambridge University Press.

    Book  Google Scholar 

  • Wong KY (2005) Critical success factors for implementing knowledge management in small and medium enterprise. Industrial Management & Data Systems 105(3): 261–279.

    Article  Google Scholar 

  • Yang E & Ding Y (2009) Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the Association for Information Science and Technology 60(10): 2107–2118.

    Google Scholar 

  • Youssef M, Haak-Saheem W, & Youssef EM (2017) A structural equation model for knowledge sharing behavior in an emerging economy. Journal of Knowledge Management 21(4): 925–945.

    Article  Google Scholar 

  • Zhang X, Liu S, Chen X, & Gong Y (2017) Social capital, motivations, and knowledge sharing intention in health Q&A communities. Management Decision 55(7): 1536–1557.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Business, Hanyang University, Seoul, Korea

    Seung Ik Baek

  2. Frost & Sullivan, Seoul, Korea

    Soon Han Bae

Authors
  1. Seung Ik Baek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soon Han Bae
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seung Ik Baek.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A previous version of this paper was published in a journal published in Korean (Journal of Society e-Business Studies, 2017).

Author Biographies

Seung Ik Baek is a professor of Business School at Hanyang University (Seoul, Korea). He holds a Bachelor degree from Sogang University (Seoul, Korea), and MBA/Ph.D. from George Washington University. Prior to joining Hanyang University, he served as an assistant professor at Georgia State University and Saint Joseph’s University. His research interests include areas such as social impact of AI, service innovation, big data analytics, information privacy, and experience design.

Soon Han Bae is an ICT analyst at Frost & Sullivan (Seoul, Korea). He holds a Ph.D. in Management Information Systems from Hanyang University (Seoul, Korea). His research interests include areas such as IT service strategy, ICT industry analysis, and social network analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baek, S.I., Bae, S.H. The Effect of Social Network Centrality on Knowledge Sharing. J Serv Sci Res 11, 183–202 (2019). https://doi.org/10.1007/s12927-019-0009-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12927-019-0009-2

Keywords

Search

Navigation