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The effects of online social networks on tacit knowledge transmission

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  • Zhu, Hong-Miao
  • Zhang, Sheng-Tai
  • Jin, Zhen

Abstract

Due to the popular use of online social networks in today’s world, how to propagate employees’ tacit knowledge via online social networks has attracted managers’ attention, which is critical to enhance the competitiveness of firms. In this paper, we propose a tacit knowledge transmission model on networks with even mixing based on the propagation property of tacit knowledge and the application of online social networks. We consider two routes of transmission, which are contact through online social networks and face-to-face physical contact, and derive the threshold that governs whether or not a kind of tacit knowledge can be shared in an organization with few initial employees who have acquired it. The impact of the degree distribution of the users’ contact network on the transmission is investigated analytically. Some numerical simulations are presented to support the theoretical results. We perform the sensitivity analysis of the threshold in terms of the propagation parameters and confirm that online social networks contribute significantly to enhancing the transmission of tacit knowledge among employees.

Suggested Citation

  • Zhu, Hong-Miao & Zhang, Sheng-Tai & Jin, Zhen, 2016. "The effects of online social networks on tacit knowledge transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 192-198.
    • Handle: RePEc:eee:phsmap:v:441:y:2016:i:c:p:192-198
    DOI: 10.1016/j.physa.2015.08.044
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    References listed on IDEAS

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    Cited by:

    1. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2021. "Intelligent Agents in Co-Evolving Knowledge Networks," Mathematics, MDPI, vol. 9(1), pages 1-17, January.
    2. Wang, Sixin & Mei, Jun & Xia, Dan & Yang, Zhanying & Hu, Junhao, 2022. "Finite-time optimal feedback control mechanism for knowledge transmission in complex networks via model predictive control," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. Wang, Haiying & Wang, Jun & Ding, Liting & Wei, Wei, 2017. "Knowledge transmission model with consideration of self-learning mechanism in complex networks," Applied Mathematics and Computation, Elsevier, vol. 304(C), pages 83-92.
    4. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analyzing knowledge transmission process considering free-riding behavior of knowledge acquisition: A waterborne disease approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    5. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analysis knowledge transmission process in complex networks by considering internalization mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    6. Zhu, He & Ma, Jing, 2018. "Knowledge diffusion in complex networks by considering time-varying information channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 225-235.
    7. Shumei Wang & Ming Sun & Yaoqun Xu, 2024. "Knowledge Transfer within Enterprises from the Perspective of Innovation Quality Management: A Decision Analysis Based on the Stackelberg Game," Sustainability, MDPI, vol. 16(16), pages 1-26, August.

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