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Research on knowledge dissemination model in the multiplex network with enterprise social media and offline transmission routes

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  • Zhu, Hongmiao
  • Wang, Yumie
  • Yan, Xin
  • Jin, Zhen

Abstract

The enterprise social media and the offline social network are the two main routes to disseminate the knowledge among employees in the enterprise. In this study, we propose an SIR knowledge dissemination model with enterprise social media and offline transmission routes in the multiplex network. Through the analysis and calculation, we obtain the threshold to distinguish whether a certain knowledge is disseminated or not in the multiplex network. Simulation experiments are carried out. The results demonstrate that if the review rate of the knowledge is smaller, the speed of dissemination of the knowledge is slower and the scope of dissemination of the knowledge is smaller. If employees appropriately use two routes to disseminate the knowledge comparing with only use one of the two routes, the speed of dissemination is faster and the scope of dissemination is wider. In addition, enterprise social media cannot replace offline transmission route completely in the dissemination of the knowledge. The face to face communication among employees by using offline transmission route still plays an important role. We also find that the efficiency of dissemination in the multiplex network with enterprise social media (scale-free sub-network) and offline transmission route (homogeneous sub-network) is higher than the efficiency of dissemination in the multiplex network with enterprise social media (homogeneous sub-network) and offline transmission route (homogeneous sub-network).

Suggested Citation

  • Zhu, Hongmiao & Wang, Yumie & Yan, Xin & Jin, Zhen, 2022. "Research on knowledge dissemination model in the multiplex network with enterprise social media and offline transmission routes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
  • Handle: RePEc:eee:phsmap:v:587:y:2022:i:c:s037843712100741x
    DOI: 10.1016/j.physa.2021.126468
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    References listed on IDEAS

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    1. Wang, Haiying & Wang, Jun & Small, Michael & Moore, Jack Murdoch, 2019. "Review mechanism promotes knowledge transmission in complex networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 113-125.
    2. Zhan, Xiu-Xiu & Liu, Chuang & Zhou, Ge & Zhang, Zi-Ke & Sun, Gui-Quan & Zhu, Jonathan J.H. & Jin, Zhen, 2018. "Coupling dynamics of epidemic spreading and information diffusion on complex networks," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 437-448.
    3. Liu, Chuang & Zhou, Nan & Zhan, Xiu-Xiu & Sun, Gui-Quan & Zhang, Zi-Ke, 2020. "Markov-based solution for information diffusion on adaptive social networks," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    4. Argote, Linda & Ingram, Paul & Levine, John M. & Moreland, Richard L., 2000. "Knowledge Transfer in Organizations: Learning from the Experience of Others," Organizational Behavior and Human Decision Processes, Elsevier, vol. 82(1), pages 1-8, May.
    5. 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.
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    Cited by:

    1. Zhu, Hongmiao & Jin, Zhen & Yan, Xin, 2023. "A dynamics model of coupling transmission for multiple different knowledge in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    2. Zenghui Yue & Haiyun Xu & Guoting Yuan & Yan Qi, 2022. "Modeling knowledge diffusion in the disciplinary citation network based on differential dynamics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7593-7613, December.
    3. Zhu, Hongmiao & Jin, Zhen, 2023. "A dynamics model of knowledge dissemination in a WeChat Group from perspective of duplex networks," Applied Mathematics and Computation, Elsevier, vol. 454(C).

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