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A dynamics model of knowledge dissemination in a WeChat Group from perspective of duplex networks

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  • Zhu, Hongmiao
  • Jin, Zhen

Abstract

Studying how to promote knowledge dissemination in a WeChat group is of great significance to organizational knowledge management in China. There are two different ways for group members disseminating a certain knowledge: just sharing it in WeChat group (many-to-many dissemination mode), or using @username function to disseminate it (point-to-point dissemination mode). We regard all group members connected by relationships of two different dissemination modes in a WeChat group as duplex networks. We build a susceptible-infected-recovered-immune (sirm) dynamics model of knowledge dissemination in the duplex networks to obtain the basic reproduction number R0 of a certain knowledge transmission. We use the actual data to estimate the parameters of this model, and verify that our model fits well with the actual data. The numerical simulations show that: 1) The total number of times that members communicate a certain knowledge via two modes per unit time should not be excessive, to avoid fatigue to it which resulting low efficiency of dissemination in the WeChat group. 2) If the benefit obtained by group members from learning a certain knowledge is higher than the required variable cost, the kind of knowledge is likely to be spread in the WeChat group. If a certain knowledge becomes obsolete or is updated for some group members, they will not disseminate it even after they have learned it and it may gradually disappear in the WeChat group. 3) When members only use either point-to-point @mention function discussion or many-to-many random communication to transmit a certain knowledge, or there is big difference in the number of times that group members use each of the above two discussion modes per unit time, the efficiency of the knowledge dissemination is reduced in a WeChat group.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:454:y:2023:i:c:s0096300323002527
    DOI: 10.1016/j.amc.2023.128083
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    References listed on IDEAS

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    1. Robert M. Costrell & Michael Podgursky, 2009. "Peaks, Cliffs, and Valleys: The Peculiar Incentives in Teacher Retirement Systems and Their Consequences for School Staffing," Education Finance and Policy, MIT Press, vol. 4(2), pages 175-211, April.
    2. Cowan, Robin & Jonard, Nicolas, 2004. "Network structure and the diffusion of knowledge," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1557-1575, June.
    3. Hammitt James K. & Robinson Lisa A, 2011. "The Income Elasticity of the Value per Statistical Life: Transferring Estimates between High and Low Income Populations," Journal of Benefit-Cost Analysis, De Gruyter, vol. 2(1), pages 1-29, January.
    4. Hammitt James K. & Robinson Lisa A, 2011. "The Income Elasticity of the Value per Statistical Life: Transferring Estimates between High and Low Income Populations," Journal of Benefit-Cost Analysis, De Gruyter, vol. 2(1), pages 1-29, January.
    5. Kiss, Istvan Z. & Broom, Mark & Craze, Paul G. & Rafols, Ismael, 2010. "Can epidemic models describe the diffusion of topics across disciplines?," Journal of Informetrics, Elsevier, vol. 4(1), pages 74-82.
    6. 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.
    7. Fu, Minglei & Feng, Jun & Lande, Dmytro & Dmytrenko, Oleh & Manko, Dmytro & Prakapovich, Ryhor, 2021. "Dynamic model with super spreaders and lurker users for preferential information propagation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    8. Piergiuseppe Morone & Richard Taylor, 2004. "Knowledge diffusion dynamics and network properties of face-to-face interactions," Journal of Evolutionary Economics, Springer, vol. 14(3), pages 327-351, July.
    9. Zhu, Hongmiao & Jin, Zhen & Yan, Xin, 2022. "A dynamics model of two kinds of knowledge transmission on duplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    10. Zhang, Yuexia & Pan, Dawei, 2021. "Layered SIRS model of information spread in complex networks," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    11. Wang, Haiying & Wang, Jun & Small, Michael, 2018. "Knowledge transmission model with differing initial transmission and retransmission process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 478-488.
    12. Qiao, Tong & Shan, Wei & Zhang, Mingli & Liu, Chen, 2019. "How to facilitate knowledge diffusion in complex networks: The roles of network structure, knowledge role distribution and selection rule," International Journal of Information Management, Elsevier, vol. 47(C), pages 152-167.
    13. 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).
    14. Jinxian Li & Yanping Hu & Zhen Jin, 2019. "Rumor Spreading of an SIHR Model in Heterogeneous Networks Based on Probability Generating Function," Complexity, Hindawi, vol. 2019, pages 1-15, June.
    15. Alexander Fliaster & Josef Spiess, 2008. "Knowledge Mobilization through Social tieS: The Cost-Benefit Analysis," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 60(1), pages 99-117, January.
    16. 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).
    17. 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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