基于知识关联性的科研合作网络隐性知识扩散模型研究:以重大科技工程为例

Translated title of the contribution: Diffusion Model for Tacit Knowledge of Scientific Cooperation Network Based on Relevance: Case Study of Major Sci-Tech Projects

Yunmeng Lu, Tiezhong Liu*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    [Objective] Tacit knowledge is an important resource for R&D and innovation of major science and technology projects. It is of practical significance to study the simultaneous diffusion of multiple types of interrelated tacit knowledge. [Methods] We proposed a method for evaluating the knowledge distance between scientific teams with the help of knowledge relevance, and constructed a tacit knowledge diffusion model based on the scientific cooperation network. We also investigated the influencing mechanism of knowledge relevance and interaction strategies on the diffusion of tacit knowledge through multi-agent simulation. [Results] In the early stage of dissemination, the speed of knowledge diffusion with strong knowledge relevance was faster than those of weak knowledge relevance. As the difference of knowledge among scientific teams became smaller, and the similarity of knowledge structure between scientific teams increased, and the influence of knowledge relevance on knowledge diffusion gradually weakened. The interaction strategy between subjects had greater impacts on knowledge diffusion. [Limitations] The carrier network of tacit knowledge is a real scientific cooperation network, but its dissemination process was simulated in the lab. [Conclusions] This paper analyzes the dynamic process and effects of tacit knowledge diffusion, and provides suggestions to promote the using of tacit knowledge.

    Translated title of the contributionDiffusion Model for Tacit Knowledge of Scientific Cooperation Network Based on Relevance: Case Study of Major Sci-Tech Projects
    Original languageChinese (Traditional)
    Pages (from-to)10-20
    Number of pages11
    JournalData Analysis and Knowledge Discovery
    Volume5
    Issue number9
    DOIs
    Publication statusPublished - Sept 2021

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