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Knowledge diffusion and knowledge transfer revisited: two sides of the medal

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  • Torben Klarl

Abstract

Understanding the way in which knowledge is interpersonally transferred and how it diffuses over time is of exceptional importance for the economic performance of a society. Although this insight is not new, the link between knowledge transfer and knowledge diffusion so far has not been picked out as a central theme in the relevant research field but instead it seems that it has been treated as a theme on the fringes yet. This paper mainly argues, first, that the speed of knowledge diffusion as well as the shape of the cumulative knowledge diffusion function is governed in most instances by knowledge transfer mechanisms. Second, these knowledge transfer mechanisms differ within and between heterophilic groups who participate in the knowledge transfer process. By perfectly disentangling between and within knowledge transfer mechanisms, the paper in general tries to uncover the link between knowledge diffusion and knowledge transfer within a dynamic model which is embedded in a stochastic environment. The model is able to replicate both, symmetric as well as asymmetric cumulative knowledge diffusion patterns. This is certainly an appealing attribute of the model, because from an empirical point of view there is no clear evidence for the existence of purely symmetric cumulative knowledge diffusion curves. Further, the model can be used directly for empirical investigations. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Torben Klarl, 2014. "Knowledge diffusion and knowledge transfer revisited: two sides of the medal," Journal of Evolutionary Economics, Springer, vol. 24(4), pages 737-760, September.
  • Handle: RePEc:spr:joevec:v:24:y:2014:i:4:p:737-760
    DOI: 10.1007/s00191-013-0319-3
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    Cited by:

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    2. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    3. Robertson, Jeandri & Caruana, Albert & Ferreira, Caitlin, 2023. "Innovation performance: The effect of knowledge-based dynamic capabilities in cross-country innovation ecosystems," International Business Review, Elsevier, vol. 32(2).
    4. Brandt, Urs Steiner & Svendsen, Gert Tinggaard, 2022. "Is the annual UNFCCC COP the only game in town?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    5. Xiaodan Kong & Qi Xu & Tao Zhu, 2019. "Dynamic Evolution of Knowledge Sharing Behavior among Enterprises in the Cluster Innovation Network Based on Evolutionary Game Theory," Sustainability, MDPI, vol. 12(1), pages 1-23, December.
    6. Jürgen Antony & Torben Klarl, 2020. "Knowledge Transfer, Transitional Dynamics and Optimal Research & Development Policy in a Dynamic Monopoly Setting," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 57(3), pages 579-606, November.
    7. Fernández, Ana María & Ferrándiz, Esther & Medina, Jennifer, 2022. "The diffusion of energy technologies. Evidence from renewable, fossil, and nuclear energy patents," Technological Forecasting and Social Change, Elsevier, vol. 178(C).

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    More about this item

    Keywords

    Knowledge diffusion; Knowledge transfer; Knowledge network; D83; D85; C62; R10;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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