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How Do Communication Structures Shape The Process Of Knowledge Transfer? - An Agent-Based Model

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  • Widad Guechtouli

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

Knowledge diffusion is a complex process. Knowledge is intangible and therefore is not easy to capitalize within an organization, or share between a set of individuals. The aim of this paper is to study the impact of two different structures of communication on both processes of knowledge transfer and individual learning, in the context of a community of practice. We will specifically compare two types of communication structures (through face-to-face interactions and through a forum) by using agent-based models. Results show that each structure has a different impact on individual learning and knowledge transfer. Though, communication through face-to-face interactions seems to make individuals learn slower than on a web forum. Conclusions are widely discussed.

Suggested Citation

  • Widad Guechtouli, 2008. "How Do Communication Structures Shape The Process Of Knowledge Transfer? - An Agent-Based Model," Working Papers halshs-00349033, HAL.
    • Handle: RePEc:hal:wpaper:halshs-00349033
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00349033
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    1. Boyer, Tristan & Jonard, Nicolas, 2014. "Imitation and efficient contagion," Journal of Economic Behavior & Organization, Elsevier, vol. 100(C), pages 20-32.
    2. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    3. repec:ipg:wpaper:2014-052 is not listed on IDEAS
    4. Brenner, Thomas, 2006. "Agent Learning Representation: Advice on Modelling Economic Learning," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 18, pages 895-947, Elsevier.
    5. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
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    Keywords

    knowledge; communication structure; communities of practice; agent-based models;
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