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Knowledge acquisition and transfer among engineers: effects of network structure

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  • Tonya Boone

    (Mason School of Business, College of William and Mary, Williamsburg, VA, USA)

  • Ram Ganeshan

    (Mason School of Business, College of William and Mary, Williamsburg, VA, USA)

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    Abstract

    This paper examines the association between the structure of formal intra-firm networks and productivity. We focus on two network structure components-department centralization and centrality, within a four department engineering organization. Centrality indicates the number of connections between one department and others within the organization, while centralization captures how much of those connections are concentrated among the workers within the department. Both of these represent specific managerial decisions in a formal network structure. We use learning curve theory to measure accumulated organizational knowledge, its depreciation and intra-firm transfers. We hypothesize that the departments are more productive, experience less depreciation and realize more knowledge transfer if they have more intra-firm connections among more workers. The findings suggest a significant yet moderate association between the formal network structure and productivity. Copyright © 2008 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/mde.1401
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Managerial and Decision Economics.

    Volume (Year): 29 (2008)
    Issue (Month): 5 ()
    Pages: 459-468

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    Handle: RePEc:wly:mgtdec:v:29:y:2008:i:5:p:459-468

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/7976

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    1. Epple, D. & Argote, L. & Murphy, K., 1995. "An Empiraical Investigation of the Micro Structure of Knowledge Acquisition and Transfer Through Learning by Doing," GSIA Working Papers, Carnegie Mellon University, Tepper School of Business 1995-17, Carnegie Mellon University, Tepper School of Business.
    2. Daniel Z. Levin & Rob Cross, 2004. "The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer," Management Science, INFORMS, INFORMS, vol. 50(11), pages 1477-1490, November.
    3. Brian Uzzi & Ryon Lancaster, 2003. "Relational Embeddedness and Learning: The Case of Bank Loan Managers and Their Clients," Management Science, INFORMS, INFORMS, vol. 49(4), pages 383-399, April.
    4. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers, Carnegie Mellon University, Tepper School of Business 89-90-02, Carnegie Mellon University, Tepper School of Business.
    5. Charles D. Bailey, 1989. "Forgetting and the Learning Curve: A Laboratory Study," Management Science, INFORMS, INFORMS, vol. 35(3), pages 340-352, March.
    6. Midgley, David F. & Morrison, Pamela D. & Roberts, John H., 1992. "The effect of network structure in industrial diffusion processes," Research Policy, Elsevier, Elsevier, vol. 21(6), pages 533-552, December.
    7. Paul S. Adler & Kim B. Clark, 1991. "Behind the Learning Curve: A Sketch of the Learning Process," Management Science, INFORMS, INFORMS, vol. 37(3), pages 267-281, March.
    8. Argote, Linda & Ingram, Paul, 2000. "Knowledge Transfer: A Basis for Competitive Advantage in Firms," Organizational Behavior and Human Decision Processes, Elsevier, Elsevier, vol. 82(1), pages 150-169, May.
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