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Exploring Depth Versus Breadth in Knowledge Management Strategies

Author

Listed:
  • Scott F. Turner

    (University of North Carolina at Chapel Hill)

  • Richard A. Bettis

    (University of North Carolina at Chapel Hill)

  • Richard M. Burton

    (Duke University)

Abstract

This paper provides an early attempt at operationalizing and testing the concept of knowledge strategy. Using a computer-simulated product development process, we compare the performance of generalist and specialist knowledge management strategies under conditions of market turbulence. The generalist knowledge strategy emphasizes breadth of knowledge in product development teams, while the specialist strategy focuses on depth of knowledge. Our generalist and specialist strategies are grounded in Eastern and Western perspectives of knowledge management, respectively. A primary difference between these two approaches is the strong emphasis on cross-learning, or learning across team members, in the Eastern perspective relative to the Western perspective. As such, we examine the performance implications of different modes of cross-learning for teams utilizing the generalist knowledge strategy. Specifically, we examine three modes of cross-learning, which are reflected in the use of three learning decision rules: (1) averaging, (2) majority, and (3) hot hand. A learning decision rule indicates how decision-makers learn from their fellow team members. Under the first rule, the decision-maker adopts an average of the beliefs held by fellow team members. Under the second rule, if a majority of fellow team members agree on a particular solution, then the decision-maker adopts the beliefs held by the majority. Under the third rule, the decision-maker learns from the team member whose beliefs have been consistent with market desires most recently. Surprisingly, we find that specialist strategies outperform generalist strategies under conditions of low and high market turbulence. We also find that cross-learning can be beneficial or detrimental, contingent upon the mode of learning. Generalist teams utilizing the averaging decision rule perform significantly worse, while generalist teams utilizing the hot hand decision rule perform significantly better.

Suggested Citation

  • Scott F. Turner & Richard A. Bettis & Richard M. Burton, 2002. "Exploring Depth Versus Breadth in Knowledge Management Strategies," Computational and Mathematical Organization Theory, Springer, vol. 8(1), pages 49-73, May.
  • Handle: RePEc:spr:comaot:v:8:y:2002:i:1:d:10.1023_a:1015180220717
    DOI: 10.1023/A:1015180220717
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    References listed on IDEAS

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    Cited by:

    1. Trevor Young-Hyman & Adam M. Kleinbaum, 2020. "Meso-Foundations of Interorganizational Relationships: How Team Power Structures Shape Partner Novelty," Organization Science, INFORMS, vol. 31(6), pages 1385-1407, November.
    2. Brian W. Kulik & Timothy Baker, 2008. "Putting the organization back into computational organization theory: a complex Perrowian model of organizational action," Computational and Mathematical Organization Theory, Springer, vol. 14(2), pages 84-119, June.
    3. Hart E. Posen & Dirk Martignoni & Daniel A. Levinthal, 2013. "E Pluribus Unum: Organizational Size and the Efficacy of Learning," DRUID Working Papers 13-09, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
    4. René Peinl & Ronald Maier, 2011. "SimKnowledge—Analyzing impact of knowledge management measures on team organizations with multi agent-based simulation," Information Systems Frontiers, Springer, vol. 13(5), pages 621-636, November.

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