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The Effect of Task Uncertainty and Decentralization on Project Team Performance

Author

Listed:
  • Jisung Kim

    (Duke University)

  • Richard M. Burton

    (Duke University)

Abstract

This paper investigates relationships among task uncertainty, level of centralization, and project team performance. Team performance is measured in three dimensions: cost, time, and quality. Adopting an information processing view and contingency theory, the authors discuss tradeoffs among the three performance dimensions of a project team. Results from the simulation study indicate that, under high task uncertainty, a decentralized team performs better in terms of cost and time, but a centralized team performs better in terms of quality. Under low task uncertainty, there is no performance difference between a centralized team and a decentralized team in terms of cost and time, but a centralized team performs better in terms of quality. The paper suggests that researchers pay attention to the relative impact of centralization and decentralization on different dimensions of organizational performance, and managers adopt an organizational structure that performs better in a performance dimension that counts more to enhance overall performance.

Suggested Citation

  • Jisung Kim & Richard M. Burton, 2002. "The Effect of Task Uncertainty and Decentralization on Project Team Performance," Computational and Mathematical Organization Theory, Springer, vol. 8(4), pages 365-384, December.
    • Handle: RePEc:spr:comaot:v:8:y:2002:i:4:d:10.1023_a:1025472702927
    DOI: 10.1023/A:1025472702927
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    References listed on IDEAS

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    1. Bart Victor & Richard S. Blackburn, 1987. "Determinants And Consequences Of Task Uncertainty: A Laboratory And Field Investigation," Journal of Management Studies, Wiley Blackwell, vol. 24(4), pages 387-404, July.
    2. Steven E. Phelan & Zhiang Lin, 2001. "Promotion Systems and Organizational Performance: A Contingency Model," Computational and Mathematical Organization Theory, Springer, vol. 7(3), pages 207-232, October.
    3. Myong-Hun Chang & Joseph E. Harrington, 1998. "Organizational Structure and Firm Innovation in a Retail Chain," Computational and Mathematical Organization Theory, Springer, vol. 3(4), pages 267-288, December.
    4. Arundhati Kumar & Peng Si Ow & Michael J. Prietula, 1993. "Organizational Simulation and Information Systems Design: An Operations Level Example," Management Science, INFORMS, vol. 39(2), pages 218-240, February.
    5. Pfeffer, Jeffrey, 1997. "New Directions for Organization Theory: Problems and Prospects," OUP Catalogue, Oxford University Press, number 9780195114348.
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    Cited by:

    1. Mehdi Hashemipour & Steven Stuban & Jason Dever, 2018. "A disaster multiagent coordination simulation system to evaluate the design of a first‐response team," Systems Engineering, John Wiley & Sons, vol. 21(4), pages 322-344, July.
    2. Kent Wickstrøm Jensen & Dorthe Døjbak Håkonsson & Richard M. Burton & Børge Obel, 2010. "The effect of virtuality on the functioning of centralized versus decentralized structures—an information processing perspective," Computational and Mathematical Organization Theory, Springer, vol. 16(2), pages 144-170, June.
    3. Timothy N. Carroll & Thomas J. Gormley & Vincent J. Bilardo & Richard M. Burton & Keith L. Woodman, 2006. "Designing a New Organization at NASA: An Organization Design Process Using Simulation," Organization Science, INFORMS, vol. 17(2), pages 202-214, April.
    4. Richard M. Burton & Borge Obel, 2013. "Design rules for dynamic organization design: the contribution of computational modeling," Chapters, in: Anna Grandori (ed.), Handbook of Economic Organization, chapter 13, Edward Elgar Publishing.
    5. 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.

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