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Information Assets, Technology and Organization

Author

Listed:
  • Erik Brynjolfsson

    (Sloan School of Management, MIT, Cambridge, Massachusetts 02142)

Abstract

Although there is good reason to expect that the growth of information work and information technology will significantly affect the trade-offs inherent in different structures for organizing work, the theoretical basis for these changes remains poorly understood. This paper seeks to address this gap by analyzing the incentive effects of different ownership arrangement in the spirit of the Grossman-Hart-Moore (GHM) incomplete contracts theory of the firm. A key departure from earlier approaches is the inclusion of a role for an "information asset," analogous to the GHM treatment of property. This approach highlights the organizational significance of information ownership and information technology. For instance, using this framework, one can determine when 1) informed workers are more likely to be owners than employees of firms, 2) increased flexibility of assets will facilitate decentralization, and 3) the need for centralized coordination will lead to centralized ownership. The framework developed sheds light on some of the empirical findings regarding the relationship between information technology and firm size and clarifies the relationship between coordination mechanisms and the optimal distribution of asset ownership. While many implications are still unexplored and untested, building on the incomplete contracts approach appears to be a promising avenue for the careful, methodical analysis of human organizations and the impact of new technologies.

Suggested Citation

  • Erik Brynjolfsson, 1994. "Information Assets, Technology and Organization," Management Science, INFORMS, vol. 40(12), pages 1645-1662, December.
  • Handle: RePEc:inm:ormnsc:v:40:y:1994:i:12:p:1645-1662
    as

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    File URL: http://dx.doi.org/10.1287/mnsc.40.12.1645
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    References listed on IDEAS

    as
    1. JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, EconWPA.
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    3. Scott Armstrong, J., 1988. "Research needs in forecasting," International Journal of Forecasting, Elsevier, vol. 4(3), pages 449-465.
    4. Robert Carbone & JS Armstrong, 2004. "Evaluation of Extrapolative Forecasting Methods: Results of a Survey of Academicians and Practitioners," General Economics and Teaching 0412008, EconWPA.
    5. Robert Carbone & Spyros Makridakis, 1986. "Forecasting When Pattern Changes Occur Beyond the Historical Data," Management Science, INFORMS, vol. 32(3), pages 257-271, March.
    6. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    7. Sanders, NR & Ritzman, LP, 1990. "Improving short-term forecasts," Omega, Elsevier, vol. 18(4), pages 365-373.
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