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Research Report—Modeling vs. Forecasting: The Case of Information Systems Spending

Author

Listed:
  • Vijay Gurbaxani

    (Graduate School of Management, University of California, Irvine, Irvine, California 92715)

  • Haim Mendelson

    (Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

Collopy, Adya and Armstrong (1994) (CAA) advocate the use of atheoretical “black box” extrapolation techniques to forecast information systems spending. In this paper, we contrast this approach with the positive modeling approach of Gurbaxani and Mendelson (1990), where the primary focus is on explanation based on economics and innovation diffusion theory. We argue that the objectives and premises of extrapolation techniques are so fundamentally different from those of positive modeling that the evaluation of positive models using the criteria of “black box” forecasting approaches is inadequate. We further show that even if one were to accept CAA's premises, their results are still inferior. Our results refute CAA's claim that linear trend extrapolations are appropriate for forecasting future IS spending and demonstrate the risks of ignoring the guidance of theory.

Suggested Citation

  • Vijay Gurbaxani & Haim Mendelson, 1994. "Research Report—Modeling vs. Forecasting: The Case of Information Systems Spending," Information Systems Research, INFORMS, vol. 5(2), pages 180-190, June.
    • Handle: RePEc:inm:orisre:v:5:y:1994:i:2:p:180-190
    DOI: 10.1287/isre.5.2.180
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    Cited by:

    1. Jan Svanberg & Tohid Ardeshiri & Isak Samsten & Peter Öhman & Presha E. Neidermeyer & Tarek Rana & Natalia Semenova & Mats Danielson, 2022. "Corporate governance performance ratings with machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 50-68, January.

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