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

Vijay Gurbaxani and Haim Mendelson
Additional contact information
Vijay Gurbaxani: Graduate School of Management, University of California, Irvine, Irvine, California 92715
Haim Mendelson: Graduate School of Business, Stanford University, Stanford, California 94305

Information Systems Research, 1994, vol. 5, issue 2, 180-190

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.

Keywords: information systems spending; positive models; forecasting; exponential smoothing (search for similar items in EconPapers)
Date: 1994
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/isre.5.2.180 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:5:y:1994:i:2:p:180-190

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().