Predictive Analytics in Information Systems Research

48 Pages Posted: 13 May 2010 Last revised: 25 Jul 2011

See all articles by Galit Shmueli

Galit Shmueli

Institute of Service Science, National Tsing Hua University, Taiwan

O. Koppius

Rotterdam School of Management, Erasmus University; Erasmus Research Institute of Management (ERIM)

Date Written: July 31, 2010

Abstract

This research essay highlights the need to integrate predictive analytics into information systems (IS) research, and shows several concrete ways in which this can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. The latter relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show the distinction between predictive analytics and explanatory statistical modeling, we present differences that arise in the modeling process of each type. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure empirical predictive model is best in terms of predictive power. We “convert” a well-known explanatory paper on TAM to a predictive context to illustrate these differences and show how predictive analytics can add theoretical and practical value to IS research.

Suggested Citation

Shmueli, Galit and Koppius, Otto, Predictive Analytics in Information Systems Research (July 31, 2010). Robert H. Smith School Research Paper No. RHS 06-138. Available at SSRN: https://ssrn.com/abstract=1606674 or http://dx.doi.org/10.2139/ssrn.1606674

Galit Shmueli (Contact Author)

Institute of Service Science, National Tsing Hua University, Taiwan ( email )

Hsinchu, 30013
Taiwan

HOME PAGE: http://www.iss.nthu.edu.tw

Otto Koppius

Rotterdam School of Management, Erasmus University ( email )

RSM Erasmus University
PO Box 1738
3000 DR Rotterdam
Netherlands
+31 10 408 2032 (Phone)
+31 10 408 9010 (Fax)

Erasmus Research Institute of Management (ERIM)

P.O. Box 1738
3000 DR Rotterdam
Netherlands

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