PREDICTIVE ANALYTICS IN INFORMATION SYSTEMS RESEARCH.

Author List: Shmueli, Galit; Koppius, Otto R.;

MIS Quarterly, 2011, Volume 35, Issue 3, Page 553-572.

This research essay highlights the need to integrate predictive analytics into information systems research and shows several concrete ways in which this goal 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. Extant IS literature relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models, and predictive power is assumed to follow automatically from the explanatory model. 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 that predictive analytics and explanatory statistical modeling are fundamentally disparate, we show that they are different in each step of the modeling process. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure 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.

Keywords: causal explanation; modeling process; Prediction; theory building; theory testing

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#133	0.429	data predictive analytics sharing big using modeling set power inference behavior explanatory related prediction statistical generated substantially novel building million
#110	0.116	theory theories theoretical paper new understanding work practical explain empirical contribution phenomenon literature second implications different building based insights need
#282	0.079	power perspective process study rational political perspectives politics theoretical longitudinal case social rationality formation construction shows multiple instead understanding fact
#191	0.069	model models process analysis paper management support used environment decision provides based develop use using help literature mathematical presented formulation
#86	0.068	methods information systems approach using method requirements used use developed effective develop determining research determine assessment useful series critical existing
#157	0.062	evaluation effectiveness assessment evaluating paper objectives terms process assessing criteria evaluations methodology provides impact literature potential important evaluated identifying multiple
#17	0.054	empirical model relationships causal framework theoretical construct results models terms paper relationship based argue proposed literature issues assumptions provide suggest