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. 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.
Keywords: Prediction, causal explanation, theory building, theory
testing, statistical model, data mining, modeling process