Cumulative Timed Intent: A New Predictive Tool for Technology Adoption

Abstract

Despite multiple calls for the integration of time into behavioral intent measurement, surprisingly little academic research has examined timed intent measures directly. In two empirical studies, the authors estimate individual-level cumulative adoption likelihood curves—curves calibrated on self-reported adoption likelihoods for cumulative time intervals across a fixed horizon—of 478 managerial decision makers, self-predicting whether and when they will adopt a relevant technology. A hierarchical Bayes formulation allows for a heterogeneous account of the individual-level adoption likelihood curves as a function of time and common antecedents of technology adoption. A third study generalizes these results among 354 consumer decision makers and, using behavioral data collected during a two-year longitudinal study involving a subsample of 143 consumer decision makers, provides empirical evidence for the accuracy of cumulative adoption likelihood curves for predicting whether and when a technology is adopted. Cumulative adoption likelihood curves outperform two single-intent measures as well as two widely validated intent models in predicting individual-level adoption for a fixed period of two years. The results hold great promise for further research on using and optimizing cumulative timed intent measures across a variety of application domains.

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