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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