000			02123pab a2200205 454500
008			140923b0 xxu||||| |||| 00| 0 eng d
040			_cWelingkar Institute of Management Development & Research, Mumbai _aWelingkar Institute of Management Development & Research, Mumbai
041			_aENG
082			_a _bItt
100			_aIttersum Koert Van
245			_aCumulative Timed Intent: A New Predictive Tool for Technology Adoption
250			_a5
260			_a _bOct 2010 _c0
300			_a808-822 Pp.
490			_vXLVII
520			_aDespite 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.
650			_aSelf-Reported Inventions, Cumulative Timed Intent, Technology Adoption, Predictive Accuracy, Hierarchical Bayes
856			_uhttp://192.168.6.13/libsuite/mm_files/Articles/AR11712.pdf
906			_a39153
999			_c31491 _d31491