A common objective of social science and business research is the mode
ling of the relationship between demographic/psychographic characteris
tics of individuals and the likelihood of certain behaviors for these
same individuals. Frequently, data on actual behavior are unavailable;
rather, one has available only the self-reported intentions of the in
dividual. If the reported intentions imperfectly predict actual behavi
or, then any model of behavior based on the intention data should acco
unt for the associated measurement error, or else the resulting predic
tions will be biased. In this paper, we provide a method for analyzing
intentions data that explicitly models the discrepancy between report
ed intention and behavior, thus facilitating a less biased assessment
of the impact of designated covariates on actual behavior. The applica
tion examined here relates to modeling relationships between demograph
ic characteristics and actual purchase behavior among consumers. A new
Bayesian approach employing the Gibbs sampler is developed and compar
ed to alternative models. We show, through simulated and real data, th
at, relative to methods that implicitly equate intentions and behavior
, the proposed method can increase the accuracy with which purchase re
sponse models are estimated.