THE STOCHASTIC MODELING OF PURCHASE INTENTIONS AND BEHAVIOR

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.