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The Stochastic Modeling of Purchase Intentions and Behavior

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Author Info

  • Martin R. Young

    (Department of Statistics and Management Science, University of Michigan School of Business Administration, Ann Arbor, Michigan 48109)

  • Wayne S. DeSarbo

    (Department of Marketing, Smeal College of Business, Pennsylvania State University, State College, Pennsylvania 16802)

  • Vicki G. Morwitz

    (Department of Marketing, Leonard N. Stern School of Business, New York University, New York, New York 10003)

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    Abstract

    A common objective of social science and business research is the modeling of the relationship between demographic/psychographic characteristics 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 individual. If the reported intentions imperfectly predict actual behavior, then any model of behavior based on the intention data should account for the associated measurement error, or else the resulting predictions will be biased. In this paper, we provide a method for analyzing intentions data that explicitly models the discrepancy between reported intention and behavior, thus facilitating a less biased assessment of the impact of designated covariates on actual behavior. The application examined here relates to modeling relationships between demographic characteristics and actual purchase behavior among consumers. A new Bayesian approach employing the Gibbs sampler is developed and compared to alternative models. We show, through simulated and real data, that, relative to methods that implicitly equate intentions and behavior, the proposed method can increase the accuracy with which purchase response models are estimated.

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    File URL: http://dx.doi.org/10.1287/mnsc.44.2.188
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 44 (1998)
    Issue (Month): 2 (February)
    Pages: 188-202

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    Handle: RePEc:inm:ormnsc:v:44:y:1998:i:2:p:188-202

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    Related research

    Keywords: Bayesian methods; hierarchical bayes; Markov chain Monte Carlo; measurement error; probit regression; purchase intentions; stochastic models;

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    Cited by:
    1. Ozer, Muammer, 2011. "Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions," European Journal of Operational Research, Elsevier, vol. 211(2), pages 359-369, June.
    2. Kemp, Katherine & Insch, Andrea & Holdsworth, David K. & Knight, John G., 2010. "Food miles: Do UK consumers actually care?," Food Policy, Elsevier, vol. 35(6), pages 504-513, December.
    3. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
    4. Carrington, Michal J. & Neville, Benjamin A. & Whitwell, Gregory J., 2014. "Lost in translation: Exploring the ethical consumer intention–behavior gap," Journal of Business Research, Elsevier, vol. 67(1), pages 2759-2767.
    5. B. Larivière & D. Van Den Poel, 2005. "Investigating the post-complaint period by means of survival analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/299, Ghent University, Faculty of Economics and Business Administration.
    6. Koert Van Ittersum, 2012. "The effect of decision makers’ time perspective on intention–behavior consistency," Marketing Letters, Springer, vol. 23(1), pages 263-277, March.

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