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The Stochastic Modeling of Purchase Intentions and Behavior


  • 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)


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.

Suggested Citation

  • Martin R. Young & Wayne S. DeSarbo & Vicki G. Morwitz, 1998. "The Stochastic Modeling of Purchase Intentions and Behavior," Management Science, INFORMS, vol. 44(2), pages 188-202, February.
  • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:2:p:188-202

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    References listed on IDEAS

    1. McNeil, John, 1974. " Federal Programs to Measure Consumer Purchase Expectations, 1946-1973: A Post-Mortem," Journal of Consumer Research, Oxford University Press, vol. 1(3), pages 1-10, December.
    2. Adams, F Gerard, 1974. " Federal Programs to Measure Consumer Purchase Expectations, 1946-1973: A Post-Mortem: Comment," Journal of Consumer Research, Oxford University Press, vol. 1(3), pages 11-12, December.
    3. William J. Infosino, 1986. "Forecasting New Product Sales from Likelihood of Purchase Ratings," Marketing Science, INFORMS, vol. 5(4), pages 372-384.
    4. Manohar U. Kalwani & Alvin J. Silk, 1982. "On the Reliability and Predictive Validity of Purchase Intention Measures," Marketing Science, INFORMS, vol. 1(3), pages 243-286.
    5. Peter J. Lenk & Ambar G. Rao, 1990. "New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures," Marketing Science, INFORMS, vol. 9(1), pages 42-53.
    6. Morwitz, Vicki G & Johnson, Eric J & Schmittlein, David C, 1993. " Does Measuring Intent Change Behavior?," Journal of Consumer Research, Oxford University Press, vol. 20(1), pages 46-61, June.
    7. Granbois, Donald H & Summers, John O, 1975. " Primary and Secondary Validity of Consumer Purchase Probabilities," Journal of Consumer Research, Oxford University Press, vol. 1(4), pages 31-38, March.
    8. F. Thomas Juster, 1966. "Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design," NBER Books, National Bureau of Economic Research, Inc, number just66-2, April.
    9. Allenby, Greg M & Lenk, Peter J, 1995. "Reassessing Brand Loyalty, Price Sensitivity, and Merchandising Effects on Consumer Brand Choice," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 281-289, July.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Islam, Towhidul & Meade, Nigel, 2013. "The impact of attribute preferences on adoption timing: The case of photo-voltaic (PV) solar cells for household electricity generation," Energy Policy, Elsevier, vol. 55(C), pages 521-530.
    5. 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.
    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.
    7. repec:eee:touman:v:41:y:2014:i:c:p:168-177 is not listed on IDEAS
    8. Elisa Martinelli & Donata Tania Vergura, 2015. "Le componenti della fedeltà all’insegna nel retail grocery: un modello multidimensionale," MERCATI E COMPETITIVITÀ, FrancoAngeli Editore, vol. 2015(2), pages 45-65.
    9. 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.
    10. repec:eee:transa:v:103:y:2017:i:c:p:343-361 is not listed on IDEAS
    11. Chun, Young H., 2016. "Designing repetitive screening procedures with imperfect inspections: An empirical Bayes approach," European Journal of Operational Research, Elsevier, vol. 253(3), pages 639-647.


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