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The Stochastic Modeling of Purchase Intentions and Behavior

Listed author(s):
  • 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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    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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    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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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    8. William J. Infosino, 1986. "Forecasting New Product Sales from Likelihood of Purchase Ratings," Marketing Science, INFORMS, vol. 5(4), pages 372-384.
    9. 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.
    10. 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.
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