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Differentiated Bayesian Conjoint Choice Designs

Author

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  • Sándor, Z.
  • Wedel, M.

Abstract

Previous conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about the parameter values. A computational procedure is developed that enables fast and easy implementation in practice. Even though the number of such different designs in the optimal set is small, it is demonstrated through a Monte Carlo study that substantial gains in efficiency are achieved over aggregate designs.

Suggested Citation

  • Sándor, Z. & Wedel, M., 2003. "Differentiated Bayesian Conjoint Choice Designs," ERIM Report Series Research in Management ERS-2003-016-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:320
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    File URL: https://repub.eur.nl/pub/320/ERS-2003-016-MKT.pdf
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    References listed on IDEAS

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    1. Arora, Neeraj & Huber, Joel, 2001. "Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 273-283, September.
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    Cited by:

    1. C. M. Dieteren & I. Bonfrer & W. B. F. Brouwer & J. Exel, 2023. "Public preferences for policies promoting a healthy diet: a discrete choice experiment," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(9), pages 1429-1440, December.
    2. John R. Hauser & Olivier Toubia, 2005. "The Impact of Utility Balance and Endogeneity in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(3), pages 498-507, August.

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    More about this item

    Keywords

    consumer preferences; discrete choice; estimator efficiency; experiments; multinomial logit;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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