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A Comparative Evaluation of Multiattribute Consumer Preference Models


  • Imran S. Currim

    (Graduate School of Management, University of California, Los Angeles, California 90024)

  • Rakesh K. Sarin

    (Graduate School of Management, University of California, Los Angeles, California 90024)


In this paper the theory and estimation procedures for several consumer preference models are discussed. Predictive accuracy in the form of internal consistency of these models is compared in an empirical application. Consumer decision situations are classified into two classes: decisions under certainty and decisions under uncertainty. For each of the two classes of decision situations two modeling strategies have been used: statistical estimation and algebraic solution. An additive conjoint, an additive and a multiplicative measurable value, and an additive and a multiplicative utility model are considered. Our main finding is that the statistical estimation procedures outperform their algebraic counterparts on the criterion of predictive accuracy. The utility model provides better predictions for decisions under uncertainty than the widely used conjoint models. The relationship between models for decisions under certainty and decisions under uncertainty is discussed. It is shown how a conjoint or a measurable value function model can be transformed into a utility model with minimum additional information from the subjects. A concept of relative risk attitude is proposed to segment consumers by the degree of their risk aversion or risk seeking propensities.

Suggested Citation

  • Imran S. Currim & Rakesh K. Sarin, 1984. "A Comparative Evaluation of Multiattribute Consumer Preference Models," Management Science, INFORMS, vol. 30(5), pages 543-561, May.
  • Handle: RePEc:inm:ormnsc:v:30:y:1984:i:5:p:543-561

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    Cited by:

    1. Yan Li & Fiona Yao & David Ahlstrom, 2015. "The social dilemma of bribery in emerging economies: A dynamic model of emotion, social value, and institutional uncertainty," Asia Pacific Journal of Management, Springer, vol. 32(2), pages 311-334, June.
    2. Musal, R. Muzaffer & Soyer, Refik & McCabe, Christopher & Kharroubi, Samer A., 2012. "Estimating the population utility function: A parametric Bayesian approach," European Journal of Operational Research, Elsevier, vol. 218(2), pages 538-547.
    3. Zanakis, Stelios H. & Solomon, Anthony & Wishart, Nicole & Dublish, Sandipa, 1998. "Multi-attribute decision making: A simulation comparison of select methods," European Journal of Operational Research, Elsevier, vol. 107(3), pages 507-529, June.
    4. Rodrigues, Teresa C. & Montibeller, Gilberto & Oliveira, Mónica D. & Bana e Costa, Carlos A., 2017. "Modelling multicriteria value interactions with Reasoning Maps," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1054-1071.
    5. Voros, Jozsef, 2002. "Product balancing under conditions of quality inflation, cost pressures and growth strategies," European Journal of Operational Research, Elsevier, vol. 141(1), pages 153-166, August.
    6. Burhan Turksen, I. & Willson, Ian A., 1995. "A fuzzy set model for market share and preference prediction," European Journal of Operational Research, Elsevier, vol. 82(1), pages 39-52, April.
    7. Vörös, József, 2003. "A minőség figyelembevételének szükségessége az egyensúlyi állapot meghatározásában
      [The need to consider quality when determining a state of equilibrium]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 6-21.
    8. Thomas L. Saaty & Daji Ergu, 2015. "When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1171-1187, November.


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