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Representation and Inference of Lexicographic Preference Models and Their Variants

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Author Info

  • Rajeev Kohli

    ()
    (Graduate School of Business, Columbia University, 506 Uris Hall, New York, New York 10027)

  • Kamel Jedidi

    ()
    (Graduate School of Business, Columbia University, 518 Uris Hall, New York, New York 10027)

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    Abstract

    The authors propose two variants of lexicographic preference rules. They obtain the necessary and sufficient conditions under which a linear utility function represents a standard lexicographic rule, and each of the proposed variants, over a set of discrete attributes. They then: (i) characterize the measurement properties of the parameters in the representations; (ii) propose a nonmetric procedure for inferring each lexicographic rule from pairwise comparisons of multiattribute alternatives; (iii) describe a method for distinguishing among different lexicographic rules, and between lexicographic and linear preference models; and (iv) suggest how individual lexicographic rules can be combined to describe hierarchical market structures. The authors illustrate each of these aspects using data on personal-computer preferences. They find that two-thirds of the subjects in the sample use some kind of lexicographic rule. In contrast, only one in five subjects use a standard lexicographic rule. This suggests that lexicographic rules are more widely used by consumers than one might have thought in the absence of the lexicographic variants described in the paper. The authors report a simulation assessing the ability of the proposed inference procedure to distinguish among alternative lexicographic models, and between linear-compensatory and lexicographic models.

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    File URL: http://dx.doi.org/10.1287/mksc.1060.0241
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 26 (2007)
    Issue (Month): 3 (05-06)
    Pages: 380-399

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    Handle: RePEc:inm:ormksc:v:26:y:2007:i:3:p:380-399

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    Related research

    Keywords: lexicographic preferences; noncompensatory preference models; linear models; optimization techniques; greedy algorithm; approximation algorithms; utility theory; conjoint analysis; hierarchical clustering; market segmentation; hierarchical market structures;

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    Cited by:
    1. Paola Manzini & Marco Mariotti, 2009. "Consumer choice and revealed bounded rationality," Economic Theory, Springer, vol. 41(3), pages 379-392, December.
    2. Heiman, Amir & Lowengart, Oded, 2011. "The effects of information about health hazards in food on consumers' choice process," Journal of Econometrics, Elsevier, vol. 162(1), pages 140-147, May.
    3. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    4. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, 09.
    5. John Hauser, 2011. "A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(5), pages 396-408, July.

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