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Empirical Generalizations in the Modeling of Consumer Choice

Author

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  • Robert Meyer

    (The Wharton School, University of Pennsylvania)

  • Eric J. Johnson

    (The Wharton School, University of Pennsylvania)

Abstract

Are there general algebraic laws which describe how consumers make choices from sets of alternatives? In this paper we review the verdict of research which has sought to answer this question. We focus on the functional forms which have been found to best characterize three component processes of consumer choice: those of attribute valuation, attribute integration, and choice. Our central conclusion is that there exists support for three major generalizations about the form of consumer decision processes: (1) subjective attribute valuations are a nonlinear, reference-point dependent, function of the corresponding objective measure of product attributes; (2) the integration rule which best describes how these attribute valuations are integrated to form overall valuations is multiplicative-multilinear, characterizing an overweighting of negative attribute information; and (3) the choice rule which links overall valuations of an option to the likelihood that it is chosen from a set is a member of a family of functions which recognize the attributewise proximity of a considered alternative to others in the set. The evidence supporting these generalizations is reviewed, as well as their implications for future theoretical and applied work in consumer choice modeling.

Suggested Citation

  • Robert Meyer & Eric J. Johnson, 1995. "Empirical Generalizations in the Modeling of Consumer Choice," Marketing Science, INFORMS, vol. 14(3_supplem), pages 180-189.
    • Handle: RePEc:inm:ormksc:v:14:y:1995:i:3_supplement:p:g180-g189
    DOI: 10.1287/mksc.14.3.G180
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    Cited by:

    1. A. Frenzel Baudisch, 2006. "Continuous Market Growth Beyond Functional Satiation. Time-Series Analyses of U.S. Footwear Consumption, 1955-2002," Papers on Economics and Evolution 2006-03, Philipps University Marburg, Department of Geography.
    2. Wilfred Amaldoss & Chuan He, 2018. "Reference-Dependent Utility, Product Variety, and Price Competition," Management Science, INFORMS, vol. 64(9), pages 4302-4316, September.
    3. Dellaert, B.G.C. & Brazell, J.D. & Louviere, J.J., 1998. "Variations in consumer choice consistency : The case of attribute-level driven shifts in consistency," Other publications TiSEM 46d342a2-53e4-4f40-a1c8-d, Tilburg University, School of Economics and Management.
    4. Neumann, Nico & Böckenholt, Ulf, 2014. "A Meta-analysis of Loss Aversion in Product Choice," Journal of Retailing, Elsevier, vol. 90(2), pages 182-197.
    5. Valadares Tavares, L., 1999. "A review of major paradigms and models for the design of civil engineering systems," European Journal of Operational Research, Elsevier, vol. 119(1), pages 1-13, November.
    6. Wong, Hartanto & Lesmono, Dharma, 2013. "On the evaluation of product customization strategies in a vertically differentiated market," International Journal of Production Economics, Elsevier, vol. 144(1), pages 105-117.
    7. Vince Barabba & Chet Huber & Fred Cooke & Nick Pudar & Jim Smith & Mark Paich, 2002. "A Multimethod Approach for Creating New Business Models: The General Motors OnStar Project," Interfaces, INFORMS, vol. 32(1), pages 20-34, February.
    8. van Oest, Rutger, 2013. "Why are Consumers Less Loss Averse in Internal than External Reference Prices?," Journal of Retailing, Elsevier, vol. 89(1), pages 62-71.
    9. Mary Tripsas, 2008. "Customer preference discontinuities: a trigger for radical technological change," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 29(2-3), pages 79-97.
    10. David R. Bell & James M. Lattin, 2000. "Looking for Loss Aversion in Scanner Panel Data: The Confounding Effect of Price Response Heterogeneity," Marketing Science, INFORMS, vol. 19(2), pages 185-200, May.
    11. Desmet, Pierre & Feinberg, Fred M., 2003. "Ask and ye shall receive: The effect of the appeals scale on consumers' donation behavior," Journal of Economic Psychology, Elsevier, vol. 24(3), pages 349-376, June.
    12. Naveen C. Amblee, 2014. "Analysis Of The Impact Of Online Product Reviews On Temporal And Cognitive Search Costs: An Eye-Tracking Approach," Working papers 157, Indian Institute of Management Kozhikode.
    13. Arnoud V. den Boer & N. Bora Keskin, 2022. "Dynamic Pricing with Demand Learning and Reference Effects," Management Science, INFORMS, vol. 68(10), pages 7112-7130, October.
    14. Mats Williander, 2007. "Absorptive capacity and interpretation system's impact when ‘going green’: an empirical study of ford, volvo cars and toyota," Business Strategy and the Environment, Wiley Blackwell, vol. 16(3), pages 202-213, March.
    15. Auke Hoekstra & Maarten Steinbuch & Geert Verbong, 2017. "Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation," Complexity, Hindawi, vol. 2017, pages 1-23, December.
    16. Seidl, C. & Traub, S., 1996. "Testing Decision Rules for Multiattribute Decision Making," Other publications TiSEM 06d7c897-6596-4359-80f8-e, Tilburg University, School of Economics and Management.
    17. Barbara E. Kahn & Mary Frances Luce, 2003. "Understanding High-Stakes Consumer Decisions: Mammography Adherence Following False-Alarm Test Results," Marketing Science, INFORMS, vol. 22(3), pages 393-410, April.
    18. Jeffrey Funk, 2007. "Technological Change Within Hierarchies: The Case Of The Music Industry," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 16(1), pages 1-16.
    19. Ron Adner & Daniel Levinthal, 2001. "Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation," Management Science, INFORMS, vol. 47(5), pages 611-628, May.
    20. Davis Brennan & Currim Imran S. & Sarin Rakesh K., 2012. "Reference Dependence and Conjoint Analysis," Review of Marketing Science, De Gruyter, vol. 10(1), pages 1-29, September.
    21. Hyun S. Shin & Dominique M. Hanssens & Kyoo il Kim, 2016. "The role of online buzz for leader versus challenger brands: the case of the MP3 player market," Electronic Commerce Research, Springer, vol. 16(4), pages 503-528, December.

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