IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v14y1995i3_supplementpg180-g189.html
   My bibliography  Save this article

Empirical Generalizations in the Modeling of Consumer Choice

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.14.3.G180
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.14.3.G180?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Wilfred Amaldoss & Chuan He, 2018. "Reference-Dependent Utility, Product Variety, and Price Competition," Management Science, INFORMS, vol. 64(9), pages 4302-4316, September.
    10. Neumann, Nico & Böckenholt, Ulf, 2014. "A Meta-analysis of Loss Aversion in Product Choice," Journal of Retailing, Elsevier, vol. 90(2), pages 182-197.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:14:y:1995:i:3_supplement:p:g180-g189. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.