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A Multiattribute Model of Consumer Choice During Product Learning

Author

Listed:
  • Robert J. Meyer

    (University of California, Los Angeles)

  • Arvind Sathi

    (Carnegie-Mellon University)

Abstract

The processes of consumer preference formation during product learning are analyzed. A hypothesis about how individuals form attribute expectations is used to derive a dynamic multinomial logit model of individual choice which endogenously recognizes product learning. The model and its underlying assumptions are then tested in the context of an interactive grocery store learning game. The results support most elements of the proposed model structure. An assumption of temporal stationarity in attribute salience, however, could not be supported. Approaches to implementation of the model are discussed, and implications for marketing management and research in individual choice modeling are addressed.

Suggested Citation

  • Robert J. Meyer & Arvind Sathi, 1985. "A Multiattribute Model of Consumer Choice During Product Learning," Marketing Science, INFORMS, vol. 4(1), pages 41-61.
    • Handle: RePEc:inm:ormksc:v:4:y:1985:i:1:p:41-61
    DOI: 10.1287/mksc.4.1.41
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    Citations

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    Cited by:

    1. Praveen K. Kopalle & João L. Assunção, 2000. "When (not) to indulge in 'puffery': the role of consumer expectations and brand goodwill in determining advertised and actual product quality," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 21(6), pages 223-241.
    2. Heiman, Amir & Just, David R. & McWilliams, Bruce P. & Zilberman, David, 2015. "A prospect theory approach to assessing changes in parameters of insurance contracts with an application to money-back guarantees," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 54(C), pages 105-117.
    3. Huang, Yanliu & Hutchinson, J. Wesley, 2013. "The roles of planning, learning, and mental models in repeated dynamic decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 122(2), pages 163-176.
    4. Ert, Eyal & Raz, Ornit & Heiman, Amir, 2016. "(Poor) seeing is believing: When direct experience impairs product promotion," International Journal of Research in Marketing, Elsevier, vol. 33(4), pages 881-895.
    5. Tansev Geylani & J. Jeffrey Inman & Frenkel Ter Hofstede, 2008. "Image Reinforcement or Impairment: The Effects of Co-Branding on Attribute Uncertainty," Marketing Science, INFORMS, vol. 27(4), pages 730-744, 07-08.
    6. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.
    7. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    8. Hauser, John R. & Urban, Glen L. & Weinberg, Bruce D., 1992. "Time flies when you're having fun : how consumers allocate their time when evaluating products," Working papers 3439-92., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    9. Anocha Aribarg & Thomas Otter & Daniel Zantedeschi & Greg M. Allenby & Taylor Bentley & David J. Curry & Marc Dotson & Ty Henderson & Elisabeth Honka & Rajeev Kohli & Kamel Jedidi & Stephan Seiler & X, 2018. "Advancing Non-compensatory Choice Models in Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 82-92, March.
    10. Naeun L. Kim & Gwia Kim & Lori Rothenberg, 2020. "Is Honesty the Best Policy? Examining the Role of Price and Production Transparency in Fashion Marketing," Sustainability, MDPI, vol. 12(17), pages 1-18, August.
    11. Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po, 2021. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models," International Journal of Forecasting, Elsevier, vol. 37(1), pages 302-321.
    12. Yonezawa, Koichi & Richards, Timothy J., 2016. "Competitive Package Size Decisions," Journal of Retailing, Elsevier, vol. 92(4), pages 445-469.
    13. Maltz, Amnon, 2016. "Experience based dynamic choice: A revealed preference approach," Journal of Economic Behavior & Organization, Elsevier, vol. 128(C), pages 1-13.
    14. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    15. Heiman, Amir & Lowengart, Oded, 2008. "The effect of information about health hazards on demand for frequently purchased commodities," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 310-318.
    16. Dhruv Goel & Anushka Goyal & Ishaan Sand, 2024. "Quantity surcharge, competition and package size: evidence from India," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(5), pages 452-460, October.
    17. Sridhar Narayanan & Puneet Manchanda, 2009. "Heterogeneous Learning and the Targeting of Marketing Communication for New Products," Marketing Science, INFORMS, vol. 28(3), pages 424-441, 05-06.

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