IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v4y1985i1p41-61.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.4.1.41
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.4.1.41?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. 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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Yonezawa, Koichi & Richards, Timothy J., 2016. "Competitive Package Size Decisions," Journal of Retailing, Elsevier, vol. 92(4), pages 445-469.
    11. 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.
    12. 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.
    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. 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.
    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. 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.
    17. 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.

    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:4:y:1985:i:1:p:41-61. 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.