IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v3y1984i3p227-246.html
   My bibliography  Save this article

A Normative Model of Consumer Information Processing

Author

Listed:
  • Michael R. Hagerty

    (University of California, Berkeley)

  • David A. Aaker

    (University of California, Berkeley)

Abstract

A model of information search is proposed which assumes that a consumer chooses the next piece of information so as to maximize his expected value of sample information. The cost of processing, the perceived correlation between attributes, and the perceived importance of attributes would all affect information choice. Three sets of propositions are derived. The model is also estimated and tested for subjects performing an information display board task.

Suggested Citation

  • Michael R. Hagerty & David A. Aaker, 1984. "A Normative Model of Consumer Information Processing," Marketing Science, INFORMS, vol. 3(3), pages 227-246.
  • Handle: RePEc:inm:ormksc:v:3:y:1984:i:3:p:227-246
    DOI: 10.1287/mksc.3.3.227
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.3.3.227
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.3.3.227?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. 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.
    2. Bingxiao Wu, 2014. "Information Presentation and Consumer Choice: Evidence from Assisted Reproductive Technology (ART) Success Rates Reports," Departmental Working Papers 201410, Rutgers University, Department of Economics.
    3. DeSarbo, Wayne S. & Choi, Jungwhan, 1998. "A latent structure double hurdle regression model for exploring heterogeneity in consumer search patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 423-455, November.
    4. Theo A. Arentze & Benedict G. C. Dellaert & Caspar G. Chorus, 2015. "Incorporating Mental Representations in Discrete Choice Models of Travel Behavior: Modeling Approach and Empirical Application," Transportation Science, INFORMS, vol. 49(3), pages 577-590, August.
    5. Gilbride, Timothy J. & Currim, Imran S. & Mintz, Ofer & Siddarth, S., 2016. "A Model for Inferring Market Preferences from Online Retail Product Information Matrices," Journal of Retailing, Elsevier, vol. 92(4), pages 470-485.

    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:3:y:1984:i:3:p:227-246. 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.