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A Descriptive Model of Consumer Information Search Behavior

Author

Listed:
  • Robert J. Meyer

    (Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

An algebraic model of the consumer information search process is described. The model is one which seeks to describe the nature of decision making during search, including how consumers form expectations with respect to sets of potential choice alternatives, decide which alternatives to “focus in on” during the search, and update expectations in light of gathered information. A series of experiments are then reported in which several aspects of the model are tested empirically. Results uniformly support the predictions of the model. A discussion of the implications of the research for work in information search behavior is provided.

Suggested Citation

  • Robert J. Meyer, 1982. "A Descriptive Model of Consumer Information Search Behavior," Marketing Science, INFORMS, vol. 1(1), pages 93-121.
    • Handle: RePEc:inm:ormksc:v:1:y:1982:i:1:p:93-121
    DOI: 10.1287/mksc.1.1.93
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    Citations

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    Cited by:

    1. Xu, Yunjie (Calvin) & Kim, Hee-Woong, 2008. "Order Effect and Vendor Inspection in Online Comparison Shopping," Journal of Retailing, Elsevier, vol. 84(4), pages 477-486.
    2. 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.
    3. Dimitrios Tsekouras & Benedict G. C. Dellaert & Bas Donkers & Gerald Häubl, 2020. "Product set granularity and consumer response to recommendations," Journal of the Academy of Marketing Science, Springer, vol. 48(2), pages 186-202, March.
    4. McEwen, William J., 1985. "Awareness, Recall And Advertising Effectiveness," Research on Effectiveness of Agricultural Commodity Promotion, April 9-10, 1985, Arlington, Virginia 279526, Regional Research Projects > NECC-63: Research Committee on Commodity Promotion.
    5. 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.
    6. Goh, Khim-Yong & Chu, Junhong & Wu, Jing, 2015. "Mobile Advertising: An Empirical Study of Temporal and Spatial Differences in Search Behavior and Advertising Response," Journal of Interactive Marketing, Elsevier, vol. 30(C), pages 34-45.
    7. Gerald Häubl & Benedict G. C. Dellaert & Bas Donkers, 2010. "Tunnel Vision: Local Behavioral Influences on Consumer Decisions in Product Search," Marketing Science, INFORMS, vol. 29(3), pages 438-455, 05-06.
    8. repec:sss:wpaper:201405 is not listed on IDEAS
    9. Moon, Junyean & Tikoo, Surinder, 1997. "Consumer Use of Available Information for Making Inferences about Missing Information," Journal of Business Research, Elsevier, vol. 39(2), pages 135-146, June.
    10. Mikolaj Czajkowski & Nick Hanley & Jacob LaRiviere, 2015. "The Effects of Experience on Preferences: Theory and Empirics for Environmental Public Goods," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 333-351.
    11. Browne, Glenn J. & Pitts, Mitzi G., 2004. "Stopping rule use during information search in design problems," Organizational Behavior and Human Decision Processes, Elsevier, vol. 95(2), pages 208-224, November.
    12. 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.

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