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Tunnel Vision: Local Behavioral Influences on Consumer Decisions in Product Search

Author

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  • Gerald Häubl

    () (School of Business, University of Alberta, Edmonton, Alberta T6G 2R6, Canada)

  • Benedict G. C. Dellaert

    () (Department of Business Economics, Erasmus School of Economics, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands)

  • Bas Donkers

    () (Department of Business Economics, Erasmus School of Economics, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands)

Abstract

We introduce and test a behavioral model of consumer product search that extends a baseline normative model of sequential search by incorporating nonnormative influences that are local in the sense that they reflect consumers' undue sensitivity to recently encountered alternatives. We propose two types of such local behavioral influences that, at each stage of a search process, can manifest themselves both in which of the products inspected up to that point is deemed to be the most preferred one (the product comparison decision) and whether to terminate the search at that stage (the stopping decision). The first of these influences is that consumers respond excessively to the attractiveness of the currently inspected product, at the expense of all others (“focalism”). The second proposed behavioral influence is that consumers overreact to the difference in attractiveness between the current product and the one encountered just prior to it (“local contrast”). Converging evidence from two experiments, which combine to guarantee both high internal and high external validity, provides support for the proposed behavioral influences. Our findings demonstrate that consumers' product comparison and stopping decisions in sequential product search are jointly governed by normative principles and by the proposed local behavioral influences.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormksc:v:29:y:2010:i:3:p:438-455
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    File URL: http://dx.doi.org/10.1287/mksc.1090.0525
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    References listed on IDEAS

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    Cited by:

    1. Yadav, Manjit S. & de Valck, Kristine & Hennig-Thurau, Thorsten & Hoffman, Donna L. & Spann, Martin, 2013. "Social Commerce: A Contingency Framework for Assessing Marketing Potential," Journal of Interactive Marketing, Elsevier, vol. 27(4), pages 311-323.
    2. repec:eee:jouret:v:90:y:2014:i:3:p:428-441 is not listed on IDEAS
    3. Nuno Camacho & Bas Donkers & Stefan Stremersch, 2011. "Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality," Marketing Science, INFORMS, vol. 30(2), pages 305-320, 03-04.
    4. Eric Johnson & Suzanne Shu & Benedict Dellaert & Craig Fox & Daniel Goldstein & Gerald Häubl & Richard Larrick & John Payne & Ellen Peters & David Schkade & Brian Wansink & Elke Weber, 2012. "Beyond nudges: Tools of a choice architecture," Marketing Letters, Springer, vol. 23(2), pages 487-504, June.
    5. Dellaert, B.G.C. & Baker, T. & Johnson, E.J., 2017. "Partitioning Sorted Sets: Overcoming Choice Overload while Maintaining Decision Quality," ERIM Report Series Research in Management 18-2, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

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