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A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm)

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  • John Hauser
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    Abstract

    Marketing science seeks to prescribe better marketing strategies (advertising, product development, pricing, etc.). To do so we rely on models of consumer decisions grounded in empirical observations. Field experience suggests that recognition-based heuristics help consumers to choose which brands to consider and purchase in frequently-purchased categories, but other heuristics are more relevant in durable-goods categories. Screening with recognition is a rational screening rule when advertising is a signal of product quality, when observing other consumers makes it easy to learn decision rules, and when firms react to engineering-design constraints by offering brands such that a high-level on one product feature implies a low level on another product feature. Experience with applications and field experiments suggests four fruitful research topics: deciding how to decide (endogeneity), learning decision rules by self-reflection, risk reduction, and the difference between utility functions and decision rules. These challenges also pose methodological cautions.

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    Bibliographic Info

    Article provided by Society for Judgment and Decision Making in its journal Judgment and Decision Making.

    Volume (Year): 6 (2011)
    Issue (Month): 5 (July)
    Pages: 396-408

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    Handle: RePEc:jdm:journl:v:6:y:2011:i:5:p:396-408

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    Related research

    Keywords: consideration sets; ecological rationality; evaluation cost model; fast-and-frugal heuristics; self-reflection learning; non-compensatory decision rules; product development; recognition heuristic.;

    References

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    1. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-54, July/Aug..
    2. John R. Hauser & Glen L. Urban & Guilherme Liberali & Michael Braun, 2009. "—Response to Comments on “Website Morphing”," Marketing Science, INFORMS, vol. 28(2), pages 227-228, 03-04.
    3. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    4. Theodoros Evgeniou & Massimiliano Pontil & Olivier Toubia, 2007. "A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation," Marketing Science, INFORMS, vol. 26(6), pages 805-818, 11-12.
    5. Little, John D. C., 1979. "Aggregate advertising response models : the state of the art," Working papers 1048-79., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Gerd Gigerenzer & Daniel G. Goldstein, 2011. "The recognition heuristic: A decade of research," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 100-121, February.
    7. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, vol. 34(2), pages 167-185, February.
    8. John R. Hauser, 1977. "Testing the Accuracy," Discussion Papers 286, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    9. Arndt Br�der & Ben Newell, 2008. "Challenging some common beliefs: Empirical work within the adaptive toolbox metaphor," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 3, pages 205-214, March.
    10. Hauser, John R & Urban, Glen L, 1986. " The Value Priority Hypotheses for Consumer Budget Plans," Journal of Consumer Research, University of Chicago Press, vol. 12(4), pages 446-62, March.
    11. Paul R. Milgrom & John Roberts, 1984. "Price and Advertising Signals of Product Quality," Cowles Foundation Discussion Papers 709, Cowles Foundation for Research in Economics, Yale University.
    12. Anja Dieckmann & Katrin Dippold & Holger Dietrich, 2009. "Compensatory versus noncompensatory models for predicting consumer preferences," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 4(3), pages 200-213, April.
    13. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    14. Catherine Tucker & Juanjuan Zhang, 2010. "Growing Two-Sided Networks by Advertising the User Base: A Field Experiment," Marketing Science, INFORMS, vol. 29(5), pages 805-814, 09-10.
    15. Rajeev Kohli & Kamel Jedidi, 2007. "Representation and Inference of Lexicographic Preference Models and Their Variants," Marketing Science, INFORMS, vol. 26(3), pages 380-399, 05-06.
    16. Michael Yee & Ely Dahan & John R. Hauser & James Orlin, 2007. "Greedoid-Based Noncompensatory Inference," Marketing Science, INFORMS, vol. 26(4), pages 532-549, 07-08.
    17. Clintin P. Davis-Stober & Jason Dana & David V. Budescu, 2010. "Why recognition is rational: Optimality results on single-variable decision rules," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(4), pages 216-229, July.
    18. John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
    19. John R. Hauser & Glen L. Urban & Guilherme Liberali & Michael Braun, 2009. "Website Morphing," Marketing Science, INFORMS, vol. 28(2), pages 202-223, 03-04.
    20. Hauser, John R & Wernerfelt, Birger, 1990. " An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, University of Chicago Press, vol. 16(4), pages 393-408, March.
    21. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
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    Cited by:
    1. Julian N. Marewski & Rudiger F. Pohl & Oliver Vitouch, 2011. "Recognition-based judgments and decisions: What we have learned (so far)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(5), pages 359-380, July.

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