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Knowledge Calibration: What Consumers Know and What They Think They Know

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  • Alba, Joseph W
  • Hutchinson, J Wesley
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    Abstract

    Consumer knowledge is seldom complete or errorless. Therefore, the self-assessed validity of knowledge and consequent knowledge calibration (i.e., the correspondence between self-assessed and actual validity) is an important issue for the study of consumer decision making. In this article we describe methods and models used in calibration research. We then review a wide variety of empirical results indicating that high levels of calibration are achieved rarely, moderate levels that include some degree of systematic bias are the norm, and confidence and accuracy are sometimes completely uncorrelated. Finally, we examine the explanations of miscalibration and offer suggestions for future research. Copyright 2000 by the University of Chicago.

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    Bibliographic Info

    Article provided by University of Chicago Press in its journal Journal of Consumer Research.

    Volume (Year): 27 (2000)
    Issue (Month): 2 (September)
    Pages: 123-56

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    Handle: RePEc:ucp:jconrs:v:27:y:2000:i:2:p:123-56

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    Cited by:
    1. Weisenfeld, Ursula & Ott, Ingrid, 2011. "Academic discipline and risk perception of technologies: An empirical study," Research Policy, Elsevier, vol. 40(3), pages 487-499, April.
    2. Anthony Ryan & Clive L Spash & Thomas G Measham, 2009. "Household Water Collection in Canberra," Socio-Economics and the Environment in Discussion (SEED) Working Paper Series 2009-06, CSIRO Sustainable Ecosystems.
    3. van Rijnsoever, Frank J. & Farla, Jacco C.M., 2014. "Identifying and explaining public preferences for the attributes of energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 71-82.
    4. Larrick, Richard P. & Burson, Katherine A. & Soll, Jack B., 2007. "Social comparison and confidence: When thinking you're better than average predicts overconfidence (and when it does not)," Organizational Behavior and Human Decision Processes, Elsevier, vol. 102(1), pages 76-94, January.
    5. Heiman, Amir & Lowengart, Oded, 2011. "The effects of information about health hazards in food on consumers' choice process," Journal of Econometrics, Elsevier, vol. 162(1), pages 140-147, May.
    6. Veale, Roberta & Quester, Pascale, 2009. "Do consumer expectations match experience? Predicting the influence of price and country of origin on perceptions of product quality," International Business Review, Elsevier, vol. 18(2), pages 134-144, April.
    7. Koll, Oliver & von Wallpach, Sylvia, 2014. "Intended brand associations: Do they really drive consumer response?," Journal of Business Research, Elsevier, vol. 67(7), pages 1501-1507.
    8. Zhan, Lingjing & He, Yanqun, 2012. "Understanding luxury consumption in China: Consumer perceptions of best-known brands," Journal of Business Research, Elsevier, vol. 65(10), pages 1452-1460.
    9. Andrade, Eduardo B., 2011. "Excessive confidence in visually-based estimates," Organizational Behavior and Human Decision Processes, Elsevier, vol. 116(2), pages 252-261.
    10. House, Lisa & Lusk, Jayson L. & Jaeger, Sara & Traill, W. Bruce & Moore, Melissa & Valli, Carlotta & Morrow, Bert & Yee, Wallace M.S., 2004. "Objective And Subjective Knowledge: Impacts On Consumer Demand For Genetically Modified Foods In The United States And The European Union," 2004 Annual meeting, August 1-4, Denver, CO 20125, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Heiman, Amir & Lowengart, Oded, 2006. "An Ostrich Or A Leopard - Communication Response Strategies To Post-Exposure On Negative Information About Health Hazards In Foods," Discussion Papers 7172, Hebrew University of Jerusalem, Department of Agricultural Economics and Management.
    12. Glaser, Markus & Weber, Martin, 2005. "Overconfidence and Trading Volume," SIFR Research Report Series 40, Institute for Financial Research.
    13. Markus Glaser & Martin Weber, 2007. "Overconfidence and trading volume," The Geneva Papers on Risk and Insurance Theory, Springer, vol. 32(1), pages 1-36, June.
    14. Kwon, Kyoung-Nan & Lee, Jinkook, 2009. "The effects of reference point, knowledge, and risk propensity on the evaluation of financial products," Journal of Business Research, Elsevier, vol. 62(7), pages 719-725, July.
    15. Cowley, Elizabeth, 2006. "Processing exaggerated advertising claims," Journal of Business Research, Elsevier, vol. 59(6), pages 728-734, June.
    16. Ingrid Ott & Ursula Weisenfeld, 2009. "Self-selection, socialization, and risk perception of technologies: An empirical study," Kiel Working Papers 1555, Kiel Institute for the World Economy.
    17. Ozer, Muammer, 2011. "Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions," European Journal of Operational Research, Elsevier, vol. 211(2), pages 359-369, June.
    18. Umarov, Alisher & Sherrick, Bruce J., 2005. "Farmers' Subjective Yield Distributions: Calibration and Implications for Crop Insurance Valuation," 2005 Annual meeting, July 24-27, Providence, RI 19396, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    19. Shang, Wenjing & Hooker, Neal H., 2006. "Scales or Stars? Consumer Preferences for Food Quality Signals," 2006 Annual meeting, July 23-26, Long Beach, CA 21237, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Philip M. Fernbach & Steven A. Sloman & Robert St. Louis & Julia N. Shube, 2013. "Explanation Fiends and Foes: How Mechanistic Detail Determines Understanding and Preference," Journal of Consumer Research, University of Chicago Press, vol. 39(5), pages 1115 - 1131.

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