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The Influence of Base Rate and Case Information on Health-Risk Perceptions: A Unified Model of Self-Positivity and Self-Negativity

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  • Dengfeng Yan
  • Jaideep Sengupta

Abstract

This research examines how consumers use base rate (e.g., disease prevalence in a population) and case information (e.g., an individual's disease symptoms) to estimate health risks. Drawing on construal level theory, we propose that consumers' reliance on base rate (case information) will be enhanced (weakened) by psychological distance. A corollary of this premise is that self-positivity (i.e., underestimating self-risk vs. other-risk) is likely when the disease base rate is high but the case information suggests low risk. In contrast, self-negativity (i.e., overestimating self-risk vs. other-risk) is likely when the disease base rate is low, but case information implies high risk. Six experiments provide convergent support for this thesis, using different operationalizations of construal level, base rate, and case risk across multiple health domains. Our findings inform the extant literature on health-risk perception and also provide theoretical implications for research on social comparisons, as well as that on the use of base rate versus case information.

Suggested Citation

  • Dengfeng Yan & Jaideep Sengupta, 2013. "The Influence of Base Rate and Case Information on Health-Risk Perceptions: A Unified Model of Self-Positivity and Self-Negativity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 39(5), pages 931-946.
  • Handle: RePEc:oup:jconrs:doi:10.1086/666596
    DOI: 10.1086/666596
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    Cited by:

    1. Lili Wang & Maferima Touré-Tillery & Ann L. McGill, 2023. "The effect of disease anthropomorphism on compliance with health recommendations," Journal of the Academy of Marketing Science, Springer, vol. 51(2), pages 266-285, March.
    2. Robertson, Nichola & Polonsky, Michael & McQuilken, Lisa, 2014. "Are my symptoms serious Dr Google? A resource-based typology of value co-destruction in online self-diagnosis," Australasian marketing journal, Elsevier, vol. 22(3), pages 246-256.
    3. Xueming Luo & Michelle Andrews & Zheng Fang & Chee Wei Phang, 2014. "Mobile Targeting," Management Science, INFORMS, vol. 60(7), pages 1738-1756, July.
    4. Freling, Traci H. & Yang, Zhiyong & Saini, Ritesh & Itani, Omar S. & Rashad Abualsamh, Ryan, 2020. "When poignant stories outweigh cold hard facts: A meta-analysis of the anecdotal bias," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 51-67.
    5. Castagna, Ana Carina & Pinto, Diego Costa & Mattila, Anna & de Barcellos, Marcia Dutra, 2021. "Beauty-is-good, ugly-is-risky: Food aesthetics bias and construal level," Journal of Business Research, Elsevier, vol. 135(C), pages 633-643.

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