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Consequence-Cause Matching: Looking to the Consequences of Events to Infer Their Causes

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  • Robyn A. LeBoeuf
  • Michael I. Norton

Abstract

This article documents a bias in people's causal inferences, showing that people nonnormatively consider an event's consequences when inferring its causes. Across experiments, participants' inferences about event causes were systematically affected by how similar (in both size and valence) those causes were to event consequences, even when the consequences were objectively uninformative about the causes. For example, people inferred that a product failure (computer crash) had a large cause (widespread computer virus) if it had a large consequence (job loss) but that the identical failure was more likely to have a smaller cause (cooling fan malfunction) if the consequence was small--even though the consequences gave no new information about what caused the crash. This "consequence-cause matching," which can affect product attitudes, may arise because people are motivated to see the world as predictable and because matching is an accessible schema that helps them to fulfill this motivation.

Suggested Citation

  • Robyn A. LeBoeuf & Michael I. Norton, 2012. "Consequence-Cause Matching: Looking to the Consequences of Events to Infer Their Causes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 39(1), pages 128-141.
    • Handle: RePEc:oup:jconrs:doi:10.1086/662372
    DOI: 10.1086/662372
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    Cited by:

    1. Gro Hege Haraldsen Nordbye & Karl Halvor Teigen, 2014. "Responsibility judgments of wins and losses in the 2013 chess championship," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(4), pages 335-348, July.
    2. Chaxel, Anne-Sophie & Wiggins, Catherine & Xie, Jieru, 2018. "The impact of a limited time perspective on information distortion," Organizational Behavior and Human Decision Processes, Elsevier, vol. 149(C), pages 35-46.
    3. Daniella Kupor & Kristin Laurin & Chris Janiszewski & J Jeffrey Inman, 2020. "Probable Cause: The Influence of Prior Probabilities on Forecasts and Perceptions of Magnitude [Perceived Intent Motivates People to Magnify Observed Harms]," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(5), pages 833-852.
    4. Zhaoxie Zeng & Yi Ding & Yue Zhang & Yongyu Guo, 2022. "What Breeds Conspiracy Theories in COVID-19? The Role of Risk Perception in the Belief in COVID-19 Conspiracy," IJERPH, MDPI, vol. 19(9), pages 1-11, April.
    5. repec:cup:judgdm:v:9:y:2014:i:4:p:335-348 is not listed on IDEAS
    6. Guangming Xie & Wenbo Du & Hongping Yuan & Yushi Jiang, 2021. "Promoting Reviewer-Related Attribution: Moderately Complex Presentation of Mixed Opinions Activates the Analytic Process," Sustainability, MDPI, vol. 13(2), pages 1-28, January.

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