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Emotion and reason in everyday risk perception

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Abstract

Although research has documented the importance of emotion in risk perception, little is known about its prevalence in everyday life. Using the Experience Sampling Method, 94 part-time students were prompted at random – via cellular telephones – to report on mood state and three emotions and to assess risk on thirty occasions during their working hours. The emotions – valence, arousal, and dominance – were measured using self-assessment manikins (Bradley & Lang, 1994). Hierarchical linear models (HLM) revealed that mood state and emotions explained significant variance in risk perception. In addition, valence and arousal accounted for variance over and above “reason” (measured by severity and possibility of risks). Six risks were reassessed in a post-experimental session and found to be lower than their real-time counterparts. The study demonstrates the feasibility and value of collecting representative samples of data with simple technology. Evidence for the statistical consistency of the HLM estimates is provided in an Appendix.

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  • Robin Hogarth & Mariona Portell & Anna Cuxart & Gueorgui I. Kolev, 2008. "Emotion and reason in everyday risk perception," Economics Working Papers 1108, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2009.
    • Handle: RePEc:upf:upfgen:1108
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    1. Robin Hogarth & Mariona Portell & Anna Cuxart, 2007. "What risks do people perceive in everyday life? A perspective gained from the experience sampling method (ESM)," Economics Working Papers 1005, Department of Economics and Business, Universitat Pompeu Fabra.
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    Cited by:

    1. Schniter, Eric & Sheremeta, Roman M. & Shields, Timothy W., 2015. "Conflicted emotions following trust-based interaction," Journal of Economic Psychology, Elsevier, vol. 51(C), pages 48-65.
    2. Gambetti, Elisa & Giusberti, Fiorella, 2012. "The effect of anger and anxiety traits on investment decisions," Journal of Economic Psychology, Elsevier, vol. 33(6), pages 1059-1069.
    3. Steudner, Tobias, 2021. "The Effects of Positive Feelings and Arousal on Privacy Decision-Making," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238055, International Telecommunications Society (ITS).
    4. Lane, Tom, 2017. "How does happiness relate to economic behaviour? A review of the literature," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 68(C), pages 62-78.
    5. Laura J. Noval & Günter K. Stahl, 2017. "Accounting for Proscriptive and Prescriptive Morality in the Workplace: The Double-Edged Sword Effect of Mood on Managerial Ethical Decision Making," Journal of Business Ethics, Springer, vol. 142(3), pages 589-602, May.
    6. Aseervatham, Vijay & Jaspersen, Johannes G. & Richter, Andreas, 2015. "The affection effect in an incentive compatible insurance demand experiment," Economics Letters, Elsevier, vol. 131(C), pages 34-37.
    7. Eric Schniter & Timothy Shields, 2013. "Recalibrational Emotions and the Regulation of Trust-Based Behaviors," Working Papers 13-16, Chapman University, Economic Science Institute.

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    More about this item

    Keywords

    Representative design; experience sampling method; risk perception; emotional reactions; self-assessment manikins (SAM); retrospective judgment; multilevel analysis;
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    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

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