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Information and Risk Perception: A Dynamic Adjustment Process

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  • Shiping Liu
  • Ju‐Chin Huang
  • Gregory L. Brown

Abstract

It is common in catastrophic food‐contamination events that consumers fail to adjust instantaneously to a normal consumption level. One explanation is that consumers only gradually accept new positive information as being trustworthy. The gradual establishment of the trustworthiness of the released information depends on both positive and negative media coverage over time. We examine the individual “trust” effects by extending the prospective reference theory (Viscusi, 1989) to include a dynamic adjustment process of risk perception. Conditions that allow aggregation of changes in risk perceptions across individuals are described. The proposed model describes a general updating process of risk perceptions to media coverage and can be applied to explain the temporal impact of media coverage on consumption of a broad range of goods (food or nonfood). A case study of milk contamination is conducted to demonstrate consumer demand adjustment process to a temporarily unfavorable shock. The results suggest that effects of positive and negative information to adjustment of consumption and risk perception are asymmetric over time.

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  • Shiping Liu & Ju‐Chin Huang & Gregory L. Brown, 1998. "Information and Risk Perception: A Dynamic Adjustment Process," Risk Analysis, John Wiley & Sons, vol. 18(6), pages 689-699, December.
    • Handle: RePEc:wly:riskan:v:18:y:1998:i:6:p:689-699
    DOI: 10.1111/j.1539-6924.1998.tb01113.x
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    2. Ling Jia & Queena K. Qian & Frits Meijer & Henk Visscher, 2020. "Stakeholders’ Risk Perception: A Perspective for Proactive Risk Management in Residential Building Energy Retrofits in China," Sustainability, MDPI, vol. 12(7), pages 1-25, April.
    3. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    4. Éric Cahuzac & Daniel Hassan & Sylvette Monier-Dilhan, 2007. "Sécurité sanitaire des aliments : fausse alerte et vraie crise," Economie & Prévision, La Documentation Française, vol. 0(1), pages 55-64.
    5. Annukka Vainio & Riikka Paloniemi & Vilja Varho, 2017. "Weighing the Risks of Nuclear Energy and Climate Change: Trust in Different Information Sources, Perceived Risks, and Willingness to Pay for Alternatives to Nuclear Power," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 557-569, March.
    6. Mazzocchi, Mario, 2004. "Modeling the Impact of Food Safety Information with No Information," 2004 Annual meeting, August 1-4, Denver, CO 20252, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. Janneke De Jonge & Hans Van Trijp & Reint Jan Renes & Lynn Frewer, 2007. "Understanding Consumer Confidence in the Safety of Food: Its Two‐Dimensional Structure and Determinants," Risk Analysis, John Wiley & Sons, vol. 27(3), pages 729-740, June.
    8. Zhou, Li & Turvey, Calum & Hu, Wuyang & Ying, Ruiyao, 2015. "Fear and Trust: How Risk Perceptions of Avian Influenza Affect the Demand for Chicken," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 202077, Agricultural and Applied Economics Association.
    9. Parker Bolstad & David G. Victor, 2024. "Growing deviations between elite and non-elite media coverage of climate change in the United States," Climatic Change, Springer, vol. 177(6), pages 1-20, June.
    10. Mario Mazzocchi & Davide Delle Monache & Alexandra Lobb, 2006. "A structural time series approach to modelling multiple and resurgent meat scares in Italy," Applied Economics, Taylor & Francis Journals, vol. 38(14), pages 1677-1688.

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