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Public Response to a Near‐Miss Nuclear Accident Scenario Varying in Causal Attributions and Outcome Uncertainty

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  • Jinshu Cui
  • Heather Rosoff
  • Richard S. John

Abstract

Many studies have investigated public reactions to nuclear accidents. However, few studies focused on more common events when a serious accident could have happened but did not. This study evaluated public response (emotional, cognitive, and behavioral) over three phases of a near‐miss nuclear accident. Simulating a loss‐of‐coolant accident (LOCA) scenario, we manipulated (1) attribution for the initial cause of the incident (software failure vs. cyber terrorist attack vs. earthquake), (2) attribution for halting the incident (fail‐safe system design vs. an intervention by an individual expert vs. a chance coincidence), and (3) level of uncertainty (certain vs. uncertain) about risk of a future radiation leak after the LOCA is halted. A total of 773 respondents were sampled using a 3 × 3 × 2 between‐subjects design. Results from both MANCOVA and structural equation modeling (SEM) indicate that respondents experienced more negative affect, perceived more risk, and expressed more avoidance behavioral intention when the near‐miss event was initiated by an external attributed source (e.g., earthquake) compared to an internally attributed source (e.g., software failure). Similarly, respondents also indicated greater negative affect, perceived risk, and avoidance behavioral intentions when the future impact of the near‐miss incident on people and the environment remained uncertain. Results from SEM analyses also suggested that negative affect predicted risk perception, and both predicted avoidance behavior. Affect, risk perception, and avoidance behavior demonstrated high stability (i.e., reliability) from one phase to the next.

Suggested Citation

  • Jinshu Cui & Heather Rosoff & Richard S. John, 2018. "Public Response to a Near‐Miss Nuclear Accident Scenario Varying in Causal Attributions and Outcome Uncertainty," Risk Analysis, John Wiley & Sons, vol. 38(5), pages 947-961, May.
    • Handle: RePEc:wly:riskan:v:38:y:2018:i:5:p:947-961
    DOI: 10.1111/risa.12920
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    1. Zhipeng Zhou & Chaozhi Li & Chuanmin Mi & Lingfei Qian, 2019. "Exploring the Potential Use of Near-Miss Information to Improve Construction Safety Performance," Sustainability, MDPI, vol. 11(5), pages 1-21, February.

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