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Modeling affective and cognitive responses to soft-target terrorism over time

Author

Listed:
  • Matt Baucum

    (University of Southern California)

  • Richard S. John

    (University of Southern California)

  • William Burns

    (Decision Research)

  • Kent E. Portney

    (Texas A&M University)

  • Jeryl L. Mumpower

    (Texas A&M University)

Abstract

There is great value in understanding the public’s reactions to terror attacks, though such reactions pose stark challenges for sound psychological investigation. Reactions to terrorism (as with any other threat) involve emotional and cognitive components, and the degree to which they reciprocally interact is not well understood. Furthermore, much of the literature on the public response to terrorism is based on the construct of “risk perception,” whose precise definition and correlates are still ambiguous. This study aimed to more clearly disentangle the various emotional and cognitive facets that predict individuals’ response to the terrorism threat in the United States. We employed a longitudinal survey from a representative U.S. sample (n = 1057) and measured attitudinal and cognitive reactions to the threat of terrorism at both time points. An autoregressive latent variable model was used to assess the stability of such variables over time, as well as their reciprocal effects on one another. Participants’ emotional and cognitive reactions to the threat of terrorism evolved independently over time, contrary to the predictions of some prior risk perception literature. Furthermore, measurements of risk perception depended mostly on prior estimates of attack likelihood, emphasizing the importance of individuals’ assessments of hazard probability in the risk judgment process.

Suggested Citation

  • Matt Baucum & Richard S. John & William Burns & Kent E. Portney & Jeryl L. Mumpower, 2021. "Modeling affective and cognitive responses to soft-target terrorism over time," Environment Systems and Decisions, Springer, vol. 41(2), pages 227-235, June.
  • Handle: RePEc:spr:envsyd:v:41:y:2021:i:2:d:10.1007_s10669-020-09789-6
    DOI: 10.1007/s10669-020-09789-6
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    References listed on IDEAS

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    Cited by:

    1. Zachary A. Collier & James H. Lambert & Igor Linkov, 2021. "Algorithms and models for decision making in advanced technology systems," Environment Systems and Decisions, Springer, vol. 41(2), pages 179-180, June.

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