Behavioral extremity moderates the association between certainty in attitudes about COVID and willingness to engage in mitigation‐related behaviors.

  • Published In: Social & Personality Psychology Compass, 2023, v. 17, n. 8. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Siev, Joseph J.; Petty, Richard E.; Paredes, Borja; Briñol, Pablo 3 of 3

Abstract

People generally intend to act more on beliefs and attitudes about which they have greater certainty. However, we introduce a boundary condition to the positive association between certainty and behavioral intentions—behavioral extremity. Uncertainty about a threatening issue like COVID‐19 can be disconcerting, and we propose that uncertain people cope in part through increased openness to extreme actions like accepting risky medical treatments and aggression toward those defying mitigation policies. Testing this, we compiled and analyzed all the data on certainty about COVID‐19 mitigation policies and willingness to engage in mitigation‐related behaviors that our lab collected during the pandemic (6 samples, 20 behaviors, Ns up to 1496). External ratings of the behaviors' extremity moderated certainty‐willingness associations: whereas greater certainty was associated with increased willingness to engage in moderate behaviors (the typical result), lower certainty was associated with increased willingness to engage in extreme behaviors, especially among those worried about becoming ill. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social & Personality Psychology Compass. 2023/08, Vol. 17, Issue 8, p1
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2023
  • ISSN:1751-9004
  • DOI:10.1111/spc3.12767
  • Accession Number:169851519
  • Copyright Statement:Copyright of Social & Personality Psychology Compass is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.