Speedy activists: How firm response time to sociopolitical events influences consumer behavior.

  • Published In: Journal of Consumer Psychology (John Wiley & Sons, Inc. ), 2023, v. 33, n. 4. P. 632 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Nam, Jimin; Balakrishnan, Maya; De Freitas, Julian; Brooks, Alison Wood 3 of 3

Abstract

Organizations face growing pressure from their consumers and stakeholders to take public stances on sociopolitical issues. However, many are hesitant to do so lest they make missteps, promises they cannot keep, appear inauthentic, or alienate consumers, employees, or other stakeholders. Here we investigate consumers' impressions of firms that respond quickly or slowly to sociopolitical events. Using data scraped from Instagram and three online experiments (N = 2452), we find that consumers express more positive sentiment and greater purchasing intentions toward firms that react more quickly to sociopolitical issues. Unlike other types of public firm decision making such as product launch, where careful deliberation can be appreciated, consumers treat firm response time to sociopolitical events as an informative cue of the firm's authentic commitment to the issue. We identify an important boundary condition of this main effect: speedy responses bring limited benefits when the issue is highly divisive along political lines. Our findings bridge extant research on brand activism and communication, and offer practical advice for firms. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Consumer Psychology (John Wiley & Sons, Inc. ). 2023/10, Vol. 33, Issue 4, p632
  • Document Type:Article
  • Subject Area:Marketing
  • Publication Date:2023
  • ISSN:1057-7408
  • DOI:10.1002/jcpy.1380
  • Accession Number:172368062
  • Copyright Statement:Copyright of Journal of Consumer Psychology (John Wiley & Sons, Inc. ) 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.)

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