JOURNAL ARTICLE

The impacts of social media bandwagon cues on public demand for regulatory intervention during corporate crises.

  • Published In: Journal of Contingencies & Crisis Management, 2023, v. 31, n. 3. P. 392 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ji, Yingru; Kim, Sora 3 of 3

Abstract

In the social media era, a growing number of corporate crises are entwined with salient social issues. To address such crises, publics may demand their government take action with regulations, legislation or public policy remediation. Through two online experiments in China, this study investigates how social media bandwagon cues contribute to public demand for regulatory intervention during corporate crises. This study finds that a social media post collecting a great number of likes, comments and shares (i.e., high levels of bandwagon cues) can directly lead to increased public demand. This study also reveals significant mediating roles of perceived crisis severity and publics' responsibility attributions to dual agents—an in‐crisis company and social systems wherein the company operates. When publics are exposed to a post with high levels of bandwagon cues, they perceive greater crisis severity, which in turn increases their responsibility attribution to the company and to social systems. The heightened responsibility attribution then spills over to public demand. Moreover, crisis blame frames of the post content moderated the effects of bandwagon cues on publics' attribution to social systems and subsequent public demand. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Contingencies & Crisis Management. 2023/09, Vol. 31, Issue 3, p392
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:0966-0879
  • DOI:10.1111/1468-5973.12446
  • Accession Number:169828490
  • Copyright Statement:Copyright of Journal of Contingencies & Crisis Management 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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