Resilience Messaging: The Effect of Governors’ Social Media Communications on Community Compliance During a Public Health Crisis

Reza Mousavi () and Bin Gu ()
Additional contact information
Reza Mousavi: McIntire School of Commerce, University of Virginia, Charlottesville, Virginia 22903
Bin Gu: Questrom School of Business, Boston University, Boston, Massachusetts 02215

Information Systems Research, 2024, vol. 35, issue 2, 505-527

Abstract: When managing major disasters, authorities may ask residents to comply with certain guidelines that change community members’ daily routines. In these situations, authorities often appeal to resilience, which refers to the ability to recover from challenges. In this study, we examine whether embedding resilience-related words (resilience messaging) in governors’ social media posts increases community compliance with government guidelines in the context of the COVID-19 disaster. First, we conducted a secondary data analysis using a panel data set of U.S. states. This analysis included community mobility data, governors’ tweets, official county tweets, approval ratings, new COVID-19 cases, and states’ response data for the period between February 2020 and August 2021 (81 weeks). We measure community compliance using the time residents spent at home and the time they spent at retail places, according to community mobility data. We also conducted an online controlled experiment to complement our secondary data analysis in order to identify the underlying mechanism. We show that governors’ resilience messaging increases community compliance (12.5% increase in time spent at home and 11% increase in avoiding unnecessary trips). We also find that the effect of resilience messaging on community compliance is mediated by residents’ perceptions of inspirational leadership.

Keywords: disaster management; social media; community compliance; resilience communication; inspirational leadership; large language models (LLM); natural language processing (NLP); generative artificial intelligence (generative AI); dynamic panel data model; controlled experiment (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/isre.2021.0599 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:35:y:2024:i:2:p:505-527

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().