Risk Perception and Preventive Behavior During the COVID-19 Pandemic : Testing the Effects of Government Trust and Information Behaviors
Abstract
Given the absence of COVID-19 treatments, the best way to control the spread of the virus is to break the chain of infection by increasing public participation in preventive behaviors recommended by health authorities. This study proposes a moderated mediation model of information behaviors (e.g. information seeking and information verification) and trust in government that explores the relationship between risk perception and preventive behaviors regarding COVID-19. Using a survey study in South Korea, we conducted the moderated mediation analysis with latent moderated structural equation modeling (LMS). We found serial mediation effects for risk perception, information behaviors, and preventive behaviors, as people both seek out information and verify that information before adopting preventive behaviors. Additionally, trust in government moderated information behaviors in the relationship between risk perception and preventive behaviors, suggesting that trust in government encourages people to adopt more preventive actions via information seeking and information verification. Further implications are discussed to promote public understanding of the health crisis and public participation in preventive measures.
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Risk perceptions have been identified as a critical motivator of information verification.
“Risk perceptions, in particular, have been identified as a critical motivator.”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Jeong and Kim 2024
Jeong and Kim (2024) found that high-risk perceptions during the COVID-19 pandemic led to increased information verification, which subsequently influenced preventive health behaviors.
“Jeong and Kim (2024), for instance, found that high-risk perceptions during the COVID-19 pandemic led to increased information verification, which subsequently influenced preventive health behaviors.”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Jeong and Kim 2024
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- Authors
- Jeong, J.-S., & Kim, S. Y.
- Affiliations
- University of Oklahoma (US)
- The University of Texas Rio Grande Valley (US)
- Corresponding author
- Kim, Soo Yun
- ORCID
- Kim, Soo Yun
- Venue
- Health Communication
- Publisher
- Informa UK Limited
- Published
- 2023-01-17
- Volume
- 39
- Issue
- 2
- Pages
- 376-387
- ISSN
- 1041-0236 (ISSN-L)
- 1532-7027
- OpenAlex
- W4317360180
- Cited by
- 27
- References
- 74
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 5 · 2023: 5
- FWCI
- 12.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Health Communication (publishedVersion)
- PubMed (publishedVersion)
- ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion)
- Funders
- no funding associated with the work featured in this article
- Topics
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Sociology and Political Science › Risk Perception and Management
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Keywords
- COVID-19 pandemic, risk perception, preventive behavior, public trust in government, information seeking, fact-checking, health information behavior, citizen participation, pandemic response, South Korea
- MeSH terms
- COVID-19, Government, Humans, Perception, Surveys and Questionnaires, Trust, Pandemics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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