Author ORCID Identifier
Date Available
5-14-2023
Year of Publication
2021
Degree Name
Doctor of Philosophy (PhD)
Document Type
Doctoral Dissertation
College
Communication and Information
Department/School/Program
Communication
First Advisor
Dr. Derek Lane
Abstract
Facing a pandemic caused by a novel coronavirus (COVID-19), the public feels uncertainty and fear. To cope with the pandemic and reduce uncertainty, the public needs accurate and prompt information. By theoretically and empirically comparing the Comprehensive Model of Information Seeking (CMIS) and the Risk Information Seeking and Processing Model (RISP), this dissertation aims to unpack the core mechanism of health and risk information seeking. Built on the two models, the author proposed an Integrated Model and explored which variables are the significant predictors of health and risk information seeking.
The author recruited 729 adult participants and analyzed 394 completed online survey responses. This dissertation examines each model’s power in predicting information seeking. Both multiple hierarchical regression and structural equation modeling (SEM) analyses showed that in CMIS, risk experience, salience, and utilities are the most significant predictors of actions.
Regarding the RISP model’s prediction of actions, multiple hierarchical regression analysis reveals that risk experience and informational subjective norms are the most substantial predictors. Moreover, moderation analyses suggest that channel beliefs and perceived information gathering capacity impact how information insufficiency predicts information-seeking intention.
Last, the Integrated Model explained the most variance of information-seeking actions surrounding COVID-19. Particularly, the most significant predictors of actions include risk experience, informational subjective norms, utilities, and seeking intention. These findings will assist researchers in discovering the fundamental motivation of information seeking. These findings can guide pragmatic intervention design to increase audiences’ information seeking and reduce the public’s uncertainty.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2021.110
Recommended Citation
Jin, Xianlin, "Exploring Health and Risk Information Seeking in the Context of COVID-19: Testing the Comprehensive Model of Information Seeking and Risk Information Seeking and Processing Model as Competing Explanatory Models" (2021). Theses and Dissertations--Communication. 103.
https://uknowledge.uky.edu/comm_etds/103
Included in
Communication Technology and New Media Commons, Health Communication Commons, Health Information Technology Commons
