Author ORCID Identifier

Date Available


Year of Publication


Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation


Communication and Information



First Advisor

Dr. Derek Lane


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)