Journal article
J. Paik, Hyesun Choung, Qinghua Yang
Health Communication, 2026
Health Communication, 2026
Semantic Scholar
DOI
PubMed
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APA
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Paik, J., Choung, H., & Yang, Q. (2026). Why People Turn to ChatGPT for Health Information: Extending UTAUT with Healthcare Dissatisfaction and Perceived Credibility. Health Communication.
Chicago/Turabian
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Paik, J., Hyesun Choung, and Qinghua Yang. “Why People Turn to ChatGPT for Health Information: Extending UTAUT with Healthcare Dissatisfaction and Perceived Credibility.” Health Communication (2026).
MLA
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Paik, J., et al. “Why People Turn to ChatGPT for Health Information: Extending UTAUT with Healthcare Dissatisfaction and Perceived Credibility.” Health Communication, 2026.
BibTeX Click to copy
@article{j2026a,
title = {Why People Turn to ChatGPT for Health Information: Extending UTAUT with Healthcare Dissatisfaction and Perceived Credibility.},
year = {2026},
journal = {Health Communication},
author = {Paik, J. and Choung, Hyesun and Yang, Qinghua}
}
Abstract
Generative artificial intelligence (AI) tools, such as ChatGPT, have become a convenient source of information. This study proposes and tests a model predicting intentions to use ChatGPT for health information and examines whether significant predictors differ by condition severity. The model included the original predictors of the unified theory of acceptance and use of technology (UTAUT). Guided by channel complementarity theory, which highlights source characteristics in a multisource information-seeking environment, dissatisfaction with human healthcare services and perceived credibility of ChatGPT were added to the model. Performance expectancy, social influence, and perceived credibility predicted attitudes toward using ChatGPT, which in turn predicted usage intentions, while effort expectancy was not significant. Condition severity moderated the effect of dissatisfaction with healthcare services, predicting greater intentions to use ChatGPT for mild conditions but not severe ones. This study extends UTAUT to health information seeking and discusses theoretical and practical implications for generative AI use in healthcare.