Effects of Affect and Source on Adoption of Health Information

Rui Shi; Ziyu Chen; Junchen Shang

doi:10.1002/pchj.70109

Psych Journal ›› 2026, Vol. 15 ›› Issue (3) :e70109 DOI: 10.1002/pchj.70109

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Effects of Affect and Source on Adoption of Health Information

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Abstract

We explored how affective states, information affect, and source impacted health information adoption. Adoption of expert-sourced information was greater than that from GAI, in line with the source credibility framework. In negative or neutral states, or when receiving content from GAI, people preferred positive over negative information, consistent with the affect-as-information hypothesis.

Keywords

adoption intention / affect / health information / information source

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Rui Shi, Ziyu Chen, Junchen Shang. Effects of Affect and Source on Adoption of Health Information. Psych Journal, 2026, 15 (3) : e70109 DOI:10.1002/pchj.70109

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References

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[1]

Bazarova, N. N., J. G. Taft, Y. H. Choi, and D. Cosley. 2013. “Managing Impressions and Relationships on Facebook: Self-Presentational and Relational Concerns Revealed Through the Analysis of Language Style.” Journal of Language and Social Psychology 32, no. 2: 121–141. https://doi.org/10.1177/0261927X12456384.

[2]	Berger, J., and K. L. Milkman. 2012. “What Makes Online Content Viral?” Journal of Marketing Research 49, no. 2: 192–205. https://psycnet.apa.org/record/2006-21670-004.

[3]

Carpenter, D. M., R. F. DeVellis, E. B. Fisher, B. M. DeVellis, S. L. Hogan, and J. M. Jordan. 2010. “The Effect of Conflicting Medication Information and Physician Support on Medication Adherence for Chronically Ill Patients.” Patient Education and Counseling 81, no. 2: 169–176. https://doi.org/10.1016/j.pec.2009.11.006.

[4]	Clore, G. L., and J. R. Huntsinger. 2007. “How Emotions Inform Judgment and Regulate Thought.” Trends in Cognitive Sciences 11, no. 9: 393–399. https://doi.org/10.1016/j.tics.2007.08.005.

[5]	Ellis, H. C., S. A. Ottaway, L. J. Varner, A. S. Becker, and B. A. Moore. 1997. “Emotion, Motivation, and Text Comprehension: The Detection of Contradictions in Passages.” Journal of Experimental Psychology: General 126, no. 2: 131–146. https://doi.org/10.1037/0096-3445.126.2.131.

[6]	Vafeiadis, M. 2023. “Message Interactivity and Source Credibility in Online Dental Practice Reviews: Responding to Reviews Triggers Positive Consumer Reactions Regardless of Review Valence.” Health Communication 38, no. 1: 80–90. https://doi.org/10.1080/10410236.2021.1935538.

[7]	Van Dis, E. A., J. Bollen, W. Zuidema, R. Van Rooij, and C. L. Bockting. 2023. “ChatGPT: Five Priorities for Research.” Nature 614, no. 7947: 224–226. https://doi.org/10.1038/d41586-023-00288-7.

[8]	Watson, D., L. A. Clark, and A. Tellegen. 1988. “Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales.” Journal of Personality and Social Psychology 54, no. 6: 1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063.