Message Framing, Source, and Personality in Older Adults' Perceptions and Behavioral Intentions with AI-driven Healthcare Treatment Recommendations

As artificial intelligence (AI) continues to shape healthcare delivery, older adults represent a group with much to gain-but also much to lose-depending on how these technologies are introduced and implemented. Adoption remains uneven, often hindered by concerns about trust, privacy, and a lack of familiarity with AI-based technology. This study examined the impact of message framing (gain vs. loss), message source (AI, human provider, or a combination of the two), and personality traits on older adults' trust in AI-driven healthcare recommendations and their predicted adherence to treatment plans. Grounded in prospect theory and personality research, a 2x3 experimental design tested how different message presentations interacted with individual differences to shape attitudes toward AI-based healthcare. The study also examined how healthcare and technological experience shaped perceptions of AI-generated recommendations. Results from the quantitative and qualitative analyses show that the source of the recommendation consistently shaped how comfortable and trusting respondents felt, with many expressing a preference for having a human involved in the process. Conscientiousness, in particular, played a role in how likely respondents said they would follow the treatment advice depending on who provided it. Comments in the open-ended responses pointed to a common trade-off: while AI was often seen as efficient and practical, many felt it lacked the personal touch they value in healthcare. `By integrating message features and individual traits, this study offers insight into how AI-based systems can be designed and communicated in ways that are more trustworthy, effective, and inclusive for older adults.

Level thesis:degree_level

Doctorate Degree

Year dc:date.issued

2025

Author dc:creator

• Wood, Kara A.

Advisor dc:contributor.advisor

• Devereux, Paul

Committee members dc:contributor.committeemember

• Ehrenreich, Samuel
• Jones, Daniel
• Sun, Haosen
• Feil-Seifer, David

• Artificial Intelligence in Healthcare
• Message Framing
• Older Adults
• Personality Traits (Big Five)
• Technology Adoption
• Trust and Adherence

Language dc:language.iso

en_US, English

Repository record dc:identifier.uri

https://scholarwolf.unr.edu/handle/11714/11582

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/11582