{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/11582"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/11582","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Message Framing, Source, and Personality in Older Adults' Perceptions and Behavioral Intentions with AI-driven Healthcare Treatment Recommendations","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Wood, Kara A."],"institution":null,"degree_name":null,"degree_level":"Doctorate Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Devereux, Paul"],"committee_chairs":[],"committee_members":["Ehrenreich, Samuel","Jones, Daniel","Sun, Haosen","Feil-Seifer, David"],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T21:46:19Z","subjects":["Artificial Intelligence in Healthcare","Message Framing","Older Adults","Personality Traits (Big Five)","Technology Adoption","Trust and Adherence"],"languages":["en_US","English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarwolf.unr.edu/handle/11714/11582","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Devereux, Paul"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Ehrenreich, Samuel","Jones, Daniel","Sun, Haosen","Feil-Seifer, David"]},{"key":"dc:creator","label":"Author","values":["Wood, Kara A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-08T18:45:40Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-08T18:45:40Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctorate Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial Intelligence in Healthcare","Message Framing","Older Adults","Personality Traits (Big Five)","Technology Adoption","Trust and Adherence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarwolf.unr.edu/handle/11714/11582"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Message Framing, Source, and Personality in Older Adults' Perceptions and Behavioral Intentions with AI-driven Healthcare Treatment Recommendations"]}]}],"canonical_facts":{"dc:contributor.advisor":["Devereux, Paul"],"dc:contributor.committeemember":["Ehrenreich, Samuel","Jones, Daniel","Sun, Haosen","Feil-Seifer, David"],"dc:creator":["Wood, Kara A."],"dc:date.accessioned":["2025-09-08T18:45:40Z"],"dc:date.available":["2025-09-08T18:45:40Z"],"dc:date.issued":["2025"],"dc:description.abstract":["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."],"dc:format":["PDF"],"dc:identifier.uri":["https://scholarwolf.unr.edu/handle/11714/11582"],"dc:language":["English"],"dc:language.iso":["en_US"],"dc:subject":["Artificial Intelligence in Healthcare","Message Framing","Older Adults","Personality Traits (Big Five)","Technology Adoption","Trust and Adherence"],"dc:title":["Message Framing, Source, and Personality in Older Adults' Perceptions and Behavioral Intentions with AI-driven Healthcare Treatment Recommendations"],"dc:type":["Dissertation"],"thesis:degree_level":["Doctorate Degree"]},"updated_at":"2026-07-27T21:46:19Z"}