Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/185846
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dc.contributor.authorWagner, Rafael Luis-
dc.contributor.authorPinto, Diego Costa-
dc.contributor.authorHildebrand, Diogo-
dc.contributor.authorPacheco, Natália Araújo-
dc.contributor.authorDhillon, Gurpreet-
dc.contributor.authorHerter, Marcia Maurer-
dc.date.accessioned2025-07-31T21:19:59Z-
dc.date.issued2025-11-
dc.identifier.issn0742-6046-
dc.identifier.otherPURE: 121778799-
dc.identifier.otherPURE UUID: 4eac6301-3404-4a3f-a38d-a2d4ca07140f-
dc.identifier.otherScopus: 105011271964-
dc.identifier.otherWOS: 001532800000001-
dc.identifier.otherORCID: /0000-0003-4418-9450/work/189008202-
dc.identifier.urihttp://hdl.handle.net/10362/185846-
dc.descriptionWagner, R. L., Pinto, D. C., Hildebrand, D., Pacheco, N. A., Dhillon, G., & Herter, M. M. (2025). Algorithm Delegation: How Embedded AI Facilitates Agency Transference in Medical Services. Psychology and Marketing, 42(11), 2902-2921. https://doi.org/10.1002/mar.70016 --- %ABS3% --- Funding: ENSILIS ‐ Educação e Formação Unipessoal, Fundação Para a Ciência e Tecnologia (FCT) (2024.07397. IACDC, UIDB/04152/2020), and European Health and Digital Executive Agency (HaDEA) (101219225).-
dc.description.abstractIntegrating insights from psychological distance and human-AI agency theories, this research explores embedded AI — algorithms physically integrated into tangible devices as a novel mechanism to help overcome resistance towards medical AI. Across four studies, including a large text-mining (n = 224,433 reviews) and three controlled experiments (n = 677 participants), we show that embedded AI reduces psychological distance between consumers and AI (Study 2B), which in turn enhances willingness to delegate medical decisions to algorithms (Studies 1-3). We further demonstrate that consumers expect better health outcomes when delegating decisions to embedded AI (Studies 2A, 2B, and 3). Moderation analysis further shows that these effects weaken when an analytical mode is primed using numerical accuracy information (Study 3). Our findings contribute to ongoing discussions on overcoming resistance to medical AI and offer implications for the design of AI-based technologies in medical services.en
dc.format.extent20-
dc.language.isoeng-
dc.relationhttps://doi.org/10.54499/2024.07397.IACDC-
dc.rightsembargoedAccess-
dc.subjectartificial intelligence-
dc.subjectdelegation-
dc.subjectembedded AI-
dc.subjecthuman-AI agency-
dc.subjectmedical services-
dc.subjectpsychological distance-
dc.subjectApplied Psychology-
dc.subjectMarketing-
dc.subjectSDG 3 - Good Health and Well-being-
dc.subjectSDG 8 - Decent Work and Economic Growth-
dc.titleAlgorithm Delegation-
dc.typearticle-
degois.publication.firstPage2902-
degois.publication.issue11-
degois.publication.lastPage2921-
degois.publication.titlePsychology and Marketing-
degois.publication.volume42-
dc.peerreviewedyes-
dc.date.embargo2027-07-22-
dc.identifier.doihttps://doi.org/10.1002/mar.70016-
dc.description.versionauthorsversion-
dc.description.versionepub_ahead_of_print-
dc.title.subtitleHow Embedded AI Facilitates Agency Transference in Medical Services-
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School-
dc.contributor.institutionNOVA Information Management School (NOVA IMS)-
Aparece nas colecções:NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)

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