{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T07:24:14Z","timestamp":1787210654683,"version":"build-2736575974"},"reference-count":66,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Artificial Intelligence (AI) is increasingly making decisions in healthcare, not just supporting them. This research introduces \u201cagentic loafing\u201d to describe the intentional yet unexamined delegation of decision\u2010making authority from clinicians to AI systems. Using netnography of professional podcasts and secondary analysis of gray literature, this study uncovers three institutional drivers that normalize AI delegation: performance culture conformity, structural isolation of responsibility, and legitimization through quantification. These drivers invert classical delegation theory. Where traditional delegation assumes monitoring, punishment, and shared objectives, AI delegation thrives in their absence. This inversion produces what this research terms \u201crisk evaporation\u201d: the systematic disappearance of accountability when AI\u2010assisted decisions fail, leaving no single party responsible. To help organizations manage this risk, this research also develops a \u201cpreliminary\u201d diagnostic tool, with its intersectional interventions, that transforms \u201cagentic loafing\u201d from an unmanaged bet into a measurable risk score. From this, a critical implication follows for risk analysis: the most dangerous AI systems are not those that fail unpredictably but those that function just reliably enough to quietly erode human judgment without ever triggering an alarm.<\/jats:p>","DOI":"10.1111\/risa.70306","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T13:00:11Z","timestamp":1785157211000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Agentic Loafing: An AI Decision Delegation Risk"],"prefix":"10.1111","volume":"46","author":[{"given":"Helmi","family":"Issa","sequence":"first","affiliation":[{"name":"ESSCA School of Management  Angers France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7289-5462","authenticated-orcid":false,"given":"Fabio James","family":"Petani","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Bourgogne Europe Burgundy School of Business, CEREN EA 7477  Dijon France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3168-2222","authenticated-orcid":false,"given":"Dejan","family":"Glavas","sequence":"additional","affiliation":[{"name":"ESSCA School of Management  Angers France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Arghistani N.2022. \u201cDigital Platforms and Their Critical Value Propositions Qualitative Comparative Analysis.\u201d InISPIM Conference Proceedings (1\u201325). 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