{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T06:07:12Z","timestamp":1789020432725,"version":"build-2803163510"},"reference-count":64,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The rapid expansion of artificial intelligence (AI) in human resource management has substantially reshaped how organizations attract, recruit, develop, evaluate, and retain their workforce. Research examining the combined employee-level effects of AI-enabled digital HRM (AI-DHRM)\u2014through psychological mediating processes and under technology-related boundary conditions\u2014remains sparse, particularly in emerging economies. Drawing on Social Exchange Theory, the Job Demands-Resources model, and UTAUT2, this study conceptualizes AI-DHRM as a reflective higher-order construct comprising four functionally distinct sub-dimensions, specifies a dual mediation model integrating an affective-relational pathway (person-organization [P-O] fit perception) and a motivational-agentic pathway (psychological empowerment), and identifies technology trust and privacy concern as critical boundary conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A time-lagged, two-wave survey was administered to full-time employees of 61 AI-HRM-adopting organizations in Bangladesh, yielding 487 matched responses. AI-DHRM practices, mediators, moderators, and controls were measured at Time 1; all five outcomes were measured five weeks later at Time 2. Data were analyzed in IBM AMOS 26.0 using a two-stage structural equation modeling approach, with bias-corrected bootstrapped mediation (\n                      <jats:italic>n<\/jats:italic>\n                      = 5,000 resamples), the index of moderated mediation, and Johnson-Neyman analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI-DHRM practices exerted significant positive effects on job satisfaction (\u03b2 = 0.41, 95% CI [0.29, 0.53]), work engagement (\u03b2 = 0.38, 95% CI [0.26, 0.50]), job performance (\u03b2 = 0.35, 95% CI [0.25, 0.45]), and employee wellbeing (\u03b2 = 0.33, 95% CI [0.21, 0.45]), and significantly reduced turnover intention (\u03b2 = \u22120.29, 95% CI [\u22120.41, \u22120.17]). Both P-O fit perception and psychological empowerment partially mediated these relationships across all five outcomes. Technology trust strengthened, and privacy concern attenuated\u2014but did not reverse\u2014the AI-DHRM-mediator pathways. Moderated mediation was confirmed across all ten conditional indirect effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings establish AI-DHRM as an integrated system that employees experience as a coherent organizational investment, transmitted through two complementary psychological channels and conditional on trust and privacy perceptions. Organizations should design AI-HRM as a coherent bundle and treat trust-building and privacy-by-design as prerequisites to deployment. Findings rest on self-report data from a single country and warrant replication.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1847241","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T05:31:37Z","timestamp":1789018297000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-enabled HRM systems in business organizations: effects on workforce outcomes and the roles of trust and privacy"],"prefix":"10.3389","volume":"9","author":[{"given":"Hriday Chandra","family":"Shil","sequence":"first","affiliation":[{"name":"Department of Management Studies, Comilla University","place":["Comilla, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. K. Md. 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