THE IMPACT OF AI-DRIVEN HRM ON AI KNOWLEDGE SHARING: MODERATING ROLE OF PROACTIVE PERSONALITY AND MEDIATING ROLE OF AI SELF-EFFICACY

Abstract

While AI is reshaping HRM, employees' willingness to share AI-related knowledge under such practices remains poorly understood. Drawing on Social Cognitive Theory (SCT), Self-Determination Theory (SDT), and Conservation of Resources (COR) Theory, this study develops and tests a first-stage moderated-mediation model in which AI Self-Efficacy (AISE) mediates the relationship between AI-Driven HRM and AI Knowledge Sharing (AIKS), with Proactive Personality (PP) moderating the first stage of this pathway. Using a cross-sectional survey design, data were collected from 197 full-time employees across Pakistan's IT, banking, telecom, and professional services sectors and analyzed using Hayes' PROCESS Macro Model 7. AI-Driven HRM significantly and positively predicted both AI Self-Efficacy (B = 2.099, p < .001) and AI Knowledge Sharing, with AI Self-Efficacy also functioning as a mediator between the two (indirect effect B = 0.239, 95% CI [.171, .313]). Proactive personality negatively moderated the path between AI-Driven HRM and AI self-efficacy (B = −.373, p < .001), and the overall moderated-mediation effect was significant (index = −0.119, 95% CI [−.185, −.061]), a pattern explained through COR and trait-activation logic. These findings position AI Knowledge Sharing as a distinct behavioral outcome of AI-HRM, identify Proactive Personality as a first-stage moderator, and extend the AI-HRM model to a South Asian emerging-economy context.