HUMAN–AI COLLABORATION IN HUMAN RESOURCE MANAGEMENT: THE IMPACT OF GENERATIVE AI ADOPTION ON EMPLOYEE PRODUCTIVITY, JOB SATISFACTION AND TRUST IN AI-MEDIATED HR DECISIONS

Abstract

This study examines how generative AI (GenAI) adoption in human resource management (HRM) affects employee productivity, job satisfaction and trust in AI-mediated HR decisions. Although GenAI now supports recruitment, performance management and employee support, evidence on its human consequences remains fragmented. The objectives were to assess these three effects. The study is grounded in human–AI collaboration (augmentation) theory, which treats AI as a partner that extends human judgement, complemented by organizational justice perspectives on fairness and trust. A positivist, quantitative, cross-sectional survey design was used. Data were collected through a structured online questionnaire with five-point Likert items. The target population was 2,500 employees and HR professionals in medium and large organizations using GenAI in HR. Stratified random sampling yielded 331 valid responses from 380 distributed questionnaires. Data were analyzed in SPSS and SmartPLS using descriptive statistics, Cronbach's alpha, factor analysis, Pearson correlation and multiple regression. GenAI adoption significantly predicted productivity (β = .57), job satisfaction (β = .43) and trust (β = .36), all p < .001, explaining 35.1%, 20.9% and 17.6% of variance respectively. Trust was rated lowest and increased with experience. The study concludes that GenAI benefits HRM, but transparency and human oversight are needed to build trust.