Artificial Intelligence-Driven Human Resource Management, Justice, Trust, and Employee Retention in Indonesia Startups - Hafied Noor Baja; Agus Suroso; Zulganef Zulganef; Sunardi Sembiring Brahmana (Goodwood, 2026)
Purpose: This study examines how AI-driven Human Resource Management (AI-HRM) influences organizational justice, trust in AI, job satisfaction, and retention intention among Indonesian digital startup employees, with technology readiness as an antecedent of AI-HRM.Research Methodology: This study used a quantitative explanatory approach. Data were collected via an online questionnaire administered through Google Forms from 390 employees of digital startups in Jakarta, Bandung, Surabaya, and Yogyakarta who had experience with AI-HRM systems. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS version 3.3.9.Results: The findings show that technology readiness positively influences AI-HRM, and that AI-HRM, in turn, positively influences organizational justice and trust in AI. Organizational justice and trust in AI also positively affect job satisfaction and retention intention. Job satisfaction is a significant predictor of retention intention. The mediation results confirm that organizational justice, trust in AI, and job satisfaction explain the mechanism by which AI-HRM improves employee retention.Conclusions: AI-HRM can strengthen employee retention when supported by ready employees and fair, trustworthy systems.Limitations: This study is limited to cross-sectional survey data from Indonesian digital startups, which may restrict broader generalization.Contributions: This study contributes to the AI-HRM, organizational justice, technology readiness, and retention literatures, while offering guidance for startup leaders and HR managers on designing ethical AI-HRM systems.