Human-AI collaboration for efficiency and employee job satisfaction in public administration: insights from a resource-based perspective

Purpose Efficiency in public administration is critical for scalable, sustainable and responsive governance, yet its relationship with employee satisfaction remains understudied. This research aims to investigate how factors such as processing time, resource utilization and service accessibility influence operational effectiveness, with Human-AI collaboration (HAIC) serving as a moderating factor. Design/methodology/approach Grounded in the resource-based view (RBV), this research adopts partial least squares structural equation modeling and necessary condition analysis (NCA) to analyze data from 616 Indonesian Government employees actively using AI-enabled tools. Findings The findings show that service accessibility and processing time significantly boost operational effectiveness, whereas resource utilization has a limited impact. Operational effectiveness emerges as a key determinant of employee job satisfaction. HAIC strengthens the impact of efficiency factors on operational outcomes. NCA identifies operational effectiveness as essential for employee satisfaction, emphasizing the need to address resource alignment challenges. Originality/value This study extends RBV by framing AI-driven efficiency within employee-focused models, highlighting HAIC as a strategic enabler. The results provide practical guidance for policymakers and administrators to enhance public sector performance while prioritizing employee well-being.