Achieving effectiveness of public service in AI-enabled service from public value theory: does human-AI collaboration matters?

PurposeArtificial intelligence (AI) integration into public services addresses long-standing challenges of trust, transparency and citizen satisfaction, yet gaps remain in understanding its role in fostering trust and meeting citizen expectations in complex contexts. This study aims to examine how service accuracy, transparency and citizen trust influence perceived service value and satisfaction, and investigate the role of human-AI collaboration as a moderating mechanism.Design/methodology/approachGrounded in public value theory (PVT), this research uses partial least squares structural equation modeling and necessary condition analysis (NCA) to analyze data collected from Indonesian citizens.FindingsThe results indicate that service accuracy, transparency and citizen trust positively influence perceived service value, with service accuracy as the strongest driver. Perceived service value significantly predicts citizen satisfaction. Human-AI collaboration strengthens the impact of transparency and trust on perceived service value but shows no significant effect on the relationship between service accuracy and perceived service value. The NCA identifies transparency and citizen trust as critical prerequisites for achieving citizen satisfaction, emphasizing their indispensable role in ensuring effective and trustworthy public service delivery.Originality/valueThis study advances PVT by examining the effectiveness of AI-enabled public services, showing how service accuracy, transparency and trust drive perceived value and citizen satisfaction. It highlights transparency and trust as essential for achieving effectiveness and positions human-AI collaboration as a key enabler for optimizing outcomes. These findings offer practical guidance for designing effective, citizen-centered public services.