Pathways to Public Trust and Acceptance of Artificial Intelligence in Public Services: A PLS-SEM and cIPMA Approach

Abstract

Artificial intelligence (AI) integration in delivering public services (PS) has changed the way citizens perceive the necessity of PS digitalization. A gap exists in the academic debate, characterized by a limited number of empirical studies clarifying the structural mechanisms leading to perceived necessity—particularly the role of governance trust and accountability in mediating this relationship. This study aims to fill this gap by investigating the relations between different constructs, like AI Engagement (AE), AI Service Prioritization (ASP), Perceived AI Efficiency in Public Services (PAEPS), Perceived AI Integration in Public Services (PAIPS), the Perceived Necessity of AI in Public Services (PNAPS), and AI Trust and Accountability (ATA). In this research, we employed PLS-SEM to conduct an in-depth analysis of our data and used combined importance–performance map analysis with the purpose of better understanding the interaction of the constructs and offering implications for the adoption of AI in public services. The main findings suggest that managers need to prioritize initiatives that effectively demonstrate the value of integrating AI into essential public services. Emphasis must be directed towards improving transparency, accessibility, and public engagement in AI-driven processes. Managers ought to facilitate opportunities for citizens to engage with AI tools. The proportion of unmet cases for AE suggests that additional support may be useful for individuals less familiar with AI—for example, through accessible interfaces and customized training programs.