Living Labs for AI-Enabled Public Services: Functional Determinants, User Satisfaction, and Continued Use

Abstract

Artificial Intelligence has emerged as a transformative force in public service delivery, promising improved efficiency and responsiveness to citizens’ needs, so it is essential to understand the functional factors that influence citizens’ adoption and intention to continue using such services. Drawing on the technology acceptance model, this study investigates the influence of six functional factors, namely, usefulness, ease of use, service reliability, service quality, responsiveness, and security, on the continued use of AI-enabled public services through the mediating effect of user satisfaction. Data were collected from an online survey of AI-enabled public services in Korea during the summer of 2021, and causal mediation analysis was conducted to examine these relationships. The results indicate that usefulness, service reliability, and security significantly influenced users’ intent to continue using AI-based services. Furthermore, causal mediation analysis confirmed that the five components of AI public services—usefulness, service reliability, service quality, responsiveness, and security—had significant effects on the continued use of AI-enabled service platforms, with user satisfaction playing a mediating role in the relationships. The main functional factors can lead to higher levels of satisfaction, and this ultimately drives the sustained adoption and continued use of AI-enabled public services by citizens.