An empirical investigation of users' switching intention to public service robots: From the perspective of PPM framework

Abstract

In recent years, the application of artificial intelligence (AI) technology has become increasingly common in the public sector. Users have been switching their experiences in handling businesses from interactions with human staff to those with robots. Prior studies have focused on investigating the key factors that influence users' adoption of public service robots; however, only a few have considered users' switching behaviors from traditional human services to robotic ones. This study employs a push–pull–mooring (PPM) framework derived from the human migration field to understand the factors that affect users' switching intentions in the context of public service robot applications. The research model was tested with 419 valid responses among users who had experienced both human services and public service robots in Chinese government service halls. The structural equation modeling (SEM) method was applied to quantitatively analyze the data. This study sheds new light on the key determinants of users' switching intentions toward public service robots from the perspectives of push, pull, and mooring effects. The results can help practitioners and managers understand users' intentions for such switches and make scientific decisions to encourage citizens' positive responses to service robots.