Consumer resistance to autonomous checkout technology: a study on active and passive innovation resistance

The study aims to understand consumers' reactions to applications that incorporate artificial intelligence technology, how they perceive these technologies, and the factors that prevent them from adopting them using innovation resistance theory. Data were collected online from 610 individuals and analysed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and partial least squares structural equation modelling (PLS-SEM) via SPSS 26 and SmartPLS 3.0. The results indicated that passive innovation resistance (PIR) and adoption barriers were not directly related to adoption intention. However, due to the sequential nature of the innovation adoption process, active innovation resistance (AIR) and adoption barriers arise as a result of information processing based on PIR. AIR is the most dominant predictor of adoption intention; it also acts as a central mediator and influences the mechanism of adoption.