A research on ai-assisted autonomous checkout technology: Investigation of active and passive innovation resistance

The emergence of big data and the latest developments in computing power are changing the way and speed of our daily lives. It is an inevitable fact that everyone at the individual or institutional level is exposed to these developments. In this context, artificial intelligence-induced change in the service sector contains great potential. This study aims to determine the reactions of consumers to applications containing artificial intelligence technology, how they perceive these technologies, and the factors that prevent their adoption. To reach this aim, the Innovation Resistance Theory is used as a theoretical base. The model is extended with certain variables that are compatible with the aim and the design of the study. In the research, 610 consumers aged 18 and over were reached by using convenience and snowball sampling methods. Data were collected through online questionnaires. Frequency, percentage, mean, and standard deviation analyses of the data were performed in the statistical package program, and then explanatory factor analysis was performed for the expressions related to the scale. Confirmatory factor analysis and path analysis were applied through the Smart PLS 3.2.9 program, which analyzes according to the variance-based structural equation model using the partial least squares method. As a result, passive innovation resistance positively affects adoption barriers and active innovation resistance, while active innovation resistance has been found to negatively affect the intention to adopt.