This study investigates consumer behaviour in unmanned retail systems, focusing on automated vending machines. To address gaps in understanding technology adoption in these settings, the consumer-adoption framework for unmanned retail (CAFUR) is proposed, integrating dimensions like automated interaction efficiency and digital trust in retail with constructs from TAM and UTAUT. Data from 300 respondents were analysed to examine perceived value, expected effort, social influence, expected performance, and customer satisfaction. Regression analysis shows expected performance as the strongest predictor of satisfaction, followed by perceived value, social influence, and expected effort. The findings emphasise optimising system efficiency, user experience, and interface design to enhance adoption. This research bridges theoretical insights with practical applications, offering strategies for leveraging analytics, improving engagement, and addressing consumer preferences to innovate unmanned retail technologies.
Chiu et al. (Thu,) studied this question.