Predicting RFID adoption in healthcare supply chain from the perspectives of users

Radio frequency identification (RFID) is an internet of things technology that provides many benefits to the healthcare industry's supply chain. However, a challenge faced by healthcare industry is the limited adoption and use of RFID by physicians and nurses. This research extended existing work by integrating unified theory of acceptance and use of technology (UTAUT) (i.e. performance expectancy, effort expectancy, facilitating conditions, social influence) and individual differences, namely personality (neuroticism, conscientiousness, openness to experience, agreeableness and extraversion) and demographic characteristics (i.e. age and gender) to predict the adoption of RFID in the healthcare supply chain. Data was collected from 252 physicians and nurses. The research model was tested by employing neural network analysis. During the course of this research, 11 variables were proposed in a bid to predict the adoption of RFID by physicians and nurses. In general, individual differences are able to predict the adoption of RFID better compared to variables derived from UTAUT. This study contributes to the growing interest in understanding the acceptance of RFID in the healthcare industry.