Attributed to the recent COVID-19 pandemic, panic buying is now a frequent occurrence in many countries, leading to stockouts and supply chain disruptions. Consequently, it has received much attention from academics and the retail industry. The aim of this study is to review, identify, and synthesise the psychological causes of panic buying, which is a relatively new and unexplored area in consumer behaviour research. A systematic review of the related literature is conducted. The review suggests that panic buying is influenced by (1) individuals’ perception of the threat of the health crisis and scarcity of products; (2) fear of the unknown, which is caused by negative emotions and uncertainty; (3) coping behaviour, which views panic buying as a venue to relieve anxiety and regain control over the crisis; and (4) social psychological factors, which account for the influence of the social network of an individual. This study contributes to the literature by consolidating the scarce and scattered research on the causes of panic buying, drawing greater theoretical insights into each cause and also offers some implications for health professionals, policy makers, and retailers on implementing appropriate policies and strategies to manage panic buying. Recommendations for future research are also provided.

Autonomous vehicles (AVs) are a key transportation technology of the future and will be integral to smart cities. An increased understanding of user adoption is necessary to promote AV usage. This study applied an integrated model based on innovation diffusion theory (IDT) and the technology acceptance model (TAM) to examine the factors influencing a user’s behavioural intention to use AVs. Then, a questionnaire was designed, and structural equation analysis was conducted on the 274 collected survey results. Results confirmed previous research findings that perceived usefulness (PU) and perceived ease of use (PEOU) positively influence users’ behavioural intention to use AVs. Moreover, the results revealed new findings that both PU and PEOU were influenced by the perceived characteristics of innovation (PCIs, i.e. relative advantage, compatibility, image, result demonstrability, visibility, and trialability). These findings contribute to theory building concerning human–computer interactions and guide policy and strategy formulation for promoting AVs.