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.

In this paper, we study a comprehensive model that addresses fleet deployment, speed optimization, and cargo allocation jointly, so as to maximize total profits at the strategic level. Our model considers a general fuel consumption function that depends on speed and load. To overcome intractability caused by nonlinear terms, we further separate fuel cost into two terms associated with ship speed and load to obtain a mixed integer linear programming formulation for approximation. Based on column generation techniques, we develop an iterative search algorithm that adaptively reorganizes the approximated formulation. We conduct extensive experiments using generated data sets from actual liner shipping services in different regions of the world to show the effectiveness of our approach as well as the significant impact of speed-load factors on fuel consumptions. Managerial insights are obtained by testing the model under different scenarios, which may greatly assist decision makers in the liner shipping industry.

Public acceptance of autonomous vehicles (AVs) is vital for a society to reap their intended benefits such as reduced traffic accidents, land usage, congestion and environmental pollution. The purpose of this paper is to use the theory of planned behaviour to pinpoint and examine the components affecting public acceptance of AVs. A model consisting of a network of hypothesised relationships is introduced. Thereafter, 526 residents in Seoul, Korea, were given a survey created for this research. Subsequently, to evaluate the collected information and estimate the model, structural equation modelling was adopted. The outcomes show individuals’ mindset on AVs, subjective customs, and behavioural influence directly influencing the acceptance of AVs. Furthermore, cognitive and emotive factors, namely comparative advantage, compatibility, complexity and hedonic motivation indirectly influence the acceptance of AVs via mindset and behavioural manipulation. Based on analysing the cumulative effect, attitude emerged with the strongest effect on public acceptance of autonomous vehicles. After this is, in decreasing order of influence, behavioural control, relative advantage, subjective norms, compatibility, hedonic motivation and complexity. The findings of this study implicate the prioritisation and allocation of resources, and policies relating to marketing, education, subsidisation and infrastructure development to better public acceptance of AVs.

This study examines how maritime transport has been researched through a comprehensive review of papers published in 19 transportation journals over the period 2000–2014. Systematic investigation shows that Maritime Policy & Management plays a prominent role in publishing maritime transport research. At the same time, ‘shipping’ is a dominant research area, with ‘port management, service, performance, efficiency and competitiveness’; ‘shipping market, industry, freight rate and economic impact’; and ‘terminal studies’ as the most popular topics. Nevertheless, few efforts have investigated ‘shipping finance’; ‘shipping service’; or ‘port risk and security.’ Since 2000, the use of quantitative analysis techniques has steadily increased in maritime transport to help participants make decisions scientifically. We here present the major data analysis techniques and highlight some limitations regarding their application. Thus, we provide a better understanding of how maritime transport research has been undertaken in a quantitative manner.