Due to the outstanding strength of advanced machine-learning techniques, they have become increasingly common in predictive studies in recent years, particularly in predicting ship energy performance. In constructing predictive models, prior studies have mostly employed vessels’ technical parameters to establish machine-learning algorithms. To bridge this research gap and enable wider applications, this paper presents the design of a multilayer perceptron artificial neural network (MLP ANN) as a machine-learning technique to estimate ship fuel consumption. We utilized the real operational data from 100–143 container ships to estimate fuel consumption for five different container ships grouped by size. We compared the performance of two ANN models and two multiple-regression models. Four input parameters (sailing time, speed, cargo weight, and capacity) were included in the first ANN and the first regression model, while the other two models only consider two inputs from physical function. The mean absolute percentage error of the ANN models with four inputs was the smallest and less than those in extended statistical models, demonstrating the MLP’s superiority over the statistical model. The MLP ANN model can thus be applied to confirm the effectiveness of the slow-steaming method for achieving energy efficiency.

The deployment of automated vehicles (AVs) can offer many benefits to the environment and society. Trust plays a crucial role in consumers’ adoption of AVs. This study examines the determinants and effects of trust on consumers' adoption of AVs. A theoretical model drawing on trust theory, health belief model, and attitude theory is presented. The results show that the health belief model’s components comprising perceived safety threat, expectation outcomes, cues to action and self-efficacy influence consumers' trust toward AVs. Consequently, trust has direct and indirect effects on consumers' adoption of AVs via attitude. Bootstrapping analysis suggests a mediated relationship. The findings implicate a wide array of transport and industry policies relating to the design of AVs, transport infrastructure development, public communication and marketing, and education and training.

The container shipping industry is receiving growing attention in driving the performance of global supply chains. This phenomenon has accelerated supply chain integration (SCI) within the industry. Although SCI could offer numerous benefits, it is often quoted to be implemented easier in theory than in practice. The high failure rate that is associated with SCI is often not addressed in the literature. Grounded on resource-based view (RBV) theory, this paper is aimed at identifying the critical success factors (CSFs) and examining their influence on SCI and supply chain performance (SCP). Survey questionnaires were administered on 164 container shipping firms. The constructs were validated empirically using confirmatory factor analysis and were subsequently analysed using structural equation modelling. The proposed CSFs in this study are found to be positively corelated with SCI, which, in turn, is positively correlated with SCP. This paper has contributed to both theory and practice by applying RBV theory to identify the key resources and capabilities that are necessary for SCI in the container shipping industry.