Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

This study explored the relationship between container throughputs and liner shipping connectivity for coastal countries. We use UNCTAD data to investigate the spatial relationship between container throughput and liner shipping connectivity across a sample of 138 coastal countries. We demonstrated empirically that container throughputs are spatially clustered: the container throughput of a coastal country is similar to those of its neighbouring countries and trade agreement partners. We also showed there is a spillover effect of container throughputs; one coastal country's throughput is explained by those of other countries through spatial connections. The growth in one coastal country's container throughput has a positive ripple effect on the global economy, leading to higher throughputs in other countries. We measure the total effect of liner shipping connectivity on container throughputs, as well as compare the effects transmitted to neighbouring countries based on sea distances with those transmitted to partner countries signed trade agreements. The study's findings suggest that understanding and enhancing liner connectivity is essential for optimising container throughputs at the country level, emphasising the critical role of network structure and technological advancements in shaping maritime transport dynamics.

Industry 5.0 introduces 'Society 5.0' along with the concept of Industry 4.0 which engages customisation with smart systems and provides smart logistics and supply chain management. As a significant component of smart logistic systems smart containers have gained global attention from academia and industry due to their flexible and traceable features to optimise logistic networks. However, the value-driven complexities of Industry 5.0 make it challenging for users to identify the best smart container. This study addresses this issue by applying an extended Pythagorean fuzzy TOPSIS (PF-TOPSIS) method, an advanced multi-criteria decision-making approach that integrates Pythagorean fuzzy sets to handle uncertainty in evaluating complex criteria. In the application section, five different smart containers are examined based on nine different diverging criteria. PF-TOPSIS method enhances the selection approach by incorporating membership, non-membership and degree of hesitation allowing for a more nuanced evaluation. The outcome shows that the usability and adaptation of decision-making approaches can help the logistics manager make informed choices. Finally, sensitivity analysis is performed to define the robustness of the experimental results. Notably, this study is the first in literature to evaluate smart containers based on Industry 5.0 criteria using extended PF-TOPSIS method.

Inefficient port and hinterland connections negatively impact both the financial bottom line and environmental performance of the logistics system. To improve hinterland connections truck appointment system (TAS) has been implemented with varying performance, indicating a need to better understand TAS from a business model perspective. Therefore, the purpose of this study is to investigate TAS from a business model perspective. Semi-structured interviews and observations were conducted with port operators, port authorities and hauliers in five European container ports. The study reveals how various approaches to TAS impact business model components. The efficiency improvements from TAS need to match the costs of the TAS to make a valid business case to motivate additional administration of the service and to provide value to customers, i.e., shippers. This paper's conceptualisation of TAS from a business model perspective can help guide efficiency improvements in port terminals.

The shipping industry currently faces a myriad of perturbation challenges, posing threats such as port service paralysis, route disruptions, trade blockages, jeopardising the stabilisation of the global supply chain. Recovery coupling emerges as a crucial interdependent interaction mechanism activated in the aftermath of network damage. To enhance the resilience and sustainability of the port-maritime-trade system, this paper formulates a recovery coupling framework across three network layers, elucidating responses to disruptions within and between them. Subsequently, a data-driven empirical analysis is conducted for the global container market datasets mapping different network layers. This study reveals prevalent nonlinear relationships and the recovery coupling mechanism, emphasising interdependencies among the maritime network layer (MNL), port network layer (PNL), and trade network layer (TNL). Notably, during sudden disruptions, TNL displays a delayed response to PNL failures, while MNL responds promptly. The derived findings propose six managerial insights tailored for port operators, ocean carriers, and trading enterprises.

The aim of this study is to uncover the association between the aircraft in the fleets of airlines involved in combined freight transportation and to assess the utilisation patterns of similar aircraft categories by airlines. In their aircraft selection process, airlines consider a variety of factors, including range, fuel efficiency, and capacity. Examining the structure of other airlines' fleets during the aircraft procurement process can give airlines a competitive edge. Airlines can employ the apriori algorithm, a data mining technique, to uncover hidden association rules among aircraft within their fleets. This study begins by showcasing the implementation of the apriori algorithm. It then delves into the operations of six airlines, each of which had an associated aircraft. Next, the cargo handling equipment used by each aircraft across these six carriers is compared. We anticipate that the results may shed light on airline executives and organisations within the aircraft supply chain.

With the deepening of port integration, the development of port clusters has become an important trend. How to determine a reasonable partition of port clusters for a number of ports (i.e., a port network) is an important research topic, which has not been addressed yet. This paper utilises the k-means algorithm and the elbow method to address port cluster partition. By using a number of statistical indicators including degree centrality, strength centrality, closeness centrality, betweenness centrality, and clustering coefficient, a hierarchical clustering method is used to further determine the hierarchical structure of port clusters. Considering ports in Asia, we construct two port networks generated by using a waterway-based physical network and a ship route-based service network, respectively. Numerical experiments show that, we can obtain properties of regionalisation and hierarchy for our obtained port clusters. Our results can provide the basis for the cooperative development among ports, the partition of port clusters, as well as understanding the hierarchical structure of port clusters.

This study introduces the spatial measurement method into the evaluation system of port competitiveness, and uses the proposed dynamic entropy weight technique for order preference by similarity to ideal solution. Exploratory spatial data analysis and geographically weighted regression methods were used to reveal the spatio-temporal differences of the coastal port competitiveness and its influencing factors in China under the 21st-century Maritime Silk Road. The results show that: 1) the competitiveness of each port is quite different, which is a 'pyramid' structure. The competitiveness of northern and southern ports continued to increase; 2) the spatial agglomeration and distribution patterns of ports are gradually emerging, showing the phenomenon of 'club convergence'. Port competitiveness shows evident marginalisation characteristics; 3) environment, infrastructure, finance and other factors have a significant positive impact on port competitiveness. The regression coefficients of different factors have great spatial heterogeneity, but they correspond to the conditions of the port.