Ports are the main arteries of global trade, handling goods circulation and serving as hubs for information, capital, and technology. Integrating digital technology has become the key for green port development to achieve resource efficiency and ecological balance. The current literature overlooks how digital technology can facilitate greener port operations. This study integrates sustainable supply chain management and system dynamics theories based on an in-depth analysis of existing research results and expert interviews. The analysis focuses on three key dimensions: integrating digital technologies with infrastructure, optimizing digital management and operations, and improving environmental and safety management in a digitally driven setting. Using the fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy Dematel) methodology, we collaborated with domain experts in port logistics to identify and confirm 12 pivotal factors that support the green digital transformation of ports. The research shows that the most critical success factors for using digital technology to drive ports’ green transformation are green supply chain information platforms, intelligent vessel scheduling, traffic optimization, and digital carbon emission monitoring. This study significantly contributes to the literature on green port transformation, offering indispensable practical insights for port operators, government entities, and shipping firms in identifying and deploying these key success factors. The findings will help maritime supply chain stakeholders develop actionable digital strategies, improving port efficiency and ecological resilience.

With its safe and efficient characteristics, container transportation has become vital for advancing the global economy. However, port congestion has become a significant obstacle to the container freight price system’s stability. There is currently no dependable engineering solution to guarantee the stability of the maritime transport system in a port congestion scenario. Therefore, decision-makers must comprehend the changing characteristics of the container freight index in the context of port congestion. Using the Shanghai container freight index as a proxy, this paper investigated the effect of port congestion on container freight rates, proposing a container freight index forecasting model. This study compiled congestion data from the Shanghai, Busan, Los Angeles, and New York ports from January 1, 2016 to January 1, 2023, to predict a Shanghai container freight index (SCFI). With its high-precision fitting effect, the RBF neural network effectively predicted the change in SCFI, and the R² reached 96%. We also confirmed the transfer effect of SCFI using the time-lag correlation model in a large congestion environment. The research results give container shipping organizations a decision-making foundation for planning shipping strategies and mitigating market risk.

The widening digital divide within the Global Critical Mineral Resource Supply Chain (GCMRS) 4.0 creates significant barriers to cross-border governance and operational efficiency. To quantify and address this disparity, this study identifies 20 Critical Success Factors (CSFs) through expert interviews with 15 industry specialists in South Korea. A hybrid multi-criteria decision-making framework integrating Fuzzy DEMATEL, Analytic Network Process (ANP), and the Choquet integral is developed to map causal relationships and determine factor weights. The empirical results reveal a distinct ‘technology-first’ dependency. Specifically, Scalable Technical Solutions and Cloud Computing Access emerge as the primary driving forces with the highest global weights, while Digital Investment Subsidies serve as the central hub for resource allocation. Unlike generic governance models, this study provides a quantifiable decision-making basis for policymakers. It demonstrates that bridging the hard infrastructure gap is a prerequisite for the effectiveness of soft collaborative mechanisms in the critical mineral sector.

Cross-border e-commerce offers consumers broader product access, yet uncertainty surrounding returns continues to suppress online purchase decisions. This study conceptualizes digital local return services as a digital assurance mechanism in cross-border e-commerce rather than merely a reverse logistics function. Drawing on UTAUT2, perceived risk theory, and trust theory, we develop and test a research model using survey data from South Korean consumers with prior experience of digital local return services (LRS). Structural equation modeling (SEM) is used to test the proposed relationships, and artificial neural networks (ANN) are employed to capture nonlinear effects and compare the relative importance of key predictors. Qualitative interview evidence is further incorporated to enrich the interpretation of the findings. The results show that performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation significantly reduce perceived risk. Perceived risk, in turn, exerts a strong negative effect on purchase intention and weakens consumer trust. Additional ANN results indicate that hedonic motivation and facilitating conditions are particularly influential in lowering perceived risk, while perceived risk is more important than trust in predicting purchase intention. These findings show that digital return service design shapes consumer decisions primarily through risk reduction rather than trust enhancement alone. The study contributes to digital commerce research by explaining how return service design functions as a customer-facing platform assurance mechanism that improves conversion in cross-border online retailing.