Inland Container Depots (ICDs) are inland multi-modal terminals where goods in intermodal loading units can be transferred directly to seaports. The contribution of ICDs to regions’ economic and social growth is undeniable. To achieve the sustainable development of ICDs, evaluating and improving their service quality is critical. This study aims to investigate the factors contributing to the service quality of ICD in a developing country. The data utilized covers some ICDs in the Red River Delta, Vietnam. Regarding analytic methods, descriptive statistics first were run to show the level of aspects of service quality of ICDs. Subsequently, attitudinal statements were analyzed using exploratory factor analysis before linear regression was applied to recognize the factors influencing the service quality of ICDs. Generally, the service quality of ICDs was evaluated at an acceptable level but far from the high one. The results suggested that the four influential service quality factors included location and accessibility, facilities, process and management, and labor. Based on the findings of contributing factors, managerial implications were proposed.

This study aimed to examine and assess the impact of the logistics industry’s environment, entry-level graduates’ characteristics and the logistics and supply chain management (LSCM) program design on the transformation of knowledge and skills at Sohar port in the Sultanate of Oman. The study employed a pragmatic research philosophy involving a structured questionnaire. The sample size included 49 mid-managers from the logistics industry who were working at Sohar Port. The study found that entry-level graduates’ characteristics and LSCM program design positively and significantly influenced the transformation of knowledge and skills. However, the organisational environment had a negative and insignificant impact on the transformation. This study revealed several dimensions that may require further research. It is pertinent to broaden the research scope to other towns, ports, and other countries in the Gulf Council Countries (GCC) to broaden the scope and generalisability of the results. According to the study findings, several recommendations are proposed for the logistics and supply chain sector in Oman to enhance the transformation of knowledge and skills by entry-level graduates, as well as for higher education institutions (HEIs). To meet the sector requirements, HEIs may improve the current university-industry collaborations by increasing the inputs of the industry in designing and developing the LSCM program. The organisational environment must reconsider the knowledge and skills transformation by entry-level graduates in their strategic plan of resources management, which must be emphasised by the remuneration system and career paths incentive. While other studies have explored knowledge and skill transformation in the context of employee training, this study aims to fill a specific research gap by focusing on the transformation of knowledge and skills by entry-level graduates, an area which has not been extensively studied before. Furthermore, this study is unique as it examines the impact of the industry’s environment, entry-level graduates’ characteristics and the LSCM program on the transformation of knowledge and skills within the unique context of Oman. This novel approach provides an opportunity to understand the specific challenges and opportunities faced by entry-level graduates in Oman and suggests strategies for addressing them.

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

This article investigates how green logistics influences Vietnam’s trade balance with Association of Southeast Asian Nations (ASEAN) countries. By using the gravity model, the article applies fixed effects (FEM) and random effects (REM) to analyze panel data on trade balance, GDP, population, trade openness, and the green logistics index of Vietnam with ASEAN countries from 2012 to 2018. The research findings indicate that green logistics has not significantly affected Vietnam’s export trade balance with ASEAN countries. The article suggests solutions for the Vietnamese government and export businesses to enhance Vietnam’s trade balance with ASEAN countries by integrating green logistics activities. By following these recommendations, Vietnam can ensure that international trade aligns with environmental conservation, laying the groundwork for sustainable and inclusive economic development in Vietnam.

In response to the increasing global emphasis on sustainability and the specific challenges faced by small and medium-sized enterprises (SMEs) in China, this study explores the integration of green reverse logistics within these enterprises to enhance their sustainability and competitiveness. The aim of this study is to understand the relationship between reverse logistics, green logistics, and sustainable development. Data analysis was conducted utilizing a combination of descriptive statistics and correlation analysis. A survey of 311 participants examined SMEs’ performance in reverse logistics practices and their initiatives in green logistics and sustainable development. The empirical findings reveal significant progress in reverse logistics practices among SMEs, reducing environmental impact and improving resource efficiency. Moreover, a notable positive correlation was identified between reverse logistics promotion and advancements in green logistics and sustainable development. SMEs’ investment in reverse logistics is closely linked to their efforts in environmentally conscious and sustainable supply chain management. These insights benefit SMEs and supply chain practitioners and offer a valuable reference for future research and practical applications in this field.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.