Grain cargo is an item that is directly related to food security, and it is important to systematically analyze the trend and fluctuation factors of the quantity of goods transported by being sensitive to seasonal supply and demand characteristics and global supply chain risks. Therefore, research that dynamically understands and predicts changes in grain volume has important academic and practical implications. In this respect, the purpose of this study is to derive the main factors affecting the quantity of grain transported in Pyeongtaek-Dangjin port, and to use them to accurately predict dynamic changes in the quantity of grain transported in Pyeongtaek-Dangjin port. System dynamics was used as a research methodology, and as a result of verifying the simulation of the quantity prediction, it was verified as a very accurate prediction model with a training section of 4.428% and a test section of 1.29%. As a result of predicting the quantity of goods transported over the next three years using the prediction model constructed in this study, it was 3,017,666 tons in 2026, 3,127,168 tons in 2027, and 2,972,667 tons in 2028, and it is expected that the annual average level of about 3.03 million tons will be maintained. The quantity of goods predicted in this study can be used as a basis for future investment plans for grain unloading and storage facilities in Pyeongtaek-Dangjin port and as a basis for calculating the size of the port operation manpower.
As the global transition to smart ports accelerates, the rapid increase in Automated Guided Vehicles (AGVs) in container yards has highlighted challenges such as obstacle interference and collision-bottlenecking in narrow intersection areas, which significantly hinder operational efficiency and safety. To address these issues, this study proposes a spatiotemporal-based multi-AGV path planning algorithm and validates its efficacy through simulation experiments. The proposed algorithm incorporates an accelerated A* search combined with Jump Point Search (JPS) to maximize computational efficiency. Furthermore, the generated paths are smoothed using Cubic Bézier curves to ensure kinematic driving stability by mitigating curvature discontinuities. To handle dynamic obstacles actively, a local path planning strategy based on a Time Reservation Table is integrated. Experimental results in complex environments containing both static and dynamic obstacles demonstrate that while the proposed method involves a slight increase in computation time due to the nature of the accelerated search module, it proves superior in path optimization performance by shortening the average path length compared to the traditional A* algorithm. Additionally, the significant improvements in collision avoidance rates and path smoothness confirm its practical utility. Consequently, this research provides a realistic and practical multi-AGV path planning solution, offering high utility for safe and efficient AGV management in smart port environments.
This study employs Dynamic Time Warping (DTW) to examine the evolution of Korea-origin container freight indices over two periods (2023-2024 and 2024-2025). The analysis reveals that, in 2023-2024, the indices were grouped into three clusters, consisting of two high-freight clusters and one low-freight cluster, reflecting a relatively straightforward structure. In the following year, however, the number of clusters increased to four. The high-freight cluster became more prominent, while the remaining indices were redistributed into low-, medium-, and ultra-low-freight clusters, illustrating greater differentiation among routes. Importantly, the independent cluster observed only for Japan (KJI) in the earlier period expanded to include China (KCI) and short-haul Southeast Asia (KSEI), underscoring the growing independence of certain routes. Overall, the findings suggest that freight index clustering is not determined solely by distance or regional factors but is subject to dynamic shifts influenced by market conditions and the structural attributes of each route. These findings provide practical implications for route-specific pricing and risk management strategies in the container shipping industry. Furthermore, the study highlights the usefulness of DTW-based clustering as a robust tool for capturing dynamic structural changes in freight markets.
This study aims to propose a Korean-style ALICE roadmap applicable to the Korean logistics system by drawing on the concept of the Physical Internet (PI). To this end, it compares and analyzes PI initiatives in the European Union (EU), the United States (US), and Japan (JP), along with related literature, policy documents, official roadmaps, and project materials, and derives a Common Standard Reference Model (CSM) through cross-case synthesis. Based on the derived CSM, the current status quo of the Korean logistics system is diagnosed, and the gaps are analyzed across five analytical domains—Physical, Digital, Service, Governance, and Sustainability—and three structural layers: Physical Layer, Digital Layer, and Operational Business Layer. The analysis shows that the Korean logistics system exhibits structural differences in such areas as a road-centered transport structure, market fragmentation, insufficient interoperability based on data models, events, and APIs, constraints on the network-level diffusion of operational services, underdeveloped governance for collaboration and sharing, and the limited internalization of carbon MRV and performance management. In contrast, standardized unit loads and hub function standardization were found to be at a level broadly comparable to the CSM's minimum requirements, although constraints remain in scaling toward corridor-hub-based multimodal connectivity. In particular, in the digital domain, event/state standards (D2) and API linkage (D3) were identified as the most critical bottlenecks, functioning as key prerequisites for the diffusion of operational services. In addition, trust, authorization management, and collaborative governance were found to operate as cross-cutting factors that shape the diffusion of digital interoperability and serviceization across layers. Based on these findings, this study proposes three design principles for a Korean-style ALICE roadmap: prioritizing prerequisites, building a diffusion system, and establishing measurable KPI-based performance management. To operationalize these principles, it suggests a policy and program package including the establishment of a minimum digital interoperability set, corridor-hub-based multimodal linkage, the development of a logistics data space, the diffusion of operational services based on capacity, reservation, and settlement, the construction of collaborative governance, and the internalization of carbon MRV and incentives. In phased terms, the short-term stage focuses on pilot projects combining a minimum digital set with basic governance; the medium-term stage emphasizes the expansion of interoperability and service-based operational systems; and the long-term stage aims at institutionalization, nationwide diffusion, modal shift, and the internalization of carbon performance management. This study is academically significant in that it does not simply borrow a single national model, but instead formalizes the minimum common elements repeatedly observed across multiple country cases into a CSM, systematically diagnoses Korea's gaps against that model, and connects them to a phased roadmap. It also suggests that the transformation of the Korean logistics system should be understood not as a series of isolated digitalization projects, but as a strategy for building an industrial logistics operating infrastructure t hat integrates standards, data, services, governance, and performance management. In this sense, the study may serve as a foundational reference for future Korean PI policy and industrial design.
This study examines how consumers’ perceptions of environmental, social, and governance (ESG) practices influence corporate image and customer loyalty in the Chinese food industry. Based on signaling and stakeholder theories, a moderated mediation model is proposed. Using survey data from 300 consumers, the results show that ESG perceptions positively affect corporate image, which in turn enhances customer loyalty. Corporate image partially mediates this relationship, and ethical consumption tendency strengthens the ESG-image link. The findings identify a clear mechanism linking ESG perception to loyalty and suggest that firms should emphasize socially oriented ESG activities and tailor strategies to ethically sensitive consumers.
The Digital Transformation(DX) of the Fourth Industrial Revolution has promoted automation, digitalization, networking, and sustainability. Through technologies such as IoT, AI, robotics, big data, cloud computing, and blockchain, logistics is evolving to streamline the entire process of transportation, storage, handling, packaging, and information, and to optimize the entire supply chain in real time. Logistics centers and warehouses, which represent the storage sector, are transitioning from the concept of traditional storage spaces to data-driven automated operational hubs. However, due to the characteristics of the equipment industry and supply chain, they are unable to respond sufficiently because of insufficient rapid technology adoption and the accumulation of capabilities. To enable companies to adapt to and accept environmental changes directly linked to competitiveness, and to explore new opportunities for continuous growth, this paper aims to identify the impact of institutional DX pressure on corporate resilience and performance through empirical analysis based on institutional theory, ultimately deriving the influence that leads to improved corporate performance. To this end, data was collected from domestic logistics centers and warehouse companies, and PLS-SEM analysis was performed. The results showed that corporate resilience significantly mediates the relationship between regulative and cognitive pressures of institutional DX and corporate performance. In other words, it was found that firms improve their performance in the face of regulative and cognitive pressures through their Proactive-resilience and Reactive-resilience capabilities.
This study aims to analyze electric freight vehicle users’ preferences for charging infrastructure expansion strategies and to propose effective policy directions. By applying capacity expansion strategy theory from manufacturing to the charging infrastructure context, user preferences for a ‘proactive expansion strategy’ based on demand forecasting versus a ‘reactive expansion strategy’ based on actual demand were analyzed using logistic regression. A nationwide survey of 222 electric freight vehicle owners revealed that 73% of respondents preferred the proactive expansion strategy. The logistic regression results indicated that users with longer daily driving distances, more frequent charging experiences at logistics hubs, and a stronger perception of charging station shortages were more likely to prefer the proactive strategy. In contrast, users with access to residential charging facilities tended to favor the reactive strategy. By empirically identifying how freight vehicle-specific operational characteristics and logistics hub-centered charging behavior distinct from those of passenger electric vehicles infrastructure preferences, this study contributes to the development of tailored charging infrastructure policies aimed at accelerating the electrification of the freight transportation sector.
This study aimed to verify whether the recent policy mandating the introduction of electronic voting as a method for exercising voting rights at general shareholder meetings benefits the Korean economy by revitalizing the capital market. In particular, it analyzed the impact of introducing electronic voting on corporate value in relation to corporate ownership structure. The results of this study's analysis are as follows: First, it was found that the higher the shareholding ratio of the largest shareholder, the more negative the impact on the introduction of electronic voting. Second, it was found that the higher the shareholding ratio of minority shareholders, the more positive the impact on the introduction of electronic voting. Third, the value of companies that have adopted electronic voting was found to be higher than that of companies that have not. Fourth, it was found that the higher the shareholding ratio of the largest shareholder, the more negative the impact on corporate value. Fifth, it was found that the higher the shareholding ratio of minority shareholders, the more positive the impact on corporate value. Finally, it was found that the impact of corporate ownership structure on corporate value may vary by industry. The results of this study are significant in that they can contribute to the activation of electronic voting as a minority shareholder protection system and help provide legitimacy for the government's policy implementation.
This study aims to analyze research trends in the pharmaceutical cold chain field before and after the COVID-19 pandemic. A total of 203 academic papers collected from major domestic and international databases were analyzed using text mining and social network analysis (SNA). The analysis period was divided into two phases based on the WHO's COVID-19 pandemic declaration: Pre-COVID (2006 2019) and Post-COVID (2020–2025). Frequency analysis, co-occurrence analysis, and centrality analysis using Gephi were conducted to identify changes in research themes and keyword network structures across the two periods. The results confirmed that Temperature Management remained the dominant research theme throughout the entire period, while keywords such as Blockchain, Traceability, Machine Learning, Sustainability, and Resilience emerged as new core topics after COVID-19. Furthermore, the betweenness centrality of Optimization Algorithm increased significantly in the Post-COVID period, reflecting a more diversified and complex research network structure. This study contributes to a quantitative understanding of pharmaceutical cold chain research trends and provides practical implications for future research directions in the field.
The post-COVID-19 disruption of global supply chains and the rapid shift toward non-face-to-face distribution channels have further deteriorated the business environment for small and medium-sized enterprises (SMEs). Under such environmental uncertainty, improving new product development (NPD) performance is essential for the survival and growth of SMEs. However, most prior studies have focused exclusively on technological innovation or internal capabilities, leaving the mechanisms through which distribution timeliness and supply chain agility affect NPD performance insufficiently explored. Drawing on Resource-Based Theory (RBT) and Dynamic Capability Theory, this study empirically examines how SMEs’ strategic agility—comprising strategic sensitivity, leadership unity, and resource fluidity— affects NPD performance, and elucidates the mediating roles of Time-to-Market speed and the degree of firm innovation in this relationship. Survey data were collected from 132 SMEs located in Gyeonggi-do, South Korea, and the hypotheses were tested using Structural Equation Modeling (SEM) and bootstrapping. The summary of the analysis results is as follows: First, higher strategic agility in SMEs leads to a faster Time-to-Market. Second, higher strategic agility leads to a greater degree of innovation. Third, both the Time-to-Market and the degree of innovation have a positive impact on new product performance.
The rapid growth of e-commerce has heightened demands for efficiency in warehouse management systems (WMS) and last-mile delivery operations. This study systematically analyzes optimization strategies leveraging artificial intelligence (AI) and big data technologies. In warehouse management, AI-driven demand forecasting, real-time inventory optimization, and robot pick-to-light systems were found to improve throughput by 30-50%. For last-mile delivery, hybrid models combining route optimization algorithms, dynamic scheduling, and drone/robot delivery reduced delivery costs by 20-35% while increasing customer satisfaction by over 15%. Through case studies of leading global implementations—Amazon Kiva systems, UPS ORION, and JD.com drone delivery—this research analyzes practical applications and proposes a four-phase roadmap for domestic logistics firms: data collection → AI modeling → pilot testing → enterprise-wide scaling.
The growing importance of climate change mitigation has increased the need to assess the effectiveness of green Official Development Assistance (ODA) in mitigating CO2 emissions. This study examines the impact of green ODA on CO2 emissions in developing countries with a particular focus on the moderating role of maritime connectivity. This study applies a dynamic panel System Generalized Method of Moments(GMM) by using panel data for 63 developing countries over the twelve-year period(2010-2021). The results indicate that green ODA alone does not consistently reduce CO2 emissions and, in some cases, is associated with increased emissions, reflecting short-term scale effects linked to economic expansion and energy demand. The fixed-effect model analysis showed that interaction had a significant negative effect on CO2 emissions suggesting that higher levels of shipping connectivity may enhance the environmental effectiveness of donors assistance. However, this moderating effect is not robust in the System GMM estimation. This finding suggests that the effectiveness of green aid depends not only on the scale of financial resources but also on structural conditions such as integration into global logistics networks. The study highlights the importance of aligning green ODA with infrastructure development and connectivity strategies to achieve meaningful emission reductions.
This study aims to empirically examine the effects of franchise network characteristics on relationship performance and cooperation. Network characteristics are conceptualized as network density and network centrality, and their effects on cooperation are analyzed through the mediating role of relationship performance using structural equation modeling. The results indicate that both network density and network centrality have significant positive effects on relationship performance, and that relationship performance, in turn, has a significant positive effect on cooperation. These findings suggest that the network structure among franchisees enhances the quality of relationships, and that such relationship performance functions as a key mediating mechanism that promotes cooperative behavior. This study contributes to the literature by extending the understanding of franchise systems from a vertical contractual perspective to a horizontal network perspective, and by providing an integrated framework linking network characteristics, relationship performance, and cooperation.
This study aims to examine the validity of the criteria defining the scope of micro businesses, which incorporate the number of regular employees in addition to a sales-based classification. Under the law, micro businesses are defined as firms with fewer regular employees based on the same sales criteria applied to small enterprises. However, due to recent technological and industrial changes, the labor productivity of micro businesses has been increasing rapidly, weakening the alignment between the number of regular employees and the notion of business smallness. Alongside reviewing the the scope of micro businesses, this study analyzed their sales distribution using the Basic Statistics of Small and Medium Enterprises, and estimated the scale of relatively high-revenue micro businesses through a comparison with small enterprises that share the same sales threshold. As a result, the study found that a significant number (More than 140,000) of micro businesses report sales higher than the median of the small enterprise group, nevertheless, they continue to receive opportunities as beneficiaries of micro business policies—focused on overcoming marginality and ensuring management stability—solely because they employ a small number of workers. This study proposes excluding the criterion based on the number of regular employees—which may act as a barrier to efficient policy implementation—from the definition of micro businesses, and suggests the need to establish new criteria, such as those based on sales.
This study developed and applied a Korean Drug Harm Index (DHI) to comprehensively assess drug-related harm in Korea across the dimensions of crime and public safety, public health and healthcare, social diffusion, and cross-border logistics and supply chains. Using official statistics and administrative data from 2013 to 2024, the study constructed an indicator framework consisting of four domains: crime and public safety, public health and healthcare, social diffusion and vulnerable groups, and cross-border logistics and supply chains. Each indicator was adjusted either by population-based normalization or raw-data transformation, followed by min-max normalization to calculate domain-specific sub-indices and the composite DHI. In addition, the study compared a baseline DHI excluding the cross-border logistics and supply chain domain with a logistics-extended DHI including that domain, in order to examine how border-seizure-based smuggling risks affect the interpretation of the overall harm index. The results indicate that drug-related harm in Korea cannot be explained by a simple increasing or decreasing trend. While the number of drug offenders and the crime and public safety index peaked in 2023 and declined in 2024, the public health and healthcare index reached its highest level in 2024. The cross-border logistics and supply chain index rose sharply in 2021, contributing to the logistics-extended DHI being higher than the baseline DHI. In contrast, the overall DHI recorded its highest value during the observation period in 2023 due to increases in the crime and public safety index and the social diffusion and vulnerable groups index. These findings suggest that interpreting drug-related harm solely through domestic crime or health indicators may fail to adequately capture smuggling risks observed at the border stage. By conceptualizing drug smuggling from the perspective of supply chain security and customs risk management, this study provides an analytical framework applicable to risk-based inspection, route-specific logistics security, inter-agency data integration, and policy priority setting.
This study aims to empirically examine the effects of franchisors’ managerial support and fair punishment on franchisees’ affective and calculative commitment as well as their cooperative behavior, while also exploring the observer effects that occur when franchisees witness the punishment of other members within the network. The analysis shows that managerial support strengthens affective commitment but does not significantly influence calculative commitment. This suggests that support activities enhance relational satisfaction and psychological bonds, yet have limited impact on altering economic considerations such as alternative attractiveness or perceived switching costs. In contrast, fair punishment positively affects both affective and calculative commitment, confirming that procedural and distributive fairness play an important role in building trust and stabilizing the relationship. Both affective and calculative commitment were also found to have positive effects on cooperative behavior. By jointly considering managerial support and punishment, this study systematically identifies key factors that shape relationship management in franchise systems. Furthermore, by empirically demonstrating the observer effects of fair punishment, it expands the understanding of control mechanisms in network-based industries. The findings provide practical implications for franchisors seeking to build fairness-based management systems and design sustainable cooperative structures.
