Search Results (80)

Container liner shipping companies operate within a complex environment where they must balance profitability and service reliability. Meanwhile, evolving regulatory policies, such as surcharges imposed on ships of a particular origin or type on specific trade lanes, introduce new operational challenges. This study addresses the heterogeneous ship routing and demand acceptance problem, aiming to maximize two conflicting objectives: weekly profit and total transport volume. We formulate the problem as a bi-objective mixed-integer programming model and prove that the ship chartering constraint matrix is totally unimodular, enabling the reformulation of the model into a partially relaxed MIP that preserves optimality while improving computational efficiency. We further analyze key mathematical properties showing that the Pareto frontier consists of a finite union of continuous, piecewise linear segments but is generally non-convex with discontinuities. A case study based on a realistic liner shipping network confirms the model’s effectiveness in capturing the trade-off between profit and transport volume. Sensitivity analyses show that increasing freight rates enables higher profits without large losses in volume. Notably, this paper provides a practical risk management framework for shipping companies to enhance their adaptability under shifting regulatory landscapes. Full article

The present study addresses the topic of European companies, including Latvian companies, sustainably entering African markets. The actuality of this topic relates to the recession and the decrease in demand in the classical export markets (such as Scandinavia and Western Europe) with which Latvian firms used to trade; this is why the re-orientation of companies to African countries was carried out. Academic research worldwide has conducted many investigations on the specifics of exporting to Africa. The lack of knowledge relating to local African business practices is considered one of the significant barriers. The aim of this study was to mitigate this barrier by exploring real-world situations in African economic sectors. Interviews with relevant African experts were conducted for this purpose. The results showed that East European entrepreneurs, including Latvian entrepreneurs, should first focus on West African French-speaking countries with big seaports (e.g., Senegal, Guinea, Ivory Coast, Benin, Togo, and Cameroon), where Latvian knowledge, professional skills, and products relating to port and transportation infrastructures are in significant demand. A case study was conducted in Cameroon as an example of a good business match with Latvian service providers. The case study also highlighted the nature of Cameroon’s sociocultural dynamics, which are distinguished by the presence of several sociocultural zones, each with its own specific characteristics that need to be taken into account. Full article

Port shipping collaboration is vital to greener, more resilient trade, yet decisions remain siloed and uncertain. This study develops a Bayesian network model grounded in empirical data from major Chinese ports, aiming to systematically analyze and enhance port shipping collaborative capacity. The methodology integrates expert knowledge and structural learning algorithms to construct a Directed Acyclic Graph (DAG), representing complex multi-stakeholder interactions among port enterprises, shipping companies, customers, and governmental bodies. Through forward and backward probabilistic inference, the study quantifies how coordinated improvements yield substantial synergistic benefits. Five leverage points stand out: customer engagement in green supply chains, perceived service quality, port digital information integration, multilateral trading maturity, and strict policy enforcement. A newly revealed feedback loop between digital integration and enforcement extends Emerson et al.’s collaborative governance framework, highlighting “digital-era connectivity” as a critical governance dimension and offering managers a focused, evidence-based action agenda. Full article

The shipping industry, a cornerstone of global trade, faces emissions reduction challenges amid tightening environmental policies. Blockchain technology, leveraging distributed symmetric architectures, enhances supply chain transparency by reducing information asymmetry, yet its dynamic interplay with emissions strategies remains underexplored. This study employs symmetry-driven differential game theory to model four blockchain scenarios in port-shipping networks: no blockchain (N), port-led (PB), shipping company-led (CB), and a joint platform (FB). By solving Hamilton–Jacobi–Bellman equations, we derive optimal emissions reduction efforts, green investments, and blockchain strategies under symmetric and asymmetric decision-making frameworks. Results show blockchain adoption improves emissions reduction and service quality under cost thresholds, with port-led systems maximizing low-cost profits and shipping firms gaining asymmetrically in high-freight contexts. Joint platforms achieve symmetry in profit distribution through fee-trust synergy, enabling win–win outcomes. Integrating graph-theoretic principles, we have designed dynamic state equations for emissions and service levels, segmenting shippers by low-carbon preferences. This work bridges dynamic emissions strategies with blockchain’s network symmetry, fostering economic–environmental synergies to advance sustainable maritime supply chains. Full article

Corporate social responsibility (CSR) disclosure plays a pivotal role in mitigating “blue” (labor standard) and “green” (environmental standard) trade barriers, optimizing the foreign trade ecosystem, fostering sustainable development of export-oriented enterprises, and advancing societal welfare objectives—all critical to maintaining high-quality social order in China. Grounded in institutional and strategic management theories, this study systematically investigates the effects of CSR disclosure on corporate export performance, focusing on mediating and moderating mechanisms, and conducts rigorous empirical testing using comprehensive firm-level CSR disclosure data from Chinese listed companies. The results reveal the following key findings: (1) CSR disclosure positively influences corporate exports; (2) enterprise financing capacity and innovation output serve as dual mediating mechanisms, through which CSR disclosure enhances export performance by improving access to external capital and stimulating product/service innovation; (3) environmental regulations amplify the export-promoting effect of CSR disclosure, indicating that institutional environmental constraints incentivize firms to leverage disclosure as a strategic response to global sustainability demands; (4) heterogeneity analysis reveals that large enterprises derive the strongest export benefits from CSR disclosure, followed by medium-sized and small enterprises; and (5) private enterprises exhibit significantly greater export gains from CSR disclosure compared to state-owned enterprises. These results underscore the context-specific and multi-dimensional nature of CSR disclosure’s impact on exports, highlighting how firm size and ownership structure shape the efficacy of disclosure strategies in global markets. This study contributes to both academic literature on corporate sustainability and practical policy by demonstrating how strategic CSR disclosure can serve as a tool for overcoming institutional barriers and enhancing international competitiveness. Full article

The use of electric vehicles in urban transportation is increasing daily due to their energy efficiency and environmental friendliness. In last-mile logistics, route optimization must consider charging station locations while balancing operational costs and customer satisfaction. In this context, solutions for cost-oriented route optimization have been presented in the literature. On the other hand, customer satisfaction is also important for third-party logistics companies. This study discusses the Capacitated Electric Vehicle Routing Problem with Time Windows (CEVRPTW) encountered in last-mile logistics. This article defines the objective function of minimizing total tardiness and compares the routes between the service provider logistics company and the customer receiving the service. In this study, the CEVRPTW was solved for the minimum total tardiness objective function with the hybrid adaptive large neighborhood search (ALNS) algorithm. The success of ALNS was proven by comparing the differences between the optimal solutions obtained with the CPLEX Solver and the ALNS solutions. Tardiness objective function-specific operators for ALNS are proposed and supported by local search and VNS algorithms. The findings of this study contribute to the literature by analyzing the balance trade-offs between customer-oriented and cost-oriented and the effect of time windows on the number of vehicles. Full article

Since the end of the COVID-19 pandemic, the economies and trade of many countries have recovered. Executives in various countries have demonstrated self-sacrificial leadership in response to the pandemic by reducing their salaries, emphasizing solidarity and social responsibility, and setting a good example of how companies can weather a storm. In this context, this study investigated the effects of self-sacrificial leadership on China’s service industry. Based on self-determination theory, a moderated mediation model was constructed to investigate the impact on the service industry in China. Surveys were conducted with 472 employees from three service companies located in Hebei Province, China, to construct a research model of self-sacrificial leadership, autonomy, competence, and creative behavior. We employed a two-wave approach with a one-month interval between waves for data collection. Statistical analysis and hypothesis testing were performed using STATA 18.0. Intriguingly, as competence intensifies, the mediating role of autonomy between self-sacrificial leadership and creative behavior intensifies. Our study underscores that increasing competence is imperative for organizations to harness self-sacrificial leadership and boost creative behavior via autonomy. Full article

Despite the fact that a decade has elapsed since the establishment of the Eurasian Economic Union (EAEU), the impact of the EAEU on the economic development of its member states remains a subject of ongoing debate. This article examines the mutual trade in services between the Eurasian Economic Union (EAEU) countries, with the aim of assessing the impact of membership on it. The difference-in-difference model has been applied for impact assessment. The model utilizes data from five EAEU member countries—Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia—capturing periods both before and after their EAEU membership, spanning 17 years in total. The results show that membership in the EAEU has significantly affected the exports of services from Russia and Belarus and has a less significant impact on the exports of services from Kazakhstan to the EAEU. At the same time, it has no significant effect on the exports of services from Kyrgyzstan and Armenia to other EAEU countries. In order to ascertain the challenges that exist, expert surveys among service exporters from Armenia have been conducted. Representatives of companies exporting various services to the EAEU have been selected as experts. The survey results indicate the presence of various barriers, including legal, logistical (for cargo transportation companies), and cultural challenges. These barriers encompass licensing difficulties, technical obstacles related to VAT refunds, a ban on cash payments, and difficulties with financial transfers due to sanctions against Russia. The findings of this research are of practical importance and can serve as a guideline for policymakers in the EAEU. Full article

Many tasks in a modern household are performed by machines, e.g., a dishwasher or a vacuum cleaner, and in the near future most household tasks will be performed by smart service robots. This will relieve the residents, who in turn can enjoy their free time. This newly gained free time will turn out to cause the so-called spare time rebound effect due to more resource consumption. We roughly quantify this rebound effect and propose a CO₂-budget model to reduce or even avoid it. In modern industry, automation and AI are taking over work from humans, leading to higher productivity of the company as a whole. This is the main reason for economic growth, which leads to environmental problems due to higher consumption of natural resources. We show that, even though the effects of automation at home and in the industry are different (free time versus higher productivity), in the end they both lead to more resource consumption and environmental pollution. We discuss possible solutions to this problem, such as carbon taxes, emissions trading systems, and a carbon budget. Full article

In today’s business landscape, the volume of transaction data is rapidly increasing. This study explores the integration of Point of Sale (POS) and Radio-Frequency Identification (RFID) technologies to enhance the analysis of customer transactions using big data tools. By leveraging these technologies, businesses can extract valuable insights to improve processes, optimize inventory, and boost customer satisfaction. The research employs an object—subject management approach, which facilitates real-time decision-making by merging retail transactions of the clients with their movement patterns. An experiment involving around 7000 customers demonstrates the effective collection and processing of POS and RFID data, highlighting the benefits of integrating these data streams. Key metrics, such as time spent in different store sections, provide deeper insights into consumer behavior. The findings reveal the potential of these technologies to transform retail services, offering opportunities for demand forecasting, risk management, and personalized customer experiences. The study concludes that merging POS and RFID data opens new avenues for developing management solutions aimed at enhancing customer engagement and the operational efficiency of the retailer. Future research will focus on further elaborating these solutions to maximize the benefits of integrated data analysis. Full article

Driven by growing demands for environmental protection and sustainable development, green finance has gained increasing attention, evolving from a peripheral topic to a core research area. Research in green finance primarily focuses on financial products, services, and policies, analyzing their impacts on society, markets, and listed companies. Through a systematic literature screening and analysis process, this study reviewed the existing body of literature on green finance, with a particular emphasis on key areas such as green financing, green financial technology, green financial products and derivatives, green building, financial reform and innovation in green finance, and carbon trading markets. Using keywords such as “green finance”, “green insurance”, “green securities”, and “green investment”, we identified 15,487 relevant publications from 2014 to 2023 across multiple databases. We then applied Latent Semantic Indexing (LSI) to cluster these documents, identifying core subfields and conducting a detailed bibliometric analysis. Our results reveal a geographic shift in green finance research prominence from the U.S. to China, with a thematic shift from green building and energy efficiency to green bonds and climate finance. Through our analysis, we provide policy recommendations informed by these findings. This study’s unique contribution lies in its systematic extension of bibliometric analysis into emerging subfields of green finance, such as green financial technology and carbon trading markets, both of which are becoming increasingly critical. Additionally, this study offers valuable insights into the evolving landscape of green finance research, bridging the gap between academic research and industry practice and providing actionable policy recommendations for stakeholders across different sectors. Full article

This study aims to explore the transformative potential of Artificial Intelligence (AI) in international trade, focusing on its key roles in optimizing trade operations, enhancing trade finance, and expanding market access. In trade optimization, AI leverages advanced machine learning and predictive analytics to enhance demand forecasting, route optimization, and customs procedures, leading to more efficient logistics and inventory management. In trade finance, AI can automate document processing and risk assessment, increasing access to finance and enhancing transactional transparency, particularly through integration with blockchain technology. In terms of market access, AI-driven analytics can identify consumer trends and competitive dynamics, enabling personalized marketing and overcoming linguistic and cultural barriers. Due to the lack of quantitative data, this study employed qualitative research methods, specifically a multiple-case-study approach. The case studies of leading companies such as Alibaba, DHL, and Maersk showcase how they leverage AI to optimize their trade operations, improve customer service, and achieve greater efficiency. These real-world examples demonstrate AI’s practical applications and significant benefits in the global trade landscape. However, the adoption of AI in international trade is not without challenges. These include issues around data quality, ethical concerns, technological complexity, and public perception. Policy recommendations highlight the need for a robust data infrastructure, establishing ethical AI guidelines, and fostering international cooperation to align data protection regulations. Full article

A thriving electric vehicles (EVs) market serves as a pivotal embodiment of the global push towards sustainable mobility. As one of the leading global EV sellers, China owns a huge used EV market, which should be spotlighted. While most studies focus on the mechanism of new EV purchases, few put their insight into the trade of used EVs. To fill this gap, this paper aims to clarify the mechanism of consumption behaviour towards used EVs. First, we identified 11 variables that have a direct or indirect impact on consumers’ purchase intention and constructed a conceptual framework. Then, we checked the structural relationships of the model through an empirical study (n = 431). The results showed that purchase intention was determined by two variables: perceived risk and attitude. We also observed an association between income and purchase intention. Functional risk had a direct and significant impact on perceived risk. Economic value, brand trust, and after-sales service were crucial predictors of attitude. Education could moderate the relationship between attitudes and purchase intention. Based on theoretical findings, we present the design strategies to enhance consumers’ purchase willingness from car companies’ and policymakers’ viewpoints. In practical situations, this article offers valuable insights for stakeholders related to the used EV industry, providing a critical reference for advancing sustainable mobility. Full article

The business behavior towards innovation and its impact on the creation of new jobs of 113 firms in the Valencian Community (Spain) were analyzed between 2014 and 2020. The sample included manufacturing, services and trading enterprises; technological and non-technological companies; micro-, small, medium, and large enterprises; and locations across the entire geographical extent of the Valencian Community. The firms were divided into quartiles based on their level of employment growth, linearly correlating this variable with 14 innovation indicators for each of the resulting four groups, reflecting the innovative capacity of these firms. It was found that the factor linked to innovation capacity that most favors or drives business employment creation is co-operation with other companies, as it had a direct and significant relationship with the two highest growth groups (quartiles 4 and 3), with no negative relationship with either of the two lower growth groups (quartiles 2 and 1). This suggests that the public administration should increase efforts to create spaces for the exchange of ideas between companies and organizations to reduce unemployment levels. Overall, this study provides new insights into the subject, and its findings lead to the conclusion that firms with higher innovative capacity create more jobs. Full article

Peer-to-Peer (P2P) energy trading is a new financial mechanism that can be adopted to incentivize the development of distributed energy resources (DERs), by promoting the selling of excess energy to other peers on the network at a negotiated rate. Current incentive programs, such as net metering (NEM) and Feed-in-Tariff (FiT), operate according to a centralized policy framework, where energy is only traded with the utility, the state-owned grid authority, the service provider, or the power generation/distribution company, who also have the upper hand in deciding on the rates for buying the excess energy. This study presents a comparative analysis of three energy trading mechanisms, P2P energy trading, NEM, and FiT, within a rural microgrid consisting of two prosumers and four consumers. The microgrid serves as a practical testbed for evaluating the economic impacts of these mechanisms, through simulations considering various factors such as energy demand, production variability, and energy rates, and using key metrics such as economic savings, annual energy bill, and wasted excess energy. Results indicate that while net metering and FiT offer stable financial returns for prosumers, P2P trading demonstrates superior flexibility and potentially higher economic benefits for both prosumers and consumers by aligning energy trading with real-time market conditions. The findings offer valuable insights for policymakers and stakeholders seeking to optimize rural energy systems through innovative trading mechanisms. Full article