Search Results (116)

In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem. Full article

This academic article seeks to clarify the state of the literature on a very pertinent topic that is based on how digital innovation, considering emerging technologies and how they could be used in business management and marketing, could increase sustainable development. The sustainable economy, which should maintain long-term development through efficient resource management, has as allies emerging technologies such as artificial intelligence, blockchain, and the Internet of Things that can help reduce waste, reduce the carbon footprint, and automate tasks. Additionally, they could present themselves as a solution to improve aspects of digital communication between companies and their consumers in remote training, distribution chain, e-commerce, and process optimization in different sectors of activity. These advances will, on the one hand, allow the possibility of conducting a greater amount of professional training, increasing the number of qualified professionals and, on the other hand, facilitate trade exchanges, promoting the economy. Based on a systematic bibliometric review of the literature using the PRISMA framework, this study investigates how digital tools catalyze transformative changes in different sectors of activity. The results indicate that, overall, the academic articles analyzed in this literature review present studies focused on digitalization and sustainability (approximately 50%). In second place are topics related to digitalization and other topics such as: smart cities; Sustainable Development Goals; academia; the digital economy; government policies; academic education; and sustainable communication (29%). Finally, in third place, there are academic articles closely linked to digitalization and the environment, more specifically to sustainable practices and the management of natural resources (21%). The article concludes that digital development, when used wisely, serves as a crucial lever to address the world’s most pressing sustainability imperatives. Future research should emphasize interdisciplinary collaboration and adaptive governance to ensure that these digital changes produce lasting impacts for people and the planet. Full article

This study adopts an evolutionary game theory framework and focuses on the strategic interaction between brands and KOLs. It examines how the two parties interact under conditions of uncertainty and risk, especially when the KOLs’ contract fulfillment capability is low, and how they adjust strategies to achieve sustainable collaboration. Different from previous studies, this paper not only examines objective parameters such as commission rate, brand value, return cost, and reputation risk, but also introduces behavioral factors, including risk preference, loss aversion, and the psychological perception of gains and losses. By modeling the decision-making process of KOLs and brands under uncertainty and risk, the key factors affecting the evolution of cooperation strategies are identified. The simulation results show that although the cooperation strategy (such as information disclosure and truthful promotion) can achieve stability under certain conditions, the system is highly sensitive to external factors (such as environmental uncertainty) and internal psychological factors (such as risk preference and loss sensitivity). This study provides practical suggestions for brands and KOLs to promote long-term cooperation, emphasizing the importance of incentive coordination, reputation risk management, commission structure optimization, and psychological perception regulation. These findings provide practical guidance for enhancing the sustainability of brand–KOL collaborations. Full article

The rapid growth of e-commerce and surges in shipment volumes have increased the pressure on transport systems, requiring innovations in collaborative logistics where consumers participate in dual roles as receivers and deliverers. However, existing research often addresses these roles in isolation, overlooking the flexibility with which users switch between them. Moreover, the literature has focused predominantly on monetary value in paid crowdsourced or social value in free social delivery, without fully exploring how users perceive value across both models. Addressing these gaps, this study profiles users of collaborative logistics services from both receiver and deliverer perspectives and examines their motivations in paid and unpaid delivery contexts. Based on survey data from 493 participants in Singapore, cluster analysis identified four distinct user segments: hesitators, potential customers, active users, and loyal advocates. The findings indicate that user preferences differ by role, with functional value prioritised in paid delivery and social value more prominent in free models. Free models attract a higher proportion of favourable users, highlighting the significance of non-monetary incentives. This study contributes to the literature by offering an integrated perspective on user roles and value perceptions and provides practical insights for developing more inclusive, community-oriented last-mile logistics solutions. Full article

Shadow fleets, operating covertly in global maritime commerce, have emerged as a significant challenge to international regulatory frameworks and trade policies. This paper introduces a novel conceptual framework that distinguishes between ‘dark fleets’ and ‘gray fleets’, offering a more nuanced understanding of these clandestine maritime activities. Through a comprehensive methodological approach integrating a literature review, case studies, and data analysis, we examine the characteristics, operational strategies, and implications of shadow fleets. Our research reveals that shadow fleets have expanded rapidly, now accounting for approximately 10% of global seaborne oil transportation. We identify key indicators of shadow fleet operations, including disabled Automatic Identification System (AIS) transmitters, inconsistent vessel information, unusual behavior patterns, obscure ownership structures, and the use of aging vessels. This paper highlights the economic disruptions caused by shadow fleets, their role in circumventing international sanctions, and the significant environmental and safety risks they pose. The study underscores the regulatory challenges in addressing shadow fleets, particularly their exploitation of flags of convenience and complex ownership structures. We propose a multifaceted approach to tackling these challenges, emphasizing the need for advanced technological solutions, enhanced international collaboration, and adaptive ocean governance frameworks. This research contributes to the evolving field of maritime security and policy, offering insights for policymakers, industry stakeholders, and researchers into developing strategies to mitigate the risks posed by shadow fleets in global maritime commerce. Full article

As digital platforms reshape the commercial landscape, brands increasingly collaborate with these platforms to enhance product sales. Many adopt livestream as a strategic tool to attract more traffic, typically choosing between Artificial Intelligence (AI) or Key Opinion Leader (KOL) approaches. Meanwhile, platforms operate under either an agency or a resale mode. However, the relative effectiveness of these strategies remains unclear. This study investigates an e-commerce supply chain comprising a single brand and platform, examining how AI and KOL livestream influence supply chain decisions across different sales modes and identifying optimal strategies for the brand and platform. Results show that when the platform’s revenue sharing rate is low, the agency mode consistently yields a Pareto improvement over resale, regardless of the livestream scheme. Moreover, when the KOL promotion fee rate is low, KOL livestream outperforms AI livestream under both sales modes. When the revenue sharing rate is high, the brand’s optimal strategy is “resale mode and KOL livestream”, while the platform prefers “agency mode and KOL livestream”. Conversely, when the revenue sharing rate is low, the platform’s best strategy is “resale mode and KOL livestream”, while the brand favors the agency mode, with livestream preferences shaped by KOL promotion fee rate. Full article

With the rapid development of e-commerce, the logistics industry faces multiple challenges, including high delivery costs, long delivery times, and a shortage of delivery personnel. Truck–drone collaborative delivery combines the high load capacity of trucks with the flexibility and speed of drones, offering an innovative and practical solution. This paper proposes the Truck–Drone Collaborative Delivery Routing Problem (TDCRPTW) and develops a multi-objective optimization model that minimizes delivery costs and maximizes time reliability under capacity and time window constraints in multi-truck, multi-drone scenarios. To solve the model, an innovative two-stage solution strategy that combines the adaptive k-means++ clustering algorithm with temperature-controlled memory simulated annealing (TCMSA) is proposed. The experimental results demonstrate that the proposed model reduces delivery costs by 10% to 50% and reduces delivery time by 15% to 40%, showcasing the superiority of the truck–drone collaborative delivery model. Moreover, the proposed algorithm demonstrates outstanding performance and reliability across multiple dimensions. Therefore, the proposed approach provides an efficient solution to the truck–drone collaborative delivery problem and offers valuable insights for enhancing the efficiency and reliability of e-commerce logistics systems. Full article

In certain newly established or niche e-commerce platforms, user–item interactions are often exceedingly sparse due to limited user bases or specialized product lines, posing significant obstacles to accurate personalized recommendations. To address these challenges, this paper proposes an enhanced recommendation approach based on a latent factor model. By leveraging factorization to uncover the hidden features of users and items and incorporating both user behavioral data and item attribute information, a multi-dimensional latent semantic space is constructed to more effectively capture the underlying relationships between user preferences and item properties. The method involves data preprocessing, model construction, user and item vectorization, and semantic-similarity-based recommendation generation. For empirical validation, we employ a real-world dataset gathered from an e-commerce platform, comprising 4645 ratings from 3445 users across 277 items in nine distinct categories. Experimental results demonstrate that, compared with conventional collaborative filtering methods, this approach achieves superior precision and recall even in highly sparse settings, showing stronger resilience under low-density conditions. These findings offer objective and feasible insights for advancing personalized recommendation techniques in newly established or niche e-commerce platforms. Full article

This study introduces APGCN-CF, an innovative graph convolutional network-based framework designed to address the distinctive challenges of minor agricultural product recommendation in rural e-commerce environments. The framework synergistically integrates graph convolutional networks (GCNs) with collaborative filtering (CF), providing an effective solution to critical challenges in minor agricultural product recommendations. APGCN-CF implements a multi-module architecture encompassing graph structure construction, convolution-based feature learning, user similarity-based candidate generation, and preference probability-driven precise recommendation. Comprehensive experimental evaluations conducted on real-world minor agricultural product datasets, featuring scallions and garlic as representative cases, demonstrate that APGCN-CF consistently outperforms existing state-of-the-art baseline methods across multiple performance metrics, including precision, recall, and F1-score. Notably, the framework exhibits superior performance in challenging scenarios characterized by data sparsity and cold-start conditions through its effective feature extraction and relationship modeling capabilities. Through deep integration of user preference features, APGCN-CF enables the dynamic generation of highly personalized recommendation lists, providing robust decision support for optimizing minor agricultural product sales. This research not only advances the theoretical landscape of recommendation systems but also delivers a practical solution tailored for personalized recommendation within rural e-commerce platforms. Full article

In recent years, with the rapid development of the internet, the accumulation of massive data has significantly propelled the advancement of artificial intelligence. Graphs, as an important data structure for representing relationships between entities, are widely used in various real-world scenarios such as social networks and e-commerce platforms. Graph Neural Networks (GNNs) have emerged as a popular research topic, capable of learning information from neighborhoods and extracting features from graph-structured data to solve tasks like graph classification, node classification, and link prediction. However, the centralized training of GNNs often faces challenges due to data isolation and privacy concerns. Federated Learning (FL) has been proposed as a solution to these issues, allowing multiple users to collaboratively train models without sharing raw data. This paper introduces a privacy-preserving mechanism based on Local Differential Privacy (LDP) to enhance the security of Federated Graph Neural Networks (FedGNNs) against inference attacks while maintaining model performance. Full article

Advancements in e-commerce and Industry 4.0 technologies have significantly improved communication and connectivity in supply chains. These technologies, particularly artificial intelligence driven by Industry 4.0 technologies (AI-IT4.0), have reshaped how products, services, and financial transactions are managed, emphasizing the importance of information sharing among supply chain participants to enhance inventory management, sales, and demand forecasting. However, sharing comprehensive information across supply chain stakeholders presents persistent challenges due to various barriers. This study seeks to review the current understanding of visibility barriers in supply chains, identify key obstacles, and suggest directions for future research. Using the PRISMA methodology, the study analyzed 20 articles, identifying 12 critical barriers to visibility. Bibliometric analysis has revealed growing interest in the topic since 2021, although evidence of collaborative research remains limited. A keyword co-occurrence analysis highlighted strong connections between visibility, supply chain management, sustainability, and artificial intelligence driven by industry 4.0 technologies, such as machine learning, predictive analytics, and digital twins. Future research should empirically investigate visibility barriers and explore the interplay between AI-TI4.0, visibility, and sustainable performance through case studies and quantitative approaches. Full article

This paper examines the synergetic development between small- and medium-sized enterprises (SMEs) and platforms in China–ASEAN cross-border e-commerce, representing a dynamic system of interrelated market entities. By integrating synergy theory with evolutionary principles, this study develops an evolutionary game model to reflect collaborative development strategies and their evolutionary trajectories. The model reveals nine distinct equilibrium states, with particular attention to a bifurcation scenario demonstrating two stable configurations: mutual collaboration and mutual non-collaboration. Model validation is conducted through numerical simulations with parameters referenced from the DHgate platform case and relevant government policy documents. The findings illuminate key parameters that influence evolutionary trajectories, including initial conditions, regulatory interventions, and resource distribution mechanisms. Specifically, strong initial collaboration propensity, balanced governmental facilitation, and equitable benefit–cost sharing mechanisms are identified as critical factors promoting synergetic development. This research enriches cross-border e-commerce studies by pioneering the integration of synergy theory and the evolutionary game model, offering novel insights into the bilateral dynamics of SME–platform system development. Full article

Circular economy theorists and advocates typically describe traditional market economies as linear “take, make, use and dispose” systems. Various policy interventions, from green taxes to extended producer responsibility, are therefore deemed essential to ensure the systematic (re)introduction of residuals, secondary materials and components in manufacturing activities. By contrast, many nineteenth- and early twentieth-century writers documented how the profit motive, long-distance trade and actors now largely absent from present-day circularity discussions (e.g., waste dealers and brokers) spontaneously created ever more value out of the recovery of residuals and waste. These opposite assessments and underlying perspectives are perhaps best illustrated in the nineteenth classical liberal and early twenty-first century interventionist writings on circularity of Fellows, members and collaborators of the near tricentennial British Royal Society for the Encouragement of Arts, Manufactures and Commerce. This article summarizes their respective contributions and compares their stance on market institutions, design, intermediaries, extended producer responsibility and long-distance trade. Some hypotheses as to the sources of their analytical discrepancies and current beliefs on resource recovery are then discussed in more detail. A final suggestion is made that, if the analysis offered by early contributors is more correct, then perhaps the most important step towards greater circularity is regulatory reform (or deregulation) that would facilitate the spontaneous recovery of residuals and their processing in the most suitable, if sometimes more distant, locations. Full article

As a new business model, cross-border e-commerce has become an important way for countries to meet new foreign trade requirements in the Internet economy. The cross-border logistics industry plays a crucial role in supporting cross-border e-commerce. Compared with domestic e-commerce, cross-border logistics faces more challenges. To address the problems in cross-border logistics, this study takes China as an example and constructs a tripartite evolutionary game model to facilitate information collaboration among cross-border e-commerce platforms, domestic logistics enterprises, and foreign logistics enterprises. The collaboration strategies in this tripartite information system are simulated using MATLAB. The study highlights key factors affecting information cooperation, such as standardization levels, risk and payoff distributions, and their implications on collaboration decisions. Specifically, the results show that higher levels of information collaboration standardization promote cooperative strategies among players; the risk associated with information collaboration is the most sensitive factor influencing decision-making within cross-border logistics alliances; and when the payoff distribution coefficient is too high, other members may resist cooperation. Full article

In the big data era, global business competition focuses on industrial chain coordination. The whole rural e-commerce industry chain, as an advanced system characterized by digital transformation, is experiencing rapid growth. This paper aims to explore the evolutionary mechanism of collaborative behavior in the digital transformation of platform enterprises and participating enterprises across the whole rural e-commerce industry chain. To achieve this, this paper combines prospect theory and evolutionary game theory, introduces the value function and decision weight of prospect theory, and constructs a two-party game model between platform enterprises and participating enterprises. Based on the demonstration of the impact of individual changes in major objective factors, such as the cooperative innovation benefit coefficient, as well as major behavioral characteristic factors, such as decision-makers’ risk attitude coefficients, on enterprises’ strategic choices, we further reveal the influence of the interaction of key factors on the evolutionary results through case simulations. The findings indicate that when the behavior characteristics of the players are introduced, the threshold interval of the cost–benefit ratio of the two sides to reach the optimal state of decision-making is obviously reduced. Under moderate risk attitudes and degrees of loss sensitivity, enhancing the resource absorption capacity of enterprises in the chain and reducing the potential risk loss of platform enterprises to alleviate the influence of subjective behavior characteristics on cooperation willingness are effective measures. Improving innovation ability is the key factor in alleviating the negative impact of uncertainty on the decision-making of both parties. This paper is one of the few studies to integrate prospect theory with evolutionary game analysis in examining the collaborative behaviors between platform enterprises and participating enterprises. Effective strategies are proposed to promote enterprises achieving synergy. Full article