Search Results (253)

Background: Rapid adoption of artificial intelligence (AI) in logistics enhances operational efficiency and firm performance; however, empirical evidence on its capability-driven impact remains limited, particularly in Saudi Arabia’s e-commerce sector. This study’s purpose is to examine the influence of AI on logistics firm performance through the mediation role of supply chain consistency and logistics capabilities. Methods: A quantitative study was conducted and data were collected using a convenience sampling technique from 275 employees working in the Saudi Arabian logistics firms. Partial Least Squares Structural Equation Modeling was used to perform data analysis. Results: The study findings indicated that AI usage has significant and positive influence on supply chain consistency (β = 0.290) and logistics capabilities (β = 0.303). Furthermore, supply chain consistency (β = 0.115) and logistics capabilities (β = 0.171) play mediating role between AI usage and firm performance. The research model exhibits substantial predictive capability, explaining 74.6% (R² = 0.746) of the variance in firm performance, while AI usage explains a smaller portion of the variance in supply chain consistency 8.4% (R² = 0.084) and logistics capabilities 9.2% (R² = 0.092). Conclusions: The findings demonstrate that AI-based logistics operations provide extensive support to streamline operations and reduce costs. Full article

Upstream textile small and medium-sized enterprises (SMEs) frequently exhibit constrained supply chain resilience owing to persistent information latency and structural dependence on downstream orders. To address these challenges, this study develops and validates a customer-to-manufacturer (C2M) intelligence framework that enables data-driven production planning using publicly available e-commerce data. The framework incorporates ethically compliant acquisition of consumer demand signals, semantic translation of unstructured market data into textile engineering attributes, machine-learning-based demand forecasting, and human-centric decision support. Utilizing 3.87 million consumer comments from 127,846 product listings, a Neural Boosted Tree model with entity embeddings for textile attributes was constructed. This model achieved a mean R² of 0.921 in cross-validation, surpassing benchmark methods. Consumer comment volume was validated as a proxy for sales activity, facilitating demand estimation. Forecasts were translated into production guidance using Monte Carlo simulation and a decision dashboard. In a 12-month field study at a Taiwanese dyeing SME, implementation resulted in a 28% reduction in inventory value, a 31% decrease in dye lot changeovers, and a 16% increase in capacity utilization. This research extends the C2M paradigm from downstream retail contexts to upstream textile SMEs, proposes an integrated and operationally feasible intelligence framework for resource-constrained manufacturers, and demonstrates how digital intelligence can enhance supply chain resilience while supporting, rather than replacing, human decision-making. The results indicate that upstream textile SMEs can leverage publicly visible e-commerce signals to enhance production planning responsiveness, minimize inventory exposure and dye-lot disruptions, and strengthen resilience to demand uncertainty through planner-centered digital decision support. Full article

Background: Last-mile logistics is one of the most complex and cost-intensive segments of supply chains, particularly in densely populated urban environments where rising customer expectations, sustainability requirements, and operational constraints increasingly intersect. Despite growing academic interest, empirical evidence remains limited regarding how convenience-related last-mile service attributes influence customer satisfaction, while the sector is undergoing a revolutionary transformation. Methods: This study applies a refined Kano model to classify last-mile convenience services according to their differentiated effects on customer satisfaction. Data were collected through a structured questionnaire administered to active e-commerce users in a metropolitan area. The methodological approach modifies and extends the traditional Kano framework. Results: The findings reveal clear patterns among last-mile service attributes. Online tracking and preferred payment options function as One-dimensional attributes, proportionally influencing customer satisfaction. Time-based delivery, flexible pickup options, and sustainability-oriented service features appear as Attractive attributes, generating additional increases in service value. In contrast, advanced technological solutions such as drone or autonomous vehicle delivery were perceived as Indifferent attributes. These interpretations are further nuanced by the fuzzy approach. Conclusions: The results provide important insights and validation for consumer-centered service design and support the prioritization of investments aimed at developing sustainable and customer-oriented last-mile logistics systems. Full article

As an effective approach to boosting consumption and facilitating the recycling of consumer goods, trade-in programs have been widely adopted by branders and e-commerce platforms. A platform supply chain system comprising e-commerce platforms and branders is investigated in this paper for this purpose. We construct a two-stage dynamic game model encompassing eight scenarios, discussing the provision of trade-in programs and product recycling issues under the resale and agency selling modes. Below are the key findings: (1) Trade-In Programs: In the resale mode, both branders and platforms prefer to adopt self-recycling when market potential is large, while opting for recycling undertaken by the other party when market potential is small. In the agency selling mode, branders prefer to adopt self-recycling (B-II) when fixed costs are high and the salvage value of used products is high, while platforms choose platform-led recycling (P-II) when fixed costs are low and the salvage value of used products is high. (2) Product Recycling: In the resale mode, branders should opt for self-recycling when facing high fixed costs, small market potential, and high salvage values, while outsourcing is more appropriate when salvage values are low. When the market potential is low, the platform ought to prefer self-recycling if the salvage value is either sufficiently high or sufficiently low; otherwise, outsourcing is preferable. In the agency selling mode, when the salvage value of used products is relatively high, platforms tend to have a free-riding mentality. When platforms provide trade-in programs, they will prioritize self-recycling if the salvage value is higher. In contrast, branders consistently achieve maximum profits when platforms adopt self-recycling. (3) Selection of Selling Mode: Branders always prefer the agency selling mode, while platforms’ mode selection depends on the trade-off between salvage value and commission rate. This study provides strategic insights for platform-based supply chain decisions. Full article

With the rapid development of e-commerce and smart manufacturing, automated warehouse systems have become critical infrastructure for modern logistics. In China’s vast market, the dynamic scheduling of Rail-Guided Vehicles (RGVs) faces significant challenges due to complex task uncertainties, hierarchical supply chain structures, and real-time collision avoidance requirements. Traditional rule-based methods and static optimization models often fail to adapt to such dynamic environments. To address these issues, this paper proposes a novel hybrid deep reinforcement learning framework integrating a Dynamic Graph Neural Network (DGNN) and a Transformer model. The DGNN captures the spatiotemporal dependencies of the warehouse network topology, while the Transformer mechanism enhances long-range feature extraction for task prioritization. Furthermore, we design a centralized Deep Q-network (DQN) framework with parameterized action spaces to coordinate multiple RGVs collaboratively. While the system manages multiple physical vehicles, the learning architecture employs a single-agent global scheduler to avoid the non-stationarity issues inherent in multi-agent reinforcement learning. Experimental results based on real-world data from a large-scale electronics manufacturing warehouse demonstrate that our method reduces average task completion time by 18.5% and improves system throughput by 22.3% compared to state-of-the-art baselines. The proposed approach demonstrates potential for intelligent warehouse management in dynamic industrial scenarios. Full article

The development of digital supply chains has significantly changed traditional supplier selection models that focus on static and cost-driven criteria. In addition to price, operational standards, service excellence, and contribution to the platform should be taken into account when evaluating sellers operating on dynamic, performance-oriented e-commerce platforms. This study addresses this gap by developing a comprehensive multi-criteria decision-making (MCDM) framework through a systematic literature review according to PRISMA methodology. Searches conducted in Web of Science, ScienceDirect, IEEE Xplore, Google Scholar, and Taylor & Francis yielded 4630 records from 2014 to 2025, of which 123 were analyzed using bibliometric mapping and thematic synthesis. The findings indicate a progressive diversification of evaluation criteria over time: while quality, delivery, and cost remain foundational, recent studies increasingly address customer service, search volume, and refined financial indicators such as profit and markup rate, pointing toward more multidimensional seller evaluation models. Through thematic synthesis of the indicators identified across the reviewed studies, we propose a four-dimensional framework encompassing financial sustainability, operational efficiency, quality and service standards, and market positioning. The study also discusses the implications of integrating artificial intelligence with multi-criteria and hybrid decision-making approaches for developing adaptive seller ranking systems. By synthesizing fragmented research, our framework offers strategic guidance for platform managers designing seller evaluation and allocation mechanisms. Full article

Under China’s rural revitalization and agricultural modernization strategies, digital village construction overcomes resource limits to drive transformation. Using 2013–2022 provincial panel data and a case study of Lin’an, Hangzhou, this study reveals how digital villages boost high-quality agriculture. The empirical results show they significantly enhance agricultural total factor productivity via three paths: IoT-driven precision production, blockchain-enabled green value addition, and e-commerce direct sales demonstrate more pronounced effectiveness in major grain-producing regions and those characterized by balanced production and sales. Simultaneously, this study employs the instrumental variable (TI) approach to address endogeneity from reverse causality and omitted variables. Mechanism testing reveals agricultural technological innovation exerts a significant 77.5% mediating effect. Finally, digital rural construction exhibits a non-linear threshold (0.3082); surpassing it triggers a gradual slowdown in growth with decreasing marginal returns. The Lin’an case validates the empirical results while revealing structural barriers, including industrial chain penetration gaps, data silos, and factor supply constraints, leading to the formulation of targeted optimization strategies. The practical contribution of this study is the proposal of a “data-value-technology” closed loop: public brands like “Tianmu Mountain Treasures” channel premiums into R&D funds, creating a self-sustaining mechanism. The findings indicate that digital villages drive high-quality agriculture primarily through direct effects, powered by full-chain tech coordination, institutional reform, and inclusive factor supply. Finally, this study proposes a coordinated governance framework encompassing “technical synergy, institutional innovation, and factor optimization,” providing theoretical support and strategic references for optimizing the pathways of regional agricultural digital transformation. Full article

This study investigates the optimal pricing and green investment strategies within a dual-channel seafood supply chain comprising a seafood manufacturer and an e-commerce platform, where the manufacturer faces capital constraints. In response to the emerging model of e-commerce platform financing, we develop a Stackelberg game model to analyze two scenarios: single green investment (by the manufacturer only) and joint green investment (by both the manufacturer and the platform). We derive the equilibrium decisions for both parties under each scenario. The results indicate that enhanced market competitiveness of the direct sales channel increases the manufacturer’s wholesale revenue from the self-operated channel and its direct sales revenue, while also enabling the platform to achieve higher profits through a premium pricing strategy. Furthermore, the choice of green investment mode is contingent upon the intensity of channel competition and the platform’s commission rate. Specifically, when both parameters are low, a single green investment strategy constitutes an equilibrium. Conversely, when direct channel competition is sufficiently intense, a joint green investment strategy emerges as the Pareto-dominant equilibrium, leading to a win–win outcome regardless of the commission rate. These findings offer actionable insights for managers of seafood companies and e-commerce platforms in formulating sustainable channel cooperation and financing strategies. Full article

Blockchain technology can enhance traceability and trust in contract farming supply chains, yet high implementation costs deter adoption by supply chain participants. This study examines the synergistic mechanisms between blockchain adoption strategies and government subsidy policies. We develop a multi-stage Stackelberg game model involving an agricultural enterprise, an e-commerce platform, and a government, and comparatively analyze six decision-making scenarios across non-subsidy, unilateral subsidy, and full-chain subsidy settings. Three key findings emerge. First, blockchain investment has a cost–effect threshold below which consumer traceability preferences do not translate into profit gains. Second, well-designed subsidies overcome investment inertia and yield Pareto improvements in both profits and social welfare, with the full-chain subsidy model (Model BG) maximizing social welfare; however, subsidies exhibit distinct efficiency boundaries, and over-subsidization causes resource misallocation. Third, both supply chain parties tend to free-ride on the other’s investment, creating strategic conflicts that necessitate differentiated subsidy mechanisms tailored to specific dominance structures. These findings provide policy guidance for facilitating agricultural digital transformation and enhancing supply chain coordination. Full article

Background: From the perspective of product liability, this study explores how agricultural product e-commerce enterprises can enhance the quality of the agricultural product supply chain through quality incentive strategies. Methods: Based on a tripartite evolutionary game model, the strategic interactions among farmers, agricultural product e-commerce enterprises, and the government are analyzed. Results: The research finds that whether the system converges to the ideal equilibrium of “high-quality production—ex-ante quality cost-sharing—collaborative governance” depends on the combined effects of revenue distribution, liability costs, and external incentives or penalties. Among these, government-led collaborative governance plays a key guiding role in incentivizing enterprises and influencing farmers’ behaviors. The incentive measures implemented by e-commerce enterprises and government penalties can effectively curb farmers’ low-quality production behaviors. Conclusions: The study further reveals how factors such as ex-ante cost-sharing, liability allocation, and farmers’ conformity psychology affect the stability of agricultural product supply chain quality, thereby providing theoretical support for constructing a “policy-platform-farmer” collaborative governance framework. Full article

Ports are critical nodes in global supply chains and play a central role in sustainability transitions in trade and logistics. This study investigates how Artificial Intelligence (AI) contributes to sustainable innovation within port ecosystems, focusing on efficiency, transparency, resilience, and environmental performance. To address the research question—how has AI supported sustainability in maritime ports?—we conducted a systematic screening combined with bibliometric performance analysis and science mapping. A total of 80 peer-reviewed articles published between 2019 and 2025 (Scopus) were analysed. The results show a strong acceleration of publications in 2025, alongside a citation–time lag for recent studies. The findings indicate three dominant application streams: (1) operational efficiency and optimisation (terminal operations, forecasting, routing, scheduling); (2) digital and smart-port enablement through IoT and data infrastructures; and (3) governance, risk, and compliance (e.g., Port State Control, inspection analytics, cyber-resilience). The mapping also evidences increasing convergence of AI with complementary technologies—particularly IoT and, in a smaller but visible subset, blockchain—to enhance trust, accountability, and interoperability. By synthesising the field’s intellectual structure and thematic evolution, this study outlines research gaps and proposes future directions toward integrated frameworks for sustainable port ecosystems and Sustainable Commerce 4.0. Full article

Wet markets remain a cornerstone of fresh food retail in Chinese cities, continuously evolving alongside urbanization. However, the drivers and implications of their transformation at the city level remain underexplored. Drawing on government documents and survey data from Nanjing and Suzhou, this study reveals that China’s wet market evolution is characterized by incremental semi-formalization and upgrading, preserving their essential role in the food supply chain without displacing other retail formats. This transformation reflects shifting government attitudes, strategic urban planning for food security, and the effective integration of public and private interests. The hybrid governance model, which combines public oversight with private operation, has enhanced wet markets’ resilience, ensuring affordability, freshness, and social interaction. Their adaptability underscores a broader lesson: inclusive urban food systems require soft–hard infrastructure synergy, where physical upgrades coexist with social functions. In this paper, we argue that wet markets exemplify social infrastructure: they are not merely food hubs but spaces fostering civic life, cultural continuity, and equitable access. Their co-evolution with supermarkets and e-commerce challenges the “supermarketization” thesis, highlighting the importance of policy flexibility and localized governance. Our findings offer insights for Global South cities grappling with food system transitions, emphasizing the need to balance modernization with the preservation of informal economies’ social fabric. Full article

The growth of e-commerce live streaming has expanded sales channel options for fresh agricultural suppliers. This study investigates a two-echelon supply chain consisting of a fresh agricultural supplier and downstream retailers. Using differential game theory, we examine the supplier’s preservation technology level and product greenness, analyzing and comparing equilibrium strategies under three different modes: e-commerce platform sales mode (SP), head streamer sales mode (SH) and ordinary streamer sales mode (SN). The results demonstrate that SP is the dominant strategy when retailers’ marginal profits are low. Conversely, under high marginal profit conditions, the optimal selection depends on streamer cooperation costs: SH is preferred with low head streamer costs; widening cost gaps introduce temporal considerations between SH and SN; further gap expansion makes SN optimal. Furthermore, product greenness is related to supplier’s marginal profit, while the preservation technology level is jointly determined by supplier’s marginal profit and retailers’ inspection costs. Finally, combinations of these modes are also investigated. Full article

Digitalization is increasingly central to economic growth strategies, yet robust macro-level evidence on the role of SME-led e-commerce remains limited. Drawing on the Resource-Based View, this study examines how SME digitalization, internet finance, and platform-based activities influence regional economic growth in China, and how these effects depend on digital infrastructure readiness (DIR). We construct an annual panel of 30 provincial-level regions in China over 2015–2024 and estimate dynamic relationships using two-step system GMM to address endogeneity and growth persistence. The results show that SME digitalization, supply-chain efficiency, mobile payment penetration, tech-driven employment growth, platform-economy contribution, and DIR all exert statistically significant positive effects on GDP growth. Quantitatively, a 10-percentage-point increase in SME digitalization is associated with approximately 0.3-percentage-point higher regional GDP growth, while a 10-point increase in DIR corresponds to about 0.4-percentage-point higher growth. Moderation analyses reveal that DIR significantly amplifies the growth effects of e-commerce expansion, mobile payments, and digital marketing, whereas its moderating role is weaker or insignificant for cross-border payments and supply-chain efficiency. These findings reconceptualize digitalization as a coordinated bundle of complementary resources and position DIR as a critical enabling capability for translating SME digital transformation into macroeconomic growth. The study offers policy-relevant evidence for targeting infrastructure investment and digital-economy strategies in emerging platform economies. Full article

The rise of the circular economy and e-commerce has led to the emergence of e-commerce closed-loop supply chains (ECLSCs). In practice, investing in process innovation (PI) is key to improving profitability and competitiveness. However, manufacturers at the downstream of ECLSCs often face financial constraints and quality uncertainty of used products, while research on how to select financing strategies under these conditions remains limited. To explore the optimal financing scheme for the ECLSC, this study investigates two financing schemes: bank financing (BF) and FinTech platform financing (FPF), which offers a combination of debt financing (DF) and equity financing (EF). Some key findings are derived. For the ECLSC, the FPF scheme is more profitable when the unit manufacturing cost for new components exceeds the threshold or PI costs are relatively low. Additionally, the FPF performs better when the FPF interest rate is low and the DF ratio is high. The BF is more beneficial when consumer sensitivity to recycling prices or service is low. The FPF enables the ECLSC to achieve maximum profits and minimize environmental impact within a specific range. Furthermore, the financing models are extended to incorporate considerations of fairness, where the optimal financing scheme is primarily influenced by the manufacturing cost. Full article