The cross-border electronic commerce has become an important channel for international trade, particularly in the field of agricultural products. This study investigates the key factors impacting the performance of enterprises specializing in the cross-border e-commerce of fresh agricultural products, with particular emphasis on optimizing cold chain logistics systems. Using a multivariate linear regression model, the authors quantify the influence of various dimensions including trade standards, logistics and supply chain efficiency, consumer trust, and customs clearance processes on enterprise performance. Key findings reveal that cross-border logistics efficiency and cold chain matching specifications significantly improve enterprise performance. These findings highlight the importance of enhancing infrastructure, improving professional service standards, and establishing integrated operational models throughout the supply chain. The study offers practical recommendations to support the sustainable development of fresh agricultural product cross-border e-commerce.
This study examines how green purchasing (GP) improves organizational performance (OP) in Chinese manufacturing firms, highlighting product innovation (PI) as the key mediator. Based on the natural resource-based view and absorptive capacity theory, a quantitative cross-sectional survey was conducted with supply chain, procurement, and operations managers in major industrial provinces. Partial least squares structural equation modeling results show GP positively affects OP (β = 0.221, p < 0.01). Strong complementary mediation is confirmed: GP significantly increases PI (β = 0.641, p < 0.001), and PI strongly enhances OP (β = 0.647, p < 0.001). The indirect effect of GP on OP through PI is substantial (β = 0.414, p < 0.001), demonstrating that PI is the primary mechanism by which sustainable procurement translates into competitive advantage. The study provides emerging-economy evidence and highlights green procurement–research and development integration and regulation-driven gains in ecology and innovation, aligned with China's “Ecological Civilization” and dual-carbon goals.
This study investigates a moderated mediation framework to explain the impact of green innovative products (GIP) on organizational performance (OP) within the Chinese institutional context. Drawing on the natural resource-based view and institutional theory, the study proposes that green supply chain management (GSCM) mediates the relationship between GIP and OP, while green organizational culture (GOC) moderates the relationship between GIP and GSCM. Using survey data from 289 Chinese firms and employing partial least squares structural equation modeling (PLS-SEM), the analysis reveals that GIP has a significant positive direct effect on OP (β = 0.227, p < .005). GIP also has a significant positive direct effect on GSCM (β = 0.433, p < .005), and GSCM has a significant positive direct effect on OP (β = 0.545, p < .005). The results further support the mediation hypothesis, showing that GSCM significantly transmits the effect of GIP to OP (indirect effect: β = 0.286, p < .005), indicating complementary partial mediation.
This paper examines the role of human resources in supply chain management practices in fostering organizational resilience and transformative resilience within logistics firms, with a focus on the moderating effect of artificial intelligence technology readiness. Drawing on dynamic capabilities theory and supply chain resilience frameworks, the study proposes a model in which cross-training, managerial support, and flexible shift design act as key enablers of resilience. Using a cross-sectional survey, data were collected from 280 employees across various logistics companies in China to explore the relationships among human resources in supply chain management practices, organizational resilience, and transformative resilience. This study contributes to the growing body of transformative supply chain research by examining the connections between workforce adaptability, digital readiness, and resilience outcomes. The results suggest that organizational resilience, supported by robust human resource practices and artificial intelligence integration, is essential for sustaining long-term resilience and competitive advantage amid global disruptions.
Digital finance has emerged as a transformative force in modern supply chain management, offering new mechanisms for enhancing operational efficiency through improved resource allocation and information processing capabilities. As enterprises increasingly adopt digital financial technologies to optimize supply chain operations, understanding the specific mechanisms through which these innovations drive efficiency gains becomes critical for both theoretical advancement and practical implementation. Using panel data from Chinese A-share listed companies (2002-2022), the authors investigate the relationship between digital finance and supply chain efficiency, focusing on innovation's moderating role. The analysis reveals three critical findings: First, digital finance significantly enhances supply chain efficiency, with robustness confirmed across diverse model specifications. Second, long-term innovation, rather than short-term efforts, acts as the primary mechanism driving this improvement, where sustained innovation capabilities magnify the positive impact of digital finance. Third, the efficiency-enhancing effect is more pronounced in large-scale firms, while no significant variation exists between younger and older firms. These results demonstrate digital finance's transformative role in optimizing supply chain operations and provide empirical guidance for enterprises seeking to leverage digital financial tools for operational enhancement.
The evolution of supply chains into complex networks necessitates intelligent and dynamic optimization methods. Although artificial intelligence (AI) shows significant potential, existing research often lacks integrated, reproducible models for full-chain dynamic decision-making. In this study the authors address this gap by proposing a novel AI-driven optimization framework. They formulate a Dynamic Demand Vehicle Scheduling Model as the core optimization problem and employ a genetic algorithm for its solution. Through comprehensive computational simulations comparing the AI-driven model against a traditional benchmark, the results demonstrate a superior performance of the smart supply chain, achieving 15-20% higher transportation volume, 30-40% reduction in delays, and 40-50% lower risk occurrence frequency. The genetic algorithm exhibits a more stable convergence trajectory compared with the baseline method. This study provides a verifiable methodology and quantitative evidence for AI applications in supply chains, offering significant theoretical and practical implications for intelligent transformation.
This paper examines the role of human resources in supply chain management practices in fostering organizational resilience and transformative resilience within logistics firms, with a focus on the moderating effect of artificial intelligence technology readiness. Drawing on dynamic capabilities theory and supply chain resilience frameworks, the study proposes a model in which cross-training, managerial support, and flexible shift design act as key enablers of resilience. Using a cross-sectional survey, data were collected from 280 employees across various logistics companies in China to explore the relationships among human resources in supply chain management practices, organizational resilience, and transformative resilience. This study contributes to the growing body of transformative supply chain research by examining the connections between workforce adaptability, digital readiness, and resilience outcomes. The results suggest that organizational resilience, supported by robust human resource practices and artificial intelligence integration, is essential for sustaining long-term resilience and competitive advantage amid global disruptions.
As the circular economy advances, green product design must balance competitiveness and transparency—goals often in tension. This paper proposes an information-driven framework to support decision-making in circular supply chains by modeling the trade-off among profitability, environmental performance, and disclosure transparency. It presents a 3D optimization model using an entropy-weighted measurement of alternatives and ranking according to compromise solution method to construct a nine-criteria indicator system and apply the non-dominated sorting genetic algorithm II to generate Pareto-optimal solutions from empirical data from 30 manufacturers. Results show that high-transparency designs increase disclosure scores by 15.3% with only a 1.8% decline in profit margins, while significantly enhancing consumer brand preference (p < 0.05). The framework enables dynamic, data-informed strategies for sustainable product development across supply chain tiers. This study provides a practical decision-support tool for firms seeking competitive advantage through transparent design and offers regulators insights for tiered oversight.
The digital economy's rapid growth has led to complex interactions among information flow, logistics, and capital flow in engineering supply chain management. Blockchain technology, with its decentralization, immutability, and full-process traceability, offers a new approach to enhancing supply chain transparency and cooperation efficiency. Common issues in engineering projects, such as information silos, data tampering, and opaque processes, significantly impede trust-building among participants. This paper explores the mechanisms and technical pathways of blockchain in improving supply chain transparency. By analyzing existing models and case studies, it identifies limitations and boundaries of current industry solutions. An innovative blockchain-based transparent management framework is proposed and validated through process modeling and virtual data. Results indicate that this framework can positively impact data transparency, protect stakeholders' rights, and foster supply chain cooperation.
The main goal of this study is to assess the maturity of BDA-AI technology implemented by medical equipment suppliers in the healthcare industry. Furthermore, it aims to measure the influence of this technology on the supplier's sustainable competitive advantage, which is mediated by operational business engagement and knowledge processes. This study utilised a cross-sectional design and an explanatory survey as a deductive method for hypothesis formation. The principal data gathering strategy entailed the self-administration of a questionnaire to medical equipment suppliers situated in the GCC. Out of 656 questionnaires distributed to medical equipment vendors, 483 were deemed usable, resulting in a response rate of 73.6%. The study employed Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN) methodologies to assess the collected data.
This study takes the retail enterprises listed on the Shanghai and Shenzhen A-shares as the research objects, selects the annual report data and financial indicators data of 54 listed retail enterprises from 2016 to 2023, and uses the double fixed effect model to conduct an in-depth analysis of the impact of digital technology application on the growth ability of retail enterprises. The results reveal that the application of digital technology has a significant positive impact on the growth ability of retail enterprises. Further mediation effect tests show that supply chain efficiency plays a partial mediating role between the application of digital technology and the growth ability of retail enterprises. In addition, the results of heterogeneity tests show that the application of digital technology has a significant positive promoting effect on the growth ability of enterprises in the eastern region, central region, and private enterprises.
This review examines a comprehensive volume on the ethical and governance challenges posed by artificial intelligence. The book is structured to guide the reader from foundational concepts-such as accountability, bias, and explainability-through evolving regulatory responses in key regions like the EU and US, and finally to in-depth, sector-specific analyses. These analyses cover the ethical implications of AI in diverse fields including autonomous vehicles, digital finance, education, smart cities, video games, and social robotics. The review concludes that the work serves as a crucial multidisciplinary resource, successfully arguing for the necessity of contextually grounded ethical frameworks and inclusive global standards to govern AI's rapid integration into society.
Supply chain disruptions pose escalating threats to operational continuity and competitive advantage, necessitating effective resilience mechanisms. This study investigated the effectiveness of collaborative strategies in enhancing supply chain resilience through agent-based simulation comparing four recovery approaches: no coordination, backup supplier, rapid response, and combined collaborative mechanisms. A 120-day simulation incorporating multipoint disruption scenarios across a multitier supply chain network revealed that the combined collaborative strategy achieved 34.6% faster recovery, maintained 6.9 percentage points higher order fulfillment rates, reduced cost volatility by 36.3%, and demonstrated 139% higher system stability compared to baseline no-coordination approaches. Statistical validation confirmed highly significant differences across all performance metrics. Critically, findings demonstrate that response speed rather than capacity redundancy emerges as the binding constraint for moderate-duration disruptions, and synergistic effects generate 6-15% performance premiums exceeding additive predictions from individual mechanisms. Results provided simulation-based validation for integrated collaborative resilience investments and offered actionable guidance for prioritizing response agility over resource redundancy.
The logistics industry plays an important role in economic development, and there is a strong interdependence between the logistics industry and regional economic growth. To better understand and evaluate this relationship, this study proposed a research project on the coordinated development of the logistics industry and regional economy based on a constrained clustering algorithm. This study used a k-means equilibrium constrained clustering algorithm and coupled coordination model to measure the coordination level between logistics development and regional economic growth. The experimental results indicated that, although there is mutual influence between the logistics industry and regional economy, their development speed varied at different stages. Even if these two systems develop well, achieving high coordination remains challenging due to their uneven growth rates. Only when the development of the logistics industry is closely integrated with the development of the regional economy can true coordinated development be achieved.
This research reveals that AR technology reshapes multi-stakeholder collaboration through virtual information overlay and real-time interaction, while its technical performance exhibits a nonlinear evolutionary trajectory characterized by diminishing marginal returns and environmental perturbation sensitivity. A tiered incentive and cross-layer constraint mechanism is introduced, demonstrating that the fraud penalty multiplier p(t) scales positively with detection sensitivity η, thereby enhancing carrier compliance. Regulatory intervention through dynamic calibration of accident penalties and technical mandates can effectively mitigate accident risks in high-hazard zones. Validated by Monte Carlo simulations using empirical data from a chemical industrial park in eastern China, the model identifies optimal technology deployment strategies under extreme weather conditions: low-risk regions favor a baseline quality threshold with flexible cost allocation, whereas high-risk areas necessitate compulsory.
This study presents the Multi-Layer Fusion Network (MLFN), a data-driven framework for enhancing precision marketing in logistics. It combines Temporal Encoder for data alignment, Cross-Domain Attention for cross-event associations, and Lightweight Auto-Encoder for compression and denoising. The framework incorporates an online gradient gating mechanism and Adaptive Recommendation Engine (AIE) to optimize real-time performance and improve customer conversion and repurchase rates. Results show significant reductions in inference latency and discount wastage, with a 12% increase in conversion and 15% in repurchase rates. MLFN outperforms baseline models in AUC, recall, and F1 scores. This framework offers an efficient solution for personalized marketing in logistics, with strong theoretical and practical value for digital transformation.
Ensuring the integrity and security of CAD design data is critical in digital supply chains, where centralized systems face risks of tampering, unauthorized access, and transmission vulnerabilities. This study proposes a blockchain-based framework to enhance data integrity, traceability, and network security in CAD environments. XML is used to standardize design data, which is encrypted and stored in a decentralized manner using blockchain and distributed file systems, while smart contracts enforce access control and validation. Experimental results show that the approach effectively prevents data tampering, ensures secure information sharing, and improves transmission efficiency—making it a promising solution for secure collaboration across supply chain ecosystems. The framework supports version control and auditability of design changes, enhancing transparency among distributed engineering teams. By integrating blockchain with CAD workflows, the system strengthens trust and data reliability in digital product development within complex supply networks.
Abnormal cigarette flows pose significant challenges to tobacco market regulation, exacerbated by fragmented data and slow inter-departmental coordination. This study proposes a blockchain-based framework to improve traceability and regulatory efficiency. Key features include decentralized consensus, immutable record-keeping, and smart contracts for automated compliance checks. Results show a detection latency reduction of 87% (from 6.2 to 0.8 days), improved departmental collaboration scores (from 5.3 to 9.2), and an 84% decrease in false positives. By integrating audit, licensing, and sales monitoring, the framework enhances overall supply chain governance and demonstrates the potential for broader application in high-stakes regulatory environments.
This study explores the strategic integration of big data analytics and the Balanced Scorecard (BSC) framework to optimize enterprise performance across financial and non-financial dimensions. By combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Analytic Network Process (ANP), the research identifies causal relationships among key big data initiatives and prioritizes their impact on organizational performance. Empirical analysis, based on expert judgments from nine valid questionnaires collected in Guangdong, China, reveals that non-financial dimensions (learning and growth, internal processes) act as causal drivers for financial outcomes. Among the nine evaluated big data strategies, appointing data-savvy leaders and implementing customer-centric analytics (e.g., personalized recommendations, customer behavior insights) are identified as the most critical enablers of performance improvement.
