Driven by the global digital economy and industrial restructuring, smart manufacturing guides manufacturing-sector transformation, with supply chain management evolving into an integrated and dynamic collaborative model. However, technological, organisational, and environmental factors exert non-linear and interactive effects on supply chain performance. Drawing on the TOE framework, this study selects 50 A-share listed pilot smart manufacturing enterprises (2022-2023) as the sample, and combines fsQCA with multiple linear regression and interaction term models to explore the multi-factor configurations leading to high supply chain performance. The results show that the linear effects of individual antecedent conditions are limited and unstable; each condition's performance contribution can only be effectively realised within appropriate configurational contexts. Furthermore, four distinct pathways are identified: governance and resource synergy, structure and resource synergy, structure driven and multi-support, and supply chain driven. These findings provide practical path references for enterprises and support targeted policy formulation for government agencies.
This study examines how food supply chain capabilities influence corporate waste reduction performance in Malaysia, with Food Security Project Management Practices (FSPMP) serving as a mediating mechanism. This study proposes a model that positions environmental capabilities as strategic resources grounded in the Natural Resource-Based View (NRBV). The data from 169 Malaysian food and beverage firms were analysed using Partial Least Squares Structural Equation Modelling. The results indicate that food safety, transportation systems, stakeholder engagement, and technological innovation significantly affect corporate performance in reducing waste. The findings reveal that FSPMP partially mediates the effects of food safety, stakeholder engagement, and technological innovation; however, it exerts a suppressive effect on transportation systems. Traceability systems demonstrate direct effects but no significant mediation, implying that they offer autonomous operational benefits independent of project management coordination. The findings position project management as a dynamic capability that converts supply chain capabilities into measurable sustainability outcomes.
Although research on supply chain resilience (SCR) has advanced considerably, governance-level antecedents remain underexplored. Drawing on social network theory and the dynamic capabilities view, this study investigates how interlocking director networks (IDNs), a form of informal inter-firm governance network, influence SCR. We argue that IDN centrality and structural holes provide information and resource advantages that can be translated into resilience outcomes. Using empirical data from Chinese A-share listed manufacturing firms, we find that firms occupying more central positions and spanning more structural holes within IDNs tend to exhibit stronger SCR. Information transparency and alleviated financing constraints mediate these relationships, while digital transformation and economic policy uncertainty strengthen the positive effects of IDN positions on SCR. This study advances SCR research by revealing how board-level governance networks shape resilience. Practically, the findings suggest that managers can strengthen SCR by translating network-derived information and resources into actionable supply chain responses.
This study identifies and prioritises readiness factors for small and medium-sized enterprises' adoption of artificial intelligence (AI)-enabled green trade. Focusing on export-capable Korean SMEs, it develops a literature-derived AHP framework covering trade digital infrastructure and data, transport and logistics AI deployment, green trade finance, regulatory governance, and SME adoption capability. Expert-based pairwise comparisons were used to derive local and global priority weights, while consistency ratios, sensitivity analysis, and a descriptive subgroup comparison were used to assess result reliability. The findings indicate that Trade Digital Infrastructure and Data is the highest-priority main criterion, followed by Transport and Logistics AI Deployment. At the sub-criteria level, Data Interoperability Infrastructure ranked first, followed by AI-Enabled Trade Documentation Automation, Financial Support for AI Adoption, and AI-Driven Load Factor and Demand Forecast Optimisation. The results suggest that AI-enabled green trade readiness is perceived primarily as a practical data and operational capability issue.
Emerging-market supply chains face growing ESG pressure, yet governance based on experiential trust often falls short in complex sustainability decisions. Drawing on the DECAS framework, this study examines how data-driven decision-making reshapes relational learning from experiential trust to computational trust and, in turn, improves ESG performance. Using panel data on Chinese A-share listed supply chain firms from 2015 to 2024, we find that DDDM significantly improves ESG performance. This effect works through two reinforcing channels: higher investment efficiency and stronger collaborative innovation. We also find that internationalisation strengthens the role of collaborative innovation. By introducing computational trust into the analysis, this study offers a clearer explanation of how data and analytics support sustainable supply chain governance. It extends the application of DECAS in ESG research and provides evidence that firms can improve sustainable performance by combining better resource allocation with stronger interorganizational innovation.
