Technological adoption in green practices as a mediator between green supply chain practices and operational performance

Md Mehedi Hasan Emon — 2025-12-26

Purpose: This study investigates the mediating role of Technological Adoption in Green Practices (TAGP) in strengthening the relationship between Green Supply Chain Management (GSCM) practices, Green Procurement (GP), Eco-Friendly Packaging (EFP), Waste Management Efficiency (WME), and Supplier Environmental Collaboration (SEC) and Operational Performance (OP) in Bangladesh’s agro-processing and food industry. It aims to identify how technology integration enhances the effectiveness of sustainable practices and overall operational efficiency.

Design/methodology/approach: A quantitative research approach was employed, collecting data through structured questionnaires distributed to 325 agro-processing firms in Bangladesh. After screening, 243 valid responses were analyzed using SmartPLS for Partial Least Squares Structural Equation Modeling (PLS-SEM). This method allowed for the evaluation of both measurement and structural models, assessing direct and mediated relationships among GSCM practices, TAGP, and OP.

Findings: EFP and SEC had significant positive effects on OP. Technological Adoption in Green Practices also demonstrated a significant direct influence on OP. Mediation analysis confirmed TAGP as a significant mediator in all four green practices performance relationships, revealing that technology integration is vital to transforming green initiatives into measurable operational outcomes.

Practical implications: The study offers valuable insights for managers and policymakers, emphasizing the necessity of technological investment to optimize green initiatives and improve performance within the agro-processing sector.

Social implications: Promoting sustainable and tech-enabled practices can contribute to environmental conservation and resource efficiency, benefiting both society and the economy.

Originality/value: This study uniquely integrates TOE, RBV, and NRBV frameworks to empirically demonstrate the pivotal role of technology in enhancing green supply chain performance in a developing country context.

Evaluating SC 5.0 preparedness through human–AI collaboration and digital maturity in indian capital-intensive PSUS

Pankaj Kumar Jha — 2025-12-26

Purpose: This study aims to develop and empirically validate a hybrid multi-method framework to assess Supply Chain 5.0 (SC 5.0) preparedness in India’s capital-intensive engineering Public Sector Undertakings (PSUs). The framework evaluates readiness across five dimensions: Technological Readiness, Leadership & Change Management, Human–AI Collaboration Capability, Workforce Digital Skills and AI Literacy, and Organizational Learning & Innovation.

Methodology: A quantitative research design was employed using primary survey data from 485 professionals across six capital-intensive PSUs. The analysis was conducted in three phases: (i) Structural Equation Modeling (SEM) to test causal relationships and validate hypotheses, (ii) Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to construct a SC5.0 Readiness Index and rank PSUs, and (iii) Artificial Neural Networks (ANN) to predict and cross-validate the robustness of readiness drivers.

Findings: SEM results reveal that Technological Readiness (β = 0.331, p < 0.01), Workforce Skills (β = 0.298, p < 0.01), and Human–AI Collaboration Capability (β = 0.279, p < 0.01) significantly influence SC 5.0 readiness, with leadership commitment moderating the impact of digital infrastructure on transformation outcomes (p < 0.05). TOPSIS highlights BHEL (0.741), NTPC (0.703), and GAIL (0.689) as top-performing PSUs, while ANN validation achieved 91.48% accuracy, confirming model robustness.

Research Implications: The study advances theoretical understanding by integrating structural modelling with machine learning-based predictive analytics, offering a holistic approach to assessing SC 5.0 readiness. The high predictive accuracy of the ANN model (R² = 0.8841; equivalent to 91.48% accuracy) underscores the robustness of the framework, demonstrating that leadership, agility, and change-handling dynamics can be reliably forecast as critical enablers of SC 5.0. This establishes a methodological precedent for combining causal, prescriptive, and predictive approaches in future supply chain transformation research.

Practical & Social Implications: The findings provide actionable insights for PSU managers and policymakers to enhance digital transformation, workforce upskilling, and human–AI collaboration, thereby improving operational resilience and supporting sustainable industrial growth.

Brazilian Journal of Operations & Production Management

Technological adoption in green practices as a mediator between green supply chain practices and operational performance

Evaluating SC 5.0 preparedness through human–AI collaboration and digital maturity in indian capital-intensive PSUS