Data-Driven Modeling of Blockchain Adoption, Supply Chain Integration, and Green Innovation Outcomes in Manufacturing Firms
Main article
Abstract
Blockchain adoption is increasingly promoted as a digital infrastructure for sustainable manufacturing, yet managers still lack practical evidence on how blockchain data capabilities become measurable green innovation outcomes. This study develops a data-driven modeling framework that links blockchain adoption, supply chain integration, and green innovation in manufacturing firms. Drawing on resource orchestration theory and a systemic supply chain perspective, the article argues that blockchain does not automatically create environmental value; rather, its value is realized when trusted, traceable, and analyzable data are converted into interorganizational integration routines and then directed toward green product and process innovation. A survey-style analytical dataset of 397 manufacturing firms was constructed to reflect realistic conditions in medium-technology and high-pollution manufacturing sectors, including metal processing, chemicals, plastics, electronics components, and industrial equipment. The study combines confirmatory measurement analysis, partial least squares structural modeling, predictive importance analysis, and sensitivity analysis. The results show that supply chain integration is the dominant transmission mechanism connecting blockchain adoption with green innovation outcomes. Blockchain adoption has a strong positive effect on integration, integration strongly predicts green innovation, and the direct blockchain-to-innovation path becomes weak after integration is included. Supply chain trust strengthens the blockchain-integration path, while task complexity weakens it. Green digital learning orientation strengthens the integration-innovation path by directing shared data resources toward sustainable experimentation. The predictive analysis further shows that traceability intensity, partner data-sharing quality, supplier environmental visibility, and joint green planning are the most important indicators for improving green innovation performance. The study contributes to business data analytics by translating a blockchain sustainability problem into a measurable analytics framework and offers practical guidance for managers seeking to build data governance, partner integration, and learning routines around blockchain-enabled green transformation.
