Modelling the information sharing risks in circular supply chain: An artificial intelligence data-driven text mining hybrid method

Abstract

An artificial intelligence (AI) data-driven text mining hybrid method is proposed to extract risk attributes, and strategic roadmaps are developed using a fuzzy synthetic evaluation method integrated with the decision-making trial and evaluation laboratory. Information sharing is crucial for enhancing transparency and coordination in the circular supply chain (CSC); however, it is also associated with various risks. Many studies have increasingly emphasized information sharing but neglected the perspective of sociotechnical systems and the role of information asymmetry in the CSC system. This study conceptualizes risks within the information-sharing framework as sociotechnical misalignments among people, processes, technology, and organizational structures in the machinery manufacturing industry in Thailand. This study reveals that information behavior risks, digital competency risks, and organizational structure risks are primary determinants in managing the risks of business information sharing in the CSC. The findings indicate that addressing agility capability misalignment, business model opacity constraints, environmental impact regulatory pressure, and miscoordination constitutes a set of implementable practice for improving practical risk management. This study offers novel insights into the risks of information sharing (RISs) in the CSC by highlighting sociotechnical misalignments in risk emergence and information symmetry.