Decoding circularity performance indicators in the nickel-battery electric vehicle supply chain: an artificial intelligence-driven model

This study develops a valid performance indicator structure for the circular nickel-battery in the electric vehicle supply chain (NB-EVSC) through an artificial intelligence (AI)-driven model. While prior studies have suggested performance metrics for circular NB-EVSC, there has been inadequate focus on a balanced performance structure that encompasses financial performance, customer and stakeholder dynamics, operational processes, and innovation, particularly in a context where governments and corporations continue to improperly prioritize economic outcomes. To address this deficiency, this study integrates a balanced scorecard with stakeholder assessment and employs an AI-driven approach utilizing BERT with a hybrid method incorporating fuzzy Delphi method, exploratory factor analysis and reliability test, fuzzy synthetic evaluation–decision-making trial and evaluation laboratory, and analytic network process to develop a hierarchical circular NB-EVSC performance structure and key attributes by considering the interrelationships among attributes.