Benchmarking blockchain adoption enablers in automotive supply chains: a hybrid machine learning–TISM–MICMAC framework

Purpose Blockchain technology is increasingly viewed as a key enabler of transparency, traceability and resilience in automotive supply chains; however, adoption priorities differ markedly between electric vehicle (EV) and traditional vehicle (TV) manufacturers. This study aims to benchmark and compare blockchain adoption enablers across EV and TV supply chains and to develop a structured, sector-specific decision framework to support managerial and strategic adoption decisions. Design/methodology/approach The study proposes a hybrid multi-method framework integrating supervised machine learning techniques (random forest, permutation importance and BORUTA) with total interpretive structural modeling and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement analysis. Expert evaluations from 100 professionals across the automotive and digital transformation domains were analyzed to shortlist and structurally model high-impact blockchain adoption enablers. Findings From an initial set of 30 enablers, 12 critical enablers were identified and hierarchically structured. Results indicate that EV manufacturers prioritize sustainability, traceability and innovation, whereas TV manufacturers emphasize cost efficiency, cybersecurity and regulatory compliance. Robust cybersecurity infrastructure, regulatory governance and risk management emerge as foundational enablers across both sectors. Research limitations/implications The study relies on expert judgment within the automotive sector, which may limit generalizability to other industries. Future research could extend the framework by incorporating Internet of Things and artificial intelligence enablers and validating the model across additional industrial contexts. Practical implications The proposed framework provides managers and policymakers with a structured roadmap for prioritizing blockchain investments, aligning adoption strategies with sector-specific objectives and targeting high-leverage enablers to accelerate digital transformation in automotive supply chains. Social implications By supporting transparent, traceable and secure supply chain operations, blockchain adoption can enhance sustainability performance, regulatory accountability and stakeholder trust across automotive ecosystems. Originality/value This study offers a novel integration of machine learning-based feature selection with interpretive structural modeling to benchmark blockchain adoption enablers across EV and TV manufacturers, delivering a decision-oriented, sector-comparative modeling framework for digital supply chain transformation.