Modeling Technological Enablers for Blockchain in Automotive Supply Chains: A Multi-Method Approach

The automotive supply chain is undergoing a profound digital transformation, with blockchain technology emerging as a promising solution to address critical challenges related to traceability, security, and interoperability. Despite its potential, the practical adoption of blockchain in this sector remains limited, often hindered by fragmented technological readiness and a lack of structured understanding of key enablers. This study aims to identify, validate, and structure the technological enablers that facilitate blockchain implementation in the automotive domain. Drawing from a systematic literature review and expert input, an initial set of 30 enablers was screened using the Fuzzy Delphi Method (FDM) to establish consensus on their relevance. A total of 12 enablers were accepted and subsequently analyzed using Interpretive Structural Modeling (ISM) to uncover their contextual interrelationships and hierarchical influence. To further categorize these enablers, MICMAC analysis was applied, mapping them into driver, linkage, and dependent categories based on their driving and dependence power. The findings reveal a multi-level structure of technological readiness, where core infrastructure elements—such as system integration, platform scalability, and technical standards—serve as foundational drivers, while trust-related and performance-focused enablers emerge as dependent outcomes. The study offers a strategic roadmap for blockchain deployment, emphasizing the prioritization of high-leverage enablers in early implementation stages. By integrating FDM, ISM, and MICMAC, this research advances both theoretical understanding and managerial practice, providing a robust framework for blockchain adoption in complex industrial ecosystems. The study concludes with recommendations for future research, including the incorporation of organizational and environmental dimensions.