Overcoming challenges in leveraging blockchain technology: Entropy-based q-rung orthopair fuzzy model for benchmarking application barriers

Blockchain technology has emerged as a transformative solution across industries, delivering enhanced transparency, security, and operational efficiency. Nevertheless, its adoption remains hindered by significant challenges, especially in complex, data-intensive domains such as logistics. This study introduces a novel integration of the entropy-based q-rung orthopair fuzzy compromise ranking of alternatives from distance to ideal solution (CRADIS) approach to systematically evaluate and prioritize key barriers to blockchain adoption. The innovation of this work lies in applying q-rung orthopair fuzzy sets which are particularly capable of handling higher degrees of uncertainty and hesitancy, and then integrated with entropy for objective criterion weighting and CRADIS for robust decision-making. A real-world case study is presented, involving five critical barriers, lack of legal and regulatory frameworks, high implementation costs, technological scalability issues, data privacy and security concerns, and cultural resistance to change evaluated against eight decision criteria. The entropy weighting revealed regulatory clarity (0.168) and security (0.154) as the most influential factors, while the CRADIS ranking identified a lack of legal frameworks as the top barrier. This framework provides a transparent, data-driven method for decision-makers to identify and prioritize adoption challenges, particularly in uncertain and multi-faceted environments. By demonstrating the model’s applicability and precision, the study contributes to the emerging body of literature on blockchain integration and supports organizations in navigating the transition towards decentralized technologies.