Unraveling technological barriers to blockchain in supply chains: a systems and complexity-based modeling approach

Purpose This study aims to systematically explore the complex interrelationships among technological barriers that hinder the adoption of blockchain technology (BT) in supply chain management (SCM). While many studies identify barriers, their interdependencies and hierarchical causal dynamics remain unclear. This study, therefore, empirically models these relationships to uncover underlying structures and influence pathways. Design/methodology/approach Grounded in complexity theory and general systems theory (GST), this study applies interpretive structural modeling (ISM) and Fuzzy MICMAC to analyze influence–dependence relationships among 15 key technological barriers. Data from 15 seasoned BT and SCM experts were gathered through structured, multi-stage interviews. This integrated approach transforms expert insights into a hierarchical, system-wide framework that highlights propagation patterns and systemic interactions among barriers. Findings Results show that foundational barriers, such as fork problems, interoperability issues and unreliable data, exert strong driving power, initiating cascading effects throughout BT implementation. Conversely, underperforming functionality appears highly dependent, representing a systemic consequence. The framework identifies four clusters (autonomous, driver, linkage and dependent) that clarify the roles, positions and interconnections of technological barriers in the BT adoption process. Originality/value This study advances literature by empirically applying complexity theory and GST through ISM and/or Fuzzy MICMAC for a hierarchical analysis focused solely on technological barriers. Unlike prior works that list isolated factors, it reveals the causal structure within the technological domain, offering a strategic tool for refining theory and targeted interventions in BT-driven SCM transformation.