Causal mapping of challenges in edge–cloud continuum for real-time decision systems

Purpose This study examines the systemic challenges associated with adopting edge–cloud continuum architectures for real-time decision systems and identifies the underlying causal drivers and dependent outcomes shaping sustainable deployment. Design/methodology/approach A Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method was employed to model causal relationships among eight key challenges: latency minimization, scalability and data growth, interoperability, cybersecurity and privacy, energy efficiency and cost, reliability and fault tolerance, network bandwidth, and regulatory governance. Pairwise influence assessments were collected from fifteen domain experts and aggregated using triangular fuzzy numbers. Sensitivity analysis was conducted to assess the structural robustness of the causal classifications. Findings The results indicate that latency minimization, scalability, energy efficiency, and reliability function as causal drivers, while governance, interoperability, cybersecurity, and bandwidth constraints emerge as effect-side outcomes. This demonstrates that performance-oriented technical enablers shape downstream institutional and coordination challenges within the edge–cloud continuum. Practical implications The findings offer a decision-support roadmap for managers and policymakers to prioritize upstream technical drivers, enabling more effective mitigation of downstream governance and security risks. Originality/value The study advances soft computing applications by providing a causal, system-level perspective on edge–cloud adoption, moving beyond isolated challenge rankings to reveal indirect influence structures in real-time digital infrastructures.