A Decision-Oriented Framework for Data Governance in Smart Airports: An Entropy–DEMATEL Approach

The rapid digitalization of airport operations has transformed airports into complex data-driven ecosystems, where effective data governance has become a critical challenge. While prior studies have explored big data applications in aviation, limited attention has been given to the interdependent structure of data governance challenges at the airport level. This study proposes a decision-oriented analytical framework integrating the entropy and DEMATEL methods in two sequential stages systematically identify, prioritize, and model the causal interactions among key big data challenges in airport ecosystems. Using Istanbul Airport (IGA) as a case study, an initial, expert-based assessment was conducted to assess nine critical challenges, including data privacy, integration, organizational culture, and regulatory compliance. The results revealed that data privacy and security is not only the most critical factor but also a primary causal driver, influencing multiple downstream challenges such as ethical considerations and regulatory compliance. The findings further demonstrate that technical and organizational barriers are strongly interconnected, requiring sequenced, system-level interventions rather than isolated solutions. By combining objective weighting with causal analysis, this study contributes to the literature by providing a holistic and actionable decision support framework for airport data governance. The proposed approach offers practical insights for airport authorities and policymakers to design more resilient, secure, and data-driven operational environments.