A Robust Hybrid Multicriteria Decision-Making Approach to Barriers of AI Adoption in Air Cargo: Addressing Interdependencies, Prioritizing Challenges, and Proposing Strategic Solutions
本研究开发了一个混合多准则决策框架，结合模糊DEMATEL、AHP和模糊TOPSIS方法，识别并优先排序航空货运中AI采纳的关键障碍，评估缓解策略，发现高层管理承诺和技术基础设施是最重要因素。
Despite the transformative potential of Artificial Intelligence (AI) in logistics, its adoption in the air cargo industry remains limited and fragmented due to several unresolved challenges. This study aims to develop and validate a hybrid decision-support framework that diagnoses barriers, prioritizes them, and evaluates mitigation strategies. Drawing on expert insights, critical barriers were identified and prioritized using a hybrid multi-criteria decision-making approach. The study applies Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) to explore causal interrelationships, Analytic Hierarchy Process (AHP) to rank barrier significance, and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to evaluate strategies for overcoming them. Key challenges include inadequate top management commitment, poor Information Technology (IT) infrastructure, data quality concerns, and cybersecurity risks. Among these, top management commitment emerged as the most influential factor affecting organizational readiness and strategic alignment. Technology and infrastructure was the highest-ranked barrier category, with data quality and maintenance costs as major sub-factors. To address these, the study recommends enhancing leadership involvement, upgrading IT systems, and improving cybersecurity. Sensitivity analysis confirms the robustness of these strategies under varying conditions. This research offers a structured framework for AI adoption, providing practical guidance for industry stakeholders and contributing to the academic discourse on digital transformation in logistics.