Akademik Veri Yönetim Sistemi
International Journal of the Analytic Hierarchy Process, cilt.17, sa.2, ss.1-37, 2025 (Scopus)
This article explores the barriers to big data adoption and proposes strategic solutions for improving disaster risk resilience to overcome these barriers in humanitarian supply chains (HSCs). Its theoretical model is grounded on the resource-based view (RBV). Based on a combination of a literature review and interviews with experts from humanitarian organizations, 24 barriers to big data adoption were identified. These barriers span infrastructural, technological, managerial, financial, and human-related factors within the humanitarian supply chain. Additionally, eight strategies were defined as solutions to overcome these barriers. The study uses the Fermatean Fuzzy Analytic Hierarchy Process (FF-AHP) to obtain the weights of barriers and the Borda Social Choice Function. The findings offer valuable insights into evaluating solutions to address barriers to big data adoption. The study ranks the barriers based on their influence. The most significant barrier is the shortage of facilities to research and develop big data tools. Other critical barriers include high costs, lack of training facilities, data quality issues, and lack of government support. The results show that the most effective solutions for improving disaster risk resilience involve increasing IT infrastructure, developing strategic plans, and securing government support to overcome barriers to big data adoption. A comprehensive understanding of the barriers to big data adoption can provide policymakers and practitioners with a roadmap for enhancing disaster risk resilience and addressing challenges associated with the adoption of big data.