Resilience in Humanitarian Supply Chains: Addressing Artificial Intelligence and Big Data Hurdles Across Borders

The study aimed to evaluate the key barriers and challenges hindering AI-BDA adoption in humanitarian supply chains in Ghana and South Africa. Guided by the sociotechnical systems theory, a descriptive research design was employed, utilizing structured surveys to collect quantitative data from 200 supply chain professionals in Ghana and South Africa. SPSS was used to analyze trends and patterns in AI-BDA adoption barriers. The findings revealed that resource-related barriers, particularly skill gaps among staff and lack of technical expertise (mean = 3.09), represent the most significant challenges to AI-BDA adoption. These were closely followed by technical barriers such as data integration complexity and limited data accessibility, while organizational barriers like management support and resistance to change were found to be relatively less prominent. The study concludes that human resource development and technical infrastructure enhancement are more critical than organizational change management in implementing AI-BDA for humanitarian supply chains in these regions. It recommends targeted training programs, improved data governance frameworks, and increased policy support to facilitate AI-BDA integration in humanitarian operations. AI and big data analytics (BDA) have the potential to revolutionize humanitarian supply chains by improving efficiency, resilience, and decision-making in disaster response. However, research on the barriers to AI-BDA adoption in humanitarian supply chains, particularly in Africa, remains scarce. Existing studies have primarily focused on AI-BDA implementation in commercial supply chains, neglecting the unique challenges faced by humanitarian organizations. This study contributes to research by identifying and analyzing the barriers to AI-BDA adoption in humanitarian supply chains within Ghana and South Africa, two emerging economies with growing digital infrastructures. The study aimed to evaluate the key challenges hindering AI-BDA adoption in humanitarian supply chains and to provide evidence-based recommendations for overcoming these barriers. A descriptive research design was employed, utilizing structured surveys to collect quantitative data from supply chain professionals in Ghana and South Africa. SPSS was used to analyze trends and patterns in AI-BDA adoption barriers. The findings revealed that resource-related barriers, particularly skill gaps among staff and lack of technical expertise (mean = 3.09), represent the most significant challenges to AI-BDA adoption. These were closely followed by technical barriers such as data integration complexity and limited data accessibility, while organizational barriers like management support and resistance to change were found to be relatively less prominent. The study concludes that human resource development and technical infrastructure enhancement are more critical than organizational change management in implementing AI-BDA for humanitarian supply chains in these regions. It recommends targeted training programs, improved data governance frameworks, and increased policy support to facilitate AI-BDA integration in humanitarian operations.