Artificial Intelligence and Humanitarian Supply Chain Resilience: Mediating Effect of Localized Logistics Capacity

Dr Wilbroad Aryatwijuka (), Assoc Prof Henry Mutebi (), Pamela Nagawa (), Assoc Prof Benjamin Tukamuhabwa (), Samuel Mayanja Ssekajja (), Kyomuhangi Diana () and Allan Akashabaluhanga ()

European Journal of Technology, 2024, vol. 8, issue 5, 1 - 20

Abstract: Purpose: The study examines the mediating effect of Localized logistics capacity on the association between Artificial intelligence and Humanitarian supply chain resilience among Humanitarian organizations. Materials and Methods: A cross-sectional survey and descriptive study involving 88 humanitarian firms in Uganda whose staff involved in relief operations were purposively selected. Data was analyzed using the Partial least squares structural equation modeling to test hypotheses and ascertain the mediating effect. Findings: The study indicates a significant indirect effect of Artificial Intelligence (AI) on humanitarian supply chain resilience (HSCR) and a direct impact of Artificial Intelligence on Localized logistics capacity (LLC). The results also confirmed a full mediation effect of LLC on the association between AI and HSCR. Implications to theory, Practice and Policy: The present study contributes deeper insights into how humanitarian organizations can develop adaptive capacities to navigate the complex landscape of humanitarian operations since it was established that logistics capacity is a conduit between artificial intelligence and humanitarian supply chain resilience. Managers should adopt artificial intelligence and build strong relationships will local logistics suppliers to achieve humanitarian supply chain resilience practices. Considering that this was a survey, a case study design with semi-structured research tools be used to have an in-depth understanding of the variables under study.

Keywords: Artificial Intelligence (AI); Humanitarian Supply Chain Resilience (HSCR); Localized Logistics Capacity (LLC); Humanitarian Firms (search for similar items in EconPapers)
Date: 2024
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