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Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
International Journal of Production Economics, Volume: 250, Start page: 108618
Swansea University Author: Yogesh Dwivedi
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This study attempts to understand the role of artificial intelligence-driven big data analytics capability in humanitarian relief operations. These disasters play an important role in mobilizing several organizations to counteract them, but the organizations often find it hard to strike a fine balan...
|Published in:||International Journal of Production Economics|
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This study attempts to understand the role of artificial intelligence-driven big data analytics capability in humanitarian relief operations. These disasters play an important role in mobilizing several organizations to counteract them, but the organizations often find it hard to strike a fine balance between agility and resilience. Operations Management Scholars’ opinion remains divided between responsiveness and efficiency. However, to manage unexpected events like disasters, organizations need to be agile and resilient. In previous studies, scholars have adopted the resource-based view or dynamic capability view to explain the combination of resources and capabilities (i.e., technology, agility, and resilience) to explain their performance. However, following some recent scholarly debates, we argue that organizational theories like the resource-based view or dynamic capability view are not suitable enough to explain humanitarian supply chain performance. As the underlying assumptions of the commercial supply chain do not hold true in the case of the humanitarian supply chain. We note this as a potential research gap in the existing literature. Moreover, humanitarian organizations remain sceptical regarding the adoption of artificial intelligence-driven big data analytics capability (AI-BDAC) in the decision-making process. To address these potential gaps, we grounded our theoretical model in the practice-based view which is proposed as an appropriate lens to examine the role of practices that are not rare and are easy to imitate in performance. We used Partial Least Squares (PLS) to test our theoretical model and research hypotheses, using 171 useable responses gathered through a web survey of international non-governmental organizations (NGOs). The findings of our study suggest that AI-BDAC is a significant determinant of agility, resilience, and performance of the humanitarian supply chain. Furthermore, the reduction of the level of information complexity (IC) on the paths joining agility, resilience, and performance in the humanitarian supply chain. These results offer some useful theoretical contributions to the contingent view of the practice-based view. In a way, we have tried to establish empirically that the humanitarian supply chain designs are quite different from their commercial counterparts. Hence, the use of a resource-based view or dynamic capability view as theoretical lenses may not help capture true perspectives. Thus, the use of a practice-based view as an alternative theoretical lens provides a better understanding of humanitarian supply chains. We have further outlined the limitations and the future research directions of the study.
Artificial intelligence; Big data analytics; Culture; Supply chain agility; Supply chain resilience; Humanitarian supply chain; Practice-based view; Humanitarian operations management; PLS-SEM
Faculty of Humanities and Social Sciences