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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

Rameshwar Dubey Orcid Logo, David J. Bryde, Yogesh Dwivedi Orcid Logo, Gary Graham, Cyril Foropon

International Journal of Production Economics, Volume: 250, Start page: 108618

Swansea University Author: Yogesh Dwivedi Orcid Logo

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Abstract

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...

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Published in: International Journal of Production Economics
ISSN: 0925-5273
Published: Elsevier BV 2022
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Operations Management Scholars&#x2019; 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. 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spelling 2023-01-06T16:39:40.4407495 v2 60882 2022-08-24 Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2022-08-24 BBU 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. Journal Article International Journal of Production Economics 250 108618 Elsevier BV 0925-5273 Artificial intelligence; Big data analytics; Culture; Supply chain agility; Supply chain resilience; Humanitarian supply chain; Practice-based view; Humanitarian operations management; PLS-SEM 28 8 2022 2022-08-28 10.1016/j.ijpe.2022.108618 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2023-01-06T16:39:40.4407495 2022-08-24T10:28:26.1011525 Faculty of Humanities and Social Sciences School of Management - Business Management Rameshwar Dubey 0000-0002-3913-030x 1 David J. Bryde 2 Yogesh Dwivedi 0000-0002-5547-9990 3 Gary Graham 4 Cyril Foropon 5 60882__26204__c1b82811e1cb413c9881f4120d1c51a5.pdf 60882.pdf 2023-01-06T16:38:18.1376661 Output 906117 application/pdf Version of Record true © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license true eng https://creativecommons.org/licenses/by/4.0/
title Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
spellingShingle Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
Yogesh Dwivedi
title_short Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
title_full Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
title_fullStr Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
title_full_unstemmed Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
title_sort Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Yogesh Dwivedi
author2 Rameshwar Dubey
David J. Bryde
Yogesh Dwivedi
Gary Graham
Cyril Foropon
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container_start_page 108618
publishDate 2022
institution Swansea University
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doi_str_mv 10.1016/j.ijpe.2022.108618
publisher Elsevier BV
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department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
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description 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.
published_date 2022-08-28T04:19:22Z
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