The influence of Artificial Intelligence techniques on disruption management: does supply chain dynamism matter?

Samadhiya, Ashutosh, Yadav, Sanjeev, Kumar, Anil, Majumdar, Abhijit, Luthra, Sunil, Garza-Reyes, Jose Arturo and Upadhyay, Arvind (2023) The influence of Artificial Intelligence techniques on disruption management: does supply chain dynamism matter? Technology in Society. ISSN 0160-791X (In Press)

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Abstract / Description

Healthcare supply chains (HSCs) have been hard hit by disruptions such as COVID-19. Keeping pace during this interruption has been a significant challenge. HSCs' adaptability and collaboration using artificial intelligence techniques (AITs) have been recognized as key components of supply chain resilience for managing disruption. The current study aims to investigate how AITs might help improve HSC resilience (HSCR) by facilitating improved adoption and collaboration throughout its operation. Data was collected from the Indian healthcare sector and analyzed using the partial least squares structural equation modeling (PLS-SEM) technique. Additionally, an examination was conducted to determine the potential moderating role of supply chain dynamism (SCD) on the adoption and cooperation of HSCs with HSCR. The research shows that AITs encourage HSC adaptability and collaboration in their operations and that HSCR results from both factors. Moreover, SCD does not moderate the link between HSC adoption and HSCR but does moderate the link between HSC cooperation and HSCR. By enhancing HSC adaptability and effective collaboration with AI-enabled systems, AITs may help HSC stakeholders deal with disruptions in healthcare sector operations.

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence; Healthcare supply chain; Supply chain dynamism; Resilience; Adaptability; Collaboration
Subjects:	600 Technology > 650 Management & auxiliary services
Department:	Guildhall School of Business and Law
Depositing User:	Anil Kumar
Date Deposited:	16 Oct 2023 09:52
Last Modified:	16 Oct 2023 09:52
URI:	https://repository.londonmet.ac.uk/id/eprint/8819

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