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Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic
International Journal of Logistics Management ; ahead-of-print(ahead-of-print):29, 2021.
Article in English | Web of Science | ID: covidwho-1309707
ABSTRACT
Purpose In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI's influence on supply chain risk mitigation (SCRM). Design/methodology/approach This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses. Findings This study's findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence. Originality/value This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Experimental Studies / Prognostic study Language: English Journal: International Journal of Logistics Management Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Experimental Studies / Prognostic study Language: English Journal: International Journal of Logistics Management Year: 2021 Document Type: Article