Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance: An Empirical Analysis on an Emerging Market
DC Field | Value | Language |
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dc.contributor.author | Wang, Mengmeng | - |
dc.contributor.author | Pan, Xiaoming | - |
dc.date.accessioned | 2023-01-11T01:40:04Z | - |
dc.date.available | 2023-01-11T01:40:04Z | - |
dc.date.created | 2023-01-06 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 2071-1050 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86519 | - |
dc.description.abstract | The global supply chain has suffered an unprecedented impact Affected by multiple factors such as anti-globalization, rising trade protectionism and the COVID-19 pandemic. Based on the technology-organization-environment framework and resource-based theory, this study attempts to explore and analyze what drives a firm’s willingness to adopt artificial intelligence technology and how such willingess to adopt artificial intelligence technology may contribute to supply chain resilience and supply chain performance. Using survey data collected from 318 firms in China, we empirically test our arguments and hypotheses through the structural equation modeling approach. The results suggest that the relative advantages of enterprise artificial intelligence technology, supply chain collaboration, and environmental uncertainty are the three major factors affecting the adoption of artificial intelligence technology, which subsequently provide a positive impact on supply chain resilience and supply chain performance. This study expands the application field and scope of artificial intelligence technology, fills the relatively large gap in the research on the behavior of enterprise users adopting artificial intelligence technology in the supply chain field. This provides a useful reference for enterprises to adopt artificial intelligence technology. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | Sustainability | - |
dc.title | Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance: An Empirical Analysis on an Emerging Market | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000902856400001 | - |
dc.identifier.doi | 10.3390/su142416836 | - |
dc.identifier.bibliographicCitation | Sustainability, v.14, no.24 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85144908125 | - |
dc.citation.title | Sustainability | - |
dc.citation.volume | 14 | - |
dc.citation.number | 24 | - |
dc.contributor.affiliatedAuthor | Wang, Mengmeng | - |
dc.contributor.affiliatedAuthor | Pan, Xiaoming | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | artificial intelligence technology | - |
dc.subject.keywordAuthor | environmental uncertainty | - |
dc.subject.keywordAuthor | supply chain collaboration | - |
dc.subject.keywordAuthor | supply chain performance | - |
dc.subject.keywordAuthor | supply chain resilience | - |
dc.subject.keywordPlus | RESOURCE-BASED VIEW | - |
dc.subject.keywordPlus | MANAGEMENT | - |
dc.subject.keywordPlus | BUSINESS | - |
dc.subject.keywordPlus | ADOPTION | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordPlus | CAPABILITY | - |
dc.subject.keywordPlus | STRATEGY | - |
dc.subject.keywordPlus | DETERMINANTS | - |
dc.subject.keywordPlus | INNOVATION | - |
dc.subject.keywordPlus | VARIANCE | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalWebOfScienceCategory | Green & Sustainable Science & Technology | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Environmental Studies | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
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