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JOURNAL OF ZHEJIANG UNIVERSITY  2024, Vol. 54 Issue (6): 5-23    DOI: 10.3785/j.issn.1008-942X.CN33-6000/C.2023.07.141
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How Does Artificial Intelligence Influence Supply Chain Resilience: Evidence from Listed Companies
Deng Huihui1, Liu Yujia1, Wang Qiang2
1.Institute of International Economy, Academy of China Open Economy Studies, University of International Business and Economics, Beijing 100029, China
2.School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China

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Abstract  Artificial intelligence (AI) has accelerated the transformation of traditional supply chains into intelligent ones, offering a way to find and predict supply chain risks and avoid disruptions. However, there is little evidence on whether AI applications have improved supply chain resilience (SCR). This study explores the impact of AI on supply chain resilience using a panel dataset from 3,240 listed companies from 2017 to 2021. We explore its mechanism from two aspects: the effectiveness of internal resource allocation and the diversity of external resource structures. The results show that: (1) Artificial intelligence could shorten inventory turnover days and improve supply chain resilience. (2) Artificial intelligence could mitigate the ineffectiveness of capital allocation. When enterprises invest inefficiently, artificial intelligence will have a greater influence on supply chain resilience. Particularly, compared to enterprises that underinvest, AI has a greater potential impact on supply chain resilience in companies that overinvest. (3) The bullwhip effect does not affect the role of artificial intelligence in improving supply chain resilience, suggesting that the effect of AI in enhancing supply chain resilience is independent of the degree of deviation from supply and demand fluctuations. In other words, artificial intelligence doesn’t mitigate the problems posed by the lack of information validity. (4) AI could improve supply chain resilience by alleviating inadequate diversity of external resources. When sourcing and distribution channels are not diverse, AI has a greater impact on supply chain resilience. (5) AI is more likely to improve supply chain resilience for large-scale firms, private firms, and firms in the Pearl River Delta city cluster and the Yangtze River Delta city cluster. However, for MSMEs, SOEs, Sino-foreign joint ventures, and firms in the Beijing-Tianjin-Hebei city cluster, the influence of AI on supply chain resilience is not significant.Our study has three contributions First, existing research is primarily theoretical and lacks empirical tests on the marginal contributions of artificial intelligence. This paper studies the level of AI in enterprises from two lenses, intention and action. And it answers questions like “Does AI improve supply chain resilience” and “How much of an improvement does artificial intelligence make”. Second, considerable studies have analyzed the impact of resource redundancy on supply chain resilience, ignoring the effectiveness of resource allocation. This paper fills this gap and proposes mechanisms of artificial intelligence to improve supply chain resilience from a resource perspective. Furthermore, we incorporate the structural diversity of external resources into the research framework and extend the perspective of resource-based theory in supply chains. Third, this paper further distinguishes differences in the firm size, property rights, and city cluster location. It assists businesses in understanding the true impact and mechanism of AI on supply chain resilience, allowing them to modify their supply chain management strategy accordingly.
Key wordsartificial intelligence      supply chain resilience      resource allocation      intelligent supply chain     
Received: 14 July 2023     
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Deng Huihui
Liu Yujia
Wang Qiang
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Deng Huihui,Liu Yujia,Wang Qiang. How Does Artificial Intelligence Influence Supply Chain Resilience: Evidence from Listed Companies[J]. JOURNAL OF ZHEJIANG UNIVERSITY, 2024, 54(6): 5-23.
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https://www.zjujournals.com/soc/EN/10.3785/j.issn.1008-942X.CN33-6000/C.2023.07.141     OR     https://www.zjujournals.com/soc/EN/Y2024/V54/I6/5
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