人工智能应用对企业供应链效率的影响OACHSSCD
The Impact of Artificial Intelligence Application on Enterprise Supply Chain Efficiency—Text Measurement Based on Large Language Models
在数字技术快速渗透、全球产业链深度调整的背景下,人工智能应用对企业供应链效率的赋能作用值得探讨.文章以2012-2024年我国A股上市企业的面板数据为研究样本,基于大语言模型对上市公司年报文本的分析构建人工智能应用的测度指标,考察人工智能应用对企业供应链效率的影响及作用机制.研究结果表明:人工智能应用可以明显提高企业供应链效率,该结论经过一系列稳健性检验后仍然成立;人工智能应用主要从两个方面对企业供应链效率产生影响,一是通过优化企业投资决策来抑制非效率投资,二是通过降低企业供应链的集中度来分散对单个供应商或客户的依赖;异质性分析发现,在非高科技企业、非国有企业中,人工智能应用对企业供应链效率的提升效应更为显著.
Against the background of rapid penetration of digital technology and deep adjustment of global industrial chains,the empowering effect of artificial intelligence applications on the efficiency of enterprise supply chains is worth exploring.This paper takes the panel data of A-share listed companies in China from 2012 to 2024 as the research sample,constructs the mea-surement indicators of artificial intelligence application based on the analysis of the annual report texts of listed companies by large language models,and examines the impact and mechanism of artificial intelligence application on the efficiency of enterprise supply chains.The research results are shown as below:The application of artificial intelligence can significantly improve enter-prise supply chain efficiency,and this conclusion still holds after multiple robustness tests.The application of artificial intelli-gence mainly affects enterprise supply chain efficiency in two ways:First,by optimizing corporate investment decisions and re-straining inefficient investment,Second,by reducing supply chain concentration and diversifying dependence on individual sup-pliers or customers.The results of the heterogeneity analysis reveal that the effect of artificial intelligence application on improving enterprise supply chain efficiency is more significant in non-high-tech enterprises and non-state-owned enterprises.
马云霄;李勇
对外经济贸易大学 国际经济贸易学院,北京 100029对外经济贸易大学 中国金融学院,北京 100029
管理科学
人工智能供应链效率非效率投资供应链集中度
artificial intelligencesupply chain efficiencyinefficient investmentsupply chain concentration
《统计与决策》 2026 (9)
36-41,6
国家自然科学基金面上项目(72373022)
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