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作 者:何新江 王钦鹏 杨铭杰 He Xinjiang;Wang Qinpeng;Yang Mingjie(School of Economics,Zhejiang University of Technology,Hangzhou 310023,China;Zhejiang University of Technology Institute for Industrial System Modernization,Hangzhou 310023,China)
机构地区:[1]浙江工业大学经济学院,杭州310023 [2]浙江工业大学现代化产业体系研究院,杭州310023
出 处:《技术经济》2025年第2期97-114,共18页Journal of Technology Economics
基 金:国家自然科学基金青年科学基金“多因素随机利率模型下利率互换期权的定价研究”(12101554);浙江省属高校基本科研业务费“流动性风险的建模及其应用研究”(GB202103001)。
摘 要:本文基于2011—2022年沪深A股上市企业的财务报告数据,系统地探讨了企业人工智能发展水平对新质生产力的影响。研究结果明确显示,人工智能的广泛应用显著地提升了企业的新质生产力。通过深入的机制分析,本文揭示了人工智能如何通过两大途径提升生产力:一是促进企业数字化和技术创新,二是通过提高企业营运效率(包括固定资产周转率和营运资本周转率)来优化资源配置效率。异质性分析进一步表明,人工智能对盈利增长、中小企业及非国有企业的正面影响尤其显著。调节效应分析进一步揭示在不同融资环境和投资效率的背景下,人工智能对新质生产力的作用存在显著差异。这些发现不仅为理解人工智能在不同企业类型中的具体作用提供了新视角,也为政策制定者和企业管理者提供了如何利用人工智能技术推动高质量企业发展的实证依据。Based on financial report data from A-share listed companies in Shanghai and Shenzhen Stock Exchanges from 2011 to 2022,a systematic investigation was conducted on the impact of enterprise artificial intelligence development level on new quality productive forces.The research results explicitly demonstrated that the widespread application of artificial intelligence significantly enhanced enterprise new quality productive forces.Through in-depth mechanism analysis,two major pathways were identified through which artificial intelligence improved new quality productive forces:promoting intelligent innovation(digitalization and technological innovation)and optimizing resource allocation efficiency through improved operational efficiency(including fixed asset turnover and working capital turnover).Heterogeneity analysis further indicated that artificial intelligence exhibited particularly significant positive effects on profit growth,small and medium-sized enterprises,and non-state-owned enterprises.Moderation effect analysis revealed substantial differences in artificial intelligence s impact on new quality productive forces under varying financing environments and investment efficiency conditions.These findings provided novel perspectives for understanding artificial intelligence s specific roles across different enterprise types.The empirical evidence also offered guidance for policymakers and enterprise managers on leveraging artificial intelligence technology to promote high-quality enterprise development.
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