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人工智能如何驱动企业绿色战略导向?——基于TOE框架的理论解释与经验检验

How does Artificial Intelligence Drive Enterprises Toward Green Strategic Orientation? Theoretical Explanation and Empirical Testing Based on the TOE Framework

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【作者】 陈明生白杨

【Author】 CHEN Ming-sheng;BAI Yang;School of Business,China University of Political Science and Law;

【通讯作者】 白杨;

【机构】 中国政法大学商学院

【摘要】 随着人工智能与实体经济的深度融合,其推动企业绿色发展的作用日益凸显。本文以中国2012-2023年上市公司为样本,检验企业人工智能水平对绿色战略导向的影响。基于TOE(Technology-Organization-Environment)分析框架,通过实证分析发现,企业人工智能水平与绿色战略导向之间呈现出明显的倒U型关系。机制检验表明,高管绿色认知与绿色持续创新不仅在企业人工智能水平对绿色战略导向的影响中存在独立中介效应,还在其中发挥链式中介作用。异质性分析显示该结果在非重污染和非高科技企业中更为显著。鉴于此,企业应建立动态适配的绿色战略调适机制,政府应结合行业特征、企业规模与技术成熟度分类施策,学界也需进一步加强非线性机制识别,挖掘多重边界条件。

【Abstract】 With the deep integration of artificial intelligence and the real economy, its role in promoting green development among enterprises has become increasingly prominent. This study examines the impact of corporate AI proficiency on green strategic orientation using listed companies in China from 2012 to 2023 as samples. Based on the TOE(Technology-Organization-Environment) analytical framework, empirical analysis reveals a distinct inverted U-shaped relationship between corporate AI proficiency and green strategic orientation. Mechanism testing indicates that executives’ green cognition and sustained green innovation not only exhibit independent mediating effects in the influence of corporate AI proficiency on green strategic orientation but also play a cascading mediating role. Heterogeneity analysis demonstrates that these findings are more pronounced in non-heavy-polluting and high-tech enterprises. In light of this, enterprises should establish dynamically adaptive green strategy adjustment mechanisms, while governments should implement tailored policies considering industry characteristics, firm size, and technological maturity. The academic community should further enhance the identification of nonlinear mechanisms and explore multiple boundary conditions.

【基金】 教育部规划基金项目“人工智能发展、机器人税与智能经济和传统经济的协调发展——以出租车行业为例”(项目编号:25YJA790004)阶段性成果之一
  • 【文献出处】 上海经济研究 ,Shanghai Journal of Economics , 编辑部邮箱 ,2026年06期
  • 【分类号】TP18;X322;F279.2
  • 【下载频次】307
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