2026
本科毕业论文 / Undergraduate Thesis
Artificial Intelligence Adoption and Corporate Investment Efficiency: Evidence from Chinese Listed Firms
人工智能应用与企业投资效率：来自中国上市公司的经验证据
Xie Na (谢娜) | Shenzhen Technology University, Business School
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Abstract: Leveraging a sample of Chinese A-share listed companies from 2007 to 2024, this study examines the impact of artificial intelligence (AI) adoption on corporate investment efficiency. Using the Richardson (2006) residual model to measure investment inefficiency and constructing a firm-level AI adoption indicator through annual report text analysis, the findings reveal that AI adoption significantly reduces investment inefficiency. The mechanism tests show that AI attracts greater analyst attention and increases research report coverage. Notably, the efficiency-enhancing effect is more pronounced among firms with lower financing constraints. Heterogeneity analysis further demonstrates that the positive impact is concentrated in non-state-owned enterprises and non-high-tech industries, suggesting diminishing marginal returns of AI in already technology-intensive settings.
Keywords: Artificial Intelligence; Investment Efficiency; Financing Constraints; Ownership Structure