AI＆Law Hub

探索人工智能与法律的交汇点 | Exploring the Intersection of AI and Law

🏛️

湾区人工智能法律评论

Greater Bay Area AI Law Review

专业视角，深度分析，洞悉法律前沿
Professional perspective, in-depth analysis, insights into legal frontiers

📅

AI＆法律信息速递周报

AI＆Law Weekly Report

每周一更新，深度解读行业动态与前沿趋势
Updated every Monday, in-depth analysis of industry dynamics and frontier trends

🚀

AI＆法律信息速递

AI＆Law Express

快速掌握最新资讯，精准把握行业脉搏
Quickly grasp the latest news, accurately capture the pulse of the industry

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全球AI治理与诉讼数据库

Global AI Governance & Litigation Database

全球视野，系统追踪AI治理动态与诉讼案例
Global perspective, systematically tracking AI governance dynamics and litigation cases

谢娜 / Sheena

Xie Na / Sheena

AI与法律领域研究者，致力于人工智能与法律交叉领域的探索与实践。
AI and Law researcher, dedicated to exploring the intersection of artificial intelligence and law.

🏆中国国际创新大赛2024广东省分赛铜奖
China International Innovation Competition 2024 Guangdong Provincial Division - Bronze Award

🏆第十四届"挑战杯"广东大学生创业计划竞赛铜奖
The 14th "Challenge Cup" Guangdong University Student Entrepreneurship Plan Competition - Bronze Award

🏆第十七届全国大学生节能减排社会实践与科技竞赛全国三等奖
The 17th National University Student Energy Conservation and Emission Reduction Social Practice and Science & Technology Competition - National Third Prize

🏆2024年省级大学生创新创业训练计划项目优秀
2024 Provincial College Students' Innovation and Entrepreneurship Training Program - Excellence

📄 学术论文 / Academic Papers

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

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