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Enhancing enterprise investment efficiency through artificial intelligence: The role of accounting information transparency

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  • Zhao, Xin
  • Zhai, Guoqing
  • Charles, Vincent
  • Gherman, Tatiana
  • Lee, Hyoungsuk
  • Pan, Tuan
  • Shang, Yuping

Abstract

In the post-COVID-19 era, with global economic recovery as a critical goal, the rapid development of artificial intelligence (AI) has emerged as a key driver of economic growth and transformation. AI not only acts as a powerful catalyst for economic development but also significantly impacts enterprise investment efficiency (EIE). This paper explores the influence of AI on EIE, with a focus on the role of accounting information transparency. Using data from Shanghai and Shenzhen A-share listed enterprises between 2010 and 2021, the findings demonstrate that AI development significantly enhances EIE. These results are confirmed through robustness tests, including variable substitution, and addressing endogeneity and sample limitations. Mechanism analysis reveals that AI improves EIE by increasing the transparency of accounting information. Additionally, heterogeneity analysis shows that AI has a greater impact on the investment efficiency of high-tech and technology-intensive enterprises, non-state-owned enterprises, and those located in highly urbanised areas, such as ‘Broadband China’ pilot cities. This paper examines how AI development affects EIE through the lens of enterprise accounting information transparency, offering actionable insights for enhancing accounting disclosures and serving as a valuable resource for enterprises navigating the technological transformation of the modern era.

Suggested Citation

  • Zhao, Xin & Zhai, Guoqing & Charles, Vincent & Gherman, Tatiana & Lee, Hyoungsuk & Pan, Tuan & Shang, Yuping, 2024. "Enhancing enterprise investment efficiency through artificial intelligence: The role of accounting information transparency," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    • Handle: RePEc:eee:soceps:v:96:y:2024:i:c:s0038012124002921
    DOI: 10.1016/j.seps.2024.102092
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