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Artificial intelligence and corporate investment efficiency

Author

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  • Lou, Zhukun
  • Li, Can
  • Tong, Chengsheng

Abstract

This study examines the relationship between artificial intelligence (AI) adoption and corporate investment efficiency. Using an archival dataset of 28,911 firm-year observations from Chinese listed companies on the Shanghai and Shenzhen stock exchanges during 2011–2022, we find that AI adoption significantly improves corporate investment efficiency, with results remaining robust across multiple robustness tests and endogeneity checks. Mechanism analysis reveals that AI enhances investment efficiency by improving information transparency and strengthening internal control. Further moderating effect tests show that the positive impact of AI on investment efficiency is more pronounced in firms with executives possessing technical expertise, operating in highly competitive product markets, or situated in high-tech industries. This research contributes to the literature by elucidating how AI empowers corporate investment decision-making and provides practical implications for enterprises aiming to leverage AI technologies to optimize their investment performance.

Suggested Citation

  • Lou, Zhukun & Li, Can & Tong, Chengsheng, 2025. "Artificial intelligence and corporate investment efficiency," International Review of Economics & Finance, Elsevier, vol. 104(C).
    • Handle: RePEc:eee:reveco:v:104:y:2025:i:c:s1059056025008767
    DOI: 10.1016/j.iref.2025.104713
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    Cited by:

    1. Cui Tiening & Liu Mengyun, 2026. "Research on Low-Carbon Development Pathways for Modern Manufacturing in the Capital of China," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 16(1), pages 1-1.
    2. Lee Bih Ni & Connie Shin@Cassy Ompok & Nur Farha Binti Shaafi & Arzizul Bin Antin, 2026. "The Use of AI in History Education in Sabah: A Comparative Study of Rural and Urban Schools," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(1), pages 1703-1712, January.
    3. Riadh GHESSIL & Sarah SLIMANI, 2026. "L’Évaluation Des Compétences Rédactionnelles À L’Épreuve De L’Ia Générative," Annals of the University of Craiova, Series Psychology, Pedagogy, Teacher Training Department, University of Craiova, vol. 48(1), pages 76-89, June.
    4. Fakhreddin Fakhrai Rad & Pejvak Oghazi & İzmir Onur & Arash Kordestani, 2025. "Adoption of AI-based order picking in warehouse: benefits, challenges, and critical success factors," Review of Managerial Science, Springer, vol. 19(11), pages 3495-3540, November.
    5. Lee Bih Ni & Connie Shin@Cassy Ompok & Nur Farha Binti Shaafi & Arzizul Bin Antin, 2026. "The Use of AI in History Education in Sabah: A Comparative Study of Rural and Urban Schools," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(1), pages 479-488, January.

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