Artificial intelligence, dynamic capabilities, and corporate financial asset allocation

Yu Li, Huiyi Zhong and Qiye Tong

International Review of Financial Analysis, 2024, vol. 96, issue PB

Abstract: The integration of artificial intelligence (AI) into corporate operations has revolutionized financial decision-making processes, yet our understanding of how AI adoption specifically impacts financial asset allocation remains limited. While existing research has explored AI's role in various financial applications, there is a critical gap in empirically examining the relationship between AI adoption and corporate financial asset allocation, particularly in understanding the organizational capabilities that enable firms to effectively leverage AI technologies. This study investigates this relationship using panel data from 25,811 firm-year observations of Chinese A-share listed companies (2008–2022). Through comprehensive regression analyses, we find that AI adoption significantly enhances corporate financial asset allocation efficiency, with this relationship being distinctly moderated by organizational dynamic capabilities. Notably, absorptive capability exhibits the strongest moderating effect, followed by innovative and adaptive capabilities. These findings advance our understanding of AI's role in corporate finance by demonstrating that the success of AI implementation in financial decision-making is contingent upon firms' underlying organizational capabilities. The results provide valuable insights for managers and policymakers in developing targeted strategies to enhance the effectiveness of AI adoption in corporate financial management.

Keywords: Artificial intelligence adoption; Corporate financial asset allocation; Absorptive capability; Innovative capability; Adaptive capability; Dynamic capabilities; Organizational learning (search for similar items in EconPapers)
JEL-codes: G32 L25 M15 O33 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:96:y:2024:i:pb:s1057521924007051

DOI: 10.1016/j.irfa.2024.103773

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