Science Research Management ›› 2025, Vol. 46 ›› Issue (5): 55-64.DOI: 10.19571/j.cnki.1000-2995.2025.05.006

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Research on the impact of artificial intelligence on enterprise growth

Lyu Zhuo1, Zhang Hengxin1, Li Lianwei2   

  1. 1. School of Statistics, Shandong Technology and Business University, Yantai 264003, Shandong, China; 
    2. School of Finance, Shandong Technology and Business University, Yantai 264003, Shandong, China
  • Received:2023-10-27 Revised:2024-11-26 Online:2025-05-20 Published:2025-05-12

Abstract:     With the deepening development of a new round of scientific and technological revolution and industrial transformation, artificial intelligence (AI) has become an important driving force for the transformation and high-quality economic development of enterprises. This paper selected A-share listed companies from 2004 to 2021 as samples, and adopted the dual machine learning method to evaluate the impact and mechanism of artificial intelligence on enterprise growth. The econometric results showed that: (1) AI significantly promotes firm growth and the conclusions remain valid after a series of robustness tests; (2) The heterogeneity analysis revealed that the promotion effect of AI on enterprise growth is more significant in non-state-owned enterprises, small and medium-sized enterprises, and traditional industries; (3) The mechanism test based on the perspective of AI′s technical-economic characteristics showed that AI mainly exerts its synergy, innovation and substitutability to promote enterprise growth by improving enterprise productivity, reducing management costs, promoting enterprise innovation, and optimizing the structure of labor employment. The paper has revealed the important role and internal mechanism of AI in promoting enterprise growth, and it will provide some important supplements and references for accelerating the development of AI and promoting enterprise transformation and upgrading.

Key words: artificial intelligence, enterprise growth, techno-economic characteristics, dual machine learning