Impact of artificial intelligence applications on enterprise market value: Evidence from Chinese enterprises

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Type of the article: Research Article

Abstract
The application of artificial intelligence (AI) in enterprises presents new opportunities for growth in their market value. This study aims to evaluate the impact of AI applications in enterprises on the growth of enterprise market value and the transmission mechanism of these impacts. Using an enterprise AI application level as the independent variable, a regression model is constructed to analyze the long-term and short-term market value of the enterprise. This study uses relevant data from Chinese listed companies from 2014 to 2023 for analysis. Findings show that for every 1% increase in AI application level, the enterprise market value increases by 0.03% and the enterprise value multiple increases by 0.44%. Increasing the level of AI application in enterprises will enhance their ability to implement low-carbon measures and investors’ expectations of corporate profits, thereby increasing the market value of enterprises. High-quality talent within the enterprise and market share can enhance the impact of these two mechanisms. The application of AI in enterprises has different impacts on different industries and companies of different sizes. This study provides new empirical evidence for enterprise market valuation.

Acknowledgments
This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (IRN BR28713593 “Sustainable development of Kazakhstan’s economy in the context of new сhallenges: foresight, strategies and scenarios of modernization, institutions”).

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    • Table 1. Mode 1 control variable
    • Table 2. Mode 2 control variable
    • Table 3. Model 1 benchmark regression results
    • Table 4. Model 2 benchmark regression results
    • Table 5. Robustness test (Model 1)
    • Table 6. Robustness test (Model 2)
    • Table 7. Model 1 endogeneity test
    • Table 8. Model 2 endogeneity test
    • Table 9. Value transmission mechanism
    • Table 10. Moderation mechanism
    • Table 11. Heterogeneity analysis
    • Conceptualization
      Liangliang Xue, Zaira Satpayeva, Altynay Tyulkubayeva, Dana Kangalakova
    • Data curation
      Liangliang Xue, Zaira Satpayeva, Altynay Tyulkubayeva
    • Formal Analysis
      Liangliang Xue, Zaira Satpayeva, Altynay Tyulkubayeva
    • Funding acquisition
      Liangliang Xue, Zaira Satpayeva
    • Investigation
      Liangliang Xue, Zaira Satpayeva, Altynay Tyulkubayeva, Dana Kangalakova
    • Methodology
      Liangliang Xue, Zaira Satpayeva
    • Project administration
      Liangliang Xue, Zaira Satpayeva
    • Resources
      Liangliang Xue, Zaira Satpayeva, Altynay Tyulkubayeva
    • Software
      Liangliang Xue, Zaira Satpayeva, Altynay Tyulkubayeva
    • Supervision
      Liangliang Xue, Zaira Satpayeva
    • Validation
      Liangliang Xue, Zaira Satpayeva
    • Visualization
      Liangliang Xue, Zaira Satpayeva, Dana Kangalakova
    • Writing – original draft
      Liangliang Xue, Zaira Satpayeva, Altynay Tyulkubayeva, Dana Kangalakova
    • Writing – review & editing
      Liangliang Xue, Zaira Satpayeva, Altynay Tyulkubayeva, Dana Kangalakova