Impact of artificial intelligence applications on enterprise market value: Evidence from Chinese enterprises
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DOIhttp://dx.doi.org/10.21511/imfi.22(4).2025.24
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Article InfoVolume 22 2025, Issue #4, pp. 303-317
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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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JEL Classification (Paper profile tab)O12, O14, М10
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References61
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Tables11
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Figures0
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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
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- Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, е103745.
- Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting & Social Change, 165, e120557.
- Bhattacharya, A., Neil, A. M., & Rego, L. L. (2022). Examining why and when market share drives firm profit. Journal of Marketing, 86(4), 73-94.
- Brey, B., & van der Marel, E. (2024). The role of human-capital in artificial intelligence adoption. Economics Letters, 244, e111949.
- Cai, X., Xiang, H., Neskorodieva, I., & Durmanov, A. (2024). Interrelation between human capital management and ESG engagement: Evidence from S&P 500 firms. Humanities and Social Sciences Communications, 11, е1654.
- Chen, Y., Chen, Y., & Guo, Y. (2023). Dynamic capabilities for value creation from AI innovation ecosystem: A comparison between IFLYTEK and Amazon. International Journal of Knowledge Management Studies, 14(2), 212-236.
- Cheng, T. C. E., Kamble, S. S., Belhadi, A., Ndubisi, N. O., Lai, K. H., & Kharat, M. G. (2022). Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms. International Journal of Production Research, 60(22), 6908–6922.
- China Stock Market & Accounting Research Database. (n.d.). Official website.
- Chinese Research Data Services Platform. (n.d.). Official website.
- Chotia, V., Cheng, Y., Agarwal, R., & Vishnoi, S. K. (2024). AI-enabled green business strategy: Path to carbon neutrality via environmental performance and green process innovation. Technological Forecasting and Social Change, 202, e123315.
- Coluccia, D., Dabić, M., Del Giudice, M., Fontana, S., & Solimene, S. (2020). R&D innovation indicator and its effects on the market: An empirical assessment from a financial perspective. Journal of Business Research, 119, 259-271.
- Corrado, C., Haskel, J., & Jona-Lasinio, C. (2021). Artificial intelligence and productivity: An intangible assets approach. Oxford Review of Economic Policy, 37(3), 435-458.
- Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), 1-25.
- Dechow, P. M., & Sloan, R. G. (1997). Returns to Contrarian Investment Strategies: Tests of Naïve Expectations Hypotheses. Journal of Financial Economics, 43(1), 3-27.
- Dong, Y., Willcott, N., Yang, X., & Yang, Y. (2025). Growing up in the modern world: How does artificial intelligence enhance firm growth? Managerial Finance, 51(4), 567-578.
- Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
- Fredström, A., Parida, V., Wincent, J., Sjödin, D., & Oghazi, P. (2022). What is the market value of artificial intelligence and machine learning? The role of innovativeness and collaboration for performance. Technological Forecasting & Social Change, 180, e121716.
- Gao, D., Tan, L., & Chen, Y. (2025). Smarter is greener: Can intelligent manufacturing improve enterprises’ ESG performance? Humanities and Social Sciences Communications, 12, e529.
- Giczy, A. V., Pairolero, N. A., & Toole, A. A. (2022). Identifying artificial intelligence (AI) invention: A novel AI patent dataset. Journal of Technology Transfer, 47(2), 476-505.
- Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, Article 120392.
- Han, F., & Mao, X. (2023). Artificial intelligence empowers enterprise innovation: Evidence from China’s industrial enterprises. Applied Economics, 56(57), 7971-7986.
- Hoffmann, A. O. I., & Kleimeier, S. (2019). Financial disclosure readability and innovative firms’ cost of debt. International Review of Finance, 19(2), 1-15.
- Hötte, K. (2023). Demand-pull, technology-push, and the direction of technological change. Research Policy, 52(5).
- Hu, L., Wang, C., & Fan, T. (2024). Sustainable operation and management of a dynamic supply chain under the framework of a community with a shared future for mankind. Sustainability, 16(17), e7780.
- Huang, C.-K., & Lin, J.-S. (2025). Firm performance on artificial intelligence implementation. Managerial and Decision Economics, 46(3), 1856-1870.
- Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50.
- Hussain, M., Yang, S., Maqsood, U. S., & Zahid, R. M. A. (2024). Tapping into the green potential: The power of artificial intelligence adoption in corporate green innovation drive. Business Strategy and the Environment, 33(5), 4375-4396.
- Jin, Y., Li, X., Tian, G., Shi, J., & Wang, Y. (2023). Employee education level and efficiency of corporate investment. Journal of Accounting Literature, 47(2), 277-297.
- Kangalakova, D., Satpayeva, Z., Nurkenova, M., & Suleimenova, A. (2024). Public management of scientists’ potential as a source of economic development: A bibliometric analysis. Problems and Perspectives in Management, 22(3), 593-605.
- Kim, T., Park, Y., & Kim, W. (2022). The impact of artificial intelligence on firm performance. In 2022 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1-7). IEEE.
- Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. The Journal of Finance, 49(5), 1541-1578.
- Lee, C. C., Atukeren, E., & Kim, H. (2024). Knowledge capital and stock returns during crises in the manufacturing sector: Moderating role of market share, Tobin’s Q, and cash holdings. Risks, 12(6), e100.
- Lee, H. H., & Oh, F. D. (2020). Corporate innovation and credit default swap spreads. Finance Research Letters, 32, e101082.
- Li, H., Lu, Z., Zhang, Z., & Tanasescu, C. (2025). How does artificial intelligence affect manufacturing firms’ energy intensity? Energy Economics, 141, e108109.
- Li, L., Liu, L., & Wu, H. (2022). Workforce education and corporate innovation. Australian Journal of Management, 47(4), 731-755.
- Li, X., Tang, H., & Chen, Z. (2025). Artificial intelligence and the new quality productive forces of enterprises: Digital intelligence empowerment paths and spatial spillover effects. Systems, 13, e105.
- Li, Y., Lin, Y., & Li, D. (2024). How does the application of artificial intelligence technology affect enterprise innovation? China Industrial Economics, 10, 155-173.
- Lin, C., Xiao, S., & Tang, P. (2024). Does artificial intelligence improve export technical complexity upgrade of manufacturing enterprises? Evidence from China. SAGE Open, 14(3), 1-18.
- McElheran, K., Li, J. F., Brynjolfsson, E., Kroff, Z., Dinlersoz, E., Foster, L., & Zolas, N. (2024). AI adoption in America: Who, what, and where. Journal of Economics & Management Strategy, 33(2), 375-415.
- Mishra, S., Ewing, M. T., & Cooper, H. B. (2022). Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, 50, 1176-1197.
- Moro-Visconti, R., Cruz Rambaud, S., & López Pascual, J. (2023). Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms. Humanities and Social Sciences Communications, 10, e795.
- National Standard Full Text Disclosure System. (n.d.). Official website. (In Chinese).
- Novy-Marx, R. (2013). The other side of value: The gross profitability premium. Journal of Financial Economics, 108(1), 1-28.
- Orazgaliyeva, Sh., Satpayeva, Z., Tazhiyeva, S., & Nurseiytova, G. (2023). E-government as a tool to improve the efficiency of public administration: The case of Kazakhstan. Problems and Perspectives in Management, 21(2), 578-591.
- Parteka, A., & Kordalska, A. (2023). Artificial intelligence and productivity: Global evidence from AI patent and bibliometric data. Technovation, 123, 102764.
- Piekkola, H., & Rahko, J. (2020). Innovative growth: The role of market power and negative selection. Economics of Innovation and New Technology, 29(6), 603-624.
- Poege, F., Harhoff, D., Gaessler, F., & Baruffaldi, S. (2019). Science quality and the value of inventions. Science Advances, 5(12), eaay7323.
- Shu, T., Tian, X., & Zhan, X. (2021). Patent quality, firm value, and investor underreaction: Evidence from patent examiner busyness. Journal of Financial Economics, 142(3), 1277-1299.
- Soto, P. E. (2025). Research in commotion: Measuring AI research and development through conference call transcripts. Finance and Economics Discussion Series, 2025-011. Washington: Board of Governors of the Federal Reserve System.
- Tingbani, I., Salia, S., Hartwell, C. A., & Yahaya, A. (2024). Looking in the rear-view mirror: Evidence from artificial intelligence investment, labour market conditions, and firm growth. International Journal of Finance & Economics, 30, 961-982.
- Wang, J., & Li, A. (2023). Design of enterprise financial performance prediction model based on artificial intelligence algorithm. Soft Computing, 28(Suppl 2), e607.
- Wang, J., Liu, Y., Wang, W., & Wu, H. (2024). Does artificial intelligence improve enterprise carbon emission performance? Evidence from an intelligent transformation policy in China. Technology in Society, 79, e102751.
- Wang, W., Gao, G., & Agarwal, R. (2023). Friend or foe? Teaming between artificial intelligence and workers with variation in experience. Management Science, 70(9), 5627-5660.
- Wang, Y., & Liu, F. (2025). Impact of artificial intelligence innovation on food company performance. International Review of Financial Analysis, 103, e104219.
- Xiao, Y., & Xiao, L. (2025). The impact of artificial intelligence-driven ESG performance on sustainable development of central state-owned enterprises listed companies. Scientific Reports, 15, e8548.
- Xue, L., Satpayeva, Z., Kangalakova, D., & Ozen, E. (2025). Trends of artificial intelligence-driven enterprise management development: A bibliometric analysis. Problems and Perspectives in Management, 23(4), 1-12.
- Ye, F., Ouyang, Y., & Li, Y. (2023). Digital investment and environmental performance: The mediating roles of production efficiency and green innovation. International Journal of Production Economics, 259, e108822.
- Zhang, L., & Shan, X. (2023). The use of intellectual property right bundles and firm performance in China. Humanities and Social Sciences Communications, 10, e210.
- Zhang, Q., Wang, A., & Li, R. (2024). Enterprise value creation effects of artificial intelligence technology from the perspective of digital agility: Evidence from China. Technology Analysis & Strategic Management, 1-15.
- Zheng, M. (2022). Advanced artificial intelligence model for financial accounting transformation based on machine learning and enterprise unstructured text data. Mobile Information Systems, 2022, e5708652.
- Zhu, H., Bao, W., & Yu, G. (2024). How can intelligent manufacturing lead enterprise low-carbon transformation? Based on China’s intelligent manufacturing demonstration projects. Energy, 313, e134032.


