Research Paper:
Can Enterprise Intelligent Transformation Resolve the “Productivity Paradox?” Evidence from Chinese Listed Companies
Jingyi Yang
, Xiuwu Zhang
, and Yarui Deng
Research Center for Quantitative Economics, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen, Fujian 361021, China
Corresponding author
Intelligent transformation is one of the primary strategies driving industrial upgrading, enhancing quality, and increasing efficiency in China. This study quantifies the extent of intelligent transformation among Chinese listed companies from 2009 to 2023, employing text processing techniques and analyzing annual reports. It subsequently investigates the comprehensive impact of intelligent transformation on these enterprises’ total factor productivity (TFP) and clarifies the dynamic mechanism enterprise environmental, social, and governance (ESG) performance plays in this process. The findings reveal that: (1) the introduction of intelligent capital leads to improved factor market competition, thereby reducing the dispersion of nominal TFP among firms and ultimately driving TFP growth; (2) intelligent transformation significantly enhances firms’ TFP, a conclusion that remains valid after considering endogeneity issues and conducting a series of robustness checks, thereby disproving the “productivity paradox;” (3) in terms of impact mechanisms, it promotes the improvement of TFP by enhancing corporate ESG performance; however, (4) the enabling effect of intelligent transformation on TFP varies significantly across firms based on the nature of their ownership, factor intensity, and geographical location.
- [1] Y. Chen, J. Li, R. Ye, R. Song, and Y. Fan, “Transformation of new quality productivity in enterprise management empowered by AI: Evolution from automation to intelligent decision making,” Academic J. of Management and Social Sciences, Vol.8, No.2, pp. 95-101, 2024. https://doi.org/10.54097/5176dn48
- [2] J. Zhou, S. Lan, Y. Liu, T. Rong, and D. Huisingh, “Research on the relations between cognition and intelligent transformation of executive teams in small and medium-sized manufacturing enterprises,” Advanced Engineering Informatics, Vol.52, Article No.101539, 2022. https://doi.org/10.1016/j.aei.2022.101539
- [3] H. Guang, Y. Liu, J. Feng, and N. Wang, “Smart manufacturing and enterprise breakthrough innovation: Co-existence test of ‘U-shaped’ and inverted ‘U-shaped’ relationships in Chinese listed companies,” Sustainability, Vol.16, No.14, Article No.6181, 2024. https://doi.org/10.3390/su16146181
- [4] S. Yang, W. Wang, and T. Ding, “Intelligent transformation and sustainable innovation capability: Evidence from China,” Finance Research Letters, Vol.55, Part B, Article No.103963, 2023. https://doi.org/10.1016/j.frl.2023.103963
- [5] Y. Gao, H. Yang, X. Sun, X. Tian, and J. Xu, “Corporate digital transformation and financing constraints: The moderating effect of institutional investors,” Heliyon, Vol.10, No.12, Article No.e33199, 2024. https://doi.org/10.1016/j.heliyon.2024.e33199
- [6] H. Chung and K. Kim, “Can open innovation improve technological outcomes for digital transformation?: Structural approach to strategic decisions of Korean ICT SMEs,” Managerial and Decision Economics, Vol.44, No.8, pp. 4404-4421, 2023. https://doi.org/10.1002/mde.3958
- [7] H. Zhang, Y. Ding, J. Niu, and S. Jung, “How artificial intelligence affects international industrial transfer – Evidence from industrial robot application,” J. of Asian Economics, Vol.95, Article No.101815, 2024. https://doi.org/10.1016/j.asieco.2024.101815
- [8] J. Chen, Z. Guo, and Z. Lei, “Research on the mechanisms of the digital transformation of manufacturing enterprises for carbon emissions reduction,” J. of Cleaner Production, Vol.449, Article No.141817, 2024. https://doi.org/10.1016/j.jclepro.2024.141817
- [9] P. Huang and X. Chen, “The impact of data factor-driven industry on the green total factor productivity: Evidence from the China,” Scientific Reports, Vol.14, Article No.25377, 2024. https://doi.org/10.1038/s41598-024-77189-w
- [10] Y. Wang, “Financial agglomeration, total factor productivity and urban economic growth,” Statistics & Decision, Vol.38, No.18, pp. 136-141, 2022 (in Chinese). https://doi.org/10.13546/j.cnki.tjyjc.2022.18.026
- [11] T. Zhang and Y. Guo, “Spatiotemporal heterogeneity of green total factor productivity in Chinese cities under technological heterogeneity,” Energy Reports, Vol.11, pp. 1535-1543, 2024. https://doi.org/10.1016/j.egyr.2024.01.034
- [12] Y. Cao and T. Xu, “The impact of environmental social responsibility on total factor productivity: Evidence from listed companies in China,” Sustainability, Vol.16, No.18, Article No.8137, 2024. https://doi.org/10.3390/su16188137
- [13] B. Lin and A. Zhang, “Impact of government subsidies on total factor productivity of energy storage enterprises under dual-carbon targets,” Energy Policy, Vol.187, Article No.114046, 2024. https://doi.org/10.1016/j.enpol.2024.114046
- [14] J. Zhang, H. Hua, L. Yang, and Z. Nie, “Impact of China’s environmental protection tax on green total factor productivity: Based on the perspective of digital transformation,” Frontiers in Environmental Science, Vol.12, Article No.1484910, 2024. https://doi.org/10.3389/fenvs.2024.1484910
- [15] W. Bu, C. Li, and S. Liu, “Environmental regulation, R&D subsidies, and industrial green total factor productivity,” Sustainable Futures, Vol.8, Article No.100333, 2024. https://doi.org/10.1016/j.sftr.2024.100333
- [16] Y. Jia et al., “Digital servitization in digital enterprise: Leveraging digital platform capabilities to unlock data value,” Int. J. of Production Economics, Vol.278, Article No.109434, 2024. https://doi.org/10.1016/j.ijpe.2024.109434
- [17] H. Zhang and S. Dong, “Digital transformation and firms’ total factor productivity: The role of internal control quality,” Finance Research Letters, Vol.57, Article No.104231, 2023. https://doi.org/10.1016/j.frl.2023.104231
- [18] Y. Wu, F. Shi, and Y. Wang, “Driving impact of digital transformation on total factor productivity of corporations: The mediating effect of green technology innovation,” Emerging Markets Finance and Trade, Vol.60, No.5, pp. 950-966, 2024. https://doi.org/10.1080/1540496X.2023.2200882
- [19] C.-T. Hsieh and P. J. Klenow, “Misallocation and manufacturing TFP in China and India,” The Quarterly J. of Economics, Vol.124, No.4, pp. 1403-1448, 2009. https://doi.org/10.1162/qjec.2009.124.4.1403
- [20] Q. Wang and G. Sun, “Intellectualization’s effect in eliminating misallocation and promoting TFP growth,” Management Review, Vol.36, No.4, pp. 39-48, 2024 (in Chinese). https://doi.org/10.14120/j.cnki.cn11-5057/f.2024.04.003
- [21] W. Jiang and J. Li, “Digital transformation and its effect on resource allocation efficiency and productivity in Chinese corporations,” Technology in Society, Vol.78, Article No.102638, 2024. https://doi.org/10.1016/j.techsoc.2024.102638
- [22] Y. Liu and Q. He, “Digital transformation, external financing, and enterprise resource allocation efficiency,” Managerial and Decision Economics, Vol.45, No.4, pp. 2321-2335, 2024. https://doi.org/10.1002/mde.4136
- [23] C. Li and Y. Wang, “Digital transformation and enterprise resilience: Enabling or burdening?,” PLOS ONE, Vol.19, No.7, Article No.e0305615, 2024. https://doi.org/10.1371/journal.pone.0305615
- [24] J. Wu, Y. Liang, and W. Liu, “The dark side of corporate digital transformation: Evidence from excess perk consumption of executives,” Finance Research Letters, Vol.61, Article No.105033, 2024. https://doi.org/10.1016/j.frl.2024.105033
- [25] Y. Li, P. Feng, T. Qi, J. Yan, and Y. Huang, “Enterprise digital transformation, managerial myopia and cost stickiness,” Humanities and Social Sciences Communications, Vol.11, Article No.1389, 2024. https://doi.org/10.1057/s41599-024-03926-1
- [26] L. Guo, Q. Zhong, and H. Wang, “Digital transformation, ESG responsibility and corporate’s export performance,” Finance Research Letters, Vol.69, Part A, Article No.106106, 2024. https://doi.org/10.1016/j.frl.2024.106106
- [27] J. Levinsohn and A. Petrin, “Estimating production functions using inputs to control for unobservables,” The Review of Economic Studies, Vol.70, No.2, pp. 317-341, 2003. https://doi.org/10.1111/1467-937X.00246
- [28] J. Yao, K. Zhang, L. Guo, and X. Feng, “How does artificial intelligence improve firm productivity? Based on the perspective of labor skill structure adjustment,” J. of Management World, Vol.40, No.2, pp. 101-116+133+117-122, 2024 (in Chinese). https://doi.org/10.19744/j.cnki.11-1235/f.2024.0018
- [29] J. Li, T. Wu, B. Liu, and M. Zhou, “Can digital transformation enhance corporate ESG performance? The moderating role of dual environmental regulations,” Finance Research Letters, Vol.62, Part B, Article No.105241, 2024. https://doi.org/10.1016/j.frl.2024.105241
- [30] Z. Wen and B. Ye, “Analyses of mediating effects: The development of methods and models,” Advances in Psychological Science, Vol.22, No.5, pp. 731-745, 2014 (in Chinese). https://doi.org/10.3724/SP.J.1042.2014.00731
- [31] Y. Cheng, X. Zhou, and Y. Li, “The effect of digital transformation on real economy enterprises’ total factor productivity,” Int. Review of Economics & Finance, Vol.85, pp. 488-501, 2023. https://doi.org/10.1016/j.iref.2023.02.007
- [32] G. Zeng and L. Lei, “Digital transformation and corporate total factor productivity: Empirical evidence based on listed enterprises,” Discrete Dynamics in Nature and Society, Vol.2021, Article No.9155861, 2021. https://doi.org/10.1155/2021/9155861
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