single-jc.php

JACIII Vol.29 No.4 pp. 880-893
doi: 10.20965/jaciii.2025.p0880
(2025)

Research Paper:

Intelligent Transformation Empowers the New Quality Productivity of Enterprises: Theoretical Mechanism and Empirical Evidence

Xiaoyang Guo ORCID Icon, Xiuwu Zhang ORCID Icon, and Yao Gui

Research Center for Quantitative Economics, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen, Fujian 361021, China

Corresponding author

Received:
December 7, 2024
Accepted:
April 11, 2025
Published:
July 20, 2025
Keywords:
intelligent transformation, new quality productivity, technological innovation, listed companies
Abstract

Accelerating the emergence of new quality productivity is a pivotal strategy for securing a proactive stance in the developmental trajectory of the contemporary era and forthcoming journey. Intelligent transformation plays a key role in this process. This study uses advanced text processing techniques and analyzes corporate annual reports to quantify the extent of intelligent transformation in listed Chinese firms between 2011 and 2022. It explores the holistic influence, underlying mechanisms, and diverse attributes of intelligent transformation in the cultivation of new quality productivity in enterprises. The results provide several key insights. (1) Intelligent transformation exerts a substantial positive effect on enterprises’ new quality productivity enhancement, which persists after considering endogeneity concerns and performing rigorous robustness checks. (2) Regarding the underlying mechanisms, intelligent transformation catalyzes an increase in enterprises’ new quality productivity by amplifying their technological innovation capabilities. (3) The magnitude of intelligent transformation’s impact on new quality productivity varies markedly across enterprises, influenced by factors such as ownership structure, geographical locale, and marketization degree.

Cite this article as:
X. Guo, X. Zhang, and Y. Gui, “Intelligent Transformation Empowers the New Quality Productivity of Enterprises: Theoretical Mechanism and Empirical Evidence,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.4, pp. 880-893, 2025.
Data files:
References
  1. [1] L. Yu and Q. Zhang, “Measurement of new qualitative productivity kinetic energy from the perspective of digital and green collaboration—Comparative study based on European countries,” J. of Cleaner Production, Vol.476, Article No.143787, 2024. https://doi.org/10.1016/j.jclepro.2024.143787
  2. [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. [3] W. Zhou and L. Y. Zhou, “On NQP: Connotation, characteristics and important focus,” J. of Reform, No.10, pp. 1-13, 2023 (in Chinese).
  4. [4] 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
  5. [5] 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
  6. [6] 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
  7. [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. [8] 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
  9. [9] S. Qin, Z. Liu, J. Wang, and Y. Wu, “The impact of digital transformation on labour demand quantity and structure: Evidence from China,” Economic Analysis and Policy, Vol.84, pp. 1452-1469, 2024. https://doi.org/10.1016/j.eap.2024.10.036
  10. [10] W. Li, X. Yang, and X. Yin, “Digital transformation and labor upgrading,” Pacific-Basin Finance J., Vol.83, Article No.102280, 2024. https://doi.org/10.1016/j.pacfin.2024.102280
  11. [11] 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
  12. [12] L. Schilling and S. Seuring, “Linking the digital and sustainable transformation with supply chain practices,” Int. J. of Production Research, Vol.62, No.3, pp. 949-973, 2024. https://doi.org/10.1080/00207543.2023.2173502
  13. [13] J. Lin and Y. Fan, “Seeking sustainable performance through organizational resilience: Examining the role of supply chain integration and digital technology usage,” Technological Forecasting and Social Change, Vol.198, Article No.123026, 2024. https://doi.org/10.1016/j.techfore.2023.123026
  14. [14] D. Du, J. Peng, and N. Wu, “Consumer behavior on digital platforms: An empirical study based on case platforms,” Academic J. of Business & Management, Vol.6, No.9, pp. 191-203, 2024. https://doi.org/10.25236/AJBM.2024.060927
  15. [15] B. Wang, I. Khan, C. Ge, and H. Naz, “Digital transformation of enterprises promotes green technology innovation – The regulated mediation model,” Technological Forecasting and Social Change, Vol.209, Article No.123812, 2024. https://doi.org/10.1016/j.techfore.2024.123812
  16. [16] 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
  17. [17] W. Han, R. Zhang, and F. Zhao, “The measurement of new quality productivity and new driving force of the Chinese economy,” J. of Quantitative & Technological Economics, Vol.41, No.6, pp. 5-25, 2024 (in Chinese). https://doi.org/10.13653/j.cnki.jqte.20240418.001
  18. [18] 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
  19. [19] H. Zhang, X. Zhang, H. Tan, and Y. Tu, “Government subsidies, market competition and firms’ technological innovation efficiency,” Int. Review of Economics & Finance, Vol.96, Part A, Article No.103567, 2024. https://doi.org/10.1016/j.iref.2024.103567
  20. [20] 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
  21. [21] G. Fan, X. L. Wang, L. W. Zhang, and H. P. Zhu, “Report on the relative process of marketization in various regions of China,” Economic Research J., Vol.03, pp. 9-18+89, 2011.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 19, 2025