single-jc.php

JACIII Vol.26 No.4 pp. 555-561
doi: 10.20965/jaciii.2022.p0555
(2022)

Paper:

Impact of Intelligent Development on the Total Factor Productivity of Firms – Based on the Evidence from Listed Chinese Manufacturing Firms

Jian Huang* and Jiangying Wei**,†

*Social Sciences Research & Management Division, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen 361021, China

**Institute of Quantitative Economics, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen 361021, China

Corresponding author

Received:
January 28, 2022
Accepted:
April 8, 2022
Published:
July 20, 2022
Keywords:
intelligent development, text mining, enterprise total factor productivity, threshold effect
Abstract
Impact of Intelligent Development on the Total Factor Productivity of Firms – Based on the Evidence from Listed Chinese Manufacturing Firms

Seed words and keywords of the intelligent development index

The new industrial revolution featuring artificial intelligence (AI) as its core is flourishing globally. However, there are many controversies surrounding the impact of AI on productivity owing to the different understandings of its development. Thus, this study adopts a text mining method to construct indicators for measuring the intelligent development of enterprises based on the information obtained from the annual reports of listed Chinese manufacturing companies from 2009 to 2019. To explore the impact of intelligent development on the total factor productivity (TFP) of enterprises, fixed-effect regression and panel threshold models are employed to empirically prove its overall and threshold effects. The result reveals that the impact of intelligent development on TFP of enterprises is significantly positive at the aggregate level. Regarding the stage characteristics, “Solow’s paradox” exists in the development of intelligence. The effect of intelligence development on TFP is not significant at its early stage; moreover, the rapid development of intelligence exerts a “promotion effect.” However, at the extreme stage (when intelligent development crosses the critical value), it exerts a negative effect on the TFP of enterprises.

Cite this article as:
J. Huang and J. Wei, “Impact of Intelligent Development on the Total Factor Productivity of Firms – Based on the Evidence from Listed Chinese Manufacturing Firms,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.4, pp. 555-561, 2022.
Data files:
References
  1. [1] J. Mokyr, C. Vickers, and N. L. Ziebarth, “The history of technological anxiety and the future of economic growth: Is this time different?,” J. of Economic Perspectives, Vol.29, No.3, pp. 31-50, 2015.
  2. [2] S. Makridakis, “The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms,” Futures, Vol.90, pp. 46-60, 2017.
  3. [3] H. Fujii and S. Managi, “Trends and Priority Shifts in Artificial Intelligence Technology Invention: A Global Patent Analysis,” Economic Analysis and Policy, Vol.58, pp. 60-69, doi: 10.1016/j.eap.2017.12.006, 2018.
  4. [4] L. S. Li, Y. F. Bao, and J. Liu, “A Study on the Impact of Intelligence on Total Factor Productivity in China’s Manufacturing Industry,” Studies in Science of Science, Vol.38, No.4, pp. 609-618, 2020 (in Chinese).
  5. [5] L. Kromann, J. R. Skaksen, and A. Sørensen, “Automation, labor productivity and employment – A cross country comparison,” Centre for Economic and Business Research (CEBR), Copenhagen Business School, 2011.
  6. [6] D. Acemoğlu and P. Restrepo, “The race between machines and humans: Implications for growth, factor shares and jobs,” American Economic Review, Vol.108, No.6, pp. 1488-1542, 2018.
  7. [7] G. Graetz and G. Michaels, “Robots at work: the impact on productivity and jobs,” Centre for Economic Performance, the London School of Economics and Political Science (LSE), Vol.20, No.1, 2015.
  8. [8] Y. D. Qi and C. W. Cai, “Research on the Multiple Effects of Digitalization on the Performance of Manufacturing Enterprises and Their Mechanisms,” Study and Exploration, Issue 7, pp. 108-119, 2020 (in Chinese).
  9. [9] R. J. Gordon, “Is US economic growth over? Faltering innovation confronts the six headwinds,” National Bureau of Economic Research Working Paper, Article No.18315, 2012.
  10. [10] M. C. Guo and C. Z. Du, “Mechanism and Effect of Information and Communication Technology on Enhancing the Quality of China’s Economic Growth,” Statistical Research, Vol.36, No.3, pp. 3-16, 2019 (in Chinese).
  11. [11] E. Brynjolfsson and T. Mitchell, “What can machine learning do? Workforce implications,” Science, Vol.358, No.6370, pp. 1530-1534, 2017.
  12. [12] P. Aghion, “Entrepreneurship and growth: lessons from an intellectual journey,” Small Business Economics, Vol.48, pp. 9-24, 2017.
  13. [13] M. Upchurch and P. V. Moore, “Deep automation and the world of work,” P. V. Moore, M. Upchurch, and X. Whittaker (Eds.), “Humans and Machines at Work,” pp. 45-71, Springer, doi: 10.1007/978-3-319-58232-0_3, 2018.
  14. [14] B. E. Hansen, “Threshold effects in non-dynamic panels: Estimation, testing, and inference,” J. of Econometrics, Vol.93, No.2, pp. 345-368, 1999.
  15. [15] A. Petrin, B. P. Poi, and J. Levinsohn, “Production function estimation in Stata using inputs to control for unobservables,” The Stata J., Vol.4, No.2, pp. 113-123, 2004.
  16. [16] G. S. Olley and A. Pakes, “The dynamics of productivity in the telecommunications equipment industry,” Econometrica, Vol.64, No.6, pp. 1263-1297, 1996.
  17. [17] X. Y. Wang, Y. Q. Li, and M. Xiao, “Do risk disclosures in annual reports improve analyst forecast accuracy?,” China J. of Accounting Studies, Vol.5, Issue 4, pp. 527-546, 2017 (in Chinese).
  18. [18] F. Yu, L. Wang, and X. Li, “The effects of government subsidies on new energy vehicle enterprises: The moderating role of intelligent transformation,” Energy Policy, Vol.141, Article No.111463, 2020.
  19. [19] Y. Yuan et al., “Quantitative Research on China’s Artificial Intelligence Industry Policy Based on Text Mining,” J. of China Academy of Electronics and Information Technology, Vol.13, No.6, pp. 663-668, 2018 (in Chinese).
  20. [20] F. He and H. X. Liu, “The Performance Improvement Effect of Digital Transformation Enterprises from the Digital Economy Perspective,” Reform, No.4, pp. 137-148, 2019 (in Chinese).
  21. [21] T. Mikolov et al., “Efficient estimation of word representations in vector space,” Proc. Int. Conf. on Learning Representations, 2013.
  22. [22] C. Y. Zhao, W. C. Wang, and X. S. Li, “How Does Digital Transformation Affect the Total Factor Productivity of Enterprises?,” Finance and Trade Economics, Vol.42, No.7, pp. 114-129, 2021 (in Chinese).
  23. [23] H. W. Wen and Q. M. Zhong, “The Influence of Intelligent Development on Total Factor Productivity – Evidence from China’s Listed Manufacturing Enterprises,” Forum on Science and Technology in China, No.1, pp. 84-94, 2021 (in Chinese).

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

Last updated on Aug. 05, 2022