Exploration of the Impact of Policy Empowerment and Digital Transformation on the Advancement of Enterprise Human Capital Structure
DOI:
https://doi.org/10.71222/yz5qpy76Keywords:
National Big Data Comprehensive Experimental Zone, digital transformation, human capital structure, entropy weight method, difference-in-differences model, PVAR modelAbstract
This study examines how enterprise digital transformation influences human capital structure advancement within China's government-driven big data initiative. Using the 2016 National Big Data Comprehensive Experimental Zone as a quasi-natural experiment, data from 2011 to 2022 are analyzed through the Difference-in-Differences (DID) and Panel Vector Autoregressive (PVAR) models. Findings indicate that national big data policies enhance human capital structure by boosting corporate innovation. Digital transformation further optimizes human capital, with both factors reinforcing each other. The experimental zone policy accelerates this process through innovation. This study underscores digital transformation as a key driver of enterprise human capital upgrading.
References
1. P. Liu and J. Wu, “Can digital transformation enable the energy enterprises to achieve high-quality development?: An em-pirical analysis from China,” Energy Rep., vol. 10, pp. 1182–1197, 2023, doi: 10.1016/j.egyr.2023.07.059.
2. C. Ding, C. Liu, C. Zheng, and F. Li, “Digital economy, technological innovation and high-quality economic development: Based on spatial effect and mediation effect,” Sustainability, vol. 14, no. 1, p. 216, 2022, doi: 10.3390/su14010216.
3. Y. Shao, “The impact of digital economy on the high-quality development of service industry in Beijing,” in Proc. 2022 Int. Conf. Bigdata Blockchain Econ. Manag. (ICBBEM 2022), Dec. 2022, pp. 133–143, doi: 10.2991/978-94-6463-030-5_15.
4. C. Xie and C. Liu, “The nexus between digital finance and high-quality development of SMEs: Evidence from China,” Sus-tainability, vol. 14, no. 12, p. 7410, 2022, doi: 10.3390/su14127410.
5. W. Gao et al., “The role of population aging in high-quality economic development: mediating role of technological inno-vation,” SAGE Open, vol. 13, no. 4, p. 21582440231202385, 2023, doi: 10.1177/21582440231202385.
6. Y. Wu, C. Jiahe, and L. Pengfei, “Application of automation technology and human capital structure of enterprises: From the perspective of supply chain,” J. Financ. Econ., vol. 49, no. 7, pp. 4–18, 2023, doi: 10.16538/j.cnki.jfe.20230221.301.
7. Z. Cui and S. Diwu, “Human capital upgrading and enterprise innovation efficiency,” Financ. Res. Lett., vol. 65, p. 105628, 2024, doi: 10.1016/j.frl.2024.105628.
8. H. Gao, S. Xu, and M. Wang, “Will the National Big Data Comprehensive Pilot Zone improve total factor productivity of enterprises?,” Energy Environ., 2024, doi: 10.1177/0958305X241241027.
9. L. Wang and J. Shao, “Digital economy and urban green development: A quasi-natural experiment based on national big data comprehensive pilot zone,” Energy Environ., 2024, doi: 0958305X241238348, doi: 10.1177/0958305X241238348.
10. W. Wang et al., “Can digital policy improve corporate sustainability? Empirical evidence from China's national comprehen-sive big data pilot zones,” Telecommun. Policy, vol. 47, no. 9, p. 102617, 2023, doi: 10.1016/j.telpol.2023.102617.
11. R. Zhao and J. Fan, “Digital policy quality and enterprise innovation: The case of China’s big data comprehensive pilot zone,” Sustainability, vol. 16, no. 12, p. 5032, 2024, doi: 10.3390/su16125032.
12. C. Qin, M. Zhang, and C. Yang, “Can national big data comprehensive experimental zones boost the development of re-gional green finance? Evidence from China,” Emerg. Mark. Financ. Trade, vol. 60, no. 3, pp. 541–556, 2024, doi: 10.1080/1540496X.2023.2228461.
13. C. Ye, “Digital economy, science and technology innovation and carbon emissions-A dynamic analysis of PVAR based on provincial panel data,” J. Risk Anal. Crisis Response, vol. 13, no. 1, 2023, doi: 10.54560/jracr.v13i1.354.
14. Y. Wang, Y. Xu, and W. Chen, “Study on the relationship between agricultural credit, fiscal support, and farmers’ in-come—empirical analysis based on the PVAR model,” Sustainability, vol. 15, no. 4, p. 3173, 2023, doi: 10.3390/su15043173.
