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Exploration of the Impact of Policy Empowerment and Digital Transformation on the Advancement of Enterprise Human Capital Structure

Jialu Xu1,* and Nier Wu1
1 College of Science, Minzu University of China, Beijing, China * Correspondence:Jialu Xu, College of Science, Minzu University of China, Beijing, China

Vol. 3 (2025): 2025 2nd International Conference on the Frontiers of Social Sciences, Education, and the Development of Humanities Arts(EDHA 2025)

Received: 2026-05-16

Accepted: 2026-05-16

Published: 2026-05-16

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Abstract

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.

Keywords

digital transformation National Big Data Comprehensive Experimental Zone human capital structure entropy weight method difference-in-differences model PVAR model

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Copyright and License

Published in2026-05-16 15:29:02

DOI 10.70088/vkjm3m18.

Creative Commons
Copyright: © 2025 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/license s/by/4.0/).

Copyright
Copyright © The Author(s), 2025. Published by EDHA 2025

Journal Information

  • Vol. 3 (2025): 2025 2nd International Conference on the Frontiers of Social Sciences, Education, and the Development of Humanities Arts(EDHA 2025)
  • 2026-05-16
  • ISSN: (Print) 3078-770X/ (Online) 3078-7718
  • Journal Homepage

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