Digital Transformation and Enterprise Total Factor Productivity - Empirical Evidence from Chinese Listed Companies

DOI

10.2991/aebmr.k.220402.013How to use a DOI?

Keywords

Digital transformation; Total factor productivity; Cost saving effect; Technological innovation effect

Abstract

Accelerating the promotion of digital transformation of enterprises is the only way to achieve high-quality economic development in China. This paper uses the method of machine learning to measure the digitalization level of enterprises, and uses the A - share data of listed companies from 2011 to 2018 to examine the impact of digital transformation on the total factor productivity of enterprises and its mechanism. The study found that digital transformation significantly improved the total factor productivity of enterprises, and after passing instrumental variables and a series of robustness tests, the basic conclusions remained robust. The results of the mechanism analysis show that digital transformation mainly improves the total factor productivity of enterprises through the effect of cost saving and technological innovation. The research in this paper not only reveals the impact mechanism of digital transformation on the total factor productivity of enterprises, but also provides micro-evidence for the integrated development of the digital economy and the real economy. The research conclusions have important policies for the formulation of digitalization-related policies and high-quality economic development revelation.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Zheng Xiao
PY  - 2022
DA  - 2022/04/12
TI  - Digital Transformation and Enterprise Total Factor Productivity - Empirical Evidence from Chinese Listed Companies
BT  - Proceedings of the 2022 International Conference on County Economic Development, Rural Revitalization and Social Sciences (ICCRS 2022)
PB  - Atlantis Press
SP  - 60
EP  - 64
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220402.013
DO  - 10.2991/aebmr.k.220402.013
ID  - Xiao2022
ER  -