The impact of network infrastructure on enterprise digital transformation ——A quasi-natural experiment from the “broadband China” Strategy

ABSTRACT

In this paper, a multi-period difference-in-difference model is constructed to investigate the impact of network infrastructure on enterprise digital transformation. The results show that network infrastructure has effectively promoted the digital transformation of enterprises. Meanwhile, as the scale of network infrastructure construction expands, the role of network infrastructure in promoting enterprise digital transformation will be enhanced. Based on the mechanism analysis, network infrastructure promotes enterprise digital transformation through two channels: easing corporate financing constraints and improving core technical capabilities. Furthermore, the role of network infrastructure in enterprise digital transformation is not invariable, the effect is more pronounced in non-state-owned enterprises, growth enterprises, manufacturing enterprises, and enterprises located in developed cities.

KEYWORDS:

• “Broadband China”
• Network infrastructure
• Digital transformation
• Financing constraints
• High-tech industry

JEL CLASSIFICATION:

• C51
• D21
• H54
• O12

Acknowledgments

The authors thank the editorial team and anonymous reviewers for their insightful comments on the earlier version of this paper.

Disclosure statement

No potential conflict of interest was reported by author(s).

Notes

¹ The data of ‘Broadband China’ strategic pilot cities comes from the ‘Notice of the General Office of the Development and Reform Commission of the Ministry of Industry and Information Technology on Launching the Work of Creating “Broadband China” Demonstration Cities (Urban Clusters)’ in 2014, 2015 and 2016. Among them, there were 41 pilot cities(districts) in 2014, 40 pilot cities(districts) in 2015 and 39 pilot cities(districts) in 2016.

² Due to the fact that the firm changed its main business, the fixed effects of firm and industry were also controlled in the model.

³ In the demonstration cities, data from autonomous prefectures (Yanbian Korean Autonomous Prefecture, Wenshan Zhuang and Miao Autonomous Prefecture, and Aba Tibetan and Qiang Autonomous Prefecture), county-level cities (Yongcheng), and Tibet are missing.

⁴ The data comes from Yuan et al. (2021) and can be downloaded from the attachment of the website of China Industrial Economy (http://ciejournal.ajcass.org).

⁵ The detailed measurement process can be found in the original attachment.

⁶ The standard deviation of Mb is higher than for the other variables, indicating a greater variation in the degree of growth among the companies. The large disparity in the administrative hierarchy and economic development levels between the cities involved in this paper explains Minteg’s high standard deviation. In particular, the level of market integration in the four municipalities directly under central government is significantly higher than in prefecture-level cities.

⁷ The first batch of treatment groups were pilot cities in 2014, the first two batches of treatment groups were pilot cities in 2014 and 2015, and the complete three-batch treatment groups were three-batch pilot cities. Non-pilot cities were the control group.

⁸ The data comes from the CSMAR database.

⁹ To test the robustness of the mechanism test results, this paper draws on the practice of Wen et al. (2004) and employs the mediating effect model. The results of the mediating effect test are not reported in the main text but are available upon request from the authors.

¹⁰ A professional financial website.