Digital transformation and innovation activities: evidence from publicly-listed firms in China

Abstract

This study investigates the relationship between digital transformation (DT) and corporate innovation activities. By studying the publicly-listed firms in China, we find that firms with higher levels of DT tend to spend more on research and development (R&D) and tend to generate more innovation outputs. The impact of DT on R&D expenditures is particularly pronounced for firms with more limited disclosure of information, more complex organizational structures, and for those who do not receive government sponsorship. We find no evidence that DT affects innovation through alleviating financial constraints or enhancing operating performance. Collectively, these findings offer novel and practical insights into the interaction between digitization and firms’ innovation.

KEYWORDS:

• Digital transformation
• R&D
• information asymmetry
• textual analysis

Acknowledgements

We thank the editor and two anonymous referees for improving our paper. We also thank Dr. Jeremy St John at Angelo State University for valuable comments. All errors are ours own. Zhan Xu acknowledges the financial supports from the Beijing Municipal Social Science Foundation (No.21GLC051)

Disclosure statement

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

Notes

1 For example, financial constraints (Li 2011; Klassen et al. 2004; Lin 2023), industry concentration (Cornett, Erhemjamts, and Tehranian 2019), the development of equity and credit markets (Hsu, Tian, and Xu 2014), uncertainties about economic policies (Bhattacharya et al. 2017; Khan et al. 2020), geopolitical risks (Jia, Yang, and Zhou 2022), governance structure (Brown and Krull 2008; Luong et al. 2017; Almazan, Hartzell, and Starks 2005; Wan et al. 2021), financial-report quality and coverage (He and Tian 2013; Park 2018), liquidity and short-selling in the stock market (He, Ren, and Tian 2023; Fang, Tian, and Tice 2014), executive background (Chen, Ho, and Ho 2014; Barker and Mueller 2002), investors’ tolerance (Tian and Wang 2014), and economic environment (Leahy and Neary 1997).

2 Boeing. Digital acceleration. Retrieved from https://www.boeing.com/defense/jadc2/digital-acceleration#overview

3 Earnest and Young. (2021). What China can teach the world about digital transformation. EY. Retrieved from https://www.ey.com/en_gl/digital/what-china-can-teach-the-world-about-digital-transformation

4 McKinsey Global Institute (2014). China’s Digital Transformation. Retrieved from https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/chinas-digital-transformation

5 In an unreported test, we conduct weak instrumental-variable and overidentification tests on the instrumental variable (IV). We find that the F-values (Cragg-Donald Wald F statistic) are greater than the critical value, indicating that the IV is not weak. Additionally, the overidentification tests (Sargan’s statistic) show that the P-values are less than the critical value, suggesting that the IV is valid and the problem of overidentifying instruments does not statistically exist.

6 In an unreported analysis, we find that the Chinese stock market reacts positively to the announcement of digital-transformation initiatives, but only when a firm reports positive earnings.

Additional information

Funding

This work was supported by Beijing Municipal Social Science Foundation [grant number: 21GLC051].

Notes on contributors

Zhan Xu

Zhan Xu is Associate Professor of Accounting School of Capital University of Economics and Business, Director of Auditing Teaching and Research Section. Professor Xu received a master's degree from the University of Illinois at Urbana-Champaign in 2012 and a Ph.D. from Renmin University of China in 2017. Dr Xu's main research interests include enterprise digitalisation and enterprise investment. She has supervised 5 projects so far, with “Digitalization and firm innovation” being the most recent research area (funded by the Beijing Municipal Social Science Foundation). She has authored 1 monograph and more than 10 publications in prestigious journals such as Quantitative Finance and Accounting.

Qingbin Meng

Qingbin Meng is Professor and doctoral advisor for the finance department of the Renmin University of China Business School. Corporate finance and capital markets are Dr. Meng's primary research focus. His publications include 70 papers in prestigious journals both domestically and abroad, including international mainstream journals like the European Journal of Operational Research, Journal of Corporate Finance, Journal of Empirical Finance, Journal of Financial Market, Quantitative Finance, and SIAM Journal on Control and Optimization, etc. Besides, he has served as project manager for three Natural Science Foundation projects.

Song Wang

Dr. Song Wang received his doctoral degree in Finance from University of Central Florida in 2012 and his master's in Applied Economics from University of North Dakota in 2008. Dr. Wang's research focuses on security short-selling, the IPO market, corporate restructuring, etc. He has numerous publications in high level finance journals, such as Journal of Banking and Finance, Managerial Finance, Research of Finance, and Applied Financial Economics. Dr. Wang has also been a panel speaker of the SXU Economic Summit jointed by Federal Reserve Bank of Chicago in 2015 and 2016.