Does digital transformation curb the formation of zombie firms? A machine learning approach

ABSTRACT

Based on the dynamic capability view theory, we examined the impact of digital transformation (DT) on zombie firm formation using a sample of listed Chinese firms. Through textual analysis of firms’ annual reports, we categorized DT into three dimensions: strategy, technology (i.e. artificial intelligence, blockchain, cloud computing, and big data), and application. Then, we employed 11 machine learning algorithms to detect zombie firms, compared the prediction performance, and calculated the contribution of each DT indicator. The results show that DT can effectively curb the formation of zombie firms. Specifically, big data contributes the most to suppressing the prevalence of zombie firms, followed by artificial intelligence, cloud computing, and DT application. Nevertheless, DT strategy and blockchain cannot reduce zombie likelihood. Finally, our research offers valuable insights for policymakers to address the issues of zombie firms.

KEYWORDS:

• Digital transformation
• zombie firms
• dynamic capability view
• machine learning

Acknowledgments

We gratefully acknowledge insightful suggestions from the editors and the anonymous reviewers, which substantively improved this article. We would also like to thank Jing Luo for his help in data processing and analysis and other members of Star-lights Machine Learning Research Team for their comments on earlier versions of the manuscript.

Disclosure statement

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

Additional information

Funding

This work was supported by the Humanities and Social Sciences Foundation of the Ministry of Education of China [grant number 21YJC630076].

Notes on contributors

Mingyue Wang

Mingyue Wang is currently a research assistant at Business School, Shandong University, Weihai, China. She is a member of Star-lights Machine Learning Research Team. Her research interest is on digital transformation, technology management, and innovation management.

Yanling Yu

Yanling Yu is a postgraduate student at Business School, Shandong University, Weihai, China. Her research interests span digital transformation, operations management and technology management.

Feng Liu

Feng Liu is an Assistant Professor at Business School, Shandong University, Weihai, China. He is Principal Investigator (PI) of Star-lights Machine Learning Research Team. He got his Ph.D. from the Department of Logistics, Service and Operations Management (LSOM) at Korea University Business School (KUBS), Seoul, Korea. His current research interests include supply chain management, operation management, technology management, and the intersection of artificial intelligence (machine learning and deep learning). He serves on the editorial board of Small Business Economics, Journal of Competitiveness, and Humanities & Social Sciences Communications. His research has appeared in in the International Journal of Production Economics, Technology Analysis & Strategic Management, Technological Forecasting and Social Change, International Journal of Physical Distribution and Logistics Management, China Economic Review, and others.