2088 
1737In China, with the advancement of technologies like big data, cloud computing, and AI, firms have gained strong technical and market support for digital transformation, which has become a key driver for China’s new industrialization and high-quality economic development. However, opinions differ on how digital transformation affects firm competitiveness. As a result, how to evaluate the effects of digital transformation has become a significant topic in both industry and academia.
Taking empirical data from China’s listed industrial firms from 2004 to 2021, this paper extends the theoretical model of Melitz and Ottaviano (2008) to investigate the impact of digital transformation on markup. The findings indicate a significant U-shaped relationship between digital transformation and markup. At a low level of digitalization, transformation may reduce markup, but as digitalization past a certain point, continued transformation can notably increase markup. Mechanism testing suggests that key factors contributing to this relationship are operating costs, maintenance investments, and production efficiency. Further, it is found that the impact of digital transformation varies among different types of firms. For those located in western regions, SOEs, larger firms, or firms with lower human capital, digital transformation does not produce a significant U-shaped impact on markup. Extended analysis shows that mid-stage transformation often yields the best results, while late-stage transformation tends to be less effective, suggesting a clear disadvantage for late adopters. A detailed analysis by dimension demonstrates that digital technologies in smart manufacturing, data processing, business modes, and digital content all have a significant impact on markup, with data processing having the greatest effect.
The contributions of this paper are that: (1) It uses a non-linear mediation effect model to study the impact of digital transformation, resulting in conclusions that differ from previous studies. (2) It provides a theoretical explanation for the staged impact of digital transformation. (3) It utilizes a rich dataset for empirical analysis, unveiling the underlying mechanism and heterogeneity of the impact of digital transformation. (4) It analyzes the economic consequences of the timing of digital transformation from a dynamic perspective, considering the dimensional heterogeneity of digital technology, thereby providing evidence for understanding the disadvantages of late adopters in the digital economy and offering guidance for firms on their digital transformation pathways.
