Author(s)

Yuyang Tang

Corresponding Author

Yuyang Tang

Abstract

As a strategic technology leading the new round of scientific and technological revolution and industrial change, whether Generative Artificial Intelligence(AIGC) can drive traditional enterprises to break through organizational boundaries and realize cross-border innovation is a core issue of concern for both academics and practitioners. This paper takes Chinese A-share listed companies in Shanghai and Shenzhen from 2011 to 2022 as research samples, constructs firm-level generative artificial intelligence development indicators, empirically examines them using a multidimensional fixed-effect model, and ensures the reliability of the conclusions after a series of robustness tests. The study finds that: firstly, AIGC significantly promotes cross-border innovation; secondly, the driving effect of technology on convergence innovation is stronger than that of pure digital innovation, but there exists the "innovation quantity-quality paradox"; thirdly, the mechanism analysis confirms that the "upgrading of human capital structure" and "upgrading of R&D structure" are more important than the "innovation quantity-quality paradox". Third, the mechanism analysis confirms that "human capital structure upgrading" and "R&D resource adsorption" are the two key paths; finally, the heterogeneity analysis shows that the technology effect is more significant in state-owned enterprises, high-tech enterprises and non-regulated industries. This study provides new empirical evidence for understanding the macroeconomic consequences of AIGC, and has important implications for policymaking in digital transformation.

Keywords

generative artificial intelligence, cross-border innovation, human capital structure upgrading, R&D resource adsorption