Attention to scientific and technological talents and corporate business model innovation – Causal inference based on double machine learning

Using a sample of Chinese A-share listed companies from 2014 to 2022, this paper employs a staggered DID model and a double machine learning approach to systematically examine the impact and mechanism of local governments’ attention to scientific and technological talent on firms’ business model innovation. The results show that heightened attention to scientific and technological talent by local governments significantly enhances the level of business model innovation among enterprises. This conclusion remains robust after a series of placebo tests, variable substitutions, and model adjustments. Mechanism analysis reveals that the effect operates through two channels: the “talent agglomeration effect” (attracting inflows of highly educated talent and optimizing firms’ human capital structure) and the “resource allocation effect” (increasing firms’ likelihood of receiving government innovation subsidies and mitigating innovation risks). Further heterogeneity analysis indicates that the promoting effect is more pronounced in high-tech industries, megacities, and technology-intensive firms. This study provides a novel perspective on how local government talent policies foster enterprise innovation by influencing both internal talent structures and external resource acquisition, and offers theoretical insights for local governments formulating talent policies and for enterprises optimizing innovation strategies.