The more the better? The nonlinear innovation effect of the big data policy

Although big data policy has been widely regarded as a key instrument for promoting firm innovation in the digital era, many policies still fall short of expectations, prompting growing scholarly attention to the relationship between big data policy and firm innovation performance. Existing studies offer inconsistent insight because they examine policy effects through linear lenses such as signaling, rent-seeking, or isolated resource provision, while paying insufficient attention to the underlying resource mechanisms embedded in big data policy and how firms' internal and external contexts shape their responses. By integrating the resource-based view and neoinstitutional theory, this study develops a resource-based and institutionally embedded explanation of how big data policy intensity affects firm innovation performance. Using panel data on Chinese A-share listed manufacturing firms from 2013 to 2022, this study reveals that the big data policy intensity has an inverted U-shaped effect on innovation performance. Furthermore, the unabsorbed slack and absorbed slack exert heterogeneous moderating effects on this inverted U-shaped relationship. This study further finds that marketization and absorbed slack can jointly moderate the relationship between big data policy intensity and innovation performance. Overall, this study extends the policy–innovation literature by revealing when big data policy facilitates or constrains firm innovation, while offering practical implications for policymakers and firms.