Data Element Agglomeration, Regional Innovation Ecosystem and Innovation Quality

The global economic landscape is currently undergoing profound structural adjustments, characterized by the ascent of emerging economies and concurrent challenges facing established economic powers. Within this context, innovation has emerged as a pivotal strategy for enhancing national competitiveness and securing strategic advantages in future development. We utilize citation counts of a patent in this article, which is more internationally recognized, as a characterizing variable of innovation quality, and use the accelerated genetic algorithm-optimized projection pursuit method (RAGA-PP) to calculate data element agglomeration. Based on the 2012 to 2022 Chinese provincial panel data, we first employ a fixed effect model to examine the direct effect of data element agglomeration on innovation quality. Secondly, the research framework includes a regional innovation ecosystem. The mediating effect model and the threshold regression model are used to investigate the indirect and non-linear effects of data element agglomeration on innovation quality, with the technological innovation subjects, R&D funds, and market environment serving as the respective constraints. It is found that: innovation quality is positively impacted by data element agglomeration, with a “central > west > east” effect. Data element agglomeration can indirectly improve innovation quality by boosting the number of technological innovation subjects, increasing the investment of R&D funds, and creating an open market environment structure; With the thresholds of technological innovation subjects, R&D funds, and market environment crossed, data element agglomeration can exert a more favorable impact on innovation quality. The conclusions offer governments some pointers for developing regulations for regional differentiation and creating a welcoming digital environment.