Research on multiple improvement paths of innovation performance in regional innovation ecosystem based on fuzzy qualitative comparative analysis

Prioritizing the development of regional innovation ecosystems (RIE) is essential for advancing China’s innovation-driven strategy. This study investigates how combinations of innovation elements, namely resources, services, achievements, and environment, affect regional innovation performance. Drawing on both innovation value-chain and ecosystem perspectives, we analyze the data from 31 Chinese provinces using fuzzy-set qualitative comparative analysis (fsQCA) and artificial neural networks (ANN). Our findings identify five distinct configurations that lead to high innovation performance and three that result in low or medium performance. High performance configurations include: resource achievement dual drive, outcome driven, and resource driven. Low performance configurations include talent shortage type, service deficiency type, and resource service dual weakness type. These results illustrate the principle of equifinality, indicating that different regions can achieve similar innovation outcomes through different pathways, and they underscore both the substitutability of certain elements and the synergy among them. Theoretically, the study advances configurational approaches in innovation research by integrating ecological and value-chain perspectives. Practically, it provides differentiated policy insights, suggesting that regions should tailor their innovation strategies to leverage specific strengths and thereby foster high-performance outcomes. First published online 4 March 2026