# Optimizing regional innovation ecosystems through actor-environment coevolution: A dynamic configurational analysis from a CAS perspective

## TL;DR

This paper studies how innovation in different regions of China evolves through interactions between actors like governments and companies, and environmental factors like openness and human capital.

## Contribution

The study introduces a novel theoretical framework using complex adaptive systems to analyze regional innovation ecosystems.

## Key findings

- Significant regional differences in innovation performance exist across China’s eastern, central, and western regions.
- Strong government investment is necessary for high innovation performance, while weak enterprise R&D leads to low performance.
- Five configurations drive high innovation, and four drive low innovation, highlighting the importance of actor-environment interactions.

## Abstract

The interaction among innovation actors and environmental uncertainties has intensified the complexity of the evolution and drivers of regional innovation ecosystems, with profound implications for regional economies. This research aims to investigate the evolution and impact mechanisms of regional innovation ecosystems through the complex adaptive systems perspective. Employing an integrated methodology, we combine quantitative performance evaluation with dynamic fuzzy-set qualitative comparative analysis to examine 30 Chinese provinces. Specifically, we investigate how innovation actors (governments, enterprises, universities) and key evolving institutional environment (openness, human capital and innovation platform) interact, adapt, and collaborate to shape innovation performance. We also analyze temporal changes and regional disparities. Our findings reveal significant differences in regional innovation performance across China’s eastern, central, and western regions, though all regions exhibit yearly improvement. Strong government investment in innovation is a necessary condition for high-level regional innovation, while weak enterprise R&D inputs are a necessary condition for low-level regional innovation. The analysis identifies five distinct configurations driving high innovation performance and four configurations associated with low-level regional innovation. The between-group consistency reveals that regional innovation increasingly depends on complex interactions among multiple actors and environments. The within-group consistency indicates that in developed eastern regions, innovation is driven by multiple innovation actors and environments, with high external knowledge dependence due to openness. In contrast, innovation in underdeveloped western regions mainly relies on internal factors like government and enterprise innovation investments. Case studies include both developed and lagging regional innovation ecosystems. Our research offers a novel theoretical perspective for understanding regional innovation ecosystem evolution. The findings provide policymakers with a scalable framework to tailor region-specific innovation strategies across diverse contexts, with insights applicable to innovation research in other regions.