Innovation systems and evolutionary economics: honoring Richard Nelson through evolutionary plasticity and insights from China
Xiaolan Fu & Qianyue Qiao
Abstract
This article first reviews the theoretical foundations of evolutionary economics, its main critiques, and intersections with other intellectual traditions, highlighting the diffusion of this perspective into the National Innovation System (NIS) framework and Richard Nelson’s seminal contributions. It then applies this evolutionary lens to analyze China’s NIS from two complementary perspectives—historical and open-system—offering a more integrative understanding of its development. Drawing on extensive industrial- and firm-level evidence, the study introduces the concept of Evolutionary Plasticity of Innovation Systems, inspired by biological evolution, to explain how similar institutional architectures can generate divergent innovation trajectories across sectors, regions, and countries. This framework contributes to evolutionary economics by extending beyond the prevailing static, discrete categorical epistemology and essentially correlational analysis in nature by providing a generative lens for understanding how innovation systems evolve, persist, and co-adapt under varying conditions, building on Nelson’s vision of the economy as an adaptive system. Together, the study advances the evolutionary approach by emphasizing institutional expression and adaptive reconfiguration over replacement, reaffirms the explanatory power of national-level analysis in an increasingly interconnected world, and calls for more analytically sophisticated and theoretically informed examination of innovation dynamics at this level.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.