Transformations  in
Business & Economics

EVOLUTIONARY EFFICIENCY EVALUATION AND INFLUENCING FACTORS OF HIGH-TECH INDUSTRY INNOVATION ECOSYSTEMS
Jianzhao Yang, Jingwei Zhu, Hairong Zhou

ABSTRACT: As a core driver of economic restructuring and technological innovation, the high-tech industry plays a crucial role in enhancing regional innovation capacity and industrial competitiveness. However, existing studies lack a systematic and phased evaluation of the evolutionary efficiency of high-tech industry innovation ecosystems. To explore the efficiency and influencing mechanisms of such evolution, drawing on ecosystem evolution and complex systems theory, a two-stage DEA-Tobit analytical framework was constructed. Using the panel data from 30 national high-tech industrial parks in China for 2018, the evolutionary efficiency of the innovation ecosystem in two stages (innovation generation and achievement transformation) was measured, and the effects of input factors as well as policy, economic, social, and technological environments on system efficiency were further identified. Results reveal that the innovation ecosystem exhibits relatively high efficiency in the intermediate output stage but shows significant inefficiency in the final output stage. Talent quality, number of enterprises, economic development, and technological factors exert significant positive effects on efficiency, whereas policy interventions and energy inputs exhibit inhibitory effects. Regional heterogeneity is also evident: eastern regions outperform others in achievement transformation efficiency, while certain central and western regions achieve breakthroughs in the intermediate stage through organizational innovation. The conclusions enrich the theoretical framework for evaluating the evolutionary efficiency of innovation ecosystems and provide empirical evidence and policy implications for optimizing regional innovation environments, improving policy systems, and promoting the high-quality development of high-tech industries.

KEYWORDS:  high-tech industry, innovation ecosystem, evolutionary efficiency, two-stage DEA model, Tobit regression

JEL classification:  O36, M21, C52.