Abstract
Previous studies have pointed out that Industry-University-Research Institution (IUR) collaborative innovation is an important means to ensure the sustainable development of regional innovation, and there may be spillover effects among different regional innovation systems. However, the impact of regional spatial correlation and IUR collaborative innovation synergy degree on regional innovation performance is not that clear. Based on the panel data of 31 regions in China from 2006 to 2015, we construct static and dynamic spatial econometrics models to analysis the relationships among regional innovation performance, IUR collaborative innovation and spatial correlation. The research results show that there are significant positive spillover effects among different regions, indicating that the dynamic flows of innovation elements among regions is conducive to improve the regional innovation performance. In addition, IUR collaboration innovation also has a positive impact on regional innovation performance: the current period of IUR synergy degree has a negative impact, while the lagged one has a positive impact. It means that it will take a while for IUR collaborative innovation to be effective and it will have far-reaching contributions to long-term improvements rather than short-term benefits in social development. The results are significant for both static and dynamic spatial econometrics models. The conclusions of this paper have important policy significance to fully understand the coordination of innovative elements and promote the sustainable development of regional innovation systems.
Highlights
There has been a lot of research discussing the importance of knowledge-based innovations, which will always occur when universities, industries and government Research & Development (R&D) institutions interact to find a solution for common problems
There have been some applications of the spatial econometric model in the field of innovation [29,38], most use the social and economic characteristics or geographical characteristics of regions to measure the spatial correlations, which ignore the regional spatial correlation affected by the flows of R&D elements among different regions
Notes: Standard Error is in the brackets; MLE-spatial autocorrelation model (SAR) represents the spatial autocorrelation (SAR) model estimated by the maximum likelihood estimation (MLE) method, MLE-SAR-FE represents the SAR fixed effect (FE) model estimated by the MLE method, GMM represents the model estimated by GMM method; the same applies below
Summary
There has been a lot of research discussing the importance of knowledge-based innovations, which will always occur when universities, industries and government Research & Development (R&D) institutions interact to find a solution for common problems. One is the use of the resources within the regional innovation systems, including the cooperation of government, industries, universities and research institutions, which we call IUR collaborative innovation. An analysis of the effects of IUR collaborative innovation synergy degree on regional innovation performance that consider regional spatial correlation is imperative. The remainder of this paper is structured as follows: the second part reviews the literature in related fields; the third part introduces the measure methods of indices used in the spatial econometric model, including regional innovation performance, collaborative innovation of IUR and spatial correlation matrix; the fourth part builds the spatial econometric model; the fifth part contains empirical analysis results and discussions; and the sixth part gives the conclusions and policy recommendations
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