Regional Innovation Capability Research Articles

Effects of Regional Innovation Capability on the Green Technology Efficiency of China’s Manufacturing Industry: Evidence from Listed Companies

The purpose of the “Made in China 2025” strategy is to enhance the innovation capabilities of the local manufacturing industry and achieve green and sustainable development. The role of innovation in the development of manufacturing is a hotspot in academic research, though only a few studies have analyzed the interaction between green technology manufacturing efficiency and its external innovation capabilities. This study used the 2011–2017 Chinese A-share listed manufacturing companies as samples to discuss whether regional innovation capabilities can promote the improvement of green technology manufacturing efficiency. The results showed that a significant spatial correlation between regional innovation capability and green technology manufacturing efficiency was prevalent within spatial heterogeneous bounds. In addition, regional innovation capability directly promoted the effective manufacturing of green technology efficiency, which was strongest in the eastern region of the country. Regional innovation capabilities also had a positive effect on human capital and government revenue, thereby further enhancing the green technology efficiency of manufacturing through the intermediary effect. Based on the above conclusions, some policy recommendations are put forward to facilitate the improvement of China’s regional innovation capabilities in terms of green technology efficiency in manufacturing.

Why Is Collaborative Agglomeration of Innovation so Important for Improving Regional Innovation Capabilities? A Perspective Based on Collaborative Agglomeration of Industry-University-Research Institution

Exploring the impact of collaborative agglomeration of industry-university-research institution innovation on regional innovation capabilities is of great significance for promoting China's high-quality economic development. This paper introduces the level of innovation collaborative agglomeration into the C-H production density model to theoretically explain the mechanism of the influence of innovation collaborative agglomeration on regional innovation capacity. On this basis, using the 2011–2017 Chinese subprovincial panel data to measure the level of regional innovation collaborative agglomeration and establishing a spatial model, the impact of innovation collaborative agglomeration on regional innovation capacity is empirically examined in two stages: knowledge innovation and outcome transformation. The study finds that the innovation collaborative agglomeration and the regional innovation capacity present a typical inverted U-shape relationship, while the human capital and the regional innovation capacity present an inverted N-shape relationship in the stage of knowledge innovation. There is a U-shaped relationship between the innovation collaborative agglomeration and the regional innovation capacity in the outcome transformation stage, while the impact of human capital on regional innovation capacity is not obvious. This result is still robust after replacing the core explanatory variables and the spatial weight matrix. In terms of three regions, the innovation collaborative agglomeration and the human capital in the middle and eastern regions have a stronger impact on regional innovation capacity than in the western region. The findings of this paper provide policy insights for the innovation collaborative agglomeration of industry, university, and research institution to promote regional innovation capacity.

Research on the influence of OFDI on Jiangsu’s regional innovation capability

초록 · 키워드 목차 오류제보하기 China"s economy is shifting from a stage of high-speed growth to a stage of high-quality development. How to use increasing foreign direct investment to enhance China"s overall technological innovation capability and promote "innovation-driven" strategic transformation has become the focus of attention everywhere. Based on the analysis of the characteristics of Jiangsu Province’s foreign direct investment, This essay also analyzed the panel data in 13 cities of Jiangsu Province in 11 years to find out its reverse technological overflow effect of OFDI. Research has found out that OFDI has positive influence on the regional innovation capacity in Jiangsu Province. After further analysis in different regions in Jiangsu province, it should be noted that different regions of Jiangsu Province are in the different development stages of OFDI. In the end, we can conclude some constructive suggestions and policy recommendations for the effective improvement of the reverse technology spillover effect of Jiangsu’s OFDI on technological progress. #OFDI逆向技术溢出 #区域创新能力 #江苏省 #OFDI reverse technology spillover #Regional innovation capability #Jiangsu Province 摘要Ⅰ. 绪论Ⅱ. 相关概念与文献综述Ⅲ. 江苏省对外直接投资现状Ⅳ. 江苏省OFDI对区域创新能力影响的实证分析Ⅴ. 结论与政策建议参考文献Abstract

The Impact of Bank Credit and Technological Innovation on Economic Growth in Jiangsu Coastal Areas

Jin, Y. and Shi, D., 2020. The impact of bank credit and technological innovation on economic growth in Jiangsu coastal areas. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 35-38. Coconut Creek (Florida), ISSN 0749-0208.Regional innovation capability is the core and driving force of regional economic development, especially its key role in economic growth has been confirmed by many related research results. With the continuous progress of the society, the pace of economic and financial development has accelerated, the living standard of people has improved obviously, and the consumption demand is also strengthening. Only when the economy develops to a certain extent can financial development promote the improvement of the quality of economic growth. It is even more necessary to encourage innovation, protect innovation and help innovative talents to stand out in the social environment. Therefore, Jiangsu coastal areas should adopt a point-axis development model, further improve the financial structure, promote the integration of financial resources, and support the innovative and coordinated development of coastal areas in order to achieve the overall and coordinated development of Jiangsu economy.

The Effects of Fiscal and Tax Incentives on Regional Innovation Capability: Text Extraction Based on Python

The regulation of fiscal and tax policies is an imperative prerequisite for improving the regional innovation capability. In view of this, an attempt was made to select 31 provinces and cities in China as the research object from 2009 to 2018, to extract the fiscal and tax policy text encouraging innovation of the Chinese provinces and cities based on Python, and analyze their impact on regional innovation capability from both a text data and numerical data perspective. It is noteworthy that most of the provincial fiscal policies just follow the national fiscal policies. Each province does not formulate fiscal and tax policy according to its own unique characteristics. Fiscal policies and regional innovation capability exhibit significant spatial heterogeneity. Based on the results of the dynamic panel data model, it is seen that the R&D input and industrial structure are the main sources of improving innovation capability. The fiscal expenditure for science and technology, fiscal and tax policy text, macro tax burden, business tax (BT), and value-added tax (VAT) have a significant boosting effect on the regional innovation capability. However, the corporate income tax hinders the regional innovation capability. Finally, through the robustness test of invention patents, it is found that the fiscal and tax policy text, macro tax burden, and business tax still have a positive effect on invention patents, but the role of value-added tax has changed from promotion to obstruction, and the corporate income tax has become a significant obstacle on invention patents. This shows that China should build a tax system that promotes fair competition, reduce the tax burden of enterprises, encourage enterprises to conduct independent R&D, and guide enterprises in the evolution from the low-tech to high-tech innovation by improving the tax structure and fiscal technology expenditures.

Natural resources, urbanization and regional innovation capabilities

This paper used panel data of 30 regions/provinces of China from year 2005–2018 to empirically test the impact of abundance of natural resources and urbanization on regional innovation capabilities. The findings from the empirical analysis indicate that (1) the spatial effects on regional innovation capabilities are significant, and cannot be ignored. It shows that the optimal model is a spatial doubly model with dual fixed effects. (2) the richer the natural resources, the more unfavorable is the improvement of the innovation capacity of the region and surrounding areas. Urbanization has improved the region's innovation capabilities at regional level, but it is negatively related to the innovation capabilities of surrounding areas. (3) In terms of control variables, the average years of education is conducive to the improvement of innovation capacity in the region, and negative impact on the innovation capacity of adjacent area. Foreign direct investment (FDI) has promoted the regional innovation capacity in the region and adjacent area. The system has strengthened the regional innovation capacity, but the impact on the surrounding areas has not been significant.

Research on the Evaluation of Innovation Efficiency and Its Influencing Factors in 11 Eastern Coastal Provinces and Cities: Based on Three-stage DEA and Tobit Models

Based on panel data from 2008 to 2017, a BCC model and a DEA-Tobit model based on a three- stage DEA method are established to study the regional innovation capabilities and their influencing factors of 11 provinces and cities in the eastern coastal areas of China. The research finds that the innovation efficiency in the eastern coastal areas is still far from the frontier of innovation efficiency, and the low scale efficiency is the main factor restricting its development. In terms of influencing factors of innovation efficiency, improving technological level, optimizing market environment, and expanding enterprise scale can improve regional innovation efficiency, while the irrational expansion of the financial industry will hinder the development of innovation capabilities. Based on the research conclusions, policy suggestions such as actively exerting the effects of industrial agglomeration, deepening the reform of old industrial bases, and optimizing the innovation environment are put forward.

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

AbstractThe relationship between collaborative innovation and innovation efficiency has attracted the attention of researchers in recent years. However, few studies have integrated intra‐regional and inter‐regional collaborative innovations (IRCI) into a unified framework to analyze the overall impact of regional innovation efficiency. To fill this gap, this paper uses an improved Data Envelopment Analysis Model to measure the innovation efficiency of Chinese cities from 2003 to 2016 based on the regional innovation capability. Using the Coupling Coordination Degree Model to measure the degree of intra‐regional collaborative innovation, we constructed a Capability Structure Model to measure the degree of IRCI, then used the Spatial Durbin Model to empirically analyze the influence of intra‐regional and IRCI on regional innovation efficiency. The results show that: (a) both intra‐regional and IRCI promote regional innovation efficiency, but the internal factors are the primary influences on regional innovation efficiency; (b) intra‐regional collaborative innovation not only promotes local regional innovation efficiency but also promotes innovation efficiency in other regions effectively, although its driving effect on the local region is higher than on other regions; (c) there is a time lag in promoting regional innovation efficiency through the cooperation and interaction among innovation in knowledge, technological, industrial, and service, and environment capabilities. The regional innovation capacities can result in good collaborative innovation effects only after a certain period of cooperation.

A Study of Regional-Level Location Factors of Car Manufacturing Companies in the EU*

The paper aims to explore how factors of regional competitiveness are associated with the location of car manufacturing companies in the EU. Although the European automotive market can be characterized by an intense dynamics in terms of location choices, literature offers little empirical guidance on how regional factors influence the location of car manufacturers in the EU. This paper aims to fill this gap by combining regional competitiveness data on 276 EU regions with the actual location of all 269 production units of car manufacturing companies currently present in the EU. Logistic regression is used to discover significant relationships, while the comparative analysis of clusters of regions is meant to offer a more detailed understanding of the role of different location factors. Results of the analysis show that the most influential location factor is related to infrastructural development, but other competitiveness factors, such as regional innovation capabilities or labour market efficiency, might also play an important role.

A Study on Innovation Radiation Effects of Shanghai Science and Technology Innovation Center from the Perspective of Field Theory

The way in which science and technology innovation center drives regional innovation capabilities depends on the spillover and radiation of innovation resources. Starting from the connotation of the science and technology innovation center, combined with the electric field theory in physics, the innovative radiation field with the science and technology innovation center as the field source is constructed, and the field source intensity is quantified from four aspects: innovation resources, innovation input, innovation achievements and innovation carrier. Taking the Shanghai science and technology innovation center as the research object, and using 2014 as the base year, the innovative radiation effects of the Yangtze River Delta urban agglomeration before and after construction was measured. The research shows that the radiation intensity of Shanghai has increased significantly since the establishment of the science and technology innovation center, but the overall radiation pattern has changed little and there is still a lot of room for improvement. Finally, the countermeasures for enhancing the innovative radiation effect of the Shanghai science and technology innovation center are proposed.

Relationship Between the Built Environment and the Location Choice of High-Tech Firms: Evidence from the Pearl River Delta

With the transition in the regional development discourse to knowledge- and innovation-based economics, the cultivation of innovation capacity has gained importance as an initiative to enhance regional sustainability and has emerged as a policy goal. An understanding of the formation of innovation clusters is critical to the cultivation of regional innovation capabilities. Except for the location and regional development conditions’ factors, researchers emphasize a built environment’s role in the formation of innovation clusters. Based on the spatial database of 12,516 high-tech firms in 2017 in the Pearl River Delta (PRD), this study developed a conceptual framework for the built environment. The framework comprises living, service, perceptual, industrial, and physical aspects. The direction and intensity of the correlation between built environment factors and high-tech firms are discussed using the spatial regression model and geographical detector (GD) technique. The results show a highly concentrated spatial distribution pattern of high-tech firms in the PRD. A significant county-level spatial autocorrelation is revealed through Moran’s I. According to the model, we determine the positive impacts of technology support, transport infrastructure, and living service levels on the agglomeration of high-tech firms as well as the negative impact of the public service level. The GD’s result demonstrates different levels of impact intensity of built environmental factors. We argue that a comprehensive understanding of the influence of built environment factors on innovation agglomeration will help policymakers develop targeted policies to foster local innovation capabilities and promote sustainable regional development.

Evaluation of government‐supported provincial innovation competitiveness—Evidence from China

AbstractGovernment support plays a crucial role in enhancing regional innovation capabilities and creating distinctive innovation clusters. Using the innovation competitiveness indicators of the 31 provinces in China, this paper examines the provincial government collaboration and innovation competitiveness values through Super‐SBM data envelopment analysis and analytic hierarchy process. Then, the particle swarm optimization (PSO) algorithm is employed to relate government collaboration to innovation competition and cooperation among provinces in hope of exploring the developmental trends of innovation cluster optimization. Research findings show that only 25.8% of the provincial government support proves effective for regional innovation, and the competitiveness values of the provinces are generally low and need to be improved. By incorporating the role of government support into the innovation competition and cooperation among provinces, there has been a tendency of innovation activities clustering in innovative regions and three major clusters have formed. The trend of clustering will continue to evolve outwards so that the overall innovation level of China would be enhanced.

DID model based research on the policy effect of national independent innovative demonstration zones

National Independent Innovation Demonstration Zone set up as combination of regional technology innovation policy in nine areas of Beijing Zhongguancun, East Lake in Wuhan, Shanghai Zhangjiang as a national independent innovation demonstration area as a sample of policy mix to when the impact of technological innovation ability by using Difference-In-Difference model. Studies have shown that compared to after the implementation of the policy, local regional technological innovation capability has increased, and fail to carry out the policy of Chongqing, Shenyang, Beijing and Wuhan, to carry out the policy of regional technological innovation capability enhancement is more obvious;The whole society investment in fixed, population density and area of technical level of regional technology innovation in inhibition, human capital, research funding, foreign real investment and government funding support to promote the regional technology innovation, After we re-estimate the same models by the stability test and conclude the similar results.

Research on the Influence of Industrial Upgrading on Independent Innovation Ability in China—Based on Inter-Provincial Panel Data

According to the different levels of economic development in China’s provinces, this paper uses principal component analysis to establish a comprehensive index system for evaluating the level of economic development, and then uses cluster analysis to classify 31 provinces in China into economically developed regions, economically relatively developed regions, and economically underdeveloped regions. Next, based on the results of regional division, this paper uses the fixed panel data model to empirically analyze the impact of national and regional industrial upgrading on independent innovation capability, and the model is tested for robustness by systematic generalized moment estimation. The results show that: Industrial upgrading can effectively promote the improvement of national and regional independent innovation capabilities. On the whole, the industrial upgrading transferred to the tertiary industry can effectively promote the improvement of independent innovation capability; moreover, in both economically developed regions and economically underdeveloped regions, the process of industrial upgrading to the secondary and tertiary industries can effectively promote the improvement of regional independent innovation capabilities. Finally, according to the results of empirical analysis, the paper puts forward policy enlightenment of industrial upgrading and suggestions on improving independent innovation ability.

The role of firm-level factors and regional innovation capabilities for Polish SMEs

The paper elaborates on the innovativeness of Small and Medium-Sized Enterprises in Poland from the regional perspective. The empirical evidence is based on data collected among 820 Polish SMEs which actively use ICT tools in their business processes. Identifying firm-level (internal) and regional drivers of innovations in these enterprises was the main aim of this study. The originality of the utilized research approach lies in combining within one framework firm-level data with meso data describing the innovative potential of the regional environment and using multilevel random-effects models to analyze the relevance of firm-level and regional drivers of SMEs’ innovativeness. By deploying a regional random effects approach, we assessed indirectly the effectiveness of innovation policies conducted in Polish NUTS 2 regions within a RIS and S3 framework. Interestingly, the research hypothesis, stating that regional (external) factors are more important to enhance innovativeness of SME than firm-level (internal) drivers, was verified negatively. The study revealed that SMEs in less-developed regions of Poland rely more on in-house capabilities, than on the regional innovative potential, to introduce different types of innovations. This suggests that the S3 framework in less-developed regions should concentrate more on linking firm-level factors and regional innovation systems to enhance companies’ innovation capacity.

Coupling and Coordination Analysis of Incubator and Regional Economy

Incubators, as a policy tool to enhance regional innovation capabilities and revitalize the regional economy, are worthy of attention in terms of regional development. It is important to understand the coupling and coordination between them. Based on the theoretical analysis of the interaction mechanism between them, this paper introduces the comprehensive index system of subsystems and constructs a coupling coordination model to carry out the development level of incubators and regional economic in 31 provinces regions in China from 2008 to 2014. The research results show that the two systems are significantly related, and the level of coupling and coordination in whole regions is steadily rising. The spatial distribution pattern of the coupling coordination degree is “pyramid”. The synchronous development of the incubator and the regional economy is not realized in most areas. The government should consider improving the incubator efficiency and regional economic development strength in improving regional economic efficiency and regional innovation capability, so as to achieve coordinated and sustainable development of them.

Measurement and Convergence of China’s Regional Innovation Capability

Regional economic differences occur in China; narrowing such differences requires solving the issues of regional innovation differences, which consist of innovation capability difference, innovation difference in structure and innovation efficiency difference. Additionally, regional innovation capability disparity is important to explain the imbalances between regional and international economic development. Thus, from the perspective of hi-tech industries, this study adopts the Theil index to analyse innovation capability difference and its evolution law in China and its five regions in 1998–2015 to explain the reasons for the differences in regional innovation capability on the spatial and factorial levels. Then convergence of innovation capability on different space scales is analysed by adopting the spatial panel of the β convergence model.

The Impact of MNCs’ R&D Centers Aggregation Level on Regional Innovation Capability

Based on the panel data of seven economically developed regions in China from 2006 to 2015, the STATA 15.0 is used to study the direct and indirect effects of the R&D center aggregation level on regional innovation capability by the fixed-effect regression method. The results show that, from its direct impact, active establishment of multinational companies R&D centers and their agglomeration would positively influence regional technological innovation ability. From the indirect effect, the aggregation of R&D center of MNCs improves regional innovation ability through the flow and agglomeration of talents, research and development funds. Finally, based on the research results, relevant suggestions are put forward.

Has the Internationalization of Universities Improved Regional Innovation Capability

In the regional innovation system, government is the main part of institutional innovation, enterprise is the main part of technological innovation, and university is the main part of knowledge innovation. In other words, government is responsible for providing institutional support for effective innovation, enterprise is responsible for adapting to the market and applying R&D to business applications, and university is responsible for the production, spreading and transfer of knowledge. With the rapid economic globalization, international cooperation and cross-border alliances among each innovation entities have an increasing influence on regional innovation capability. Existing literature has done a lot of research on the internationalization of enterprises and innovation. However, university as an important part of social innovation system in the era of knowledge economy, the influence of its degree of internationalization on innovation capability is rarely researched. In theory, the internationalization of university is an international learning-by-alliances”. On the one hand, it increases the international spread of knowledge through international textbooks, scientific research projects, and conference exhibitions. On the other hand, it increases the spread of tacit knowledge by increasing the international exchange of students and teachers. These all provide conditions for the production of new knowledge and the spreading of old knowledge in different groups and regions, which in turn will improve the regional innovation capability. The paper makes a quantitative analysis on the internationalization of university and the regional innovation capability based on the triple-helical innovation model of government-enterprise-university. It is found that the innovation coordination environment for government-enterprise-university is generally regionalized and the level of cooperation is low. Both openness and marketization of the regional scientific research system are not high, and the overall regional innovation coordination is poor. In the close coordination regions, the regional innovation coordination is related well and the degree of university’s internationalization has great influence on the regional innovation capability. Furthermore, in the less close coordination regions, the ability of university’s internationalization has a relatively larger impact on regional innovation capability. On the whole, in the close coordination regions, the internationalization of university in the form of no matter go abroad” bring in” or face to face” all have a significant positive impact on the innovation capability. Affected by variable selection, the impact from codified university’s internationalization is not significant. In the less close coordination regions, the R&D input from university is the main factor affecting regional innovation capability. In addition to this, it is necessary to adopt the internationalization of university in the form of bring in” as well as face to face” to promote local regional innovation capability.

The Development of Knowledge Transfer Networks and Regional Innovation Capabilities in High Technology Clusters:

The Development of Knowledge Transfer Networks and Regional Innovation Capabilities in High Technology Clusters: