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In the process of vigorously promoting urbanization, the city development became a new driving force for China's prosperity. To comprehensively analyze the urban regional innovation system, the typical urban city was taken as an empirical sample to consider as an urban regional innovation system. The fuzzy comprehensive evaluation method was used to evaluate the operation of typical urban regional innovation system, combined the cluster analysis and Bayesian discrimination for the research and analysis. The results showed that the evaluation of regional innovation system was a national innovation strategy. Therefore, the model provides an important evidence for the urban innovative development. The proposed targeted innovation development strategy is conducive to grasp the advantages and disadvantages of the typical urban regional innovation system construction as a whole. The analysis of the structural change layers and sub-distribution of urban regional innovation effectiveness helps to clarify the stage of urban development.

The purpose of this paper is to reveal the paradox between diversification and specialization from a dynamic perspective. More precisely, this paper will analyze the impact of diversification and specialization as well as their interaction on regional innovation in different development stages.

Based on the principles of new economic geography and innovation geography, data from 30 provinces from 2001 to 2017 was used to explore the relationship. Least squares regressions with fix effect were used to examine the hypotheses.

The results show that both diversification and specialization have a significant and positive impact on regional innovation. The interaction of diversification and specialization also significantly and positively impacts regional innovation. The effect of industrial agglomeration is heterogeneity under different development stages.

This paper verifies the positive role of diversification and specialization and their interaction in promoting regional innovation. The impact of industrial agglomeration on innovation is dynamic and changes with the regional development process. Emerging economies should make appropriate industrial agglomeration strategies according to their development stages.

This paper introduces diversification, specialization and their interaction into the research framework at the same time to analyze their impact on innovation performance which deepened the research of industrial agglomeration. Taking China as an example, this paper also examines the impact of industrial agglomeration on regional innovation in different development stages that expands the dynamic perspective of industrial agglomeration.

This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.

A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.

On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.

This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.

Although recent studies have increased attention on the effects of related and unrelated variety on innovation, a Chinese test has until now been missing from the literature. This paper aims to investigate how related and unrelated variety affect regional innovation in Chinese provinces. In particular, emphasis was placed on differentiating the analysis for the industry and services sectors at a detailed sectoral level.

This paper’s sample is composed of 30 provinces in China from 2003 to 2016. Feasible generalized least squares was used to estimate the effects of related and unrelated variety on regional innovation.

The results show that related variety in all sectors promotes regional innovation, whereas unrelated variety in all sectors does not play a role. In-depth analyses were performed by comparing the industry and services sectors. Only related variety in the industry sector and unrelated variety in the services sector promote regional innovation, whereas unrelated variety in the industry sector exerts a negative effect. After dividing the country into eastern, central and western regions, different findings appear in the sub-samples.

This study contributes to the literature on evolutionary economic geography and innovation by exploring how related and unrelated variety promote regional innovation in a developing country context (China). It also sheds light on the sectoral and regional differences in the influence of related and unrelated variety on regional innovation.

Industry 4.0 or digitization, from a regional innovation system (RIS) and policy perspective to improve regional innovation, is over-looked. Specifically, this paper aims to focus on analyzing the nascent European Commission (EC) digital innovation hub (DIH) program, designed for fostering transition into Industry 4.0 in regions and facilitating new path development.

Empirically, 10 Spanish DIH is explored through interviews and secondary data analysis.

The results suggest that DIHs despite their emerging and trial-and-error stage are designed for promoting multi-actor collaborative platforms including non-local actors to stimulate transition into Industry 4.0 by promoting place-based collaboration alliances that respond to local/regional contextual specificities and demands. These regional-based platforms facilitate public-private partnerships that co-design policy initiatives resulting from co-participation and negotiation of spatially-bounded oriented initiatives for digitizing.

The authors answer: what are the key characteristics of emerging European-level regional innovation policies aimed at facilitating Industry 4.0 in regions? This is the first study on the topic.

The purpose of this paper is to explore the relationship between culture and regional innovation performance in China where innovation is deemed as a key for sustainable economic development. The diversity of China’s regional culture and its rising economic and innovative capability enhancement provides an opportunity for such an exploration.

This paper adopts the GLOBE’s nine cultural dimensions to empirically examine the relationship between culture and Chinese regional innovation performance through multiple regression analysis.

The study results find that performance orientation and gender egalitarianism have positive and significant influences on regional innovation performance, while institutional collectivism has a negative and significant influence. The remaining six GLOBE cultural dimensions show no significant effect on regional innovation performance.

To the best of the authors’ knowledge, this is the first research exploring the relationship between culture and regional innovation performance in a Chinese context by using GLOBE’s cultural dimensions that are deemed as a valuable empirical alternative to Hofstede’s cultural measures. The results of this study help further the understanding of the cultural influence in China’s regional innovation performance.

The purpose of this paper is to present a longitudinal case study of a regional innovation policy initiative, in which ideas with regard to how innovation might be facilitated were changing over time. Through the scrutiny of insights in industrial network studies (IMP), the authors seek to shed light on the challenges created by policy interventions aimed at constructing complementary networks for the facilitation of innovation. That is to say, the authors endeavour to understand the interfaces between innovation networks and industrial networks, and the way in which they may influence innovation.

This study is based on a longitudinal case study of four successive regional innovation projects in Norway. Data are drawn from relevant policy documents and project documentations, as well as from participatory observation of application processes and project activities.

This study shows that regional innovation policy concerns first and foremost the interaction within and between relatively established diverse networks, which affects both structuring and restructuring. Changes in innovation policy required the re-configuring of constellations of business networks, research networks and policy networks. All initiatives required mobilisation input by persistent actors – often boundary organisations or researchers. The construction of innovation networks served as an instrument in the production of new interfaces between businesses, researchers and policy makers. The use and usefulness of these networks as perceived by the business actors were heavily influenced by the way in which the networks were configured.

Generalisation based on in-depth qualitative case research requires further testing across similar and varying cases, and there have hitherto been relatively few studies of the interfaces between industrial and innovation networks. Despite this it can be argued that the conceptual distinction between constructed and emerging networks is a productive one in the study of networked innovation dynamics. During the research into this longitudinal case, it has been interesting to observe the way in which innovation research, and thus its influence on innovation policy, has changed over time. It would be beneficial if further studies were to be conducted on the way in which this has played out.

The administration of the public funding of innovation network activities requires great care. Where innovation policy initiatives are closely related to established industrial networks, it may be possible to strengthen innovation dynamics, challenge established practices and conceptions, and contribute to expanding, or even initiate innovation activities. In the first place, new activities need to be initiated in a way that supports the long-term development of actual business networks; and second, innovation policy bodies should be prepared to stimulate activity over longer periods of time.

This paper engages in, and combines, two parallel and rarely interacting debates on, respectively, innovation within innovation policy (innovation systems, clusters, networks) and industrial network studies (IMP and others). The authors make an “ideal type” distinction between alternative “constructed” networks and “emerging” networks, and the way in which they influence innovations.

This study aims to measure the moderating effect of geographical and organizational proximity by focusing on readily available Chinese regional economic data over a five-year period.

The authors used multilevel regression analysis to analyze the relationship.

Results show that increasing government investment in research and development (R&D) can improve innovation performance during this period, organizational proximity and geographic proximity have a positive moderate effect on the relationship between R&D investment and Innovation performance.

This study enriches the existing theories on government innovation input and output from the perspective of regional differences and provides meaningful guidance for current Chinese regional innovation policies.

The purpose of this paper is to examine whether foreign direct investment (FDI) inflow impacts on regional innovation efficiency in China and whether the impacts of FDI are contingent on regional conditions that may maximize the effect of FDI on regional innovation efficiency.

Using panel data of 30 provinces from 2000 to 2010, the authors first used data envelopment analysis to measure regional innovation efficiency, followed by a spatial panel model to test research hypotheses concerning the effect of FDI on regional innovation efficiency and the direct and moderating effects of regional characteristics such as regional innovation environment, regional absorptive capacity and regional complementary assets.

The paper finds that there are considerable inter-regional and intra-regional variations in innovation efficiency in China and that regional variations in innovation efficiency in China can first be explained by the differences in inflow FDI and then be accounted for by the direct and moderating effect of regional innovation environment, absorptive capacity and complementary assets.

The research findings have three policy implications. First, governments should continue their efforts to increase the transparency and predictability of the framework for inward FDI and align FDI with the region’s strategic priorities of development to improve innovation efficiency. Second, governments should develop holistic and coherent policies that address the key aspects of regional conditions conducive to the inflow of FDI. Third, governments at the regional level should cultivate an open innovation environment and support the development of financial markets to maximize the positive effect of FDI technology spillover and externalities.

This paper fills a gap in research on the spatial heterogeneity characteristics of spillover effects of FDI on regional innovation efficiency.

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended for regional development. However, regional development policies have led economies to very different competitive positions in matters of innovation. To address these issues, this paper aims to identify the variables that most contribute to the positioning of economies in terms of performance innovation in Europe.

The data for this study were collected at the regional innovation scoreboard. This paper uses a quantitative methodology through a multivariate statistical technique (discriminant analysis).

The results suggest that specific innovation strategies explain the competitive positioning of economies within each group of countries. It was possible to demonstrate that economies with leader classification show greater comparative robustness in the variables “Small and medium enterprise (SMEs) with product or process innovations,” “SMEs with product or process innovations,” “research and development (R&D) expenditure public sector” and “population with tertiary education,” constituting an effective instrument of innovation policy. Furthermore, it was possible to show that the economies belonging to the modest group do not have a competitive advantage in any of the variables under study, thus providing a reflection opportunity for policymakers at this level.

The present research identifies which variables are most relevant to the classification considering the regional innovation performance in leader, strong, moderate and modest. Several suggestions were given to companies, policymakers and higher education institutions in the sense that the regions where they operate can improve their innovative performance, which may help to a change in their current classification.