Notice of Retraction: The current situation analyses and lifting countermeasure of regional innovation capacity in Hebei

2011 ◽  
Author(s):  
Wang Jun Ling ◽  
Zhao Rui Fen
Keyword(s):  
Current Situation ◽  
Regional Innovation ◽  
Innovation Capacity

A Tale of Different Realities

2019 ◽  
pp. 262-280
Author(s):  
Pedro Nuno Rebelo Pavão ◽  
João Pedro Almeida Couto ◽  
Maria Manuela Santos Natário
Keyword(s):  
Innovation Policy ◽  
Regional Governance ◽  
Regional Level ◽  
The European Union ◽  
Regional Innovation ◽  
Structural Funds ◽  
Innovation Dynamics ◽  
Innovation Capacity ◽  
And Cluster Analysis ◽  
Private Actors

This chapter aims to identify the determinants that affect innovation capacity at regional level in Europe. It proposes modelling the territorial innovation capacity and identifies relevant factors with influence on the innovation capacity at a regional level. The chapter uses the Regional Innovation Scoreboard database and cluster analysis to detect behavioral patterns in terms of innovation performance in European regions. The results show that innovation capacity is related to regional governance, and particularly regional autonomy, regional control of innovation policy, influencing the affectation of structural funds, and the region's location within the European Union. Cohesion policy criteria is also a significant factor, demonstrating the adequacy of the European regional policy's new programming regarding innovation policy. These results point to the importance of the participation of regions in formulation, and implementation bottom-up strategies to develop innovation dynamics and develop partnerships with other public and/or private actors.

The Regional Innovation Capacity Model

2012 ◽  
pp. 243-256
Author(s):  
Antonio Lerro ◽  
Giovanni Schiuma
Keyword(s):  
Local System ◽  
Point Of View ◽  
Future Research ◽  
Regional Innovation ◽  
Knowledge Based ◽  
Future Research Agenda ◽  
Capacity Model ◽  
Innovation Dynamics ◽  
Innovation Capacity ◽  
Definition Of

This chapter aims to present a conceptual model aimed to understand the Intellectual Capital-based (IC) characteristics of the regional innovation capacity. The proposed Regional Innovation Capacity Model (RICM) can be used for interpretative and normative purposes to analyse the innovation dynamics taking place at regional and territorial level. From an interpretative point of view, the model identifies the pillars grounding the innovation capacity of a local system. While, from a normative perspective, the model can inspire the definition of guidelines driving the design and the implementation of actions, projects and programmes aimed to stimulate and sustain regional development dynamics. The RICM adopts a knowledge-based perspective assuming that IC, in the forms of regional knowledge assets, and knowledge dynamics, in the form of knowledge transfer and learning processes, are the drivers of innovative processes and outputs. The chapter concludes proposing a future research agenda.

scholarly journals How Does Regional Innovation Capacity Affect the Green Growth Performance? Empirical Evidence from China

2019 ◽  
Vol 11 (18) ◽  
pp. 5084 ◽  
Author(s):  
Hu ◽  
Liu ◽  
Li ◽  
Lin
Keyword(s):  
Growth Performance ◽  
Regional Differences ◽  
Relative Effectiveness ◽  
Practical Significance ◽  
Eastern Region ◽  
Green Growth ◽  
Regional Innovation ◽  
Institution Innovation ◽  
Coordination Effect ◽  
Innovation Capacity

Behind the high development of technology, backward institution systems and imperfect incentive mechanisms are not conducive to the green transformation of the economic society in China. Meanwhile, the relative effectiveness of both technical and institution innovation in encouraging green growth has yet to be tested empirically in China. It is of great practical significance to assess the effect of regional innovation capacity (RIC) on the green growth performance. This paper firstly exploits a model to measure regional innovation capacity from the perspective of technological and institutional respect. The panel data of 30 provinces in China during 2008–2017 is then used to examine the coordination effect of technological and institutional instruments on green growth performance. The empirical results demonstrate the following: (i) regional innovation capacity significantly affects the green growth performance of 30 provinces in China, showing regional differences. The elasticity of RIC on the green total factor efficiency in the eastern region is larger at approximately 0.48, followed by central and western areas, at about 0.47 and 0.45, respectively; (ii) technological innovation is able to incentivize green growth performance for all regions in China, while the institutional innovation induces green growth in the eastern region only; (iii) the coordination of technical and institutional instruments has a significant effect on green growth performance, positive in the eastern region and negative in central region respectively.

scholarly journals Application of Multiattribute Decision-Making for Evaluating Regional Innovation Capacity

2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Su ◽  
Dezhi Liang ◽  
Wen Guo
Keyword(s):  
Decision Making ◽  
Evaluation System ◽  
Analytic Network Process ◽  
Regional Innovation ◽  
Innovation Development ◽  
Research Institutes ◽  
Capacity Evaluation ◽  
Network Process ◽  
Output Efficiency ◽  
Innovation Capacity

The growing imbalance in regional innovation development has become an urgent issue in China’s strategy to build an innovative country. To enrich the regional innovation capacity evaluation system, scientifically assess regional innovation capacity, and explore available pathways to improve regional innovation capacity, this paper introduces a multiattribute decision-making method for evaluating regional innovation capacity. First, a random forest model and the DEMATEL-based analytic network process (DANP) method are applied to calculate the weights of the evaluation attributes. Second, the multiobjective optimization by the ratio analysis method based on the maximum and minimum (MOORA-min-max method) is used to calculate the evaluation attribute gap ratios and regional innovation capacity of each region. Finally, the limitations of regional innovation development are identified based on the evaluation attribute gap ratios and the critical influence strength roadmap (CISR) to explore the regional innovation capacity improvement pathways. The results show that “output capacity of R&D personnel in universities and research institutes” is the most fundamental evaluation attribute in the regional innovation capacity evaluation, while “output efficiency of R&D funds in universities and research institutes” is the most influential evaluation attribute. Research in Sichuan and Inner Mongolia reveals that regions need to identify critical constraints in four aspects: knowledge creation, knowledge acquisition, enterprise innovation, and innovation environment, to improve regional innovation capacity.

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