scholarly journals The regional innovation potential of industrial clusters

2017 ◽  
Vol 15 (5) ◽  
pp. 881-895
Author(s):  
R.A. Abramov ◽  
◽  
S.K. Podchufarov ◽  
2017 ◽  
Vol 22 (2) ◽  
pp. 175-186
Author(s):  
R.A. Abramov ◽  
◽  
S.K. Podchufarov ◽  

2017 ◽  
Vol 22 (2) ◽  
pp. 175-186
Author(s):  
R.A. Abramov ◽  
◽  
S.K. Podchufarov ◽  

Author(s):  
G. A. AKZHANOVA ◽  
◽  
G. A. SHMARLOUSKAYA ◽  

The article considers the terms “innovation potential”, “regional innovation potential”, and the state of innovation potential. The purpose of this article is to determine the current state and prospects for the development of the innovative potential of the Akmola region. The research methodology consists in systematization of innovative indicators of the region by comparison and economic and statistical groupings. The analysis of the formation of the innovative potential of the region on the example of the Northern regions of the Republic of Kazakhstan is carried out. In particular, the dynamics of the gross regional product and innovation activity in the Akmola region is presented.


2021 ◽  
Vol 17 (3) ◽  
pp. 987-1003
Author(s):  
Dorota Ciołek ◽  
Anna Golejewska ◽  
Adriana Zabłocka-Abi Yaghi

The literature emphasises the role of regional and local innovation environment. Regional Innovation Systems show differences in innovation outputs determined by different inputs. Understanding these relationships can have important implications for regional and innovation policy. The research aims to classify Regional Innovation Systems in Poland according to their innovation capacity and performance. The analysis covers 72 subregions (classified as NUTS 3 in the Nomenclature of Territorial Units for Statistics) in 2004–2016. Classes of Regional Innovation Systems in Poland were identified based on a combination of linear and functional approaches and data from published and unpublished sources. It was assumed that innovation systems in Poland differ due to their location in metropolitan and non-metropolitan regions, thus, the Eurostat NUTS 3 metro/non-metro typology was applied for this purpose. Panel data regressions as models with individual random effects were estimated separately for metropolitan and non-metropolitan groups of subregions. The study identified common determinants of innovation outputs in both NUTS 3 types: share of innovative industrial enterprises, industry share, unemployment rate, and employment in research and development. Next, NUTS 3 were classified within each of two analysed types in line with output- and input-indices, the latter being calculated as non-weighted average of significant inputs. Last, the subregions were clustered based on individual inputs to enable a more detailed assessment of their innovation potential. The cluster analysis using k-means method with maximum cluster distance was applied. The results showed that the composition of the classes identified within metropolitan and non-metropolitan systems in 2004– 2016 remains unstable, similarly to the composition of clusters identified by inputs. The latter confirms the changes in components of the capacity within both Regional Innovation System types. The observed situation allows us to assume that Regional Innovation Systems in Poland are evolving. In further research, the efficiency of Regional Innovation Systems should be assessed, taking into account the differences between metropolitan and non-metropolitan regions as well as other environmental factors that may determine the efficiency of innovative processes.


Author(s):  
N. Lapina

The author eexamines the policy initiated by French President Nicolas Sarkozy and aimed at the development of innovation. This policy is reflected by the support of high-tech industries, by the creation of scientific-industrial clusters, by the development of regional innovation networks, by upgrading of higher education and scientific research. Altogether it is to raise the France’s competitive force and to turn it into one of the leading powers of the world.


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