Triple Helix as a Social and Legal Phenomenon of Regional Innovative Development: on the Example of the Penza Region as an Average Subject of the Russian Federation

Fomin, Alexey

doi:10.18690/lexonomica.11.2.175-194.2019

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The regional dynamics, and the interaction of its actors (Academy, Industry, Government), known as the triple helix, have to work oriented towards the needs of the industry, thereby directing the research towards emerging domains previously selected in RIS3 [43][44][45]. Consequently, it is intended to enhance regional talent, physical assets, and local resources, to make the most of them, thereby, disseminating the benefits of research and innovation throughout the region, increasing the country's competitiveness, and enhancing the country's ability to manage the challenges that are being posed in society [12,46].…”

Section: Literature Review In Regional Governance Of Smart Specializationmentioning

confidence: 99%

The Role of Open Innovation, and the Performance of European Union Regions

Lopes

Gomes

Oliveira

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

This research aims to detect the factors that best explain the performance of regional innovation in the European Union (EU), in the year 2019, and compare the obtained results with the factors used to elucidate the performance of regional innovation in the EU during the year 2016. This comparison allowed us to identify the variations that have occurred during these 3 years. The methodology used is quantitative and served to identify the factors that clarify the performance of regional innovation in the EU in 2019. The data collected was treated in the econometric software Eviews10. Estimations used a multiple linear regression method. The attained results show that with the implementation of the Research and Innovation Strategy for Smart Specialization (RIS3), the Leader and Strong Regions benefited from its implementation. On the other hand, Moderate and Modest Regions failed to improve their innovative performance with the implementation of RIS3. On the practical contributions, it provides suggestions to the actors of the triple helix (Academy–Government–Industry) to improve the performance of innovation. Furthermore, it contributes to the theory by updating the knowledge of the existing literature with new dimensions from the 2019 RIS database. This research is original as it allows to appraise the evolution of the open innovative performance of the regions, by using comparative data from 2019 and 2016.

show abstract

Section: Literature Review In Regional Governance Of Smart Specializationmentioning

confidence: 99%

The Role of Open Innovation, and the Performance of European Union Regions

Lopes

Gomes

Oliveira

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

show abstract

Regions of Russia: Clustering Results Based on Economic and Innovation Indexes

Zavarukhin

Чинаева

Churilova

2022

Statistika i ekonominka

View full text Add to dashboard Cite

Currently, one of the main trends is the study of the features and benefits of regional development, increasing the importance of the role of regions in national and world politics. The differences in technological results that can be observed at the national and regional levels are largely due to the peculiarities of the institutional environment, i.e. the degree of concentration at the regional level of high-tech companies, modern production and innovation infrastructures. The regions of the Russian Federation demonstrate noticeable differences regarding the level of socio-economic development, the availability of human and natural resources, the development of educational, scientific and innovative potentials, depending on the historical development of infrastructure. This study examines the results of clustering Russian regions according to the main indexes characterizing the economic, scientific and innovative activity. The classification of regions was carried out by the method of cluster analysis.Purpose of the study. The aim of the study was to identify homogeneous groups of regions that are similar in their economic and innovation indexes, statistical analysis of these groups based on non-parametric methods and methods of correlation and regression analysis, the formation of conclusions and recommendations regarding innovation.Materials and methods. The information base of the study was statistical data and analytical information characterizing the state of economic and innovation activity in the Russian regions. The following statistical methods were used in the study: non-parametric (Spearman’s rank correlation coefficients, Mann-Whitney test), correlation (Pearson’s coefficients, coefficients of determination), regression (non-linear regression models), multivariate classifications (cluster analysis), descriptive statistics (averages, structural averages, indicators of variation, etc.).Results. As a result of clustering the regions of Russia using the k-means method, 4 cluster groups were obtained, which are statistically homogeneous within the studied indexes. In order to identify the relationships between the considered indexes, paired linear Pearson correlation coefficients were calculated. The study tested three hypotheses about statistically significant differences between the indexes of the third and fourth clusters. The set of indexes was as follows: the coefficient of inventive activity, internal costs of research and development per employee, the average per capita size of innovative goods and services. For these purposes, the nonparametric Mann-Whitney test was used. The analysis showed that the regions of the Russian Federation are extremely diverse and heterogeneous in terms of their economic and innovative development. When analyzing them, it is advisable to first use cluster analysis methods to obtain homogeneous groups of territories with similar social and economic characteristics, which is confirmed in this study by testing hypotheses about statistically significant differences between the indexes of the third and fourth clusters (differences between the first and second clusters with other clusters and between themselves obvious and do not require any mathematical proof).Conclusion. The leaders in scientific and innovative development are Moscow, St. Petersburg, the Moscow region and the Republic of Tatarstan. They have the highest rates of inventive activity of the population and the volume of production of innovative goods and services. Such regions of the Russian Federation as the Tyumen region, the Republic of Sakha (Yakutia), Magadan region, Sakhalin region and Chukotka formed a cluster group with the highest per capita GRP, investments and fixed assets, but they have almost the lowest rates of innovation activity. The extractive industry is the main engine of the economy of these regions. A separate cluster was formed by 26 regions with average levels of economic and innovative development in the Russian Federation. In particular, it includes the areas: Belgorod, Lipetsk, Smolensk, Arkhangelsk, Vologda, Leningrad, Murmansk, Chelyabinsk, Irkutsk, Tomsk, etc. These regions are promising in terms of innovation, but require significant federal investments for their further development. The fourth group of regions united economically weak territories with low rates of innovation activity. These regions accounted for more than half of the total (47 regions). Statistical analysis within the resulting clusters made it possible to identify the relationship between economic indexes and describe them using regression models.

show abstract

Triple Helix as a Social and Legal Phenomenon of Regional Innovative Development: on the Example of the Penza Region as an Average Subject of the Russian Federation

Cited by 2 publications

References 0 publications

The Role of Open Innovation, and the Performance of European Union Regions

The Role of Open Innovation, and the Performance of European Union Regions

Regions of Russia: Clustering Results Based on Economic and Innovation Indexes

Contact Info

Product

Resources

About