Targeted management of innovative development of Russian regions
DOI:
https://doi.org/10.21638/spbu05.2020.207Abstract
At present, the Russian Federation lags significantly behind the economically developed countries of the world in terms of innovative development. The situation in Russia is exacerbated by the active processes of divergence of the country’s regions in the field of innovative development. Abroad, the process of transforming a three-tier into a fourtier spiral of innovation, involving the interaction of not only the state, science, business community, but also the civil society of the country. The key factor in achieving the competitiveness of regions of economically developed countries of the world is the development of regional innovation systems through the coordination and synchronization of actions of such actors. By conduct- ing an empirical study, the degree of differentiation of the subjects of the Russian Federation is evaluated by the level of innovative development. Based on neuro-modeling, the regions of the country are clustering (using Kohonen’s self-organizing maps) and forecasting their innovative development in the short term by forming an adequate Bayesian ensemble of dynamic neural networks. As a result of the conducted empirical research, it was established that even now the polarization of Russian regions in terms of the level of innovative development is characteristic. At the same time, in 2015–2016, there were negative changes in the cluster structure (based on the level of innovative development) of the constituent entities of the Russian Federation. So, in particular, the share of Russian regions with low and very low levels of innovative development has significantly increased (from 48,8 to 66,3 %). Despite the optimistic short-term outlook in the field of innovative development for most of the leading regions (Moscow, the Republic of Tatarstan and the Nizhny Novgorod Region), a significant “breakaway” from a number of subjects of the Russian Federation, for example, the Republic of Bashkortostan, is expected. This indicates the need to update the provisions of the innovation policy of most Russian regions.
Keywords:
factors of innovative development, innovation system, Russian regions, clustering of regions, Bayesian assembly of neural network, forecast of innovation development
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мРогова Е. М. (2014) Бизнес-инкубаторы как элементы системы технологического трансфера российских университетов. Инновации. No 7. С. 53–58.
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