Managing innovation activity factors in Russian regions through econometric modeling

The paper aims to identify the main innovation activity factors in the Russian regions using econometric analysis. Assessment of the current state of the innovation environment in the Russian Federation reveals that there is a number of problems impeding innovation growth, which affects the country’...

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Autores principales:	Oleg S. Mariev, Karina M. Nagieva, Viktoria L. Simonova
Formato:	article
Lenguaje:	RU
Publicado:	Ural State University of Economics 2020
Materias:	management innovation innovation activity genetic algorithm econometric model russian regions Business HF5001-6182 Finance HG1-9999
Acceso en línea:	https://doaj.org/article/c1aa15ddcd2f42abb07ba9e14355c3f1
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spelling	oai:doaj.org-article:c1aa15ddcd2f42abb07ba9e14355c3f12021-12-02T05:46:30ZManaging innovation activity factors in Russian regions through econometric modeling10.29141/2218-5003-2020-11-1-62218-5003https://doaj.org/article/c1aa15ddcd2f42abb07ba9e14355c3f12020-03-01T00:00:00Zhttp://upravlenets.usue.ru/images/83/6.pdfhttps://doaj.org/toc/2218-5003The paper aims to identify the main innovation activity factors in the Russian regions using econometric analysis. Assessment of the current state of the innovation environment in the Russian Federation reveals that there is a number of problems impeding innovation growth, which affects the country’s position in international rankings. The methodological basis of the research includes the theoretical principles of innovation economics, innovation management and regional development. There is a plethora of approaches to modeling the factors of innovative development of countries and regions, as well as to measuring innovation. In the article, we analyze patent activity in Russia’s regions and stimulating factors. The information base includes Rosstat panel data for 1999–2015 in 77 subjects of the Russian Federation. To optimize the set of variables of the econometric model, the authors apply a genetic algorithm. We find that factors reflecting human capital, financial performance of enterprises, market competition, ownership and general macroeconomic indicators of regions affect the indicators of patent activity. The research results can be used to formulate recommendations for improving regional innovation policy. In particular, it is reasonable to encourage the management and research staff of organizations to design and introduce innovations, develop venture business, promote active interaction of business with universities and research institutes, as well as strengthen international cooperation.Oleg S. MarievKarina M. NagievaViktoria L. SimonovaUral State University of Economicsarticlemanagementinnovationinnovation activitygenetic algorithmeconometric modelrussian regionsBusinessHF5001-6182FinanceHG1-9999RUУправленец, Vol 11, Iss 1, Pp 57-69 (2020)
institution	DOAJ
collection	DOAJ
language	RU
topic	management innovation innovation activity genetic algorithm econometric model russian regions Business HF5001-6182 Finance HG1-9999
spellingShingle	management innovation innovation activity genetic algorithm econometric model russian regions Business HF5001-6182 Finance HG1-9999 Oleg S. Mariev Karina M. Nagieva Viktoria L. Simonova Managing innovation activity factors in Russian regions through econometric modeling
description	The paper aims to identify the main innovation activity factors in the Russian regions using econometric analysis. Assessment of the current state of the innovation environment in the Russian Federation reveals that there is a number of problems impeding innovation growth, which affects the country’s position in international rankings. The methodological basis of the research includes the theoretical principles of innovation economics, innovation management and regional development. There is a plethora of approaches to modeling the factors of innovative development of countries and regions, as well as to measuring innovation. In the article, we analyze patent activity in Russia’s regions and stimulating factors. The information base includes Rosstat panel data for 1999–2015 in 77 subjects of the Russian Federation. To optimize the set of variables of the econometric model, the authors apply a genetic algorithm. We find that factors reflecting human capital, financial performance of enterprises, market competition, ownership and general macroeconomic indicators of regions affect the indicators of patent activity. The research results can be used to formulate recommendations for improving regional innovation policy. In particular, it is reasonable to encourage the management and research staff of organizations to design and introduce innovations, develop venture business, promote active interaction of business with universities and research institutes, as well as strengthen international cooperation.
format	article
author	Oleg S. Mariev Karina M. Nagieva Viktoria L. Simonova
author_facet	Oleg S. Mariev Karina M. Nagieva Viktoria L. Simonova
author_sort	Oleg S. Mariev
title	Managing innovation activity factors in Russian regions through econometric modeling
title_short	Managing innovation activity factors in Russian regions through econometric modeling
title_full	Managing innovation activity factors in Russian regions through econometric modeling
title_fullStr	Managing innovation activity factors in Russian regions through econometric modeling
title_full_unstemmed	Managing innovation activity factors in Russian regions through econometric modeling
title_sort	managing innovation activity factors in russian regions through econometric modeling
publisher	Ural State University of Economics
publishDate	2020
url	https://doaj.org/article/c1aa15ddcd2f42abb07ba9e14355c3f1
work_keys_str_mv	AT olegsmariev managinginnovationactivityfactorsinrussianregionsthrougheconometricmodeling AT karinamnagieva managinginnovationactivityfactorsinrussianregionsthrougheconometricmodeling AT viktorialsimonova managinginnovationactivityfactorsinrussianregionsthrougheconometricmodeling
_version_	1718400256355336192

Managing innovation activity factors in Russian regions through econometric modeling

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