Clustering of AIRR regions as a prerequisite of interregional innovation policy | Vestnik Tomskogo gosudarstvennogo universiteta – Tomsk State University Journal. 2015. № 399.

Clustering of AIRR regions as a prerequisite of interregional innovation policy

The relevance of studying innovation systems in the spatial context of Russia is due to high differentiation of innovative potential in the country. In addition the evolutionary changes in the properties of the innovation process are responsible for the spread of open innovation approaches (formation of innovation multidirectional flows between business entities, taking into account the complementarity of their resources), which dictates a need to revise and improve approaches to the regional and national innovation policy. Russia formed an association of regions (grassroots initiative) for which innovative development is a priority of regional policy: the Association of Innovative Regions of Russia (AIRR). This form of regional cooperation is interesting from the scientific point of view, in particular, as analysis of opportunities for inter-regional cooperation in the innovation field, i.e. implementing openness in the regional innovation processes management. The article proposes a version of AIRR regions clustering, which aims to describe the different models of regional innovation development (specified groups of regions) and their features, advantages and disadvantages. The main method of indices clustering was factor analysis which allows constructing a quotient space of regions' innovative development and conduct regions' clustering, depending on the characteristics of their innovative development. Using factor analysis, a 3-factor model of innovative development indicators of AIRR regions (for the year 2012) was built. According to the results of clustering at the level of a 4-cluster model of AIRR regions, four main groups (regional models) were identified: industrial innovation-active regions, regions with a developed scientific and educational complex, industrial regions which provide demand for innovation, and a mixed catching model. This article describes the features, advantages and disadvantages of the regional models. Advantages and disadvantages of the models neutralize each other when looking at AIRR as a single entity innovation. Features of various innovation models allow each region to achieve effective innovation on the basis of complementarity of resources if each region performs its function (role). It is concluded that the problem of regional disparities in the field of innovation can be transformed into an additional source of regional development through the establishment of inter-regional innovation policy which aims to strengthen and enhance the effectiveness of innovation in all regions based on the use of regional synergy potentials.

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Keywords

кластеризация регионов, инновационная политика, Ассоциация инновационных регионов России, региональные модели инновационного развития, regional clustering, innovation policy, Association of Innovative Regions of Russia, regional innovation models

Authors

NameOrganizationE-mail
Akerman Elena N.Tomsk Polytechnic Universityaker@tomsk.gov.ru
Mihalchuk Aleksandr A.Tomsk Polytechnic Universityaamih@rambler.ru
Burets Yu.S.Tomsk Polytechnic Universityburetsys@tomsk.gov.ru
Всего: 3

References

Бурец Ю.С. Эволюция моделей управления инновационным процессом // Вестник Томского государственного университета. Экономика. 2014. № 4. С. 125-139.
Бурец Ю.С. Теоретико-методологические аспекты подхода открытых инноваций: сущность, формы, инструменты, модель // Экономика и предпринимательство. 2014. № 12-3. С. 705-711.
Унтура Г.А., Есикова Т.Н., Зайцев И.Д., Морошкина О.Н. Проблемы и инструменты аналитики инновационного развития субъектов РФ // Вестник Новосибирского государственного университета. Сер. Социально-экономические науки. 2014. Т. 14, вып. 1. С. 81-100.
Акерман Е.Н., Михальчук А.А., Трифонов А.Ю. Типология регионов как инструмент со-организации регионального развития // Вестник Томского государственного университета. 2010. № 331. С. 126-131.
Сошникова Л.А., Тамашевич В.Н., Уебе Г., Шефер М. Многомерный статистический анализ в экономике. М. : ЮНИТИ-ДАНА, 1999. 598 с.
Айвазян С.А., Мхитарян В.С. Теория вероятностей и прикладная статистика. М. : ЮНИТИ-ДАНА, 2001. Т. 1. 656 с.
Дубров А.М., Мхитарян В.С., Трошин Л.И. Многомерные статистические методы. М. : Финансы и статистика, 1998. 352 с.
Дюран Б., Оделл П. Кластерный анализ. М. : Статистика, 1977. 128 с.
Каплан А.В. и др. Решение экономических задач на компьютере. СПб. : Питер, 2004. 600 с.
Боровиков В.П. Statistica. Искусство анализа данных на компьютере. СПб. : Питер, 2003. 688 с.
Боровиков В.П., Боровиков И.П. Statistica. Статистический анализ и обработка данных в среде Windows. М. : Филинь, 1997. 608 с.
Халафян А.А. Statistica 6. Статистический анализ данных. М. : ООО «Бином-Пресс», 2008. 512 с.
 Clustering of AIRR regions as a prerequisite of interregional innovation policy | Vestnik Tomskogo gosudarstvennogo universiteta – Tomsk State University Journal. 2015. № 399.

Clustering of AIRR regions as a prerequisite of interregional innovation policy | Vestnik Tomskogo gosudarstvennogo universiteta – Tomsk State University Journal. 2015. № 399.

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