Моделирование и оценка влияния инновационной системы на региональное развитие (на примере Новосибирской области)
Monitoring trends is a key requirement for national and corporate policy makers to stay up-to-date with socio-economic and technological transformations, to anticipate emerging developments at the global and local levels, and to use this intelligence to prioritize areas for innovation and investment. This chapter aims at discussing how the results of trend monitoring can be integrated into the process of Science, Technology and Innovation policy formulation and business Research & Development planning processes. The chapter starts with an overview of the relevant innovation literature that gives a background in a broader theoretical context, where the technology monitoring activities can be better justified conceptually. This background provides to generate two models, which will portray positioning and functioning of Global Trend Monitoring in the policy and business planning process. Some practical aspects of how and in what form the results of Global Trend Monitoring should be provided to the target communities of policy makers and business planners are elaborated throughout the chapter.
In recent years, links between spatial proximity, and firms’ formal and informal contacts have become a sufficient subject for research in the field of innovation, competitiveness and sustainable economic growth. We introduce a model for the quantitative evaluation of the relationship between cluster participation and innovation capabilities, as well with a company’s growth in value. The paper focuses on comparison in mutual effects for companies from transitional and developed economies. We use a sample of 284 traded European companies between 2005 and 2009 which were carefully applied and subjected to panel data analysis techniques. Our empirical findings show the positive mutual effects on innovation capabilities measured as intangible assets and companies’ economic value added for both transitional and developed economies. Hereby these links are stronger in developed countries. Moreover, we identify the complementary factors to clustering, such as industry-level public R&D expenses, country innovation infrastructure development and location in a megalopolis.
The technology of the comparative analysis of innovation systems of the subjects of the Russian Federation is proposed. The technology is based on usage of non-parametric approaches to metric multidimentional scaling. General technology is illustrated by econometric comparison of the sunjects of the Russian Federation in the direction of "Technological innovations" using data from official statistics.
The article in memoriam summarizes theoretical approaches to sociological analysis of innovation developed by Vladislav Kelle. The focus is made on the key papers describing Russian scientific and innovation system. Key theoretical considerations and observations complemented with the recent statistical data on innovation development of the country.