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Zabolotskii A.A. Institute of Economics and Industrial Engineering, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russian Federation ( firstname.lastname@example.org )
Journal: Finance and Credit, #46,
Subject The article addresses component parts and their impact on the aggregating innovation effect as constituents of innovation.
Objectives The aim of the paper is to study the structure of analytical systems of search for and integration of component parts, and the advantage of their application in Russia.
Methods The paper rests on evaluation of data and experience accumulated from the cooperation with international corporations, and employs data visualization for comparison and analysis.
Results I adjust the cluster model of analytical innovation systems development in Russia. The model is based on searching for production chain participants through tenders, and this precludes from accumulating the necessary innovation capacity. The paper offers to create innovative systems that rely on a wide and multistage selection of elementary components with a deep level of integration, rather than a simple assembly of finished components. It identifies major classes of elementary components and their impact on final innovative products.
Conclusions The paper updates the process of competitive advantages formation at the level of elementary components, and highlights key problems in the formation of components of the country's innovative competitiveness when developing the import substitution programs.
Zabolotskii A.A. Institute of Economics and Industrial Engineering, Siberian Branch of RAS (IEIE SB RAS), Akademgorodok, Novosibirsk, Russian Federation ( email@example.com )
Journal: Finance and Credit, #1,
Subject This article discusses the issues of technological development, namely its limits, influencing the economic and innovation growth.
Objectives The article aims to study and prove the existence of the limits of technological development.
Methods The research uses an innovative method of forecasting on the basis of weight vectors of neural self-organizing maps (SOM).
Results The article explores the transformation of investment and the changing structure of innovative progress that has led to this transformation. It shows two main factors, namely the limit of scientific breakthrough and the technological limit that influence the transformation. The article also models the process of generating scientific breakthroughs using a two-dimensional self-organizing map neural network (MATLAB neural clustering tool), and on the basis of these results, it makes predictions up to 2050.
Conclusions and Relevance The neural network weight map can be used to model and forecast evolutionary systems, as well as for accurate forecasting of technologies that may appear in the future, or can show the exhaustion of the potential of innovative development for these input component sets and technologies.
Untura G.A. Institute of Economics and Industrial Engineering, Siberian Branch of Russian Academy of Sciences (IEIE SB RAS), Novosibirsk, Russian Federation ( firstname.lastname@example.org )
Kaneva M.A. Gaidar Institute for Economic Policy, Moscow, Russian Federation ( email@example.com )
Zabolotskii A.A. Institute of Economics and Industrial Engineering, Siberian Branch of Russian Academy of Sciences (IEIE SB RAS), Novosibirsk, Russian Federation ( firstname.lastname@example.org )
Journal: National Interests: Priorities and Security, #12,
Subject The article examines how knowledge spillover influences economic factors.
Objectives The study evaluates how the growth rate of GRP per capita responds to the impact of expenditures on S&T, knowledge spillover due to geographical neighborhood, organization of economic activities, concentration and specialization of production enterprises and engineering efforts of people in the Russian regions.
Methods The study relies upon the cross-sectional regression analysis of 80 regions and four sectors of economy by section of the Russian Classifier of Types of Economic Activity (OKVED).
Results In those regions that have the moderate and definite specialization (Herfindahl–Hirschman index) the growth rate of GRP per capita appeared to be higher than the average of 80 regions. Analyzing economic sectors, we figured out that strong innovative regions had a higher growth rate of GRP per capita as compared to the other regions. Those regions that finance R&D demonstrated a growth in GRP per capita coupled with an increase in engineering efforts.
Conclusions and Relevance Referring to calculations based on the 2013 data, we found the effect generated with a combination of two related groups of growth drivers increasing the growth rates of GRP per capita, i.e. investment in technological innovation and economic structure across regions that feature various economic sectors and industries or concentration of production enterprises. Our conclusions and recommendations are of applied nature with respect to goals set by the Strategy of Spatial Development of Russia.
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