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Composition of Innovative Activity’ Parameters for a System of Analysis and Making Decision in the Innovations

Author

Listed:
  • Irina Anatolyevna Razumova
  • Nadezhda Nikolaevna Pokrovskaya
  • Lilia Vilyevna Akhmerova

Abstract

Innovative activity determines the ability of an enterprise, region or country to occupy leading or profitable positions within the global economic system. To understand and interpret the situation of the country in the context of world innovative economic growth, it is necessary to develop a system of criteria and indicators for assessing innovation activity. The definition of the criteria will allow both an assessment of the existing situation and the effectiveness of the programs being conducted, so the research main purpose is to develop a system of indicators of innovation activity and to increase the effectiveness of government programs that are aimed to support and to stimulate innovation activities at the regional and national levels. Given the complexity, diversity and versatility of innovation and of functioning of modern economy, the most effective approach to solve this problem is the use of modern technologies, in particular, artificial intelligence to solve this problem. In this regard, in this article, based on the theoretical analysis of existing approaches to the definition and evaluation of innovation activity, a structured system of characteristics of innovation activity developed for the authors to prepare machine learning and to develop a system of analysis and making decision in the innovations’ sphere is proposed. The necessity to develop the characteristics of innovation activity includes both statistical and economic characteristics, as well as wider possibilities for processing large data with the use of neural network technologies, namely, analysis of weak signals from various sources, taking into account industry specific features and analysis of the dependence of innovation activity on Demand by local, regional or national populations for innovative products and services. This determines the multidimensionality of the set of characteristics of innovation activity, which is effectively solved with the help of neural network technologies, in particular, system of analysis and making decision.

Suggested Citation

  • Irina Anatolyevna Razumova & Nadezhda Nikolaevna Pokrovskaya & Lilia Vilyevna Akhmerova, 0. "Composition of Innovative Activity’ Parameters for a System of Analysis and Making Decision in the Innovations," Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 10.
  • Handle: RePEc:acf:journl:y::id:674
    DOI: 10.22394/1726-1139-2017-10-59-72
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    References listed on IDEAS

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    1. Bronwyn Hall & Jacques Mairesse, 2006. "Empirical studies of innovation in the knowledge-driven economy," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(4-5), pages 289-299.
    2. Crepon, B. & Duguet, E. & Mairesse, J., 1998. "Research Investment, Innovation and Productivity: An Econometric Analysis at the Firm Level," Papiers d'Economie Mathématique et Applications 98.15, Université Panthéon-Sorbonne (Paris 1).
    3. Bruno Crepon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation And Productivity: An Econometric Analysis At The Firm Level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 7(2), pages 115-158.
    4. Polterovich, Victor, 2007. "Institutional Trap," MPRA Paper 20595, University Library of Munich, Germany.
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