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Information × Registration Number 0220U103585, 0117U003922 , R & D reports Title Innovative drivers of national economic security: structural modeling and forecasting. popup.stage_title Head Zakharkina Liudmyla S., Кандидат економічних наук Registration Date 22-10-2020 Organization Sumy State University popup.description2  The object of the research is processes of ensuring of country's national economic security in the face of global challenges, based on the innovative breakthrough of business entities. The purpose of the study is formulating methodological foundations and methodical tools for modeling and forecasting interconnection innovative development of economic entities and ensuring economic security in Ukraine. Based on the conducted researches, the following scientific results are received: 1) it was proposed methodical tools for assessing the degree of readiness of Ukraine for the challenges of Industry 4.0 and strengthening innovation activity based on a comparative analysis of the main drivers, trends and risks of Industry 4.0; 2) it was proposed methodological tools for the formation of directions and choice of financial policy instruments in the implementation of economic incentives for economic development of economic entities, which provides a combination of empirical study of formalization of the impact of financial policy on innovation development on the overall level of innovation development and taking into account potential corruption risks; 3) it was developed a methodological approach to measuring the attractiveness of territories as a basis for ensuring their national economic security, which, in contrast to existing research, involves assessing the economic, innovative and marketing attractiveness of territories, positioning them on the matrix and developing appropriate management strategies; 4) it was developed scientific and methodological approach to cluster analysis of regions of Ukraine on indicators of innovation activity, which is based on the distribution of regions by groups using clustering by k-means and hierarchical algorithms of cluster analysis, which allows to increase the accuracy of such analysis. Product Description popup.authors Bilous Yuliia H. Hrybinichenko Ruslana A Domashenko Maryna D. Kreshchik Olha S. Lyulyov Oleksii V. Matvieieva Yuliia T. Myronova Alona M. Myroshnichenko Yulia O. Novikov Volodymyr M. Saher Liudmyla Yu. Samoilikova Anastasiia V. Syhyda Liubov O. Syhyda Nataliia O. Smolennikov Denys O. Solomko Yuliia O Strilets Viktoriia Yu Tverezovska Оleksandra I. Tiutiunnyk Inna V Shamkalo Kateryna Yu. Shcherbachenko Viktoriia O. popup.nrat_date 2021-03-17 Close