ABSTRACT
This paper focuses on the current theoretical views of venture capital that predetermines a "narrow" treatment. In the light of the existing "narrow" treatment, venture investors seek private commercial interests in financial support for Industry 4.0, ignoring other interests that fall beyond the limits of the current "narrow" treatment of venture capital. A "wide" treatment of venture capital 4.0 proposed in this paper allows for improving venture investors' market strategies. Implementing this treatment, they will strive for providing a whole range of advantages for society. Due to this novel approach, venture capital 4.0 might become a tool of corporate social responsibility. To substantiate this novel approach, this paper considers data for 2020 that reflect the influence of venture capital 4.0 on the economy in the period of its stability for 33 countries of the OECD, including developed and developing countries. Econometric modelling based on the official statistics data proves that Industry 4.0 venture capital will help achieve such growth goals as innovative development, global competitiveness, and increasing digital competitiveness. The limitations of this research are due to the impossibility of achieving such goals as sustainable development, economic growth, and implementation of human potential; what's more, the specifics of developing countries have not been studied sufficiently. The conclusions are oriented mainly at developed countries and could merely partially be applied to developing countries. During further research, it is expedient to explore - more thoroughly - the experience of the influence of Industry 4.0 venture capital on emerging economies.
ABSTRACT
The article shows the possibilities of Data Set "Interactive Statistics and Intelligent Analytics of the Balanced State of the Regional Economy of Russia in Terms of Big Data and Blockchain - 2020". For creation of the data set, we formed time rows of the values of the selected indicators, which characterize the balance of Russia's regional economy. The indicators are systematized and classified into two categories. The first category includes the indicators of the level of socio-economic development: GDP per capita and "underdevelopment whirlpools", balanced financial result of companies' activities, population's employment level, and balance of the regional budget, calculated by finding the difference between revenues and expenditures. The second category includes the indicators of potential of socio-economic development: investments into fixed capital per capita, share of innovations-active organizations, share of innovative products, and digitization level. The data are collected in a table, based on which - with the help of programming and web-design - a data set is created - an interactive platform for working with data. The data set is available in Russian and English at the official web-site of the Institute of Scientific Communications [12]. The data set unifies and transfers into the digital form the data on the level and potential of Russia's regions' potential of socio-economic development. The data set allows for flexible setting of data for any research. The data set allows - based on the statistics of a region's socio-economic development - determining its position in the Russian national ranking, category by the "TRMS" matrix, calculating the integral index for each region, and comparing its values. Using the proprietary methodology of calculation of "underdevelopment whirlpools" of Prof. Popkova, the data set reflects the balance of Russia's regional economy in 2005-2024. The data set allows for automatic visualization of data by creating a blockchain polygon of region's socio-economic development, which includes the chronogram of the region's development and the process of region's pulling into an "underdevelopment whirlpool". The data set's data are presented in the form of an interactive map of Russia's regional economy. Map's color shows categories that are assigned to regions and the borders of regions, as well as information on each region's position in the 2020 rating. The data set allows for large-scale studies of Russia's regional economy with application of technologies of Big Data processing, machine learning, and intellectual analytics.
