Analyzing the Use of the Production Potential in the Russian Federation’s Territories During the Transition to the Digital Economy

Bezdudnaya, Anna Geroldovna; Gundorova, Marina A.; Gerashchenkova, Tatyana; Gerasimov, Kirill; Fraimovich, Denis Yurievich

doi:10.1007/978-3-030-29586-8_22

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…A similar study that characterizes the potential of the Russian economy during the transition to a digital economy is presented in Bezdudnaya et al (2020). The study provides a quantitative assessment of the level of use of production potential in the regions of the Russian Federation over a long period of time.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Society: New Challenges for Education

Гапсаламов¹,

Bochkareva²,

Akhmetshin³

et al. 2020

PTQ

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The world has entered a new phase of development – the era of digital society. Under the influence of a new round of technological progress, the mechanisms and practices of the world order, as well as equipment and technologies, are changing. The range of innovations today is not limited to one or two industries; changes cover almost everything – telecommunications, information and communication sectors, nanotechnology, space, bioengineering, robotics, etc. The research aims to study the processes of changing the paradigm of social development under the influence of the formation of a digital society. The article analyzes the concept of "digital society," shows how the tools of the digital economy are used to justify a paradigm shift in social development for the modern generation. The research methodology is based on a systematic and structural-functional approach. Also, methods of sociological survey and statistical processing of information were used. As a result of the analysis, plans to the definition of the digital economy are formulated, the stages of development of digital society are presented, the trajectories of technological changes in the digital era are identified, and problems that inhibit the digitalization process are formulated.

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Section: Literature Reviewmentioning

confidence: 99%

Digital Society: New Challenges for Education

Гапсаламов¹,

Bochkareva²,

Akhmetshin³

et al. 2020

PTQ

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“…The methods that are used in forecasting are quite extensive (Bezdudnaya et al, 2020;Kron et al, 2019;Kryshtaleva, 2017;Longhi, & Nijkamp, 2007;Schanne et al, 2010;Zinoviev & Schetinin, 2014): extrapolation method; modeling method; method of mathematical analysis; intuitive models and correlation method. However, public employment services use only intuitive models and the extrapolation method.…”

Section: Problem Statementmentioning

confidence: 99%

Methodological Tools For Assessing The Situation On The Russian Labor Market

Бавыкина¹,

Zakharov²,

Eremeev³

et al. 2020

European Proceedings of Social and Behavioural Sciences

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Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation

Frolov

Bosenko

2021

Vysš. obraz. Ross. (Print)

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The article analyzes the statistical data relating to training specialists for digitalized economy by secondary vocational and higher education institutions. The purpose of the study was to develop and test personnel support indices for digitalization of the economy, as well as to identify social and economic factors that significantly affect the level of personnel support for the processes of digital transformation of the economy. The authors applied data from the official statistical reporting of the Russian Federation. The proposed staffing indices were modeled as objective functions depending on socio-economic factors characterizing the development of the economy in different dimensions. At the same time, the indices themselves were calculated as values in which the parameters of the output of digital specialists and their relevance in the economy were correlated. In the course of the study, a comparison of statistical and neural network data modeling methods and generalizing indices was performed. An analysis of the obtained regression models and an analysis of the sensitivity of trained neural networks made it possible to evaluate their accuracy in predicting the trends in the staffing of the digital economy and to identify factors that significantly affect the achievement of the goal of matching the output of specialists and the demands of economic sectors.

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Analyzing the Use of the Production Potential in the Russian Federation’s Territories During the Transition to the Digital Economy

Cited by 4 publications

References 4 publications

Digital Society: New Challenges for Education

Digital Society: New Challenges for Education

Methodological Tools For Assessing The Situation On The Russian Labor Market

Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation

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