The paper seeks to develop a predictive model for assessing the impact of the (COVID-19) pandemic on the economies of Eastern Europe, taking into account quarantine measures. Functions of the dependence on the number of the infected populations in Eastern Europe on pandemic duration were calculated based on trend analysis. Factors affecting the intensity of disease and the number of infected persons have been determined. Integral model of their influence has been built using regression analysis. Based on the values of the factors, the number of infected people and the rate of infection were predicted for each of the Eastern European countries. The prognostic duration of the stage of exponential disease growth and the total duration of quarantine (epidemiological saturation point) are substantiated. The predicted decline in Eastern European GDP due to COVID-19 has been estimated based on the construction of a prognostic regression model. The results obtained can be used by state authorities and economic agents as a tool for active and preventive response. They can also serve as an example of the urgent need to develop, especially in non-standard situations, mechanisms and products of open innovation.

University social responsibility (USR) is an important assessment criterion of the QS Stars. In the context of the COVID-19 pandemic, the social orientation of universities as intellectual leaders in the development of society gains particular importance. The research purpose is to analyze the impact of the COVID-19 pandemic on the university activity directions in the framework of strategies (USR). An empirical assessment of the level and complementary factors of USR in the BRICS countries (Brazil, Russia, India, China, and South Africa) was conducted, using the method of integral and expert assessment. Grounded on scoring according to the principal component analysis, the structure of the factors of the USR development in the BRICS countries was determined. Multifactor regression modeling allowed substantiating the priority of factors stimulating the development of USR in the BRICS countries in modern conditions and arguing the main barriers to introducing the concept of social responsibility into university activities and expanding the stakeholders’ circle in it. The research results showed that the university management creativity, effective communication with the public and stakeholders, the quality of the educational process and the development of scientific activities stimulate USR development in the BRICS countries and should be used as the basis for the strategic planning of activities in the context of the continuing COVID-19 pandemic. Conceptual trends in the USR development can be useful for universities in the studied countries when adapting strategic development plans regarding the social needs of modern society.

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The paper deals with the problems of Russian tourism with the use of statistic analysis methods. The structure of tourist economic sector is represented based on the international recommendations. The usage of adjusting factors having regard to the share of tourism within the activity of enterprises of various branches is suggested. Tourism development trends in Russia and in the world in general are revealed. Regional inequality of international incomes and expenses is demonstrated. The reasons of the Russian tourism low competitiveness on the world market are defined.

The COVID-19 pandemic has significantly affected the employee lifecycle management (ELM) sphere, leading to the adoption of new human resource (HR) technologies and policies. This study investigates the impact of megatrends, artificial intelligence, digital technologies, and innovation on ELM and human resource management (HRM) policies in China, Russia, and Indonesia. Data were collected through structured interviews and publicly available information from companies in these countries between 2021 and 2022. The study evaluates the effects of artificial intelligence (AI), digital transformation (DT), and innovations on the sustainable development of ELM and identifies differences in technological responses to ELM in companies depending on their level of digital maturity. The results show that the majority of companies have continued the process of ELM digital transformation, but the percentage varies based on the scope of activity, labor, and readiness of the country to implement new technologies. The study reveals that large companies in each analyzed country with over 10,000 employees have a greater need and opportunity to implement HR digital transformation, whereas small companies with up to 100 people can operate without automation. In addition, the findings of this study provide propositions for designing how AI and innovations contribute to ELM. This article contributes to the current debate in the literature by substantiating the positive impact of AI, digital technology, and innovation on ELM and HRM strategies, offering practical applications for companies to improve productivity. Overall, this study highlights the importance of adopting innovative HR technologies in response to global challenges and workplace trends.

In the context of speculatively priced Russian oil on the world energy market, the oil exchange market development in the Russian Federation is updated. The purpose of the article was to rationalize conditions ensuring the crude oil market pricing by means of exchange trade development. The main objective of the scientific search was to justify state oil purchase as the main factor in improving the Russian oil exchange market liquidity at the present stage of its development. An optimal level of the ratio of public expenditures for oil exchange purchase to Russia's GDP was determined. The optimal amount of public expenditures for oil exchange purchase for the second quarter of 2018 amounted to 2,384.64 billion rubles. The optimal amount of public expenditures for oil exchange purchase is 89.9 million tons per quarter. State procurement of such oil volumes as of today could ensure an increase in the oil exchange market liquidity. The research results may lay the groundwork for enhancing the state strategy efficiency to improve the pricing of Russia's energy resources. Some practical focus areas substantiated in the article would contribute to the exchange market development at the present stage as a factor in the formation of an actual market price for Russian oil

The aim of this article was to develop scientific activity in Russian universities. Using the survey method, a sample of quantitative indicators of research output for the years 2011–2019 was formed. The respondents were comprised of 934 lecturers from 13 universities in Russia with different work experience, academic degrees, and indicators of scientific activity. The main components method was used to study the structure of scientific activity productivity indicators. Using the method of additive convolution, a five-factor integral model of the structure of scientific activity of a teacher was formed to evaluate the individual and collective productivity of scientific activity. The factor values and the integral indicator of scientific activity output were calculated, showing that the individual growth rates of the young lecturers’ output exceeded the growth rates of the older lecturers. Destructive factors relating to the scientific activity output of the lecturers and researchers in Russian universities (divided into two groups of young scientists and senior scientists) were determined and systematized based on the level of dominant influence. The features of the influence of the factor of emotional burnout on the scientific productivity of university teachers were revealed.

This research aims to substantiate the impact of using open innovation (OI) in the energy sector in readiness to implement artificial intelligence (AI) technologies and their effectiveness. The empirical method was proposed to determine the readiness level of OI for the implementation of AI technologies by comparing Russian and French energy companies. Readiness level indicators of companies for AI implementation using the Fibonacci sequence, Student’s t-test, and the method of fuzzy sets were empirically determined. The integrated readiness indicator for AI implementation by companies was calculated using the method of fuzzy sets and expressed through variance, allowing for these significant factors. Russian companies are at a low level of developmental readiness to implement AI, which is in contrast to companies operating in a developed market where the determining factor is the AI technology cost. The example of the innovative business model “Energy-as-a-Service” shows the synergistic effects of OI use and AI technology introduction. This paper is novel because it seeks to contribute to the current debate in the literature, justifying the position that energy companies that have in the past actively applied the concept of open innovation in business, are the most competitive and most efficient in implementing AI technologies.