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.

The decrease in the economic activity level around the world due to the COVID-19 pandemic spread has led to a sharp decrease in the crude oil price and provoked an oil war outbreak in the global energy market. The current situation has provoked the need for a total decrease in the crude oil production in the world. Considering that Russia is one of the main oil exporters on the world market, the need to determine the supply and demand levels for Russian oil is becoming relevant. The aim of the paper is to model predictive scenarios of Russian oil industry development, considering the specifics of the current economic environment given the COVID-19 pandemic. The multifactor correlation modeling method was used to form the system of indicators determining the level of demand and supply for Russian oil used and the total level of their influence. The functions determine the probability of implementing various scenarios of oil industry development depending on the predicted values of demand and supply. The three-sigma rule and the fuzzy sets method were used to estimate three scenarios of oil industry development for 2020–2021. Changes in revenues of the industry under the influence of forecast indicators of supply and demand for oil have been assessed and the probability of implementation of each of the scenarios has been reasoned. The results obtained are of a practical nature and can be used by government agencies, financial intermediaries, and scientists to diagnose Russian oil industry development. The results will be useful for oil companies to develop a strategy of open innovations for further design of the scientific information field for the effective functioning of the industry in complete uncertainty conditions.

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.

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.

The study is aimed at determining the oscillators of crisis manifestations when the Russian economy tries to make transition to the path for accelerating technological development and forming an innovative economy. Short-term cycles were determined in the development of the Russian economy from 1995 to the first half of 2020 through the Fourier spectral analysis. Using the Granger test, causal relationships between the leading indicators of the economic crisis and the real GDP index in Russia were identified and substantiated. They reflect the influence of the key rate dynamics on the volume of lending, savings, investments, the yield on securities and the exchange rate; volumes of bank loans per the share of non-performing and bad loans and innovative development of the economy. Based on the constructed neural models of the oscillator influence on the level of real GDP in Russia, it was determined that the rapid growth of bank and mortgage lending, the devaluation of the ruble, a decreased volume of gross foreign investment and the level of innovative development predetermine crisis manifestations in the national economy. The lags of the influence of changes in the leading indicators of the economic crisis on the development of the economy were calculated. The results obtained can contribute to the effectiveness of the anti-crisis regulation strategy in Russia. They can serve as a basis for increasing the efficiency of long-term innovative development and creating appropriate conditions for increasing the scientific and technological potential of the country.