Background and purpose: The use of artificial intelligence (AI) models for data-driven decision-making in different stages of employee lifecycle (EL) management is increasing. However, there is no comprehensive study that addresses contributions of AI in EL management. Therefore, the main goal of this study was to address this theoretical gap and determine the contribution of AI models to EL management. Methods: This study applied the PRISMA method, a systematic literature review model, to ensure that the maximum number of publications related to the subject can be accessed. The output of the PRISMA model led to the identification of 23 related articles, and the findings of this study were presented based on the analysis of these articles. Results: The findings revealed that AI algorithms were used in all stages of EL management (i.e., recruitment, on-boarding, employability and benefits, retention, and off-boarding). It was also disclosed that Random Forest, Support Vector Machines, Adaptive Boosting, Decision Tree, and Artificial Neural Network algorithms outperform other algorithms and were the most used in the literature. Conclusion: Although the use of AI models in solving EL management problems is increasing, research on this topic is still in its infancy stage, and more research on this topic is necessary.

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 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.

The main aim of this article is to analyze the prerequisites for the development and the promotion of cryptocurrencies, covering the period from the emergence of the first forms of money to recent period. The paper focuses on findings of comparative analysis of consumer value of precious metals, cash and non-cash money and cryptocurrencies as different forms of money. The authors provide comprehensive descriptions and expert quantitative estimates for each of the listed forms of money, as well as determine the place of cryptocurrency in the modern financial system as a means of payment. The research presents a detailed analysis of the advantages and disadvantages of new technologies. The paper defines the status of cryptocurrencies in various countries of the world and names promising development trends. The authors determine the main obstacles hindering the circulation of cryptocurrencies in certain markets and assess the risks and prospects associated with the introduction and use of cryptocurrencies as a new form of money which aims at full/partial replacement of fiat money in the modern financial system. As a conclusion it is noted that weak integration into the financial system is the main obstacle to major distribution of cryptocurrencies as a means of payment.

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.