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 this research, we applied the DEA method (data envelopment analysis) for a cross-country analysis of the comparative efficiency of government support for coal production in eight countries: The leading producers of coal and lignite, three OECD countries with developed economies (the USA, Germany, and Australia), four BRICS countries with developing economies and emerging markets (China, India, Russia, and South Africa), and Indonesia -the largest producer of coal and lignite in Southeast Asia from 2013 to 2018. An extended version of the DEA method allowed us to evaluate not only technicalities, but also price efficiency of budget support for natural gas production in the considered countries. The data for the empirical model characterizing the volume of financial support to oil producers through budgetary transfers and tax expenditures was taken from the OECD statistical base. The obtained results indicate low efficiency of state support for coal and lignite production in Russia, the industry that is responsible for the largest generation and emission of greenhouse gases. In accordance with international obligations, Russia should solve this problem. To achieve this goal, the government should legislatively limit the funding of coal projects and exclude coal projects from the sphere of credit and export agencies, development banks, and state banks.
This research focuses on the multi-cycle production development planning for sustainable power systems to maximize the usage of renewable energy sources. The intention of this study is to offer a comprehensive review of the research on the potential of multi-cycle production development planning for the development of sustainable power systems. In pursuit of this objective, the study has incorporated a qualitative research approach to analyze the volume of data available on the research topic to delineate how multi-cycle production development planning can be used for sustainable power systems and the maximization of the use of renewable energy sources. The study also highlights the major models that can be incorporated into the multi-cycle production development planning for sustainable power systems to maximize the use of renewable energy sources. The existing literature was extracted from databases, namely, Google Scholar, EBSCOHost, and Springer. The data comprised peer-reviewed journal articles, books, and credible online sources. Lastly, the practical and theoretical relevance of the study, along with limitations and recommendations for future practitioners, is provided in the conclusion. Doi: 10.28991/CEJ-2022-08-11-018 Full Text: PDF
Stimulation of productivity increase is a key task at the present stage of development of the economies of both Russia and Eurasian countries. The purpose of this article is to identify quantitative assessments of how various factors impact productivity increase and conduct a cluster analysis of the regions, based on the considered indicators that evaluate the impact of relevant factors on productivity. The authors use general scientific methods such as analysis and synthesis, econometric analysis and multidimensional statistics. To build the model, the authors of the article used statistical data relating to socioeconomic development indicators for 85 Russian regions. As a result of the correlation and regression analysis, the following factors were identified: the average monthly wage, consumption of fixed capital, internal R&D costs, innovative activity of organisations, and tax burden. These factors have both positive and negative impacts on productivity. A cluster analysis was also conducted. It enabled to group the regions in terms of their productivity. Based on the analysis, the authors proposed the directions of improving the policy to increase productivity for each of the three clusters. For the regions included in the first cluster, it is necessary to apply methods of direct state regulation, for the regions of the second cluster-to pursue a policy of improvement of tax incentive mechanisms through the application
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