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This study investigates the relationships between sustainable human capital management practices, ESG performance, ESG disclosure, and firm financial performance. Using a sample of 387 S&P 500 firms from 2013 to 2023 and a panel data regression approach, we examine the impact of training expenditure, workforce diversity and inclusion, pay equity, and employee benefits on ESG performance. We also explore the association between ESG performance and ESG disclosure, the effect of ESG performance on financial performance, and the moderating role of ESG disclosure in the ESG-financial performance relationship. Our findings reveal that sustainable human capital management practices have a positive and significant impact on ESG performance, which in turn positively influences firm financial performance. We also find a positive relationship between ESG performance and ESG disclosure, and that ESG disclosure moderates the ESG-financial performance link, with the positive association being stronger for firms with higher levels of ESG disclosure. This study contributes to the literature by offering an integrated approach to examine the relationships between sustainable human capital management, ESG performance, ESG disclosure, and financial performance, providing novel insights into the drivers and outcomes of corporate sustainability in the context of human capital management. Doi: 10.28991/HEF-2024-05-02-08 Full Text: PDF

The objective of this study is to compare and underscore the advantages and disadvantages of learning English in non-English speaking countries to propose the concept of a new English teaching method for students from non-English speaking countries (the case of Russia, Spain, Serbia, and Indonesia). The study used a mixed-methods approach with qualitative analysis of literature from Russia, Serbia, Spain, and Indonesia, and a research questionnaire was developed. 1595 participants were recruited for a survey determining their experiences of learning English and students’ perspectives on English teaching methods in non-English speaking countries. The data went through thematic analysis in the qualitative part of the research and descriptive analysis in the quantitative survey-based design. Findings of the qualitative analysis revealed both advantages and disadvantages of teaching English to students in selected countries; however, the main findings reported the presence of cultural barriers and students finding it difficult to form meanings from the English language context. Conceptual thinking helped to understand the role of memory and comprehension when learning a foreign language, whereby research focuses on more novel concepts. Future researchers can focus on the area of neural development of students' memory, which can help guide strategies to teach the English language effectively. Doi: 10.28991/ESJ-2023-07-06-020 Full Text: PDF

The study determined the influence of the Central Bank Digital Currency (CBDC) on the financial markets during the COVID-19 pandemic. It investigated if the CBDC had the potential to improve the efficiency and lending power of the financial system. The research relied on secondary data from a range of financial markets, such as the FTSE, MSCI, Gold Index, S&P 500, and Dow Jones, collected during the COVID-19 period. The quantitative approach was employed using the statistical software SPSS to perform descriptive statistics and regression analysis. The results revealed that the financial markets were negatively impacted by COVID-19, but the CBDC saw a higher level of development during the same period. The statistical analysis showed a positive and significant relationship between the CBDC Attention Index, the Uncertainty Index, and various financial market indices, with regression analysis indicating a significant impact of the CBDC Attention Index on all financial market indices except the Gold Index. However, this study has some limitations, including its limited scope of financial markets. Despite this, these results have important implications for academics and policymakers. It is recommended to increase the use of CBDC through well-crafted policies and legislation to fully realize its potential impact on the financial system. Doi: 10.28991/ESJ-2023-SPER-013 Full Text: PDF

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.

The aim of the study is to elaborate an econometric model to determine the conditions for ensuring the balanced development of environmental, economic, and social components of the Russian agricultural sector within the Eastern European region. The method of fuzzy sets was used to build an integral model for assessing the level of sustainable development of the agricultural sector in Eastern Europe on the basis of economic, environmental, and social sustainability indicators. The control indicators (independent variables) and integral economic, environmental, social sustainability (dependent variables) helped build multifactor linear regression models and calculate the indicators of elasticity of dependent variables from independent variables, which characterize the change in sustainable development indicators with the growth of controlling factors by 1%. This model allows us to define and analyze the levels of sustainable development of the industry, both in a specific country and within the region in general. The study shows that, for Russia and Eastern European countries, innovation is one of the crucial factors in ensuring sustainable development of agriculture in the region, taking into account the current state and level of economic development.

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.