Факультет цифрових технологій та автоматизації виробництва
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Документ Assessment of the EU Countries’ Economic Security based on the Composite Indicators(Rutgers University, New Jersey, 2022) Khadzhynova, O.; Simanaviciene, Z.; Mints, A. Yu.; Burak, P.; Khachatrian, V.; Мінц, О. Ю.The authors propose an integral indicator of the economic security of a country, based on a study of economic, social, political and environmental indicators of security of 28 European Union countries. The study used panel regression methods, correlation analysis, nonlinear approximation, graphical methods. The research results make it possible to explain up to 58% of the variations in the studied indicators. The calculated values of the integral indicator of economic security correspond to empirical data. The indicator proposed by authors comprehensively characterizes the current state of the country’s economic security in the economic, social, political and environmental spheres. This indicator makes it possible to determine the level and disproportions of the country’s development and can become the basis for the formation of directions for ensuring its economic security.Документ Corporate Social Responsibility Impact on Financial Performance: a Case for the Metallurgical Industry(University of Naples "Federico II", 2021) Mints, A. Yu.; Kamyshnykova, E. V.; Zherlitsyn, D. M.; Bukrina, K.; Bessonova, A.; Мінц, О. Ю.; Жерліцин, Д. М.Assessing the impact of methods of corporate social responsibility management on financial performance is one of the key aspects to implement strategic management into practices. There are contradictory results of this impact’s study in the literature due to the difference in the applied methods of measuring variables, errors in models etc. The available literature is still inconclusive about this aspect, in particular, for the metallurgical industry, which plays a significant role in Ukrainian and world economy. The purpose of the paper is to evaluate the impact of corporate social responsibility on the company financial performance and to determine the financial efficiency of socially responsible initiatives for the metallurgical industry in particular. It proposes methodology for assessing the impact of corporate social responsibility on the corporate financial performance, and it uses data from a socially oriented balanced scorecard. The research methodology includes correlation and regression analysis with panel data techniques based on data from a balanced scorecard for a sample of four dominant market participants in the Ukrainian metallurgy in 2010-2018. Authors assess the level of corporate social responsibility by indicators of four perspectives, such as: internal processes, learning and growth, environmental, and relational perspective that characterizes the level of satisfaction of various stakeholder groups with the company’s activities in the field of corporate social responsibility. The initial data for the analysis have been taken from the financial and non-financial statements and results of expert assessment. The study uses linear and panel regression models with fixed and random effects in order to demonstrate the impact of four independent variables (internal processes, learning and growth, environmental, and relational perspectives) on the financial perspective as a dependent variable. The panel effects made it possible to obtain more accurate model’s parameters compared to simple linear regression model. The empirical finding from the study illustrates a strong and statistically significant relationship between the relational perspective, which is a corporate social responsibility indicator, and the financial perspective in the socially oriented balanced scorecard. This means that the costs of creating and maintaining a positive image of metallurgical companies are fully justified by improving their bottom line. Future research directions compare the effectiveness of statistical methods evaluating the impact of corporate social responsibility on the company financial performance with alternative methods, e.g. data mining techniques, in terms of forecasting accuracy.