Socially Relevant Factors of Organizational Mortality of Enterprises: Context of Corporate Sustainability in European Countries

• Autorzy: Tetiana Dotsenko , Marek Dvořák , Sergij Lyeonov , Attila Kovács
• Języki publikacji: EN

Abstrakty

EN

The business-demographic problem of organizational mortality of enterprises is considered an essential area of social welfare in a society. It affects the lives and activities of many people and their organizations, as well as the corporate sustainability and the overall socioeconomic situation in the country. The study aims to determine relevant factors influencing the organizational mortality of enterprises from the group of social indicators by using data mining methods. The paper identifies the most influential among such social factors, using the Sigma-restricted parameterization method and correlation analysis. The significance of these social factors is explored through the univariate tests, the Pareto chart of t-values, and a correlation matrix of their interdependence. The factors' force and areas of the influence have been quantified. A model of a linear relationship in multiple regression between organizational mortality of enterprises and relevant social factors is constructed using the ordinary least squares method (OLS method). Meanwhile, a graph of the normal distribution of residuals is built for an analogous linear regression model. The research results highlight the problematic social aspects and factors of enterprises' activity for corporate sustainability, suggesting a practical use for the calculated dependencies between relevant indicators. The application of the methodology will make it possible to influence the pattern of entrepreneurial activity in developing countries to reduce the national economic downturn. (original abstract)

Słowa kluczowe

EN

Enterprise survival; Sustainable development; Corporation; Correlation analysis; Multiple regression; Linear regression; Data Mining

PL

Przetrwanie przedsiębiorstwa; Rozwój zrównoważony; Korporacje; Analiza korelacji; Regresja wielokrotna; Regresja liniowa; Data Mining

• Czasopismo: Economics & Sociology
• Rocznik: 2023
• Tom: 16
• Numer: nr 1
• Strony: 299--314

Twórcy

autor

Tetiana Dotsenko

• Sumy State University, Sumy, Ukraine; Technical University of Berlin, Berlin, Germany

autor

Marek Dvořák

• Czech University of Life Sciences

autor

Sergij Lyeonov

• Sumy State University, Ukraine

autor

Attila Kovács

• Budapest Metropolitan University, Budapest, Hungary

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Identyfikatory

DOI

10.14254/2071-789X.2023/16-1/18