Analyzing the impact of corporate social responsibility on employee satisfaction using a hybrid SEM-ANN approach
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Date
2025
Authors
Stojanović, Anđelka
Arsić, Sanela
Milošević, Isidora
Mihajlović, Ivan
Spasojević Brkić, Vesna
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Source
Sustainability
Volume
17
Issue
9
Abstract
In the conditions of modern market dynamics, corporate social responsibility (CSR) is increasingly evolving from a formal ethical principle into a powerful strategic instrument, key to achieving sustainable growth and long-term competitive advantage. Companies that integrate CSR into their business not only affirm the values of social responsibility, but also position themselves as reliable and ethically oriented actors, thereby winning the trust of investors, motivating employees and building a stable base of loyal consumers. Hence, the aim of this research is to determine, through empirical analysis, to what extent and in what way individual aspects of corporate social responsibility influence employee perception and satisfaction, as well as to develop a predictive model of their mutual connection. The specific hybrid SEM-ANN methodology in the CSR field was applied to obtain more precise results than standard data analysis methods, which fulfilled the literature gap in this research field. The detailed hypotheses designed were empirically tested using SEM methodology and indicated a positive association between the social and stakeholder aspects with employee satisfaction. These outcomes were confirmed by the results of the ANN models. The findings obtained are not only theoretical, but also have a useful application in the real business environment, which is reflected in the development of strategies that can serve as a road map for organizations in achieving employee satisfaction. This could lead to a change in organizational culture with an emphasis on ethical business and greater responsibility towards society and stakeholders.
Description
Keywords
CSR aspects, employee satisfaction, SEM-ANN method, company
Citation
DOI
10.3390/su17094009
Scopus
ISSN
2071-1050
ISBN
License
CC-BY