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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Organizational Psychology
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1442990

Organizational Commitment in the Private and Public Sector: A Regression Analysis based on Personality Traits, Subjective Wellbeing, Organizational Orientations and Perceived Employment Uncertainty in Serbia

Provisionally accepted
Dusan Todorovic Dusan Todorovic 1Petar M. Mitic Petar M. Mitic 2Nenad Stojiljkovic Nenad Stojiljkovic 2Mihai Olanescu Mihai Olanescu 3*Adrian Suciu Adrian Suciu 3Danut Popa Danut Popa 3
  • 1 Department of Psychology, Faculty of Philosophy, University of Niš, Nis, Serbia
  • 2 Faculty of Sport and Physical Education, University of Nis, Nis, Serbia
  • 3 Technical University of Cluj-Napoca, Cluj-Napoca, Romania

The final, formatted version of the article will be published soon.

    This study aims to explain the variability in organizational commitment by examining a range of individual and organizational factors. The predictors include personality traits from the HEXACO model, organizational orientations, subjective well-being, perceived employment uncertainty, duration of employment, and income satisfaction. The sample consisted of 1,127 employees, with 49.4% from the private sector and 50.6% from the public/state-owned sector. Multiple regression analysis revealed that the models were statistically significant for both sectors. Public sector employees demonstrated higher levels of continuance commitment, likely due to job security, while private sector employees exhibited higher levels of affective and normative commitment. The model accounted for 51.8% of the variance in organizational commitment for public sector employees and 39.2% for private sector employees. These findings underscore the distinct commitment patterns between sectors and emphasize the role of both dispositional and contextual factors in shaping organizational commitment.

    Keywords: organizational commitment, private and public work organizations, personality traits, Employment uncertainty, Regression Analysis

    Received: 04 Jun 2024; Accepted: 18 Oct 2024.

    Copyright: © 2024 Todorovic, Mitic, Stojiljkovic, Olanescu, Suciu and Popa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mihai Olanescu, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.