Emotional Intelligence and Employee Outcomes among Special School Teachers in Kerala: An Integrated CFA and Structural Equation Modelling Approach
Authors
Dr. Josheena Jose
Associate Professor, Christ College (Autonomous), Irinjalakuda, University of Calicut (IN)
Dr. Femy O. A
Assistant Professor, Sree Kerala Varma College, Thrissur, University of Calicut (IN)
Dr. Muvish K. M
Assistant Professor, Christ College (Autonomous), Irinjalakuda, University of Calicut (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2025.1412000139
Subject Category: Commerce
Volume/Issue: 14/12 | Page No: 1589-1596
Publication Timeline
Submitted: 2026-01-18
Published: 2026-01-17
Abstract
Emotional intelligence has become an essential psychological asset impacting job results in emotionally taxing fields, including special education. This study investigates the structural links between emotional intelligence and employee outcomes among special school teachers in Kerala, employing a CFA–SEM integrated analytical approach. Data were gathered from 606 special school teachers using a structured questionnaire and evaluated using confirmatory factor analysis to validate the measurement model, subsequently employing covariance-based structural equation modeling to assess the proposed correlations. The findings demonstrate that emotional intelligence exerts a substantial and favorable influence on job satisfaction, job commitment, job performance, and organizational citizenship behavior. Mediation research indicates that emotional intelligence has a substantial direct impact on job commitment, although job satisfaction does not significantly mediate this link. These results indicate that emotional intelligence serves as an inherent attribute that directly improves professional efficacy, rather than functioning through intermediary processes. The study emphasizes the significance of enhancing emotional intelligence to elevate employee outcomes and maintain efficacy in special education settings.
Keywords
Emotional Intelligence, Employee Outcomes, Confirmatory Factor Analysis, Structural Equation Modelling, Special School Teachers
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References
1. Goleman, D. (1995). Emotional Intelligence: Why It Can Matter More Than IQ. New York: Bantam Books. [Google Scholar] [Crossref]
2. Goleman, D. (1998). Working with Emotional Intelligence. New York: Bantam Books. [Google Scholar] [Crossref]
3. Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality, 9(3), 185–211. [Google Scholar] [Crossref]
4. Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality, 9(3), 185–211. [Google Scholar] [Crossref]
5. Mayer, J. D., Salovey, P., & Caruso, D. R. (2004). Emotional intelligence: Theory, findings, and implications. Psychological Inquiry, 15(3), 197–215 [Google Scholar] [Crossref]
6. Brackett, M. A., & Katulak, N. A. (2006). Emotional intelligence in the classroom. In J. Ciarrochi et al. (Eds.), Emotional Intelligence in Everyday Life. New York: Psychology Press. [Google Scholar] [Crossref]
7. Jennings, P. A., & Greenberg, M. T. (2009). The prosocial classroom: Teacher social and emotional competence. Review of Educational Research, 79(1), 491–525. [Google Scholar] [Crossref]
8. Sutton, R. E., & Wheatley, K. F. (2003). Teachers’ emotions and teaching. Educational Psychology Review, 15(4), 327–358. [Google Scholar] [Crossref]
9. Sutton, R. E., & Wheatley, K. F. (2003). Teachers’ emotions and teaching. Educational Psychology Review, 15(4), 327–358. [Google Scholar] [Crossref]
10. Meyer, J. P., Stanley, D. J., Herscovitch, L., & Topolnytsky, L. (2002). Affective, continuance, and normative commitment. Journal of Vocational Behavior, 61(1), 20–52. [Google Scholar] [Crossref]
11. Organ, D. W. (1988). Organizational Citizenship Behavior: The Good Soldier Syndrome. Lexington, MA: Lexington Books. [Google Scholar] [Crossref]
12. Podsakoff, P. M., MacKenzie, S. B., Paine, J. B., & Bachrach, D. G. (2000). Organizational citizenship behaviors. Journal of Management, 26(3), 513–563. [Google Scholar] [Crossref]
13. Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement. Journal of Happiness Studies, 3(1), 71–92. [Google Scholar] [Crossref]
14. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall. [Google Scholar] [Crossref]
15. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson Education [Google Scholar] [Crossref]
16. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis. Structural Equation Modeling, 6(1), 1–55. [Google Scholar] [Crossref]
17. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models. Journal of Marketing Research, 18(1), 39–50. [Google Scholar] [Crossref]
18. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models. Journal of Marketing Research, 18(1), 39–50. [Google Scholar] [Crossref]
19. National Council for Teacher Education (NCTE). (2014). Teacher Education in India: Vision and Mission. New Delhi. [Google Scholar] [Crossref]
20. Government of Kerala. (2020). Status of Special Education Institutions in Kerala. Thiruvananthapuram. [Google Scholar] [Crossref]
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