Job Satisfaction and Union Commitment of Teachers: A Survey of Senior High Schools in the Ashanti Region of Ghana

This study assessed teachers’ job satisfaction and union commitment in senior high schools in the Ashanti Region of Ghana. This quantitative descriptive study sampled 400 teachers for the survey, and analyzed the responses using SPSS. A correlational design was used to examine the associations among job satisfaction and union commitment variables of teachers. The data were analyzed using the Smart PLS 4—Structural Equation Model (SEM). The choice of SEM was informed by its appropriateness for predictive research models and to facilitate the derivation of significant conclusions. The key findings of the study revealed that 268 (67%) agreed that they received recognition for good performance, whereas 71 (17.5%) disagreed. Additionally, 234 (58.5%) reported feeling connected to their colleagues, compared to only 21 (5.3%) who did not. However, wages emerged as the most contentious factor, eliciting the highest level of dissatisfaction. Regarding union commitment, 290 (72.5%) expressed pride in their membership, while 66 (16.6%) did not, yielding an overall positive perception with an average score of 70%. The study recommends that teacher unions should improve their relationships with school management and collectively work towards better compensation and working conditions for their members. This will help improve their union commitment and job satisfaction and eventually improve learning outcomes. Furthermore, teacher unions should expand welfare services, including mental health support, flexible loan schemes, and professional development opportunities, guided by regular member needs assessments to improve teacher job satisfaction and commitment. It is also recommended that school administrators cultivate supportive work environments by strengthening mentorship, recognition systems, and community engagement initiatives. Finally, routine monitoring of teacher well-being using validated assessment tools is recommended to support data-driven decision-making and timely interventions that reinforce teacher satisfaction and retention.