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Email this article The Effect of Transformational-Instructional Leadership and Work Motivation on Teacher Performance: Evidence from Private Senior High Schools in Manado City, Indonesia
Abstract
Teacher performance is a critical determinant of school effectiveness and educational quality. However, leadership practices in many private schools tend to rely on a single approach, limiting their adaptability to contextual demands. This study addresses a gap in the literature by examining the integrated effect of transformational–instructional leadership and work motivation on teacher performance. This study employed a quantitative cross-sectional survey design involving 64 teachers from private senior high schools in Manado City, Indonesia, selected through proportional random sampling. Data were collected using validated Likert-scale questionnaires and analyzed using multiple linear regression. Prior to hypothesis testing, classical assumption tests (normality, multicollinearity, and heteroscedasticity) were conducted to ensure model adequacy. The findings reveal that both transformational–instructional leadership and work motivation have significant positive effects on teacher performance. Transformational–instructional leadership emerged as the strongest predictor (β = 0.758; p < 0.001), followed by work motivation (β = 0.230; p = 0.001). Simultaneously, both variables explain a substantial proportion of variance in teacher performance (Adjusted R² = 0.814), indicating a strong model fit. The results suggest that the integration of transformational and instructional leadership creates a synergistic effect that enhances teacher performance by addressing both motivational and instructional dimensions. However, the cross-sectional design and reliance on self-reported data limit causal interpretation and may introduce response bias. This study highlights the importance of adopting a hybrid leadership approach supported by strong teacher motivation to improve performance in private school contexts. Future research should employ longitudinal designs and more robust analytical models to validate these findings.
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DOI: https://doi.org/10.35445/alishlah.v18i1.9004
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