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Transformational Leadership and Work Motivation as Predictors of Elementary-School Dapodik Operator Performance: Evidence from Pinrang Regency, Indonesia Masni Masni; Andi Hendra Syam; Rina Rina
International Journal of Business, Law, and Education Vol. 7 No. 2 (2026): International Journal of Business, Law, and Education (on progres)
Publisher : IJBLE Scientific Publications Community Inc.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56442/ijble.v7i2.1535

Abstract

This study examines whether transformational leadership and work motivation predict the performance of elementary-school operators of Indonesia’s Basic Education Data system (Data Pokok Pendidikan, Dapodik) in Pinrang Regency. A quantitative, cross-sectional explanatory design was used. From a population of 315 operators, 176 respondents were selected through simple random sampling using a finite-population formula with a 5% precision level. Data were collected using a 13-item questionnaire and documentation and were analysed through multiple linear regression in IBM SPSS Statistics 25. Item-total correlations exceeded the critical value of .147. Visual diagnostic checks indicated no substantial violations of normality, multicollinearity, or homoscedasticity assumptions. Transformational leadership was positively but not significantly associated with operator performance (B = .087, SE = .137, β = .119, t = .615, p = .539), whereas work motivation was a positive and statistically significant predictor (B = .409, SE = .071, β = .502, t = 5.746, p < .001). The model was significant overall, F(2, 173) = 17.560, p < .001, explaining 16.9% of the variance in performance (adjusted R² = .159). The findings indicate that motivation is the more proximal predictor of performance in a highly standardized digital-administration role. Education authorities should therefore combine recognition, professional development, adequate work support, and adaptive leadership with continuing digital-capability development. The cross-sectional, single-region, and self-report design limits causal interpretation and generalizability.