Digital human resource management and academic performance: Evidence from Indonesian associate professors

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Type of the article: Research Article

Digital transformation has changed the way human resource management is organized in higher education. In private universities with strong accreditation demands, digitally supported human resource practices are increasingly relevant for helping academic staff manage teaching, research, publication, and institutional service responsibilities. The study analyzes the relationship between Digital Human Resource Management practices, human resource agility, and self-reported academic staff performance among full-time Associate Professors in Indonesian private universities with “Excellent” accreditation status. A quantitative explanatory approach was used, based on cross-sectional survey data collected from June to December 2025. Of 537 responses collected, 500 valid responses were analyzed using Partial Least Squares Structural Equation Modeling. The results show that Digital Human Resource Management practices are positively associated with self-reported academic staff performance (β = 0.297, t = 7.367, p < 0.001) and human resource agility (β = 0.495, t = 12.713, p < 0.001). Human resource agility is also positively associated with self-reported academic staff performance (β = 0.422, t = 10.463, p < 0.001). Mediation analysis indicates a positive indirect effect of Digital Human Resource Management practices on self-reported academic staff performance through human resource agility (β = 0.209, 95% Boot CI [0.160, 0.264]), with a total effect of β = 0.506. The model explains 29.9% of human resource agility and 48.7% of self-reported academic staff performance. The findings suggest that digital human resource practices are positively associated with perceived academic performance when accompanied by agile academic work behavior. Practically, universities should align digital human resource systems with agility-oriented staff development.

 
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    • Table 1. Measurement items
    • Table 2. Common method bias assessment
    • Table 3. Response summary
    • Table 4. Respondent profile based on available data
    • Table 5. Descriptive statistics and correlations
    • Table 6. Reliability and convergent validity
    • Table 7. Outer loadings and outer VIF
    • Table 8. HTMT discriminant validity with bootstrapped confidence intervals
    • Table 9. Path coefficients and hypothesis testing
    • Table 10. Coefficient of determination (R2)
    • Table 11. Mediation analysis
    • Conceptualization
      Nursaimatussaddiya, Horas Djulius, Tria Aziti Meisya
    • Data curation
      Nursaimatussaddiya, Horas Djulius, Tria Aziti Meisya
    • Formal Analysis
      Nursaimatussaddiya, Horas Djulius, Tria Aziti Meisya
    • Funding acquisition
      Nursaimatussaddiya, Horas Djulius, Tria Aziti Meisya
    • Project administration
      Nursaimatussaddiya
    • Software
      Nursaimatussaddiya
    • Supervision
      Nursaimatussaddiya, Horas Djulius, Tria Aziti Meisya
    • Investigation
      Horas Djulius, Tria Aziti Meisya
    • Resources
      Horas Djulius, Tria Aziti Meisya