Organizational culture and commitment among Generation Z: The mediating role of employee engagement

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Type of the article: Research Article

Abstract
As the global workforce undergoes significant demographic change, organizations in Indonesia face increasing challenges in maintaining organizational commitment among Generation Z employees during shifting work values and high labor mobility. This study examines the effect of organizational culture on organizational commitment and investigates the mediating role of employee engagement. Based on job demands–resources theory and social exchange theory, a quantitative cross-sectional survey was conducted. Data were collected in 2025 through purposive sampling from 271 Generation Z employees working in organizations located in Jakarta, Indonesia. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS using a two-stage approach for higher-order constructs. The findings show that organizational culture is strongly positively associated with employee engagement (β = 0.793; t = 34.590; p < 0.001), explaining 62.9% of the variance in engagement (R2 = 0.629). Employee engagement significantly affects organizational commitment (β = 0.524; t = 4.847; p < 0.001) and demonstrates a significant indirect mediating role in the relationship between organizational culture and organizational commitment (β = 0.416; t = 4.685; p < 0.001). However, the direct association between organizational culture and organizational commitment is not statistically significant (β = 0.168; p = 0.092). These results suggest that organizational commitment is shaped mainly by engagement experiences rather than by organizational culture alone, highlighting engagement as an important instrument for sustaining workplace bonds.

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    • Figure 1. Conceptual model
    • Figure 2. First-order measurement model results
    • Figure 3. Second-order measurement model results
    • Figure 4. Structural model with path coefficients and t-statistics
    • Table 1. Demographics of respondents
    • Table 2. Convergent validity assessment of first-order constructs
    • Table 3. Discriminant validity assessment of first-order constructs using the heterotrait–monotrait ratio (HTMT)
    • Table 4. Reliability assessment of first-order constructs
    • Table 5. Variance inflation factor assessment of first-order constructs
    • Table 6. Convergent validity and reliability of second-order constructs
    • Table 7. Discriminant validity assessment of second-order constructs using the heterotrait–monotrait ratio (HTMT)
    • Table 8. Variance inflation factor (VIF) assessment of second-order constructs
    • Table 9. Coefficient of determination
    • Table 10. Effect size (f2)
    • Table 11. Predictive relevance (Q2)
    • Table 12. Variance inflation factor (VIF) assessment
    • Table 13. Model fit assessment
    • Table 14. Hypothesis testing results
    • Table 15. PLSpredict assessment
    • Conceptualization
      Jefta Harlianto, Winona Rahmania Imansari
    • Data curation
      Jefta Harlianto, Winona Rahmania Imansari, Rudi
    • Formal Analysis
      Jefta Harlianto, Winona Rahmania Imansari, Rudi
    • Methodology
      Jefta Harlianto, Winona Rahmania Imansari
    • Resources
      Jefta Harlianto, Rudi
    • Supervision
      Jefta Harlianto
    • Validation
      Jefta Harlianto, Rudi
    • Visualization
      Jefta Harlianto, Rudi
    • Writing – original draft
      Jefta Harlianto, Rudi
    • Writing – review & editing
      Jefta Harlianto
    • Investigation
      Winona Rahmania Imansari
    • Project administration
      Winona Rahmania Imansari, Rudi
    • Software
      Winona Rahmania Imansari, Rudi