The effect of talent and knowledge management on employee retention: The mediating role of employee engagement

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Type of the article: Research Article

Abstract
Amid rising labor mobility and competition for skilled employees, this study aims to examine how talent and knowledge management relates to employee engagement and retention, with employee engagement as a mediator, among private-sector employees in Indonesia’s manufacturing, banking, retail, service, and logistics sectors. Using a combination of purposive and snowball sampling, 543 valid responses (from 964 distributed questionnaires across 97 companies) were analyzed using partial least squares structural equation modeling (PLS-SEM). Talent management (β = 0.554) and knowledge management (β = 0.379) were positively associated with employee engagement (R² = 0.721), and both were also directly associated with employee retention (β = 0.275 and β = 0.315, respectively; R² = 0.632). Employee engagement was positively associated with employee retention (β = 0.290), and its indirect effects for talent management (β = 0.161) and knowledge management (β = 0.110) were statistically significant (p < 0.001), confirming employee engagement as a significant partial mediator (VAF = 36.93% and 25.88%, respectively). Direct effects of talent and knowledge management on retention remained somewhat larger than their indirect effects through engagement, but the substantial VAF values indicate that employee engagement is a meaningful complementary pathway, alongside direct organizational investment in talent development and knowledge management, through which these practices are associated with employee retention. The study extends social exchange theory and the resource-based view and offers practical implications for strengthening retention in the sampled Indonesian private companies.

Acknowledgments
Thanks to the lecturers of the Faculty of Economics and Business, Universitas Muhammadiyah Jember, for their support of this research.

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    • Figure 1. Research model
    • Figure 2. SmartPLS test
    • Table 1. Questionnaire distribution
    • Table 2. Respondent characteristics
    • Table 3. Summary of operational definitions and measurement scale
    • Table 4. Inter-item correlation matrix: Talent management (X1) and knowledge management (X2)
    • Table 5. Pilot test reliability results (n = 30)
    • Table 6. Descriptive statistics
    • Table 7. Outer loading / loading factor results
    • Table 8. Heterotrait-monotrait ratio (HTMT)
    • Table 9. Heterotrait-monotrait ratio (HTMT) bootstrapping
    • Table 10. Fornell–Larcker criterion
    • Table 11. Reliability and multicollinearity test results
    • Table 12. Collinearity assessment (Outer and inner VIF)
    • Table 13. R-squared results
    • Table 14. Model fit results
    • Table 15. Hypothesis testing
    • Table 16. Indirect effect contribution (VAF)
    • Table 17. PLS predict
    • Table 18. Robustness check with control variables
    • Table 19. Multi-group analysis: Permanent vs Contract
    • Table 20. MICOM Step 2 – Compositional invariance
    • Table 21. MICOM Step 3 – Equality of means & variances
    • Table 22. Cluster dependence diagnostics and robustness check
    • Table A1. Questionnaire
    • Conceptualization
      Nursaid
    • Formal Analysis
      Nursaid
    • Investigation
      Nursaid
    • Methodology
      Nursaid
    • Software
      Nursaid
    • Writing – original draft
      Nursaid
    • Data curation
      Trias Setyowati, Wenny Murtalining Tyas
    • Project administration
      Trias Setyowati, Wenny Murtalining Tyas
    • Resources
      Trias Setyowati, Wenny Murtalining Tyas
    • Supervision
      Trias Setyowati, Wenny Murtalining Tyas
    • Validation
      Trias Setyowati, Wenny Murtalining Tyas
    • Visualization
      Trias Setyowati, Wenny Murtalining Tyas
    • Writing – review & editing
      Trias Setyowati, Wenny Murtalining Tyas
    • Funding acquisition
      Wenny Murtalining Tyas