Retaining millennial professionals: Strategies for sustaining tech industry talent in Malaysia

  • 20 Views
  • 3 Downloads

Creative Commons License DMCA.com Protection Status
This work is licensed under a Creative Commons Attribution 4.0 International License

Type of the article: Research Article

Abstract
Highly skilled and experienced professionals are essential to most profit-making organizations to sustain and remain competitive. This study aims to examine the central challenge of retaining skilled professionals in Malaysia, a key factor in sustaining organizational competitiveness and long-term profitability. A sample of 200 millennials was used to evaluate the potential for employee retention in relation to work-life balance, work environment, compensation, and supervisor support. Structural equation modeling was used to analyze the hypothesized relationships among the constructs. The analysis shows that compensation (β = 0.353, t = 2.869, p = 0.002, f2 = 0.153) is the strongest predictor of employee retention, followed by work environment (β = 0.283, t = 1.653, p = 0.049, f2 = 0.082) and work-life balance (β = 0.246, t = 2.253, p = 0.012, f2 = 0.097). Together, these three factors explain 66.6% of the variance (R2 = 0.666) in employee retention, which is considered substantial. In contrast, supervisor support (β = 0.080, t = 0.681, p = 0.248, f2 = 0.010) does not significantly influence employee retention. Overall, the results show that financial rewards, supportive working conditions, and work-life balance are key drivers of professional retention in Malaysia. Instead of losing highly skilled employees to competitors, it is important to retain them by offering competitive pay, a positive work environment, and a fair work-life balance.

view full abstract hide full abstract
    • Table 1. Respondents’ characteristics
    • Table 2. Measurement items
    • Table 3. Loadings, AVE, and composite reliability
    • Table 4. Fornell and Larcker сriterion
    • Table 5. Heterotrait-monotrait criterion
    • Table 6. Variance inflator factor (VIF)
    • Table 7. Hypotheses testing results
    • Table 8. Model fit
    • Table 9. PLS predict
    • Table 10. Predictive power
    • Table A1. Cross-loading
    • Conceptualization
      Hasliza Hassan, Andrina Sequerah
    • Formal Analysis
      Hasliza Hassan, Andrina Sequerah, Aysa Siddika
    • Funding acquisition
      Hasliza Hassan
    • Investigation
      Hasliza Hassan, Andrina Sequerah
    • Methodology
      Hasliza Hassan, Andrina Sequerah, Aysa Siddika
    • Resources
      Hasliza Hassan
    • Software
      Hasliza Hassan
    • Supervision
      Hasliza Hassan
    • Validation
      Hasliza Hassan, Andrina Sequerah
    • Visualization
      Hasliza Hassan, Aysa Siddika
    • Writing – original draft
      Hasliza Hassan, Andrina Sequerah, Aysa Siddika
    • Data curation
      Andrina Sequerah
    • Writing – review & editing
      Aysa Siddika