Student satisfaction with learning outcomes and intention to stay with current employers within dual education

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Type of the article: Research Article

The growing demand for practice-oriented workforce development has increased the importance of dual education in vocational education and training systems. The purpose of this study is to examine the relationships between students’ satisfaction with learning outcomes and their intention to stay in their current job within dual education programs in the vocational education and training (VET) system in Kazakhstan. The methodological approach is based on a quantitative research design using survey data collected from 440 students enrolled in four VET institutions in Almaty. Structural equation modeling (SEM) with SmartPLS software was employed to test the proposed hypotheses and assess the relationships between the studied variables. The results demonstrate that enterprise internship satisfaction positively affects general learning satisfaction (β = 0.380), job involvement (β = 0.274), and intention to stay in the current job after graduation (β = 0.142). Job involvement has the strongest effect on intention to stay with the employer (β = 0.416) and significantly increases learning involvement (β = 0.636). However, general satisfaction with learning outcomes does not directly influence intention to stay in the current job. The findings indicate that practical workplace experience and active integration into the work environment are more important determinants of students’ intention to stay with their current employer than general perceptions of educational quality. The study contributes to understanding the role of dual education in strengthening workforce retention intention and early career commitment in vocational education contexts.

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    • Figure 1. Structural model results
    • Table 1. Research design and data collection
    • Table 2. Constructs and variables
    • Table 3. Path coefficients
    • Table 4. Total effect
    • Table 5. Total indirect effects
    • Table A1. Reliability and validity
    • Table B1. Discriminant validity, Fornell-Larcker
    • Table B2. Discriminant validity, HTMT
    • Table C1. Variance inflation factor (VIF)
    • Table C2. Comparison of saturated and estimated models
    • Table C3. Specific indirect effects
    • Table С4. Factor loadings (outer loadings)
    • Conceptualization
      Gulashar Doskeyeva
    • Data curation
      Gulashar Doskeyeva, Roza Kuzembekova, Makpal Nurpeisova
    • Formal Analysis
      Gulashar Doskeyeva, Roza Kuzembekova
    • Funding acquisition
      Gulashar Doskeyeva, Nurzhan Bizhanov, Makpal Nurpeisova
    • Investigation
      Gulashar Doskeyeva, Nurzhan Bizhanov
    • Methodology
      Gulashar Doskeyeva, Nurzhan Bizhanov
    • Project administration
      Gulashar Doskeyeva
    • Resources
      Gulashar Doskeyeva, Nurzhan Bizhanov, Roza Kuzembekova
    • Software
      Gulashar Doskeyeva, Nurzhan Bizhanov, Roza Kuzembekova, Makpal Nurpeisova
    • Supervision
      Gulashar Doskeyeva
    • Validation
      Gulashar Doskeyeva, Nurzhan Bizhanov, Roza Kuzembekova
    • Visualization
      Gulashar Doskeyeva, Nurzhan Bizhanov, Makpal Nurpeisova
    • Writing – original draft
      Gulashar Doskeyeva, Nurzhan Bizhanov, Roza Kuzembekova, Makpal Nurpeisova
    • Writing – review & editing
      Gulashar Doskeyeva, Nurzhan Bizhanov, Roza Kuzembekova, Makpal Nurpeisova