Bridging the gap: How career development learning mediates higher education and employability outcomes in Nepal

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Higher education institutions are vital for preparing future professionals and integrating education with the job market. Nevertheless, the limited opportunities for career-orientated education in Nepal continue to pose a significant challenge to the employability of youths. This study examines the impact of the higher education institutional environment and career development learnings on the employability of Nepalese youth. It also examines how career development learning influences the connection between the higher education environment and employment preparedness. The study employs a descriptive and causal methodology, using data from a survey of 411 respondents aged 21–35 actively pursuing work and engaging in career-preparatory courses in Nepal. It employed structural equation modeling to analyze the data and assess the proposed hypotheses. The results suggest that the higher education institution environment does not significantly improve employability (β = 0.038, p > 0.05). Career development learning influences this association (β = 0.803, p < 0.05), underscoring its essential function in converting educational experiences into competencies and preparedness for the job market. The paper stresses the importance of self-determination theory, illustrating how autonomy, competence, and interpersonal interactions contribute to personal development and motivation. Despite the specific cultural and economic context, the outcomes highlight the importance of higher education institutions in fostering career-oriented learning opportunities, advancing employability, and promoting broader developmental goals.

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    • Figure 1. Conceptual framework
    • Figure 2. Structural equation model and path analysis
    • Table 1. Respondent characteristics
    • Table 2. Survey instrument items
    • Table 3. Standardized regression weight, Cronbach’s alpha, construct reliability, and average variance extracted
    • Table 4. Discriminant validity – HTMT criterion
    • Table 5. Descriptive statistics
    • Table 6. Model fit indices with cut-off values
    • Table 7. Hypotheses testing– Direct effects
    • Table 8. Hypotheses testing – Direct, indirect, and total effects of the model
    • Table 9. Hypotheses summary
    • Conceptualization
      Rewan Kumar Dahal, Binod Ghimire, Dilli Raj Sharma, Dipendra Karki, Surendra Prasad Joshi
    • Data curation
      Rewan Kumar Dahal, Dilli Raj Sharma, Dipendra Karki, Surendra Prasad Joshi
    • Formal Analysis
      Rewan Kumar Dahal
    • Methodology
      Rewan Kumar Dahal, Dipendra Karki
    • Project administration
      Rewan Kumar Dahal, Binod Ghimire
    • Software
      Rewan Kumar Dahal, Dipendra Karki
    • Supervision
      Rewan Kumar Dahal, Binod Ghimire
    • Writing – original draft
      Rewan Kumar Dahal, Binod Ghimire, Surendra Prasad Joshi
    • Writing – review & editing
      Rewan Kumar Dahal, Binod Ghimire, Dilli Raj Sharma, Dipendra Karki
    • Funding acquisition
      Binod Ghimire
    • Resources
      Binod Ghimire, Dilli Raj Sharma, Surendra Prasad Joshi
    • Validation
      Binod Ghimire, Dilli Raj Sharma, Dipendra Karki, Surendra Prasad Joshi
    • Investigation
      Dilli Raj Sharma