Types of employee training, organizational identification, and turnover intention: evidence from Korean employees

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Many organizations are willing to increase human capital investment through various employee training programs. This study empirically examines a proposed model that explains the relationship between the different types of employee training, including general and firm-specific training and employee turnover in Korean firms. This study used a survey sample of 10,069 employees in 467 publicly traded firms in South Korea. 78% of participating companies provided training programs to the employees. This study conducted quantitative cross-sectional regression analyses to test the hypotheses. The study suggests empirical evidence that general training and firm-specific training reduce employee turnover intention. Moreover, the magnitude of firm-specific training on turnover intention is much higher than general training. Furthermore, employee organizational identification has a partial mediating effect on training and turnover intention. However, the study found no substantial evidence of the moderating effect of employees’ justice perception of receiving training opportunities. Based on the human capital theory and social exchange perspective, the results indicate that both types of training programs help employee retention, and cultivating employee organizational identification can be critical in the training-turnover process.

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    • Figure 1. Research model
    • Table 1. Descriptive statistics and correlations
    • Table 2. Results of regression analysis of turnover intention (mediation model)
    • Table 3. Results of regression analysis of organizational identification (moderating model)
    • Conceptualization
      Ji-Young Ahn, Shilu Huang
    • Formal Analysis
      Ji-Young Ahn, Shilu Huang
    • Methodology
      Ji-Young Ahn, Shilu Huang
    • Project administration
      Ji-Young Ahn
    • Supervision
      Ji-Young Ahn
    • Validation
      Ji-Young Ahn
    • Writing – review & editing
      Ji-Young Ahn
    • Data curation
      Shilu Huang
    • Investigation
      Shilu Huang
    • Writing – original draft
      Shilu Huang