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Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques.

Jungryeol Park1, Yituo Feng2, Seon-Phil Jeong3

  • 1Technology Policy Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.

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High turnover among new college graduates is a growing concern. This study uses machine learning to predict turnover intention, finding job security is a key factor, outperforming traditional predictors.

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Area of Science:

  • Organizational Behavior
  • Human Resource Management
  • Data Science

Background:

  • Intensifying turnover among new college graduates poses significant financial challenges for businesses due to recruitment and training costs.
  • Previous studies identified factors influencing turnover intention but lacked predictive power for actual job changes.
  • Effective identification and management of new employees at risk of turnover are crucial for organizational stability.

Purpose of the Study:

  • To develop a machine learning-based model for predicting the turnover intention of new college graduates.
  • To overcome the limitations of traditional econometric models in predicting employee turnover.
  • To identify key predictors of turnover intention in early-career professionals.

Main Methods:

  • Utilized data from the Korea Employment Information Service's Job Movement Path Survey for college graduates.
  • Employed Ordinary Least Squares (OLS) regression to analyze predictor influences.
  • Implemented machine learning classifiers including Logistic Regression (LR), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGB) for model learning and classification.

Main Results:

  • Job security emerged as the most significant predictor of turnover intention, surpassing traditional factors like workload importance and major relevance.
  • The Extreme Gradient Boosting (XGB) model achieved the highest prediction accuracy at 78.5%.
  • Demonstrated a diminished or reversed influence of certain traditional factors on turnover intention compared to previous research.

Conclusions:

  • Machine learning models, particularly XGB, offer a significant advancement in predicting new graduate turnover intention.
  • Job security and organizational satisfaction are critical factors for retaining early-career talent.
  • Organizations should re-evaluate traditional factors influencing turnover and prioritize job security to improve retention strategies.