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Estimating Successful Internal Mobility: A Comparison Between Structural Equation Models and Machine Learning

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This summary is machine-generated.

Predicting job satisfaction is key for internal mobility success. This study found that traditional Structural Equation Modeling (SEM) and Machine Learning algorithms like Bagging k-NN offer comparable predictive power for employee job satisfaction.

Keywords:
internal mobilityjob relocationjob satisfactionmachine learningpredictive HR analyticsresistance to changestructural equation models

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Area of Science:

  • Human Resources Analytics
  • Organizational Psychology
  • Machine Learning in HR

Background:

  • Internal mobility programs require accurate prediction of future job satisfaction for employee retention and success.
  • Traditional statistical models and modern machine learning algorithms offer different approaches to predictive analytics in HR.
  • Comparing these diverse methodologies is crucial for advancing predictive capabilities in human resources.

Purpose of the Study:

  • To compare the predictive accuracy of Structural Equation Modeling (SEM), Lasso regression, and Bagging k-NN for job satisfaction.
  • To identify key predictors of job satisfaction within internal mobility programs in a large Italian banking group.
  • To evaluate the effectiveness of different modeling approaches in Human Resources analytics.

Main Methods:

  • Employed Structural Equation Modeling (SEM), a traditional statistical approach.
  • Utilized Machine Learning algorithms: Lasso for feature selection and Bagging meta-model with k-NN as a base estimator.
  • Trained and tested models on data from 348 employees and 35 supervisors, with a test set of 79 employees.

Main Results:

  • SEM and Bagging k-NN demonstrated average predictive power, with accuracies between 61-66% and F1 scores of 0.51-0.73.
  • SEM and Lasso algorithms identified 'resistance to change' and 'orientation to relation' as significant predictors.
  • Personality and motivation variables also emerged as important predictors in various models.

Conclusions:

  • Both traditional SEM and machine learning (Bagging k-NN) provide viable, comparable predictive performance for job satisfaction.
  • Specific personality traits like resistance to change and orientation to relation are crucial for predicting successful internal mobility.
  • Integrating diverse analytical methods enhances the predictive accuracy and theoretical understanding in Human Resources analytics.