Authors:
Ana Živković
;
Dario Šebalj
and
Jelena Franjković
Affiliation:
Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, Trg Ljudevita Gaja 7, Osijek, Croatia
Keyword(s):
Employee Turnover, Employee Job Satisfaction, Machine Learning, Organizational Commitment.
Abstract:
This study examines the effectiveness of Decision Tree methodology in predicting employee turnover intention, an area in which this method has received limited research. In this paper, primary research was conducted and four Decision Tree algorithms were applied to a sample of 511 respondents. The study incorporates several predictor variables into the model, including job satisfaction, perceived organizational commitment, perceived organizational justice, perceived organizational support, and perceived alternative job opportunities, to assess their influence on turnover intention. The assessment measure of the model was Recall. The results indicate that the Decision Tree model using the RandomTree algorithm is relatively successful in predicting turnover intentions (almost 60% accuracy rate), with job satisfaction, especially opportunities for personal growth and affective organizational commitment being significant predictors. Other influencing factors include satisfaction with sal
ary and the job itself, as well as interpersonal relationships. This study underscores the potential of the Decision Tree method in human resource management and provides a basis for future research on the role of predictive analytics in understanding employee turnover dynamics.
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