Predictors of Turnover Intention in U.S. Federal Government Workforce: Machine Learning Evidence That Perceived Comprehensive HR Practices Predict Turnover Intention

In Gu Kang · Ben Croft · Barbara A. Bichelmeyer

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Job Satisfaction and Organizational Behavior Employee Performance and Management AI and HR Technologies

Abstract

This study aims to identify important predictors of turnover intention and to characterize subgroups of U.S. federal employees at high risk for turnover intention. Data were drawn from the 2018 Federal Employee Viewpoint Survey (FEVS, unweighted N = 598,003), a nationally representative sample of U.S. federal employees. Machine learning Classification and Regression Tree (CART) analyses were conducted to predict turnover intention and accounted for sample weights. CART analyses identified six at-risk subgroups. Predictor importance scores showed job satisfaction was the strongest predictor of turnover intention, followed by satisfaction with organization, loyalty, accomplishment, involvement in decisions, likeness to job, satisfaction with promotion opportunities, skill development opportunities, organizational tenure, and pay satisfaction. Consequently, Human Resource (HR) departments should seek to implement comprehensive HR practices to enhance employees’ perceptions on job satisfaction, workplace environments and systems, and favorable organizational policies and supports and make tailored interventions for the at-risk subgroups.

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2009Predicting and Changing Behavior: The Reasoned Action Approach 2000A Meta-Analysis of Antecedents and Correlates of Employee Turnover: Update, Moderator Tests, and Research Implications for the Next Millennium 1986Employee Turnover: A Meta-Analysis and Review with Implications for Research 2004The effects of organizational learning culture and job satisfaction on motivation to transfer learning and turnover intention 2007The effects of perceived organizational support and perceived supervisor support on employee turnover 2015Does Turnover Intention Matter? Evaluating the Usefulness of Turnover Intention Rate as a Predictor of Actual Turnover Rate 2011Turnover Intention and Turnover Behavior 1981Interactional Psychology and Research on Human Behavior in Organizations

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