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.

这项研究旨在确定离职意图的重要预测因素，并表征高离职意图风险的美国联邦雇员的子群体。数据来自2018年联邦雇员观点调查（FEVS，未加权N= 598,003），是美国联邦雇员的全国代表样本。进行了机器学习分类和回归树（CART）分析以预测离职意图并考虑样本权重。CART分析确定了六个有风险的亚组。预测指标重要性评分显示，工作满意度是离职意向的最强预测指标，其次是组织满意度，忠诚度，成就，参与决策，工作相似性，对晋升机会，技能发展机会，组织任职期和薪酬满意度的满意度。因此，人力资源部门应寻求实施全面的人力资源实践，以增强员工对工作满意度，工作场所环境和系统的认识，