A Machine-Learning Classification Tree Model of Perceived Organizational Performance in U.S. Federal Government Health Agencies

Perceived organizational performance (POP) is an important factor that influences employees’ attitudes and behaviors such as retention turnover, which in turn improve or impede sustainability. The current study aims to identify interaction patterns of risk factors differentiate public health human services employees who perceived their agency low. 2018 Federal Employee Viewpoint Survey (FEVS), a nationally representative sample U.S. federal government employees, was used for this study. included 43,029 (weighted n = 75,706) among 10 sub-agencies the sector. machine-learning classification decision-tree modeling identified several tree-splitting variables classified 33 subgroups with 2 high-risk, 6 moderate-risk 25 low-risk POP. predicting POP performance-oriented culture, satisfaction, procedural justice, task-oriented leadership, work security safety, commitment agency, interacted one another risks Complex high- subgroups, importance approach sustainable resource management industry 4.0, limitations future research are discussed.