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

In-Gu Kang, Nayoung Kim, Wei-Yin Loh and Barbara A. Bichelmeyer
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In-Gu Kang: Department of Organizational Performance and Workplace Learning, College of Engineering, The Boise State University, Boise, ID 83706, USA
Nayoung Kim: Center for Tobacco Research and Intervention, School of Medicine and Population Health, The University of Wisconsin-Madison, Madison, WI 53711, USA
Wei-Yin Loh: Department of Statistics, The University of Wisconsin-Madison, Madison, WI 53706, USA
Barbara A. Bichelmeyer: Office of the Provost, The University of Kansas, Lawrence, KS 66045, USA

Sustainability, 2021, vol. 13, issue 18, 1-14

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

Keywords: perceived organizational performance; U.S. federal government public health and human services employees; sustainable human resource management; machine-learning classification tree model; industry 4.0 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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