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Article

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

1
Department of Organizational Performance and Workplace Learning, College of Engineering, The Boise State University, Boise, ID 83706, USA
2
Center for Tobacco Research and Intervention, School of Medicine and Population Health, The University of Wisconsin-Madison, Madison, WI 53711, USA
3
Department of Statistics, The University of Wisconsin-Madison, Madison, WI 53706, USA
4
Office of the Provost, The University of Kansas, Lawrence, KS 66045, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Lenka Ližbetinová, Eva Nedeliaková, Miloš Hitka and Christian Vandenberghe
Sustainability 2021, 13(18), 10329; https://doi.org/10.3390/su131810329
Received: 21 August 2021 / Revised: 3 September 2021 / Accepted: 12 September 2021 / Published: 16 September 2021
(This article belongs to the Special Issue Sustainable Human Resource Management in Industry 4.0)
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. View Full-Text
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 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
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MDPI and ACS Style

Kang, I.-G.; Kim, N.; Loh, W.-Y.; Bichelmeyer, B.A. A Machine-Learning Classification Tree Model of Perceived Organizational Performance in U.S. Federal Government Health Agencies. Sustainability 2021, 13, 10329. https://doi.org/10.3390/su131810329

AMA Style

Kang I-G, Kim N, Loh W-Y, Bichelmeyer BA. A Machine-Learning Classification Tree Model of Perceived Organizational Performance in U.S. Federal Government Health Agencies. Sustainability. 2021; 13(18):10329. https://doi.org/10.3390/su131810329

Chicago/Turabian Style

Kang, In-Gu, Nayoung Kim, Wei-Yin Loh, and Barbara A. Bichelmeyer. 2021. "A Machine-Learning Classification Tree Model of Perceived Organizational Performance in U.S. Federal Government Health Agencies" Sustainability 13, no. 18: 10329. https://doi.org/10.3390/su131810329

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