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

Nayoung Kim,Barbara A. Bichelmeyer,Wei-Yin Loh,In-Gu Kang

doi:10.3390/su131810329

Nayoung Kim, Barbara A. Bichelmeyer + Show 2 more

Open Access

https://doi.org/10.3390/su131810329

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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.

Highlights

An organization’s member shapes her or his self-concept and self-esteem by how they perceive the image and prestige of the organization that s/he belongs to [1]
This study identified factors associated with public health and human services employees’ perceived organizational performance (POP), classified high, moderate, and low-risk subgroups, and developed targeted interventions for those at-risk subgroups
The machine-learning classification decision-tree model efficiently identified that the two most important split variables of performanceoriented culture and organizational satisfaction interacted with other variables while worsening public health and human services employees’ low POP

Summary

Introduction

An organization’s member shapes her or his self-concept and self-esteem by how they perceive the image and prestige of the organization that s/he belongs to [1]. Being a member of a high performing and highly regarded organization helps organizational members develop self-continuity, self-distinctiveness, and self-esteem [2] since people tend to “bask in reflected glory”, even though successes or achievements are not a direct consequence of their efforts or activities [3]. Employees who perceive that their organization performs better than its competitors have a more favorable view of the prestige and status of their organization and strong organizational identification with the high level of perceived organizational performance (POP) [1]. POP is referred to as “the subjective measurement of employee perceptions regarding an organization’s overall performance when compared to the rivals in the same sector” [4].

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