Predictors of Turnover Intention in U.S. Federal Government Workforce: Machine Learning Evidence That Perceived Comprehensive HR Practices Predict Turnover Intention

• Author: Barbara A. Bichelmeyer,Ben Croft,In Gu Kang
• DOI: 10.1177/0091026020977562
• Date: 01 December 2021
• Published date: 01 December 2021
• Subject Matter: Articles

https://doi.org/10.1177/0091026020977562

Public Personnel Management

2021, Vol. 50(4) 538 –558

© The Author(s) 2020

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DOI: 10.1177/0091026020977562

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Article

Predictors of Turnover

Intention in U.S. Federal

Government Workforce:

Machine Learning

Evidence That Perceived

Comprehensive HR Practices

Predict Turnover Intention

In Gu Kang1, Ben Croft1,

and Barbara A. Bichelmeyer2

Abstract

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.

1Boise State University, ID, USA

2University of Kansas, Lawrence, USA

Corresponding Author:

In Gu Kang, Assistant Professor, Department of Organizational Performance and Workplace Learning,

College of Engineering, Boise State University, 1910 University Drive ENGR 329, Boise, ID 83725-2070,

USA.

Email: ingukang@boisestate.edu

977562PPMXXX10.1177/0091026020977562Public Personnel ManagementKang et al.

research-article2020

Kang et al. 539

Keywords

federal government, organizational behavior, public management, turnover, CART

analysis

Turnover among U.S. federal government workforces has been of great concern for

the last several decades (Pitts et al., 2011). Turnover intention has been widely accepted

as a proxy of actual turnover in public administration research (Bertelli, 2007; S. Kim,

2005). There are two rationales for justifying why turnover intention is commonly

used by researchers. First, according to the Reasoned Action Approach (RAA; Fishbein

& Ajzen, 2010), it is assumed that a person’s behavior is determined by their intention

to perform the behavior. From this theoretical standpoint, turnover intention is assumed

to be the strongest predictor of actual turnover. Empirical evidence supports this claim

in the public sector (S. Kim, 2005; Pitts et al., 2011). Second, from a practical perspec-

tive, self-reported intention on turnover tends to be more favorable than direct observa-

tion on actual turnover by researchers due to some reasons. For instance, turnover

intention is ease of use and more cost-effective by measuring employees’ self-reported

perceptions via surveys than actual turnover with a longitudinal approach. In addition,

while observing actual turnover may cause an ethical issue of revealing personal infor-

mation on leaving their organizations, measuring turnover intention can avoid this kind

of ethical consideration by administering surveys anonymously (Dalton et al., 1999).

Cumulative research evidence on turnover intention consistently shows that job

satisfaction is significantly and negatively associated with turnover intention (Chiu

et al., 2005). According to a 2018 government-wide management report, 68% of fed-

eral employees “strongly agree” or “agree” with the statement that they are satisfied

with their jobs (United States Office of Personnel Management, 2018). Despite a high

level of job satisfaction among federal employees, 33% of federal employees stated

they intend to leave their organization in the next year. Thus, given the importance of

job satisfaction, other individual and organizational/workplace environmental factors

should be examined to better understand why federal employees still intend to leave

when they have high job satisfaction.

Many scholars have investigated various antecedents of turnover intentions in fed-

eral agencies, including diverse individual characteristics, job characteristics, organi-

zational policies, and workplace environments (i.e., Ertas, 2015; S. Kim, 2012; S. Y.

Kim & Fernandez, 2017; Ko & Hur, 2013; Liss-Levinson et al., 2015; Pink-Harper &

Rauhaus, 2017; Pitts et al., 2011; Weaver, 2015; Wynen et al., 2013; Wynen & Op de

Beeck, 2014). These studies primarily identified numerous main effect predictors in

regression models, indicating most of the predictors can be applied to the entire popu-

lation. However, these regression approaches have some limitations for conducting

more accurate prediction on turnover intention. Because they aim for prediction for the

entire population, they may not predict variables that impact turnover intentions of a

relatively small subgroup within the entire population. In addition, if there are too

many predictors in a regression model, results can be difficult to interpret. Because

standard parametric methods (e.g., traditional regression models) are unable to exam-

ine or uncover complex predictor interactions, a novel and advanced methodological