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A study of job involvement prediction using machine learning technique

Youngkeun Choi (Sangmyung University, Jongno-gu, Republic of Korea)
Jae Won Choi (Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, Texas, USA)

International Journal of Organizational Analysis

ISSN: 1934-8835

Article publication date: 24 August 2020

Issue publication date: 7 May 2021




Job involvement can be linked with important work outcomes. One way for organizations to increase job involvement is to use machine learning technology to predict employees’ job involvement, so that their leaders of human resource (HR) management can take proactive measures or plan succession for preservation. This paper aims to develop a reliable job involvement prediction model using machine learning technique.


This study used the data set, which is available at International Business Machines (IBM) Watson Analytics in IBM community and applied a generalized linear model (GLM) including linear regression and binomial classification. This study essentially had two primary approaches. First, this paper intends to understand the role of variables in job involvement prediction modeling better. Second, the study seeks to evaluate the predictive performance of GLM including linear regression and binomial classification.


In these results, first, employees’ job involvement with a lot of individual factors can be predicted. Second, for each model, this model showed the outstanding predictive performance.

Practical implications

The pre-access and modeling methodology used in this paper can be viewed as a roadmap for the reader to follow the steps taken in this study and to apply procedures to identify the causes of many other HR management problems.


This paper is the first one to attempt to come up with the best-performing model for predicting job involvement based on a limited set of features including employees’ demographics using machine learning technique.



Choi, Y. and Choi, J.W. (2021), "A study of job involvement prediction using machine learning technique", International Journal of Organizational Analysis, Vol. 29 No. 3, pp. 788-800.



Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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