Job satisfaction and turnover decision of employees in the Internet sector in the US

Abstract

This paper proposes that high value on the work-life balance, compensation, career opportunity and fitness of culture and management style would improve job satisfaction. A turnover risk prediction model based on the random forest is constructed to understand the turnover risk feature and identify risk. Using a sample of 17,724 online reviews of employees from Glassdoor, the positive effect of antecedents, the job satisfaction variable as a mediator, and the unemployment rate variable as a moderator is verified. Finally, job satisfaction is identified as the most important feature for predicting turnover based on the random forest algorithm.

Publication DOI: https://doi.org/10.1080/17517575.2022.2130013
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/)
Uncontrolled Keywords: Information Systems and Management,Computer Science Applications
Publication ISSN: 1751-7583
Last Modified: 13 Jun 2024 07:23
Date Deposited: 17 Oct 2022 10:53
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Related URLs: https://www.tan ... needAccess=true (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-10-07
Published Online Date: 2022-10-07
Accepted Date: 2022-09-26
Authors: Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Mou, Yeqing
Xu, Qianwen Ariel
Xu, Yue

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