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

Victor Chang; Yeqing Mou; Qianwen Ariel Xu; Yue Xu

doi:10.1080/17517575.2022.2130013

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

Victor Chang, Yeqing Mou, Qianwen Ariel Xu, Yue Xu

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Number of pages	33
Journal	Enterprise Information Systems
Early online date	7 Oct 2022
DOIs	https://doi.org/10.1080/17517575.2022.2130013
Publication status	E-pub ahead of print - 7 Oct 2022

Bibliographical note

© 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/)

Keywords

Information Systems and Management
Computer Science Applications

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10.1080/17517575.2022.2130013Licence: CC BY 4.0

Changetal_2022_VoR
© 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/)
Final published version, 2.49 MBLicence: CC BY 4.0

Cite this

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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.",

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N2 - 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.

AB - 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.

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M3 - Article

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ER -

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

Abstract

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