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Does work overload of odd-job platform workers lead to turnover intention? An empirical study on platform workers

Does work overload of odd-job platform workers lead to turnover intention? An empirical study on... Against the background of the digital economy, odd-job platforms rely on artificial intelligence algorithms to efficiently allocate tasks and monitor platform workers’ performance, putting these workers under enormous pressure. This paper explores the relationship between work overload and turnover intention of platform workers on odd-job platforms and the factors that lead to platform workers’ turnover.Design/methodology/approachBased on the job demands–resources model (JD-R), we construct a theoretical model to explain the relationship between work overload and turnover intention of platform workers. We test job burnout as a mediator variable and perceived algorithmic fairness and job autonomy as moderating variables. We conducted a study at food delivery platforms and ride-hailing platforms in China.FindingsThe empirical results show that: (1) work overload increases the turnover intention of platform workers by increasing job burnout and (2) perceived algorithmic fairness and job autonomy moderate the positive relationship between work overload and job burnout.Originality/valueWe provide a theoretical basis to explain the influence of work overload on turnover intention of odd-job platform workers and provide practical recommendations for management of platform workers. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Baltic Journal of Management Emerald Publishing

Does work overload of odd-job platform workers lead to turnover intention? An empirical study on platform workers

Liu, Shan; Xu, Guang; Zhong, Jie; Xu, Yuling
Baltic Journal of Management , Volume 19 (5): 15 – Nov 13, 2024
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References (60)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1746-5265
DOI
10.1108/bjm-10-2023-0390
Publisher site
See Article on Publisher Site

Abstract

Against the background of the digital economy, odd-job platforms rely on artificial intelligence algorithms to efficiently allocate tasks and monitor platform workers’ performance, putting these workers under enormous pressure. This paper explores the relationship between work overload and turnover intention of platform workers on odd-job platforms and the factors that lead to platform workers’ turnover.Design/methodology/approachBased on the job demands–resources model (JD-R), we construct a theoretical model to explain the relationship between work overload and turnover intention of platform workers. We test job burnout as a mediator variable and perceived algorithmic fairness and job autonomy as moderating variables. We conducted a study at food delivery platforms and ride-hailing platforms in China.FindingsThe empirical results show that: (1) work overload increases the turnover intention of platform workers by increasing job burnout and (2) perceived algorithmic fairness and job autonomy moderate the positive relationship between work overload and job burnout.Originality/valueWe provide a theoretical basis to explain the influence of work overload on turnover intention of odd-job platform workers and provide practical recommendations for management of platform workers.

Journal

Baltic Journal of Management – Emerald Publishing

Published: Nov 13, 2024

Keywords: Odd-job platform; Work overload; Perceived algorithmic fairness; Job autonomy; Turnover intention

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