Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement
Abstract Background With the emergence of the gig economy as a new economic form, the influence of algorithmic technology control on gig workers’ perceptions and engagement has become a topic of academic concern. This study explores the emotional impact of perceived algorithmic control on gig worker...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Psychology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40359-023-01402-0 |
| _version_ | 1797451015473070080 |
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| author | Jiao Jiao Lang Li Feng Yang Chen Cheng Xiang Yang Cheng Fei Yu Chen |
| author_facet | Jiao Jiao Lang Li Feng Yang Chen Cheng Xiang Yang Cheng Fei Yu Chen |
| author_sort | Jiao Jiao Lang |
| collection | DOAJ |
| description | Abstract Background With the emergence of the gig economy as a new economic form, the influence of algorithmic technology control on gig workers’ perceptions and engagement has become a topic of academic concern. This study explores the emotional impact of perceived algorithmic control on gig workers and how it affects their work engagement. Methods This study takes gig workers as the research object to build a structural equation model. Based on the background of gig economy and the Job Demands-Resources model, this paper constructs a mechanism model of the influence of perceived algorithmic control on the work engagement of gig workers. The research data in this paper are collected by questionnaire, and the research hypothesis is tested by the SEM structural model. Results The gig workers in this study believed that perceived algorithmic control positively affects employee work engagement. In addition, burnout was positively correlated with employee work engagement. Burnout played a partial mediating role in the relationship between perceived algorithmic control and employee work engagement. And flow experience played a moderating role through the indirect effect of burnout on employees’ work engagement. Conclusion Perceived algorithmic control causes burnout among gig workers, but strong algorithmic technology support provides them with rich work resources that can help them meet their work needs. That is, the gig workers may still demonstrate a high level of work engagement even if they experience burnout symptoms. |
| first_indexed | 2024-03-09T14:48:50Z |
| format | Article |
| id | doaj.art-eca1875276f54bf8824e19d72cc86bfa |
| institution | Directory Open Access Journal |
| issn | 2050-7283 |
| language | English |
| last_indexed | 2024-03-09T14:48:50Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychology |
| spelling | doaj.art-eca1875276f54bf8824e19d72cc86bfa2023-11-26T14:37:03ZengBMCBMC Psychology2050-72832023-10-0111111510.1186/s40359-023-01402-0Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagementJiao Jiao Lang0Li Feng Yang1Chen Cheng2Xiang Yang Cheng3Fei Yu Chen4Endicott College, Woosong UniversitySchool of Economics, Fuyang Normal UniversitySchool of Business, Fuyang Normal UniversitySchool of Business, Fuyang Normal UniversitySchool of Economics and Management, Fei Yu Chen, China University of Mining and TechnologyAbstract Background With the emergence of the gig economy as a new economic form, the influence of algorithmic technology control on gig workers’ perceptions and engagement has become a topic of academic concern. This study explores the emotional impact of perceived algorithmic control on gig workers and how it affects their work engagement. Methods This study takes gig workers as the research object to build a structural equation model. Based on the background of gig economy and the Job Demands-Resources model, this paper constructs a mechanism model of the influence of perceived algorithmic control on the work engagement of gig workers. The research data in this paper are collected by questionnaire, and the research hypothesis is tested by the SEM structural model. Results The gig workers in this study believed that perceived algorithmic control positively affects employee work engagement. In addition, burnout was positively correlated with employee work engagement. Burnout played a partial mediating role in the relationship between perceived algorithmic control and employee work engagement. And flow experience played a moderating role through the indirect effect of burnout on employees’ work engagement. Conclusion Perceived algorithmic control causes burnout among gig workers, but strong algorithmic technology support provides them with rich work resources that can help them meet their work needs. That is, the gig workers may still demonstrate a high level of work engagement even if they experience burnout symptoms.https://doi.org/10.1186/s40359-023-01402-0Gig economyAlgorithmic technologyFlow experienceEmployee work engagement |
| spellingShingle | Jiao Jiao Lang Li Feng Yang Chen Cheng Xiang Yang Cheng Fei Yu Chen Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement BMC Psychology Gig economy Algorithmic technology Flow experience Employee work engagement |
| title | Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement |
| title_full | Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement |
| title_fullStr | Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement |
| title_full_unstemmed | Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement |
| title_short | Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement |
| title_sort | are algorithmically controlled gig workers deeply burned out an empirical study on employee work engagement |
| topic | Gig economy Algorithmic technology Flow experience Employee work engagement |
| url | https://doi.org/10.1186/s40359-023-01402-0 |
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