Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8–10, 2023, Guangzhou, China

Research Article

Decoding the Digital Dilemma: Unraveling the Relationship Between Algorithmic Management and Employee Turnover Intentions in Online Work Platforms

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  • @INPROCEEDINGS{10.4108/eai.8-12-2023.2344749,
        author={Ying  Zou and Lu  Cui},
        title={Decoding the Digital Dilemma: Unraveling the Relationship Between Algorithmic Management and Employee Turnover Intentions in Online Work Platforms},
        proceedings={Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8--10, 2023, Guangzhou, China},
        publisher={EAI},
        proceedings_a={MSIEID},
        year={2024},
        month={4},
        keywords={online labor platform; algorithm management application; employee engagement; turnover intention},
        doi={10.4108/eai.8-12-2023.2344749}
    }
    
  • Ying Zou
    Lu Cui
    Year: 2024
    Decoding the Digital Dilemma: Unraveling the Relationship Between Algorithmic Management and Employee Turnover Intentions in Online Work Platforms
    MSIEID
    EAI
    DOI: 10.4108/eai.8-12-2023.2344749
Ying Zou1,*, Lu Cui2
  • 1: Shanghai University
  • 2: University of New South Wales
*Contact email: ying_zou@shu.edu.cn

Abstract

With the continuous development of big data, artificial intelligence, and cloud computing, the platform economy is steadily rising, giving rise to diverse forms of employment. Online platform algorithm management, facilitated by network information technology, utilizes online platforms as carriers and employs data-driven algorithms to achieve highly efficient and precise matching services, as well as intelligent labor process management. To comprehensively understand the underlying influence mechanism of online platform algorithm management on employees, this paper focuses on studying the impact of algorithm management on employee turnover intention at the individual level. Through an extensive review of literature, questionnaire surveys, and analysis, this study aims to investigate the mechanism by which algorithm management affects turnover intention, as well as the mediating role of employee engagement in the relationship between turnover intention. By conducting a detailed analysis of the questionnaire data, this study provides a fresh perspective and theoretical foundation for human resource management in the context of online labor platforms.