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作 者:陈万明[1] 徐国长 马欢欢 CHEN Wanming;XU Guozhang;MA Huanhuan(College of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
机构地区:[1]南京航空航天大学经济与管理学院,江苏南京211106
出 处:《商业经济与管理》2024年第9期49-61,共13页Journal of Business Economics
基 金:国家社会科学基金项目“经济新形态下更充分就业和更高质量就业的协同推进机制研究”(20BGL203)。
摘 要:算法控制是数字零工平台的显著特征,零工工作者对算法控制的感知差异会影响其工作态度及行为,但鲜有研究关注感知算法控制对零工工作者离职倾向的影响机制。基于资源保存理论,通过对430名零工工作者的三阶段时间滞后调查,探究感知算法控制通过工作倦怠和工作投入对零工工作者离职倾向的双路径作用机制,以及谋生工作价值观的调节作用。研究发现:工作倦怠和工作投入在感知算法控制与离职倾向关系间发挥双重中介作用,即感知算法控制既可以通过提升零工工作者工作倦怠,增强其离职倾向,又可以通过提高零工工作者工作投入,降低其离职倾向;谋生工作价值观负向调节感知算法控制与工作倦怠的关系,而正向调节感知算法控制与工作投入的关系;此外,谋生工作价值观还调节了工作倦怠、工作投入在感知算法控制与离职倾向之间的中介作用。研究结论丰富了零工经济领域员工离职倾向的理论研究,为数字零工平台的算法控制实践提供了启示。Algorithmic control is a significant feature of digital gig platform,and the difference of gig workers perception of algorithmic control would affect their work attitudes and behaviors.However,few studies have focused on the mechanism of the perceived algorithmic control on the turnover intention of gig workers.Based on the Conservation of Resources Theory,this paper explores the dual-path mechanism of perceived algorithmic control on turnover intention of gig workers through work burnout and work engagement,and the moderating effect of survival work values by the questionnaire survey on 430 gig workers.The results are as follows:work burnout and work engagement play double mediating roles in the relationship between perceived algorithmic control and turnover intention,that is,perceived algorithmic control can not only enhance turnover intention of gig workers by improving work burnout,but also reduce their turnover intention by improving work engagement.Moreover,survival work value negatively moderates the relationship between perceived algorithmic control and work burnout,but positively moderates the relationship between perceived algorithmic control and work engagement.In addition,survival work value moderates the mediating effect of work burnout and work engagement.The results enrich the research on the turnover intention in the gig economy,and provide practical enlightenment for the algorithmic control of digital gig platform.
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