Legitimacy judgments of algorithmic management and gig workers' proactive service behavior: the roles of work motivation and colleague community support

合法性 调解 自治 调解 服务(商务) 社会心理学 心理学 知识管理 工作(物理) 透视图(图形) 多级模型 工作制度 测量数据收集 公共关系 感知组织支持 自举(财务) 自决论 业务 编配 服务交付框架 控制(管理) 社会学
作者
Jiazhen Shi,Meng Xi,Xin Li,Jiaxin Li
出处
期刊:Personnel Review [Emerald Publishing Limited]
卷期号:: 1-24
标识
DOI:10.1108/pr-05-2025-0486
摘要

Purpose As algorithmic management grows central to platform-mediated work, understanding how gig workers evaluate and respond to such systems represents an important theoretical and practical imperative. Integrating legitimacy judgment perspective and Self-Determination Theory, this study aims to investigate how workers' assessments of autonomy, privacy, and fairness legitimacy shape proactive service behavior via autonomous and controlled motivation. We further examine the moderating role of colleague community support in these linkages. Design/methodology/approach Data were collected from 272 food delivery riders and ride-hailing drivers in China using a three-wave time-lagged survey design. Hierarchical regression analyses and bootstrapping techniques were used to test the hypothesized direct, mediating, and moderated mediation effects. Findings Autonomy legitimacy and fairness legitimacy were positively related to proactive service behavior, whereas privacy legitimacy was negatively related. Autonomous and controlled motivation served as distinct mediating mechanisms. Furthermore, colleague community support weakened the indirect effects of legitimacy judgments through autonomous motivation but showed no significant moderating effect on the controlled motivation pathway. Originality/value This study contributes to the algorithmic management literature by showing that workers' responses depend not only on the characteristics of algorithmic systems but also on how those systems are interpreted and evaluated by workers themselves. It provides a process-based account of proactive service behavior in gig work and clarifies how informal peer support shapes worker reactions to algorithmic control.
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