How the Interplay Between Algorithmic HRM Systems Promotes Gig Workers' Self‐Efficacy: The Role of Technostressors
区分了算法监控与算法控制的概念差异，基于压力与应对理论，通过对407名零工工人的三波时滞调查数据进行分析，发现两种算法系统通过挑战性和威胁性技术压力对工人自我效能感产生不同影响，挑战了算法控制总是有害的普遍观点。
ABSTRACT The increasingly crucial algorithmic Human Resource Management (HRM) field is spawning two research streams: Algorithmic monitoring and algorithmic control. Yet, the conceptual differences and interplay between them have been largely confused and ignored in research and practice. This study clarifies their conceptual differences by exploring their interplay effect on gig workers' technostressors. Based on the stress and coping theory, a partial least squares structural equation modeling analysis by running data from 407 gig workers participating in a three‐wave time‐lagged survey was conducted. Results show that observational or interactional algorithmic monitoring hinders or promotes gig workers' self‐efficacy via both challenge and threat technostressors, respectively. While enhancing the positive effect of interactional algorithmic monitoring on self‐efficacy via threat technostressors, guiding algorithmic control attenuates the negative effect of observational algorithmic monitoring on self‐efficacy via challenge and threat technostressors, which contrasts with prior algorithmic HRM literature considering algorithmic control as a universally “bad thing” by workers. These findings deepen the understanding of the algorithmic HRM realm by revealing the differences and interplay between algorithmic monitoring and algorithmic control. Operators should differentiate and synergize control and monitoring functions by emphasizing outcomes that the interplay between algorithmic HRM systems has on the workforce.