How the Interplay Between Algorithmic HRM Systems Promotes Gig Workers' Self-Efficacy: The Role of Technostressors

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.