How Do Algorithmic Management Practices Impact Ride-hailing Drivers Service Performance?

Speaker: Prof. HUANG Qihai (University of Huddersfield)

Host: Prof. HU Qiongjing (Zhejiang University)

Venue: Room 723, Building A, School of Management, Zijingang Campus

Abstract: 

Online labor platforms (OLPs) employ algorithmic management practices to manage app-workers. Drawing upon the self-determination theory, this research examines the impact of perceived algorithmic management practices (PAMP) on the service performance of ride-hailing drivers through three studies. We theorise the process by investigating the mediating roles of individual needs satisfaction (competence, autonomy, and relatedness) and work engagement, and the moderating role of online community support (OCS). We developed and validated a PAMP scale using a rigorous multistep process in the first study. In the second study, we empirically tested our conceptual model with a sample of 431 ride-hailing drivers. Our findings reveal that PAMP influences drivers’ competence, autonomy, and relatedness needs satisfaction differently. The satisfaction of these three basic needs enhances work engagement, subsequently improving service performance.

Furthermore, OCS positively moderates the relationship between PAMP and drivers’ needs satisfaction. To deepen our understanding of how PAMP influences drivers’ needs satisfaction and subsequent behaviors, we conducted a qualitative investigation with 103 ride-hailing drivers. Our study provides valuable insights for future research and practice in managing app-workers in the gig economy.