Linking perceived algorithmic management and app-workers’ prosocial service behaviors: a needs satisfaction perspective

Purpose – Grounded in self-determination theory, this study explores how perceived algorithmic management (PAM) influences app-workers’ prosocial service behaviors through psychological needs satisfaction and work engagement, while considering the moderating role of algorithmic transparency (AT). 

Design/methodology/approach – We conducted a 4-wave field study with a sample of 431 ride-hailing drivers in China. Structural equation modeling was performed using Mplus to test the proposed conceptual model. 

Findings – PAM positively influences competence need satisfaction but negatively impacts autonomy and relatedness needs satisfaction. AT moderates the relationship between PAM and app-worker needs, strengthening the positive effects via competence and weakening the negative effects via autonomy and relatedness. However, the total sequential indirect effect of PAM on prosocial service behaviors via need satisfaction and work engagement was not significant. 

Research limitations/implications – The study’s reliance on a single sample of app workers limits generalizability. Self-reported data may also introduce common method variance. Practical implications – Our findings highlight PAM’s tension between enhancing competence and undermining autonomy and relatedness, with AT playing a critical role in reducing negative effects and fostering trust. 

Originality/value – This study uncovers the dual-edged effects of PAM on app-worker needs and highlights the moderating role of AT, contributing to ongoing discussions on human–algorithm interactions and sustainable management in app-work environments.