Information Systems Faculty Publications and Presentations
Document Type
Article
Publication Date
8-2025
Abstract
As algorithm governance becomes increasingly crucial in gig economy ecosystems, understanding the behavioral impact of algorithmic transparency remains a key research gap. Using the stimulus-organism-response framework and uncertainty management theory, this study examines how platform algorithmic transparency affects gig workers’ in-role and extra-role service behaviors, with emotional labor acting as a mediator and work gamification as a moderator. Analyzing survey data from 325 ride-hailing drivers using partial least squares structural equation modeling (PLS-SEM), the findings reveal that greater algorithmic transparency enhances both in-role and extra-role behaviors. Emotional labor mediates this relationship: deep acting strengthens both behaviors, while surface acting primarily supports in-role behavior. Meanwhile, work gamification diminishes the positive effect of algorithmic transparency on extra-role service behavior. These insights clarify the mechanisms and boundary conditions of algorithmic transparency in gig work, offering practical guidance for designing platform algorithms that optimize worker performance and satisfaction.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
Computers in Human Behavior
DOI
10.1016/j.chb.2025.108681
Comments
Original published version available at https://doi.org/10.1016/j.chb.2025.108681