Online Labor Platforms and the Role of Job Security and Compensation (Mis)Fits for Gig Workers

Online labor platforms (OLPs) use algorithms to manage their workers. Prior research has examined how OLP management shapes workers’ attitudes, implicitly assuming that workers have the same preferences. However, research has largely neglected the role of (mis)fits between what workers need and what they receive from OLPs. Therefore, we conducted a survey with 121 Mechanical Turk workers and used polynomial regression analysis of workers’ actual needs and perceived OLP supplies. Our results suggest that higher job security fit decreases perceived exploitation and increases system satisfaction, whereas higher compensation fit decreases perceived exploitation. In addition, we find that job security misfit has decreasing marginal effects on perceived exploitation and increasing marginal effects on system satisfaction. Overall, we contribute to a more nuanced understanding of workers’ reactions to the design of OLPs by shedding light on the interplay between what gig workers need and what they perceive to receive from such platforms