Pay Model Intuitiveness and Transparency on On-Demand Service Platforms (with Basak Kalkanci and Chris Parker)
Abstract: On-demand service platforms have been experimenting with models that determine workers' compensation. While some use commission- or effort-based models that are intuitive to workers, others, in their efforts to better match customer demand, have transitioned to models where pay is not strictly tied to effort, but depends on other, potentially exogenous factors. Platforms have also kept these pay models opaque. Despite the move towards less-intuitive and opaque pay models in practice, workers' reactions to them are not systematically examined or understood. Through incentivized online experiments on Prolific, we present real-effort tasks as work opportunities for payment to participants and examine how pay model intuitiveness, transparency, and a change in the pay model from intuitive to non-intuitive affect worker participation. We examine these effects over a range of outside options available to workers capturing the competitiveness of the labor market. We find that pay model intuitiveness can boost worker participation because workers view their pay more negatively under a non-intuitive pay model, particularly after completing more difficult orders. Transparency can improve worker participation under a non-intuitive pay model by reducing the negative impacts of workers' disappointing pay experiences. In contrast, transparency into a pay model that is already intuitive may backfire because it enables pay comparisons, which can erode pay satisfaction among workers fulfilling lower-paying orders. Furthermore, although a transparent platform experiences a reduction in worker participation after switching to a non-intuitive pay model, commitment to transparency can be beneficial: Worker participation with transparency is not dominated by that without transparency, while transparency can be more effective at motivating positive perceptions towards the platform.
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