Title:
Information Sharing and Operational Transparency on On-Demand Service Platforms
Information Sharing and Operational Transparency on On-Demand Service Platforms
Author(s)
Kulkarni, Swanand
Advisor(s)
Kalkanci, Basak
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Abstract
The three essays in this dissertation examine the operational practices of on-demand service platforms, pertaining to information sharing and operational transparency on the supply-side. On-demand service platforms such as ridesharing, food delivery, grocery delivery, and courier delivery platforms critically depend on the services of workers, who are independent contractors. Given that these workers have discretion over their labor supply decisions, the platform's information provision to workers plays a key role in influencing workers' decisions and in eventually meeting the customer demand. In this dissertation, I employ game-theoretic modeling and conduct incentivized experiments with human subjects to evaluate the implications of a platform's practices around information sharing and operational transparency with workers, for workers' decisions and potentially for the platform's operational outcomes.
Demand-Supply Information Sharing: We investigate how an on-demand service platform's mechanism to share demand-supply mismatch information spatially affects drivers' relocation decisions and the platform's matching efficiency. We consider three mechanisms motivated by practice: the platform either shares demand-supply mismatch information about zones(s) with excess demand (i.e., surge zone(s)) with all drivers (surge information sharing, common practice today), all zones with all drivers (full information sharing), or about surge zone(s) only with drivers sufficiently close by (local information sharing). We develop a game-theoretic model with three zones; drivers in two non-surge zones decide whether to relocate to the surge zone. We incorporate two spatial aspects: drivers' relocation costs, and initial supply across non-surge zones. Theoretically, full can yield a lower matching efficiency than surge information sharing under low relocation costs because drivers do not relocate as much when demand in non-surge zones is high. Local information sharing is strictly dominated by other mechanisms on matching efficiency under limited supply near the surge zone, and weakly dominated otherwise by surge information sharing. We test these theoretical predictions in the lab with human participants as drivers. Experimentally, surge information sharing serves fewer customers than predicted because drivers relocate too often, compromising efficiency in the non-surge zones. The alternatives, full and local, are not dominated by surge information sharing, and serve more customers than theoretically predicted, providing support for their potential benefits. A behavioral equilibrium incorporating loss aversion through mental accounting and decision errors describes drivers' behavior in our experiments better than the rational equilibrium.
Payment Algorithm Transparency: On-demand service platforms have been experimenting with algorithms to determine compensation for their workers. While some use commission- or effort-based algorithms that are intuitive to workers, others, in their efforts to better match customer demand, have transitioned to algorithms where pay is not strictly tied to effort, but depends on other, potentially exogenous factors. Platforms have also kept these algorithms opaque. Despite the move towards less-intuitive and opaque algorithms 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 human participants, and examine how individual features of a pay algorithm, specifically its intuitiveness to workers and transparency, affect workers' engagement (measured by work rejection rates and willingness to pay to accept a work opportunity) and perceptions of the platform. We also examine the effect of an algorithm change from intuitive to non-intuitive, and how transparency interacts with this change. For workers with prior experiences on the platform, intuitiveness, and transparency both are effective at sustaining engagement in our experiments. Transparency is particularly motivating for workers under a non-intuitive algorithm and can fully compensate for the reduction in worker engagement from implementing a non-intuitive algorithm. Furthermore, even though a transparent platform experiences a drop in worker engagement after switching to a non-intuitive algorithm, commitment to transparency is still beneficial: Worker engagement with transparency is at least as much as that without transparency, while transparency is more potent at motivating positive perceptions towards the platform.
Platform Commission Transparency: On-demand service platforms in the role of an intermediary that matches service-seeking customers and service-providing independent contractors, typically charge workers a commission on each service request that they complete. Early on, most on-demand service platforms operated a fixed commission model, where the platform determines the price on a service request such that the worker completing it is compensated for effort, while the platform keeps a fixed percentage of the price as a commission from the worker. While several platforms continue to operate this model, some platforms transitioned to a model where the platform's commission is inconsistent across service instances. Thereby, while the platform continues to compensate workers based on effort-based factors, it utilizes several factors that do not influence workers' wage to determine the price, leading to the platform commission being variable across service instances. Platforms argue that this helps to improve customers' experience through better prices while drivers continue to earn for their effort. Anecdotal evidence suggests that workers are concerned about the large commission that platforms charge them in some service instances. In response, platforms have experimented with workers' visibility of the platform's commission under the variable commission model, which has reportedly contributed to worker suspicion and distrust. Motivated by these practices, we design incentivized experiments with human subjects to examine the influence of a platform's commission on workers' participation decisions under the fixed and variable commission models. We study the impact of consistency in platform commission on workers' participation decisions and their perceptions of the platform. Furthermore, we evaluate how the visibility of platform commission influences workers' participation decisions and their perceptions of the platform.
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Date Issued
2023-07-27
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Text
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Dissertation