I cordially invite you to attend my dissertation proposal scheduled for Wednesday,  April 19th, 10:30AM EST. The location will be Scheller College of Business, Room 464. If you are unable to attend in person, you can join us via Zoom https://gatech.zoom.us/j/94539654679

Area: Operations Management

Committee Members: Dr. Basak Kalkanci (Chair), Dr. Chris Parker (American University), Dr. Ravi Subramanian, Dr. Beril Toktay

Dissertation title: Information Sharing and Operational Transparency on On-Demand Service Platforms

Chapter 1: Spatial Information Sharing on On-Demand Service Platforms: A Behavioral Examination 

Abstract: 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 with all drivers (surge information sharing, most widely practiced mechanism today), all zones with all drivers (full information sharing), or zone(s) with excess demand only with drivers sufficiently close by (local information sharing). We develop a game-theoretic model with three zones wherein drivers in two non-surge zones decide whether to relocate to the surge zone with excess demand. We incorporate two spatial aspects:  drivers' relocation costs and initial supply across different non-surge zones. Theoretically, full information sharing can hurt the platform's matching efficiency compared to surge information sharing under low relocation costs because drivers in non-surge zones, facing high demand locally do not chase the surge as much. Local information sharing is strictly dominated by other mechanisms in terms of matching efficiency when the supply of drivers near the surge zone is limited, and weakly dominated otherwise by surge information sharing. We test these theory predictions in the lab with human participants as drivers, in an environment where theoretical matching efficiency is highest with surge and lowest with local information sharing. Experimentally, the platform serves fewer customers than predicted with surge information sharing. Full and local information sharing serve more customers than predicted and yield comparable performance to surge information sharing in terms of platform matching efficiency and driver payoffs. Therefore, these alternate mechanisms can help to alleviate coordination problems observed under surge information sharing. A behavioral equilibrium incorporating risk-aversion and decision errors describes drivers' behavior in our experiments better than the rational equilibrium. 

Chapter 2: Payment Algorithm Transparency on On-Demand Service Platforms 

Abstract: 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. 

Chapter 3: Commission Transparency on On-Demand Service Platforms 

Abstract: Early in their conception, most on-demand service platforms operated under a fixed commission model, where a worker received a fixed portion of the price charged to the customer. Subsequently, some platforms including Uber and Lyft have implemented a decoupled pricing model, where a worker's payment is determined independently of the price charged to the customer and hence the platform’s commission is not consistent across service tasks. Motivated by this transition and platforms’ experimentation with transparency under the decoupled pricing model, we examine how the platform’s commission level and workers’ transparency into it affect workers’ decision to work for the platform and their perceptions of the platform.