cordially invite you to attend my dissertation defense scheduled for Wednesday, July 12th, 11:30AM EST. The location will be Scheller College of Business, Room 201. If you are unable to attend in person, you can join us via Zoom: https://gatech.zoom.us/j/7518971947

The abstract is included below, and a copy of the dissertation is available upon request. 

Best Regards, 

Swanand 

Area: Operations Management 

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

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 (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. 

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: Platform Commission and its 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 the price to be charged to the customer is determined independently of the worker's payment 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.