Date of Award
Doctor of Philosophy (PhD)
Operations & Information Management
Gerard P. Cachon
Gig-economy platform like Uber, Lyft, Postmates, and Instacart have created markets in which independent service providers provide on-demand service to consumers. A hallmark of this arrangement is that providers decide for themselves when, where, and how much to work. In other words, the platform does not set its capacity's schedule; instead its capacity "self-schedules." This decentralization of decision making can create value for providers. The platform's challenge is then to devise a contract with its capacity that allows it to capture some of this value. I study the platform's contracting problem in three chapters. In the first, I show that the platform can benefit from allowing its providers to self-schedule. In the second, I study the platform's strategy when coordinating supply and demand across multiple states of the world. I show that the resulting dynamic pricing policy can be beneficial to consumers, despite widespread dislike of the real-world practice. I also show that, in many cases, the platform need not independently vary payments to providers to achieve near-optimal profit. Instead the platform may pay its providers a fixed percent commission on the price paid by consumers per completed service. In the final chapter, I argue that the findings above are distinct from the traditional two-sided markets literature. Though a classic two-sided market model experiences near-optimal performance of the fixed commission in many cases, the market conditions that produce poor fixed commission performance differ between the gig-economy model and the two-sided markets model. Because the two-sided market model does not accurately predict poor gig-economy fixed commission performance, it is important to study a model tailored the gig-economy to understand gig-economy specific applications.
Daniels, Kaitlin Marie, "Managing Self-Scheduling Capacity" (2017). Publicly Accessible Penn Dissertations. 3065.