{"id":{"repo_id":"penn","oai_identifier":"oai:repository.upenn.edu:20.500.14332/30048"},"canonical_url":"https://search.dev.ndltd.org/etd/penn/oai:repository.upenn.edu:20.500.14332/30048","repository":{"repo_id":"penn","name":"University of Pennsylvania","base_url":"https://repository.upenn.edu/server/oai/request"},"display":{"title":"Managing Self-Scheduling Capacity","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Daniels, Kaitlin Marie"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Gerard P. Cachon"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-24T03:45:15Z","subjects":[],"languages":["en"],"rights":["Kaitlin Marie Daniels"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.upenn.edu/handle/20.500.14332/30048","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gerard P. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy (PhD)"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Managing Self-Scheduling Capacity"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gerard P. Cachon"],"dc:creator":["Daniels, Kaitlin Marie"],"dc:date":["2023-05-17T21:07:47.000"],"dc:date.accessioned":["2023-05-22T17:25:06Z"],"dc:date.available":["2001-01-01T00:00:00Z"],"dc:date.issued":["2017"],"dc:description.abstract":["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."],"dc:description.degree":["Doctor of Philosophy (PhD)"],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://repository.upenn.edu/handle/20.500.14332/30048"],"dc:language":["en"],"dc:rights":["Kaitlin Marie Daniels"],"dc:title":["Managing Self-Scheduling Capacity"],"dc:type":["Dissertation/Thesis"]},"updated_at":"2026-07-24T03:45:15Z"}