Managing self-scheduling capacity / Kaitlin M. Daniels.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Daniels, Kaitlin M., author.

Contributor:

Cachon, Gerard P., degree supervisor.

Gans, Noah, degree committee member.

Lobel, Ruben, degree committee member.

Su, Xuanming, degree committee member.

Veeraraghavan, Senthil, degree committee member.

University of Pennsylvania. Department of Operations and Information Management, degree granting institution.

Language:

English

Subjects (All):

Penn dissertations--Operations and information management.

Operations and information management--Penn dissertations.

Local Subjects:

Penn dissertations--Operations and information management.

Operations and information management--Penn dissertations.

Physical Description:

xi, 135 leaves : illustrations ; 29 cm

Production:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2017.

Summary:

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.

Notes:

Ph. D. University of Pennsylvania 2017.

Department: Operations and Information Management

Supervisor: Gerard P. Cachon.

Includes bibliographical references.

OCLC:

1291362482