Abstract

My dissertation investigates supply behavior within the sharing economy, focusing specifically on the ride-sharing market. Leveraging quantitative methods such as structural modeling, field experiments, and lab experiments, I examine supplier behaviors to help platforms design more effective incentives—including subsidies and strategically framed rewards—to better match supply with demand. In Chapter 1, “Using Field Experiments to Infer Cross-Side Network Effects in the Ride-Sharing Market: How Does Driver Supply Impact Rider Orders, Cancellations, and Customer Lifetime Value?”, I address the central question of how shifts in driver supply affect rider behavior. Collaborating with a leading ride-sharing platform, I implement a natural field experiment that exploits an instrumental variable strategy. Specifically, by exogenously altering driver subsidy schedules, I employ these subsidies as instruments to causally identify cross-side network effects. The findings indicate that increasing the number of active drivers by 1% leads to a 2.01% rise in rider orders and simultaneously reduces cancellation rates by 0.48%. Furthermore, the results show significant implications for the platform’s long-term profitability: a 1% increase in afternoon or night driver availability enhances aggregate customer lifetime value (CLV) by 1.62% and 0.50%, respectively. These insights inform platform operations, guide strategic incentive adjustments, and ultimately enhance the overall rider experience. In Chapter 2, “Subsidizing Drivers in the Ride-Sharing Market: A Full-Heterogeneity Supply Model”, I explore cost-effective strategies to subsidize drivers while accounting for the flexible and autonomous nature of supplier participation in the sharing economy. By developing a structural model that explicitly incorporates driver-level heterogeneity in work costs and income sensitivity, I combine data from a field experiment and observational sources to estimate the parameters governing driver decision-making. Recognizing the computational complexity introduced by high-dimensional heterogeneity, I propose a novel nested iteration method that significantly enhances estimation scalability. Subsequent counterfactual analyses reveal that conventional, non-targeted time-based subsidies prove economically inefficient due to limited income sensitivity among drivers. However, individualized subsidy programs tailored to estimated driver-specific costs can substantially reduce incentive expenditures—by approximately 40-60%. This chapter highlights the critical role of supplier heterogeneity in the design of effective incentives, underscoring significant profit-enhancing opportunities for platforms. Finally, Chapter 3, “The Effect of the Order of Incentive Framing on Performance”, examines the psychological dimensions of incentive structures. Beyond monetary rewards, I explore how the sequence and framing of goals influence supplier performance and motivation. Through both a controlled lab experiment and a real-world field experiment partnered with a ride-sharing platform, I investigate scenarios in which goals are structured to appear progressively less challenging. The lab experiment employs a video game task to test consumer performance under varied goal sequences, while the field experiment analyzes how framing drivers' ride-completion goals impacts their subsequent activity. Results from both experiments consistently show enhanced performance when participants perceive goals as becoming easier over time—even without additional monetary incentives. This chapter demonstrates the practical value of psychological framing, highlighting an effective, low-cost approach to improve supplier motivation. Collectively, these chapters provide comprehensive insights into how ride-sharing and similar sharing-economy platforms can leverage both economic mechanisms and behavioral insights to more effectively engage, motivate, and manage their supplier bases.

Committee Chair

Tat Chan

Committee Members

Dennis Zhang; Qiyuan Wang; Stephen Nowlis; Xiang Hui

Degree

Doctor of Philosophy (PhD)

Author's Department

Marketing

Author's School

Olin Business School

Document Type

Dissertation

Date of Award

5-22-2025

Language

English (en)

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