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Recent years have witnessed the emergence and dramatic growth of platform businesses. This dissertation addresses two challenges of significance to ride service companies: 1) it investigates promotion effects on two-sided platforms; and 2) it models platform pricing and staffing strategies under a hybrid employment mode.In the first chapter, I broadly discuss the new challenges faced by the ride service platforms in recent years and provide perspectives on related research questions. In the second chapter, I study the network effects of different promotion methods in two-sided markets. Using data from a transportation-service platform, I specify a structural model that quantifies the respective promotional effects for price discounts and service upgrades. The results show that the primary effect of price discounts is to increase demand within the same service tier, whereas upgrades have stronger stickiness effects and spillover effects. Based on the estimates, I calculate the return on investment (ROI) and find that the ROI for upgrades is higher than that for discounts. Our counterfactual analyses show that as the platform matures, the importance of upgrades increases while the importance of price discounts decreases. These results provide important managerial implications for platforms on how to design optimal promotions to grow their business. In the third chapter, I model an on-demand platform that adopts a hybrid employment mode. This work is motivated by the recent public debate over the status of drivers for the major ride- hailing platforms as contract workers. My hybrid employment environment includes both contractors and full-time employees who receive a benefits package. In the hybrid model with driver control, drivers have the flexibility to decide how long to work and consequently whether to be an employee or a Contractor. Those who work over a certain number of hours will be classified as employees and receive a benefits package. The platform is a profit-maximizer and decides the optimal price based on the required benefit amount. As the benefit amount increases, the platform's profit decreases, which is consistent with strong gig company opposition to providing benefits. Moreover, I show that higher benefits make consumers and full-time drivers better off but decrease part-time drivers' welfare as well as overall social welfare. I consider a second model to better balance the platform's profitability and drivers' welfare. Under the hybrid model with platform control, the platform hires a limited number of full-time employees while guaranteeing that a minimum proportion of all work will be fulfilled by those employees. In this way, the profit loss due to the required benefits is capped, making this alternative policy a potentially viable solution from the platform’s perspective.