In my research, I use mathematical models to solve problems related to revenue management, service operations and platform economy. I am especially interested in (1) pricing and design of two-sided digital platforms including ride-sharing platforms (such as Uber and Lyft) and online marketplaces (such as eBay), and (2) equilibrium behavior of strategic customers and providers in services involving queues. I use techniques and theories from industrial organization, queueing theory, stochastic processes, optimization, mechanism design and game theory.
Below I summarize three papers that form my Ph.D. dissertation.
 Ride Solo or Pool: Designing Price-Service Menus in a Ride-Sharing Platform, by Jagan Jacob and Ricky Roet-Green. R & R at Production and Operations Management.
Consider a ride-sharing platform (RSP) such as Uber or Lyft. They offer passengers an option to share the ride with another passenger and split the fare. This is suitable for people who want to save money but do not mind sharing the space with a stranger (who could be unpleasant) and taking a bit longer than a regular ride to reach their destination. At the first glance, offering such an option seems rational for the RSP, especially in big cities. But is it always beneficial?
To answer this question, we develop a queueing model to find the RSP’s optimal revenue at equilibrium when passengers are strategic and drivers are independent agents, and design the RSP’s revenue-maximizing price-service menu. We find that offering both solo and pooled rides is optimal when the distribution of passenger-type is not skewed and congestion is not high. Counter intuitively, when congestion is high, the RSP benefits from offering only one ride choice, and simulation-based results extend these findings when more than one route exists.
Furthermore, we find that revenue per driver can be non-monotonic with respect to the number of cars, even though total revenue is monotonic. Numerical analysis provides important insights when the number of drivers is endogenous. For instance, equilibrium revenue per driver can decrease when the passenger arrival rate increases. We find that when the driver supply side thickens, revenue per driver decreases. The compensation drivers receive (as a fraction of total revenue generated) increases with their reservation price and decreases with the arrival rate of passengers. When demand is low, a higher wage-payout fraction can increase RSP’s equilibrium revenue.
 Pricing and Quality Competition under Logit Demand Functions, by Jagan Jacob and Harry Groenevelt.
What happens to quality when competition increases? Should the firm try to differentiate from its competitor? How does the pace of technological innovation and market expansion affect a firm’s choice? Despite the extensive existing literature on price-quality competition, there is no clear consensus on answers to such questions. Furthermore, despite being one of the most widely used discrete choice models by empirical researchers, analytical studies on joint price-quality competition under logit demand are scarce.
Therefore, we consider a duopoly where a firm and its competitor each chooses its product’s optimal quality and price, and each gets a market-share according to a logit demand model. Interestingly, we find that the myopic optimal quality depends neither on the competitor’s choices nor the sequence of actions (simultaneous vs. sequential). We show that the firm need not always differentiate from the competitor.
However, in a dynamic (multi-period) setting, depending on the pace of technological innovation and market expansion, the firm’s optimal product quality is either a quasi-concave or a quasi-convex function in the competitor’s quality. When customers are homogeneous, we prove the existence and uniqueness of equilibrium during price-quality competition under logit demand. But when there are multiple customer segments, neither uniqueness nor existence can be guaranteed. We provide bounds for a compact search space to improve computational efficiency. We show how minor changes in model parameters can produce results that make seemingly opposite suggestions, which could explain why prior studies on price-quality competition point in different directions.
 Screening Mechanism When Online Users Have Privacy Concerns by Jagan Jacob. International Journal of Revenue Management Vol. 11, Nos. 1/2, 2019.
In consumer-to-consumer online platforms that enable selling (e.g., eBay, Taobao) or sharing (e.g., Airbnb, Uber) of goods and services, information asymmetry between providers (e.g., sellers, hosts, and drivers) and consumers (e.g., buyers, guests, passengers) pose challenges. Such platforms facilitate transactions between users (providers and consumers), who are often strangers. Stricter screening, background checks, and identity verification requirements may reduce the probability of bad users entering the platform. However, users are reluctant to share personal information on the Internet. In this paper, I design a matching mechanism to maximize platform profit when users are heterogeneous with some more likely to be good than others, but the platform does not know who. I argue that in some cases, the platform increases its profit by allowing users with a higher probability of being bad to join as well.
Works-in-progress and future plans
My research agenda for the future is to work on revenue management problems in innovative platforms and service systems. My immediate plan is to publish papers  and  and continue working on my on-going research (papers -) listed below. I am very much interested in collaborating with empirical researchers so that on-going research findings can be empirically tested, eventually leading to stronger publications.
 Optimal Admission Policy with Independent Agents, co-authored with Ricky Roet-Green.
 On Incentive-Compatible Repair Contracts, co-authored with Vera Tilson.
 Optimal Pricing in a Peer-to-Peer Solar Electricity Trading Platform, co-authored with Fang Wan.
 Should Competing Firms Cooperate to Reduce Congestion? by Jagan Jacob. R & R at Transportation Research: Part E.