Date of Award
Doctor of Philosophy (PhD)
Operations & Information Management
My dissertation develops stylized models using analytical and numerical tools to understand innovative business models in the context of operations management.
In the first chapter, using airlines as a backdrop, I study optimal overbooking policies with endogenous customer demand, when customers internalize their expected cost of being bumped. I first consider the traditional setting in which compensation for bumped passengers is fixed and booking limits are the airline's only form of control. I provide sufficient conditions under which demand endogeneity leads to lower overbooking limits in this case. I then consider the broader problem of joint control of ticket price, bumping compensation, and booking limit. I show that price and bumping compensation can act as substitutes, which reduces the general problem to a more tractable one-dimensional search for optimal overbooking compensation and effectively allows the value of flying to be decoupled from the cost of being bumped. Finally, I extend our analysis to the case of auction-based compensation schemes and demonstrate that these generally outperform fixed compensation schemes. Numerical experiments that gauge magnitudes suggest that fixed-compensation policies that account for demand endogeneity can significantly outperform those that do not and that auction-based policies bring smaller but still significant additional gains.
In the second chapter, I study the design of an emerging fundraising method for Blockchain-based startups, Initial Coin Offerings (ICOs), with a particular focus on capped ICOs. I propose a simple model of matching supply and demand with ICOs by companies involved in production of physical goods, aka inventory/asset ``tokenization''. I examine how ICOs should be designed---including optimal token floating and pricing for both utility and equity tokens (aka, security token offerings, STOs)---in the presence of moral hazard, production risk and demand uncertainty, make predictions on ICO failure, and discuss the implications on firm operational decisions and profits. I show that in the current unregulated environment, ICOs lead to risk-shifting incentives (moral hazard), and hence to agency costs, underproduction, and loss of firm value. These inefficiencies, however, fade as product margin increases and market conditions improve, and are less severe under equity (rather than utility) token issuance. Importantly, the advantage of equity tokens stems from their inherent ability to better align incentives, and hence continues to hold even in unregulated environments.
In the third chapter, I aim to understand how uncapped ICOs can fund service platforms under network effects. I propose an infinite horizon model that incorporates the interaction between the firm, speculators, service providers and customers. I find that both the platform's service capacity and service providers' profitability are enhanced by stronger network effect, larger customer base, and/or lower unit service cost. Moreover, I show that uncapped ICO is successful if and only if the cost of building the platform does not exceed the total service cost per period. I also extend the base model to account for firm's moral hazard and show that under loose regulation, uncapped ICO can still be successful if the firm charges the right amount of service fee.
Gan, Jingxing (rowena), "Operations Management In The Presence Of Strategic Agents" (2020). Publicly Accessible Penn Dissertations. 4041.