2025 Theses Doctoral
Empirical and Behavioral Operations Management of Digital Healthcare and FinTech
This dissertation is motivated by two significant trends in the operations management of modern businesses. First, emerging technologies have transitioned traditional in-person services, such as healthcare, to online platforms. Second, advancements in machine learning and artificial intelligence, including large language models, provide promising tools to improve service quality. This dissertation aims to explore how emerging technologies and digitalization can be effectively utilized to manage customer behavior and the financial market.
In Chapter 1, I study operational factors that lead to service incompletions and customers' heterogeneous behaviors online versus in-person, using telemedicine as a canonical example. The adoption of online services, such as telemedicine, has increased rapidly over the last few years. To better manage online services and effectively integrate them with in-person services, we need to better understand customer behaviors under the two service modalities. Utilizing data from two large internal medicine outpatient clinics, I take an empirical approach to study service incompletion for in-person and telemedicine appointments respectively. I focus on estimating the causal effect of provider availability on service incompletion. When providers are unavailable, patients may be more likely to leave without being seen, leading to service incompletion. I introduce a multivariate probit model with instrumental variables to handle estimation challenges due to endogeneity, sample selection, and measurement error. The estimation results show that intra-day delay increases the telemedicine service incompletion rate, but it does not have a significant effect on the in-person service incompletion rate. This suggests that telemedicine patients may leave without being seen, while in-person patients are not sensitive to intra-day delay. I conduct counterfactual experiments to optimize the intra-day sequencing rule when having both telemedicine and in-person patients. This analysis indicates that not correctly differentiating the types of incompletions due to intra-day delays from no-shows can lead to highly suboptimal patient sequencing decisions.
In Chapter 2, I study the optimal usage of sunk cost, along with delay announcement, to mitigate service incompletion. Sunk-cost bias occurs when decisions are influenced by the time, energy, and money already invested, rather than considering the future costs necessary to achieve success. This phenomenon of "irrational behavior" is well-documented in decision-making studies and is generally recognized as a factor that can lead to suboptimal decisions. In this work, however, I investigate how sunk cost (and the behavioral bias associated with it) can be used as an operational lever to increase service completion rates in a congested service system. I run a controlled online experiment and find that the abandonment rate is significantly reduced for the group of participants who incur a larger sunk cost. To better capture the dynamics of service systems and their impact on customers' behavior, I study a queueing model with sunk cost and strategic customers, where customers experience a disutility of balking that is proportional to the sunk cost they incur. I characterize the equilibrium behavior of the customers, from which I further derive the optimal strategy for the service provider in terms of whether to provide real-time queue length information to customers as well as the optimal level of sunk cost to impose. The results show that the sunk cost strategy is effective only when waiting information is provided and that using a non-zero sunk cost is optimal when the system is moderately congested. Through a comprehensive numerical study, I demonstrate that implementing a non-zero sunk cost can substantially improve the throughput of the queuing system. In addition, I reveal an interesting asymmetric pattern in the robustness of the service provider's optimal policy when the customers' sensitivity to sunk cost cannot be accurately estimated, which suggests that if the service provider cannot accurately estimate the customer's sensitivity to sunk cost, using an underestimated value will give more robust performance improvements.
In Chapter 3, I quantify news novelty -- changes in the distribution of news text -- through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language. Entropy risk cannot be explained by existing long-short factors.
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More About This Work
- Academic Units
- Business
- Thesis Advisors
- Chan, Carri W.
- Dong, Jing
- Degree
- Ph.D., Columbia University
- Published Here
- January 29, 2025