Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Explanatory Model for the Use of Traditional Medicines in Kilimanjaro, Tanzania

Introduction: Traditional medicines are one of the most important means of achieving total health care coverage globally, and their importance in Tanzania extends beyond the impoverished rural areas. Their use remains high even in urban settings among the educated middle and upper classes. They are a critical component healthcare in Tanzania, but they also can have harmful side effects. Therefore we sought to understand the decision-making and reasoning processes by building an explanatory model for the use of traditional medicines in Tanzania.

Methods: We conducted a mixed-methods study between December 2013 and June 2014 in the Kilimanjaro Region of Tanzania. Using purposive sampling methods, we conducted focus group discussions (FGDs) and in-depth interviews of key informants, and the qualitative data were analyzed using an inductive Framework Method. A structured survey was created, piloted, and then administered it to a random sample of adults. We reported upon the reliability and validity of the structured survey, and we used triangulation from multiple sources to synthesize the qualitative and quantitative data.

Results: A total of five FGDs composed of 59 participants and 27 in-depth interviews were conducted in total. 16 of the in-depth interviews were with self-described traditional practitioners or herbal vendors. We identified five major thematic categories that relate to the decision to use traditional medicines in Kilimanjaro: healthcare delivery, disease understanding, credibility of the traditional practices, health status, and strong cultural beliefs.

A total of 473 participants (24.1% male) completed the structured survey. The most common reasons for taking traditional medicines were that they are more affordable (14%, 12.0-16.0), failure of hospital medicines (13%, 11.1-15.0), they work better (12%, 10.7-14.4), they are easier

to obtain (11%, 9.48-13.1), they are found naturally or free (8%, 6.56-9.68), hospital medicines have too many chemical (8%, 6.33-9.40), and they have fewer side effects (8%, 6.25-9.30). The most common uses of traditional medicines were for symptomatic conditions (42%), chronic diseases (14%), reproductive problems (11%), and malaria and febrile illnesses (10%). Participants currently taking hospital medicines for chronic conditions were nearly twice as likely to report traditional medicines usage in the past year (RR 1.97, p=0.05).

Conclusions: We built broad explanatory model for the use of traditional medicines in Kilimanjaro. The use of traditional medicines is not limited to rural or low socioeconomic populations and concurrent use of traditional medicines and biomedicine is high with frequent ethnomedical doctor shopping. Our model provides a working framework for understanding the complex interactions between biomedicine and traditional medicine. Future disease management and treatment programs will benefit from this understanding, and it can lead to synergistic policies with more effective implementation.

Carbon mitigation costs for the commercial building sector: Discrete-continuous choice analysis of multifuel energy demand

We estimate a carbon mitigation cost curve for the U.S. commercial sector based on econometric estimation of the responsiveness of fuel demand and equipment choices to energy price changes. The model econometrically estimates fuel demand conditional on fuel choice, which is characterized by a multinomial logit model. Separate estimation of end uses (e.g., heating, cooking) using the U.S. Commercial Buildings Energy Consumption Survey allows for exceptionally detailed estimation of price responsiveness disaggregated by end use and fuel type. We then construct aggregate long-run elasticities, by fuel type, through a series of simulations; own-price elasticities range from -0.9 for district heat services to -2.9 for fuel oil. The simulations form the basis of a marginal cost curve for carbon mitigation, which suggests that a price of $20 per ton of carbon would result in an 8% reduction in commercial carbon emissions, and a price of $100 per ton would result in a 28% reduction. © 2008 Elsevier B.V. All rights reserved.

Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.

OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.