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.

Essays on the Role of Government Incentives in the Private Provision of Social Goods

The economic rationale for public intervention into private markets through price mechanisms is twofold: to correct market failures and to redistribute resources. Financial incentives are one such price mechanism. In this dissertation, I specifically address the role of financial incentives in providing social goods in two separate contexts: a redistributive policy that enables low income working families to access affordable childcare in the US and an experimental pay-for-performance intervention to improve population health outcomes in rural India. In the first two papers, I investigate the effects of government incentives for providing grandchild care on grandmothers’ short- and long-term outcomes. In the third paper, coauthored with Manoj Mohanan, Grant Miller, Katherine Donato, and Marcos Vera-Hernandez, we use an experimental framework to consider the the effects of financial incentives in improving maternal and child health outcomes in the Indian state of Karnataka.

Grandmothers provide a significant amount of childcare in the US, but little is known about how this informal, and often uncompensated, time transfer impacts their economic and health outcomes. The first two chapters of this dissertation address the impact of federally funded, state-level means-tested programs that compensate grandparent-provided childcare on the retirement security of older women, an economically vulnerable group of considerable policy interest. I use the variation in the availability and generosity of childcare subsidies to model the effect of government payments for grandchild care on grandmothers’ time use, income, earnings, interfamily transfers, and health outcomes. After establishing that more generous government payments induce grandmothers to provide more hours of childcare, I find that grandmothers adjust their behavior by reducing their formal labor supply and earnings. Grandmothers make up for lost earnings by claiming Social Security earlier, increasing their reliance on Supplemental Security Income (SSI) and reducing financial transfers to their children. While the policy does not appear to negatively impact grandmothers’ immediate economic well-being, there are significant costs to the state, in terms of both up-front costs for care payments and long-term costs as a result of grandmothers’ increased reliance on social insurance.

The final paper, The Role of Non-Cognitive Traits in Response to Financial Incentives: Evidence from a Randomized Control Trial of Obstetrics Care Providers in India, is coauthored with Manoj Mohanan, Grant Miller, Katherine Donato and Marcos Vera-Hernandez. We report the results from “Improving Maternal and Child Health in India: Evaluating Demand and Supply Side Strategies” (IMACHINE), a randomized controlled experiment designed to test the effectiveness of supply-side incentives for private obstetrics care providers in rural Karnataka, India. In particular, the experimental design compares two different types of incentives: (1) those based on the quality of inputs providers offer their patients (inputs contracts) and (2) those based on the reduction of incidence of four adverse maternal and neonatal health outcomes (outcomes contracts). Along with studying the relative effectiveness of the different financial incentives, we also investigate the role of provider characteristics, preferences, expectations and non-cognitive traits in mitigating the effects of incentive contracts.

We find that both contract types input incentive contracts reduce rates of post-partum hemorrhage, the leading cause of maternal mortality in India by about 20%. We also find some evidence of multitasking as output incentive contract providers reduce the level of postnatal newborn care received by their patients. We find that patient health improvements in response to both contract types are concentrated among higher trained providers. We find improvements in patient care to be concentrated among the lower trained providers. Contrary to our expectations, we also find improvements in patient health to be concentrated among the most risk averse providers, while more patient providers respond relatively little to the incentives, and these difference are most evident in the outputs contract arm. The results are opposite for patient care outcomes; risk averse providers have significantly lower rates of patient care and more patient providers provide higher quality care in response to the outputs contract. We find evidence that overconfidence among providers about their expectations about possible improvements reduces the effectiveness of both types of incentive contracts for improving both patient outcomes and patient care. Finally, we find no heterogeneous response based on non-cognitive traits.

Development and Optimization of Four-dimensional Magnetic Resonance Imaging (4D-MRI) for Radiation Therapy

A tenet of modern radiotherapy (RT) is to identify the treatment target accurately, following which the high-dose treatment volume may be expanded into the surrounding tissues in order to create the clinical and planning target volumes. Respiratory motion can induce errors in target volume delineation and dose delivery in radiation therapy for thoracic and abdominal cancers. Historically, radiotherapy treatment planning in the thoracic and abdominal regions has used 2D or 3D images acquired under uncoached free-breathing conditions, irrespective of whether the target tumor is moving or not. Once the gross target volume has been delineated, standard margins are commonly added in order to account for motion. However, the generic margins do not usually take the target motion trajectory into consideration. That may lead to under- or over-estimate motion with subsequent risk of missing the target during treatment or irradiating excessive normal tissue. That introduces systematic errors into treatment planning and delivery. In clinical practice, four-dimensional (4D) imaging has been popular in For RT motion management. It provides temporal information about tumor and organ at risk motion, and it permits patient-specific treatment planning. The most common contemporary imaging technique for identifying tumor motion is 4D computed tomography (4D-CT). However, CT has poor soft tissue contrast and it induce ionizing radiation hazard. In the last decade, 4D magnetic resonance imaging (4D-MRI) has become an emerging tool to image respiratory motion, especially in the abdomen, because of the superior soft-tissue contrast. Recently, several 4D-MRI techniques have been proposed, including prospective and retrospective approaches. Nevertheless, 4D-MRI techniques are faced with several challenges: 1) suboptimal and inconsistent tumor contrast with large inter-patient variation; 2) relatively low temporal-spatial resolution; 3) it lacks a reliable respiratory surrogate. In this research work, novel 4D-MRI techniques applying MRI weightings that was not used in existing 4D-MRI techniques, including T2/T1-weighted, T2-weighted and Diffusion-weighted MRI were investigated. A result-driven phase retrospective sorting method was proposed, and it was applied to image space as well as k-space of MR imaging. Novel image-based respiratory surrogates were developed, improved and evaluated.

Beam Angle Optimization for Automated Coplanar IMRT Lung Plans

Purpose: To investigate the effect of incorporating a beam spreading parameter in a beam angle optimization algorithm and to evaluate its efficacy for creating coplanar IMRT lung plans in conjunction with machine learning generated dose objectives.

Methods: Fifteen anonymized patient cases were each re-planned with ten values over the range of the beam spreading parameter, k, and analyzed with a Wilcoxon signed-rank test to determine whether any particular value resulted in significant improvement over the initially treated plan created by a trained dosimetrist. Dose constraints were generated by a machine learning algorithm and kept constant for each case across all k values. Parameters investigated for potential improvement included mean lung dose, V20 lung, V40 heart, 80% conformity index, and 90% conformity index.

Results: With a confidence level of 5%, treatment plans created with this method resulted in significantly better conformity indices. Dose coverage to the PTV was improved by an average of 12% over the initial plans. At the same time, these treatment plans showed no significant difference in mean lung dose, V20 lung, or V40 heart when compared to the initial plans; however, it should be noted that these results could be influenced by the small sample size of patient cases.

Conclusions: The beam angle optimization algorithm, with the inclusion of the beam spreading parameter k, increases the dose conformity of the automatically generated treatment plans over that of the initial plans without adversely affecting the dose to organs at risk. This parameter can be varied according to physician preference in order to control the tradeoff between dose conformity and OAR sparing without compromising the integrity of the plan.