Browsing by Author "Guo, Yuan"

In this dissertaion we discuss customer choice and company decisions in three emerging retail settings. We first consider subscription box services in which a provider selects the assortment of products to include in the box by taking into account the customer's preferences. Customers interested in purchasing a product choose between engaging in searching physical stores or subscribing to a box delivery service. We study the subscription box company's problem of selecting the optimal contents of the box to maximize expected revenue (by driving demand from customers). We model the interaction between customer search and subscription by applying a cross-nested logit framework that correlates the contents in the box with the products available at the stores. We find that if a preview of the box is available, for customers with intermediate values of the search cost, it may be optimal to include a so-called utility loss leader, i.e., a product with relatively low valuation, to entice customers to subscribe to the box delivery service and therefore increase the likelihood of a sale. Next, we study companies' problem selling in social media, where brands attach product tags directly in their posts and users can complete their purchase by clicking on these tags. We propose a novel choice model that captures the users' impulsive behavior and limited attention span while browsing products in social media platforms. We consider a seller's product display problem on a product page and examine different strategies to sell through social media. We find that, depending on the product preferences and the users' pattern of attention decay, the optimal display set can have different structure. Finally, we discuss the fashion retailer's production problem selling in a market where a secondhand platform collects old clothes from customers and resells them. We construct a customer decision model that captures their consumption of new and/or used products under transitioning fashion trends over time. We find that if the platform operates independently and the hassle cost related to buying and selling used goods is high, the retailer's optimal production decision will not be affected by the existence of the secondhand market.

In the United States, heart failure is a leading cause of hospitalization. The medical industry places great emphasis on diagnosing heart disease through cardiac metrics like ejection fraction. Left ventricular ejection fraction is a commonly used diagnostic indicator for heart efficiency and is measured with echocardiography through different volume calculation techniques. However, ejection fraction results can drastically vary from one examiner to another. Generally cardiologists still give ejection fraction measurements a plus or minus 10 percent error range.

A program developed at Duke called 4DViz is robust enough for users to process 3D ultrasound data. 4DViz allows examiners to determine heart chamber volumes by constructing a surface model over an imaged heart chamber with many mouse click inputs. Through 4DViz programming, a viable approach for calculating ejection fraction is attempted in this thesis. Using feature tracking, surface drawing, and voxel filling, the new approach aims to reduce examiner input and improve ejection fraction consistency. Water filled balloons were used to calibrate the algorithm's parameters. In testing, several volunteers were asked to use the 4DViz. Their results are compared to volume measurements where user input was standard. The results show promise and may remove some of the inconsistency behind ejection fraction measurements.