An identity-based motivational model of the effects of perceived discrimination on health-related behaviors

Perceived discrimination is associated with increased engagement in unhealthy behaviors. We propose an identity-based pathway to explain this link. Drawing on an identity-based motivation model of health behaviors (Oyserman, Fryberg, & Yoder, 2007), we propose that erceptions of discrimination lead individuals to engage in ingroup-prototypical behaviors in the service of validating their identity and creating a sense of ingroup belonging. To the extent that people perceive unhealthy behaviors as ingroup-prototypical, perceived discrimination may thus increase motivation to engage in unhealthy behaviors. We describe our theoretical model and two studies that demonstrate initial support for some paths in this model. In Study 1, African American participants who reflected on racial discrimination were more likely to endorse unhealthy ingroup-prototypical behavior as self-characteristic than those who reflected on a neutral event. In Study 2, among African American participants who perceived unhealthy behaviors to be ingroup-prototypical, discrimination predicted greater endorsement of unhealthy behaviors as self-characteristic as compared to a control condition. These effects held both with and without controlling for body mass index (BMI) and income. Broader implications of this model for how discrimination adversely affects health-related decisions are discussed.

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.