Why Does Employment Discrimination Persist against People with Mental Illness? Effects of Negative Stereotypes, Power, and Differential Discrimination

Mental illness affects a sizable minority of Americans at any given time, yet many people with mental illness (hereafter PWMI) remain unemployed or underemployed relative to the general population. Research has suggested that part of the reason for this is discrimination toward PWMI. This research investigated mechanisms that affect employment discrimination against PWMI. Drawing from theories on stigma and power, three studies assessed 1) the stereotyping of workers with mental illness as unfit for workplace success, 2) the impact of positive information on countering these negative stereotypes, and whether negatively-stereotyped conditions elicited discrimination; and 3) the effects of power on mental illness stigma components. I made a series of predictions related to theories on the Stereotype Content Model, illness attribution, the contact hypothesis, gender and mental health, and power. Studies tested predictions using, 1) an online vignette survey measuring attitudes, 2) an online survey measuring responses to fictitious applications for a middle management position, and 3) a laboratory experiment in which some participants were primed to feel powerful and some were not. Results of Study 1 demonstrated that PWMI were routinely stigmatized as incompetent, dangerous, and lacking valued employment attributes, relative to a control condition. This was especially evident for workers presented as having PTSD from wartime service and workers with schizophrenia, and when the worker was a woman. Study 2 showed that, although both war-related PTSD and schizophrenia evoke negative stereotypes, only schizophrenia evoked hiring discrimination. Finally, Study 3 found no effect of being primed to feel powerful on stigmatizing attitudes toward a person with symptoms of schizophrenia. Taken together, findings suggest that employment discrimination towards PWMI is driven by negative stereotypes; but, stereotypes might not lead to actual hiring discrimination for some labeled individuals.

UNDERSTANDING CUSTOMER CHOICES IN SERVICE OUTSOURCING AND REVENUE MANAGEMENT

This dissertation investigates customer behavior modeling in service outsourcing and revenue management in the service sector (i.e., airline and hotel industries). In particular, it focuses on a common theme of improving firms’ strategic decisions through the understanding of customer preferences. Decisions concerning degrees of outsourcing, such as firms’ capacity choices, are important to performance outcomes. These choices are especially important in high-customer-contact services (e.g., airline industry) because of the characteristics of services: simultaneity of consumption and production, and intangibility and perishability of the offering. Essay 1 estimates how outsourcing affects customer choices and market share in the airline industry, and consequently the revenue implications from outsourcing. However, outsourcing decisions are typically endogenous. A firm may choose whether to outsource or not based on what a firm expects to be the best outcome. Essay 2 contributes to the literature by proposing a structural model which could capture a firm’s profit-maximizing decision-making behavior in a market. This makes possible the prediction of consequences (i.e., performance outcomes) of future strategic moves. Another emerging area in service operations management is revenue management. Choice-based revenue systems incorporate discrete choice models into traditional revenue management algorithms. To successfully implement a choice-based revenue system, it is necessary to estimate customer preferences as a valid input to optimization algorithms. The third essay investigates how to estimate customer preferences when part of the market is consistently unobserved. This issue is especially prominent in choice-based revenue management systems. Normally a firm only has its own observed purchases, while those customers who purchase from competitors or do not make purchases are unobserved. Most current estimation procedures depend on unrealistic assumptions about customer arriving. This study proposes a new estimation methodology, which does not require any prior knowledge about the customer arrival process and allows for arbitrary demand distributions. Compared with previous methods, this model performs superior when the true demand is highly variable.