BEYOND THE LIBRETTO: HOW THE COMPOSER GUIDES THE CREATION OF CHARACTER THREE CASE STUDIES

When performing in opera, a singer portrays a character. A libretto is used as the principal resource for the research. Music can also reveal insights into the composer’s ideas regarding characterization. This performance dissertation examines how musical devices such as genre, texture, meter, melody, instrumentation and form can be used to inform choices of characterization. Three roles from diverse operas were examined and performed. The first role, Estelle Oglethorpe in Later the Same Evening (2007) by John Musto (b 1954) was performed November 15, 16, 17, 18 2007. The second role, Dorabella in Così fan tutte (1789) by Wolfgang Amadeus Mozart (1756-1791) was performed April 20, 25, 27, 2008. The third role, Olga in Eugene Onegin (1878) by Pyotr Ilyich Tchaikovsky (1840-1893) was performed on April 19, 2009. All operas were presented by the University of Maryland Opera Studio at the Ina and Jack Kay Theater in the Clarice Smith Performing Arts Center, University of Maryland College Park. DVD recordings of all performances can be found in the University of Maryland library system.

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.