The Effect of Attentional Focus on Singing Voice Quality: Towards the Interdisciplinary Experimental Investigation of Singing Pedagogy

Instructional methods employed by teachers of singing are mostly drawn from personal experience, personal reflections, and methods encountered in their own voice training (Welch & Howard, 2005). Even in Academia, singing pedagogy is one of the few disciplines in which research of teaching/learning practice efficacy has not been established (Crocco, et al., 2016). This dissertation argues the reason for this deficit is a lack of operationalization of constructs in singing, which, to date has not been undertaken. The researcher addresses issues of paradigm, epistemology, and methodology to suggest an appropriate model of experimental research towards the assessment of teaching/learning practice efficacy. A study was conducted adapting attentional focus research methodologies to test the effect of attentional focus on singing voice quality in adult novice singers. Based on previous attentional focus studies, it was hypothesized that external focus conditions would result in superior singing voice quality than internal focus conditions. While the hypothesis was partially supported by the data, the researcher welcomed refinement of the suggested research model. It is hoped that new research methodologies will emerge to investigate singing phenomena, yielding data that may be used towards the development of evidence-based frameworks for singing training.

PHYTOPLANKTON AND NUTRIENT DYNAMICS WITH A FOCUS ON NITROGEN FORM IN THE ANACOSTIA RIVER, IN WASHINGTON, D.C. AND WEST LAKE, IN HANGZHOU, CHINA

Nutrient loading has been linked with severe water quality impairment, ranging from hypoxia to increased frequency of harmful algal blooms (HABs), loss of fisheries, and changes in biodiversity. Waters around the globe are experiencing deleterious effects of eutrophication; however, the relative amount of nitrogen (N) and phosphorus (P) reaching these waters is not changing proportionately, with high N loads increasingly enriched in chemically-reduced N forms. Research involving two urban freshwater and nutrient enriched systems, the Anacostia River, USA, a tributary of the Potomac River feeding into the Chesapeake Bay, and West Lake, Hangzhou, Zhejiang Province, China, was conducted to assess the response of phytoplankton communities to changing N-form and N/P-ratios. Field observations involving the characterization of ambient phytoplankton communities and N-forms, as well as experimental (nutrient enrichment) manipulations were used to understand shifts in phytoplankton community composition with increasing NH4+ loads. In both locations, a >2-fold increase in ambient NH4+:NO3- ratios was followed by a shift in the phytoplankton community, with diatoms giving way to chlorophytes and cyanobacteria. Enrichment experiments mirrored this, in that samples enriched with NH4+ lead to increased abundance of chlorophytes and cyanobacteria. This work shows that in both of these systems experiencing nutrient enrichment that NH4+ supports communities dominated by more chlorophytes and cyanobacteria than other phytoplankton groups.

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.