Thesis (Ed.D.)--University of West Florida, 2009. / Submitted to the Dept of Professional and Community Leadership. Title from title page of source document. Document formatted into pages; contains 176 pages. Includes bibliographical references.

Thesis (M.A.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (February 20, 2007) Includes bibliographical references.

The study Demographics of Central Indiana Wal-Mart and Target Stores for Analysis of Intended Consumers and Store Locations sought to answer whether or not each store chose locations based upon their demographic needs. Through the use of flow charts a methodology was derived and then completed using Geographic Information Systems (GIS) and Business Analyst software from ESRI. Data for income, age, education, distance, drive time, market penetration, city demographics and businesses were broken down into customer profiles, market analysis and proximity analysis categories. These were used to compile maps and graphs for final analysis. The data from the maps and graphs were then compared from one store to another and a conclusion was made about whether or not each store did a good job of locating near its average intended potential customers. / Department of Geography

Thesis (M.S.) -- Central Connecticut State University, 2010. / Thesis advisor: Brian Sommers. "... in partial fulfillment of the requirements for the degree of Master of Science in Geography." Includes bibliographical references (leaves 62-65). Also available via the World Wide Web.

This qualitative analysis examines Wal-Mart managers‟ perspectives of the strategies that the U.S. corporation has implemented in order to increase its sales and profits at more than 4,000 stores in local U.S. communities. Two theoretical paradigms are specifically used: glocalization and grobalization. The former refers to cultural adaptation; the latter means standardization. The ultimate goal of the researcher is to identify the dialectical tensions between those two current forms of globalization. In-depth, face-to-face, qualitative interviewing of ten Wal-Mart managers in Central Florida allowed the researcher to actually comprehend managers‟ perspectives, gather fresh data, and construct a final product to enlighten readers on the current Wal-Martization of the United States. Throughout the data reduction process, four key themes surfaced as the most relevant to the initial research questions: (1) Awareness of Glocalization as Key to Success, (2) Grobalization Strategies Implemented, (3) Centralization as a Pattern of Grobalization, and (4) Organizational Socialization. Overall, it was found that Wal-Martization is a process that requires complex strategies and efforts to match the contemporary conditions of globalization. Meeting the needs of local Wal-Mart stores varies from one geographical location to the next. While, by definition, grobalization is a reversal of the meaning of glocalization, this study has revealed that part of Wal-Mart‟s phenomenal success is to be both grobalizing and glocalizing. Wal-Mart offers its customers the opportunity of consuming locally (e.g., Hispanic products, Mediterranean food), globally (e.g., universal U.S. merchandise), or both simultaneously (like products and traditions found in Orlando stores). In this sense, both glocalization and grobalization are effective for the successful Wal-Martization of U.S. communities

Task-oriented dialogue systems have increasingly become integral to our daily lives. However, collecting dialogue data is notably expensive due to the necessity of human interaction. These systems are used in various applications, such as customer service chatbots, virtual assistants, and automated scheduling tools. Given the critical role of these systems, it is essential to develop methods that can leverage limited resources efficiently, especially in data-driven models like neural networks, which have demonstrated superior performance and widespread adoption. This dissertation proposes systematic approaches to address the limited-data problem in both modeling and data aspects, aiming to enhance the effectiveness and efficiency of task-oriented dialogue systems even when data is scarce. This dissertation is divided into three main parts. The first part introduces three modeling techniques to tackle limited-data challenges. As the base dialogue model evolves from traditional recurrent neural networks to advanced large language models, we explore meta-learning methods, meta-in-context learning, and pre-training sequentially. Besides modeling considerations, the second part of our discussion emphasizes evaluation benchmarks. We start by discussing our work on correcting MultiWOZ, one of the most popular task-oriented dialogue datasets, which enhances training and provides more accurate evaluations. We also investigate biases within this dataset and propose methods to mitigate them. Additionally, we aim to improve the dataset by extending it to a multilingual dataset, facilitating the development of task-oriented dialogue systems for a global audience. The last part examines how to adapt our methods to real-world applications. We address the issue of database-search-result ambiguity in Meta’s virtual assistants by constructing disambiguation dialogue turns in the training data. Furthermore, we aim to enhance Walmart’s shopping companion by synthesizing high-quality knowledge-based question-answer pairs and constructing dialogue data from the bottom up. Throughout this dissertation, the consistent focus is on developing effective approaches to building task-oriented dialogue systems with limited resources. Our strategies include leveraging limited data more efficiently, utilizing data from other domains, improving data quality, and distilling knowledge from pre-trained models. We hope our approach will contribute to the field of dialogue systems and natural language processing, particularly in building applications involving real-world limited data and minimizing the need for manual data construction efforts. By addressing these challenges, this dissertation aims to lay the groundwork for creating more robust, efficient, and scalable task-oriented dialogue systems that better serve diverse user needs across various industrial applications.