Content: Global student dissertations and literature reviews.
Purpose: Use for foundational research, to locate test instruments and data, and more.
Special Features: Search by advisor (chair), degree, degree level, or department. Includes a read-aloud feature
The ProQuest Dissertations & Theses database (PQDT) is the world's most comprehensive collection of dissertations and theses. It is the database of record for graduate research, with over 2.3 million dissertations and theses included from around the world.
The study provided a better understanding of existing big data management techniques and allowed the examination of the usefulness of predictive analytics in improving organizational effectiveness. Author: Mohamadou Seck, Capella University
In this dissertation, we contribute to this domain and address three key financial problems through the development of novel solutions to: (i) unravel linkages among institutional investments and equity market co-movements, (ii) forecast high impact economic events, and (iii) mine illegal trading activities driven by material non-public information. Author: Taruna Seth, State University of New York at Buffalo
Predictive analytics is a rapidly growing area of technology, but little research has been completed in the adoption of predictive analytics by consumers. Specific to this study, little research has been completed on the use of social media sentiment-based predictive analytics by individual investors in the U.S. stock market. Author: Nicki Susman, Capella University
The purpose of the study was to investigate the effect of customer retention strategies on churn within the Small Package Logistics Industry (SPLI) to assist United Parcel Service, Inc. (UPS) in determining predictive churn management strategies that would increase the efficiency of retention efforts. Author: Wesley Dante Mitchell, Northcentral University
This research presents the application of various single machine learning algorithms (generalized linear models, random forest, decision tree, gradient boosted trees, support vector machine, deep Learning) and ensemble (bagging) to predict the cost and schedule of a software project. Author: Emmanuel Awolumate, The George Washington University