E-banking services in Ethiopia are increasing among low-income populations; however, with over 53 million mobile service users countrywide, more than 85% of the population still lacks access to banking services. A single case study was used to explore e-business strategies that bank managers use to promote the adoption of electronic banking services to the unbanked population in Ethiopia. The extended resource-based view of strategy served as the conceptual framework for this study. Data were collected from interviews with 12 experienced bank managers from leading commercial bank in Ethiopia. Data were analyzed using coding techniques and word clustering, with the help of qualitative data analysis software. After member checking and methodological triangulation, data were sorted into 5 themes including ensuring leadership, creating accessibility, fostering customers' acceptance, leveraging unique features and organizational resources, and building an e-banking ecosystem. The result showed that bank managers need to develop a customer-centric organizational posture and they should focus to build e-banking ecosystem inside and outside the country so that they can realize their vision to become global competitors. The findings from the study may contribute to positive social change for the unbanked communities in Ethiopia by informing bank managers options of e-banking adoption strategies thereby improve the convenience and accessibility of banking services.

The focus of this dissertation has been on improving the non-negative tensor factorization technique of text mining. The improvements have been made in both pre-processing and post-processing stages, with the goal of making the non-negative tensor factorization algorithm accessible to the casual user. The improved implementation allows the user to construct and modify the contents of the tensor, experiment with relative term weights and trust measures, and experiment with the total number of algorithm output features. Non-negative tensor factorization output feature production is closely integrated with a visual post-processing tool, FutureLens, that allows the user to perform in depth analysis and has a great potential for discovery of interesting and novel patterns within a large collection of textual data. This dissertation necessitated a number of significant modifications and additions to FutureLens in order to facilitate its integration into the analysis environment.

While solutions for file system encryption can prevent an adversary from determining the contents of files, in situations where a user wishes to hide even the existence of data, encryption alone is not enough. Indeed, encryption may draw attention to those files, as they most likely contain information the user wishes to keep secret, and coercion can be a very strong motivator for the owner of an encrypted file system to surrender their secret key. Herein we present DEFY, a deniable file system designed to work exclusively with solid-state drives, particularly those found in mobile devices. Solid-state drives have unique properties that render previous deniable file system designs impractical or insecure. Further, DEFY provides features not offered by any single prior work, including: support for multiple layers of deniability, authenticated encryption, and an ability to quickly and securely delete data from the device. We have implemented a prototype based on the YAFFS and WhisperYaffs file systems. An evaluation shows DEFY performs comparatively with WhisperYaffs.

Manufacturing hiring managers in the United States who fail to implement adequate recruitment strategies for skilled production workers experience reduced profits and sustainability challenges. The purpose of this multiple case study was to explore the strategies that successful manufacturing hiring managers in North Carolina used to recruit skilled production workers to sustain business profitability. Inductive analysis was guided by the human capital theory, and trustworthiness of interpretations was strengthened by member checking. The population for the study consisted of 4 business leaders who demonstrated the use of effective recruitment strategies to sustain profitability in manufacturing businesses in southeastern North Carolina. Data were collected from face-to-face interviews with the leaders and review of artifacts pertaining to employee recruitment strategies. Six themes emerged: fueling competition for local labor market, enhancing advertising methods, networking, providing job training, growing talent, and building new perceptions of manufacturing. The application of the findings from this study could contribute to positive social change by providing manufacturing business leaders with effective strategies for recruiting skilled workers. Business leaders could contribute to positive social change by increasing workplace stability and employees' abilities to support their families. Businesses and individuals could benefit from improved standards of living, thereby contributing to the sustainment and prosperity of communities.

Poor software quality leads to lost profits and even loss of life. U.S. organizations lose billions of dollars annually because of poor software quality. The purpose of this multiple case study was to explore the strategies that quality assurance (QA) leaders in small software development organizations used for successful software quality assurance (SQA) processes. A case study provided the best research design to allow for the exploration of organizational and managerial processes. The target population group was the QA leaders of 3 small software development organizations who successfully implemented SQA processes, located in Saint John, New Brunswick, Canada. The conceptual framework that grounded this study was total quality management (TQM) established by Deming in 1980. Face-to-face semistructured interviews with 2 QA leaders from each organization and documentation including process and training materials provided all the data for analysis. NVivo software aided a qualitative analysis of all collected data using a process of disassembling the data into common codes, reassembling the data into themes, interpreting the meaning, and concluding the data. The resulting major themes were Agile practices, documentation, testing, and lost profits. The results were in contrast to the main themes discovered in the literature review, although there was some overlap. The implications for positive social change include the potential to provide QA leaders with the strategies to improve SQA processes, thereby allowing for improved profits, contributing to the organizations' longevity in business, and strengthening the local economy.

Nowadays the largest E-commerce or E-service websites offer millions of products for sale. A Recommender system is defined as software used by such websites for recommending commercial or noncommercial product items to users according to the users’ tastes. In this project, we develop a recommender system for a private multimedia web service company. In particular, we devise three recommendation engines using different data filtering methods – named weighted-average, K-nearest neighbors, and item-based – which are based on collaborative filtering techniques, which work by recording user preferences on items and by anticipating the future likes and dislikes of users by comparing the records, for prediction of user preference. To acquire proper input data for the three engines, we retrieve data from database using three data collection techniques: active filtering, passive filtering, and item-based filtering. For experimental purpose we compare prediction accuracy of those three recommendation engines with the results from each engine and additionally we evaluate the performance of weighted-average method using an empirical analysis approach – a methodology which was devised for verification of predictive accuracy.

The problem researched was small business leaders' early and limited adoption of cloud computing. Business leaders that do not use cloud computing may forfeit the benefits of its lower capital costs and ubiquitous accessibility. Anchored in a diffusion of innovation theory, the purpose of this quantitative cross-sectional survey study was to examine if there is a relationship between small business leaders' view of cloud-computing attributes of compatibility, complexity, observability, relative advantage, results demonstrable, trialability, and voluntariness and intent to use cloud computing. The central research question involved understanding the extent to which each cloud-computing attribute relate to small business leaders' intent to use cloud computing. A sample of 3,897 small business leaders were selected from a commerce authority e-mail list yielding 151 completed surveys that were analyzed using regression. Significant correlations were found for the relationships between the independent variables of compatibility, complexity, observability, relative advantage, and results demonstrable and the dependent variable intent to use cloud computing. However, no significant correlation was found between the independent variable voluntariness and intent to use. The findings might provide new insights relating to cloud-computing deployment and commercialization strategies for small business leaders. Implications for positive social change include the need to prepare for new skills for workers affected by cloud computing adoption and cloud-computing ecosystem's reduced environmental consequences and policies.

The growth of mobile phone use has paralleled increased reports of identity theft. Identity theft can result in financial loss and threats to a victim's personal safety. Although trends in identity theft are well-known, less is known about individual cell phone users' attitudes toward identity theft and the extent to which they connect it to cell phone use. The purpose of this qualitative study was to determine how cell phone use is affected by attitudes toward privacy and identity theft. The study was based on social impact theory, according to which people's attitudes and behavior are affected by the strength and immediacy of others' attitudes and behavior. The research questions concerned the extent to which participants connected cell phone use with decreasing privacy and increasing cybercrime, how the use of biometrics affected cell phone users' attitudes and behavior, and what steps can be taken to reduce the misuse of private information associated with cell phone use. Data collection consisted of personal interviews with representatives from 3 groups: a private biometrics company, individual cell phone users who earn more than {dollar}55,000 a year, and individual cell phone users who earn less than {dollar}55,000 a year. Interviews were transcribed and coded for themes and patterns. Findings showed that interviewees were more likely to see identity theft as a problem among the public at large than in the industries in which they worked. Participants recommended a variety of measures to improve cell phone security and to reduce the likelihood of identity theft: passwords, security codes, voice or fingerprint recognition, and encryption. The implications for positive social change include informing government officials and individual users about the use and abuse of cell phones in order to decrease violations of privacy and identity theft while still promoting national security.

In order to fully examine the application of association analysis to medical data for the purpose of deriving medical diagnoses, we survey classical association analysis and approaches, the current challenges faced by medical association analysis and proposed solutions, and finally culminate this knowledge in a proposition for the application of medical association analysis to the identification of food intolerance. The field of classical association analysis has been well studied since its introduction in the seminal paper on market basket research in the 1990's. While the theory itself is relatively simple, the brute force approach is prohibitively expensive and thus, creative approaches utilizing various data structures and strategies must be explored for efficiency. Medical association analysis is a burgeoning field with various focuses, including diagnosis systems and gene analysis. There are a number of challenges faced in the field, primarily stemming from characteristics of analysis of complex, voluminous and high dimensional medical data. We examine the challenges faced in the pre-processing, analysis and post-processing phases, and corresponding solutions. Additionally, we survey proposed measures for ensuring the results of medical association analysis will hold up to medical diagnosis standards. Finally, we explore how medical association analysis can be utilized to identify food intolerances. The proposed analysis system is based upon a current method of diagnosis used by medical professionals, and seeks to eliminate manual analysis, while more efficiently and intelligently identifying interesting, and less obvious patterns between patients' food consumption and symptoms to propose a food intolerance diagnosis.

We propose that most data stores for large organizations are ill-designed for the future, due to limited searchability of the databases. The study of the Semantic Web has been an emerging technology since first proposed by Berners-Lee. New vocabularies have emerged, such as FOAF, Dublin Core, and PROV-O ontologies. These vocabularies, combined, can relate people, places, things, and events. Technologies developed for the Semantic Web, namely the standardized vocabularies for expressing metadata, will make data easier to utilize. We gathered use cases for various data sources, from human resources to big enterprise. Most of our use cases reflect real-world data. We developed a software package for transforming data into these semantic vocabularies, and developed a method of querying via graphical constructs. The development and testing proved itself to be useful. We conclude that data can be preserved or revived through the use of the metadata techniques for the Semantic Web.