Thesis (Ph.D.)--Murdoch University, 2005. / Thesis submitted to the Division of Arts. Includes bibliographical references (p. 342-372).

A Dissertation submitted to the Faculty of Arts University of the Witwatersrand, Johannesburg, for the degree of Master of Arts Johannesburg 1975 / No abstract

<p> The challenges of Big Data (BD) and Big Data Analytics (BDA) have attracted disproportionately less attention than the overwhelmingly espoused benefits and game-changing promises. While many studies have examined BD challenges across multiple industry verticals, very few have focused on the challenges of implementing BDA solutions. Fewer of these studies have focused directly on the financial services industry, and none have quantifiably measured the severities of the challenges. That created gaps as BDA solution implementers in the financial services industry could neither access a roadmap to guide their steps against obstacles that lay ahead, nor compute the severities of the challenges. This study addressed those gaps through two research questions: (1) What are the challenges of implementing BDA solutions in the financial services industry; and (2) What are the rankings of these challenges, in terms of importance and relative severities, such that BDA implementations can devote more research attention to or hedge better against those challenges? To answer these questions, the study used a mixed methods approach to content-analyze 75 BDA documents and collate a comprehensive list of 22 BDA challenges. As well, 36 financial services industry BDA-subject-matter-experts (SMEs) were surveyed to validate the list, rank the challenges, and measure their impacts. The research findings showed that the challenges of implementing BDA solutions in the financial services industry are mostly strategic and people-driven, rather than process-induced or technology-driven. Specifically, miscommunications and misconception of the meanings, intents, and the value-added benefits of BDA implementation in the financial services industry were found to be the top challenge. Details of the results, its implications for the BDA communities of practice and discourse, and opportunities for future research were discussed. The results can be generalized if scaled with a bigger sample size and better measures-of-intangibles.</p>

<p> This study investigated the influence of professional training and personal factors on five categories of technostress: techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty. The goal of the study was to determine whether experience and knowledge gained during professional training influenced the level of technostress individuals experienced in the workplace. The research also sought to determine how personal factors influence technostress directly and whether those factors moderate the relationship between professional training and technostress. The specific personal factors that were examined included gender, generational cohort, race, religion, and education level. A sample of 212 individuals who regularly use information technology as part of their daily work routine were surveyed on their levels of stress related to techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty. Multiple linear regression analyses were then conducted to examine the relationship between the criterion variables (categories of technostress) and the predictor variables (professional training and personal factors). The data analysis demonstrated that while professional training had little influence on technostress, women experienced significantly higher levels of techno-overload, older participants experienced significantly higher levels of stress related to techno-complexity, non-Whites experienced significantly higher levels of techno-insecurity, and age impacted levels of techno-uncertainty in individuals in the IT/engineering field who had received professional training. These results suggest that there is a need for further investigation into how specific elements of technostress impact individual populations.</p><p>

Thesis (Ed. D.)--University of Georgia, 1990. / Directed by Allen B. Moore. Includes bibliographical references (leaves 84-92).

Thesis (Ph. D.)--University of California, Los Angeles, 1992. / Vita. Includes bibliographical references (leaves 288-300).

Thesis (B.A.)--Haverford College, Dept. of Computer Science, 2005. Thesis (B.A.)--Haverford College, Dept. of Economics, 2005 / Includes bibliographical references.

Thesis (Ed. D.)--University of Georgia, 1990. / Directed by Allen B. Moore. Includes bibliographical references (leaves 84-92).

Thesis (Ph. D.)--State University of New York at Binghamton, Department of Systems Science and Industrial Engineering, 2005. / Includes bibliographical references (leaves 255-272).

Thesis (Ph. D.)(Informatics)--University of Pretoira, 2002. / Appendices mostly in Portuguese. Includes bibliographical references.