Dissertation/Thesis Abstract

Predicting Enterprise Data System Success: A Study Of Knowledge Competency Using Partial Least Squares
by Kadwell, Frank E., D.I.T., Capella University, 2019, 137; 27667768
Abstract (Summary)

This quantitative, nonexperimental, predictive study was an examination of how knowledge maturity predicts data system impact. Data volumes are increasing exponentially, and organizations need to understand how data impacts information systems—specifically data systems. Studies have been completed that examined the predictive relationship between knowledge maturity and the overall system impact with transactional systems, but not data systems. The research questions asked to what extent do knowledge creation, knowledge transfer, knowledge retention, and knowledge application predict system quality? To what extent do knowledge creation, knowledge transfer, knowledge retention, and knowledge application predict information quality? To what extent do knowledge creation, knowledge transfer, knowledge retention, and knowledge application predict individual impact? To what extent do knowledge creation, knowledge transfer, knowledge retention, and knowledge application predict organizational impact? The research methodology was a nonexperimental quantitative study using Partial Least Squares, which provided the ability to analyze multiple independent variables and dependent variables. Similar studies used Partial Least Squares and proved to be effective. The target population of 115,000 consisted of data managers who work for U.S. companies earning $1B or more in annual revenue. The sample size was 113 using G*Power software. Cronbach’s alpha and composite validity returned results that proved the collection was reliable and valid. Limitations included data managers being the only participants in U.S.-based organizations with annual revenue over $1B. The quantitative, nonexperimental, predictive study did not analyze the demographics of the respondents other than the industry of the participants. There were transcription errors, so the survey was not exactly as expected; however, the survey errors did not affect the overall validity of the results, which validated the theory. Previous studies proved that knowledge maturity predicted transaction system quality; this quantitative, nonexperimental, predictive study proved that knowledge maturity predicts data system quality. Knowledge maturity predicted all variables. This quantitative, nonexperimental, predictive study may provide organizations with a better understanding of how data collection and the accumulation of data affects competitive advantage and validated that converting tacit knowledge into explicit knowledge affords organizations a competitive advantage. This quantitative, nonexperimental, predictive study concluded that (a) knowledge maturity predicts data system quality, and (2) organizations benefit from improved knowledge management. Increasing knowledge with quality information is vital to organizations.

Indexing (document details)
Advisor: Babb, Danielle
Commitee: Gagnon, Sharon, Vucetic, Jelena
School: Capella University
Department: School of Business and Technology
School Location: United States -- Minnesota
Source: DAI-A 81/6(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Information science, Information Technology
Keywords: Data, Data management, Data systems, Enterprise systems, Knowledge
Publication Number: 27667768
ISBN: 9781392627549
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