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| Autor/in | Huebner, Richard A. |
|---|---|
| Titel | A Quantitative Analysis of Organizational Factors That Relate to Data Mining Success |
| Quelle | (2017), (147 Seiten)
PDF als Volltext Ph.D. Dissertation, Capella University |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| ISBN | 978-0-3550-6394-3 |
| Schlagwörter | Hochschulschrift; Dissertation; Data Analysis; Data Collection; Information Retrieval; Surveys; Correlation; Multiple Regression Analysis; Hypothesis Testing; Administrative Organization; Decision Making; Progress Monitoring; Organization Size (Groups); Predictor Variables |
| Abstract | The ubiquity of data in various forms has fueled the need for advanced data-mining techniques within organizations. The advent of data mining methods used to uncover hidden nuggets of information buried within large data sets has also fueled the need for determining how these unique projects can be successful. There are many challenges associated with ensuring that a data-mining project is successful. Previous literature explored technical and individual factors related to data mining, however, organizational factors related to data mining have not been widely explored to date. Identifying and understanding these factors provides business leaders and data mining professionals with a strategy and roadmap for improving data mining project outcomes and improving data mining success rates. This study examined the extent to which top management support and organizational size are related to data mining success. This study followed a quantitative correlational, non-experimental approach. A survey instrument containing a top management support and organizational impact scale was used for collecting data. The survey was hosted at SurveyMonkey.com, where a random sample of data mining professionals was asked to complete the survey. The sample consisted of 175 data-mining professionals. A correlation analysis and hierarchical multiple regression model were used to test the hypotheses. Top management support was found to be a significant predictor of data mining success. A hierarchical multiple regression model revealed that top management support accounted for approximately 18% of the overall variability of data mining success, leaving 82% to other factors. Specific top management behaviors, including participating in decision making and monitoring the project, were found to be significant predictors of data mining success. Results indicated that organizational size was not significantly related to data mining success. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided). |
| Anmerkungen | ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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