Critical success factors in business intelligence user satisfaction

No Thumbnail Available
Journal Title
Journal ISSN
Volume Title
School of Business | Master's thesis
Degree programme
Information and Service Management (ISM)
46 + 9
For many organizations, BI systems have become critical organizational resources, but many fail to take full advantage of the full resources and opportunities enabled by these systems and information. Furthermore, there is no clear measure for BI success as it remains context specific. Some academics refer to BI success as a more presumptive category, while others take an approach of defining it based on quality, use or net benefit. Further lack of consensus remains on whether BI success should be measured at an organizational or individual level. Even though the research on BI success and user satisfaction is still in the early stages, several different models have been adapted and implemented by academics. Like most past studies, this framework uses the Deloan and McLean (D&M) model as a foundation. However, the D&M models of 1992 and 2003 only concentrated on the technology category. Therefore, the suggested conceptual framework for this thesis takes both organizational and technological categories into consideration and is based on the D&M model and aframework by Kulkarni & Robles-Flores (2013). This quantitative model was chosen to target a more generalizable cross-sectional BI success model for multiple organizations, and data was collected in the form of a survey. For the data analysis, two different models were utilized. The partial least square model with the software SmartPLS 3.0 was used to assess the measurement model and structural model. In fsQCA, the final outcome is a group of configurations of conditions, so-called factors, leading to an outcome. These configurations show what groups of configurations of conditions lead to outcomes, such as high user satisfaction. The model was first assessed using the SmartPLS partial least square method. However, the results were not supporting most of the hypotheses as only three out of nine hypotheses showed significant results. Unlike the PLS analysis, the solutions of fsQCA provided valuable insights on the relationships of the predicting variables towards user satisfaction. However, as fsQCA identifies combinations and every observation is viewed as its own, containing its own mix of conditions connected or possible unconnected conclusion instead of separation to discrete variables, the results of fsQCA do not help us in confirming our suggested framework itself.
Thesis advisor
Liu, Yong
Lin, Yanqing
business Intelligence, BI, BI success, success model, user satisfaction, fsQCA, fs/QCA
Other note