CEC Theses and Dissertations

Date of Award

2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Information Systems (DISS)

Department

Graduate School of Computer and Information Sciences

Advisor

Yair Levy

Committee Member

Raymond Hackney

Committee Member

James Parrish

Abstract

Business intelligence (BI) systems have been widely recognized as a leading technology for many years. However, despite the high priority and importance placed on BI, there has been a significant lack of BI system implementation (BISI) success. BI systems are not considered to be conventional information systems (IS) and often rely on the integration of a complex information infrastructure. Consequently, the degree of information quality (IQ) and system quality (SQ) have not met expectations for BISI success.

This study was designed to determine how an organization may gain benefits in the context of BISI by uncovering the antecedents and critical value factors (CVFs) of SQ and IQ necessary to derive greater BISI success. In phase one, a list of BISI SQ and IQ characteristics were collected through literature discovery and an open-ended questionnaire delivered to a group of BI user experts. The collected items were grouped and categorized based on their similarities. In phase two of the study 257 survey responses were collected from BI users to measure the level of importance, i.e. value, they placed on SQ and IQ characteristics. Exploratory factor analysis (EFA) via principal component analysis (PCA) was then used to uncover the CVFs of SQ and IQ that influence BISI success. Two highly reliable CVFs for SQ of BISI with a cumulative variance of nearly 62% and three highly reliable CVFs for IQ of BISI with a cumulative variance of over 75% were subsequently identified. In phase three of the study, an extended conceptual model for IS success was validated to assess the uncovered CVFs of SQ and IQ, as well as their influence on the constructs of perceived SQ of BISI and perceived IQ of BISI. Employing partial least squares (PLS), a subset of structural equation modeling (SEM), the research model was then used to assess the dimensions of perceived SQ of BISI and perceived IQ of BISI as antecedents of the constructs of perceived user systems satisfaction and perceived user information satisfaction from BISI. The crossover effects of perceived user systems and information satisfaction from BISI were also analyzed. The results identified two SQ CVFs of BISI (integration flexibility SQ and reliability SQ) that demonstrated a significant positive impact on perceived SQ for BISI as well as three IQ CVFs of BISI (representation IQ, intrinsic IQ, and accessibility IQ) that had a significant positive impact on perceived IQ of BISI. The constructs of perceived user systems satisfaction and perceived user information satisfaction from BISI had explained variances of R2 = .576 and .589 respectively. Additionally, 12 items of SQ for BISI and 14 items of IQ for BISI were identified as possessing high reliability.

This study makes two important contributions to the IS body of knowledge. First, it investigated the universal set of antecedents of SQ and IQ to establish the CVFs of IQ (integration flexibility SQ and reliability SQ) as well as the CVFs of IQ (representation IQ, intrinsic IQ, and accessibility IQ) for BISI success. Second, this study evaluated the crossover effects of system and information satisfaction in BISI success highlighting the importance that BI users place on the need to distinguish between the BI system, the IQ of the output produced, and the influence of IQ on perceived user system satisfaction from BISI. This study benefits stakeholders by focusing on what is important to BISI success and identifies those areas that are most likely to lead to better use of scarce resources while providing the greatest benefits.

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