CCE Theses and Dissertations

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Date of Award


Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy in Information Systems (DISS)


Graduate School of Computer and Information Sciences


William Hafner

Committee Member

Michael Reid

Committee Member

Yair Levy


Over the past decade, there has been a change in the citizen–government relationship. Citizens have moved from a traditional face–to–face communication with their government, to an electronic interaction through the use of e–government systems. Emerging technology has enabled citizens to communicate with their government remotely. However, trust in e–government systems has been a problem. E–government systems lack personal interaction, and this creates resistance and uncertainty. Citizens also have a concern about turning over information to the government due to the concern that the information could be used to harm them.

This predictive study was designed to determine citizens’ trust in e–government systems. In Phase one, a key list of e–government’s information quality (IQ) characteristics was developed through literature research, and via an open–ended questionnaire delivered to a focus group of about 20 citizens. After the first phase, key IQ factors that affect trust in e–government systems were determined using Keeney’s approach. IQ characteristics collected from the open–ended questionnaire in the first phase were grouped based on their similarities and categorized based on the four IQ categories proposed by Y. W. Lee (1997). In Phase two, 363 citizens were surveyed via the Internet to determine their level of trust in e–government systems. Exploratory factor analysis (EFA) via principal component analysis (PCA) was used to determine the key IQ factors that affect citizens’ trust in e–government systems. A theoretical model was proposed, and the ordinal logistic regression (OLR) statistical method was used to formulate model and test predictive power. OLR developed the predictive model using IQ factors as the independent variables and trust as the dependent variables. OLR helped determine the relative weight of each of the IQ factors when predicting user’s trust in e–government systems. Based on the results, the three IQ factors: accuracy/dependability, accessibility/completeness, and representational were confirmed to have positive weights on citizens’ trust in e–government systems. Additionally, results demonstrated that two factors – –accessibility/completeness and representational had a significant contribution to trust. Accuracy/dependability showed a positive weight on the dependent variable, trust, but was not a significant contributor to trust. Results from the Mann–Whitney U Test determined that there were no significant differences between males and females on trust in e–government systems.

The study makes two important contributions to the Information Systems body of knowledge. First, it investigated the IQ factors that citizens feel are important in e–government systems. IQ is important for information systems success. IQ in e–government systems is important for persuading citizens to trust e–government systems. Second, it investigated key IQ factors contributing to citizens’ trust in e–government systems. Trust in the IQ of e–government systems is crucial to the success of such Web–based technology due to its involvement with most citizenry as users.

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