E-government adoption and user’s satisfaction: an empirical investigation
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the relationships among e-government attributes in terms of social influence, perceived effectiveness, system quality, and information quality user satisfaction and intention to use e-government services in United Arab Emirates (UAE). Design/methodology/approach – This study is based on previous research on e-government services, and has relied partially on Delone and Mclean’s (2004) updated IS success model and the new theoretical and accepted model (UTAUT). These models were used to examine the impact of some selected attributes of e-government on the adoption of e-government services in the UAE from the perspective of a citizen. A questionnaire survey was used to collect data from a total of 1,800 e-government users in the UAE and structural equation modeling was utilized to test the theoretical model. Findings – This study has identified five main findings. First, it provides evidence of the direct effects of e-government attributes on user satisfaction. Second, it provides evidence of the direct effects of user satisfaction on user intention to use e-government services. Third, it provides evidence of the direct effects of e-government attributes on intention to use. Fourth, it demonstrates the positive total effects (direct and indirect) of e-government attributes on the intention to use e-government services in the UAE through user satisfaction. Fifth, two attributes of e-government – system quality and information quality – have emerged as having a strong effect on the intention to use e-government services. Research limitations/implications – The research study was limited to the UAE geographical region. It would be very interesting to see if there are regional variations when compared to the findings of this research study. The target respondents for this study are internet users. Future research needs to be extended to include both users and nonusers of the internet for the purpose of comparison, and looking at the perception of a technology divide among citizens as a factor influencing citizen intention to adopt e-government services. Practical implications – This study would help policy makers understand e-government users and to implement policies at large to meet the citzens ' needs of e-government services. The results of the study and the proposed model can be used as a guideline for e-government strategy formulation and implementation for the Government of UAE. The results of the study imply that decision makers of e-government programs should take into considerations these attributes when developing e-government programs because they affect users’ satisfaction and in turn enhances users’ intentions to use e-government services and consequently increase the success of e-government strategic programs. Originality/value – This paper is one of the few studies on e-government adoption in an Arab country and the first study on e-government adoption in the UAE. The paper’s originality and value comes from its theoretical contribution as a first paper in this region to empirically test the impact of some selected variables (social influence, perceived effectiveness, system quality, and information quality) on user satisfaction and intention to adopt e-government services. In contrast to previous research, this paper selected user satisfaction as a mediated, rather than a dependent variable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".