Adoption of E-Government among Bahraini Citizens
Bibliographic record
Abstract
Many governments worldwide have been investing heavily in e-Government project as a strategy to provide the best governmental services to citizens. However, many governments and academic researchers recognized the problem of low-level of citizens' adoption toward e-Government services. Bahrain e-Government Authority is one of the governments that suffer from the lack of citizens' adoption of e-Government services, what caused serious problems to the authority. Therefore, this study is dedicated to address the factors that affect citizen's intention to adopt e-Government services from cultural, awareness and trust perspectives. Regression analysis is conducted to determine the relationship between culture, awareness and trust with adoption of e-Government. The results of the regression test showed strong evidence of a significant relationship between culture, awareness and trust and adoption of e-Government. However, the test indicated that trust had the highest level of relationship toward e-Government adoption. These results reveal that trust construct should be more considered by the e-Government authorities in Bahrain because it has a considerable impact on citizens' intention to use e-Government services. This will enable the authority to take actions to enhance citizens' trust, what will increase the rate of e-Government adopters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".