An Empirical Investigation on the Adoption of e-Government in Developing Countries: The Case of Jordan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While e-Government has the potential to improve public administration effectiveness as well as efficiency by increasing convenience, performance and accessibility of different government services to citizens, the success of these initiatives is dependent not only on government support, but also on citizens’ willingness to accept and adopt those e-government services. Although there is a great body of literature that discuss e-Government in developed countries, e-government in developing countries, in general, and Arab countries, in particular, has not received equal attention. The objective of this study is to determine the factors that influence the adoption of e-government services in a developing country, namely Jordan. An extended version of Technology Acceptance Model (TAM) is utilized as the theoretical base of this study. Overall, the study proposes that citizens’ perceptions about e-Government services influence their attitude towards adopting e-government initiatives. A survey collected data from 853 online users of Jordan’s e-government services. Using partial least squares (PLS) of structural equation modeling (SEM) analysis technique, the results show that all the four factors, namely: Perceived Credibility, Perceived Usefulness, Perceived Ease of Use and Computer Self Efficacy have significant effect on the adoption of e-government services in Jordan. Moreover, the study findings show that Perceived Ease of Use as the most important factor in predicting Jordanian citizens’ adoption of e-government services. The research limitations, implications for research and practice are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it