The effect of quality, security and privacy factors on trust and intention to use e-government services
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
In keeping abreast with the digitized and automated world today, governments of developing and developed nations must provide appropriate e-government services to assure confidence and effective and efficient usage among their citizens. The quality, security, and privacy of current e-government implementation have been impairing the trust and participation of users, in Jordan especially. Hence, this study examined the impacts of quality, security and privacy of e-government services on the intention to use e-government services among Jordanian citizens. Questionnaires were used to gather data, and questionnaire items covered the constructs of quality factors (information quality, system quality, and service quality), perceived security, and perceived privacy as independent variables, and the constructs of trust and intention to use as dependent variables. The study samples comprised academics in Jordanian public universities. The universities were selected using stratified sampling method, while the respondents were chosen using simple random sampling method - 212 respondents were selected. SPSS Version 18 and PLS Version 3.3.6 were used in data analyses and hypotheses testing. Results affirmed a positive and significant link between information quality, system quality, service quality, perceived security, perceived privacy and trust in e-government services, and a positive and significant link between trust in e-government services on intention to use. In e-government services implementation, Jordanian government should take into account the quality factors (information quality, system quality, and service quality), perceived privacy, and perceived security, to increase trust of the citizens and consequently their intention to use the e-government services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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