Determinants of smart government continuous use: A two-staged structural equation modeling-artificial neural network approach
Bibliographic record
Abstract
This study aimed to develop and empirically validate an integrated model for continuous smart government service usage. This model integrates constructs from the unified theory of acceptance and use of the technology framework with the expectation-confirmation model, along with an additional construct: trust. Structural equation modeling (SEM) was used to analyze data collected via online questionnaires from 369 people who utilized smart government services in the United Arab Emirates. Next, an artificial neural networks model was used to rank the relative influence of the significant predictors identified through SEM analysis. The findings reveal that, among the significant predictors affecting the continuous use of smart government services, facilitating conditions, satisfaction, and perceived usefulness had the most substantial impact. Furthermore, this study highlights the direct influence of perceived usefulness, confirmation, facilitating conditions, effort expectancy, social influence, and public trust on citizen satisfaction.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".