Exploring Public Attitudes toward E-Government Health Applications Used During the COVID-19 Pandemic: Evidence from Saudi Arabia
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
This study sought to explore factors that determine the public’s acceptance of and adoption behavior toward e-government health applications launched in Saudi Arabia (SA) by the Ministry of Health (MOH) during the COVID-19 pandemic. The research relied on several theories: the technology acceptance model (TAM), information system success model (ISSM), mobile services acceptance model (MSAM), and unified theory of acceptance and use of technology (UTAUT). The constructs of perceived ease of use (PEOU), perceived usefulness (PU), attitude (ATT), trust (TR), information quality (IQ), facilitating condition (FC), and social influence (SI) were utilized to investigate the user’s intention toward using e-government health applications. The proposed model and its seven hypotheses were tested by conducting a survey across social media among citizens and residents in SA. A total of 785 valid responses were analyzed by SmartPLS and a structural equation modeling technique. After analysis, the results showed that PEOU, PU, ATT, TR, IQ, FC, and SI have positive effects on behavioral intentions. As for contributions, this paper is the first research paper to investigate the adoption of e-government health applications launched by MOH in SA during the COVID-19 pandemic and to provide a theoretical framework for pursuing future research work in a similar scope.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.001 | 0.001 |
| 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