Factors affecting acceptance and use of online technology in Thai people during COVID-19 quarantine time
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 aimed to investigate factors affecting behavioral intention and use behavior of technologies of Thai people under the COVID-19 circumstances.390 respondents were participated in our survey as sample size for statistical analysis.The authors utilized PLS-SEM assessment for testing the research hypotheses.Descriptive analysis revealed that Thai people in the quarantine period or work from home had suffered from a moderate to high levels of anxiety or stress.This has made Thai people increasingly use online and mobile technology or programs compared to the past.The study revealed four key factors that had significant and positive effects on the intention of users in using online technology including performance expectancy, effort expectancy, trust, and perceived risk.In addition, it indicated that behavioral intention positively affected the actual use behavior of technologies during quarantine time.The authors expect that policymakers or strategists could be used to manage the use of online and mobile technology for people, especially during the tough time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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