Understanding compliance intention of SNS users during the COVID-19 pandemic: a theory of appraisal and coping
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
Purpose The purpose of this paper is to explore the factors affecting the intention of social networking sites (SNS) users to comply with government policy during the COVID-19 pandemic. Design/methodology/approach Based on the theory of appraisal and coping, the research model is tested using survey data collected from 326 SNS users. Structural equation modeling is used to test the research model. Findings The results show that social support has a positive effect on outbreak self-efficacy but has no significant effect on perceived avoidability. Government information transparency positively affects outbreak self-efficacy and perceived avoidability. Outbreak self-efficacy and perceived avoidability have a strong positive impact on policy compliance intention through problem-focused coping. Practical implications The results suggest that both government and policymakers could deliver reliable pandemic information to the citizens via social media. Originality/value This study brings novel insights into citizen coping behavior, showing that policy compliance intention is driven by the ability to cope with problems. Moreover, this study enhances the theoretical understanding of the role of social support, outbreak self-efficacy and problem-focused coping.
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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 | 0.007 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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