Trust as a mediating effect of social media marketing, experience, destination image on revisit intention in the COVID-19 era
Bibliographic record
Abstract
The Covid-19 pandemic has a huge impact on the economy, which causes a substantial decline in the tourism sector. On the other hand, the detrimental impact of proactive measures taken to control the Covid-19 pandemic has had a negative impact on all industries around the world including tourism. Therefore, the purpose of this study is to learn on how trust can have an impact on the intention to revisit during the Covid-19 pandemic. The study uses 125 respondents from domestic tourists who visited Bali during the Covid-19 pandemic and had come to Bali before the Covid-19 pandemic. The study uses PLS as a statistical analysis. Our survey shows that while destination image influences intention, it does not have any meaningful effect on trust. Also, trust influences on intention, and experience influences positively on both trust and intention. In our survey, while social media marketing influences significantly on intention, it has no meaningful effect on trust.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".