Social network for the choice of tourist destination: attitude and behavioral intention
Bibliographic record
Abstract
Social media is one of the most important elements in the process of industry 4.0. It has transformed itself into not just a platform for social interactions, but into a powerful marketing tool. People posting pictures or information through social media will likely influence others seeing it. This research investigates the impact of social media postings on millennial (Gen Y) tourists' decision in choosing destinations, as well as the perceptions of social media users toward the information they gather from the social media about the tourism destinations. World Travel and Tourism Council in its release in 2018 revealed that tourism industry represents 10.4% of total global GDP. The industry has also contributed 20% of new jobs globally created in the last decade. The findings from samples taken from respondents in greater Jakarta (Jakarta, Bogor, Depok, Tangerang and Bekasi) show that social media contributes significantly on shaping the millennial tourists' decision in choosing their holiday destinations.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".