Does the Advertising Strategy and Tourism Attraction of the ‘Cap Go Meh’ Festival Can Affect the Decision and Intention of Tourists Revisiting?
Bibliographic record
Abstract
This paper's priority aims to examine the effects of advertising strategies and tourist attraction on visiting decisions and their impact on tourists' revisiting interest.The identity of Singkawang, which is often dubbed the 'City of a Thousand Temples', has attracted the attention of both local and domestic tourists.The 'Cap Go Meh' Festival, which is regularly held every year, complements the tourist attraction.Associative-quantitative are some characteristics of the research.They invited 100 informants to be interviewed by purposive sampling.The survey data is processed by path analysis, where the SmartPLS software supports statistical interpretation.Empirical investigation found that advertising strategy had a significant positive effect on the decision and interest of visiting tourists.Likewise, for tourist attractions that have a positive-significant effect on decisions and interest in visiting tourists again.Interestingly, the decision to visit also has a positive-significant effect on the interest of returning tourists.Limitations of the study need to evaluate a more extensive approach in order to help practitioners and researchers consider the dimensions of promotion, such as advertising strategy and tourism attraction, to support the decision and intention of tourists revisiting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".