Factors influencing weekend travel destination choice: A study in Ho Chi Minh city, Vietnam
Bibliographic record
Abstract
Nowadays, weekend travel is gradually gaining people's attention due to societal impacts, with the desire to improve health, relax, rest, and entertain after days of exhausting work. Therefore, the development of weekend travel is a strategy of interest to managers and leaders, leading to intense competition among destinations. Although it has been long established worldwide, weekend travel in Vietnam has only recently gained popularity, primarily among young people. Therefore, researching the factors influencing the decision to choose weekend travel destinations is significant in developing strategies for this type of tourism. The research results show that the choice of weekend travel destinations by Ho Chi Minh City tourists is driven by various internal and external factors. Among them, internal motivations, income, convenience in the trip, the image of the destination, etc., are factors rated highly by tourists. There are significant differences in some internal and external factors according to age groups. Model testing and research hypotheses indicate that 66.4% of destination choices are influenced by the proposed factors in the model. Among them, the destination image has the most significant impact, followed by income, internal motivations, and distance. The remaining factors in the model have low or no impact on the satisfaction and commitment to return to weekend travel destinations for Ho Chi Minh City tourists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".