How Travel Vlogs Contribute to Destination Marketing: A Comparison with <scp>DMO</scp> Promotional Videos and the Moderating Role of Destination Competitiveness
Bibliographic record
Abstract
ABSTRACT This two‐part study examines how travel vlogs influence tourist behaviors and, consequently, their value in destination marketing. A convenience sample of 196 North Americans who belonged to Generation Y was collected via an online experiment. The first part adopted the Attention‐Interest‐Desire‐Action (AIDA) principle as the theoretical underpinning of how travel vlogs influence Gen Y travel behaviors, contrasting them with Destination Marketing Organization (DMO) promotional videos. It was found that travel vlogs impact tourist behavior by attracting tourists' attention, delivering realistic destination information, and inspiring them. The second part examined the relationship between destination competitiveness levels and willingness to pay (WTP), and the impact of travel vlogs and DMO promotional videos on this relationship. It was shown that destination competitiveness levels exert different impacts on WTP between travel vlogs and DMO promotional videos. This study enriches the tourism destination marketing literature and suggests that DMOs tailor their strategies based on destination competitiveness.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".