Influence of the Preference Factor on the Behavior Patterns of Participation in Festival Activities
Bibliographic record
Abstract
Festival activity marketing is one of the most popular tourism strategies around the world. Festival activities combined with marketing for interacting and communicating with the tourists, can enhance tourists’ preferences and impressions on the tourism destinations and it becomes an important source in leading the development of regional economic. Festival activities held in each and every region shall be coordinated with relevant factors to integrate into distinguishing features and be implanted deeply into people’s mind, and only after different marketing strategies are prepared for tourists with different preferences, can the best result of festival activities be achieved.This study mainly discusses whether there are any difference in the pattern of associations of the tourist groups with different preference on festival activity in regards to relevant factors on festival activities, festival attractiveness, tourists’ cognitive values, and behavioral intention. The result witnesses that: (1) the cognitive value of tourist groups with high preference for festival activities further promotes their behavioral intention for participating in festival activities; (2) tourist groups with medium preference for festival activities feel attracted by the favorable atmosphere of the environment, which fosters their intention to participate in festival activities. Therefore, suitable and satisfactory festival marketing strategies shall be established for different types of tourist division.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".