Bibliographic record
Abstract
Event marketing is the important strategy for the regional tourism development. It is worthwhile to be discussed that the cooperation between regional image and physical environment can shape the regional features and intensify tourists’ attitudes and tourist willingness towards regions. This study took the regular event marketing activities (Xinshe Sea of Flowers events in Taichung; Sakura Festival in Formosa Aboriginal Culture Village in Sun Moon Lake) held in two regions in Taiwan as the examples to discuss the effect of event marketing activities in different regions, regional image and physical environment on tourists’ experiential value, satisfaction, trust and commitment, so as to establish the competing model, compare the intensity difference in each path relationship and deeply analyze the effect of different event marketing activities.After the analysis of 500 valid questionnaires, it can be found that: (1) the event marketing activities and physical environment in two regions both have the significantly positive effect on tourists’ experiential value; (2) The tourists’ experiential value has the significantly positive effect on satisfaction and trust; (3) The tourists’ trust has the significantly positive effect on commitment; (4) However, the regional image has no significant effect on tourists’ experiential value. Besides, there is significant difference in the influencing intensity of the two paths: (1) The tourists’ satisfaction for Xinshe Sea of Flowers events in Taichung has the significantly positive effect on trust, while there is no significantly positive effect in the other region; (2) The influencing intensity of tourists’ experiential value for Sakura Festival in Formosa Aboriginal Culture Village in Sun Moon Lake on trust is significantly greater than that in the other region. It can be seen that the event marketing in different regions can generate the impact with different intensity. Therefore, each region should cooperate with its physical environment to plan the characteristic event marketing strategies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.004 |
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".