Construction and Comparison of Relationship Models of Urban Tourism Development
Bibliographic record
Abstract
Tourism image and innovation marketing of cities are critical in tourism strategy and urban development since they demonstrate the regional characteristics and images. By image promotion and innovation marketing, we can construct tourists’ attitude and influence their comments and travel intention in the regions. This study focuses on Taipei, Taichung, and Kaohsiung in Taiwan and explores the effects of tourism image and innovation marketing strategy of cities on tourists’ experiential value, satisfaction and loyalty in different regions and the difference of intensity in order to explore the characteristics of the cities. According to investigation by 750 questionnaires, tourism image and innovation marketing of Taipei, Taichung, and Kaohsiung have significantly different effects on tourists’ experiential effect. It shows the difference of different cities. In addition, (1) tourism image of three cities significantly and positively influences experiential value; (2) experiential value of three cities significantly and positively influences satisfaction; (3) satisfaction with three cities significantly and positively influences loyalty; (4) innovation marketing strategy of Taipei and Taichung significantly and positively influences experiential value, except for Kaohsiung; (5) tourism image of Taichung significantly and positively influences loyalty, except for the other two cities. The findings can serve as reference for decision making of urban development and operational strategy of the cities.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".