Branding Hunan Province as an International Travel Destination: \nAn Analysis of the Potential E-marketing Strategies towards Taiwan Market
Bibliographic record
Abstract
In 2008, the agreement between Mainland China and Taiwan of building Interactive Relationship of the Economic Trade was implemented, which created positively economic effect on both sides as well as improved the cross-relations. \n \nIn order to seize the great opportunity of reopening Taiwan market to Mainland China, the Hunan province tourism bureau had established some policies of promoting Hunan province to Taiwanese consumers. Meanwhile, it also could be an opportunity to further develop inbound tourism in Hunan province. \n \nThe purpose of this research was to investigate the information about power of destination branding and images, their travel preferences, internet use patterns and attitudes towards travelling in Mainland China and Hunan province, including the appropriate e-marketing strategies employed to promote travelling Hunan province to Taiwan market. \n \nThe e-survey of this research was conducted by the end of August, 2010 though the famous online questionnaire engine called my3q.com in Taiwan. Meanwhile, the link of the e-questionnaire was distributed to the major Taiwanese online society forums based in Taiwan, UK and Canada. In total, 107 respondents had involved in this research, and finally 96 answers were valid. \n \nIt appeared that respondents aged from 24 to 35 years old seem to be more valuable and could be considered as target market, as it mainly have higher budget as well as the higher anticipate. Meanwhile, destinations of natural scenery, heritages and heritages were found to be more attractive to Taiwanese consumer. Furthermore, the result also suggested the increasing power of internet in tourism market and emerging of travel blogs and forums.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".