An analysis of students' travel motivations and images of China as a tourist destination
Bibliographic record
Abstract
Despite China's rapid growth in inbound tourism, the nature of its Canadian tourist \nmarket has been insufficiently studied. In response to this need, the objectives of this \nstudy are to identify China's destination image in Canadian students' minds, their \npossible internal motivations for visiting China as well as examining demographic \ninfluences on people's destination image formation. The study reviews image formation \nprocess and travel motivation categorisation, discusses their relationship, and implements \nBaloglu and McCleary's (1999) perceptual and affective image formation model and \n"push and pull factors" theory as its framework. A self-administered survey was applied \nto 424 undergraduate students in a Canadian university in early 2004. Exploratory factor \nanalyses were conducted to identify perceived images and travel motivation. Summated \nmeans were calculated to illustrate the affective attitudes. A series of f-test and ANOVA \ntests were employed to examine the influence of demographics. An open-ended question \nformat was adopted to analyse other images, motivations and visitation barriers that \nstudents may have. Findings demonstrate that cultural and natural attractions are the \npredominant image which the Canadian students have of China'; some stereotypes and \nnegative images still influence the students' perception; travel service quality is largely \nunknown; increasing knowledge and seeking excitement and fun are the significant \nmotivators in the likelihood of the Canadian students choosing to visit China; and \npersonal interests may be a factor that significantly influences an individual's destination \nimage and travel motivation. Raising awareness and increasing familiarity through \npromotion are suggested as methods to create a positive destination image of China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".