Developing a Path Model of International Tourism Demand: A Case of Canada to USA
Bibliographic record
Abstract
The objective of this study was to develop an empirical path model for the international tourism demand. Through series regression analyses of time series data for 18 years, the determinants of tourism demand in a case of tourists flow from Canada to the USA were investigated. Six determinants (TPI, exchange rate, population, GDP, percentage of imports, and percentage of exports) and four demand measures (arrivals, receipts from Canada as intervening variables, total arrivals, and receipts from USA as dependent variables were employed to examine four different regression equations of the hypothesized model. The results indicated that the explained variances (R-square) of each model range from 99, 97, 90, and 92 (%) respectively (p < .001). TPI, exports, imports and exchange rate are identified as major determinants that have been generating tourism demand from Canada to the USA. Additionally, the results showed that arrivals and receipts from Canada have been affecting the total number of arrivals and receipts in the USA, meaning that Canada is one of the significant markets for the USA international tourism demand. Finally, this study suggested that the results of this study may help to efficiently develop and promote tourism policy and marketing programs for international tourism demand.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".