Understanding Canadian and US tourists: A self-concept based segmentation study
Bibliographic record
Abstract
This study aims to identify the distinctive market segments based on tourists’ self-concept, gain a better understanding of U.S. and Canadian tourists’ travel patterns, and provide implications that are beneficial to destination marketing organizations (DMOs). This study advances the knowledge of self-concept in the tourism context by validating its measurement and employing it as a segmentation base. This study used 2 percent of cases (N=1,012) of secondary data collected by an Ontario government agency, and a factor-cluster approach for analysis. Principal component analysis was utilized to identify specific characteristics of self-concept items and the results yielded three selves (extravert self, explorative self, and depressive self). Then, the study segmented U.S. and Canadian tourists by three self-concept factors and obtained four distinctive segments: Energetic Segment (ENT), Adventurous Segment (ADT), Conservative Segment (COT), and Escaping Segment (EST). ENT tourists are characterized as active, inquisitive and confident with a medium level of perceived value, satisfaction, and recommendation. ADT represents tourists who are older, open-minded, and optimistic with the highest level of perceived value, satisfaction, and recommendation. COT is relatively passive and had the lowest level of perceived value, satisfaction, and recommendation. EST is a group of nervous and stressful young female tourists who had a low level of perceived value and a medium degree of satisfaction and recommendation. This paper concludes with appropriate advertising and promotional strategies for the different segments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".