Understanding Canadian and US tourists: A self-concept based segmentation study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it