Relationships Between Student Characteristics and Perception of the Quality of Tourism, Hospitality and Leisure Courses According to the SERVQUAL Scale
Bibliographic record
Abstract
This study sought to identify relationships between the characteristics of students and their perceptions of the quality of tourism, hospitality and leisure courses provided by the Federal Institute of Santa Catarina (FISC) at the Florianopolis-Mainland campus by using the SERVQUAL scale. The study’s methodological approach is classified as a quantitative, descriptive survey in which regression analysis was used to assess relationships between the respondents’ characteristics (independent variables) and perceived quality (dependent variables). The resulting data indicated that the respondents’ characteristics are more related to the perceived quality than to their expectation of it. Still, it was also observed that the perceived quality was statistically significantly related to age, including the variables ‘do not know/do not want to take another course at FISC’ and ‘intend to start a business’. These results will allow the managers to design strategies for maximisation of the quality of services on the basis of knowing that students who ‘do not know/do not want to take another course at FISC’, ‘choose the course in the field in which they already work’ and ‘choose the course intending to open a business’ have expectations and perceptions of the courses.
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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 | 0.005 |
| 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 | 0.000 |
| 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".