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Record W4388923688 · doi:10.5539/jel.v12n6p189

Relationships Between Student Characteristics and Perception of the Quality of Tourism, Hospitality and Leisure Courses According to the SERVQUAL Scale

2023· article· en· W4388923688 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Education and Learning · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSERVQUALHospitalityTourismPsychologyMarketingQuality (philosophy)Hospitality management studiesMedical educationService qualityBusinessService (business)MedicineGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.168
GPT teacher head0.452
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it