Evaluating the Service Quality of the Hotel Establishments in Sports Tourism With Regard to the Athletes
Bibliographic record
Abstract
The purpose of this study was to evaluate the service quality of the hotels which are provided sport tourism by athletes according to some variables.The research was conducted with cross-sectional research method as one of the general survey models and relational screening model. Target group of the study also constituted the sample group. This sample was formed of 389 athletes in total as 247 males and 142 females from team sports in Turkish Football, Volleyball, Basketball leagues in years 2016-2017 and individual sports.“Sport Tourism Service Quality Scale” (STSQS) developed by Osmanoğlu et al. (2017) was employed as data collecting tool in this study. The scale was formed of 28 items and 5 sub-dimensions as “Sport facilities, α=0.96”, “Staff, α=0.82”, “Entertainment, α=0.90”, “Hygiene, α=0.85” and “Room quality, α=0.80”. The data was analysed by applying one-way analysis of variance (ANOVA), Tukey’s Post-Hoc test and Pearson Correlation analysis. The level of significance for this study was decided as p<0.05.In conclusion with the study, service quality perception scores of the athletes were established to be high-level at room quality, hygiene and staff sub-dimensions and medium level at sport facilities and entertainment sub-dimensions. It was noted that service quality perception of the individual sports athletes is higher than the team sports athletes at staff and hygiene sub-dimensions (p<0.05). Regarding duration of stay, the mean of the ones that stay for 5 nights and above was observed to be significantly higher than the ones that stay one-two days and three-four days at sport facilities and staff sub-dimensions. Between the sport facilities, staff, hygiene and entertainment sub-dimensions of hotel classification and service quality, a significant distinction was seen in favor of the five star hotels (p<0,05).
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".