SERVQUAL: Can It Be Used to Differentiate Guest’s Perception of Service Quality of 3 Star from a 4 Star Hotel
Bibliographic record
Abstract
A 3-star and 4-star rated hotel, what do they mean? Are there significant perceived differences between the ratings? This research work investigates on what customers think of the quality of service of 3-Star and 4-Star hotel using SERVQUAL measures. In spite of the criticality of service quality, there are still misunderstanding about the perception of quality of service between the hotel and their customer, where research has shown that many service organizations develop their own perception of customer needs. The respondents for this study were the customers of the 3 and 4 stars hotels. Data were collected through a self-administered survey SERVQUAL questionnaire distributed to the respondents based on convenient random sampling method. Data were then analyzed statistically using the linear regression and independent t-Test from the SPSS package to determine how service quality is related to hotel guest’s satisfaction, and the differences between 3-Star and 4-Star hotel respectively. Practical business strategy implications of this study are highlighted.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".