Measuring Customers' Perceived Service Quality in Hotel Industry
Bibliographic record
Abstract
This research attempts to study customer's perceived service quality in the hotel industry. This paper aims to discover what customers think of the quality of service as can be found in the hotel industry by looking into factors influential on this perception such as personal service, technological innovations and quality of food served. The method employed to gather the research resources was adopted from SERVQUAL which is a popular method in measuring perceived service quality. The descriptive and inferential methods were also used in testing and analysing the hypotheses. Data were analysed by using the SPSS package. The research findings indicated that generally, customers were dissatisfied with the service quality provided by the hotel management. From the research, it was also discovered that personal services technology innovation and quality of food served were vital in improving customers' outlook on the service quality. Therefore, the hotelier should try to meet or exceed the customers' expectations, in order to ensure the customers are satisfied. It is very important for the hotelier to take an effort in comprehending and understanding customers' expectations in order to deliver good service, in which if the perceived service equal or exceeded the expected service, they perceived that there is a quality in the service.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".