A study of the degree of customer satisfaction with hotel services during COVID
Bibliographic record
Abstract
The purpose of the article is to develop proposals to increase the degree of satisfaction of hotel services consumers during COVID among residents of Kazakhstan and Russia. The article examines the issues of service, service, cost and quality of services of hotel enterprises, including measures to counteract coronavirus infection. Marketing research was conducted in the third quarter of 2021, the methodology for determining the degree of satisfaction consisted of six stages. The research results have shown that, despite the existing mobility restrictions in the studied countries, the economically active population feels the need for attractive emotions, rest, and a change of scenery. The survey data showed that the epidemiological situation in the examined countries impacted tourist trips over the past year and a half. The respondents noted the need to comply with health safety measures. The choice of recreation and accommodation facilities was influenced by the cost of PCR analysis, especially for families traveling with children, and the availability of a vaccination passport. Also, in the conditions of COVID, the choice of consumers was made in favor of personal vehicles. This period has increased the demand for domestic tourism facilities. However, most previous consumers’ requirements for services have remained the same – for quality of service, cost, and friendly attitude. The respondents attributed to the key factors: hotel location, clean rooms, and quality of food. 85% of respondents are satisfied with the hotel services and measures of safe stay. Conclusions and recommendations based on the study findings can be applied by the enterprises providing hotel and catering services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".