The Impact of Using the Continuous Improvement Technique (KAIZEN) on the Human Resources Performance in Five-Star Hotels in Greater Cairo
Bibliographic record
Abstract
This research aims to measure The Impact of using the continuous improvement technique (KAIZEN) on the human resources performance in five-star hotels in Greater Cairo. To achieve this objective, questionnaire was developed and distributed on a random sample of employees in five-star hotels in all departments. The number of valid form questionnaires for statistical analysis was 396 (81.6%) valid, before the start of the training program, and after the end of the training, it was a period of six months for the possibility of using kaizen by the study sample hotels, then 396 questionnaires were distributed again, and the number of questionnaires was valid 396 to see the difference in results before and after kaizen training. The general result Kaizen staff experiences and practices before the kaizen training, , the average value was 1.8 and after the training it became a greater value, which is 3.42. the general recommendation Kaizen Technique, big achievements come from small and incremental changes. Eliminate obstacles in operation that slow down order completion or cash collection. Find ways to provide customers with more value and a better accommodation experience with greater profit, because quality plus speed equals lower cost.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".