Relationship Between Job Satisfaction and Business Excellence: Empirical Evidence from Hospital Nursing Departments
Bibliographic record
Abstract
Business excellence is important in terms of encouraging successful quality implementations, disseminating the result of such implementations to society, making quality culture widespread, creating a basis for comparison of quality implementations, and directing the quality implementer to continuous improvement. Excellence models affect performance and help organizations achieve organizational excellence. Furthermore, employee satisfaction is another concern of organizational excellence. The measurement of job satisfaction has become an important issue in TQM. In this respect, the extent to which employees are satisfied with what they are responsible for may directly influence the level of customer satisfaction with their services and products. The main purpose of the study is to determine the relationship between business excellence and job satisfaction. In order to reach this goal, a survey that contains Job Descriptive Index with 5 factors and EFQM Criteria with 6 factors is applied to different nursing departments of two research hospitals. Both hospitals are in the business excellence process. Data obtained in the study has been analyzed at the base of multivariate data analysis and the results show that the canonical correlation between job satisfaction and business excellence model is significant. Theoretical and practical implications of the findings are also discussed in the paper.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".