The effect of process quality improvement and lean practices on competitive performance in the UAE healthcare industry
Bibliographic record
Abstract
The purpose of this paper is to empirically examine a theoretical model which identifies the effect of process quality improvement and lean practices on competitive performance in the healthcare industry in the United Arab Emirates (UAE). The study uses a quantitative research technique with convenient cluster sampling through applying a descriptive, causal and analytical research design. A valid sample size of 270 respondents is used for analysis by linear regression and hypothesis testing using SPSS. The results indicate a direct significant relationship between process quality improvement and a direct significant relationship between lean practices and competitive performance. Given the substantial resources spent and efforts to improve healthcare quality, the absence of studies demonstrating the impact of quality-related operations and activities would require future research. Hospitals in one city in UAE have access that limits the research results. It is recommended that future research assess more variables dependency on the healthcare sector that can affect increase competitiveness. The research provides some managerial implications that could help hospital managerial members improve their healthcare delivery system's leanness and quality improvement to get competitiveness. Quality improvement and lean practices help increase competitive performance and can assist the healthcare sector in providing better service using these practices that have never been considered in research.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".