The effect of lean and agile operations strategy on improving order-winners: Empirical evidence from the UAE food service industry
Bibliographic record
Abstract
This research aims to assess the impact of lean and agile operational strategies on improving order winners in the food service industry in the UAE. Research disclosed a few attributes with a dimensional review of lean and agile strategies that enhance strategic alignment in the food service industry of UAE to achieve the maximum benefits that have never been identified in research before. Data from 85 Sharjah-based food service companies were used for the analysis. A quantitative method with descriptive, causal and exploratory research design was used, along with convenient cluster sampling. A valid sample size of 255 respondents was used to assess the model through regression and ANOVA using SPSS. Research findings show a significant direct impact of lean strategies on order winners, and agile strategies significantly positively impact order winners. In contrast, both variables have a significant direct impact on order winners. This research is limited to assessing the impact of lean and agile strategies to achieve maximum order winners. Future research should consider a manufacturing industry to increase generalizability and a comprehensive focus on the lean and agile dimensional impact on competitive advantage. Customer loyalty and satisfaction lead a business toward order winners. An exemplary implementation of lean and agile strategies can translate into high business performance.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".