Strategic agility and its impact on competitive capabilities in healthcare industry
Bibliographic record
Abstract
This paper identifies the degree of strategic agility and its relationship with the competitive advantage in the private hospitals in Jordan. To achieve this goal, a special questionnaire was developed for a sample of managers in hospitals. The study distributed 208 questionnaires to 27 hospitals, and managed to collect 95% of them, properly. The target group was mainly hospital directors, deputy directors, district directors and department heads. The researcher presented a set of recommendations that could be considered necessary to achieve a level of competitive advantage in all fields of hospital work. In the light of the problem of research and its questions and the review of relevant studies, the current research sought to test the validity of the hypotheses of the study. The results of the statistical analysis revealed that there is a statistically significant relationship between the extent of the Agility exercise and the competitive advantage of private hospitals in Jordan (α=0.05). The results also showed that there was a statistically significant relationship between the strategic sensitivity and competitive advantage of the private hospitals in Jordan (α = 0.05). However, the results of the hypothesis did not differ from the second sub-hypothesis, where there was a statistically significant relationship between the substantial and competitive advantages of private hospitals in Jordan (α = 0.05). Finally, the statistical analysis found that there is a statistically significant relationship between technology and competitive advantage in private hospitals in Jordan (α = 0.05).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".