Impact of strategic agility on the financial performance of commercial banks in Jordan
Bibliographic record
Abstract
The Jordanian banking sector has been able to achieve remarkable growth rates at various levels. This sector is based on a robust infrastructure and is subject to the supervision of the Central Bank, which guarantees the likes of banks operating to financial reporting standards in order to maintain good financial soundness indicators. Therefore, this study aimed to examine the impact of strategic agility on the financial performance of commercial banks in Jordan. The study population was represented by senior managers. The purposeful sampling method was used to collect primary data from (188) respondents who constituted (81.74%) of the sent questionnaires. Structural Equation Modeling (SEM) was applied to test the study's hypotheses. The results showed that strategic agility had a positive impact on financial performance, as the greatest impact was strategic sensitivity, followed by resource fluidity, and finally leadership unity. This study contributed to the development of a logical framework supported by empirical evidence about the possibility of developing financial performance in dynamic environments. Hence, senior managers recommended focusing on making rational decisions derived from studying the reality of the work environment and adjusting organizational structures to become more flexible in response to the volatile business environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".