The Impact of Internal Environment Factors in Achieving Strategic Agility During COVID- 19 Pandemic at Jordanian Commercial Banks: The Moderating Role of Information Technology
Bibliographic record
Abstract
To remain competitive in today's uncertain business environment, banks must develop capabilities that enable them to adapt and respond quickly to market changes.Therefore, this study aims to examine the impact of internal environmental factors on achieving strategic agility through the moderating role of information technology at the Jordanian Commercial Banks.Out of the 13 banks, 10 took part in the survey.Internal environmental factors being investigated include agile human resources, organizational structure, and organizational culture.The 240 middle and first-line managers who worked at the headquarters of the 10 banks made up the sampling unit.To get the information and data needed, 240 questionnaires were sent out, and 203 of them could be used for statistical analysis.The results indicate a statistically significant impact of internal environmental factors in achieving strategic agility.The findings of the moderation hypothesis also reveal that information technology as a moderator has improved the impact of internal environmental factors in achieving banks' strategic agility by 0.04.The results show that the agility of human resources has the highest impact in achieving banks' strategic agility.Consequently, it was recommended to enhance the skills and competencies of the banks' staff and to equip them with the needed training courses to be able to adapt to change successfully.
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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 | 0.007 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".