Organizational Agility: The Key to Improve Organizational Performance
Bibliographic record
Abstract
Purpose: The purpose of this study is to highlight the significant role of Organizational Agility (OA) in improving Organizational Performance (OP) at the pharmaceutical industry in Egypt. Research Design/Methodology: To assess positive OA, refer to (OA Questionnaire, Jaworski, & Kohli 1993), and OP (OP Questionnaire & Darroch, 2003; Pathirage et al., 2007; and Chen & Mohamed, 2008). The data was collected from 310 employees. Out of the 356 questionnaires that were distributed, 310 usable questionnaires were returned, a response rate of 87%. Multiple Regression Analysis (MRA) was used to confirm the research hypotheses. Findings: The research has found that there is significant relationship between OA and OP. The finding reveals that OA affects OP. Accordingly, the study provided a set of recommendations including the necessity to pay more attention to OA as a key source for improving OP. Practical implications: This research contributes to boosting scientific research, particularly in terms of testing the model content, as well as studying the study variables and the factors affecting them. In addition, this research pointed to the need for organizations to practice OA in order to improve OP. Originality/value: This research dealt with OA in terms of its concept and dimensions, in addition to dealing with the role of OA in improving OP at the pharmaceutical industry in Egypt.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".