The role of knowledge management in delivering the organization to the state of performance excellence: Mediating role of technological vigilance
Bibliographic record
Abstract
Current study aims to examine the relationship between knowledge management and performance excellence through the effect of knowledge management (KM) on technological vigilance within pharmaceutical manufacturing organizations in Jordan. Depending on quantitative approach, 270 questionnaires were distributed among employees and leaders of 49 pharmaceutical manufacturing organizations in Jordan to examine the effect of KM and its chosen variables, Organizational Culture, Leadership, Organizational Processes, Organization's Politics and Strategies, on delivering organization to performance excellence through the mediating role of technological vigilance. Results of the study indicate a positive influence of KM on delivering organization to excellence; this influence was attributed to mainly leadership as the basic driver. The results also indicate that technological vigilance mediates the relationship between knowledge management and delivering the organization to the state of performance excellence. Moreover, the study results indicate that KM enhances the organization's ability to retain and improve organizational performance and helps deliver it to excellence based on experience and knowledge. Knowledge management allows the institution to define the required knowledge, document, develop, share, apply and evaluate this knowledge and it is an approach for excellent performance. Study recommends increasing efforts towards providing the requirements of applying knowledge management, and the need for organizational structures to be horizontal and flexible, and there must be conscious and eager leadership to apply knowledge management and encourage the exchange of information, and the need for organizational culture to be conducive to the application of knowledge.
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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 | 0.017 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".