The role of knowledge-oriented leadership in knowledge management and innovation
Bibliographic record
Abstract
Therefore, improving innovative performance is critical for creating com-petitive advantage. On the other hand, availability of information and knowledge can be defined as one the best ways to increase the innovation ability of organizations. Many theorists as well as practitioners emphasize on knowledge management as an enabler in enhancing organizational inno-vation. Hence, This study is carried out in the Fars governor in Iran during the year of 2017 to in-vestigate the relationship between the knowledge-based leadership and knowledge management and innovation performance. This study is descriptive / survey and the data collection is a cross-sectional and data questionnaire is used to collect the required data. Data analysis and hypotheses testing have indicated a significant relationship between knowledge-based leadership and knowledge management and innovation performance in Fars governor. The results also suggest a relationship between knowledge-based leadership and the knowledge management activities with a coefficient of 0.97. In addition, There is also a positive and meaningful relationship between knowledge management and innovation performance with a coefficient of 0.73 and between knowledge-based leadership and innovation performance with a coefficient of 0.73. The results al-so led to the existence of a relationship between knowledge based leadership, knowledge manage-ment practices and innovation performance with a coefficient of 0.7081.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".