The role of knowledge-oriented leadership in knowledge management and innovation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it