The impact of knowledge-oriented leadership on innovation performance with e-based knowledge management system as mediating variable
Bibliographic record
Abstract
This study investigates how knowledge-oriented leadership impacts on innovation performance both directly and indirectly mediated electronic based knowledge management systems (e-based KMS). Where, electronic knowledge management system consists of knowledge management infrastructures, and knowledge management processes. Primary data was obtained through a questionnaire given to 110 managers or directors of the manufacturing company as respondents. In addition, data were also obtained through observation techniques, and interviews with respondents. Both descriptive statistical analysis, and structural equation modelling (SEM) was used as an analysis method. The findings suggest that knowledge-oriented leadership has a positive, direct, and significant influence on knowledge management infrastructures as well as knowledge management processes. However, knowledge-oriented leadership has no direct influence on innovation performance. The findings also indicate that electronic knowledge management systems that consist of knowledge management infrastructures, and knowledge management processes have a direct, positive, and significant influence on innovation performance. This study suggested that there are two key pathways for businesses to improve their innovation performance i.e.: enhancing the technological, cultural, and structural infrastructure of the company, and enhancing knowledge creation, use, and utilization.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".