Exploring the Association Between Knowledge Management and Innovation Capability in R&D Centers
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
Businesses striving to survive in today's highly competitive market conditions are continuously trying to utilize innovation related strategies to sustain their position and competitiveness. Knowledge management, on the other hand, has been shown to have a significant influence on the innovation capability of the organization. Thus, the aim of this study is to examine the relationship between knowledge management practices and innovation capability in research and development (R&D) centers operating in Istanbul and Kocaeli / Turkiye through an empirical study. The data used in the study was collected from the managers of R&D centers using a web-based questionnaire, as well as face-to-face meetings. A complete census method was used as the sampling technique, and 220 R&D center managers in the region were contacted. Among the managers contacted, only 182 managers provided data and were included in the study. Multiple hierarchical regression analysis was used to analyze the data obtained. As a result of the analyses, it is found that the knowledge acquisition dimension has a significant positive relationship with the learning capability, production capability, marketing capability and strategic planning capability. In addition, the results revealed that storing and sharing knowledge have significant and positive relationship with production capability, and transforming knowledge has a significant and positive relationship with both marketing and organizational capability. In particular, it is concluded that knowledge acquisition and sharing are important in terms of learning, production, marketing and strategic planning dimensions of innovation capability specifically in R&D centers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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