Exploring the Association Between Knowledge Management and Innovation Capability in R&D Centers
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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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".