Impact of Knowledge Sharing and Leakage on Innovative Performance
Bibliographic record
Abstract
Organizations share knowledge in order to achieve its strategic objectives and enhance innovative performance, most important business process and critical knowledge are subjected to unwanted leakage. This study focused on the aspects and concerns associated with knowledge sharing within and outside organizations. To achieve this objective, the study construct, validate, and test the structure equation modeling for knowledge sharing, knowledge leakage, and innovation performance. A survey was designed and the data were collected in 2015 from managers and owners of 600 industrial companies in KSA. The total responses were 276. Confirmatory factor analysis was used to validate the constructs, then subjected to structure equation modeling to test hypothesis. Model fit parameters indicated normal fit and suitability as the research model. Finding indicated that knowledge leakage negatively mediate the positive impact of knowledge sharing on innovation performance, and some of leakage may be beneficial and have a positive impact, especially when sharing knowledge with customers and competitors in order to develop market innovation. Theoretical implication supplement few empirical studies that shown knowledge leakage as mediator leads to minimize positive results of knowledge sharing. Since KSA recently began the transition to knowledge economy, where much of knowledge sharing occurs dramatically, the study provides analytical framework for Managerial and practical implication about important aspects of knowledge sharing, the method and measures proposed by this study allow organizations to map, formalize and measure their knowledge activities, model can be applied in corporate level to identify the specific impact among organizational units and company's networks.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".