Analysis the mediating role of knowledge sharing and innovation value chain on the company's sustainable competitive advantage
Bibliographic record
Abstract
In an era where knowledge has become one of the most valuable assets, the ability to effectively gather, store, manage, and utilize knowledge is critical. Given the ever-changing market needs, the sustainable competitive advantage of a company is crucial. The aim of this research is to analyze the role of knowledge management capabilities in sustainable competitive advantage and to assess the effectiveness of the impact provided by knowledge sharing and the innovation value chain in enhancing a company's competitive advantage. The research method employed is quantitative research using a questionnaire with a 1 – 7-point Likert Scale for data collection. This study was conducted on employees, staff, and managers of a state-owned bank in Indonesia, with a sample size of 206 respondents. Data analysis will be performed using Structural Equation Modeling (SEM) technique with SmartPLS 4 software. The results of hypothesis testing indicate that the influence of knowledge management capabilities on knowledge sharing, and the innovation value chain is statistically significant. Furthermore, there is a statistically significant influence between knowledge sharing and sustainable competitive advantage, and knowledge sharing mediates the relationship between knowledge management capabilities and sustainable competitive advantage. The influence of the innovation value chain on sustainable competitive advantage is also significant, however, there is no significant evidence found that the innovation value chain mediates the relationship between knowledge management capabilities and sustainable competitive advantage.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".