Knowledge Management Capabilities and Its Impact on Product Innovation in SME’s
Bibliographic record
Abstract
Purpose–This study aims to explore the impact of Knowledge Management Capabilities (KMC), captured by six dimensions, on product innovation in Information Technology (IT) Small and Medium Enterprises (SMEs). Design/methodology/approach– Survey data were collected from 300 managers in (45) IT SMEs located in Jordan. SPSS was employed to analyze the data. Findings–Two key findings emerged: first, among the six dimensions of KMC, only acquisition, sharing, application, and protection were found to be positively associated with products innovation, whereas knowledge creation and storing were not. Second, no significant differences were identified in employees' answers due to company size. Research limitations/implications – This study was restricted to small and medium size enterprises, and therefore, the findings of this study may not be generalized to large enterprises. Additionally, this study was confined to the Jordanian IT sector only, thus, the findings need to be interpreted with cautious as they may not be generalized to other sectors. Originality/value – this study advances our understanding of the nature of the relationship between knowledge and innovation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".