Innovation Capability and Sustainable Performance Through Knowledge Management in Small and Medium-sized Enterprises
Bibliographic record
Abstract
Knowledge management (KM) across organizations is based on the realization that integrating knowledge or more broadly intellectual capital (IC) both inside and outside the enterprise helps enhance the innovation capability (INC) and sustainable performance (SUP) of enterprises. The purpose of this study is to examine the effects of internal intellectual capital (IIC), external intellectual capital (EIC), and social capital (SOC) on INC and SUP through KM in SMEs. The survey questionnaire was sent to managers working in Vietnamese SMEs. SPSS and AMOS software were used for data analysis. The results indicate that IIC, EIC, and SOC have positive impacts on the INC and SUP of SMEs through KM activities. These findings hope to be useful for scholars and especially SME owners to understand more thoroughly the relationships between IC, SOC, and KM, as well as the influence of these relationships on INC and SUP, while providing new insight and useful suggestions for owners of SMEs in acquiring and exploiting knowledge from outside to fill knowledge gaps within the enterprise.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".