Enhancing innovation performance of small and medium enterprises in Malaysia
Bibliographic record
Abstract
Small medium enterprises (SMEs) hold 98.5% of businesses and serve as economy backbone in Malaysia. However, the global competitiveness of Malaysia in innovation has been declined recently. The declining innovation index has been reflected a low level of innovation in SMEs. This study serves as one of the pioneer studies conducted to foster the achievement of Malaysia Master Plan (2012-2020), focusing on a fresh approach to bring SMEs to the next level through innovation. The study aims to examine which innovation factors affect innovation performance, as there are relatively little empirical evidences in previous researches and very little innovative activities in SMEs Malaysia. This study uses quantitative research methodology, 300 sample sets have been collected from Malaysia SMEs and the data was analyzed by using Structural Equation Modelling (SEM). All proposed factors in this study (absorptive capacity, internal R&D collaboration and knowledge sharing) are significantly affect innovation performance, except technology transfer. The findings of this study provide theoretical contribution and practical contribution for small medium enterprise, stakeholder, academic institution, policy makers as well as a reference for government to help SME achieve higher 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".