Strengthening SMEs’ Innovation Through HRM and Organizational Learning
Bibliographic record
Abstract
This study is to investigate the role of HRM, OL, KM capability in increasing innovation of SMEs in Central Java, Indonesia. This study used 128 SMEs in the fashion creative industry in Jepara, Indonesia, and Semarang, Indonesia. Besides, it used a questionnaire distributed to the owners of SMEs. For data analysis, this study used descriptive statistics and SEM with PLS program. This study found that HRM do not affect KM but affects innovation. Organizational Learning (OL) is also able to strengthen KM and innovation. KM capability also significantly affects innovation. The capability of SMEs to adapt and explore knowledge, innovate, think creatively, which is strong and conducts exploration and exploitation of OL is very important in responding to changes in the very competitive environment. The challenges of SMEs in the industrial revolution 4.0 include economic globalization and the digital economy. SMEs need to be more intensive in increasing their intellectual capital (IC), knowledge management (KM), and dynamic abilities to improve performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".