The Influence of Allocating the Residual Value of MSMEs’ Cluster on the Growth of MSMEs and the Cluster Based on the Theory of Structural Hole
Bibliographic record
Abstract
The objectives of this research were to explicate the influence mechanism between MSMEs and MSMEs’ clusters; to explicate the generation mechanism of cluster residual value, and to determine whether there is a significant effect of structural hole on allocating the residual value of MSMEs’ cluster. The research was designed as quantitative research and used survey questionnaires to collect data from 475 entrepreneurs or senior managers of MSMEs. After passing the validity (KMO) and reliability (Cronbach’s Alpha) tests, the correlations between independent and dependent variables have been examined by Pearson Correlation. Then One-Way ANOVA was employed for further specifying the causal direction of correlation between variables. The findings of this research showed that there are positive correlations between cluster’s value and growth of MSMEs; between structural hole and allocation of cluster’s residual value; between structural hole and growth of cluster. In addition, structural hole as a moderating variable effect on the relationship between cluster’s value and growth of MSMEs significantly.
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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 | 0.004 | 0.030 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".