Determinants on Small Scale Business: An Empirical Evidence from Indonesia
Bibliographic record
Abstract
In 1997-1998, the resilience of small and medium enterprises (SMEs) was tested when the monetary recession paralysed Indonesia. At that time, only SMEs were detected as shining and the most prominent from other sectors. This study is oriented to investigate the effect of the quality of human resources (HR), capital, and business length on turnover, labor cost, market share, and profit. The study design is offline survey, where primary data is collected from a sample that invites 285 respondents in three zones of Indonesia. Sources of information focused on and addressed to three SME scales covering the fields of trade, industry, and services. Then, the data is processed, filtered, and set using the structural equation model (SEM). The findings confirm that the HR quality and capital drives an increase in turnover, labor cost, market share, and profit. At one point, the business length actually only stimulated turnover, labor cost, and market share, but did not generate significant profits. But, significant of labor cost, market share, and profit followed the increase in turnover. Similarly, between labor cost to market share and profit, where the results are significant. The market share affects profit. It is important for a country to realize that disruptions in financial access, HR capabilities, and experience attributes trigger the inhibition of domestic market performance. These three alternatives give birth to strong SMEs.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".