Using the Balanced Scorecard to Measure the Performance of Small and Medium- Sized Garment Enterprises in Vietnam
Bibliographic record
Abstract
Improving performance is always a strategic issue for any business operating in the market economy, as it is an important basis for the survival and development of the business. In order to evaluate the performance of enterprises, it is necessary to use financial and non-financial indicators. In models of appreciation of performance, the Balanced Scorecard (BSC) is one of the best model. Thus, this research sought to determine the application of BSC to measure the performance suitable for small and medium-sized (SMEs) garment enterprises in Vietnam. The research design was a survey conducted on a target population of the garment companies in Vietnam with a sample size of 238 garment SMEs. The study used questionnaires in data collection. In order to analyze the data, the research tested the reliability of the observation variable and performed exploratory factor analysis to examine the convergence of the observed variables in appling BSC to set up a rating system for garment SMEs. The study found that the indicators in the financial perspective for garment SMEs only include the traditional financial criteria taken from accounting books. On the other hand, in terms of internal processes, the research also adds the following criteria: Supplier-to-Supplier ratio, Supplier-to-Supplier Timeliness, Supplier Percentage Regularly supplied to the enterprise. These indicators are highly appreciated by managers of garment SMEs and in line with the production characteristics of garment SMEs in Vietnam.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".