Identify factors affecting business efficiency of small and medium enterprises (SMEs): Evidence from Vietnam
Bibliographic record
Abstract
As operating the business in the economy, all enterprises are interested in the business efficiency.Business efficiency is not only a measure of organizational performance and business management but also it is a vital issue for businesses operations.That is why defining the factors affecting business performance in enterprises, especially small and medium-sized enterprises accounted for more than 80 percentage of the enterprises in Vietnam plays a very important role in helping business managers find solutions to improve the business efficiency.This paper is conducted to determine the factors affecting the business performance of these groups of businesses from the perspective of business managers and the business.This study is based on a survey from 100 business managers and 400 small and medium enterprises in Vietnam.Through the EFA analysis tool, we found some similarities in terms of the groups of factors affecting the business performance in small and medium enterprises in Vietnam between the perspective of business managers and perspective of the enterprises.Both the surveyed groups defined that there were three factor groups influencing the business efficiency: (1) group of institutional, policy and infrastructure factors, (2) group of factors associated with enterprises and (3) group of factors related to the environment outside the enterprise.
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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".