Determinants of business performance of the firms: A case of the construction listed enterprises in Vietnam Stock Market
Bibliographic record
Abstract
This research aims to investigate the determinants of business performance in Vietnam small and medium sized firms. The study employs a sample of 100 construction-listed enterprises in the Vietnam Stock Market. The author collects data on time series of October 2020 based on the financial statement and annual reports of the Vietnam construction listed companies. The survey data are collected by email and internet sources. This study also identifies the factors that affect the business performance enterprises in Vietnam. The author processed data via STATA 14.0 and SPSS 20.0 software. The research results indicate that (1) government support policies, (2) education level of enterprises owner, (3) enterprises scale, (4) society relationships of enterprises and (5) revenue growth rate affect the business performance of construction- listed enterprises in Vietnam. In addition, the government support policies, education level of enterprises owner, enterprises scale, society relationships of enterprises and revenue growth rate have positive impact on business performance of the Vietnam Construction Listed Companies. In which, education level of enterprises owner and government support policies have mostly positive impacts on business performance. The findings of this study also suggest that the education level of enterprise owners is the highest impact to business performance of the construction- listed firm 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".