Determinants of profitability: evidence from construction companies listed on Vietnam Securities Market
Bibliographic record
Abstract
The profitability of businesses is influenced by many different factors such as financial structure, financial leverage, size and age of enterprises, business characteristics, etc.Therefore, the determination of the factors influencing on the trend of the profitability of enterprises is an essential and important basis for managers to provide useful solutions to improve performance measurement.This study was conducted based on data collected from 73 listed construction companies in Vietnam for the period 2008-2015 with 584 observations and using quantitative methods in combination with the FEM regression model through Hausman test with the help of Stata software 14.0.The research results show that: (1) The age of the company (AGE) and debt ratio (TD) negatively affect the profitability (2) Growth rate (GROW) and asset utilization performance (TURN) have positive impacts on profitability (3) Company size (SIZE) has a positive impact on profitability, and (4) The proportion of fixed assets in total assets (TANG) maintains an opposite effect on profitability although the effect is not clear.Based on the research results, the authors have provided a number of specific recommendations and solutions to improve the profitability of the construction companies listed on the Vietnam Stock Exchange.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".