The Impacts of Selling Expense Structure on Enterprise Growth in Large Enterprises: A Study from Vietnam
Bibliographic record
Abstract
This study intends to examine the impact of selling expense structure on the business growth of 255 Vietnamese large-scale enterprises in three different industries (Consumer Staples, Industrials, and Manufacture) listed on the Vietnamese Stock Exchange over four years from 2015 to 2018. By using STATA software (StataCorp LLC, 4905 Lakeway Drive, College Station, Texas 77845-4512, USA), the research outcomes indicate that both labour expense and depreciation expense have a negative influence on revenue growth and firm size growth but positive influence on profit growth while materials and tools expenses negatively affect all three dependent variables. Furthermore, an increase in the proportion of outsourcing expenses and other selling expenses would result in a significant increase in revenue but a decline in the profit of these companies. From this research results, large-scale enterprises should consider changing the selling expense structure as they spend too much on outsourcing and other selling expenses (60%–70% total selling expense) but too little on labour, which plays an important role in upgrading the profitability of these enterprises.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".