Management Accounting Information in Vietnamese Small and Medium Sized Enterprises
Bibliographic record
Abstract
The small and medium-sized enterprises (SMEs) in Vietnam play an increasingly important role in the economy by the amount (representing 97.7% of Vietnam firms), contribute economic development and create more employment opportunities. However, because of economic crisis, financial downturn, unhealthy competitions, free trade agreements and others, the number of SMEs recently is downsizing in firm size, human resources and more and more SMEs go bankruptcy in the context of Vietnam. This situation may be due to the enterprise use ineffective management accounting tools.This article reviews and assesses the creation and use of management accounting information which has an important part to play with respect to planning, decision-making, monitoring and controlling of the activities of SMEs in Vietnam. Data collected from a posted survey of five enterprises with twenty two interviews of directors, chief accountants and management accountants. The results show that management accounting information has not really been interested from managers and accountants. Management accounting information is weak in quantity and poor in quality; administrators are operating firms primarily based on personal experiences. Therefore, management accounting information has not been promoted in the management, monitoring and decision making of SMEs in Vietnam. The addition of management accounting knowledge for managers and accountants is necessary for development of SMEs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".