The impact of cost management accounting techniques on supply chain performance using the balanced scorecard approach: A case of logistics companies in Vietnam
Bibliographic record
Abstract
This study aims to discover the relationship between CMA Techniques and Supply Chain Performance at logistics enterprises in Vietnam. Based on qualitative research methods combined with quantitative research, the survey subjects were 300 accounting staff and managers of large logistics enterprises in Vietnam. Qualitative research is used to summarize the business situation of the companies, build research models and hypotheses based on literature review and get opinions from the managers and accountants about the quality of the questionnaire. The study has developed a scale to evaluate the Supply Chain Performance of the businesses according to the Balanced Scorecard model. Quantitative research is used to measure and explain the relationship between factors in the model using SPSS and AMOS tools. The results of the linear structural model show that CMA Techniques have a positive impact on the Supply Chain Performance of logistics businesses in Vietnam. This study also demonstrates that CMA Techniques have a positive impact on Financial Performance, Internal Business Process, Learning and Growth, Customer Perspectives of the businesses. Based on these findings, the author proposes recommendations for governments and the companies to improve the effectiveness of CMA Techniques, thereby improving their Supply Chain Performance.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".