Firm size, business sector and quality of accounting information systems: Evidence from Vietnam
Bibliographic record
Abstract
This paper increases the understanding of the quality of accounting information systems in emerging economies, using data from Vietnam as an example. The quality of accounting information systems is a measure combining system quality and information quality. It is important to figure out what aspects of this measure are critical for business to enhance firm performance. This research investigates the level of accounting information system quality and examines the relationships between system quality and firm size, information quality and firm size, system quality and business sector as well as information quality and business sector, respectively. We employed descriptive statistics to illustrate the quality of accounting information systems and One-Way ANOVA to test four hypotheses. The descriptive statistics results demonstrate the level of system quality and information quality, in general, is not excellent. And there are differences in system quality and information quality in each business sector groups and firm size groups. The test result highlights a relationship between system quality and firm size but there are no links between information quality and firm size, system quality and business sector, and information quality and business sector. In conclusion, the paper extends the literature of the quality of accounting information systems and assists state agencies and executives to have a framework to improve the business performance as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| 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 | 0.008 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".