Factors Influencing Key Audit Matter Reporting in the Stock Exchange of Thailand: Empirical Evidence from 2016–2020 Data
Bibliographic record
Abstract
This study aims to respond to the new auditing standard on the information reporting of Key Audit Matters (KAMs) as a separate section in the auditor’s report, which will increase the transparency and quality of the report. It not only explores the current practice of KAM reporting among Thai listed companies but also seeks factors that influence KAM reporting in Thailand. This study explores the quantitative methodology through secondary data collected from the Thai Stock Exchange. This archival research explores 343 listed companies in the Thai Stock Exchange from 2016 to 2020. Descriptive statistics, a correlation matrix, and regression analysis are employed. The results suggest that the type of auditor (Big 4 or non-Big-4 audit firms), audit fee, audit independence, and industry have a direct positive impact on Key Audit Matter reporting at a 0.05 significance level. However, the evidence also suggests that the presence of females on the board, year, ROA (return on asset), risk, and size were not validated factors that have direct positive impacts on Key Audit Matter reporting at a 0.05 significance level.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.002 | 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".