The impact of key audit matter characteristics on financial statement understandability and investor decision-making: An empirical stud
Bibliographic record
Abstract
This study investigates how key audit matter (KAM) characteristics influence financial statement understandability and subsequent investor decision making. Using Structural Equation Modeling-Variance Based (SEM-VB) through Partial Least Squares (PLS), the analysis was conducted on a diverse global sample of investors from Europe, North America, Asia-Pacific, Africa, and Latin America. The results indicate that KAM accuracy, reliability, audit quality, and financial reporting quality significantly enhance perceived financial statement understandability, which in turn positively impacts investor judgments. The mediating role of understandability is confirmed, emphasizing its crucial influence on investors’ decisions. Additionally, an importance-performance map analysis (IPMA) identified KAM reliability and accuracy as the most critical factors. This study contributes to the theoretical and managerial understanding of audit practices and investor behavior.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".