Audit Committee Characteristics and Quality of Financial Information: The Role of the Internal Information Environment and Political Connections
Bibliographic record
Abstract
This study explores the relationship between audit committee characteristics and accounting information quality by justifying the role of the internal information environment and political connections under the theocracy state of Iran with syncretic politics. Using panel data of 558 firms from the Tehran Stock Exchange (TSE) for 2011–2016, we rank firms using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy method for determination of the weight of evaluating indicators. The firms are positioned into high- to low-level political connections, and two proxies for audit committee characteristics are used: independence of audit committee and financial knowledge. Furthermore, three proxies are used for an internal information environment: earning announcement speed, the accuracy of earning forecasting and lack of financial restatements. Our findings show that there is a significant and positive relationship between the audit committee and financial information quality characteristics in high-level political connections, as well as between financial knowledge and financial information quality. Furthermore, the findings of this study suggest that the application of political economy theories could be appropriate for more inquiry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".