The Impact of Family Firms on Financial Reporting Quality: The Mediating Role of High-Quality Auditors
Bibliographic record
Abstract
This study empirically examines how Big4 audit firms mediate the relationship between family-controlled enterprises and their earnings management practices. Analyzing a dataset of 61 non-financial family-listed companies listed on the Indonesia Stock Exchange from 2017 through 2019 reveals that family-controlled businesses and Big4 auditors are associated with lower earnings management, resulting in improved financial reporting quality. The study also shows that family-owned enterprises are more inclined to hire a higher-quality auditing firm for their financial statement assessments. Moreover, our results suggest that Big4 auditors partially mediate the relationship between family businesses and their earnings management practices. The additional tests conducted in this study highlight the significant role of family-run firms and Big4 auditors in curbing earnings management, primarily when corporate management is prone to decrease reported earnings. Robustness tests validate the reliability of the conclusions drawn from the primary findings. Our study shows that family managers align their goals with the firm and shareholders, enhancing company financial reporting integrity. Our finding also emphasizes the crucial role of Big4 auditors in minimizing intra-family agency conflicts in family firms, promoting transparency, and aligning family managers’ interests with external stakeholders.
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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".