Bibliographic record
Abstract
Income smoothing is an act of accounting engineering by exploiting gaps in accounting standards. This study aims to determine the motives for income-shifting management. Based on agency theory, this study tested three hypotheses on two income-smoothing objects: operating income and net income. This research is a quantitative study with data in Indonesian public manufacturing companies’ financial statements dated December 31, 2009 - 2018 obtained from the Indonesian Capital Market Directory. Hypothesis testing uses a binary logistic regression approach. The practice of income smoothing exists in manufacturing companies in Indonesia. Management shift income with object engineering is gross profit by 30.2% and net income by 21.7%. Hypothesis testing confirms that the commissionaire board size is not a mechanism of supervision effectiveness. The independent commissioners’ size was able to suppress income smoothing in manufacturing companies. Audit tenure has a negative effect on income smoothing. The audit period is directly proportional to the auditor’s ability to limit income smoothing. These results contribute to the formulation of policies, especially in improving the quality of corporate governance. Even the public and investors can understand the indications of income smoothing practices. New evidence suggests that income smoothing is less likely to be desired by corporate governance mechanisms. The motive for income smoothing is considered opportunistic. Audit tenure improves the quality of oversight of accounting engineering actions, contrary to the previous opinion that tenure reduces auditor independence.
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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 | 0.003 | 0.012 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".