Transaction cost analysis of corporate sustainability: The influence of profit management, audit committee, and women board directors
Bibliographic record
Abstract
The main criterion of a company is its financial performance. The purpose of this study is to examine the impact of Profit Management, Audit Committee, and Female Director Characteristics on firm sustainability as measured by financial performance at companies in the real estate construction sub-sector listed on the Indonesia Stock Exchange. Profit Management, Audit Committee, and Female Director Characteristics are independent factors, while company sustainability is dependent. Financial performance is the control variable. Discretionary Terms Net Profit - Cash and Cash Equivalents / Total Assets t-1) is used to measure earnings management, while ROA (Return on Assets) is used to measure financial performance. The data used in this study are secondary. The subjects of this study include 17 companies in the real estate construction sector listed on the Indonesia Stock Exchange. Purposive sampling was used to collect data for this study and data were obtained from eight companies. Classical hypothesis testing, multiple linear regression testing, coefficient of determination testing, path analysis and hypothesis testing are the analytical methods used. Based on the results, it can be concluded that earnings management has a positive and significant impact on financial performance; Audit committee has a positive and significant impact on financial performance; Characteristics of female directors have a positive and significant impact on financial performance; financial performance has a positive and significant impact on corporate sustainability; and financial performance can mediate the relationship between financial performance and corporate sustainability. Accordingly, strengthening earnings management, audit committees and the special role of female directors must be considered to improve a company's financial performance. Earnings management, audit committees and the characteristics of good female directors can then improve corporate sustainability. It will be higher if supported by strong financial performance. Earnings management, audit committees and female director characteristics can be optimized if supported by good financial performance.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".