The Effect Of Green Intellectual Capital, CEO Characteristic, Free Cash Flow On Prudence Moderated By Enviromental Performance
Bibliographic record
Abstract
The rapid development of many industries in the world has resulted in environmental damage due to excessive use and exploitation of natural resources. As a result, there is a decline in environmental quality resulting in global warming, ozone depletion, pollution, and acid rain. The impact of environmental conventions in the world Montreal Convention, Kyoto Protocol, Ban on the Use of Certain Hazardous Materials and increasing consumer environmentalism can change the context of competition in industries around the world. This study aims to examine the effect of Green Intellectual Capital, CEO Characteristic, Free Cash Flow on Prudence moderated by Environmental Performance. This study took the research population in the energy sector companies listed on the Indonesia Stock Exchange for the period 2019-2023. The type of data used in this study is secondary data in the form of financial reports of companies that are used as samples. The research method used in this study is a quantitative research method. The sample was selected using the purposive sampling method. For hypothesis testing, this study uses multiple linear regression analysis. Based on the results of this study, it shows that Green Intellectual Capital, Busy Director, and Free Cash Flow have an effect on prudence but CEO Tenure has no effect on Prudence. Environmental Performance strengthens the influence of Green Intellectual Capital, Busy Director, and Free Cash Flow on prudence and Environmental Performance strengthens the influence of CEO Tenure on prudence.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".