The Intervening Effect of the Dividend Policy on Financial Performance and Firm Value in Large Indonesian Firms
Bibliographic record
Abstract
This study aims to examine the relationship between financial performance with firm value with dividend policy as an intervening variable in an emerging market, Indonesia. The samples in this study are large firms listed on the Indonesia Stock Exchange (IDX). The sampling method uses a purposive sampling technique according to research criteria, especially members of the LQ45 index. The study used the data analysis by using multiple regression analysis, path analysis, and Sobel test to find the direct, indirect, and intervening effect and significance in this study. The results indicated that profitability and activity have a positive effect, leverage has a negative effect, but liquidity has no effect on the value of the firms. The subsequent analysis shows that profitability and leverage do not affect dividend policy, liquidity has a negative effect, while activity has a positive effect, significantly. Dividend policy has a positive effect on firm value. Liquidity and leverage do not affect the firm value, but profitability and activity effect positively on the firm value through intervening dividend policy. Conclusions: in general, financial performance indicates an influence on firm value and less effect on dividend policy. As an intervening variable, dividend policy weakens the effect of financial performance on firm value.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".