Exploring the Nexus of Dividend Policy, Third-Party Funds, Financial Performance, and Company Value: The Role of IT Innovation as a Moderator
Bibliographic record
Abstract
This research investigates the connection between dividend policy, third-party funds, financial performance, and company value, with a focus on IT Innovation as a moderating factor. This research was conducted using a quantitative approach, utilizing Commercial Banks listed on the Indonesia Stock Exchange categorized as BUKU 4 Banks during the period of 2016–2022. This study employed Partial Least Squares (PLS) analysis with WarpPLS 6.0 software as the tool for data analysis. This research concludes that dividend policy does not significantly impact financial performance and company value, while third-party funds have a significant positive effect on both financial performance and company value. Although dividend policy does not directly affect company value, its impact may occur through the mediation of financial performance. Additionally, IT Innovation serves as a moderating factor that strengthens the positive relationship between third-party funds and financial performance towards company value. The novelty of this research lies in the development of a more comprehensive model or concept regarding dividend policy, third-party funds, financial performance as a mediating variable, and company value when considering IT Innovation as a moderating variable.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".