The role of profitability in moderating the factors affecting transfer pricing
Bibliographic record
Abstract
This study aims to analyze the influence of tax expense, bonus mechanism, and incentive tunneling on transfer pricing with profitability as moderating. The population is mining companies listed on the Indonesia Stock Exchange in 2016-2019. The sample selection used a purposive sampling technique and obtained 45 analysis units. Data analysis method used moderated regression analysis (MRA). The study showed that tunneling incentive had a significant positive on transfer pricing decision. Tax expense and bonus mechanism had no significant effect on transfer pricing decisions. Profitability strengthened the effect of tax expense on transfer pricing decisions. However, profitability was unable to moderate the influence of bonus mechanism and tunneling incentive towards on transfer pricing decisions. The conclusions are that shareholders the majority of a controlled by the foreign shown to improve the transfer pricing decision. An increase in profitability followed the transfer pricing decision high to reduce tax expense in the company.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".