Do tax disputes affect firm value?
Bibliographic record
Abstract
This study examines the effect of tax disputes on firm value with industry profiles as a moderator. The population of this study is non-financial companies that are listed on the Indonesia Stock Exchange and disclosed tax disputes during the 2014-2019 period. The purposive sampling technique was applied, and 292 observations were obtained. A mixed-method approach is used in this study. First, a panel data regression analysis was performed using a tool called EViews 12. Second, to deepen the empirical nature of this research, and with the results of the panel data regression analysis having already been obtained, tax consultants who have legal power of attorney at the tax court were invited to a focus group discussion (FGD) that was held in Bali. The results of this study find that tax disputes have a negative effect on firm value. This study also demonstrates that an industry having a high profile weakens the negative effect of tax disputes on firm value. The research findings provide an understanding of tax disputes, firm values, and industry profiles within the framework of signaling theory and legitimacy theory. The limitation of this research is that it does not discuss typical tax dispute cases (whether material disputes or judicial disputes) due to data limitations.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".