The Moderating Effect of Non-audit Services Fee on Aggressive Tax Planning: Empirical Evidence From Malaysian Listed Companies
Bibliographic record
Abstract
The purpose of this study is to examine the moderating effect of non-audit services fees on the relationship between size, profitability, leverage, capital intensity, inventory intensity, financial distress and ethnicity with aggressive tax planning. This study uses a sample from companies listed on the Malaysian (Access, Certainty, Efficiency (ACE) Market from 2014 to 2018, comprising of 105 firm year-observations. The finding shows that the non-audit services fee moderate the relationship between size, profitability, leverage, inventory intensity, financial distress and ethnicity with aggressive tax planning except for capital intensity. It is hoped that the finding can assist readers in understanding the nature of companies listed on the ACE Market, particularly their behaviour towards tax planning. This study contributes to knowledge in the areas of financial accounting and taxation specifically on aggressive tax planning, by introducing the moderating variable of non-audit services fee. The uniqueness of the use of companies listed on the Malaysian ACE market will provide new avenue on the discussion on an aggressive tax planning issue, which usually more focus on big firms. The framework used in the present study could serve as a basis for research in other developing countries or regions.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".