Can Sharia-Compliance Protect the Financial Performance of Companies from the Effect of Value Added Tax
Bibliographic record
Abstract
Sharia-compliant companies differ from other companies in its financing sources and business activities. These differences may affect its ability to protect its financial performance from changes in tax policies. The main queries in this study were whether value-added tax can affect the financial performance of companies and whether sharia-compliance can mitigate that effect. To answer these queries, a quantitative research method was utilized using data about listed companies in Abu Dhabi stock exchange in the United Arab Emirates. Data were collected from the financial reports of the listed companies for two years before the imposing of VAT in UAE (2016, 2017) and for two years after the VAT imposition and before the pandemic of COVID-19 (2018, 2019). Collected data were used to calculate financial ratios for the included companies first and then these ratios were further analyzed using Nonparametric regression. Conclusions of this study indicated that VAT had a negative and insignificant effect on the financial performance of all companies while this effect is positive and insignificant for Sharia-non-compliant companies implying that Sharia-compliance cannot mitigate the negative effect of VAT.
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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 | 0.007 | 0.028 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".