The Effect of Tax Fairness, Peer Influence, and Moral Obligation on Sales Tax Evasion among Jordanian SMEs
Bibliographic record
Abstract
Tax evasion remains a complex issue for tax authorities, policymakers, and researchers. While socio-psychological factors have been researched, their impact on tax evasion among SMEs has not yet been determined. This paper empirically analyses the effects of tax fairness, peer influence and moral obligation, on sales tax evasion among Jordanian SME owners/managers. A survey was used to obtain data from three regions of Jordan (north, middle, south). Random sampling was utilized in selecting the prospective respondents from SMEs in three sectors (trade, service, manufacturing). A total of 212 usable questionnaires retrieved from the SMEs were analysed using Smart-PLS 3.0. The results revealed that tax fairness and moral obligation had a significant negative effect on sales tax evasion behaviour among SME owner-managers. On the other hand, peer influence positively and significantly impacted sales tax evasion behaviour. Thus, policymakers and tax authorities should incorporate these factors in developing effective strategies to reduce tax evasion in Jordan, which could result in an improvement in the country’s overall revenue collection. The findings also contribute to the scarcity of literature about the significance of socio-psychological factors in mitigating tax evasion by examining the effects of tax fairness, peer influence, and moral obligation on sales tax evasion.
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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 | 0.000 |
| 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) | 0.002 | 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".