Forging a resilient pathway: Uncovering the relationship between the supply chain sustainability and the tax compliance, and the sustainable future of the micro, small, and medium enterprise industry
Bibliographic record
Abstract
The prolonged duration of the Covid epidemic has contributed to the challenging situation faced by land-based Micro, Small, and Medium Enterprises (MSMEs), leading to difficulties in meeting tax obligations. This study aims to identify the factors causing low tax compliance among micro, small, and medium-sized businesses. Employing quantitative methods, the research relies on primary sources and specifically focuses on small and medium-sized companies in Sidoarjo Regency. Purposive sampling was employed, resulting in the inclusion of 164 micro, small, and medium-sized firms meeting the specified criteria. Data analysis was conducted using Structural Equation Modeling Partial Least Square. The study reveals that supply chain sustainability significantly and positively influences taxpayer compliance. Additionally, supply chain sustainability, quality of staff tax services, financial attitudes, and tax comprehension all demonstrate substantial and positive impacts on taxpayer compliance. Indirectly, Supply Chain Sustainability, Tax Employee Service Quality, Financial Attitude, and Tax Understanding exert a significant influence on Development Sustainability in Sidoarjo Regency, East Java Province, Indonesia, through Taxpayer Compliance as an intervening variable for MSMEs. This research contributes to the existing literature by confirming that taxpayer compliance is influenced by Supply Chain Sustainability, Tax Employee Service Quality, Financial Attitude, and Tax Understanding. Furthermore, the study validates the application of the theory of planned behavior in exploring the moderating effect of tax compliance between Supply Chain Sustainability, Tax Employee Service Quality, Financial Attitude, and Tax Understanding on development sustainability, particularly in the context of developing countries.
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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 | 0.004 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".