Flourishing MSMEs: The Role of Innovation, Creative Compliance, and Tax Incentives
Bibliographic record
Abstract
This study explores the interplay between tax incentives, creative compliance, and innovation in enhancing business resilience and sustainability among micro, small, and medium enterprises (MSMEs) in Indonesia, addressing gaps in the existing literature regarding their interrelationships during crises. A cross-sectional survey of 360 MSMEs was conducted, utilizing the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to analyze complex relationships among variables. The findings reveal that creative compliance, including tax planning and avoidance, does not directly impact resilience or sustainability. While tax incentives did not significantly enhance resilience during crises, they contributed to long-term sustainability. Innovation emerged as a critical factor linking creative compliance to business success and fully mediating the effects of tax incentives on resilience. This study emphasizes the necessity for MSMEs to prioritize innovation in their strategies, particularly in conjunction with effective tax practices, and highlights the need for government support through simplified regulatory frameworks to foster an innovative business environment. Limitations include the challenges of incorporating control variables in SEM and the need for further research into the long-term effects of these factors on sustainable performance.
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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 | 0.009 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".