Tax Incentives and Financial performance of Small and Medium Sized Enterprises in Kabale District, Uganda
Bibliographic record
Abstract
Purpose: The purpose of the study was to determine the effect of tax incentives on financial performance of SMEs in Kabale district Uganda. This study was guided by the normative theory and the political systems theory. Materials and Methods: The study employed the descriptive research design. The data was collected using structured questionnaires. Data was later edited, coded, and fed into the SPSS computer package to generate both inferential and descriptive statistics. Findings: The study findings as indicated by the coefficients of determination show that tax exemptions have a significant impact on growth of SMEs (β= .325, t=4.075, p<0.000). It was observed that there are various tax incentives that have been formulated to accelerate the financial performance of SMEs in Uganda although their practical implementation has not been fully realized. Unique Contribution to Theory, Practice and Policy: The study recommends that the development of policies by Ugandan government geared to accelerate the financial performance of SMEs should have the target beneficiaries’ input before implementation to prevent the formulation of impractical and undesirable policies. The government should also provide frequent trainings to SMES on the available incentives, which they could leverage on to boost their financial 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".