Factors Affect the Success of SME in Bangladesh: Evidence from Khulna City
Bibliographic record
Abstract
Small and Medium Enterprises (SMEs) occupy dominant positions in economy. SMEs bear special significancefor counties where density of population is very high because it offers huge employment opportunities andincome generations at low cost. Considering the importance this study has strived to identify those factorscontribute the success of SMEs using causal model. Data were collected from SMEs owners of the Khulna city,divisional city of Bangladesh. A total 195 respondents were finally interviewed for the study. Several factorswere selected typically affect the success of SMEs from pervious literature. Important factors were identifiedfirst using rotated components matrix later regression statistics was applied to find out which are statisticallysignificant. Business plan, channel of distribution, management skills and government support are identifiedstatistically significant in determining success of SMEs in Khulna City.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".