Determinants of successful entrepreneurship in a developing nation: Empirical evaluation using an ordered logit model
Bibliographic record
Abstract
Entrepreneurship has always been a crucial issue in the economic development of the countries as it has the ability to enhance standards of living and create wealth, not only for the entrepreneurs but also for related businesses and people. This paper aims to provide insights into how to build successful entrepreneurship in Kathmandu valley (KV). Using the descriptive method, we have applied non- probability sampling technique to select 302 entrepreneurs from KV. The structured questionnaire is used for data collection. Descriptive statistics, correlation, regression, pre-estimation, and post-estimation are used for data analysis. The research finds that entrepreneurs are more successful when possessing such qualities as creativity and leadership. Furthermore, the results reveal that technology plays a vital role while initiating entrepreneurship and education helps raise positive output in entrepreneurship. Based on the findings, the study concludes that entrepreneurship has been one of the important issues in the context of Nepal to open up employment opportunities and bring economic progress.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".