Identifying critical success factor of Indian entrepreneur / Siti Farah Edwin
Bibliographic record
Abstract
The Malaysian economy recorded a stronger growth of 4.3% (QI 2013: 4.1%) during the second quarter of 2013. Growth was driven by strong domestic economic activities amid a weakening external sector. On the supply side, growth in the services sector remained firm at 4.8% (Ql 2013: 5.9%), driven largely by the wholesale and retail trade, business services, and communication subsectors (Ministry of Finance Malaysia. 2013 }. An interesting feature of Malaysian economic history that has to be understood in order to appreciate the current context of entrepreneurship in Malaysia is the historical practice of segregating economic activity along racial lines, a practice that was introduced by the British under their colonial rule of Malaya (pre-1957). Entrepreneurship is the terminal stage of the entrepreneurial process wherein after setting up a venture one looks for diversification and growth. An entrepreneur is always in search of new challenges. That is why the entrepreneurs with a track record of success are much more likely to succeed than first-time entrepreneurs and those who have previously failed. In particular, they exhibit persistence in selecting the right industry and time to start new ventures. Entrepreneurs with demonstrated market timing skill are also more likely to outperform industry peers in their subsequent ventures. This is consistent with the view that if suppliers and customers perceive the entrepreneur to have market timing skill, and is therefore more likely to succeed, they will be more willing to commit resources to the firm. In this way, success breeds success and strengthens performance persistence and don't give up. Don't expect it to be a smooth ride, expect it to be irritating. But once the entrepreneurs are there, they'll enjoy it and they'll make a lot of money as well. By contrast, first-time entrepreneurs have only an 18% chance of succeeding and entrepreneurs who previously failed have a 20% chance of succeeding (Gompers. P., & et al, 2008).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".