Learning Sources and Methods Used by Famous Entrepreneurs: A Comparative Study about Three Entrepreneurs from Iran, Japan and United States of America
Bibliographic record
Abstract
Entrepreneurial perspective emphasizes on idea generation and putting them into action or creation of business. Learning methods used by entrepreneurs have a crucial impact on their capabilities. Entrepreneurs use various sources and methods of learning to achieve the expected capabilities. The main question of this article is: Which sources and methods of learning are mostly used by famous entrepreneurs? Do entrepreneurs who live in different countries use similar learning sources and methods? This research is aimed to examine Kuratko’s framework and has added a source and some methods. This framework contains four main sources of learning: “Publications”, “Observation”, “Speeches and Presentation” and “doing business activities”. Content Analysis of documents is used as the research method, which is done by reviewing reliable documents on the three famous entrepreneurs. Amir Kabir, Matsushita and Welch respectively from Iran, Japan and America are selected as research sample. The logic and the reason of our selection are based on their influence in business. Results show that top entrepreneurs learn mostly from informal learning methods including: doing activities, duties, observations and conversations or dialogues, although there are differences for each of the chosen entrepreneurs dependent on his environment. It seems that entrepreneurs select their own sources and learning methods based on contingency approach. Authors suggest that the sources and methods of learning used by top entrepreneurs should be identified and used at universities as formal educational sources. In other words, informal learning sources and methods are recommended for simulation in schools of entrepreneurship.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".