Connection between Entrepreneurial Skills and Intelligences of High-Tech Entrepreneurs
Bibliographic record
Abstract
The uncertainties and risks of high-tech ventures are well known, and, despite the increasing number of start-up companies founded annually, few survive. This study examines the correlation between the entrepreneurial skills and multiple intelligences of entrepreneurs, as well as their influence on success in high-tech ventures. The theoretical foundation of the study is based on Salamzadeh and Kirby’s (2017) venture-creation model and Gardner’s (1983) multiple-intelligences theory. A convenience sample of three hundred entrepreneurs (281 men, 19 women) in different stages of their ventures was evaluated via an online Qualtrics questionnaire. The results indicate that the most successful entrepreneurs have the highest levels of logical intelligence but also the lowest levels of linguistic, intrapersonal, and interpersonal intelligences. Entrepreneurial skills were found to be related to all types of intelligences, as well as to success in entrepreneurial ventures. The study adds to the limited literature on the connection between personal characteristics and the success of entrepreneurial startup companies, and may contribute to improving entrepreneurship education programs. Future research is needed to examine other characteristics of both successful and unsuccessful entrepreneurs across a wider range of venture stages.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".