The Antecedents of Entrepreneurial Success: A Mixed Methods Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this research is to understand: (1) the main themes that appear to contribute to entrepreneurial success, (2) the various combinations of antecedents that can lead to entrepreneurial success, and; (3) the role that travel plays in entrepreneurial success. We first use a qualitative methodology to assess the themes that emerge in our conversations with 14 highly-successful Canadian entrepreneurs. The main categories that emerged from our interviews that contribute to entrepreneurial success involve: learning, travel, adversity quotient, and mentorship. From these results, we conduct a qualitative comparative analysis (QCA) and find that the input variables that were most important to entrepreneurial success were: learning, experiencing failure, learning from mentors, and adversity quotient. The contributions to knowledge of this research are twofold. First, we show that travel is an important construct to entrepreneurial success, which is significant as travel has largely been omitted from the entrepreneurship literature. Second, we show that entrepreneurial success is dependent on a complex combination of variables of varying levels of importance.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it