The Antecedents of Entrepreneurial Success: A Mixed Methods Approach
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".