The Nature of Entrepreneurial Opportunities: Understanding the Process Using the 4I Organizational Learning Framework
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
In this article, we drew upon insights from two rather disparate streams of literature—entrepreneurship and organizational learning—to develop an informed understanding of the phenomenon of entrepreneurial opportunities. We examined the nature of entrepreneurial opportunities from two contrasting views—Schumpeterian and Kirznerian—and delved into their ontological roots. By applying the 4I organizational learning framework to entrepreneurial opportunities, we were able to not only resolve the apparently conflicting explanations of opportunities arising out of the contrasting ontological positions but also to achieve a level of pragmatic synthesis between them. In highlighting the article's contributions to theory and practice, we suggest that just as research on entrepreneurial opportunities benefits from applying organizational learning theory, so is organizational learning informed by research arising within the field of entrepreneurship studies.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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