Entrepreneurship as a Process: Toward Harmonizing Multiple Perspectives
Bibliographic record
Abstract
Are there any common denominators within the diversity of entrepreneurship literature that may serve as foundations for understanding the entrepreneurial process in a systematic and comprehensive way that is useful to both scholars and practitioners? The objective of this paper was to discover about the entrepreneurial process what, if anything, is both generic ( all processes that are “entrepreneurial” do this) and distinct ( only entrepreneurial processes do this). Our approach was to evaluate published models of entrepreneurial process to discover what scholars have argued about what entrepreneurs do and how they do it (the processes they use) and to seek out any key commonalities that scholars claim are associated with the phenomenon. Unfortunately for the field, the investigation demonstrates that, as at the time of our investigation, the 32 extant models of entrepreneurial process are highly fragmented in their claims and emphases and are insufficient for establishing an infrastructure upon which to synthesize an understanding of entrepreneurial process that is both generic and distinct. Insights gained in the study lead to suggestions for future research and theory development of which the most urgent is the need to develop a single harmonized model of entrepreneurial process capable of embracing the best of what is on offer and adding new theoretical arguments in areas where practice shows that they are lacking.
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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 | 0.042 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.029 | 0.050 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.010 |
| 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".