Artificial Intelligence and Entrepreneurship: A Call for Research to Prospect and Establish the Scholarly AI Frontiers
Bibliographic record
Abstract
Entrepreneurship has entered a new era shaped by artificial intelligence (AI), demanding accelerated scholarly advances to keep pace with this transformative technology—yet this demands that academics bridge the gap between the AI revolution’s ambiguities and meaningful scholarly contributions. To motivate and guide future research on AI’s transformative role in entrepreneurship, we introduce an ongoing special issue in Entrepreneurship Theory and Practice ( ETP ) and outline multiple compelling opportunities for future research. Unlike typical editorials, we offer a prospective vision—rather than retrospective, after the articles have been accepted and published—at this project’s outset, to empower the field to prospect and establish new scholarly foundations in the relatively uncharted world of AI in the domain of entrepreneurship. Accordingly, we highlight the “AI PEN” ( P rospecting and E stablishing N exus) as a desirable research approach to advance this literature going forward. We hope, and anticipate, that our invitation to submit proposals to this special issue facilitates novel empirical as well as theory-focused contributions to the literature.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.027 | 0.032 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".