A Temporal Typology of Entrepreneurial Opportunities
Bibliographic record
Abstract
Entrepreneurial opportunities emerge and dissipate over time, yet little is known about how and why they vary in their temporality and what the implications of temporal variance are for the urgency and timing of entrepreneurial action. Building on the actualization theory of opportunity and signal processing theory, we propose that opportunities for entrepreneurial action can be understood as a convolution of consumer desire, production feasibility, and economic viability of an innovation. Conceiving consumer desire – the necessary ingredient of any profit opportunity – as either known or unknown and fleeting versus enduring, we identify four possible distributions of consumer desire over time. We then show how the interaction of these distributions with potential feasibility functions produce a temporal typology of entrepreneurial opportunities. Our analysis suggests that despite sharing conceptual similarities in structure, each type of opportunity emphasizes a different form of asymmetry across opportunity categories, which is likely to differentially affect the urgency and optimal timing of entrepreneurial action. We conclude by pointing out how considerations of time facilitate the move away from a highly acontextual and abstract understanding of entrepreneurial opportunity and toward a more theoretically nuanced and empirically informative view of the concept.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".