Commercializing Emerging Technologies: A Learning Journey and Framework
Bibliographic record
Abstract
Technology entrepreneurship exists at the intersection between technology innovation management and entrepreneurship. Uncertainty is a key element of both fields, but the term is often imprecisely or inconsistently used in the literature. This study focuses on entrepreneurial actions that support the mitigation or management of uncertainty, such as entrepreneurial learning or hedging. In this paper we first review the literature to understand the different definitions of uncertainty. We argue that different types of uncertainty correspond with different modes of learning and thus a different set of entrepreneurial learning actions. We analyze nine cases of new technology-based firms that face uncertainty commercializing emerging technologies to understand how they mitigate or manage uncertainty through their actions. We then develop a learning framework to describe the process of commercializing emerging technologies. The study contributes to the discussion in the academic literature about the construct of uncertainty as it relates to technological innovation and entrepreneurial learning while also providing a practical learning roadmap for technology entrepreneurs.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".