Harnessing the potential of additive manufacturing technologies: Challenges and opportunities for entrepreneurial strategies
Bibliographic record
Abstract
Additive manufacturing (AM), a revolutionary production technique that will make many previously inconceivable innovations a reality, is transforming the manufacturing sector. The technologies behind AM have a generic character and possess immense and transformative potential that will be felt across different sectors of the economy. This paper studies how entrepreneurs can access, exploit, and diffuse this new disruptive knowledge, as well as the potential challenges that lie ahead. Further, we examine the magnitude of the knowledge pool created by the Top 10 countries in AM research from 2011 to 2016; specifically, we determine the leading countries, the pattern of organizational and international collaborations, leading disciplines contributing to AM, and the pattern in authors' affiliations. We find that the US is clearly the dominant country, while China witnessed a notable acceleration in AM research between 2014 and 2016; we also show that universities are the predominant actor in codified AM knowledge creation. Using this information, we paint a vivid picture of the technological and market opportunities for entrepreneurs and identify the gaps in the current knowledge production environment; we find that the challenges currently faced by entrepreneurs could be overcome through academic entrepreneurship, collective entrepreneurship, and the actions of an entrepreneurial state.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".