From Technology Transfer to Disruptive Innovation: The Case of EMBRAER
Bibliographic record
Abstract
The aircraft industry is a high-tech market with very high barriers to entry. Particularly considering the commercial aircraft market, only a handful of competitors account for most of the market share. Amongst this select group, Embraer is the only one from the developing world. This paper proposes Embraer approach to innovation as a major factor for The Company's global success. As part of a country-wide strategy, Embraer has for decades, systematically employed technology transfer to build R&D capabilities. More recently, Embraer identified a gap in the market for larger regional jets and in collaborating with risk partners has launched a very competitive product line, considered an example of disruptive innovation. The regional jet market has recently received a lot of attention with the launch of Bombardier's C-Series. In this context, this paper attempts to validate its claims about Embraer by triangulating, primary and secondary sources of information, describing Embraer's trajectory and current position in the market. From Embraer's case, practical and conceptual implications are proposed. Additionally, limitations and opportunities for future research are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".