Research and Development Projects Upon Real Options View
Bibliographic record
Abstract
We investigate the importance of R&D expenditures for SMC (small and medium companies) and for Blue Chips, focusing on the existence of relation between Research and Development (R&D) option value and some variables such as relative probability of innovation, level of capital expenditures, expected innovation rents, expenditures with respect to the implementation of new technologies, proportions of money, proportions of indebtedness, operating cash flows, patents of affiliated companies, numbers of workers, market concentration and the efficiency of work. Empirical analysis also includes R&D projects valuation worksheet based upon the competition duopoly model that we applied to Brazilian Embraer and Canadian Bombardier. Embraer and Bombardier are 3rd and 4th largest suppliers of commercial aircrafts. These are main rival competitors in the segment of small commuter planes. Our main objective was to study changes of R&D projects performance when alterations of environmental factors are simulated. Basically, we observed significant difference between SMCs and Blue Chips. SMC tend to start new R&D projects on their own while Blue Chips buy other companies that already have access to new technologies. Moreover, in the group of small companies, R&D costs are significantly positive, while Blue Chips show opposite results as R&D costs are negative and statistically significant in this group. In addition, R&D projects and patents possessed by investigated companies affect positively R&D projects valuation. Future growth, which forms part of the value of a company, depends on the number of patents pertaining to companies and newly started R&D projects which subsequently will become patents possessed by those companies. DOI: http://dx.doi.org/10.5755/j01.ee.25.3.2737
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".