VALUE RELEVANCE OF R&D SPENDING BY RIVALS
Bibliographic record
Abstract
INTRODUCTION Research and development (RD Los & Verspagen, 2000). These results point to knowledge spillovers from rivals. On the other hand, evidence from Canada suggests that managers in leading technology-based industries are very concerned about the possibility of negative effects from their own R&D disclosures (Entwistle, 1999). This concern is mostly due to the possibility of competitors gaining valuable intelligence about the firm's technology and thus hurting the firm's competitive advantage. Moreover, there may be negative impacts on their customers. These impacts are negative spillovers. Accounting profits have negative elasticity with respect to the capitalized R&D pool from a firm's rivals (Jaffe, 1986). In addition, public knowledge on rivals' R&D such as patents has negative impact on the profitability of Canadian firms (Hanel & St-Pierre, 2002). For a concrete example of spillovers, consider the case of tablet computers. Apple Inc.'s (Apple) iPad created a new product category called tablet computers. iPad users downloaded one million software applications and 250,000 electronic books on the first day that it was introduced (Wortham, 2010). This implies that, in addition to the direct profits from the sale of iPads, Apple profited from the sale of electronic books. Yet, electronic books were invented by other firms. Therefore, Apple benefited from the knowledge of the makers of electronic book readers (e.g. Amazon Kindle). This is a positive externality created by Apple's rivals' R&D. Moreover, other rivals of Apple, like Samsung and Motorola introduced tablet computers with similar characteristics to iPad (Brustein, 2011). …
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".