A story of breakthrough versus incremental innovation: corporate entrepreneurship in the global pharmaceutical industry
Bibliographic record
Abstract
Abstract Breakthrough innovations are difficult to create, yet they are critical to long‐term competitive advantage. This highlights the considerable opportunities and risks that face corporate entrepreneurs. We study the complex explorative and exploitative entrepreneurial processes of multinational firms operating in the global pharmaceutical industry. We analyze over 1,500 new drug approvals by the U.S. Food and Drug Administration (FDA). We find that a successful track record in breakthrough innovation significantly increases the likelihood of a current breakthrough, while achievements in nongeneric incremental innovation do not have a significant effect. A strong foundation in generic incremental innovation hinders breakthrough performance. Thus, incremental innovation processes appear to be heterogeneous. Products that emerge from joint ventures and alliances are more likely to be breakthroughs. Foreign subsidiary participation in innovation processes did not significantly inhibit breakthroughs. These suggestive findings support the decentralization literature that highlights the benefits associated with exploiting knowledge from foreign centers of excellence. Contrary to the literature arguing that younger firms tend to have greater advantages in exploration, we do not find firm age to be a significant predictor of the likelihood of breakthrough innovation. Copyright © 2010 Strategic Management Society.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".