Which alliance partners become attractive targets for acquisitions in biotech? Prior experience vs. relational capabilities
Bibliographic record
Abstract
Recent years have seen a growth in strategic alliances, mergers and acquisitions and collaborative networks involving knowledge-intensive and hi-tech industries. However, there have been relatively few studies looking at this form of collaboration as a strategy to drive firms’ innovative performances. This book specifically focuses on the role of strategic alliances, M&A and innovation networks, providing insights on if and how they contribute to boosting firms’ innovation performances. The book has a double purpose. Firstly, it investigates at an industry level the role played by the alliance, M&As and networks in high-tech environments such as biotechnology, pharmaceutical, software and nanotechnology in creating, transforming and reshaping the dynamics inside and between industries. Secondly, it explores the impact at the firm level of factors such as cognitive distance, management capabilities, and relational and social capabilities, on firms’ global innovation capacity, measured as innovation quantity, innovation quality and innovation novelty. The book will be of interest to scholars working on the economics of innovation, innovation management studies, strategic management, regional science and evolutionary economics, among other areas.Introduction Part I: Alliances and Networks 1. The emergence of the red biotech niche and its evanescent dissolution into the integrated parallel "knowledge system" of a new bio-pharmaceutical filière. An Evolutionary Perspective 2. Innovation in US metropolitan areas: the role of global connectivity 3. Competition and Cooperation in Entrepreneurial Ecosystems: A Life-Cycle Analysis of a Canadian ICT Ecosystem Part II: Alliances and Innovation 4. Partnering strategies in biotech firms: A longitudinal perspective 5. The management of the collaboration network of the Italian biotech firms. Do firms experiments a diminishing return from alliances? 6. Which alliance partners become attractive targets for acquisitions in biotech? Prior experience vs. relational capabilities 7. Do acquisitions increase acquirers’ innovative performance in the bio-pharma industry? An empirical investigation 8. Evaluating post-acquisition technological performance by measuring absorption-related invention 9. Are M&As driving exploitation or exploration? Part III Alliances in High Tech Environments 10. Pasteur scientists meet the market. An empirical illustration of the innovative performance of university-industry relationships 11. The relational models of the software industry in Italy and Spain relative to Germany 12. How alliances in biotech are shaping the national systems of innovation in three European countries Part IV: Case Studies 13. Evolving through innovation, knowledge re-utilisation and exaptation: the case of L’Oreal 14. The case of Fab: from start-up through open innovation to acquisition
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".