Alliance Portfolio & Technology Brokering:The Effect of Diversity and Familiarity of Portfolio Firms
Bibliographic record
Abstract
This study explores the structure and composition of alliance portfolios that enables a firm to access and integrate diverse external knowledge for the purpose of technology brokering. More specifically, we investigate the knowledge benefits of a technologically diverse alliance portfolio comprised of new and familiar partners and how familiarity of partner specific knowledge and experience gained from vertical and horizontal alliances enhances these benefits. The research model is tested using patent, publication and alliance data of 222 biotechnology firms from 1990-2000. Technology brokering in our study refers to the extent to which patents applied by sample firms have referred to patents from diverse classes other than the focal patents' own class. The results confirm the positive association between diverse alliance portfolio and technology brokering and that the relationship is strengthened by focal firm's familiarity of its partners with prior horizontal alliances but not partners with prior vertical alliances. Further, familiarity among portfolio partners has an overall enhancing effect. From our interviews with some founders of biotech firms, we can infer from the findings that familiarity of partners provide not only the strategic direction for finding potentially valuable knowledge but also the experience crucial for knowledge recombination.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".