It's Time to Break Up: Dynamics Surrounding Young-Established Firm Alliance Duration
Bibliographic record
Abstract
While young firms often benefit from their relationships with established firms, these relationships can be risky. Hence, during their relationship with established firms, young firms must constantly monitor signs of a failing partnership and terminate it before being in a disadvantageous position. However, discontinuing the alliance with an established firm can also be risky, especially if the young firm has limited alternative collaborative opportunities. Our study adopts the young firm's perspective and dynamically weighs the tradeoffs between the risks of continuing and discontinuing its relationship with established firms, thereby deciding on its termination. We first develop an analytical model to understand how the alliance duration (time from alliance formation to termination) between young and established firms is affected by alliance, firm, and industry characteristics. We then test the resulting hypotheses on a sample of 1,111 alliances with licensing deals formed between 159 established pharmaceutical firms and 448 young biotechnology firms during the 1986 to 2000 period, which straddles the technological discontinuity of combinatorial chemistry. Our empirical results provide partial support for the hypotheses derived from the analytical model, informing us of firms’ rational alliance duration decisions as well as their deviations from rationality. In presenting both optimal alliance duration decisions and suboptimal alliance duration practices, our mixed-method approach offers important implications for theory and practice.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".