Greener Pastures: Outside Options and Strategic Alliance Withdrawal
Why this work is in the frame
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Bibliographic record
Abstract
Departing from prior work that demonstrates the stickiness and stability of alliance networks resulting from embeddedness, we extend matching theory to study firms' withdrawal from alliances. Viewing alliance withdrawal as a result of firms' pursuit of more promising alternative partners (outside options) rather than failures in collaboration, we predict that a firm is more likely to withdraw from an alliance when there is a higher density of outside options that have better match quality than the current partners. We also propose that, because matching is two-sided, outside options have a greater impact on a firm's withdrawal when they are more likely to initiate new alliances. Using data on alliances in the global liner shipping industry, we show that, controlling for internal tensions in the alliance, outside options predict alliance withdrawals. Thus, despite the alliance stickiness and stability, firms alter their alliances in response to the availability of promising outside options, even leaving alliances that appear successful.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it