Understanding alliance evolution and termination: Adjustment costs and the economics of resource value
Bibliographic record
Abstract
Alliances have been studied extensively in the past and various arguments have been suggested to explain their evolution and eventual termination. We argue that one important explanation of alliance termination has remained overlooked, one where the mechanism revolves around resource value and is independent of any mismanagement, opportunism, lack of trust, interpretive misunderstanding, or perceptions of inequity. In this explanation, we recognize explicitly that resources undergo transformation through an alliance, and this transformation reveals new previously imperfectly predicted costs to remain in the alliance as well as new opportunities outside the alliance. We apply the concepts of direct and indirect adjustment costs and inter-temporal economies of scope to explain these phenomena and demonstrate that, depending on the particular structure of incentive asymmetry between the two firms after alliance formation, the new circumstances may motivate a revised cost/profit sharing arrangement, a change in ownership of alliance resources, or a complete dissolution of the alliance. Some determinants of adjustment costs are explored in detail, covering resource characteristics, resource combination characteristics, and environment characteristics. Based on the economics of resource value, our argument has implications not just for alliance evolution and termination but also provides a distinct lens to explain the evolution of firm boundaries and the manner of transition of alliances into acquisitions.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".