Alliance portfolio reconfiguration following a technological discontinuity
Bibliographic record
Abstract
Research summary : We study how technological discontinuities generate first‐ and second‐order effects on alliance formation and termination, leading to reconfiguration of firms' alliance portfolios. Following technological shocks, we argue that firms often seek alliances that provide new resources while also having incentives to form alliances for reinforced and challenged resources that complement the new resources. In parallel, alliance terminations, even involving resources otherwise unaffected by the discontinuity, increase due to limits in firms' alliance carrying capacity. We study biopharmaceutical firms between 1990 and 2000, which faced a technological discontinuity in 1995 in the form of combinatorial chemistry and high‐throughput screening. We improve understanding of how technological discontinuities affect the value of resources and how firms reconfigure alliance portfolios in response . Managerial summary : When firms form alliances to gain new resources during technological discontinuities that disrupt their industry, they cannot consider only the focal new partnerships. Instead, new alliances create complementarity and substitution pressures that lead to broader reconfiguration of the firms' alliance portfolios: (1) complementarity creates incentives to also form alliances for resources that the technological discontinuity reinforces or challenges in order to improve the collective value of co‐specialized assets; (2) substitution creates incentives to terminate existing alliances, even if their value is otherwise unaffected by the discontinuity, in order to create carrying capacity for new alliances. Thus, one new alliance can generate a cascade of reconfiguration that challenges the balance between the benefits of stability and the need for change in an alliance portfolio . Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".