Do firms with more alliance experience outperform others with less? A three‐level sigmoid model and the moderating effects of diversification
Bibliographic record
Abstract
Abstract Whether firms with more alliance experience perform better than those with less and whether the alliance strategy interacts with diversification strategy to shape firm performance are two critical but underexplored questions. To address these queries, this study develops a three‐level sigmoid framework built upon a marginal analysis that contrasts alliance benefits and alliance costs, and considers the moderation of diversification that often closely works with the alliance in shaping firm performance. Empirical results obtained from firms in two alliance‐populated industries support first that the alliance experience‐performance relationship is S‐shaped in that the linkage is negative to alliance novices, positive to alliance experts, and negative again to alliance overusers; and second, that the shape of this sigmoid curve varies systematically between high‐ and low‐diversified firms. Copyright © 2015 ASAC. Published by 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".