Alliance Portfolio Configurations and Competitive Action Frequency
Bibliographic record
Abstract
We advance competitive dynamics research by introducing alliance portfolio configuration as an important antecedent of competitive action frequency. We propose and test a model for developing effective alliance portfolio configurations that enhance a firm’s ability to discover, conceptualize, and carry out new competitive actions. Our model consists of three overlapping components: (a) opportunity recognition capacity as evidenced by the portfolio attribute of structural holes, (b) opportunity development capacity as indicated by R&D alliance scope, and (c) action execution capacity as exhibited by equity alliances with trusted partners. We hypothesize and find a multiplicative effect of the configuration of all three alliance portfolio attributes on the frequency of competitive actions carried out by 12 large global automobile manufacturing firms with 1,471 unique partners and 37,520 alliances formed over a 16-year period (1988 to 2003). The three-way configuration of portfolio attributes was stronger for more complex competitive actions requiring more time, expertise, and resources to develop and execute.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".