Implementing Project-Based Alliances: Three Paradoxes of Brokerage
Bibliographic record
Abstract
We theorize that organizations with higher brokerage positions (which we call brokers) benefit from access to capital and distribution that, paradoxically, causes them to implement poorer projects but to survive longer. Whereas prior research proposes that such organizations implement alliance projects because of their superior quality, we argue that they can do so despite project quality, because they have better access to capital and distribution. This access subjects their ventures to fewer implementation hurdles. As a result, there emerge three paradoxes and associated hypotheses: (a) brokers are able to implement more alliance projects, but their projects will perform on average more poorly; (b) brokers will tend to ally with other brokers but will do better when they ally with those having lower brokerage positions; and (c) despite the poorer performance of alliance projects, brokers will experience fewer risks and be less likely to exit the business. Paradoxically, brokerage makes organizations perform worse in each alliance but benefit from longer-term sustainability. In a study of 2,694 movie production companies in the Hollywood film industry from 1994 to 2009, we find considerable support for our hypotheses and develop a novel perspective on brokerage.
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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 | 0.022 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".