Bibliographic record
Abstract
We study two main questions in this paper: 1) How do spillovers of knowledge created by manufacturers' investments in process innovation affect channel structure and effort investment incentives? 2) What are the interactions between organizational incentives to form joint ventures and strategic alliances with competitors, and coordinate decisions vertically with downstream channel members? We focus on situations where spillovers are involuntary, firms' innovative activities are non-overlapping, and firms benefit directly from the results of competitors' innovations. Under these conditions, we find that spillovers in process knowledge increase the likelihood of observing decentralized channel structures. Surprisingly, decentralized manufacturers invest more in process innovation than perfectly coordinated manufacturers do when spillovers are large. Moreover, in industries where large spillovers exist, horizontal cooperation among manufacturers induces higher levels of process innovation investments than channel coordination does. From a public policy perspective, however, the desirability of such cooperative arrangements among competitors depends on channel structure: joint ventures among decentralized manufacturers are more likely to meet the regulators' criteria of raising effort investments than cooperation among integrated manufacturers would be. Investment incentives are best provided when firms share their process knowledge and are buffered from subsequent price competition by independent retailers.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".