Understanding Value-Added Resellers’ Assortments of Multicomponent Systems
Bibliographic record
Abstract
Interconnect standards increase choices. For example, in cardiac pacemakers, the IS-1 standard enables the “pulse generator” from 6 manufacturers to be combined with the “lead set” from the other 5 to create up to 30 additional mixed-brand pacemakers. However, observed assortment additions are much smaller, which is puzzling because manufacturers in extant models have welcomed such additions to reduce price competition and increase variety. Instead, conflict with the value-added resellers that create and carry these additions is commonplace. The authors extend the literature with an analytical model showing that value-added resellers limit the number and composition of additions to gain better upstream terms. This conflict is exacerbated when “keystone” components are relatively more decisive in influencing customer choices, so their exclusion from an addition represents a larger loss. The empirical study of the multibillion-dollar auto paint refinish market finds assortment additions consistent with the authors’ predictions. The article concludes with a discussion of the role of channel support programs in ameliorating these conflicts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".