Channel Structure Design for Complementary Products under a Co‐Opetitive Environment
Bibliographic record
Abstract
ABSTRACT In the high‐tech industry, firms can be partners in one respect (e.g., resellers) and competitors in another. In this article, we investigate the channel structure problem for two firms‐each selling competing products in two complementary markets—who are deciding whether to sell their products to customers directly or distribute one of them through a competitor. The customers are heterogeneous and both firms have products that are horizontally differentiated. When selling products directly, the firm can coordinate the prices of the two complementary products and avoid the inefficiency of double marginalization. However, selling (indirectly) through the competing manufacturer can mitigate competition because the competitor shares the profit of both competing products and therefore does not price its own products aggressively. One might expect that when the externality across the markets is strong, firms would prefer to sell both products directly (rather than through the competitor) in order to take advantage of the complementarity between markets and eliminate the inefficiency of double marginalization. Interestingly, we find that even though the first mover chooses to sell both products directly, the second mover forsakes the opportunity to coordinate the prices of its products and instead opts to distribute one of the products through the first mover.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".