Bibliographic record
Abstract
Conventional wisdom suggests that when firms face a negative externality like gray marketing (i.e., the selling of branded goods outside of the manufacturer’s authorized channels), an effective strategy to reduce the negative impact is to centralize decision making. Nevertheless, in industries with significant gray marketing, we observe many firms with decentralized decision making. Our study assesses whether decentralized decision making can be optimal when a manufacturer faces gray market distribution. We consider a market where a focal firm competes with an existing competitor that produces a differentiated product and a gray marketer that sources an identical product from a lower-priced foreign market. We find that decentralization is optimal under quantity-based competition, provided the gray market is relatively uncompetitive and the level of competitive intensity between the focal firm and the competitor is high. Decentralization leads a firm to make aggressive production decisions, which leads to lower prices, yet it also leads to higher market share for the firm compared to centralization. When the level of competitive intensity between a firm and its competitor is high, the gain in market share more than offsets the loss due to lower prices. As a result, the focal firm is better off decentralizing its operations independent of (a) whether the competitor operates in the foreign market, and (b) the competitor’s organizational structure. This finding contradicts the belief that centralized decision making is always optimal when authorized manufacturers attempt to limit the negative impact of gray markets. The findings also provide insight to understand why firms might employ decentralized decision making in industries where gray markets are active.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".