Demand information acquisition strategy in a dual channel supply chain
Bibliographic record
Abstract
Abstract This article examines the information acquisition strategy of a dual‐channel supply chain, in which a manufacturer sells a product both through a retailer and through its own direct channel. Either the manufacturer or the retailer can acquire demand information from a third‐party marketing research company. The manufacturer first decides whether or not to acquire such information, and then the retailer decides whether or not to acquire information. This setup implies a signaling game (either the manufacturer or the retailer may have private demand information) with an endogenous information structure. We identify conditions under which neither of the firms will acquire demand information, even when the cost of implementation is negligible. We also show that information acquisition can have a negative impact on the retailer, the supply chain, customers, and society. The manufacturer who acquires information always prefers to share information with the retailer, which benefits the retailer. The retailer who acquires information, however, may not want to share information with the manufacturer. The managerial insight of our paper is that firms that have more accurate demand data must develop strategies for the appropriate use of that information, both in their own planning and within the context of their dual‐channel supply chain.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| 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".