Managing Channel Profits with Positive Demand Externalities
Bibliographic record
Abstract
Demand externalities arise when past sales stimulate future demand. They pervade many consumer markets. To penetrate such markets, how should manufacturers contract with retailers? We formulate the problem as a dynamic game, wherein the retailer can privately observe and control evolving market conditions, and consumers can act either myopically or strategically. Our contribution is threefold. (i) We characterize the optimal contract: It resolves a dynamic tradeoff between exploiting demand externalities, screening new information, and optimizing channel efficiency; moreover, it has a simple implementation of quantity discount. (ii) We characterize the dual role of demand externalities. Although demand externalities can improve channel surplus by expanding market size, they can also exacerbate information friction by enhancing the retailer’s ability to manipulate the market. Ignoring the dark side of the agency cost, previous studies may have overestimated the benefit of demand externalities. (iii) We provide new practical guidance. We show private information per se need not hurt channel efficiency: The manufacturer can use recursive advance selling to extract new information for free. Our results also shed light on when and why manufacturers should moderate demand externalities and prefer long-term contracts. By highlighting the dual role of demand externalities in long-run channel performance, this study sharpens our understanding of channel theory and practice. This paper was accepted by Dmitri Kuksov, marketing. Funding: L. Gao was partly supported by Academic Senate COR Grants of UCR. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2021.00008 .
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".