Incentive mechanisms for price and advertising coordination in dynamic marketing channels
Bibliographic record
Abstract
Abstract This paper examines the issue of price and advertising coordination in bilateral monopolies from a dynamic perspective. Its main objectives are to design incentive mechanisms that the manufacturer can use in order to reduce double marginalization, and to investigate whether these mechanisms can be implemented by the manufacturer and accepted by the retailer. I consider a differential game where a manufacturer controls its wholesale price and its national advertising effort. The carry‐over effects of national advertising build up brand goodwill, which represents the model's state variable. The retailer controls the product's retail price and the local advertising effort, which affect consumer demand, in conjunction with the brand goodwill. I compute strategies and outcomes under three scenarios: (a) a fully coordinated scenario where channel members act as if the channel was vertically integrated; (b) a noncoordinated scenario, considered as the status quo , where the channel is decentralized; and (c) a scenario where incentive mechanisms are offered by the manufacturer. The main results indicate that two incentive mechanisms, namely, a retail‐price incentive and an advertising allowance offered by the manufacturer to the retailer can mitigate the double‐marginalization problem. With these incentives, and an appropriate choice of the channel‐coordinating wholesale price, the manufacturer can induce the retailer to fix its control variables at their channel‐coordinating levels without committing to implement the channel‐coordinating level of national advertising. Our results highlight the role of the channel‐coordinating wholesale price level on the Pareto‐optimality and the implementation of the incentives. I compute the values for the channel‐coordinating wholesale price that can be used as benchmarks by channel members in order to evaluate if the incentives should be implemented or not. I illustrate our results with numerical simulations indicating the channel‐coordinating wholesale price values where we observe an increase in both channel members' individual profits when the incentives are implemented (i.e., with respect to the status quo ). These results can be explained by the decrease in retail price and the increase in local and national advertising. Both effects explain the increase in brand goodwill and demand when the incentives are implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".