The Link between Purchase Delay and Resale Price Maintenance: Using the Real Options Approach
Bibliographic record
Abstract
Why would a manufacturer want to impose resale price maintenance (RPM)? The traditional explanation of RPM is that it prevents retailers from free riding in providing services. In this paper, we show that the manufacturers still have incentives to impose RPM even their products do not need special services. When making a purchase decision, consumers choose from various alternatives, including options to delay the purchase, especially if they feel the price will be lower in the future. This paper connects frequent markdowns, purchase delay, and resale price maintenance (RPM) by using the real options analysis. The results indicate that the demand quantity under flexible pricing is lower than that under RPM, due to purchase delay. The profits of manufacturer will be lower without the use of RPM. This research also suggests that the manufacturer has more incentives to impose RPM on products with higher demand price elasticity. The results from real options analysis suggests that what we call consumers’ purchase delay, which is caused by retailers’ frequent markdowns makes RPM a desirable strategy for manufacturers.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".