Optimal Intertemporal Pricing Strategies for Firms Introducing New Products
Bibliographic record
Abstract
Firms can significantly improve their performance upon the introduction of a new product by following an intertemporal pricing strategy which predicts the adoption of the product through time. Four reasons for gradual adoption are explored: delayed purchase, awareness, social pressures and informational needs. The firm does better by pricing a straightforward new product at a lower introductory price when the product is quite visible to other potential adopters when an individual adopts. Differences in price-sensitivity among consumers also impact the firm's optimal strategy. Products for which the social relevance varies considerably or for which the average perceived social risk of adoption is high cannot necessarily benefit from a low introductory price. A high initial price which decreases through time is better when consumers are varied in their need for information, when on average, much information is needed and when the information generated by other adopters is forgotten more quickly.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".