Demonstrations and Price Competition in New Product Release
Bibliographic record
Abstract
We incorporate product demonstrations into a game theoretic model of price competition. Demonstrations may include product samples, trials, return policies, online review platforms, or any other means by which a firm allows consumers to learn about their value for a new product. In our model, demonstrations help individual consumers to learn whether they prefer an innovative product over an established alternative. The innovative firm controls demonstration informativeness. When the innovative firm commits to demonstration policies and there is flexibility in prices, the firm is best off offering fully informative demonstrations that divide the market and dampen price competition. In contrast, when a firm can adjust its demonstration strategy in response to prices, the firm prefers only partially informative demonstrations, designed to maximize its market share. Such a strategy can generate the monopoly profit for the innovative firm. We contrast the strategic role of demonstrations in our framework with the strategic role of capacity limits in models of judo economics, which also allow firms to divide a market and reduce competition. This paper was accepted by J. Miguel Villas-Boas, marketing.
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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 | 0.023 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".