Product Launches and Buying Frenzies: A Dynamic Perspective
Bibliographic record
Abstract
Buying frenzies caused by a firm's intentional undersupplying of a new product are frequently evident in several industries including electronics (cell phones, video games), luxury automobiles, and fashion goods. We develop a dynamic model of buying frenzies that incorporates the firm's manufacturing and sale of a product over time and characterizes the conditions under which inducing such frenzies is an optimal strategy. We find that buying frenzies occur when customers are sufficiently uncertain about their valuations of the product and when they discount the future sufficiently but not excessively. We propose measures of “customer desperation” and of the extent of scarcity to measure the depth and breadth of buying frenzies, respectively. We also demonstrate that such frenzies can have a significantly positive effect on firm profits and partially recover the loss due to non‐commitment to future prices. This study provides managerial insights on how firms can influence market response to a new product through production, pricing, and inventory decisions to induce profitable frenzies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".