Intertemporal Segmentation via Flexible-Duration Group Buying
Bibliographic record
Abstract
Problem definition : We study a special form of group buying: the group buying succeeds only if the number of sign-ups reaches a preset threshold, with no duration constraint. Customers with heterogeneous valuations arrive sequentially and decide between signing up for the group buying or purchasing a regular product. To decide whether to join the group buying, customers need to estimate their expected waiting time, which varies depending on the cumulative sign-ups by the time of their arrival. The firm decides on the prices for the group-buying product and regular product, with the product quality levels and group-buying size exogenously determined. Academic/practical relevance : This type of group buying is often adopted for a special edition of the product and offered alongside a constantly available regular product. Methodology : We study the product line design with the group-buying sign-up behavior of customers characterized by the rational expectations equilibrium in a random pledging process. Results : We show that group buying with flexible duration can result in intertemporal customer segmentation, as different segments might be admitted at different times in the dynamic sign-up process. Such intertemporal segmentation is a natural discrimination scheme and has nontrivial implications. First, the efficiency loss due to waiting for enough sign-ups may decrease when a larger batch size is required for economic production. Second, as valuation heterogeneity in the market increases, the firm may not always benefit from offering group buying along with the regular product. Third, group buying can achieve a win-win-win situation for both high-end and low-end customers as well as the firm. Managerial implications : In addition to demonstrating the profitability of flexible-duration group buying, we show that the firm can strengthen its profitability by contingently setting prices or concealing sign-up information in group buying. We also confirm the robustness of our main insights by considering customers’ heterogeneous patience levels and horizontally differentiated products, among other factors.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".