The Strategic Value of a Seller's Advance Booking Discount Program
Bibliographic record
Abstract
In an Advance Booking Discount Program (ABDP), a firm offers a product at a price discount prior to the selling season. In the selling season the product is sold at a regular price. The aim of the paper is to study the strategic value of an ABDP under uncertainty with respect to a specific consumer characteristic. The setup is a duopolistic market, modelled by Hotelling’s “linear city” where two firms, A and B, are located at the boundaries of the city. A and B are uncertain about consumers’ transportation costs. Firm A only has the option to implement an ABDP. Three scenarios emerge, each generating a particular outcome. In the first scenario it is not optimal for A to set up an ABDP. The reason typically is that the selling price of B is considerably larger than that of A and there is no incentive to implement the program. In the two other scenarios, A implements an ABDP in which the reduced price enables A to attract customers from B. If consumers’ transportation costs are sufficiently low, B actually exits the market. We show that when A does not implement an ABDP, its expected profit increases as uncertainty about consumers’ transportation cost increases. We also show that the gain of implementing an ABDP may depend on uncertainty in various ways. Resume Un Programme d’Achat Anticipe (PAA) consiste pour une entreprise a proposer aux consommateurs d’acheter a un prix reduit un produit qui sera livre durant la saison reguliere. Le produit est disponible au prix regulier durant la saison. L’objectif de cet article est d’evaluer la valeur strategique d’un PAA en presence d’incertitude sur une caracteristique des consommateurs. Le cadre analytique est celui d’un marche duopolistique a la Hotelling ou deux firmes, A et B, sont localisees aux deux extremites de la ligne. Les deux firmes sont incertaines quant au cout de transport des consommateurs. Seule la firme A peut offrir un PAA. Trois scenarios engendrant des gains differents peuvent emerger. Dans le premier, il n’est pas optimal pour A d’offrir un tel programme. La raison est typiquement que le prix regulier au cours de la saison est considerablement plus eleve que le prix reduit. Dans les deux autres scenarios, A implante un PAA pour attirer une partie de la clientele de B. Si le cout de transport est en fait suffisamment bas, la firme B sort du marche. On montre que quand A n’offre pas un PAA, son profit espere augmente avec l’incertitude sur le cout de transport des consommateurs. On montre aussi que le gain de proposer un PAA peut dependre de plusieurs facons de l’incertitude. Acknowledgments: This paper was presented in a seminar at Department of Business and Economics, University of Southern Denmark, Odense, and in The Second Workshop on Game Theory in Marketing, Montreal. Les Cahiers du GERAD G–2007–38 1
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".