Demand-Based Pricing versus Past-Price Dependence: A Cost–Benefit Analysis
Bibliographic record
Abstract
The authors develop a conceptual framework of the factors that motivate a retailer's decision to rely on demand conditions and past prices in setting current and future prices. Specifically, they examine the circumstances under which retailers choose demand-based pricing versus past-price dependence for different brands and categories. Given scarce resources and costs of price adjustments, demand-based pricing is more likely when the customer-driven and firm-driven costs of adjusting pricing patterns are low or when the benefits of such adjustments are high. First, the customer-driven benefits of demand-based pricing are expected to be greater in categories with higher penetration and for brands with higher market share and higher demand sensitivity to price. Second, the firm-driven benefits are greater for categories with higher private-label share. Finally, the customer-driven costs are greater for expensive categories, whereas the firm-driven costs are greater for categories with many stockkeeping units. The empirical findings support the conceptual framework, implying that customer-driven and firm-driven benefits are the main stimulants in the retailer's choice of demand-based pricing. In contrast, customer-driven and firm-driven costs significantly hinder retailer implementation of demand-based pricing. These insights enable retailers to identify problem areas and opportunities to improve the allocation of scarce pricing resources. The results also contribute to the ongoing debate in economics and marketing on the rationality of observed past-price dependence. Whereas previous research points to the negative impact on gross margins of this practice, the authors find that retailers weigh the costs and benefits of demand-based pricing rather than adhere to past-pricing patterns.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".