When “More” Seems Like Less: Differential Price Framing Increases the Choice Share of Higher-Priced Options
Bibliographic record
Abstract
Four experiments supported by six supplemental studies show that premium but higher-priced products (e.g., direct flights, larger-capacity data storage devices) are more popular when the additional cost is made explicit using differential price framing (DPF; e.g., “for $20 more”) rather than being left implicit, as in standard inclusive price framing (IPF; e.g., “for $60 total”). The DPF effect is driven by pricing focalism: relative to IPF, DPF creates a focus on the price difference, which, because it is smaller than the total price, leads to lower perceived expensiveness and thus greater choice share for the premium option. This price framing effect is robust to displaying the total cost of the purchase, bad deals, and easy-to-compute price differences, and it appears to be uniquely effective in pricing contexts. However, DPF effects are reduced among consumers who adopt a slow and effortful decision process. These findings have implications for research on price partitioning, the design of effective pricing strategy, the sources of expensiveness perceptions in the marketplace, and consumer welfare.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".