Consumer Perceptions of Price Reframing in an In-Store Decision Context
Bibliographic record
Abstract
This article compares consumer responses to price reframing methods in an in-store decision context where price comparisons among brands can be conducted. The methods examined are measure-based unit pricing, usage-based unit pricing, and temporal reframing of price. They differ in the unit used for calculating reframed prices: measure-based unit pricing uses weight or volume, usage-based unit pricing uses usage account, and temporal reframing of price uses time. This area of research has not been fully explored. Results from a laboratory experiment showed that measure-based and usage-based unit pricings were evaluated better than temporal reframing of price on usability, likeability, and comprehensibility. Also, measure-based unit pricing was the best at making the choice perceived easier, whereas usage-based unit pricing was the best at increasing the attractiveness of retail prices. Moreover, consumers who chose the brand with the lowest reframed price generated favorable price and quality perceptions for the chosen brand when usage-based unit pricing or temporal reframing of price was used but generated only favorable price perception when measure-based unit pricing was used.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".