Bibliographic record
Abstract
Purpose This paper aims to improve understanding of the effects of price endings on consumers' choice behavior. The research study described here was driven by three central questions. First, do consumers process a price holistically or process each digit as a stimulus? Second, do consumers “round” prices? Third, do price endings such as 9 or 0 have specific effects on purchase intentions? Design/methodology/approach The study is based on a discrete choice experiment where consumers responded to two‐digit prices. Tomato soup and backpacks were the two product categories selected for the study. A total of 188 university students who had purchased these products completed an online survey indicating their choices for one of the four alternative products, with an option of not purchasing anything. Tomato soup prices were varied from 40 cents to 99 cents (every potential price ending was included) and backpack prices varied from $30 to $59 (no pennies). Each respondent made 20 choices for each product and the resulting database was used to construct the nested logit models. Findings Estimated models suggest that consumers do not process price holistically. In other words, respondents processed prices by splitting numbers into two parts. Furthermore, the use of truncation and the effects of “odd/even” and “0” appeared to be statistically significant for both canned soup and backpack products. Although there was rounding of prices for the soup category, there was no statistically significant support for that in the backpack category. Finally, the effect of a 9‐ending was statistically significant for the backpack category but not for the soup category. Practical implications The findings suggest that consumers may not process prices holistically. This, in turn, means that price endings are likely to influence consumer price sensitivity and both retailer and manufacturer profits. Originality/value This is the first paper that examines price endings for all numbers from 0 to 9. In addition, the use of a discrete choice modeling method to infer individual choice behaviour in this context is new and innovative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".