Is Top 10 Better than Top 9? The Role of Expectations in Consumer Response to Imprecise Rank Claims
Bibliographic record
Abstract
Many marketing communications are carefully designed to cast a brand in its most favorable light. For example, marketers may prefer to highlight a brand's membership in the top 10 tier of a third-party list instead of disclosing the brand's exact rank. The authors propose that when marketers use these types of imprecise advertising claims, subtle differences in the selection of a tier boundary (e.g., top 9 vs. top 10) can influence consumers’ evaluations and willingness to pay. Specifically, the authors find a comfort tier effect in which a weaker claim that references a less exclusive but commonly used tier boundary can actually lead to higher brand evaluations than a stronger claim that references a more exclusive but less common tier boundary. This effect is attributed to a two-stage process by which consumers evaluate imprecise rank claims. The results demonstrate that consumers have specific expectations for how messages are constructed in marketing communications and may make negative inferences about a brand when these expectations are violated, thus attenuating the positive effect such claims might otherwise have on consumer responses.
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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 | 0.012 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".