Counter‐stereotypical products: Barriers to their adoption and strategies to overcome them
Bibliographic record
Abstract
Abstract Counter‐stereotypical products (CSPs) are targeted at groups that are opposite to the stereotypical users of these products (e.g., face‐cream for men, construction tools for women). Such products entail adoption barriers, as they are associated with a dissociative out‐group (e.g., men avoid products used by women). A theoretical framework is developed to investigate such barriers by outlining consumers’ cognitive and affective responses to CSPs; namely: stereotyping (CSP is considered appropriate only for the stereotypical user group), subtyping/subgrouping (CSP is useful for certain individuals or subgroups), and derogating (disparaging the CSP due to a perceived threat to self). Study 1 verifies these responses and demonstrates their effect on the evaluation of CSPs targeting men versus women. Overall, CSPs targeting men faced more barriers than those targeting women, and this was especially so for publicly consumed CSPs (e.g., purse for men) as compared to privately consumed ones (e.g., hair‐remover for men). Study 2 examined the effect of a common marketing tool—product design color (e.g., using blue for men and pink for women)—in reducing the above barriers. It was found that blue is effective in reducing stereotype‐based barriers for CSPs targeting men. For CSPs targeting women, using pink was only effective for women scoring high on femininity, and it backfired for those scoring low on femininity.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".