Building age-inclusive brands: the case of 19/99 Beauty
Bibliographic record
Abstract
Purpose This paper aims to answer the under-researched question: how can brand managers design an age-inclusive brand? Design/methodology/approach An extended case study approach was used to trace the creation of a successful age-inclusive beauty brand (19/99 Beauty) through a variety of data, including archival, social media, interviews and participant observation. Findings This study finds three forms of institutional work institutional entrepreneurs undertake to design an age-inclusive brand by fostering belonging: narrative work that positively reframes stereotypes associated with the stigmatized group, material work that expands the possibilities for the stigmatized group to engage with the market and relational work that gives voice to a range of market actors from the stigmatized group. Research limitations/implications This paper defines age-inclusive branding as fostering a sense of belonging in the marketplace of the traditionally overlooked aging consumer segment. Originality/value To the best of the authors’ knowledge, this is one of the first empirical studies exploring how branding professionals create age-inclusive brands. It suggests that entrepreneurs and brand managers should treat age-inclusive consumers as an intersectional segment that cuts across traditional segmentation tools. Additionally, it provides an age-inclusive branding checklist for branding professionals.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| 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".