Luxury brands’ use of CSR and femvertising: the case of jewelry advertising
Bibliographic record
Abstract
Purpose Luxury brands seek to differentiate themselves from competitors by engaging in corporate social responsibility (CSR) practices. Although many luxury brands participate in CSR activities, it is unclear if luxury brands communicate these CSR activities to consumers. Therefore, this study aims to explore two questions: are luxury jewelry brands communicating CSR (including women’s empowerment) in their advertising? And how should luxury jewelry brands communicate CSR messages in their advertising? Design/methodology/approach This paper uses a content analysis of luxury jewelry print advertisements and in-depth interviews with 20 female jewelry consumers analyzed using grounded theory to construct the luxury brand CSR advertising strategies theory. Findings Very few (3%) of print advertisements contain CSR messages, including femvertising and the theory presents four paths for brands to consider when promoting CSR practices, namely, ethical sourcing, cause-related marketing product, a signal of product care and quality and signal of an authentic relationship with the consumer. Practical implications The model provides four potential CSR advertising strategies and guidelines luxury jewelry brands can use to create successful advertising campaigns. Originality/value Luxury jewelry advertising has not been empirically examined and the study fills gaps in the understanding of luxury brands’ communication strategies. It adds to the knowledge and theorizing of the use and appropriateness of CSR appeals in a luxury brand context.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".