Selling sustainability: investigating how Swedish fashion brands communicate sustainability to consumers
Bibliographic record
Abstract
Over the last thirty years, sustainability has become a growing concern in the fashion industry. While there is agreement among a growing range of actors regarding the need to engage with the social and environmental challenges created by the fashion industry, there is less consent regarding what sustainability entails. Although “sustainability” may be intuitively understood, it has different meanings, depending on how it is applied, and who it is applied by. Without a clear-cut definition, sustainability becomes subjective. In this context, there is a need for research at the intersection of brand-sustainability initiatives and their communication to consumers, who play a vital role in this transition. Drawing on a case study of the Swedish fashion industry, we explore how evolving industrial business models and emerging best practices are informed by a robust understanding of sustainability. We evaluate how brands communicate sustainability to consumers across three key sites: brand websites (including corporate social responsibility reports), social media platforms, and in-store campaigns. We found that not only do brands use a range of practices to define sustainability differently, but furthermore, these definitions vary depending on the context. Considering the industry’s ongoing history with greenwashing, it is vital to address and confront this issue head on. We argue that there is a need to determine what constitutes sustainability in the fashion industry and, in turn, hold businesses to that standard. As COVID-19 has only magnified and intensified these challenges, the article explores the implications of a more robust approach for both theory and practice.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".