Circular Economy and Marketing Strategy: How Adidas Builds Brand Loyalty Through Recycled Plastics
Bibliographic record
Abstract
This paper analyzes how Adidas applies circular economy (CE) in its marketing to increase brand loyalty and credibility. Based on stakeholder theory, brand equity theory, and consumer behavior models, this paper explores how consumers’ perception, engagement, and loyalty are affected by its CE practices, such as collaboration with Parley for the Oceans and “Run for the Oceans” campaigns. By means of qualitative content analysis of CSR reports, marketing communications, and consumer comments on social media, the research findings show that CE practices benefit Adidas by improving consumers’ trust and emotional bonding as brand identity is confirmed with environmental values. Meanwhile, doubts and skepticism emerge from the limited environmental contribution by the small proportion of recycled products and the potential of greenwashing. Our research concludes that as CE marketing brings competitive advantages to Adidas currently, it needs to transform into a genuine and scalable change to maintain differentiation in a sustainability landscape increasingly becoming mature. This study contributes to the understanding of how circular economy strategy can combine ecological image and brand equity in global sportswear industries.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".