Is it fashionable to swap clothes? The moderating role of culture
Bibliographic record
Abstract
Abstract The fashion industry has received harsh criticism about its increasing environmental footprint. As a result, formal clothing swapping has evolved into collaborative sharing practices, sometimes leading to circular social and economic developments. In either case, it transforms how individuals behave around possessing and sharing clothes for what it can bring to them and their collective well‐being. This study explores what factors (e.g., economic, hedonic, environmental, and activism) motivate individuals to swap their clothes and why culture may be an important moderating factor to consider. An online Amazon Mechanical Turk (MTurk) survey comprised of the NextGen (individuals between 18 and 35), 51.6% of workers, and 28% of students ( n = 279) from various countries analyzed through ANOVA regressions led to clear evidence of the moderating effects of culture on motivating factors in swapping behavior for clothes: (1) economic motives are stronger in masculine cultures; (2) hedonic motives are stronger in collectivist cultures; (3) environmental motives are stronger in collectivist, low power distance, and indulgent cultures; (4) activist motives are relatively strong in collectivist, feminine, low power distance, low uncertainty avoidance, and indulgent cultures; (5) the collectivist culture had a moderated influence due to hedonic, environmental, and activist motives as in this culture, greater mental emotions may be aroused. This study highlights economic and hedonist motives as influential variables that correspond to more actual consumer needs. Results also indicate specificities on NextGen culture identity and motivations to push forward a shift in up‐swapping systems, engaging decision‐makers for more transparent, sustainable efforts.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".