Dress to Impress or Distress! The Value Conflict Behind Gen Z's Fast Fashion remorse
Bibliographic record
Abstract
This study will investigate the determining factors of Gen Z's intention to discontinue fast fashion (FF) brands, based on the interaction of the push-pull-mooring (PPM) framework and cognitive dissonance theory. This research provides new perspectives on responsible buying habits in emerging economies by linking psychological deterrents, such as remorse after fast fashion purchases, to subsequent behavioural shifts towards more sustainable alternatives. The data is collected through a structured questionnaire of 251 Gen Z respondents who experienced remorse after purchasing FF apparel. The study reveals that buyer remorse (BR) is significantly influenced by factors such as ascribed responsibility, inferior quality products, social influence, and environmental concern. The findings indicate that Generation Z may experience heightened remorse regarding fast fashion purchases when they perceive a sense of ascribed responsibility and face disapproval from their social networks. The findings suggest that FF brands should focus on product quality with genuine environmental measures rather than resorting to greenwashing claims. FF Brands must prioritise social influence, community development, and accountability in alignment with the values of Generation Z. FF brands may mitigate consumer remorse by implementing sustainable processes and fostering transparent communication.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".