One size fits all? Segmenting consumers to predict sustainable fashion behavior
Bibliographic record
Abstract
Purpose This study segmented consumers by combining emotional and shopping characteristics to develop typologies that classify their consumption patterns and disposal behaviors. Design/methodology/approach To identify segments of fashion consumers, an online questionnaire was administered measuring emotional and shopping characteristics, including perspective taking, empathic concern, personal distress, hedonism, and frugality. An online questionnaire involving 168 US-based participants were used to accomplish the purpose of the study. A cluster analysis was conducted to identify segments of participants based on these variables. Consumption patterns and disposal behavior, including motivation to buy environmentally friendly items, consciousness for sustainable consumption, buying impulsiveness, likelihood to follow fashion trends, and tendencies to dispose of or repair damaged or unwanted items were also measured via the questionnaire as dependent variables to be predicted by identified segments. Findings Three clusters of consumers were identified as: Distressed and Self-Oriented, Warm and Thrifty, and Cold and Frivolous. Distressed and Self-Oriented individuals reported the highest levels of personal distress and hedonism. Warm and Thrifty individuals reported the highest levels of empathic concern, perspective taking and frugality, and the lowest levels of personal distress and hedonism. Cold and Frivolous individuals reported the lowest levels of perspective taking, empathic concern, and frugality. Originality/value The classification of consumers into segments brings a new dimension to the field of sustainable fashion. Clusters were created according to the variables of emotional characteristics (i.e. perspective taking, empathic concern, and personal distress) and shopping characteristics (i.e. hedonism and frugality). The analysis unveiled three distinct clusters that can be utilized to develop tailored strategies to successfully promote sustainable fashion consumption.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".