Fashion Rewind: A Segmentation of Second-hand Shoppers in Luxury Fashion
Bibliographic record
Abstract
<p>The purpose of this study is to uncover to what extent economic and hedonic motivations, as well as general attitudes towards second-hand shopping influence consumers to purchase second-hand luxury fashion. Prior discussions of luxury consumption have focused on either brand-new luxury goods, or second-hand clothing in general, thus largely neglecting the emergence of markets for used luxury fashion. The data for this study was generated through an online survey questionnaire with second-hand luxury owners from the United States (N=302). This research utilized exploratory factor analysis, as well as hierarchical and K-means cluster analysis to produce three cluster segments of second-hand luxury shoppers: hedonic shoppers, high-spirited shoppers, and indifferent shoppers. Furthermore, consumers self-extension tendencies, shopping frequencies, and expenditures were analyzed to guide the assessment of the segments and allow for better understanding of the second-hand luxury consumer. This study suggests important implications for retailers and luxury brand marketers.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".