Why buy used? Motivators and barriers for re-commerce luxury fashion
Bibliographic record
Abstract
Purpose The sale of second-hand goods in the luxury fashion space continues to soar. However, existing literature on this segment is limited and the factors that draw consumers to this space are not well understood. This study aims to fill this gap and proposes a conceptual model demonstrating the linkage between the motivators and barriers toward re-commerce in the luxury fashion space and actual shopping behaviors. Design/methodology/approach A survey sample of USA second-hand luxury fashion shoppers was collected. Participants were asked questions about various motivators and barriers toward re-commerce, as well as the participants' attitudes and shopping behavior. The results were analyzed using SmartPLS structural equation modeling (SEM). Findings Economic reasons, originality and self-extension were found to be statistically significant motivators of attitudes toward re-commerce, while status consumption, nostalgia and ecological motivators were not. Superstitious beliefs were also found to be statistically significant motivators toward attitudes of re-commerce. Attitudes were also found to be a significant predictor of shopping behavior as measured by dollars spent and shopping frequency. Originality/value This study is among the first to propose a conceptual model depicting the relationship between motivators and barriers to actual shopping behavior in the second-hand luxury fashion space. Many of the motivators and barriers examined in this study are novel and have not been considered in prior research. Superstitious beliefs in particular have not been studied in the context of re-commerce.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".