Digital Innovation for Sustainable Fashion: Virtual Fitting Rooms as a Strategy to Minimize Waste and E-commerce Returns
Bibliographic record
Abstract
This study aims to determine the efficacy of digital innovations and Virtual Fitting Rooms (VFRs) in minimizing online returns and waste in the fashion industry. This study used both primary and secondary data, including 1) a narrative literature review approach based on data from multiple secondary sources, including scientific journals, e-commerce reports, tech company websites, and other relevant and verified sources, 2) an online survey with sixteen respondents and eight semi-structured interviews with tech companies and virtual fitting rooms developers were conducted to establish baseline data of their perception on the role of developers and VFR tech companies concerning sustainable fashion and reverse logistics. This study assessed information of current VFR apps, including issues with technology development, relationships with e-commerce retailers, effect of VFR in reducing online returns and increasing customer satisfaction. Collected data determined the effects of existing VFRs and digital solutions adopted by the fashion industry to help customers in the sizing and fitting process to reduce online returns and e-commerce waste. The results highlight that VFRs are effective in minimizing returns, increasing customer satisfaction, and promoting sustainability by reducing CO2 emissions due to transportation. This study shows that VFRs could reduce 31.3% of reverse logistic costs and minimize store sizing sampling. There is room for future research on the usage of VFR in 3D cloth simulation engines in pre-production and designers’ collections, which could potentially have a more significant environmental impact on the minimization of fashion waste and overproduction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".