Digital future of luxury brands: Metaverse, digital fashion, and non‐fungible tokens
Bibliographic record
Abstract
Abstract Leading luxury brands have incorporated technologies to recreate brand images and reinvent consumer experience. The fashion industry is experiencing a historic transformation thanks to emerging technologies such as blockchain and non‐fungible tokens (NFTs) along with impactful technologies such as artificial intelligence (AI), machine learning (ML), and virtual reality (VR). With metaverse as a new social platform around the corner, academics and industry alike are querying how these new technologies might reshape luxury brands, reinvent consumer experience, and alter consumer behavior. This research charts new academic territory by investigating how newly evolved technologies affect the fashion industry. With practical examples of luxury brands, this article has theorized the irreversible trend of digital fashion: the attraction of NFT collectibles. It then proposes intriguing questions for scholars and practitioners to ponder, such as will young consumers, essentially living online, buy more fashion products in the digital world than in the real world? How can the fashion industry strategize for the coexistence of digital collections and physical goods?
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".