Digital future of luxury brands: Metaverse, digital fashion, and non‐fungible tokens
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Insufficient payload (model declined to judge)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: QualitativeConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.664
- Threshold uncertainty score
- 0.996
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.165 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
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.
The record
- Venue
- Strategic Change
- Topic
- Fashion and Cultural Textiles
- Field
- Arts and Humanities
- Canadian institutions
- University of OttawaUniversity of British Columbia
- Funders
- not available
- Keywords
- Fashion industryBusinessMetaverseEmerging technologiesEmerging marketsMarketingAdvertisingDigital transformationDigital marketingComputer scienceVirtual realityWorld Wide WebClothingHuman–computer interactionPolitical science
- Has abstract in OpenAlex
- yes