Revolutionizing consumption: Unveiling the Allure of NFTs and digital twins for sustainable luxury fashion
Bibliographic record
Abstract
Non-fungible tokens (NFTs) are revolutionizing luxury fashion by offering digital experiences that promise innovation, exclusivity, and sustainability. While luxury brands increasingly experiment with these technologies, little is known about how they influence consumer perceptions of sustainability, brand legitimacy, and purchase likelihood. Drawing on dematerialization theory, institutional and legitimacy theory, and the sufficiency model, this research investigates NFTs’ role in promoting sustainable consumption and brand legitimacy. Building on insights from a preliminary qualitative study, three experiments test how product type (non-NFT, NFT, digital twin) affects purchase likelihood and how perceived product sustainability and brand legitimacy moderate and mediate these effects. Study 1 shows that digital twin products combining physical and NFT components yield the highest likelihood of purchase. Study 2 finds the positive effect of NFTs strengthens when perceived product sustainability is high. Study 3 reveals perceived product sustainability acts as a boundary condition, shaping how product type influences brand legitimacy and purchase likelihood. Findings offer theoretical insights and actionable guidance for managers.
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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 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".