Resale Royalty in Non-Fungible Token Marketplaces: Blessing or Burden for Creators and Platforms?
Bibliographic record
Abstract
Resale royalties, first introduced in the 1920s to support artists through a share of future resales, have now adopted by nonfungible token (NFT) marketplaces for digital art trading. Although these royalties are often viewed as beneficial for creators, our research reveals unexpected consequences. Using data from a major NFT marketplace, we find that NFTs with higher royalty rates sell for significantly lower prices and take longer to sell. Surprisingly, creators do not recoup these initial losses through royalty payments within four years. We discover that higher up-front minting costs lead creators to set higher royalty rates. We reveal a delayed gratification effect where creators with higher royalties accept lower up-front prices in hopes of future royalty income. We also find an overconfidence effect where confident creators, measured by their past sales and follower count, are more likely to lower initial prices. Our research contributes to the ongoing debate about royalty enforcement in NFT marketplaces and offers empirical evidence to inform platforms and creators. Platform managers should carefully consider both reducing up-front minting costs and implementing royalty rate limits to improve market liquidity. Creators should be cautious about setting high royalty rates as they may not provide the expected financial benefits.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".