Blockchain, Cryptos and NFTs in the gaming industry: A tale of two worlds
Bibliographic record
Abstract
The gaming industry is at a crossroads with Web 3.0. The ever-growing gaming industry is at a tipping point of mass blockchain adoption. Based on public and secondary data analysis, we show that the gaming industry is shifting toward Web 3.0, which could significantly affect its practices and business models. Aligned with this change, big gaming companies have launched new technologies associated with Web 3.0, such as NFTs and cryptocurrencies. The sector’s reaction could indicate the society’s response to including cryptocurrencies and NFTs in an increasing number of industries. Reluctant North American players. North American players have been unwilling to include cryptocurrencies and NFTs in their games. They felt this integration was threatening the “gaming” logic of the industry and creating some ecological issues. Welcoming Asian and Latin American players. In sharp contrast, Asian and Latin American countries such as China and the Philippines have seen a more significant intake from gamers, who perceived cryptocurrencies and NFTs as an opportunity to generate more revenues. A tale of two worlds. Our study argues that consumers associated cryptocurrencies and NFTs with a financial logic, and would prefer not to use those technologies if their primary goal was to play. This prevented the mass adoption of those technologies in North America, where most gamers associated gaming with something other than revenue generating (i.e., playing, escaping in a virtual world). Instead, Asia-Pacific gamers saw the opportunities in these technologies to generate additional revenues and create new markets. This led to a strong divide between both parts of the world regarding Web 3.0. The report outlines some implications for the future of the industry and the rest of society.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".