Digital Assets in the Eyes of Generation Z: Perceptions, Outlooks, Concerns
Bibliographic record
Abstract
The recent decade saw an explosion of digital assets and digitalisation of financial services. The present contribution poses several research questions incorporated into a survey questionnaire and grouped into two categories: (1) associations with, knowledge of, and familiarity with notions relevant to digital assets and (2) perceptions of digital assets and attitude towards investing in them. Invitations to participate were sent to a group of 570 random representatives of Generation Z with 387 correctly completed questionnaires employed in the study. The research demonstrated that it was not insufficient funds that posed the greatest barrier to the growth in digital assets investments. The respondents justified their concerns about digital assets with poor knowledge of cryptocurrencies and non-fungible tokens (NFTs). The scepticism is fuelled mostly by the nontangible nature of digital assets (approx. 23%). The respondents most commonly (123, approx. 47%) associated NFTs with digital works of art, virtual objects, and NFT graphics. Blockchain most often brought to the minds of the respondents databases, algorithms, data recording, transaction data transfer, data cloud transactions, cryptocurrencies, cryptography, and decentralised financial systems. The research seems to suggest a certain difficulty with representing (characterising) the digital ecosystem and virtual reality. The media narrative emphasises the intangible nature of the digital ecosystem, often depicting it as impalpable and unreal, which does not help with how prospective investors view it. Some recommendations emerge from the research that should be considered when drawing a strategy for presenting digital assets, cryptocurrencies, and NFT markets.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".