Non-Adoption of Crypto-Assets: Exploring the Role of Trust, Self-Efficacy, and Risk
Bibliographic record
Abstract
Over the last years, crypto-assets have gained significant interest from private investors, academia, and industry. While the user population and their motivations, perceptions, and behaviors have been studied, non-adopters and factors influencing their decision have been left unexplored. This work fills this knowledge gap and sheds light on the effects of trust, perceived self-efficacy, and risk, which have been shown to be the key antecedents to technology acceptance, on the adoption intention of non-users. We propose and empirically test a theoretical model that explains the adoption intention of crypto-assets among those, who decided against using them. The validity of the model is assessed in a structural equation model analysis of 204 non-users. Results revealed that trust is a critical factor affecting adoption intention, with perceived self-efficacy having a mediating effect. Building on the results, practical recommendations are offered that could lower the entry barriers and facilitate the adoption of crypto-assets.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".