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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".