Lackluster Adoption of Cryptocurrencies as a Consumer Payment Method in the United States—Hypothesis: Is This Independent Technology in Need of a Brand, and What Kind?
Bibliographic record
Abstract
Cryptocurrencies were supposed to replace traditional payment methods when they were invented over 13 years ago, but adoption by the general consumer is still lacking, at least in the United States. Instead, crypto is often used as a speculative investment, by illicit actors, or for use cases unrelated to everyday purchases. A literature review on general adoption barriers and interviews with experts has only unearthed factors like usability, performance, and political drivers, among other barriers. Brand as an adoption barrier is mostly missing from literature, at least for cryptocurrencies. This led to the formation of a hypothesis related to crypto’s lack of adoption as a payment method. A framework is being designed based on the technology adoption model to find out if “brand” has an impact on cryptocurrency adoption, which was paradoxically designed to be brandless and not needing any institutional trust. The intent is to focus on what “Bitcoin 2.0” might look like, and to also delve further and gauge perceptions about various types of brands getting involved in the next generation of cryptocurrencies, including traditional banks, governments, technology companies, and also some of the decentralized and hybrid consortia currently vying to get consumers to use stablecoins, nation-issued cryptocurrencies, and other forms of digital instruments. While other studies had focused on trust, early adopter usability, or performance of blockchain networks, this work intends to focus on the general consumer’s perceptions about digital money, and the types of brands and evolution of this instrument liable to increase uptake.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".