Exploring Cryptocurrency Acceptance Patterns: An In-depth Review of Influencing Factors from Adoption to Adaption for Human Resource Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cryptocurrencies are rapidly emerging as a novel virtual financial system with significant implications across various industries. This study systematically reviews the factors influencing cryptocurrency adoption, identifying key motivators and barriers that affect individuals’ decisions to embrace this technology. Our findings reveal that while there is increasing interest in cryptocurrencies, substantial gaps remain in understanding the underlying motivations for adoption and the disparities in acceptance across different regions. We categorize these gaps and propose future research directions aimed at bridging them. Ultimately, this review contributes to a deeper understanding of cryptocurrency adoption dynamics and highlights the need for more comprehensive studies in this evolving field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it