Cryptocurrency Technology Adoption: A Bibliometric Analysis, Systematic Literature Review, and Future Research Agendas
Bibliographic record
Abstract
Purpose: The cryptocurrency’s growth as an emergent and disruptive financial technology has resulted in financial and economic systems and addressed some critical issues of them. Numerous academic studies have examined the adoption/acceptance of cryptocurrency. Despite the growing body of extant research and published review articles, no review has provided an integrative look at the cryptocurrency adoption/acceptance models (C-TAMs). Methodology: The main aim of this research is to provide a quantitative (bibliometric analysis) and qualitative review (systematic literature review) of the most relevant research from 2008 until 2023. Findings: This analysis extracted 98 publications from 75 journals, written by 292 authors, from the Institute for WOS and Scopus databases. Multidisciplinary or interdisciplinary journals typically publish studies on C-TAMs. The theories most commonly used in this field are the ‘unified theory of adoption and use technology’ (UTAUT) and the ‘technology adoption model’ (TAM). Systematic literature reviews show that these theories and their components are the most frequently mentioned factors affecting cryptocurrency adoption. The most important factors investigated for cryptocurrency adoption are ‘intention to use’, ‘perceived trust’, ‘perceived risk’, ‘perceived usefulness’, and ‘perceived ease of use’. Also, the research identifies variables at the micro-level (individual factors), mezzo-level (technological factors), and macro-level (socioeconomic factors) that affect cryptocurrency acceptance. Originality/value: The findings provide a valuable resource for researchers and stakeholders seeking to understand the dynamics of cryptocurrency adoption. While this study does not include new experimental data, it suggests that future research could benefit from comparative case studies and practical experimentation across various countries to understand further how local contexts influence cryptocurrency adoption.
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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 | 0.037 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.158 | 0.152 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".