Government Oversight and Institutional Influence: Exploring the Dynamics of Individual Adoption of Spot Bitcoin ETPs
Bibliographic record
Abstract
Spot Bitcoin Exchange Traded Products (ETPs) are financial instruments enabling Bitcoin to be traded on traditional brokerage platforms, reducing the risks associated with direct Bitcoin exposure while addressing fraud and market manipulation concerns. This study examines the adoption of Spot Bitcoin ETPs, emphasizing the roles of financial and digital literacy, market dynamics, and regulatory frameworks in influencing individual investor behavior. Based on a survey of 428 U.S. respondents, financial literacy and early adopter traits were found to significantly enhance adoption likelihood (β = 0.458, p < 0.001). Government factors, such as compliance guidelines and tax policies, improved investor confidence and adoption rates (β = 0.409, p < 0.001). Market factors, including volatility and sentiment, played a notable yet secondary role (β = 0.34, p < 0.001). Institutional investment mediated the effects of regulatory and market dynamics on individual adoption, legitimizing Spot Bitcoin ETPs and fostering trust (β = 0.298, p < 0.001). The findings emphasize the need for clear regulations, robust disclosure requirements, and investor education to enhance adoption. Policymakers should focus on regulatory transparency to build investor confidence, while financial institutions can advance adoption by promoting financial and digital literacy. This study contributes to understanding how individual, market, and regulatory factors collectively drive the integration of regulated cryptocurrency products into mainstream finance.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".