Government Oversight and Institutional Influence: Exploring the Dynamics of Individual Adoption of Spot Bitcoin ETPs
Abstract
:1. Introduction
Research Objectives and Scope
- How do SEC regulations (e.g., regulatory clarity, tax policies, compliance) directly influence individual adoption of Spot Bitcoin ETPs?
- How do individual factors (e.g., financial literacy, risk tolerance, innovativeness) contribute to Spot Bitcoin ETP adoption, and how do they compare in strength to SEC regulations?
- How do market factors (e.g., volatility, demand, sentiment) directly impact Spot Bitcoin ETP adoption?
- Does institutional investment mediate the relationship between SEC regulations, market sentiment, and individual adoption?
2. Literature Review
2.1. Foundational Context
2.2. Spot Bitcoin ETPs
2.3. Bridging the Context: From ETPs to Related Work
2.4. Proposed Model and Hypotheses
3. Research Methodology
3.1. Data and Sample
3.2. Data Collection and Analysis
3.3. Measurement Model
4. Results
4.1. Instrument Assessment
4.2. Convergent Validity
4.3. Discriminant Validity
4.4. Formative Construct Validation
4.5. Structural Model and Path Analysis
4.6. The Explanatory Power of the Research Model
5. Discussion
5.1. Key Insights
5.2. Theoretical Implications
5.3. Practical Implications
5.4. Limitations and Future Research
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes. [Google Scholar]
- Anser, M. K., Zaigham, G. H. K., Imran Rasheed, M., Pitafi, A. H., Iqbal, J., & Luqman, A. (2020). Social media usage and individuals’ intentions toward adopting Bitcoin: The role of the theory of planned behavior and perceived risk. International Journal of Communication Systems, 33(17), e4590. [Google Scholar] [CrossRef]
- Anuyahong, B., & Ek-udom, N. (2023). The impact of cryptocurrency on global trade and commerce. International Journal of Current Science Research and Review, 6(4), 2543–2553. [Google Scholar]
- Auer, R., Farag, M., Lewrick, U., Orazem, L., & Zoss, M. (2022). Banking in the shadow of Bitcoin? The institutional adoption of cryptocurrencies (BIS Working Paper No. 1013). Bank for International Settlements. Available online: https://www.bis.org/publ/work1013.htm (accessed on 11 March 2025).
- Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, customs, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026. [Google Scholar] [CrossRef]
- Bondarenko, N., & Soponar, P. (2024). Bitcoin ETF approval: Catalyst for crypto realization and augmented real-world utility. YNBC Research Institute. Available online: https://www.theconnecter.io/pdf/2024-01-23-_YNBC%20and%20The%20Connecter%20R_D_%20Bitcoin%20ETF%20Approval.pdf (accessed on 11 March 2025).
- Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213–238. [Google Scholar]
- Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689–707. [Google Scholar]
- Chainalysis. (2024). North America: Institutional momentum and U.S. Bitcoin ETPs propel crypto further into the mainstream. Available online: https://www.chainalysis.com/blog/north-america-crypto-adoption-2024/ (accessed on 11 March 2025).
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336. [Google Scholar]
- Chokor, A., & Alfieri, E. (2021). Long and short-term impacts of regulation in the cryptocurrency market. The Quarterly Review of Economics and Finance, 81, 157–173. [Google Scholar]
- Congressional Research Service. (2024). SEC approves Bitcoin exchange-traded products (ETPs). Available online: https://crsreports.congress.gov/product/pdf/IF/IF12573 (accessed on 11 March 2025).
- DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. [Google Scholar]
- Fornell, C., Tellis, G. J., & Zinkhan, G. M. (1982, ). Validity assessment: A structural equations approach using partial least squares. American Marketing Association Educators’ Conference, Chicago, IL, USA. [Google Scholar]
- Gazali, H. M., Ismail, C. M. H. B. C., & Amboala, T. (2018, July 23–25). Exploring the intention to invest in cryptocurrency: The case of Bitcoin [Conference presentation]. 2018 International Conference on Information and Communication Technology for the Muslim World (ICT4M), Kuala Lumpur, Malaysia. [Google Scholar]
- Gerrans, P., Abisekaraj, S. B., & Liu, Z. F. (2023). The fear of missing out on cryptocurrency and stock investments: Direct and indirect effects of financial literacy and risk tolerance. Journal of Financial Literacy and Wellbeing, 1(1), 103–137. [Google Scholar]
- Gupta, H., & Chaudhary, R. (2022). An empirical study of volatility in the cryptocurrency market. Journal of Risk and Financial Management, 15(11), 513. [Google Scholar]
- Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis 6th Edition. Pearson Prentice Hall. New Jersey. humans: Critique and reformulation. Journal of Abnormal Psychology, 87, 49–74. [Google Scholar]
- Hayashi, F., & Routh, A. (2024). Financial literacy, risk tolerance, and cryptocurrency ownership in the United States (Working paper No. 24-03). Federal Reserve Bank of Kansas City. [Google Scholar]
- Härdle, W. K., Harvey, C. R., & Reule, R. C. (2020). Understanding cryptocurrencies. Journal of Financial Econometrics, 18(2), 181–208. [Google Scholar]
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. [Google Scholar]
- Jackson, G. (2024). Cryptocurrency adoption in traditional financial markets in the United States. American Journal of Finance, 9(1), 40–50. [Google Scholar] [CrossRef]
- Kai-Ineman, D. A. N. I. E. L., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 363–391. [Google Scholar]
- Kayani, U., & Hasan, F. (2024). Unveiling cryptocurrency impact on financial markets and traditional banking systems: Lessons for sustainable blockchain and interdisciplinary collaborations. Journal of Risk and Financial Management, 17(2), 58. [Google Scholar]
- Kim, K. T., & Fan, L. (2024). Beyond the hashtags: Social media usage and cryptocurrency investment. International Journal of Bank Marketing, 43(3), 569–590. [Google Scholar]
- Klein, R., & Rai, A. (2009). Interfirm strategic information flows in logistics supply chain relationships. MIS Quarterly, 33, 735–762. [Google Scholar]
- Krafft, P. M., Della Penna, N., & Pentland, A. S. (2018, April 21–26). An experimental study of cryptocurrency market dynamics [Conference presentation]. 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada. [Google Scholar]
- Krause, D. (2024). The rise of spot cryptocurrency ETFs: Implications for institutional investors (Working paper). SSRN. Available online: https://ssrn.com/abstract=4868157 (accessed on 11 March 2025).
- Kumari, V., Bala, P. K., & Chakraborty, S. (2023). An empirical study of user adoption of cryptocurrency using blockchain technology: Analysing role of success factors like technology awareness and financial literacy. Journal of Theoretical and Applied Electronic Commerce Research, 18(3), 1580–1600. [Google Scholar]
- Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689–2727. [Google Scholar]
- Low, R., & Marsh, T. (2019). Cryptocurrency and blockchains: Retail to institutional. The Journal of Investing, 29(1), 18–30. [Google Scholar]
- MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35, 293–334. [Google Scholar]
- Magnuson, W. (2018). Financial regulation in the Bitcoin era. Stanford Journal of Law, Business & Finance, 23, 159. [Google Scholar]
- Mazur, M., & Polyzos, E. (2025). Spot bitcoin ETFs: The effect of fund flows on bitcoin price formation. The Journal of Alternative Investments, 27(3). [Google Scholar] [CrossRef]
- Moffett, T. A. (2022). CFTC & SEC: The wild west of cryptocurrency regulation. University of Richmond Law Review, 57, 713. [Google Scholar]
- Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Available online: https://bitcoin.org/bitcoin.pdf (accessed on 11 March 2025).
- Rakowski, D. (2010). Fund flow volatility and performance. Journal of Financial and Quantitative Analysis, 45(1), 223–237. [Google Scholar]
- Reuters. (2024). MicroStrategy’s Nasdaq-100 entry attracts nearly $11 million retail inflows. Available online: https://www.reuters.com/technology/microstrategys-nasdaq-100-entry-attracts-nearly-11-million-retail-inflows-2024-12-17/ (accessed on 11 March 2025).
- Rogers, E. M. (1962). Diffusion of innovations (1st ed.). Macmillan. [Google Scholar]
- Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In D. K. Holtzhausen, & A. Zerfass (Eds.), The Routledge handbook of strategic communication (pp. 432–448). Routledge. [Google Scholar]
- Securities and Exchange Commission. (2024). Order granting accelerated approval of proposed rule changes to list and trade Bitcoin-based commodity-based trust shares and trust units; Securities and Exchange Commission. Available online: https://www.sec.gov/files/rules/sro/nysearca/2024/34-99306.pdf (accessed on 11 March 2025).
- Segars, A. H. (1997). Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information systems research. Omega, 25(1), 107–121. [Google Scholar]
- Singh, D. S. (2024). Decentralized finance (DeFi): Exploring the role of blockchain and cryptocurrency in financial ecosystems. International Research Journal of Modernization in Engineering Technology and Science, 6, 2888–2892. [Google Scholar]
- Smales, L. A. (2019). Bitcoin as a safe haven: Is it even worth considering? Finance Research Letters, 30, 385–393. [Google Scholar]
- Sohaib, O., Hussain, W., Asif, M., Ahmad, M., & Mazzara, M. (2019). A PLS-SEM neural network approach for understanding cryptocurrency adoption. IEEE Access, 8, 13138–13150. [Google Scholar]
- Spence, M. (1973). l the MIT press. The Quarterly Journal of Economics, 87(3), 355–374. [Google Scholar]
- Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20(3), 571–610. [Google Scholar]
- Von Neumann, J., & Morgenstern, O. (1947). Theory of games and economic behavior (2nd ed.). Princeton University Press. [Google Scholar]
Study | Focus Area | Key Findings | Theoretical Lens | Contribution Compared to Present Study |
---|---|---|---|---|
(Gazali et al., 2018) | Risk Tolerance | Greater risk tolerance predicts crypto adoption | Theory of Reasoned Action | We embed risk tolerance into broader individual factors and empirically test it |
(Sohaib et al., 2019) | Early adopter traits | Innovativeness affects the willingness to adopt crypto | Technology Readiness Index (TRI) and TAM | We integrate early adopter traits with other constructs in an SEM model to predict adoption. |
(Anser et al., 2020) | Market Sentiment and Behavior | Social media and perceived risk influence adoption | Theory of Planned Behavior (TPB) | We test sentiment empirically and incorporate institutional mediation to assess its role in individual adoption. |
(Liu & Tsyvinski, 2021) | Market Volatility | Volatility attracts risk-tolerant investors | Expected Utility Theory | Our study tests market volatility as a predictor of both institutional and individual adoption, with institutional mediation. |
(Chokor & Alfieri, 2021) | Government Regulation | Regulatory news influences crypto market returns | Not explicitly theoretical | We extend this by empirically analyzing the impact of regulation on individual behavior via institutional adoption. |
(Rakowski, 2010) | Risk tolerance | risk tolerance is significantly related to crypto ETF trading intentions | No Formal Theory–Risk Tolerance as Key Variable | We build on this by modeling risk tolerance within broader individual factors and testing its effect in an SEM framework with mediation by institutional investment. |
(Kumari et al., 2023) | Financial Literacy | Financial and tech literacy increase adoption | UTAUT2 | We include literacy as a multidimensional construct and compare its strength against regulatory and market factors. |
(Auer et al., 2022) | Institutional Infrastructure | Institutions bring liquidity and legitimacy to crypto | Institutional Legitimacy Theory | Our study examines this as a mechanism that links macro policies to individual behavior. |
(Jackson, 2024) | Regulatory Clarity | Clear regulations facilitate institutional adoption | Regulatory Signaling Theory | We test this empirically and extend it to individual adoption via institutional investment pathways. |
(Kim & Fan, 2024) | Market Sentiment | Social media impacts investor behavior | Theory of Planned Behavior (TPB) | We extend this by analyzing sentiment alongside market and regulatory dimensions in a unified framework. |
(Bondarenko & Soponar, 2024) | Institutional Legitimacy | ETF approval increases legitimacy and adoption | Institutional Legitimacy Theory | We empirically test institutional investment as a mechanism mediating between macro (market/regulation) and micro (individual) drivers. |
Category | Subcategory | Frequency | Percent |
---|---|---|---|
Gender | Male | 262 | 61.2 |
Female | 166 | 38.8 | |
Age Group | 18–30 | 122 | 28.5 |
31–45 | 212 | 49.5 | |
45–60 | 78 | 18.2 | |
60 and older | 16 | 3.7 | |
Education Level | High School | 77 | 18 |
College | 138 | 32.2 | |
Undergrad | 107 | 25 | |
Graduate | 106 | 24.8 | |
Annual Income | Less than 20 k | 40 | 9.3 |
20 k to 40 k | 53 | 12.4 | |
40 k to 60 k | 101 | 23.6 | |
60 k to 80 k | 83 | 19.4 | |
80 k to 100 k | 59 | 13.8 | |
more than 100 k | 92 | 21.5 | |
Total | 428 |
Construct | Sub-Dimensions | Items | Standardized Factor Loading (>0.7) | Composite Reliability (>0.7) | Cronbach’s Alpha (>0.7) | AVE (>0.5) |
---|---|---|---|---|---|---|
Individual Factors | Financial Literacy | Q1, Q2, Q3 | Q1: 0.80, Q2: 0.78, Q3: 0.76 | 0.88 | 0.8 | 0.68 |
Digital Literacy | Q1, Q2, Q3 | Q1: 0.85, Q2: 0.82, Q3: 0.87 | 0.91 | 0.83 | 0.73 | |
Early Adopter Trait | Q1, Q2, Q3 | Q1: 0.79, Q2: 0.83, Q3: 0.81 | 0.89 | 0.78 | 0.71 | |
Market Factors | Perceived Volatility | Q1, Q2, Q3 | Q1: 0.81, Q2: 0.85, Q3: 0.86 | 0.92 | 0.85 | 0.76 |
Perceived Market Demand | Q1, Q2, Q3 | Q1: 0.80, Q2: 0.84, Q3: 0.82 | 0.9 | 0.81 | 0.7 | |
Perceived Market Sentiment | Q1, Q2, Q3 | Q1: 0.78, Q2: 0.79, Q3: 0.80 | 0.87 | 0.79 | 0.69 | |
Government Factors | Security Perception | Q1, Q2, Q3 | Q1: 0.81, Q2: 0.83, Q3: 0.82 | 0.91 | 0.82 | 0.74 |
Disclosure Requirement | Q1, Q2, Q3 | Q1: 0.79, Q2: 0.84, Q3: 0.81 | 0.88 | 0.8 | 0.68 | |
Tax Policy | Q1, Q2, Q3 | Q1: 0.76, Q2: 0.79, Q3: 0.81 | 0.85 | 0.77 | 0.66 | |
Compliance Guideline | Q1, Q2, Q3, Q4, Q5, Q6 | Q1: 0.77, Q2: 0.80, Q3: 0.83, Q4: 0.78, Q5: 0.82, Q6: 0.81 | 0.89 | 0.82 | 0.71 |
Construct | Financial Literacy | Digital Literacy | Early Adopter Trait | Perceived Volatility | Perceived Market Demand | Perceived Market Sentiment | Security Perception | Disclosure Requirement | Tax Policy | Compliance Guideline |
---|---|---|---|---|---|---|---|---|---|---|
Financial Literacy | 0.82 | |||||||||
Digital Literacy | 0.55 | 0.81 | ||||||||
Early Adopter Trait | 0.47 | 0.5 | 0.8 | |||||||
Perceived Volatility | 0.43 | 0.48 | 0.49 | 0.81 | ||||||
Perceived Market Demand | 0.41 | 0.42 | 0.46 | 0.52 | 0.8 | |||||
Perceived Market Sentiment | 0.39 | 0.41 | 0.45 | 0.5 | 0.51 | 0.79 | ||||
Security Perception | 0.37 | 0.39 | 0.43 | 0.48 | 0.44 | 0.42 | 0.8 | |||
Disclosure Requirement | 0.42 | 0.43 | 0.44 | 0.46 | 0.45 | 0.41 | 0.45 | 0.81 | ||
Tax Policy | 0.4 | 0.44 | 0.41 | 0.47 | 0.43 | 0.44 | 0.42 | 0.43 | 0.81 | |
Compliance Guideline | 0.45 | 0.4 | 0.43 | 0.45 | 0.46 | 0.43 | 0.44 | 0.46 | 0.45 |
Construct | Financial Literacy | Digital Literacy | Early Adopter Trait | Perceived Volatility | Perceived Market Demand | Perceived Market Sentiment | Security Perception | Disclosure Requirement | Tax Policy | Compliance Guideline |
---|---|---|---|---|---|---|---|---|---|---|
Financial Literacy | ------ | |||||||||
Digital Literacy | 0.67 | ------ | ||||||||
Early Adopter Trait | 0.65 | 0.68 | ------ | |||||||
Perceived Volatility | 0.6 | 0.63 | 0.64 | ------ | ||||||
Perceived Market Demand | 0.58 | 0.61 | 0.62 | 0.66 | ------ | |||||
Perceived Market Sentiment | 0.57 | 0.6 | 0.61 | 0.64 | 0.67 | ------ | ||||
Security Perception | 0.56 | 0.59 | 0.58 | 0.61 | 0.64 | 0.59 | ------ | |||
Disclosure Requirement | 0.59 | 0.64 | 0.63 | 0.65 | 0.62 | 0.6 | 0.63 | ------ | ||
Tax Policy | 0.55 | 0.62 | 0.6 | 0.63 | 0.61 | 0.62 | 0.61 | 0.65 | ------ | |
Compliance Guideline | 0.61 | 0.6 | 0.63 | 0.64 | 0.65 | 0.61 | 0.62 | 0.66 | 0.64 | ------ |
Second-Order Construct | Dimensions (First-Order) | Weights | Significance | VIF | R2a |
---|---|---|---|---|---|
Individual Factors | Financial Literacy | 0.45 | p < 0.001 | 2.34 | 0.75 |
Digital Literacy | 0.53 | p < 0.001 | 2.15 | 0.75 | |
Early Adopter Trait | 0.48 | p < 0.001 | 2.4 | 0.75 | |
Market Factors | Perceived Volatility | 0.56 | p < 0.001 | 2.22 | 0.78 |
Perceived Market Demand | 0.54 | p < 0.001 | 2.18 | 0.78 | |
Perceived Market Sentiment | 0.49 | p < 0.001 | 2.12 | 0.78 | |
Government Factors | Security Perception | 0.52 | p < 0.001 | 2.36 | 0.72 |
Disclosure Requirement | 0.51 | p < 0.001 | 2.28 | 0.72 | |
Tax Policy | 0.47 | p < 0.001 | 2.19 | 0.72 | |
Compliance Guideline | 0.5 | p < 0.001 | 2.25 | 0.72 |
Fit Index | Value | Threshold |
---|---|---|
Chi-Square | 145.67 | Lower is better; non-significant indicates good fit |
Degrees of Freedom (df) | 90 | N/A |
p-value | 0.001 | Should be >0.05 for good fit |
RMSEA | 0.054 | <0.08 indicates acceptable fit |
CFI | 0.95 | >0.90 indicates good fit |
SRMR | 0.042 | <0.08 indicates good fit |
GFI | 0.92 | >0.90 indicates good fit |
Hypothesis | Path | Standardized Coefficient (β) | t-Value | Significance | Results |
---|---|---|---|---|---|
H1 | Individual Factors Individual Spot Bitcoin ETPs Adoption | 0.458 | 9.359 | p < 0.001 | Supported |
H2 | Market Factors Individual Spot Bitcoin ETPs Adoption | 0.34 | 11.361 | p < 0.001 | Supported |
H3 | Government Factors Individual Spot Bitcoin ETPs Adoption | 0.409 | 12.029 | p < 0.001 | Supported |
H4 | Market Factors Institutional Spot Bitcoin ETPs Investment | 0.48 | 12.919 | p < 0.001 | Supported |
H5 | Government Factors Institutional Spot Bitcoin ETPs Investment | 0.409 | 12.029 | p < 0.001 | Supported |
H6 | Institutional Spot Bitcoin ETPs Investment Individual Spot Bitcoin ETPs Adoption | 0.298 | 9.867 | p < 0.001 | Supported |
Relationship | Indirect Effect | SE | 95% CI |
---|---|---|---|
Market Factors Individual Spot Bitcoin Adoption | 0.278 | 0.058 | [0.165, 0.391] |
Government Factors | 0.245 | 0.052 | [0.145, 0.345] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hasavari, S.; Maddah, M.; Esmaeilzadeh, P. Government Oversight and Institutional Influence: Exploring the Dynamics of Individual Adoption of Spot Bitcoin ETPs. J. Risk Financial Manag. 2025, 18, 175. https://doi.org/10.3390/jrfm18040175
Hasavari S, Maddah M, Esmaeilzadeh P. Government Oversight and Institutional Influence: Exploring the Dynamics of Individual Adoption of Spot Bitcoin ETPs. Journal of Risk and Financial Management. 2025; 18(4):175. https://doi.org/10.3390/jrfm18040175
Chicago/Turabian StyleHasavari, Shirin, Mahed Maddah, and Pouyan Esmaeilzadeh. 2025. "Government Oversight and Institutional Influence: Exploring the Dynamics of Individual Adoption of Spot Bitcoin ETPs" Journal of Risk and Financial Management 18, no. 4: 175. https://doi.org/10.3390/jrfm18040175
APA StyleHasavari, S., Maddah, M., & Esmaeilzadeh, P. (2025). Government Oversight and Institutional Influence: Exploring the Dynamics of Individual Adoption of Spot Bitcoin ETPs. Journal of Risk and Financial Management, 18(4), 175. https://doi.org/10.3390/jrfm18040175