Cryptocurrency Research and Decision-Making: A Multi-Framework Systemic Review and Future Agenda

Kaushik Mitra; Aparajita Sanyal; Sanjib Biswas; Ambar Dutta; Dragan Pamucar; Vladimir  Simic

doi:10.31181/sdmap31202680

Authors

Kaushik Mitra Amity Institute of Information Technology & Amity Business School, Amity University Kolkata, Rajarhat, Kolkata, India Author https://orcid.org/0000-0002-3507-5999
Aparajita Sanyal Amity Business School, Amity University Kolkata, Rajarhat, Kolkata, India Author https://orcid.org/0000-0002-9968-5817
Sanjib Biswas Amity Business School, Amity University Kolkata, Rajarhat, Kolkata, India Author https://orcid.org/0000-0002-9243-2403
Ambar Dutta Amity Institute of Information Technology & Amity Business School, Amity University Kolkata, Rajarhat, Kolkata, India Author https://orcid.org/0000-0001-6828-4044
Dragan Pamucar 1) Industrial Engineering Department, Faculty of Engineering and Natural Sciences, İstinye University, İstanbul, Türkiye; 2) BEU-Scientific Research Center, Baku Engineering University, Baku, Azerbaijan; 3) Department of Applied Mathematical Science, College of Science and Technology, Korea University, Sejong 30019, Republic of Korea Author https://orcid.org/0000-0001-8522-1942
Vladimir Simic 1) University of Belgrade, Faculty of Transport and Traffic Engineering, Belgrade, Serbia; 2) Department of Industrial Engineering, Faculty of Engineering, Dogus University, Istanbul, Türkiye; 3) Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia Author https://orcid.org/0000-0001-5709-3744

DOI:

https://doi.org/10.31181/sdmap31202680

Keywords:

Investment decision-making, Cryptocurrency , Bibliometric Analysis, ADO, PRISMA, TCCM, PICO

Abstract

This study addresses the growing importance of cryptocurrency (CC) as a financial asset and its increasing popularity as an investment option. Given the rapid expansion of research in this field, the main objective is to systematically synthesize the existing literature on cryptocurrency investment and decision-making, focusing on its intellectual structure, dominant themes, theoretical foundations, and emerging research trends. To achieve this, a hybrid review framework is employed, combining a theory-based systematic literature review with bibliometric analysis, following PRISMA guidelines. The analysis covers 1,184 articles indexed in Scopus and published between 2015 and 2025. Additionally, the study integrates the TCCMR, ADO, and PICO frameworks to provide a comprehensive, multidimensional evaluation of the selected body of literature. The findings reveal that cryptocurrency research is predominantly focused on volatility, market connectedness, portfolio diversification, and behavioral aspects of investment. The results also indicate a strong reliance on econometric and predictive modeling approaches. Emerging research directions highlight increasing attention to sustainability concerns, regulatory challenges, and the application of artificial intelligence in investment analytics. Based on these insights, the study proposes a future research agenda emphasizing theoretical integration, methodological diversification, sustainability perspectives, and decision-oriented modeling. The implications of the research are relevant for investors, regulators, and financial institutions, as they provide a deeper understanding of risks, governance, and decision-making processes in cryptocurrency markets. This study contributes to the literature by offering a comprehensive knowledge structure and research roadmap, representing one of the first attempts to combine bibliometric mapping with TCCMR, ADO, and PICO frameworks in the context of cryptocurrency investment research.

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Author Biography

Aparajita Sanyal, Amity Business School, Amity University Kolkata, Rajarhat, Kolkata, India

Analysts, Marketing

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Sharma, G. D., Tiwari, A. K., Chopra, R., & Dev, D. (2024). Past, present, and future of block-chain in finance. Journal of Business Research, 177, 114640. https://doi.org/10.1016/j.jbusres.2024.114640

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Bouteska, A., Abedin, M. Z., Hajek, P., & Yuan, K. (2024). Cryptocurrency price forecasting–a comparative analysis of ensemble learning and deep learning methods. International Review of Financial Analysis, 92, 103055. https://doi.org/10.1016/j.irfa.2023.103055

Felföldi-Szűcs, N., & Králik, B. (2024). Put–call parity in a crypto option market—Evidence from Binance. Finance Research Letters, 61, 104874. https://doi.org/10.1016/j.frl.2023.104874

Kyriazis, N., & Corbet, S. (2024). Evaluating the dynamic connectedness of financial assets and bank indices during black-swan events: A Quantile-VAR approach. Energy Economics, 131, 107329. https://doi.org/10.1016/j.eneco.2024.107329

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Feng, J., Yuan, Y., & Jiang, M. (2024). Are stablecoins better safe havens or hedges against global stock markets than other assets? Comparative analysis during the COVID-19 pandemic. International Review of Economics & Finance, 92, 275-301. https://doi.org/10.101

Gorman, M., & Hughen, W. K. (2024). Does bitcoin still enhance an investment portfolio in a post Covid-19 world?. Finance Research Letters, 62, 105170. https://doi.org/10.1016/j.frl.2024.105170

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Umar, Z., Usman, M., Umar, M., & Ktaish, F. (2024). Interdependencies and risk management strategies between green cryptocurrencies and traditional energy sources. Energy Economics, 136, 107742. https://doi.org/10.1016/j.eneco.2024.107742

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Karim, M. M., Shah, M. E., Noman, A. H. M., & Yarovaya, L. (2024). Exploring asymmetries in cryptocurrency intraday returns and implied volatility: New evidence for high-frequency traders. International Review of Financial Analysis, 96, 103617. https://doi.org/10.1016/j.irfa.2024.103617

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Chen, Y., Liu, Y., & Zhang, F. (2024). Coskewness and the short-term predictability for Bitcoin return. Technological Forecasting and Social Change, 200, 123196. https://doi.org/10.1016/j.techfore.2023.123196

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Zięba, D. (2024). If GPU (time)== money: Sustainable crypto-asset market? Analysis of similarity among crypto-asset financial time series. International Review of Economics & Finance, 89, 863-912. https://doi.org/10.1016/j.iref.2023.10.036

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Chen, Y., Chiu, J., Chung, H., & Lien, D. (2024). Bitcoin market connectedness across political uncertainty. International Review of Economics & Finance, 96, 103623. https://doi.org/10.1016/j.iref.2024.103623

Cheng, Z., Li, M., Cui, R., Wei, Y., Wang, S., & Hong, Y. (2024b). The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis. International Review of Financial Analysis, 95, 103454. https://doi.org/10.1016/j.irfa.2024.103454

Lei, H., Xue, M., & Ye, J. (2024). The nexus between ReFi, carbon, fossil energy, and clean energy assets: Quantile time–frequency connectedness and portfolio implications. Energy Economics, 132, 107456. https://doi.org/10.1016/j.eneco.2024.107456

Lu, X., Huang, N., & Mo, J. (2024). Time-varying causalities from the COVID-19 media coverage to the dynamic spillovers among the cryptocurrency, the clean energy, and the crude oil. Energy Economics, 132, 107442. https://doi.org/10.1016/j.eneco.2024.107442

Yang, M. Y., Chen, Z. K., Hu, J., Chen, Y., & Wu, X. (2025). Multidimensional information spillover between cryptocurrencies and China’s financial markets under shocks from stringent government regulations. Journal of International Financial Markets, Institutions and Money, 100, 102134. https://doi.org/10.1016/j.intfin.2025.102134

Yousaf, I., Youssef, M., & Goodell, J. W. (2024). Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes. Journal of International Financial Markets, Institutions and Money, 91, 101929. https://doi.org/10.1016/j.intfin.2023.101929

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Osman, M. B., Urom, C., Guesmi, K., & Benkraiem, R. (2024). Economic sentiment and the cryptocurrency market in the post-COVID-19 era. International Review of Financial Analysis, 91, 102962. https://doi.org/10.1016/j.irfa.2023.102962

de Almeida Junior, I. N., Palazzi, R. B., Klotzle, M. C., Pinto, A. C. F., & Gomes, L. L. (2024). Beyond hype: Unveiling the herd effect in ESG and non-ESG cryptocurrency portfolios. Finance Research Letters, 65, 105500. https://doi.org/10.1016/j.frl.2024.105500

Drăgan, G. B., Arfi, W. B., Tiberius, V., Ammari, A., & Khvatova, T. (2025). Navigating the green wave: Understanding behavioral antecedents of sustainable cryptocurrency investment. Technological Forecasting and Social Change, 210, 123909. https://doi.org/10.1016/j.techfore.2024.123909

Gill-de-Albornoz, B., Lafuente, J. A., Monfort, M., & Ordonez, J. (2024). Bitcoin attention and economic policy uncertainty. Finance Research Letters, 60, 104839. https://doi.org/10.1016/j.frl.2023.104839

Lee, H. C., De Vries, E. L., & Hosseini, R. (2025). Balancing generosity with profitability: The role of relative market price and value perceptions in crypto philanthropy. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2025.02.001

Luo, J., Zhang, S., & Zhang, C. (2025). Drivers of investment intentions across diverse cryptocurrency categories. Finance Research Letters, 77, 107024. https://doi.org/10.1016/j.frl.2025.107024

Beckmann, J., Geldner, T., & Wüstenfeld, J. (2024). The relevance of media sentiment for small and large scale bitcoin investors. Journal of International Financial Markets, Institutions and Money, 92, 101963. https://doi.org/10.1016/j.intfin.2024.101963

Hodula, M. (2025). Retail crypto investors when facing financial constraints: Evidence from energy shocks and the use and downloads of crypto trading apps. Energy Economics, 144, 108338. https://doi.org/10.1016/j.eneco.2025.108338

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Agnese, P., & Rios, F. (2024). Spillover effects of energy transition metals in Chile. Energy Economics, 134, 107589. https://doi.org/10.1016/j.eneco.2024.107589

Bouteska, A., Sharif, T., Isskandarani, L., & Abedin, M. Z. (2025). Market efficiency and its determinants: Macro-level dynamics and micro-level characteristics of cryptocurrencies. International Review of Economics & Finance, 98, 103938. https://doi.org/10.1016/j.iref.2025.103938

Buse, R., Görgen, K., & Schienle, M. (2025). Predicting value at risk for cryptocurrencies with generalized random forests. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2024.12.002

Cheng, J., Tiwari, S., Khaled, D., Mahendru, M., & Shahzad, U. (2024a). Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models. Technological Forecasting and Social Change, 198, 122938. https://doi.org/10.1016/j.techfore.2023.122938

Harris, R. D., Mazibas, M., & Rambaccussing, D. (2024). Bitcoin replication using machine learning. International Review of Financial Analysis, 93, 103207. https://doi.org/10.1016/j.irfa.2024.103207

Mizdrakovic, V., Kljajic, M., Zivkovic, M., Bacanin, N., Jovanovic, L., Deveci, M., & Pedrycz, W. (2024). Forecasting bitcoin: Decomposition aided long short-term memory based time series modeling and its explanation with Shapley values. Knowledge-Based Systems, 299, 112026. https://doi.org/10.1016/j.knosys.2024.112026

Atree, M. K., & Tripathy, N. (2025). Cryptocurrency research: Bibliometric review and content analysis. International Review of Economics & Finance, 98, 103940. https://doi.org/10.1016/j.iref.2025.103940

Conlon, T., De Mingo‐López, D. V., & Urquhart, A. (2025). Persistence and Market Timing Ability of Cryptocurrency Funds. Financial Management. https://doi.org/10.1111/fima.12498

Feng, L., Qi, J., Liu, Y., & Wang, W. (2025). The spillover effects of the "Binance Incident" on financial markets: A study based on machine learning approach. Finance Research Letters, 71, 106383. https://doi.org/10.1016/j.frl.2024.106383

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