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The Cryptocurrencies in Emerging Markets: Enhancing Financial Inclusion and Economic Empowerment

Author

Listed:
  • Mohammad El Hajj

    (Business School, Holy Spirit University of Kaslik, Jounieh P.O. Box 446, Lebanon)

  • Imad Farran

    (Faculty of Business Administration, City University, Abou Samra, Tripoli P.O. Box 676, Lebanon)

Abstract

The present study discusses how adopting cryptos affects financial inclusion in developing economies. Primary constructs like financial inclusion (FI), perceived economic empowerment (PEE), trust in financial institutions (TFI), user satisfaction (US), and cryptocurrency adoption (CA) were tested through Structural Equation Modeling (SEM). The results indicated that CA significantly and positively influenced FI, US, TFI, and PEE. These relationships extend to the interaction effects: US, TFI, and PEE, all positively related to FI. This is a reflection of cryptocurrencies as an opportunity to redress most of the afflictions characteristic of traditional finance systems and to promote financial inclusion and economic empowerment in developing countries. Future research should also investigate whether digital literacy and regulatory environments support cryptocurrency access.

Suggested Citation

  • Mohammad El Hajj & Imad Farran, 2024. "The Cryptocurrencies in Emerging Markets: Enhancing Financial Inclusion and Economic Empowerment," JRFM, MDPI, vol. 17(10), pages 1-27, October.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:467-:d:1500377
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    References listed on IDEAS

    as
    1. Satish Nambisan, 2017. "Digital Entrepreneurship: Toward a Digital Technology Perspective of Entrepreneurship," Entrepreneurship Theory and Practice, , vol. 41(6), pages 1029-1055, November.
    2. Raphael Auer & Rainer Boehme, 2020. "The technology of retail central bank digital currency," BIS Quarterly Review, Bank for International Settlements, March.
    3. Ghazanfar Ali Abbasi & Lee Yin Tiew & Jinquan Tang & Yen-Nee Goh & Ramayah Thurasamy, 2021. "The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-26, March.
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