Cryptocurrency Adoption Under Institutional Dualism Extending Technology Acceptance Model Among Indonesian Generation Z

Erix Galih Pratama; Melati Oktafiyani; Hayu Wikan  Kinasih; Dian Festiana Hadi Saputro; Mohamed Abdualwhab Ali Alfared

doi:10.33633/jpeb.v11i2.17793

Authors

Erix Galih Pratama Universitas Dian Nuswantoro Semarang
Melati Oktafiyani Universitas Dian Nuswantoro Semarang
Hayu Wikan Kinasih Universitas Dian Nuswantoro Semarang
Dian Festiana Hadi Saputro Universitas Dian Nuswantoro Semarang
Mohamed Abdualwhab Ali Alfared Ain Shams Higher Institute

DOI:

https://doi.org/10.33633/jpeb.v11i2.17793

Keywords:

Technology Acceptance Model, Cryptocurrency Adoption, Institutional Dualism, Generation Z, Government Support

Abstract

This study examines how perceived usefulness, ease of use, and perceived risk mediate the relationship between technology awareness and behavioral intention to adopt cryptocurrency among Indonesian Generation Z, with government support as a moderator under institutional dualism. A quantitative cross-sectional design was executed using Partial Least Squares Structural Equation Modeling (PLS-SEM) on a sample of 335 respondents ( ). Results reveal that perceived usefulness (indirect effect = 0.291, ) and ease of use (indirect effect = 0.158, ) significantly mediate adoption intention, whereas perceived risk does not (indirect effect = 0.011, ). Crucially, perceived risk directly predicts intention positively ( , ), indicating Generation Z perceives risk as a signal of high return potential rather than a barrier. Government support moderates only the awareness, ease of use, and intention pathway (Index of Moderated Mediation = 0.014, ). This study establishes institutional ambiguity as a critical boundary condition for technology acceptance models, where regulatory dissonance suppresses or inverts classical risk mediation. Policy effectiveness hinges on harmonizing regulatory frameworks between financial and monetary authorities.

References

Albayati, H., Kim, S. K., & Rho, J. J. (2020). Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach. Technology in Society, 62, 101320. https://doi.org/10.1016/j.techsoc.2020.101320

Arias-Oliva, M., Pelegrin-Borondo, J., & Matias-Clavero, G. (2019). Variables influencing cryptocurrency use: A technology acceptance model in Spain. Frontiers in Psychology, 10, 475. https://doi.org/10.3389/fpsyg.2019.00475

Bhardwaj, A. K., Garg, A., & Gajpal, Y. (2021). Determinants of blockchain technology adoption in supply chains by small and medium enterprises in India. Mathematical Problems in Engineering, 2021, 5537395. https://doi.org/10.1155/2021/5537395

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

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. https://doi.org/10.2307/2095101

Falk, R., & Miller, N. (1992). A Primer for Soft Modeling. The University of Akron Press: Akron, OH. https://www.researchgate.net/publication/232590534_A_Primer_for_Soft_Modeling

Folkinshteyn, D., & Lennon, M. (2016). Braving Bitcoin: A technology acceptance model analysis. Journal of Information Technology Case and Application Research, 18(4), 220–249. https://doi.org/10.1080/08874417.2016.1232503

Ha, D., Nguyen, M., Nguyen, K., & Sensoy, A. (2025). Unveiling financial inclusion dynamics: Fintech’s resonance in Association of Southeast Asian Nations (ASEAN). International Journal of Finance & Economics, 30(2), 1348–1371. https://doi.org/10.1002/ijfe.2963

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling using R. Springer.

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. https://doi.org/10.1007/s11747-014-0403-8

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Ikhlash, M., & Purba, R. A. (2025). Pengaruh keamanan sistem, kemudahan penggunaan, dan literasi digital terhadap intensitas penggunaan e-wallet pada generasi Z. Jurnal Riset Manajemen Dan Bisnis, 11(1), 45–62. https://doi.org/10.30871/jaat.v10i2.11428

Jariyapan, P., Mattayaphutron, S., Gillani, S. N., & Shafique, O. (2022). Factors influencing the behavioural intention to use cryptocurrency in emerging economies during the COVID-19 pandemic. Frontiers in Psychology, 12, 814087. https://doi.org/10.3389/fpsyg.2021.814087

Khofifah, S., & Kardiyem, K. (2023). Intensitas Penggunaan E-wallet pada Mahasiswa Fakultas Ekonomika dan Bisnis: Perspektif Teori TAM dan UTAUT. Jurnal Ekonomi & Pendidikan, 20(2), 64–76. https://doi.org/10.21831/jep.v20i2.63121

Kock, N. (2015). Common Method Bias in PLS-SEM: International Journal of E-Collaboration, 11, 1–10. https://doi.org/10.4018/ijec.2015100101

Lim, N. (2003). Consumers’ perceived risk: sources versus consequences. Electronic Commerce Research and Applications, 2(3), 216–228. https://doi.org/10.1016/S1567-4223(03)00025-5

Mannheim, K. (1952). The problem of generations. In P. Kecskemeti (Ed.), Essays on the Sociology of Knowledge (pp. 276–322).

Mattke, J., Maier, C., Reis, L., & Weitzel, T. (2021). Bitcoin investment: A mixed methods study of investment motivations. European Journal of Information Systems, 30(3), 261–285. https://doi.org/10.1080/0960085X.2020.1760227

Namahoot, K. S., & Rattanawiboonsom, V. (2022). Integration of TAM model of consumers’ intention to adopt cryptocurrency platform in Thailand. Human Behavior and Emerging Technologies, 2022, 9642998. https://doi.org/10.1155/2022/9642998

Nuryyev, G., Wang, Y. P., Achyldurdyyeva, J., Jaw, B. S., Yeh, Y. S., Lin, H. T., & Wu, L. F. (2020). Blockchain technology adoption behavior and sustainability of the business in tourism and hospitality SMEs. Sustainability, 12(3), 1256. https://doi.org/10.3390/su12031256

Putri, N. P. A. R. T., Putri, I. G. A. P. D., & Utami, N. W. (2024). Analisis faktor yang mempengaruhi minat pengguna e-wallet DANA dengan metode Technology Acceptance Model di Bali. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 9(4), 2146–2161. https://doi.org/10.29100/jipi.v9i4.5605

Ramadhan, R. P., & Putra, I. S. (2025). Adopsi Aplikasi Investasi oleh Investor Generasi Z dan Milenial: Analisis Berdasarkan Technology Acceptance Model (TAM). JOINS (Journal of Information System), 10(1), 14–31. https://doi.org/10.33633/joins.v10i1.12048

Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. SmartPLS. https://www.smartpls.com

Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology, 91(1), 93–114. https://doi.org/10.1080/00223980.1975.9915803

Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning. Information and Management, 42(2), 317–327. https://doi.org/10.1016/j.im.2003.12.011

Sagheer, N., Khan, K. I., Fahd, S., Mahmood, S., Rashid, T., & Jamil, H. (2022). Factors Affecting Adaptability of Cryptocurrency: An Application of Technology Acceptance Model. Frontiers in Psychology, 13, 903473. https://doi.org/10.3389/fpsyg.2022.903473

Saputra, U. E., & Darma, G. S. (2022). The Intention to Use Blockchain in Indonesia Using Extended Approach Technology Acceptance Model (TAM). CommiT (Communication and Information Technology), 16(1), 27–35. https://doi.org/10.21512/commit.v16i1.7609

Scott, W. R. (2014). Institutions and Organizations: Ideas, Interests, and Identities (4th ed.). Sage Publications.

Shahzad, F., Xiu, G., Wang, J., & Shahbaz, M. (2018). An empirical investigation on the adoption of cryptocurrencies among the people of mainland China. Technology in Society, 55, 33–40. https://doi.org/10.1016/j.techsoc.2018.05.006

Treiblmaier, H., & Sillaber, C. (2021). The impact of blockchain on e-commerce: A framework for salient research topics. Electronic Commerce Research and Applications, 48, 101054. https://doi.org/10.1016/j.elerap.2021.101054

Tsang, E. W. K., & Kwan, K. M. (1999). Replication and theory development in organizational science: A critical realist perspective. Academy of Management Review, 24(4), 759–780. https://doi.org/10.2307/259353

Turner, A. (2015). Generation Z: Technology and social interest. Journal of Individual Psychology, 71(2), 103–113. https://doi.org/10.1353/jip.2015.0021

Violinda, Q., & Khorunnisya, N. V. (2022). Minat generasi milenial menggunakan e-wallet LinkAja: Perspektif teori persepsi dan perilaku. Jurnal Aplikasi Bisnis Dan Manajemen, 8(1), 181–191. https://doi.org/10.17358/jabm.8.1.181

Zafar, S., Riaz, S., & Mahmood, W. (2021). Conducting the cashless revolution in Pakistan using enterprise integration. International Journal of Education and Management Engineering, 11(4), 12–25. https://doi.org/10.5815/ijeme.2021.04.02

Zhao, X., Lynch, J., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and Truths About Mediation Analysis. Journal of Consumer Research, 37, 197–206. https://doi.org/10.1086/651257

Zreik, M., & Iqbal, B. A. (2025). Navigating the global fintech regulatory landscape: Balancing innovation and protection. In Examining Global Regulations During the Rise of Fintech (pp. 71–102). IGI Global. https://doi.org/10.4018/979-8-3693-3803-2.ch004