Determinants of cryptocurrency investment decisions (Study of students in Bali)

  • Received February 5, 2023;
    Accepted May 8, 2023;
    Published May 17, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.20(2).2023.17
  • Article Info
    Volume 20 2023, Issue #2, pp. 193-204
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This work is licensed under a Creative Commons Attribution 4.0 International License

The investment world today is vying for profit from investing in cryptocurrencies, so this encourages young people, especially students, to invest in cryptocurrencies, but financial literacy, herding behavior, and risk perception are things that influence investment decisions. The aim of this study was to identify the factors that influence students’ decisions to invest in cryptocurrencies. The research method used is quantitative, using questionnaires distributed to students in Bali; the sample in this study was active students currently studying at universities in Bali, Indonesia, totaling 179 samples; questionnaires were distributed using the Google form and analyzed using Warp PLS. The results show that investment decisions, herding behavior, and risk perception are all significantly and positively influenced by financial literacy. Perceived risk and herding behavior have a significant influence on investment decisions. Perceived risk and herding behavior can partially mediate financial literacy on investment decisions. The influence of financial literacy on investment decisions will be stronger if it is through perceived risk with a coefficient value of 0.412 and herding behavior with a coefficient value of 0.422. Based on the study’s conclusion, it is important for investors, especially students, to prioritize improving their financial literacy before investing in cryptocurrencies. Additionally, investors should be aware of the potential impact of herding behavior and perceived risk on their investment decisions and take steps to mitigate their influence.

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    • Figure 1. Conceptual framework
    • Figure 2. Results of the analysis of the relationship between variables
    • Table 1. Goodness of fit
    • Table 2. Output latent variable coefficients
    • Table 3. Coefficient relationship between variables
    • Table 4. Indirect effect of two segments
    • Conceptualization
      Henny Rahyuda, Made Reina Candradewi
    • Data curation
      Henny Rahyuda, Made Reina Candradewi
    • Formal Analysis
      Henny Rahyuda, Made Reina Candradewi
    • Funding acquisition
      Henny Rahyuda, Made Reina Candradewi
    • Investigation
      Henny Rahyuda, Made Reina Candradewi
    • Methodology
      Henny Rahyuda, Made Reina Candradewi
    • Project administration
      Henny Rahyuda, Made Reina Candradewi
    • Resources
      Henny Rahyuda, Made Reina Candradewi
    • Software
      Henny Rahyuda, Made Reina Candradewi
    • Supervision
      Henny Rahyuda, Made Reina Candradewi
    • Writing – original draft
      Henny Rahyuda, Made Reina Candradewi
    • Writing – review & editing
      Henny Rahyuda, Made Reina Candradewi