Financial literacy, technological progress, financial attitudes and investment decisions of Gen Z Indonesian investors

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Rapid technological advances have made financial markets more accessible and encouraged individual investors to engage in investment decision-making actively. Generation Z, or Gen Z, characterized by higher levels of digital literacy, a high sense of curiosity, and acceptance of innovation, tends to make investment decisions quickly. This study aimed to analyze the effect of technological progress, financial literacy, and financial attitudes on investors’ investment decisions. There are 125 Gen Z investors in Jakarta, Indonesia, selected as research samples using the non-probability sampling method. The survey method was employed to collect data, and the study instrument was a questionnaire. For data analysis, Partial Least Squares version 4.0 was used. The study’s findings revealed that financial literacy and financial attitude positively influence Gen Z investment decisions. Technological progress does not affect Gen Z in determining investment in the financial market. Financial literacy and financial attitude are more dominant for Gen Z investors than technological progress in determining investment allocation. This finding implies that Gen Z must improve their understanding of correct financial literacy and financial attitudes that align with individual investors’ character. Further investigation needs to reveal the insignificance of technological progress in determining investment decisions. Technological progress and financial literacy likely have the same factor characteristics related to three dimensions: knowledge, skills, and attitudes. The attitude of Gen Z investors towards the progress of financial technology by investors is preceded by good financial literacy. Therefore, it is necessary to test the relationship between variables, both mediation and moderation, in investment decisions.

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    • Table 1. Convergent validity test results (outer loading)
    • Table 2. Discriminant validity results (cross loading)
    • Table 3. AVE test results
    • Table 4. Discriminant validity test results (Fornell-Lacker criterion)
    • Table 5. Composite reliability test results
    • Table 6. Cronbach’s Alpha test results
    • Table 7. R-squared coefficients
    • Table 8. Hypothesis test results (bootstrapping)
    • Conceptualization
      Kiandra Putri Susanto, Wenny Candra Mandagie, Endri Endri
    • Formal Analysis
      Kiandra Putri Susanto, Endri Endri
    • Investigation
      Kiandra Putri Susanto, Arjuna Wiwaha
    • Methodology
      Kiandra Putri Susanto, Wenny Candra Mandagie, Endri Endri
    • Supervision
      Kiandra Putri Susanto, Endri Endri
    • Visualization
      Kiandra Putri Susanto, Wenny Candra Mandagie
    • Writing – original draft
      Kiandra Putri Susanto, Wenny Candra Mandagie, Endri Endri
    • Writing – review & editing
      Kiandra Putri Susanto, Endri Endri, Arjuna Wiwaha
    • Funding acquisition
      Wenny Candra Mandagie, Arjuna Wiwaha
    • Project administration
      Wenny Candra Mandagie, Arjuna Wiwaha
    • Software
      Wenny Candra Mandagie, Arjuna Wiwaha
    • Data curation
      Endri Endri, Arjuna Wiwaha
    • Resources
      Endri Endri, Arjuna Wiwaha
    • Validation
      Endri Endri, Arjuna Wiwaha