Beyond age: Decoding the investment DNA of generations Z and Y in Indonesia

  • 68 Views
  • 13 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Investment decisions are a matter of how individuals should allocate funds into investment forms that provide future benefits. This paper investigates the impact of financial literacy, perceptions of risk and returns, family background, income, and financial technology proficiency on investment decisions among Generations Z and Y in Indonesia. This study uses a quantitative approach, using primary data from 240 respondents through purposive sampling. Primary data were collected through a questionnaire survey to collect respondents’ perceptions and investment decisions. The Likert scale assesses indicators by eliciting responses to statements and questions. The Structural Equation Model Partial Least Square (SEM-PLS) approach was employed for analysis utilizing WarpPLS 8.0 software. The results show that financial literacy, risk and return perception, income, and fintech proficiency significantly influence investment decisions (p < 0.05), while family background does not (p > 0.05). In addition, fintech proficiency mediates the effects of financial literacy, risk perception, family background, and income on investment decisions (p < 0.05). These findings suggest that improving financial literacy and fintech skills can lead to better investment decisions among young investors. This study highlights the need for targeted financial education programs and innovative fintech solutions to support informed investment choices. Further research is recommended to explore additional factors influencing investment decisions and to develop strategies to improve financial decision-making in this demographic group.

view full abstract hide full abstract
    • Figure 1. Research framework
    • Table 1. Loading factor
    • Table 2. Average Variance Extracted (AVE)
    • Table 3. Fornell-Larcker criterion
    • Table 4. Cronbach’s alpha and construct reliability
    • Table 5. R-square
    • Table 6. Estimated results
    • Conceptualization
      Debbi Chyntia Ovami
    • Formal Analysis
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika
    • Investigation
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Methodology
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Project administration
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Supervision
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Validation
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Software
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana
    • Resources
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Visualization
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Writing – original draft
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Writing – review & editing
      Debbi Chyntia Ovami, Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari
    • Data curation
      Henny Zurika Lubis, Esa Setiana, Ita Mustika, Sari Wulandari