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dc.contributor.authorYusup, Adi Kurniawan
dc.contributor.authorGunawan, Kartikasari
dc.date.accessioned2025-02-11T08:50:03Z
dc.date.available2025-02-11T08:50:03Z
dc.date.issued2024
dc.identifier.issnP-ISSN : 20854587 E-ISSN : 2549404X
dc.identifier.urihttps://dspace.uc.ac.id/handle/123456789/7886
dc.description.abstractThe number of Generation Z individuals who invest in high-risk financial instruments is increasing. This trend is interesting because high-risk investments commonly tend to attract older and more experienced investor. This study aims to analyze the impact of financial literacy and financial interest on investment decisions of Generation Z in high-risk assets, with risk tolerance as a moderating factor. Theory of Planned Behavior (TPB) is used to formulate the hypotheses. This study used quantitative method and the data is collected by distributing the questionnaires to the respondents. The sample of this study is 401 Generation Z individuals in Surabaya who are over 17 years old and thus legally able to invest according to Indonesian regulations. We conduct logistic regression analysis to test the hypotheses. The findings reveal that financial literacy does not affect investment decisions in high-risk assets, whereas financial interest has a positive effect. Furthermore, risk tolerance does not moderate the relationship between financial literacy and investment decisions. However, a high level of risk tolerance strengthens the positive effect of financial interest on investment decisions in high-risk assets. This research contributes new insight for investment companies and government to boost financial interest through engaging activities like gamified financial tools and interactive workshops.en_US
dc.publisherUNIVERSITAS MULTIMEDIA NUSANTARAen_US
dc.subjectFinancial Literacyen_US
dc.subjectFinancial Interesten_US
dc.subjectGeneration Zen_US
dc.subjectInvestment Decisionen_US
dc.subjectRisk Toleranceen_US
dc.titleGEN Z INVESTMENT DECISION: ROLE OF FINANCIAL LITERACY, INTEREST AND RISK TOLERANCE USING LOGISTIC REGRESSIONen_US
dc.typeArticleen_US


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