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All Journal Jurnal Mirai Management Lokawati: Jurnal Penelitian Manajemen dan Inovasi Riset
Moody Wahongan
Universitas Klabat
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Financial Literacy and Financial Behaviour on Investment Decision Making Among Generation Z: The Mediating Role of Financial Attitude Niel Ananto; Moody Wahongan
Lokawati : Jurnal Penelitian Manajemen dan Inovasi Riset Vol. 4 No. 4 (2026): Juli: Jurnal Penelitian Manajemen dan Inovasi Riset
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/lokawati.v5i4.2678

Abstract

This study examines the influence of financial literacy and financial behaviour on investment decision making among Generation Z, with financial attitude as a mediating variable. A quantitative survey design was employed, involving 125 Generation Z respondents (aged 18–27) currently investing in mutual funds, equities, or digital/crypto assets. Data were analysed using a regression-based path analysis on standardized composite construct scores, with mediation tested through bootstrapping (5,000 resamples). The measurement model showed adequate convergent validity, discriminant validity, and reliability for all constructs. Financial behaviour significantly affected financial attitude (β = 0.434, p < .001), while the effect of financial literacy on attitude was not significant (β = 0.150, p = .069). Financial attitude significantly affected investment decision making (β = 0.424, p < .001), and both financial literacy (β = 0.301, p < .001) and financial behaviour (β = 0.158, p = .043) significantly affected it directly. Financial attitude partially mediated the effect of financial behaviour on investment decisions (VAF = 53.3%), but did not significantly mediate the effect of financial literacy. These findings suggest that financial behaviour shapes investment decisions partly through attitude formation, while financial literacy operates mainly through a direct pathway.
Predicting Fintech Adoption Intention Among Generation Z Using Machine Learning: A Comparative Analysis of Classification Algorithms Niel Ananto; Moody Wahongan
Jurnal Mirai Management Vol 12, No 1
Publisher : STIE AMKOP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/mirai.v12i1.12784

Abstract

Abstract Generation Z's rapid uptake of financial technology (fintech) presents both an opportunity for financial inclusion and a challenge for providers seeking to understand the drivers of adoption. While prior studies have relied predominantly on covariance or variance based structural equation modeling (SEM) to explain fintech adoption intention, machine learning (ML) classification algorithms offer a complementary, prediction-oriented approach capable of capturing non-linear relationships among adoption determinants. This study compares eight supervised classification algorithms Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Naïve Bayes, a Multi layer Perceptron Neural Network, and XGBoost in predicting fintech adoption intention among Generation Z users, using predictors derived from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit) augmented with perceived risk and financial literacy. Using dataset a survey of 300 Generation Z respondents model performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and 10-fold cross validation. In the illustrative run, the Neural Network and XGBoost models achieved the most favorable discrimination (AUC ≈ 0.57–0.61), while effort expectancy, facilitating conditions, social influence, and habit emerged as the most important predictors based on Random Forest feature importance. These findings, once confirmed with primary survey data, are expected to provide fintech providers and policymakers with a prediction oriented complement to traditional SEM-based adoption studies, supporting more targeted onboarding and engagement strategies for Generation Z users. Keywords: fintech adoption; Generation Z; machine learning; classification algorithms; comparative analysis.
Co-Authors Niel Ananto