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All Journal Jurnal Mirai Management Jurnal Penelitian Pendidikan IPA (JPPIPA) SEIKO : Journal of Management & Business YUME : Journal of Management Aksara: Jurnal Ilmu Pendidikan Nonformal Literatus The Eastasouth Management and Business Klabat Accounting Review Klabat Journal of Management Lokawati: Jurnal Penelitian Manajemen dan Inovasi Riset JER
Niel Ananto
Fakultas Ekonomi Dan Bisnis Universitas Klabat
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Physics Social Sciences Other

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Logistic Regression Model for Predicting Buy Now Pay Later Default Risk Among Generation Z Users Niel Ananto; Cherry Efraim Lumingkewas
Jurnal Mirai Management Vol 12, No 1
Publisher : STIE AMKOP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/mirai.v12i1.12779

Abstract

consumption patterns among Generation Z, a demographic characterized by high digital adoption but comparatively limited financial experience. This study develops a binary logistic regression model to predict BNPL default risk among Generation Z users, incorporating financial literacy, financial self-control, impulsive buying tendency, income level, digital transaction frequency, and peer influence as predictors. Using an illustrative dataset structured to mirror a survey of 300 Generation Z BNPL users (aged 17–27). The model was evaluated using odds ratios, the Hosmer Lemeshow goodness-of-fit test, Nagelkerke R², classification accuracy, and the ROC AUC. In the illustrative run, financial literacy and financial self-control were associated with lower odds of default, while impulsive buying tendency, transaction frequency, and peer influence were associated with higher odds of default, consistent with the hypothesized directions; income level was not statistically significant. The model demonstrated adequate discriminative ability (illustrative AUC = 0.72) and good calibration (Hosmer-Lemeshow p > 0.05). These findings, once confirmed with primary survey data, are expected to offer practical implications for credit risk management, responsible lending policy, and product design in fintech lending platforms. Keywords: buy now pay later; logistic regression; default risk; generation Z; fintech; credit risk management; machine learning.
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 Ani Budi Astuti Aprilia Nurul Azizah Baso Intang Sappaile Cherry Efraim Lumingkewas Cherry Lumingkewas Danny Ivan Rantung EFENDI Hartini Pop Koapaha Hartiny Pop Koapaha Jimmy H Moedjahedy Moody Wahongan Nindi Dwike Bella Astari Noldin Jerry Tumbel Nouke Sysca Oroh Sabil Shapely Ambalao Toetik Wulyatiningsih Ubaidillah Utiarahman, Nurnaningsih Wahongan, Moody