Predicting Fintech Adoption Intention Among Generation Z Using Machine Learning: A Comparative Analysis of Classification Algorithms
Sari
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.
Teks Lengkap:
Download PDFReferensi
Al-Qudah, A. A., Al-Okaily, M., Shiyyab, F. S., Taha, A. A., Almajali, D. A., & Masa'deh, R. (2024). Determinants of digital payment adoption among Generation Z. Journal of Risk and Financial Management, 17(11), 521. https://doi.org/10.3390/jrfm17110521
Backovic, N. (2023). Predicting the success of FinTech startups using machine learning [Master's thesis, University of Belgrade]. PHAIDRA Repository.
Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418-430. https://doi.org/10.1016/j.chb.2015.04.024
Berlianawati, D. I., Nurabiah, N., & Ridhawati, R. (2024). Exploring the mind of Gen Z: Deciphering e-wallet adoption through the lens of TPB theory. Jurnal Ilmiah Akuntansi dan Bisnis, 19(1), 118-131. https://doi.org/10.24843/JIAB.2024.v19.i01.p09
Blanco-Oliver, A., Lara-Rubio, J., Irimia-Diéguez, A., & Liébana-Cabanillas, F. (2024). Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption. Financial Innovation, 10, Article 76. https://doi.org/10.1186/s40854-024-00625-3
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953
Chinedu, P., et al. (2023). A machine learning approach to fintech adoption drivers: Evidence from Nigeria during the pandemic. Cogent Business & Management, 10(2), Article 2242985. https://doi.org/10.1080/23311975.2023.2242985
Coulibaly, A. (2019). Credit risk estimation in the age of peer to peer lending [Master's thesis, St. Cloud State University]. The Repository at St. Cloud State.
Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1-30.
Fahrunnisa, A., & Puspawati, D. (2024). Determinants of behavioural intention to use Islamic financial technology in Generation Z. Proceedings of ISETH, Universitas Muhammadiyah Surakarta.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36. https://doi.org/10.1148/radiology.143.1.7063747
Indriartiningtias, R., Permatasari, D. A., Ilhamsyah, H. A., Agustina, F., & Novianti, T. (2025). The influence of trust, perceived usefulness, attitude, and promotion on e-wallet adoption among Generation Z in Bangkalan, Indonesia. E3S/EPJ Web of Conferences (AIPTEC 2025), Article 01046.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), 2, 1137-1143.
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765-4774.
Ridhawati, R., et al. (2024). Analysis of factors influencing Generation Z's interest in using e-wallets in Balikpapan City. Prosiding, Politeknik Negeri Balikpapan.
Saputra, U. W. E., Dananjaya, I. B. G., Nurfakhrurozi, R., & Basmantra, I. N. (2024). The determinant of fintech adoption among Gen Z in Indonesia. Atlantis Press.
Sari, S. N., & Tasman, A. (2025). Financial technology adoption, financial literacy, and financial behavior among Generation Z: The mediation role of financial attitude. Indonesian Journal of Development Studies, Universitas Pendidikan Indonesia.
Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015). Determinants of default in P2P lending. PLOS ONE, 10(10), e0139427. https://doi.org/10.1371/journal.pone.0139427
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
DOI: https://doi.org/10.37531/mirai.v12i1.12784
Refbacks
- Saat ini tidak ada refbacks.
