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Jurnal Ekonomi Malaysia

59 (2) 2025 –

Modeling Intention and Frequency of Cashless Behavior: Integrating Big Data and Cluster-Structural Equations Modeling on Bank Customers
Pemodelan Niat dan Kekerapan Tingkah Laku Tanpa Tunai: Mengintegrasikan Data Raya dan Pemodelan Kluster-Persamaan Berstruktur terhadap Pelanggan Bank

Faculty of Social Sciences and Humanities
University of Walisongo Gempol
Pasuruan, Jawa Timur, INDONESIA.

rita.alfin@stiegwalisongo.ac.id

University of Brawijaya
Jalan Veteran, Lowokwaru
Kota Malang, Jawa Timur, 65145, INDONESIA.

Faviandeanova@student.ub.ac.id

Faculty of Mathematics and Natural Sciences
University of Brawijaya
Jalan Veteran, Lowokwaru,
Kota Malang, Jawa Timur, 65145, INDONESIA.

solimun@ub.ac.id

Faculty of Mathematics and Natural Sciences
University of Brawijaya
Jalan Veteran, Lowokwaru,
Kota Malang, Jawa Timur, 65145, INDONESIA.

Sepriadi1412@student.ub.ac.id

Faculty of Mathematics and Natural Sciences
University of Brawijaya
Jalan Veteran, Lowokwaru,
Kota Malang, Jawa Timur, 65145, INDONESIA.

Fernandes@ub.ac.id

Faculty of Mathematics and Natural Sciences
University of Brawijaya
Jalan Veteran, Lowokwaru,
Kota Malang, Jawa Timur, 65145, INDONESIA.

fachiraneinaj@student.ub.ac.id

Abstract

This study aims to determine and model the determinant factors that increase the intensity and frequency of use of cashless payments in the community among Bank customers in Indonesia. The novelty in this research is to combine big data methods (web scraping & LDA) and statistical modeling (cluster and SEM) in modeling the intensity and frequency of use of cashless society. This research uses research data obtained from questionnaires distributed using indicators obtained using big data methods and modeled using cluster-SEM. The results show that there are three groups of cashless payment usage levels, the three groups produce three SEM models where each group has different determinant factors in modeling the intensity and frequency of cashless usage. For the public to increase their sensitivity to the use of a cashless society because its development is very fast. For banks to maximize economic, payment, adoption, policy, and consumer focus variables which can increase the intensity and frequency of use of a cashless society.

Keywords

Big data; cashless society; Clustering; structural equation modeling
http://dx.doi.org/10.17576/JEM-2025-5902-5
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Bibliography

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Alfin, R., Atha Valentino, F. D., , S., Sepriadi, H., Rinaldo Fernandes, A. A., & Junianto, F. H. (2025). Modeling Intention and Frequency of Cashless Behavior: Integrating Big Data and Cluster-Structural Equations Modeling on Bank Customers. Jurnal Ekonomi Malaysia, 59(2), –. http://dx.doi.org/10.17576/JEM-2025-5902-5

@article{valentino2025modeling,
  title={Modeling Intention and Frequency of Cashless Behavior: Integrating Big Data and Cluster-Structural Equations Modeling on Bank Customers},
  author={Alfin, Rita and Atha Valentino, Favian Deanova and , Solimun and Sepriadi, Hanifa and Rinaldo Fernandes, Adji Achmad and Junianto, Fachira Haneinanda},
  journal={Jurnal Ekonomi Malaysia},
  volume={59},
  number={2},
  pages={—},
 

year={2025},
}

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