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Analisis Faktor yang Mempengaruhi Pengguna pada Aplikasi Mobile Banking

Department of Informatics, Universitas Diponegoro, Indonesia

Received: 28 Oct 2023; Revised: 27 Nov 2023; Accepted: 29 Nov 2023; Available online: 30 Nov 2023; Published: 30 Nov 2023.
Editor(s): Guruh Aryotejo
Open Access Copyright (c) 2023 The authors. Published by Department of Informatics, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Sejak pandemi virus Covid-19, masyarakat terbiasa jauh lebih berhati-hati dalam melakukan transaksi dimana masyarakat mulai terbiasa lebih memilih melakukan transaksi yang meminimalisir terjadinya kontak fisik. Salah satu cara yang dipilih adalah melakukan transaksi perbankan menggunakan teknologi mobile banking. Oleh karena itu, penelitian ini bertujuan untuk mengetahui faktor apa saja yang mempengaruhi pengguna pada aplikasi mobile banking. Penelitian ini dilakukan dengan menggunakan model DeLone & McLean serta tambahan variabel trust, social influence, intimacy, loyalty, dan continuance intention. Berdasarkan model dan variabel yang digunakan, penelitian ini dilakukan untuk mencari faktor yang memengaruhi penggunaan, kepuasan, keintiman, loyalitas, niat berkelanjutan, dan dampak individual pengguna mobile banking dalam menggunakan mobile banking. Data dikumpulkan dengan syarat responden pernah menggunakan mobile banking. Data yang terkumpul sebanyak 204 responden. Data dianalisis dengan metode Partial Least Square – Structural Equation Modeling (PLS-SEM) menggunakan aplikasi SmartPLS 3.2.9. Hasil penelitian menunjukkan bahwa faktor yang memengaruhi pengguna dalam menggunakan mobile banking adalah kualitas sistem, kualitas layanan, kepercayaan, pengaruh sosial, penggunaan, kepuasan, dan keintiman.

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Keywords: Mobile banking; DeLone & McLean; PLS-SEM; IT Adoption

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  1. Y. Hanif and H. S. Lallie, “Security factors on the intention to use mobile banking applications in the UK older generation (55+). A mixed-method study using modified UTAUT and MTAM - with perceived cyber security, risk, and trust,” Technology in Society, vol. 67, p. 101693, Nov. 2021, doi: 10.1016/j.techsoc.2021.101693
  2. G. Mediatama, “Digital banking tumbuh di tengah pandemi, masyarakat kian sering bertransaksi online - Page all,” kontan.co.id. Accessed: Nov. 27, 2023. [Online]. Available: https://newssetup.kontan.co.id/news/digital-banking-tumbuh-di-tengah-pandemi-masyarakat-kian-sering-bertransaksi-online
  3. L. H. Trihandayani, I. Aknuranda, and Y. T. Mursityo, “Penerapan Model Kesuksesan Delone dan Mclean pada Website Fakultas Ilmu Komputer (FILKOM) Universitas Brawijaya,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 12, Art. no. 12, Aug. 2018
  4. S. Loaba, “The impact of mobile banking services on saving behavior in West Africa,” Global Finance Journal, vol. 53, p. 100620, Aug. 2022, doi: 10.1016/j.gfj.2021.100620
  5. W. H. DeLone and E. R. McLean, “Information Systems Success: The Quest for the Dependent Variable,” Information Systems Research, vol. 3, no. 1, pp. 60–95, 1992
  6. W. H. Delone and E. R. McLean, “The DeLone and McLean Model of Information Systems Success: A Ten-Year Update,” Journal of Management Information Systems, vol. 19, no. 4, pp. 9–30, Apr. 2003, doi: 10.1080/07421222.2003.11045748
  7. A. Geebren, A. Jabbar, and M. Luo, “Examining the role of consumer satisfaction within mobile eco-systems: Evidence from mobile banking services,” Computers in Human Behavior, vol. 114, p. 106584, Jan. 2021, doi: 10.1016/j.chb.2020.106584
  8. S. Yuan, L. Liu, B. Su, and H. Zhang, “Determining the antecedents of mobile payment loyalty: Cognitive and affective perspectives,” Electronic Commerce Research and Applications, vol. 41, p. 100971, May 2020, doi: 10.1016/j.elerap.2020.100971
  9. R. F. Malaquias and Y. Hwang, “Mobile banking use: A comparative study with Brazilian and U.S. participants,” International Journal of Information Management, vol. 44, pp. 132–140, Feb. 2019, doi: 10.1016/j.ijinfomgt.2018.10.004
  10. F. B. Franque, T. Oliveira, and C. Tam, “Understanding the factors of mobile payment continuance intention: empirical test in an African context,” Heliyon, vol. 7, no. 8, p. e07807, Aug. 2021, doi: 10.1016/j.heliyon.2021.e07807
  11. G. Dash and J. Paul, “CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting,” Technological Forecasting and Social Change, vol. 173, p. 121092, Dec. 2021, doi: 10.1016/j.techfore.2021.121092
  12. N. K. Avkiran, “Rise of the Partial Least Squares Structural Equation Modeling: An Application in Banking,” in Partial Least Squares Structural Equation Modeling: Recent Advances in Banking and Finance, N. K. Avkiran and C. M. Ringle, Eds., in International Series in Operations Research & Management Science. , Cham: Springer International Publishing, 2018, pp. 1–29. doi: 10.1007/978-3-319-71691-6_1
  13. A. Safi’i et al., “The effect of the adversity quotient on student performance, student learning autonomy and student achievement in the COVID-19 pandemic era: evidence from Indonesia,” Heliyon, vol. 7, no. 12, p. e08510, Dec. 2021, doi: 10.1016/j.heliyon.2021.e08510

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