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Abstract

In this digital age, governments are increasingly embracing online payment of car taxes and other public services made possible by advances in information technology. A digital software called SIGNAL was created to make it easier to validate car registrations and tax payments online. The lower utilization rate compared to traditional services at Samsat offices indicates that its implementation is still unsatisfactory, especially in Lampung Province. The purpose of this research is to use an expanded Technology Acceptance Model (TAM) to determine what variables impact taxpayers' intent to utilize the SIGNAL application. Perceived safety, trust, and subjective standards are some of the extraneous factors included in the model. Data was collected from 120 participants chosen at random using a survey approach. Partial Least Squares (PLS) analysis using SmartPLS software was used to examine the data. Findings show that the desire to use the SIGNAL app is heavily impacted by subjective norms, perceived security, perceived trust, perceived simplicity of use, and perceived utility. The public sector may improve its plans for adopting digital services by using the insights provided by these studies.

Keywords

Technology Acceptance Model SIGNAL Intention to Use Perception Subjective Norm

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Suci, N. D., Bangsawan, S., & Pandjaitan, D. R. H. (2025). The Intention to Use Signal Application for Paying Motor Vehicle Taxes. Amkop Management Accounting Review (AMAR), 5(1), 760–773. https://doi.org/10.37531/amar.v5i1.2815
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References

  1. Abdillah, W., & Hartono, J. (2015). Partial Least Square (PLS): Alternatif Structural Equation Modeling (SEM) dalam Penelitian Bisnis (D. Prabantini (ed.); 1st ed.). Andi.
  2. Al Kurdi, B., Alshurideh, M., Nuseir, M., Aburayya, A., & Salloum, S. A. (2021). The Effects of Subjective Norm on the Intention to Use Social Media Networks: An Exploratory Study Using PLS-SEM and Machine Learning Approach. Advances in Intelligent Systems and Computing, 1339, 581–592. https://doi.org/10.1007/978-3-030-69717-4_55
  3. An, S., Eck, T., & Yim, H. (2023). Understanding Consumers’ Acceptance Intention to Use Mobile Food Delivery Applications through an Extended Technology Acceptance Model. Sustainability (Switzerland), 15(1). https://doi.org/10.3390/su15010832
  4. Balapour, A., Nikkhah, H. R., & Sabherwal, R. (2020). Mobile application security: Role of perceived privacy as the predictor of security perceptions. International Journal of Information Management, 52(November), 102063. https://doi.org/10.1016/j.ijinfomgt.2019.102063
  5. Davis, F. D., Granić, A., & Marangunić, N. M. (2024). The technology acceptance model: 30 years of TAM. Springer International Publishing AG.
  6. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate Data Analysis. Pearson Education Limited. https://doi.org/10.1016/j.ijpharm.2011.02.019
  7. Harris, M. A., Brookshire, R., & Chin, A. G. (2016). Identifying factors influencing consumers’ intent to install mobile applications. International Journal of Information Management, 36(3), 441–450. https://doi.org/10.1016/j.ijinfomgt.2016.02.004
  8. Liébana-Cabanillas, F., Marinkovic, V., Ramos de Luna, I., & Kalinic, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change, 129(October), 117–130. https://doi.org/10.1016/j.techfore.2017.12.015
  9. Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2021). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197(2021), 512–520. https://doi.org/10.1016/j.procs.2021.12.168
  10. Nurkholis. (2017). Hukum Berdasarkan Undang-. Jurnal Pemikiran Hukum Dan Hukum Islam, Vol. 8, h. 77.
  11. Silaen, S. (2018). Metodologi Penelitian Sosial untuk Penulisan Skripsi dan Tesis. IN MEDIA.
  12. Sugiyono. (2019). Metode Penelitian dan Pengembangan Pendekatan Kualitatif, Kuantitatif, dan R&D. Alfabeta.
  13. Türker, C., Altay, B. C., & Okumuş, A. (2022). Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM. Technological Forecasting and Social Change, 184(July). https://doi.org/10.1016/j.techfore.2022.121968
  14. Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  15. Wilson, N. (2019). the Impact of Perceived Usefulness and Perceived Ease-of-Use Toward Repurchase Intention in the Indonesian E-Commerce Industry. Jurnal Manajemen Indonesia, 19(3), 241. https://doi.org/10.25124/jmi.v19i3.2412
  16. Wilson, N., Keni, K., & Tan, P. H. P. (2021). The role of perceived usefulness and perceived ease-of-use toward satisfaction and trust which influence computer consumers’ loyalty in china. Gadjah Mada International Journal of Business, 23(3), 262–294. https://doi.org/10.22146/gamaijb.32106
  17. Wu, J., & Song, S. (2021). Older Adults’ Online Shopping Continuance Intentions: Applying the Technology Acceptance Model and the Theory of Planned Behavior. International Journal of Human-Computer Interaction, 37(10), 938–948. https://doi.org/10.1080/10447318.2020.1861419

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