Explaining Tax Digitalization Adoption: The Mediating Role of Digital Literacy in the Effects of AI-Driven Automation, Effort Expectancy, and Facilitating Conditions

Della Fadhilatunisa; M. Miftach Fakhri; Aprilianti Nirmalasari; Andi Dio Nurul Awalia; Soeharto Soeharto

Authors

Della Fadhilatunisa Universitas Islam Negeri Alauddin Makassar, Indonesia
M. Miftach Fakhri Universitas Negeri Makassar, Indonesia
Aprilianti Nirmalasari Universitas Negeri Makassar, Indonesia
Andi Dio Nurul Awalia Universitas Negeri Makassar, Indonesia
Soeharto Research Center for Education, National Research and Innovation Agency (BRIN), Indonesia

Keywords:

AI-driven automation, Digital literacy, Effort expectancy, Facilitating conditions, Tax digitalization

Abstract

The acceleration of tax digitalization through artificial intelligence (AI) has redefined modern taxation systems; however, its success largely depends on users’ digital literacy and readiness to embrace automation. This study investigates the mediating role of digital literacy in the relationship between AI-driven automation, facilitating conditions, and effort expectancy on tax digitalization adoption in Indonesia. Employing a quantitative approach with a cross-sectional survey design, data were collected from 161 individual and professional taxpayers using purposive sampling methods. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that digital literacy exerts the strongest direct and significant influence on the adoption of tax digitalization. It also mediates the effects of AI-driven automation and facilitating conditions, whereas effort expectancy shows a positive but statistically insignificant relationship. These findings underscore that digital literacy is not merely a supporting factor but a fundamental determinant of successful digital tax transformations. This study implies that policies aimed at promoting tax digitalization should prioritize digital literacy enhancement through systematic education, technical training, and user-friendly system design. By strengthening digital competence, tax authorities can increase user engagement, improve compliance, and facilitate an equitable digital transformation within tax administration.

References

Barclay, et. al. (1995). The Partial Least Squares (PLS) Approach to Causal Modeling, Personal Computer Adoption and Use as an Illustration. Technology Studies, 2(2), 296–297.

Belahouaoui, R., & Attak, E. H. (2024). Digital taxation, artificial intelligence and Tax Administration 3.0: Improving tax compliance behavior – a systematic literature review using textometry (2016–2023). Accounting Research Journal, 37(2), 172–191. https://doi.org/10.1108/ARJ-12-2023-0372

Bélisle-Pipon, J. C. (2025). AI, universal basic income, and power: Symbolic violence in the tech elite’s narrative. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1488457

Chigisheva, O., Soltovets, E., Dmitrova, A., Akhtyan, A. G., Litvinova, S. N., & Chelysheva, Y. V. (2021). DIgital Literacy And Its Relevance To Comparative Education Researchers: Outcomes Of Scival Analytics. Eurasia Journal of Mathematics, Science and Technology Education, 17(10), 1–12. https://doi.org/10.29333/ejmste/11183

Cresswell, J. W. (2017). Planning, Conducting, and Evaluating Quantitative and Qualitative Research. Pearson Education, Inc.

Djafri, I. A., Damawati, I., Suharto, S., Satwika, I. G. A. R. P., & Rahmatullah, R. (2023). Utilization of Information and Communication Technology in the Tax Administration System to Increase Taxpayer Compliance. Ilomata International Journal of Tax and Accounting, 4(1), 14–25. https://doi.org/10.52728/ijtc.v4i1.670

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312

Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/IntR-12-2017-0515

Gavrilenko, I. V., Rada, A. O., & Fedulova, E. A. (2022). Assessment of the Effect of Using Geographic Information Technologies in Identifying Taxation Objects and Violations of Land Legislation. Proceedings of the International Scientific and Practical Conference Strategy of Development of Regional Ecosystems “Education-Science-Industry” (ISPCR 2021). https://doi.org/10.2991/aebmr.k.220208.021

Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis: A Global Perspective. Pearson.

Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

Hair Joseph F. (2017). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). sage.

Hanrahan, D. (2021). Digitalization as a Determinant of Tax Revenues in OECD Countries: A Static and Dynamic Panel Data Analysis. Athens Journal of Business & Economics, 7(4), 321–348. https://doi.org/10.30958/ajbe.7-4-2

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Kabakus, A. K., Bahcekapili, E., & Ayaz, A. (2023). The effect of digital literacy on technology acceptance: An evaluation on administrative staff in higher education. Journal of Information Science. https://doi.org/10.1177/01655515231160028

Ketchen, D. J. (2013). A Primer on Partial Least Squares Structural Equation Modeling. Long Range Planning, 46(1–2), 184–185. https://doi.org/10.1016/j.lrp.2013.01.002

Kock, N. (2014). Advanced Mediating Effects Tests, Multi-Group Analyses, and Measurement Model Assessments in PLS-Based SEM. International Journal of E-Collaboration, 10(1), 1–13. https://doi.org/10.4018/ijec.2014010101

Mohamed, K. A. (2024). Effectiveness of Gamified Cooperation and Competition in Blended Learning Environment for EFL Business Writing in TVET. European Scientific Journal, ESJ, 20(17), 107. https://doi.org/10.19044/esj.2024.v20n17p107

Nepal, S., & Nepal, B. (2023). Adoption of Digital Banking: Insights from a UTAUT Model. Journal of Business and Social Sciences Research, 8(1), 17–34. https://doi.org/10.3126/jbssr.v8i1.56580

Nikou, S., De Reuver, M., & Mahboob Kanafi, M. (2022). Workplace literacy skills—How information and digital literacy affect adoption of digital technology. Journal of Documentation, 78(7), 371–391. https://doi.org/10.1108/JD-12-2021-0241

Odunayo Adewunmi Adelekan, Olawale Adisa, Bamidele Segun Ilugbusi, Ogugua Chimezie Obi, Kehinde Feranmi Awonuga, Onyeka Franca Asuzu, & Ndubuisi Leonard Ndubuisi. (2024). EVOLVING TAX COMPLIANCE IN THE DIGITAL ERA: A COMPARATIVE ANALYSIS OF AI-DRIVEN MODELS AND BLOCKCHAIN TECHNOLOGY IN U.S. TAX ADMINISTRATION. Computer Science & IT Research Journal, 5(2), 311–335. https://doi.org/10.51594/csitrj.v5i2.759

Paiva, J. (2024). Exploring the Drivers of AI Adoption: A Meta-Analysis of Technological, Organizational and Environmental (TOE) Factors. Research Square. https://doi.org/10.21203/rs.3.rs-5634577/v1

Patricia Aguilera-Hermida, A. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, 100011. https://doi.org/10.1016/j.ijedro.2020.100011

Rahayu, P., & Suaidah, I. (2025). Peran Artificial Intelligence dalam Perpajakan terhadap Kepatuhan Wajib Pajak E-Commerce: Literasi Digital sebagai mediator. Owner, 9(1), 479–490. https://doi.org/10.33395/owner.v9i1.2516

Santoro, F., Prichard, W., & Mascagni, G. (2024). Digital IDs and Digital Payments – Opportunities and Challenges for Tax Administration. Institute of Development Studies. https://doi.org/10.19088/ICTD.2024.021

Sarstedt, M., and Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012). Long Range Planning, 47(3), 154–160. https://doi.org/10.1016/j.lrp.2014.02.007

Susanty, L. (2024). Critical Analysis of the Research on Digital Literacy. Sinergi International Journal of Education, 2(1), 12–25. https://doi.org/10.61194/education.v2i1.149

Tinta, J. K., Zerbo, M., Santoro, F., Diouf, A., & Pale, K. (2024). Electronic Services and Tax Compliance: Evidence from Medium and Small Businesses in Burkina Faso. Institute of Development Studies. https://doi.org/10.19088/ICTD.2024.099

Tushar Ranjan Barik & Priyanka Ranawat. (2024). Transformation of Traditional Corporate Tax Planning into AI-Driven Corporate Tax Planning. Involvement International Journal of Business, 1(4), 269–280. https://doi.org/10.62569/iijb.v1i4.68

Zakaria, M., Wan Ahmad, W. N., Che Hussin, N., Hassan, R. A., Madah Marzuki, M., Syukur, M., & Sari, E. N. (2024). Adoption of tax digitalisation among Malaysian tax practitioners. TELKOMNIKA (Telecommunication Computing Electronics and Control), 22(3), 567. https://doi.org/10.12928/telkomnika.v22i3.25959