Abstract :

Over the last decade, Indonesian Tax Authority, DGT (Directorate General of Taxes) has introduced digital transformations on tax administration as a part of Tax Reform in Indonesia. These practices were done to improve taxpayer trust and tax compliance. The objective of this study is to look into the relationship between tax administrations’ digital transformation on trust, as well as taxpayers’ tax compliance decisions. We emphasized on Indonesian taxpayers’ perceptions of fairness and voluntary tax compliance following the digital transformation of tax administrations. An online questionnaire was distributed as part of this study’s survey approach. To collect samples from all throughout Indonesia, the online survey used a Google form. Indonesian taxpayers were used as the study’s population. A simple random sample strategy was used in this study. Structural equation modelling (SEM) was utilized to examine the collected data. The result was that tax administration’s digital transformation has a positive impact on voluntary tax compliance.

---

Keywords :

Digital transformation, Slippery Slope Framework, Tax Administration, Trust, Voluntary Tax Compliance.

---

References :

1. Badara, M. S. (2012). The Effect of Tax Audit on Tax Compliance in Nigeria (A Study of Bauchi State Board of Internal Revenue). Assessment, 3(4).
2. Baisalbayeva, K., Enden, E., Tenan, R., & Flores, R. (2018). Digital Transformation of Tax Administration. Microsoft and PricewaterhouseCoopers Publication.
3. Barclay, D., Higgings, C., & Thompson, R. (1995). The partial least squares (PLS) approach to casual modeling: Personal computer adoption and use as an illustration. Technology Studies, 2: 285–309, 1995. Hypothesis Testing. View At.
4. Ben-Ner, A., & Halldorsson, F. (2010). Trusting and trustworthiness: What are they, how to measure them, and what affects them. Journal of Economic Psychology, 31(1), 64–79. https://doi.org/https://doi.org/10.1016/j.joep.2009.10.001
5. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.
6. Chin, W. W., Thatcher, J. B., Wright, R. T., & Steel, D. (2013). Controlling for Common Method Variance in PLS Analysis: The Measured Latent Marker Variable Approach. Springer Proceedings in Mathematics and Statistics, 56, 231–239. https://doi.org/10.1007/978-1-4614-8283-3_16
7. Christoph, K., Stephan, M., & Erich, K. (2013). Trust, power, and tax compliance: Testing the slippery slope framework among self-employed taxpayers. University of Vienna, Faculty of Psychology.
8. Daniela, C. W., & Luís, P. (2014). Tax compliance, corruption and deterrence: An application oftheslippery model. Taxation Review, 6(6), 56–76.
9. Devos, K. (2008). Tax evasion behaviour and demographic factors: An exploratory study in Australia. Revenue Law Journal, 18, 1–44.
10. Dijke, M. van, … D. D. C.-J. of A., & 2010, undefined. (2010). The role of authority power in explaining procedural fairness effects. Psycnet.Apa.Org. https://doi.org/10.1037/a0018921
11. Feld, L. P., & Frey, B. S. (2002). Trust breeds trust: How taxpayers are treated. Economics of Governance, 3(2), 87–99.
12. Fornell, C., research, D. L.-J. of marketing, & 1981, undefined. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journals.Sagepub.Com, 18(1), 39–50. https://journals.sagepub.com/doi/abs/10.1177/002224378101800104
13. Gracia, D., Marketing, L. A.-R. E. de I. de, & 2015, undefined. (n.d.). Rebuilding public trust in government administrations through e-government actions. Elsevier. Retrieved July 4, 2022, from https://www.sciencedirect.com/science/article/pii/S1138144214000072
14. Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management and Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130/FULL/HTML
15. Hair, J., Ringle, C., Practice, M. S. M. theory and, & 2011, undefined. (2011). PLS-SEM: Indeed a silver bullet. Taylor & Francis, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
16. Hair Jr, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis a global perspective. Pearson Education Inc. USR, New Jersey, 7458.
17. 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
18. Hofmann, E., Hoelzl, E., Psychologie, E. K.-Z. für, & 2008, undefined. (n.d.). Preconditions of voluntary tax compliance: Knowledge and evaluation of taxation, norms, fairness, and motivation to cooperate. Psycnet.Apa.Org. Retrieved July 4, 2022, from https://psycnet.apa.org/record/2008-15781-003
19. Ivancevich, J., Matteson, M., & Konopaske, R. (1990). Organizational behavior and management. https://www.academia.edu/download/61483596/book_10_edition20191211-121005-1yj6hma.pdf
20. Jr, J. H., Hult, G., Ringle, C., Sarstedt, M., & Danks, N. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. https://library.oapen.org/handle/20.500.12657/51463
21. Kastlunger, B., Lozza, E., Kirchler, E., & Schabmann, A. (2013). Powerful authorities and trusting citizens: The Slippery Slope Framework and tax compliance in Italy. Journal of Economic Psychology, 34, 36–45.
22. Kirchler, E., Psychology, I. W.-J. of E., & 2010, undefined. (2010). Tax compliance inventory TAX-I: Designing an inventory for surveys of tax compliance. Elsevier. https://doi.org/10.1016/j.joep.2010.01.002
23. McAllister, D. J. (1995). Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1), 24–59.
24. Murphy, K. (2004). The role of trust in nurturing compliance: A study of accused tax avoiders. Law and Human Behavior, 28(2), 187–209.
25. Nazarov, M. A., Mikhaleva, O. L., & Chernousova, K. S. (2019). Digital transformation of tax administration. International Scientific Conference “Digital Transformation of the Economy: Challenges, Trends, New Opportunities,” 144–149.
26. Reddy, S. K., & Reinartz, W. (2017). Digital transformation and value creation: Sea change ahead. GfK Marketing Intelligence Review, 9(1), 10.
27. Ringle, C., Sarstedt, M., Management, R. M.-… R., & 2020, undefined. (2018). Partial least squares structural equation modeling in HRM research. Taylor & Francis, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655
28. Robbin, P. (2007). Stephen and Judge A, Timothy, Organizational Behavior. New Jersey: Pearson Education, Inc.
29. Tabachnick, B., Fidell, L., & Ullman, J. (2007). Using multivariate statistics. https://www.pearsonhighered.com/assets/preface/0/1/3/4/0134790545.pdf
30. Verboon, P., criminology, M. van D.-I. journal of, & 2012, undefined. (n.d.). The effect of perceived deterrence on compliance with authorities: The moderating influence of procedural justice. Lifescienceglobal.Com. Retrieved July 4, 2022, from https://lifescienceglobal.com/pms/index.php/ijcs/article/view/953
31. Verboon, P., & van Dijke, M. (2007). A self-interest analysis of justice and tax compliance: How distributive justice moderates the effect of outcome favorability. Journal of Economic Psychology, 28(6), 704–727. https://doi.org/10.1016/J.JOEP.2007.09.004
32. Wahl, I., Erich, K., Harina Gangl, K., Ant, G., Goslinga, S., Kirchler, E., & Hoelzl, E. (2007). Enforced versus voluntary tax compliance: The “slippery slope” framework. Elsevier. https://doi.org/10.1016/j.joep.2007.05.004
33. Yi, Y., Bagozzi, R., Turner, L., & Iacobucci, D. (2009). Everything you always wanted to know about SEM (structural equations modeling) but were afraid to ask. Elsevier. https://doi.org/10.1016/j.jcps.2009.09.002