Digitalisation, Artificial Intelligence, and Corporate Tax Compliance in Emerging Economies: The Moderating Roles of Institutional Quality and Taxpayer Trust

Harshitaben Dineshbhai Suva; Dineshkumar Ramjibhai Chavda

doi:10.68090/IJBSIVol1Iss2-21

Authors

Harshitaben Dineshbhai Suva Research Scholar, Department of Commerce and Management, Bhakta Kavi Narsinh Mehta University, Junagadh, Gujarat, India Author
Dineshkumar Ramjibhai Chavda Assistant Professor, Department of Commerce and Management, Bhakta Kavi Narsinh Mehta University, Junagadh, Gujarat, India Author

DOI:

https://doi.org/10.68090/IJBSIVol1Iss2-21

Keywords:

Tax Compliance, Digitalisation of Tax Administration, Artificial Intelligence, Institutional Quality, Taxpayer Trust, Slippery Slope Framework

Abstract

Tax compliance remains a major challenge in emerging economies due to institutional weaknesses, limited administrative capacity, and low taxpayer trust. This study examines how the digitalisation of tax administration and the adoption of artificial intelligence (AI) tools influence corporate tax compliance behaviour. Drawing on the Slippery Slope Framework, the Technology Acceptance Model, and institutional theory, we develop and test a model in which digitalisation and AI adoption enhance compliance both directly and indirectly, with institutional quality and taxpayer trust acting as key moderators. Using survey data from 387 tax managers and finance executives of medium and large enterprises across India, Serbia, and three other Southeast European emerging economies, combined with World Bank institutional quality indicators, we apply structural equation modelling (SEM) and multi-group analysis. Results show that digitalisation and AI-enabled tools significantly improve voluntary tax compliance, with stronger effects under higher institutional quality and greater taxpayer trust. AI adoption partially mediates the digitalisation–compliance relationship. Robustness checks confirm the stability of the findings. The study integrates technological, behavioural, and institutional perspectives and offers practical insights for modernising tax systems in emerging markets.

References

Acemoglu, D., & Robinson, J. A. (2012). Why nations fail: The origins of power, prosperity, and poverty. Crown Business.

Alm, J., & Torgler, B. (2011). Do ethics matter? Tax compliance and morality. Journal of Business Ethics, 101(4), 635–651. https://doi.org/10.1007/s10551-011-0761-9

Alm, J., Martinez-Vazquez, J., & Torgler, B. (Eds.). (2016). Developing alternative frameworks for explaining tax compliance. Routledge.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Besley, T., & Persson, T. (2014). Why do developing countries tax so little? Journal of Economic Perspectives, 28(4), 99–120. https://doi.org/10.1257/jep.28.4.99

Bird, R. M. (2015). Improving tax administration in developing countries. Journal of Tax Administration, 1(1), 23–45.

Bird, R. M., & Zolt, E. M. (2008). Technology and taxation in developing countries: From hand to mouse. National Tax Journal, 61(4), 791–821. https://doi.org/10.17310/ntj.2008.4S.02

Collosa, A. (2022). Artificial intelligence and tax administration. Inter-American Center of Tax Administrations (CIAT).

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Faúndez-Ugalde, A., Mellado-Silva, R., & Aldunate-Lizana, E. (2020). Use of artificial intelligence by tax administrations: An analysis regarding taxpayers’ rights in Latin American countries. Computer Law & Security Review, 38, 105441. https://doi.org/10.1016/j.clsr.2020.105441

Gangl, K., Hofmann, E., & Kirchler, E. (2015). Tax authorities’ interaction with taxpayers: A conception of compliance in social dilemmas by power and trust. New Ideas in Psychology, 37, 13–23. https://doi.org/10.1016/j.newideapsych.2014.12.001

Gangl, K., Torgler, B., Kirchler, E., & Hofmann, E. (2020). Effects of supervision on tax compliance: Evidence from a field experiment in Austria. Economics Letters, 189, 109028. https://doi.org/10.1016/j.econlet.2014.03.027

Gobena, L. B., & Van Dijke, M. (2016). Power, justice, and trust: A moderated mediation analysis of tax compliance among Ethiopian business owners. Journal of Economic Psychology, 52, 24–37. https://doi.org/10.1016/j.joep.2015.11.004

Hofmann, E., Hartl, B., Gangl, K., Hartner-Tiefenthaler, M., & Kirchler, E. (2017). Authorities’ coercive and legitimate power: The impact on cognitions underlying cooperation. Journal of Economic Psychology, 63, 1–15. https://doi.org/10.3389/fpsyg.2017.00005

IMF. (2023). Fiscal monitor: On the path to policy normalisation. International Monetary Fund.

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The worldwide governance indicators: Methodology and analytical issues. World Bank Policy Research Working Paper No. 5430. https://doi.org/10.1596/1813-9450-5430

Kirchler, E. (2007). The economic psychology of tax behaviour. Cambridge University Press.

Kirchler, E., & Wahl, I. (2010). Tax compliance inventory TAX-I: Designing an inventory for surveys of tax compliance. Journal of Economic Psychology, 31(3), 331–346. https://doi.org/10.1016/j.joep.2010.01.002

Kirchler, E., Hoelzl, E., & Wahl, I. (2008). Enforced versus voluntary tax compliance: The “slippery slope” framework. Journal of Economic Psychology, 29(2), 210–225. https://doi.org/10.1016/j.joep.2007.05.004

Kochanova, A., Hasnain, Z., & Larson, B. (2020). Does E-Government Improve Government Capacity? Evidence from Tax Compliance Costs, Tax Revenue, and Public Procurement Competitiveness. The World Bank Economic Review, 34(1), 101–120. https://doi.org/10.1093/wber/lhx024

OECD. (2016). Technologies for better tax administration: A practical guide for revenue bodies. OECD Publishing. https://doi.org/10.1787/9789264256439-en

OECD. (2021). Tax administration 2021: Comparative information on OECD and other advanced and emerging economies. OECD Publishing. https://doi.org/10.1787/cef472b9-en

OECD. (2022). Revenue statistics in Asia and the Pacific 2022: Strengthening Tax Revenues in Developing Asia. OECD Publishing. https://doi.org/10.1787/db29f89a-en

Okunogbe, O., & Santoro, F. (2023). The promise and limitations of information technology for tax mobilization. The World Bank Research Observer, 38(2), 295–324. https://doi.org/10.1093/wbro/lkac008

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879

Scholz, J. T., & Lubell, M. (1998). Trust and taxpaying: Testing the heuristic approach to collective action. American Journal of Political Science, 42(2), 398–417. https://doi.org/10.2307/2991764

Torgler, B. (2007). Tax compliance and tax morale: A theoretical and empirical analysis. Edward Elgar.

World Bank. (2022). GovTech maturity index: The state of public sector digital transformation. World Bank. https://doi.org/10.1596/978-1-4648-1765-6

Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. https://doi.org/10.1111/rego.12158

Yilmaz, F., & Coolidge, J. (2013). Can e-filing reduce tax compliance costs in developing countries? World Bank Policy Research Working Paper No. 6647. https://doi.org/10.1596/1813-9450-6647

Zarsky, T. Z. (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology, & Human Values, 41(1), 118–132. https://doi.org/10.1177/0162243915605575