Artificial Intelligence and International Business Law: Transforming Global Trade, Governance, and Compliance
DOI:
https://doi.org/10.61978/legalis.v4i2.1348Keywords:
artificial intelligence, international business law, compliance effectiveness, international trade performance, regulatory clarity, digital infrastructure readiness, cross-border data governanceAbstract
Artificial Intelligence (AI) is increasingly reshaping international business law by transforming how firms manage regulatory compliance, governance processes, and cross-border trade operations. In practice, AI is applied to legal mechanisms such as automated customs compliance, regulatory monitoring, sanctions screening, and cross-border data transfer governance. Despite growing adoption, empirical evidence remains limited on how AI deployment and institutional conditions jointly influence compliance effectiveness and international trade performance. To address this gap, this study examines the effects of AI adoption, regulatory clarity, digital infrastructure readiness, and cross-border data governance quality on international trade performance, with compliance effectiveness as a mediating mechanism. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) on 350 survey responses, the findings show that all four antecedent factors have a significant positive impact on compliance effectiveness. Compliance effectiveness, in turn, strongly enhances firm-level international trade performance, measured through improvements in trade efficiency, risk reduction, and market expansion. Moreover, compliance effectiveness significantly mediates the relationship between the institutional and technological factors and trade performance. Among the predictors, cross-border data governance quality exerts the strongest influence. These findings highlight that AI-enabled trade outcomes depend not only on technological adoption but also on regulatory clarity, robust digital infrastructure, and harmonized data governance frameworks, offering practical insights for policymakers and firms integrating AI into international business law.
References
Agarwal, A., & Nene, M. J. (2025). A five-layer framework for AI governance: integrating regulation, standards, and certification. Transforming Government: People, Process and Policy. https://doi.org/10.1108/TG-03-2025-0065 DOI: https://doi.org/10.1108/TG-03-2025-0065
Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23. https://doi.org/10.1016/j.jretconser.2014.09.003 DOI: https://doi.org/10.1016/j.jretconser.2014.09.003
Alfano, V., & Guarino, M. (2024). Why Do You Make Things So Complicated? Understanding the Texts of Regulations During the COVID-19 Pandemic. Italian Economic Journal, 10(2), 929–955. https://doi.org/10.1007/s40797-023-00243-5 DOI: https://doi.org/10.1007/s40797-023-00243-5
Arsyida, T., van Delft, S., Rukanova, B., & Tan, Y. H. (2017). Trade and compliance cost model in the international supply chain. 12th Annual WCO PICARD Conference, 1–22.
Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve sustainable business performance: application of technology-organization-environment framework. Sustainability, 16(5), 1864. https://doi.org/10.3390/su16051864 DOI: https://doi.org/10.3390/su16051864
Chin, Y. C., & Zhao, J. (2022). Governing cross-border data flows: International trade agreements and their limits. Laws, 11(4), 63. https://doi.org/10.3390/laws11040063 DOI: https://doi.org/10.3390/laws11040063
Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1). https://doi.org/10.7275/jyj1-4868
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312 DOI: https://doi.org/10.1177/002224378101800104
Fuadi, A., Wulandari, D. S., & Loka, S. P. (2024). Enhancing tax compliance: the role of awareness, regulation understanding, and perceived system effectiveness among self-employed individuals. Jurnal Akuntansi Bisnis Pelita Bangsa, 9(01), 93–104. https://doi.org/10.37366/akubis.v9i01.1800 DOI: https://doi.org/10.37366/akubis.v9i01.1800
Gammisch, M., & Balina, S. (2014). The effectiveness of compliance management systems: an experimental approach. Procedia - Social and Behavioral Sciences, 156, 236–240. https://doi.org/10.1016/j.sbspro.2014.11.181 DOI: https://doi.org/10.1016/j.sbspro.2014.11.181
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 213–236. https://doi.org/10.2307/249689 DOI: https://doi.org/10.2307/249689
Gupta, S. K., & Saranya, T. S. (2025). Developing a Robust Digital Industry Infrastructure: Macro and Micro Levels. In Digital Transformation and IT Implementation: Driving Sustainable Development Across Nations (pp. 34–60). https://doi.org/10.36690/DTIT DOI: https://doi.org/10.36690/DTIT
Hair, J. F. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage.
He, Y. (2025). Research on Enterprise Cross-border Data Compliance. Lecture Notes in Education Psychology and Public Media, 112(1), 74–83. https://doi.org/10.54254/2753-7048/2025.BR26817 DOI: https://doi.org/10.54254/2753-7048/2025.BR26817
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 DOI: https://doi.org/10.1007/s11747-014-0403-8
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 DOI: https://doi.org/10.1080/10705519909540118
Huang, B., & Niyomsilp, E. (2025). The impact of artificial intelligence on organizational decision-making processes. Edelweiss Applied Science and Technology, 9(4), 794–808. https://doi.org/10.55214/25768484.v9i4.6081 DOI: https://doi.org/10.55214/25768484.v9i4.6081
Jones, E. (2023). Digital disruption: artificial intelligence and international trade policy. Oxford Review of Economic Policy, 39(1), 70–84. https://doi.org/10.1093/oxrep/grac049 DOI: https://doi.org/10.1093/oxrep/grac049
Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling. Guilford Publications.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101 DOI: https://doi.org/10.4018/ijec.2015100101
Liao, D. X., & Parkouda, M. (2023). Paving the Road to Global Markets. International Journal of Standardization Research, 20(1). https://doi.org/10.4018/IJSR.319023 DOI: https://doi.org/10.4018/IJSR.319023
Mirishli, S. (2025). Ethical implications of AI in data collection: Balancing innovation with privacy. ArXiv Preprint. https://doi.org/10.48550/arXiv.2503.14539 DOI: https://doi.org/10.2139/ssrn.5168593
Mirzaye, S., & Mohiuddin, M. (2025). Digital transformation in international trade: Opportunities, challenges, and policy implications. Journal of Risk and Financial Management, 18(8), 421. https://doi.org/10.3390/jrfm18080421 DOI: https://doi.org/10.3390/jrfm18080421
Morrow, P., Smart, M., & Swistak, A. (2022). VAT compliance, trade, and institutions. Journal of Public Economics, 208, 104634. https://doi.org/10.1016/j.jpubeco.2022.104634 DOI: https://doi.org/10.1016/j.jpubeco.2022.104634
Nallapu, S. (2025). Enterprise Automation in Transportation Finance: Digital Infrastructure for Compliance. Journal of Information Systems Engineering & Management, 10(59s), 97–105. https://doi.org/10.52783/jisem.v10i59s.12811 DOI: https://doi.org/10.52783/jisem.v10i59s.12811
Parker, C., & Nielsen, V. L. (2017). Compliance: 14 questions. In Regulatory Theory: Foundations and Applications (pp. 217–232). https://doi.org/10.22459/RT.02.2017.13 DOI: https://doi.org/10.22459/RT.02.2017.13
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879 DOI: https://doi.org/10.1037/0021-9010.88.5.879
Porumbescu, G. A., Lindeman, M. I., Ceka, E., & Cucciniello, M. (2017). Can transparency foster more understanding and compliant citizens? Public Administration Review, 77(6), 840–850. https://doi.org/10.1111/puar.12790 DOI: https://doi.org/10.1111/puar.12790
Rai, A., Constantinides, P., & Sarker, S. (2019). Next-generation digital platforms: toward human-AI hybrids. MIS Quarterly, 43(1), iii–ix. https://doi.org/10.25300/MISQ/2019/431E0 DOI: https://doi.org/10.25300/MISQ/2019/431E0
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019). Diffusion of Innovations. In Diffusion of Innovations (pp. 415–434). https://doi.org/10.4324/9780203710753-35 DOI: https://doi.org/10.4324/9780203710753-35
Roida, R., Werdiningsih, R., & Pudjiarti, E. S. (2025). The implementation of Tax Administration Digitalization Policy on tax Compliance in manufacturing industry companies. Dynamics Social International Journal of Social Sciences and Communication, 1(3), 45–54. https://doi.org/10.70062/dynamicssocial.v1i3.231 DOI: https://doi.org/10.70062/dynamicssocial.v1i3.231
Shah, I. H. (2025). Assessing the Influence of Artificial Intelligence on Corporate Tax Compliance and Financial Reporting. Research Square. https://doi.org/10.21203/rs.3.rs-7206981/v1 DOI: https://doi.org/10.21203/rs.3.rs-7206981/v1
Shakhnazarov, B. A. (2024). Legal Approaches in International Trade Compliance. Kutafin Law Review, 11(4), 761–787. https://doi.org/10.17803/2713-0533.2024.4.30.761-787 DOI: https://doi.org/10.17803/2713-0533.2024.4.30.761-787
Singh, S. K., Bandyopadhyay, P., & Mishra, A. (2025). The role of artificial intelligence in enhancing compliance in banks. Journal of Economic Surveys. https://doi.org/10.1111/joes.12693 DOI: https://doi.org/10.1111/joes.12693
Tripathi, D., Jain, M., & Kharche, P. (2024). Data quality and data governance: investigating the impact on data science outcomes. International Journal for Research in Applied Science and Engineering Technology, 12(10), 343–349. https://doi.org/10.22214/ijraset.2024.64518 DOI: https://doi.org/10.22214/ijraset.2024.64518
Uddoh, J., Ajiga, D., Okare, B. P., & Aduloju, T. D. (2021). Cross-border data compliance and sovereignty: a review of policy and technical frameworks. Journal of Frontiers in Multidisciplinary Research, 2(2), 68–74. https://doi.org/10.54660/IJFMR.2021.2.2.68-74 DOI: https://doi.org/10.54660/.IJFMR.2021.2.2.68-74
Umbet, M., Askarov, D., Rudzioniene, K., Christauskas, C., & Alikulova, L. (2025). Evaluating the implementation of information technology audit systems within tax administration: a risk governance perspective for enhancing digital fiscal integrity. Journal of Risk and Financial Management, 18(8), 422. https://doi.org/10.3390/jrfm18080422 DOI: https://doi.org/10.3390/jrfm18080422
Xue, G. (2025). Building a Three-Pronged Governance Model for Cross-Border Data Compliance in the Biomedical Industry under the Dual Circulation Pattern. Frontiers in Business Economics and Management, 20(3), 89–92. https://doi.org/10.54097/44c7mx79 DOI: https://doi.org/10.54097/44c7mx79
Zhao, Y. (2025). Is AI Usage Relevant to Audit Effectiveness: Correlation Findings from the Big Four Firms. Advances in Economics Management and Political Sciences, 214(1), 181–189. https://doi.org/10.54254/2754-1169/2025.cau27063 DOI: https://doi.org/10.54254/2754-1169/2025.CAU27063
Zulaikhah, S. (2025). Transforming Tax Compliance in Optimizing State Revenue: Innovation and Challenges in the Digital Era. International Journal of Economics, 4(1). https://doi.org/10.55299/ijec.v3i2.1109 DOI: https://doi.org/10.55299/ijec.v3i2.1109
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Legalis : Journal of Law Review
This work is licensed under a Creative Commons Attribution 4.0 International License.
