ANALYSIS ANALYSIS DETERMINANT ADOPTION Artificial Generative INTELLIGENCE IN SYSTEM CLOUD-BASED ACCOUNTING INFORMATION: A PERSPECTIVE TRUST AND ALGORITHMIC Accountability
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2026-07-16Downloads
Abstract
Generative AI is experiencing very rapid growth in use in the business and accounting world, cloud-based Accounting Information systems are becoming the main platform for modern AI implementation, the level of user trust determines the success of using AI technology, algorithmic accountability is a strategic issue related to transparency and governance of AI. The purpose of this study can be a basis for organizations in designing AI implementation strategies that are more responsible and acceptable to users. By conducting a study of the relationship between trust and algorithmic accountability on the adoption of Generative AI in the accounting context, the results of partial hypothesis testing show the influence of trust t count of 6.004 with a significance level of 0.000 <0.05 and a t table value of 2.024, meaning that trust provides evidence of influencing the perception of generative AI adoption. The algorithmic accountability hypothesis has not provided a significant influence on the perception of generative AI adoption with a t count of 0.489 with a significance level of 0.627 (>0.05). The hypothesis simultaneously provides a large influential contribution to the two independent variables with a calculated F value of 25.955 with a significance of 0.000 which can be compared with the F table of 3.25. Meanwhile, the contribution of the two variables trust and algorithmic accountability has a moderate relationship in influencing the dependent variable with a coefficient value of 0.764 and gives a sign that 58.4% of the independent variables can influence the dependent variable while the remaining 41.6% is influenced by other variables outside this model.
Keywords:
Generative_AI Trust algorithmmithmic_Accountability AIS CloudReferences
1. A. Abdo-Salloum and M. Chehade, “Generative Artificial Intelligence Adoption in Accounting,” SAGE Open, vol. 16, no. 1, 2026
2. A. Appelbaum, M. Kogan, and G. Vasarhelyi, “Analytical Procedures in External Auditing,” Journal of Emerging Technologies in Accounting, vol. 17, no. 1, pp. 45–62, 2020.
3. A. Kaplan and M. Haenlein, “Siri, Siri, in my hand: Who’s the fairest in the land?,” Business Horizons, vol. 63, no. 1, pp. 15–25, 2020.
4. ACCA, Accounting and Finance in the Age of AI, London, United Kingdom, 2025.
5. Deloitte, State of Generative AI in the Enterprise Report 2025, London, UK: Deloitte Insights, 2025.
6. Deloitte, Generative AI in Finance and Accounting, 2024.
7. D. McKnight et al., “Developing and Validating Trust Measures for E-Commerce,” Information Systems Research, vol. 13, no. 3, pp. 334–359, 2002.
8. G. Grote, S. K. Parker, and K. Crowston, “Taming Artificial Intelligence: A Theory of Control–Accountability Alignment Among AI Developers and Users,” Academy of Management Review, vol. 49, no. 2, pp. 312–337, 2024.
9. G. Vasarhelyi and M. Alles, “Continuous Auditing and Continuous Monitoring,” Accounting Horizons, vol. 34, no. 4, pp. 221–239, 2020.
10. Ghozali, Imam. 2018. Aplikasi Analisis Multivariate dengan Program IBM SPSS 25. Badan Penerbit Universitas Diponegoro: Semarang.
11. H. Wu, "Research on Enterprise Accounting Risk Control Systems Based on Artificial Intelligence Technology," 2024 3rd International Conference on Artificial Intelligence and Autonomous Robotic Systems (AIARS), Bristol, United Kingdom, 2024, pp. 129-133, doi: 10.1109/AIARS63200.2024.00029.
12. IBM, Predictive Analytics for Finance and Accounting, Armonk, NY, USA, 2024.
13. IFAC, Artificial Intelligence and the Future of Accountancy, 2024.
14. J. Panduro-Ramirez, P. Malyadri, K. Mayi, C. Poma-Garcia, D. R. Sisodia and H. A. Almashaqbeh, "A Comparative Study in Understanding the Emerging Field of Blockchain Toward Effective Accounting Information Systems in Large Corporations," 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2022, pp. 2468-2472, doi: 10.1109/ICACITE53722.2022.9823723.
15. J. W. Creswell and J. D. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 6th ed. Thousand Oaks, CA, USA: Sage Publications, 2023.
16. J. Żywiołek, “Empirical Examination of AI-Powered Decision Support Systems: Ensuring Trust and Transparency in Information and Knowledge Security,” Information, vol. 15, no. 6, pp. 1–18, 2024.
17. J. D. Lee and K. A. See, “Trust in Automation: Designing for Appropriate Reliance,” Human Factors, vol. 46, no. 1, pp. 50–80, 2020.
18. K. Dwivedi et al., “So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” International Journal of Information Management, vol. 71, 2023.
19. KPMG, AI-Enabled Financial Reporting Transformation, Amsterdam, Netherlands, 2024.
20. M. Mökander, “Auditing Generative AI Systems,” AI and Ethics, vol. 4, no. 1, 2023.
21. McKinsey & Company, The State of AI in Early 2025: Generative AI's Breakout Year, New York, NY, USA: McKinsey Global Institute, 2025.
22. M. Feuerriegel et al., “Generative AI,” Business & Information Systems Engineering, vol. 66, no. 1, pp. 111–126, 2024.
23. M. Mökander, “Auditing AI Systems: Challenges and Opportunities,” AI and Ethics, vol. 4, no. 2, pp. 301–315, 2023.
24. M. Romney and P. Steinbart, Accounting Information Systems, 15th ed., Pearson, 2021.
25. Microsoft, Integrated Cloud Data Platforms, 2024.
26. M. Glikson and A. Woolley, “Human Trust in Artificial Intelligence,” Academy of Management Review, vol. 45, no. 4, pp. 627–660, 2020.
27. M. Bach et al., “Human-AI Trust and Adoption,” Information Systems Frontiers, vol. 26, no. 1, pp. 1–18, 2024.
28. M. Feuerriegel et al., “Generative AI and Trustworthy Systems,” Business & Information Systems Engineering, vol. 66, no. 1, pp. 111–126, 2024.
29. M. Glikson and A. Woolley, “Human Trust in Artificial Intelligence,” Academy of Management Review, vol. 45, no. 4, pp. 627–660, 2020.
30. M. Wieringa, “What to Account for When Accounting for Algorithms,” FAT Conference Proceedings, 2020.
31. M. G. Alles and M. A. Vasarhelyi, “Continuous Auditing and Continuous Monitoring: Current Status and Future Directions,” Accounting Horizons, vol. 34, no. 4, pp. 221–239, 2020.
32. M. Janssen, P. Brous, E. Estevez, L. S. Barbosa, and T. Janowski, “Data Governance and Algorithmic Accountability in AI Systems,” Government Information Quarterly, vol. 37, no. 3, 2020.
33. N. P. Wulandari and N. K. Dewi, “Adoption of Cloud-Based Accounting in the Villa Accommodation Sector: An Analysis of UTAUT Model Implementation,” International Journal of Economics and Management Research, vol. 4, no. 1, pp. 112–128, 2025.
34. N. Kristiyanthi, I. G. A. M. Asri Dwija Putri, and N. P. S. Dewi, “Impact of Cloud-Based Accounting Information Systems on Decision-Making Quality,” International Journal of Accounting and Information Management, vol. 32, no. 2, pp. 145–162, 2024.
35. OECD, Artificial Intelligence in Society, Paris: OECD Publishing, 2023.
36. PwC, AI-Powered Financial Reporting and Disclosure, London, United Kingdom, 2024.
37. R. Mayer, J. Davis, and F. Schoorman, “An Integrative Model of Organizational Trust,” Academy of Management Review, vol. 20, no. 3, pp. 709–734, 1995.
38. R. Novianti and A. Aligarh, “MSME Adoption of Cloud Accounting: A TAM and UTAUT Perspective,” Journal of Economics, Business and Accounting, vol. 8, no. 1, pp. 55–69, 2025.
39. R. Ayyadurai, K. Parthasarathy, N. K. R. Panga, J. Bobba and V. R. Prasaath, "Cloud-Based Business Intelligence with AutoML for Financial Risk Management, Budget Optimization, and Accounting Simulation Using Cloudsim," IEEE 2025 International Conference on Advances in Computational Research on Engineering and Science Technology (ACROSET), INDORE, India, 2025, pp. 1-6, doi: 10.1109/ACROSET66531.2025.11281174.
40. S. Minaee et al., “Large Language Models: A Survey,” ACM Computing Surveys, 2024.
41. S. Siau and W. Wang, “Building Trust in Artificial Intelligence Systems,” Journal of Computer Information Systems, vol. 60, no. 1, pp. 47–53, 2020.
42. S. Agrawal and S. Jethy, “Factors Affecting the Adoption of Cloud-Based Accounting: A Case Study in Bhubaneswar,” International Journal of Research in Commerce and Management Studies, vol. 5, no. 4, pp. 45–58, 2023.
43. Supardianto et al. (2019). The Role of Information Technology Usage on Startup Financial Management and Taxation. Procedia Computer Science 161 (2019) 1308–1315. Published by Elsevier B.V.
44. Stahl, B. C., Brooks, L., Hatzakis, T., Santiago, N., & Wright, D. (2023). Exploring ethics and human rights in artificial intelligence – a delphi study. Technological Forecasting and Social Change, 191, Article 122502. https://doi.org/10.1016/j. techfore.2023.122502.
45. Sugiyono, D. (2018). Metode penelitian kuatintatif, kualitatif dan R & D/Sugiyono. Bandung: Alfabeta, 15(2010).
46. T. Stratopoulos and Y. Wang, “Generative AI in Accounting: Emerging Research Directions,” 2025.
47. U. Sekaran and R. Bougie, Research Methods for Business: A Skill Building Approach, 9th ed. Hoboken, NJ, USA: Wiley, 2024.
48. UNESCO, Recommendation on the Ethics of Artificial Intelligence, Paris, France, 2023.
49. V. Venkatesh et al., “Artificial Intelligence and Trust in Organizations,” MIS Quarterly Executive, vol. 22, no. 1, pp. 15–29, 2023.
50. World Bank, AI and Digital Transformation Report, 2025.
51. World Economic Forum, AI-Powered Decision Intelligence, Geneva, Switzerland, 2025.
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