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All Journal International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS)
Agustina Br Surbakti
Universitas Islam Kebangsaan Indonesia
Economics, Econometrics & Finance

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ANALYSIS ANALYSIS DETERMINANT ADOPTION Artificial Generative INTELLIGENCE IN SYSTEM CLOUD-BASED ACCOUNTING INFORMATION: A PERSPECTIVE TRUST AND ALGORITHMIC Accountability Lakharis Inuzula; Erika Fahmi Ginting; Agustina Br Surbakti; Koko Bustami
International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Vol. 6 No. 4 (2026): August (ON PROGRESS)
Publisher : CV. Radja Publika

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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.
Co-Authors Erika Fahmi Ginting Koko Bustami Lakharis Inuzula