توسعه و اعتبارسنجی مدل ساختاری به‌کارگیری هوش مصنوعی در گذار به حسابداری مدیریت با تأکید بر ملاحظات اخلاقی

امیرحسین   ابراهیمی; علی اکبر فرزین فر; حسن  قدرتی; میثم  عرب زاده; محمدرضا  محققی

Authors

Amirhosein Ebrahimi PhD Student, Department of Accounting Department, Kas.C., Islamic Azad University, Kashan, Iran Author https://orcid.org/0009-0002-3638-9081
Ali Akbar Farizin Far Department of Accounting, Kas.C., Islamic Azad University, Kashan, Iran. Corresponding author https://orcid.org/0000-0002-0998-4478
Hassan Ghodrati Department of Accounting Department, Kas.C., Islamic Azad University, Kashan, Iran Author https://orcid.org/0000-0001-6209-3335
Meysam Arabzadeh Department of Accounting Department, Kas.C., Islamic Azad University, Kashan, Iran Author https://orcid.org/0000-0001-6000-7827
Mohammadreza Mohaghgheghi Department of Accounting Department, Kas.C., Islamic Azad University, Kashan, Iran Author https://orcid.org/0000-0002-7080-283X

Keywords:

Artificial intelligence, management accounting, ethical considerations, data governance, algorithmic transparency

Abstract

This study aimed to develop and validate a structural model for the adoption of artificial intelligence (AI) in the transition to management accounting, with particular emphasis on ethical considerations. This applied study employed an exploratory mixed-methods qualitative–quantitative design conducted in three stages. First, influential factors were identified through a systematic analysis of the literature and qualitative content analysis and were coded using MAXQDA 2020. Content validity was assessed using the content validity ratio, while coding reliability was evaluated using Cohen’s kappa, which yielded a coefficient of 0.90. Second, Interpretive Structural Modeling (ISM) and the judgments of 18 academic and professional experts were used to structure the relationships among the identified factors. Third, the resulting model was empirically validated using Partial Least Squares Structural Equation Modeling (PLS-SEM) based on data obtained from 118 respondents and analyzed in SmartPLS. Multicollinearity, structural paths, bootstrapped t-statistics, and the coefficient of determination were examined to evaluate the structural model. Inferential analyses indicated that variance inflation factor values ranged from 1.299 to 4.805, with none exceeding the critical threshold of 5, demonstrating the absence of serious multicollinearity among the predictor constructs. Bootstrapping results supported the statistical significance of the structural relationships at the 95% confidence level based on the criterion of t>1.96. Moreover, the coefficient of determination was R²=0.62, indicating that the predictor constructs explained 62% of the variance in the endogenous constructs and confirming the model’s satisfactory explanatory power. Overall, the PLS-SEM findings provided empirical support for the conceptual structure and relationships derived from the ISM model. The validated model indicates that a successful AI-driven transition in management accounting requires the integration of technological and organizational infrastructure with ethical governance, accountability, data protection, algorithmic transparency, professional competence, and preservation of human judgment. Accordingly, AI adoption should be managed as a simultaneous technological, professional, organizational, and ethical transformation.

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