Validation of an Artificial Intelligence–Based Model for Accounting Practices
Keywords:
Accounting Procedures, AI Model Validation, Financial Decision Support, Machine Learning in AccountingAbstract
The objective of this study was to develop and validate an artificial intelligence–based model for accounting practices by identifying the key determinants of AI adoption and examining its implications for financial reporting quality and organizational decision-making. This developmental study employed a quantitative research design. Data were collected using a structured questionnaire administered to financial managers, auditors, and information technology specialists. The validity and reliability of the instrument were confirmed through convergent validity, Cronbach’s alpha, and composite reliability assessments. Data analysis was conducted using SPSS and SmartPLS software, and the proposed model was tested and validated through Structural Equation Modeling (SEM). The SEM results revealed that contextual factors, including technological infrastructure and institutional support, intervening factors such as organizational culture, regulations, and employee acceptance, and causal factors including the demand for transparency, accuracy, and high-quality financial reporting, significantly influenced the adoption of AI-based accounting practices. Furthermore, strategies such as AI-driven process development, system integration, workforce training, and big-data analytics significantly facilitated technology implementation and reduced resistance to change. Model fit indices indicated a strong level of model adequacy (GOF = 0.62). Significant positive relationships were also observed among artificial intelligence adoption, financial reporting transparency, risk management effectiveness, data security, and employee acceptance. The findings demonstrate that artificial intelligence serves as a transformative force in accounting by improving the accuracy, efficiency, and transparency of financial reporting. AI-enabled accounting systems enhance financial analysis, strengthen risk management capabilities, support managerial decision-making, and contribute to sustainable organizational value creation and competitive advantage. Successful implementation, however, requires adequate technological infrastructure, continuous workforce development, effective data governance, and supportive regulatory frameworks.
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Copyright (c) 2025 Amir Aghaalizadeh Darandashi (Author); Gholamreza Zomorodian (Corresponding author); fatemeh Samadi, Hossein Badiei (Author)
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