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THE IMPACT OF ARTIFICIAL INTELLIGENCE ON ACCOUNTING PERFORMANCE: SUSTAINABLE DEVELOPMENT AS A MEDIATING VARIABLE
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The UN plans to achieve several development objectives by 2030. These objectives address global warming, a major issue. This method aims to improve sustainable accounting performance (AP). In this circumstance, AI is being applied in various fields, notably in economic, social, and environmental (ESE) domains. This research investigates how sustainable development (SD) influences AI methodologies and AP improvement. The research examined a sample of Iraqi banks listed on the Iraq Stock Exchange from 2014 to 2022. AI was measured by ATM and POS prevalence. A three-dimensional approach examined economic, social, and environmental (ESE) sustainability. Meanwhile, the performance of sustainable accounting was measured through the return on assets (ROA) index. The findings of this study revealed a general weakness in the AP of Iraqi banks during the study period. However, it was found that the integration of AI dimensions contributes significantly to achieving SD and enhancing AP. Furthermore, the study demonstrated that SD, in turn, plays a critical role in improving the level of AP. These results highlight the importance of adopting AI-driven technologies within the banking sector to promote sustainability and strengthen accounting outcomes. Additionally, the study emphasizes the need for a deeper focus on SD as a vital intermediary that links technological advancements to improved organizational performance. This research offers valuable insights for both policymakers and financial institutions striving to achieve the United Nations' SD goals by 2030, particularly in regions facing similar challenges to those in Iraq.

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Publication Date
Wed Apr 25 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Different Estimation Methods for System Reliability Multi-Components model: Exponentiated Weibull Distribution
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        In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through  Monte Carlo simulation technique were made depend on mean squared error (MSE)  criteria

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