The pattern of increasing the quality of decision-making according to the digital accounting system
Subject Areas :Masoud Saljoghi 1 , Mehdi Dasineh 2 , حجت الله سالاری 3 , علی امیری 4
1 - Accounting doctoral student of Islamic Azad University, Bandar Abbas branch, Iran
2 -
3 - Assistant Professor of Accounting Department, Bandar Abbas Branch, Bandar Abbas Islamic Azad University, Iran
4 - Assistant Professor of Accounting Department, Bandar Abbas Branch, Bandar Abbas Islamic Azad University, Iran
Keywords: Digital Accounting, Decision Making, Quality.,
Abstract :
This research is done with the aim of increasing the quality of decision-making with emphasis on the digital accounting system. The information required for the research for Interpretive Structural Modeling (ISM) to provide the model was collected from interviews with 15 experts, i.e. high-ranking managers with a master's degree and a doctorate in accounting and digital accounting experience of at least 5 years. Data collection in the structural equation modeling section was done by a researcher-made questionnaire from 384 accounting and financial employees of listed companies. In this research, using Interpretive Structural Modeling (ISM),a model for digital accounting system was designed to increase the quality of dicision makinig. Then, based on the obtained model, the relationships of the variables were re-examined using Structural Equation Modeling (SEM). The results of the interpretative structural analysis (ISM) by the exploratory model showed that the factors of the digital accounting system including blockchain technology, cloud processing, artificial intelligence, big data, process automation, intelligent information systems, electronic data exchange and intelligent analysis and computing as the most effective factors in The model promotes the quality of decision-making, including the increase of optimal and desirable decisions, optimal monitoring and supervision, intelligent decision-making and reassuring decision-making. Also, structural equation modeling showed that all exploratory mode variables have significant relationships.
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