Optimal selection of non-human actors affecting the accounting information system

An accounting information system is a platform in which the basis of accounting is formed and managers and all users make decisions based on its product. Playing such a role necessitates the accurate identification of the actors playing a role in this system in order to plan in a principled and principled way in the development and productivity of the country's financial and economic system. Therefore, if it is possible to accurately predict and identify the actors affecting the accounting information system before the start of activities, a fundamental change in the field of management of the accounting information system can be created. The purpose of this study was to predict the prioritization of non-human actors affecting the accounting information system in Iran from the perspective of the network of actors using four algorithms: support vector machine (SVC), random forest (RF) ٬ Boosting and K-nearest neighbor (k- NN) is. Findings show that the most important actors in the accounting information system are organizational actors, followed by technical actors, economic actors, technology actors, and socio-political actors who have the most predictive power in the accounting information system. The RF model is also known as the best model for predicting the most effective characteristics of non-human actors in the accounting information system.