Accounting information system

This study aims at investigating the factors that affect a user’s satisfaction of accounting information systems based on the quality and the perceived usefulness of such systems, and the quality of data involved. The paper analyses the given factors on data collected from Bank Sumut,  Indonesia. In addition, this research also aims to find out whether the influence of perceived ease of use can be utilized as a moderating variable or not. the research is quantitative in terms of methodology and the sample pool consists of 112 people. The data used is primary data, which has been collected through the distribution of a questionnaire. Data has been analyzed through the Multiple Linear Regression and moderator test has been done using the Eviews 9 and SPSS software. The results contained in this study show that the variables can influence the accounting information system (AIS) user satisfaction significantly and that the information system quality plays an important role in the process. In addition, the quality of information systems, the information quality and perceived usefulness simultaneously affect the AIS of user satisfaction.

The accounting software is considered to be of the most critical components of accounting information system, with particular significance as of accounting and financial systems. the most important problems with accounting education systems is that students do not adequately learn the financial software required by the accounting profession, which, in turn, reduces the credibility and position of the accounting profession. That the main objective of accounting software education is to educate skilled and expert accountants to enter the accounting profession, which is considered as of the success factors of country’s economy. In this study, employ data mining techniques to investigate the accuracy, precision, and recall performance measures and to predict the rate of financial software learning based on accounting students’ emotional intelligence (EI), gender and education level. Accordingly, a machine-learning-based multivariate statistical analysis is performed on 100 Iranian accounting students. The results show that emotional intelligence has the most impact on the rate of financial software learning among the variables. Gender and education level were influential. Also, among the five algorithms, the highest precision and recall are achieved by both Decision Tree and XGBoost and are presented as the most appropriate models for the prediction rate of financial software learning.