Application of Data Mining Technology in Accounting Information Systems of Government Institutions: Drivers and Scenarios

Hossein Fotoohi Ardakani; Akram Taftiyan; Nasim Shahmoradi

Authors

Hossein Fotoohi Ardakani Accounting Department, Ya.C., Islamic Azad University, Yazd, Iran Author https://orcid.org/0000-0001-5116-1603
Akram Taftiyan Accounting Department, Ya.C., Islamic Azad University, Yazd, Iran https://orcid.org/0000-0001-8627-3461
Nasim Shahmoradi Accounting Department, Ya.C., Islamic Azad University, Yazd, Iran Author https://orcid.org/0000-0002-1430-553X

Keywords:

foresight, data mining technology, accounting information systems, government institutions, scenario planning

Abstract

In the contemporary era, data mining, as an advanced tool for data analysis and the discovery of hidden patterns, plays an important role in enhancing information quality and improving financial processes. The present study aimed to conduct a foresight analysis of the use of data mining technology in the accounting information systems of Iranian government institutions using a scenario-planning approach. This study employed a mixed-methods (qualitative–quantitative) design. In the qualitative phase, a review of the literature and semi-structured interviews with 19 academic and professional experts identified 30 influential factors. Subsequently, using an expert assessment questionnaire, 22 statistically significant factors were selected. In the quantitative phase, a cross-impact analysis questionnaire was distributed among the experts, and the data were analyzed using MICMAC software, resulting in the identification of three key drivers. Subsequently, Scenario Wizard software was used to develop plausible scenarios for the future use of data mining, and appropriate implementation strategies were proposed for each scenario. Among the 22 significant factors, three key drivers with an influence score greater than 80 and a dependence score below 30 were identified: development of information technology infrastructure, availability of specialized human resources, and managers’ inclination toward data-driven decision-making. The remaining factors were positioned in dependent, linkage, or autonomous zones, indicating their dependence on these three drivers. Ultimately, 21 plausible scenarios were developed. The findings can provide a basis for policymaking, planning, and enhancing financial transparency and accountability in the public sector.

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