IRCI Article ID: IRCI-AR-0000000296

Optimizing Decision-Making in Higher Education Institutions through AI-Driven Business Intelligence in the Digital Era

Journal: International Journal of Economics, Management and Accounting (IJEMA)

Publication: 2025-08-05 · Vol. 3 No. 3 · pp. 221–229

DOI: 10.47353/ijema.v3i3.328

Cite this article

Citation

Choose a citation style or copy BibTeX for your reference manager.

 
View Original Publication

Abstract

Digital transformation in higher education requires institutions to adopt intelligent technologies that support more accurate and data-driven decision-making. This study investigates the integration of Artificial Intelligence (AI) into Business Intelligence (BI) systems within Indonesian universities using the Technology Acceptance Model (TAM) as the theoretical framework. A mixed-methods approach was employed, involving surveys (n = 75) and interviews with academic leaders and information technology (IT) staff. The results show that AI-enhanced BI systems significantly improve decision-making effectiveness, particularly in academic planning and administrative efficiency. Regression analysis revealed that perceived usefulness and perceived ease of use explained 58% of the variance in decision effectiveness, while all variables combined (including management support and digital literacy) accounted for 69%. These findings validate the TAM in the context of AI-based decision systems in education. This study contributes both theoretically and practically by offering evidence-based recommendations for strengthening data-driven culture and institutional readiness for adopting intelligent information systems.

0
IRCI Cited By
15
Indexed References

Authors

References

DOI: 10.1504/IJTEL.2012.051815

DOI: 10.1145/1978542.1978562

DOI: 10.2307/249008

DOI: 10.1111/caim.12224

DOI: 10.1016/j.ijinfomgt.2014.10.007

DOI: 10.33395/owner.v9i2.2646

DOI: 10.14710/jiab.2024.42452

DOI: 10.1057/ejis.2014.17

DOI: 10.1111/isj.12071

DOI: 10.1109/AECT47998.2020.9194157

DOI: 10.1016/j.techfore.2015.12.019

DOI: 10.1080/23311975.2023.2195027

DOI: 10.1057/ejis.2010.21

DOI: 10.1007/978-1-4614-3305-7_4

DOI: 10.2139/ssrn.1819486

Cited By (0)

No indexed citing article has been matched by IRCI yet.