Business Intelligence Models to Support the Digital Transformation of MSMEs in Indonesia in the Digital Economy Era

Authors

  • Mar’atus Solikhah Sekolah Tinggi Manajemen Informatika dan Komputer LIKMI, Indonesia
  • Sandi Agus Maulana Sekolah Tinggi Manajemen Informatika dan Komputer LIKMI, Indonesia

DOI:

https://doi.org/10.59141/jist.v7i2.9178

Keywords:

business intelligence, big data analytic, transformasi digital MSMEs, SEM-PLS, competitiveness

Abstract

The background of this research is motivated by the increasingly rapid digital transformation in various sectors, including in the MSME industry in Indonesia. Although the MSME sector makes a significant contribution to the economy, many MSME actors have difficulty in implementing digital technology effectively, especially in data-driven decision-making. One of the solutions that can support this digital transformation is Business Intelligence (BI) based on Big Data Analytics, which allows MSMEs to manage big data and increase operational efficiency and competitiveness. The purpose of this research is to develop a Business Intelligence (BI) model that can be adopted by MSMEs in Indonesia to support their digital transformation in the digital economy era. This study uses a quantitative approach with an explanatory research design, as well as Structural Equation Modeling (SEM-PLS) to test the relationship between variables that affect the adoption of BI in the MSME sector. The results of the study show that Business Intelligence based on Big Data Analytics has a significant influence on the digital transformation of MSMEs in Indonesia, especially in improving data-based decision-making and operational efficiency. The proposed BI model is proven to support MSMEs to adapt to market dynamics and increase their competitiveness.

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Published

2026-02-25

How to Cite

Solikhah, M., & Maulana, S. A. . (2026). Business Intelligence Models to Support the Digital Transformation of MSMEs in Indonesia in the Digital Economy Era. Jurnal Indonesia Sosial Teknologi, 7(2). https://doi.org/10.59141/jist.v7i2.9178