Optimising International SME Competitiveness through Machine Learning-Driven Market Analysis : A Mixed Methods Approach

Wenny Eka Prasetiawan; Sudarmiatin Sudarmiatin

doi:10.62951/ijecm.v2i3.677

Authors

Wenny Eka Prasetiawan Universitas Negeri Malang
Sudarmiatin Sudarmiatin Universitas Negeri Malang

DOI:

https://doi.org/10.62951/ijecm.v2i3.677

Keywords:

Global Competitiveness, International MSMEs, Machine Learning, Market Analysis, Mixed-Methods

Abstract

International Micro, Small and Medium Enterprises (MSMEs) face significant challenges in improving global competitiveness due to limited resources and access to effective market analysis, despite contributing 45% to the global economy (OECD, 2025). This research aims to develop an integrated machine learning (ML) model with a mixed-methods approach to optimise cross-border MSME market analysis. A combination of quantitative (transaction data analysis of 500 Indonesian export MSMEs 2020-2024 using XGBoost and SEM-AMOS) and qualitative (interviews with 15 MSME players) methods revealed that the XGBoost model achieved 89% accuracy in predicting market trends, with key variables including social media sentiment (28%) and exchange rate fluctuations (19%). Qualitative results show that 65% of MSMEs face cross-border regulatory barriers that ML models do not detect. The findings extend the Resource-Based View theory by validating AI-driven market intelligence as a strategic asset (β = 0.67, p 0.7. This research highlights the importance of technology integration and contextual adaptation in the digital transformation of MSMEs.

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