Enhancing International SME Competitiveness through Machine Learning Driven Market Analysis : A Mixed Methods Approach - Muhammad Tody Arsyianto; Budi Eko Soetjipto (International Journal of Economics, Management and Accounting, 2025)
Despite their 45% contribution to the global economy, international micro, small, and medium-sized enterprises (MSMEs) face considerable obstacles in enhancing their global competitiveness because they lack the resources and access to efficient market analysis (OECD, 2025). In order to optimize cross-border MSME market analysis, this research attempts to construct a machine learning (ML) model coupled with a mixed-methods approach. A combination of quantitative (XGBoost and SEM-AMOS were used to analyze transaction data of 500 Indonesian export MSMEs 2020–2024) and qualitative (interviews with 15 MSME players) methods showed that the XGBoost model achieved 89% accuracy in predicting market trends, with key variables including exchange rate fluctuations (19%) and social media sentiment (28%). According to qualitative findings, the ML model does not identify cross-border regulatory constraints that 65% of MSMEs must deal with. These results validate market intelligence powered by AI as a strategic asset, extending the Resource-Based View paradigm. The significance of contextual adaptation and technological integration in the digital transformation of MSMEs is emphasized by this study.