Purpose: This study investigates how financial and entrepreneurial capability configurations differentiate internationalized from domestically oriented Thai SMEs. By adopting a decision-science perspective, the study identifies the capability patterns that shape firms’ strategic international orientation. Design/methodology/approach: This research, which utilizes survey and financial data from 179 Thai SMEs (2021–2023), employs an inductive machine learning approach based on Extreme Gradient Boosting (XGBoost). The analytical framework integrates profitability, liquidity, leverage, operational efficiency, international experience, team readiness, market knowledge, and institutional connectivity. Feature importance scores and confirmatory statistical tests are used to validate differentiating capability structures. Findings: Internationalized SMEs are characterized by stronger financial agility, higher profitability, disciplined leverage, and more efficient resource utilization, together with entrepreneurial preparedness reflected in international experience, risk tolerance, team coordination, and institutional embeddedness. Localized SMEs, by contrast, display liquidity-heavy but lower-dynamism profiles, weaker network engagement, and more limited organizational readiness. Practical implications: The findings suggest that SME support should move beyond finance-only assistance and address capability alignment. Programs that strengthen financial agility, team readiness, international market knowledge, digital channels, and institutional networks may improve SME readiness for international engagement. Originality/value: The study contributes to international business theory by reframing SME internationalization as a selective and configurational process rather than a linear or binary outcome. It extends the resource-based view by emphasizing capability bundles rather than isolated resources and contributes to the dynamic capabilities perspective by showing how financial, entrepreneurial, and institutional capabilities are associated with SMEs’ differentiated participation in international markets.

Financial markets are connected, and several articles illustrate a network of financial markets. This study shows the connectedness of the economic sector in emerging markets, Indonesia, Malaysia, and Thailand, by the Granger causality graph. Moreover, the CNN-LSTM, a hybrid machine learning approach, was applied to make a prediction based on the connectedness of economic sectors. As a result, CNN-LSTM improves prediction accuracy in most sectors of each country, showing the interdependence among assets in the same market. On the other hand, the network's isolated sector cannot gain more precise predictions in the Thailand stock market. However, the isolated sector gained a better prediction score in Malaysia, emphasizing that machine learning can extract non-linear relationships compared to traditional statistics. On the other hand, the noisy path of the interconnected structure deteriorates forecasting performance as connected sectors in the Malaysian market cannot improve the accuracy of the prediction. As most sectors gain a better prediction score, incorporating the interaction among asset returns, especially economic activities connection, should be used for testing the asset pricing model.

According to the Granger-causality test, this study investigates the causation of business sector return in the Thailand stock market. Moreover, the result reveals that sectors' return connectedness contributes to the predictive model for forecasting price. First, we construct vector autoregression (VAR) to extract the intersection causation. Next, the hybrid machine learning method, CNN-LSTM, is proposed and modified to make a prediction based on the linkage assumption. The finding shows that a given sector influences other sectors, and the CNN-LSTM, which incorporates stated assumption, gains better performance than plain LSTM. Consequently, we found supporting evidence for the assumption that there is a linkage of return where money moves around from asset to asset, as stated in the network theory in finance. Therefore, the forecasting model gains a better performance by adding this finding.

Purpose: This study investigates how financial and entrepreneurial capability configurations differentiate internationalized from domestically oriented Thai SMEs. By adopting a decision‐science perspective, the study identifies the capability patterns that shape firms’ strategic international orientation. Design/methodology/approach: This research, which utilizes survey and financial data from 179 Thai SMEs (2021–2023), employs an inductive machine learning approach based on Extreme Gradient Boosting (XGBoost). The analytical framework integrates profitability, liquidity, leverage, operational efficiency, international experience, team readiness, market knowledge, and institutional connectivity. Feature importance scores and confirmatory statistical tests are used to validate differentiating capability structures. Findings: Internationalized SMEs demonstrate stronger financial agility, higher profitability, disciplined leverage, and superior resource utilization combined with entrepreneurial preparedness, as evidenced by international experience, risk tolerance, effective team coordination, and institutional embeddedness. In contrast, localized SMEs exhibit liquidity-heavy but low-dynamism profiles, limited network engagement, and weaker organizational readiness. The machine learning model highlights the non-linear interactions among these capabilities, achieving high predictive accuracy (92.59%). Practical implications: The findings offer a capability-based diagnostic tool for managers and policymakers. Strengthening financial agility, team readiness, and institutional ties can enhance the global competitiveness of SMEs. Originality/value: This study introduces a multidimensional, data-driven capability configuration model for SME internationalization, advancing decision science, dynamic capabilities, and network-based theories in emerging-market contexts.