Abstract

This study aims to suggest a machine learning-based analysis model to predict the business process effectiveness and to analyze the informatization level survey data of small and medium-sized enterprises (SMEs). The study also focuses on identifying the most effective methodology and providing practical insights for establishing informatization strategies. This study predicts the business process effectiveness of small and medium-sized enterprises (SMEs) by utilizing the survey data of their informatization level. Representative machine learning classification models - Random Forest, XGBoost, and LightGBM - were applied, and SHAP (SHapley Additive exPlanations) was then used to analyze the key variables influencing the prediction performance with the highest-performance analysis model. According to the findings, the Random Forest and LightGBM models demonstrated the best performance in terms of AUC and accuracy for predicting business process effectiveness. A SHAP analysis suggested that the informatization capabilities of enterprises and the use of appropriate information systems could be key variables for business process effectiveness.