Predicting Factors Influencing MSME Subscription Behavior Towards Digital Services Using Machine Learning
DOI:
https://doi.org/10.12695/jmt.2026.25.1.4Keywords:
MSME, Digital Service Subscription, Explainable Artificial Intelligence, Random Forest.Abstract
Abstract. The adoption of digital services among Micro, Small, and Medium Enterprises (MSMEs) has become increasingly important for supporting business operations and digital transformation initiatives. Understanding the factors associated with subscription behavior toward digital services may assist service providers in developing effective customer retention strategies. This study aims to examine the associations among MSME characteristics, digital service utilization, and subscription behavior toward Telkom Indonesia's digital services using a combination of statistical analysis and explainable machine learning. A total of 150 MSME respondents were selected through stratified random sampling. Data were collected through structured online questionnaires and secondary data sources. The analysis included descriptive statistics, Pearson correlation analysis, and Random Forest classification integrated with SHAP (Shapley Additive Explanations) for model interpretability. The results indicate that MSME characteristics and digital service utilization are positively associated with subscription behavior. The Random Forest model achieved an accuracy of 0.91 and an AUC of 0.94, while SHAP analysis identified digital service utilization, service usage duration, and technological readiness as the variables contributing most strongly to model predictions. These findings suggest that behavioral engagement with digital services and organizational readiness are both relevant to understanding subscription continuity among MSMEs. The study contributes to the literature by integrating correlation analysis, machine learning classification, and explainable artificial intelligence to examine subscription behavior within the context of MSME digital services.
Keywords: MSMEs; digital service utilization; subscription behavior; random forest; explainable artificial intelligence; shap.
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