A Comparative Analysis of Decision Tree, Random Forest, and Logistic Regression Models in Predicting Business Readiness for Digital Technology Integration

This study compared the performance of Decision Tree, Random Forest, and Logistic Regression models in predicting business readiness for digital technology integration using survey data from 400 business respondents in Pangasinan, Philippines. The analysis utilized variables related to technology utilization, perceived helpfulness, willingness to integrate technology, and challenges encountered in adopting digital marketing, e-commerce, and digital payment technologies. Business readiness was operationalized from respondents’ willingness scores and classified into Ready and Less Ready categories. Descriptive results revealed very low technology utilization despite high perceived helpfulness, indicating a gap between awareness of digital technologies and their actual adoption. Lack of awareness emerged as the most frequently reported barrier, followed by data security concerns. Using an 80:20 train test split, the machine learning models achieved moderate predictive performance, with Decision Tree and Random Forest attaining the highest accuracy of 63.75%. Random Forest produced the best overall performance, achieving the highest weighted F1 score and demonstrating a more balanced classification capability than the other models. The findings highlight the potential of machine learning as a decision support tool for assessing business readiness and generating evidence-based insights that can support digital transformation planning, technology adoption strategies, and capacity building initiatives among businesses.

@article{Ducut2026,
  title     = {A Comparative Analysis of Decision Tree, Random Forest, and Logistic Regression Models in Predicting Business Readiness for Digital Technology Integration},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Gloria M. Ducut},
  doi       = {10.14569/IJACSA.2026.0170642},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170642}
}