Business Model Innovation for Sustainable Competitive Advantage: A Machine Learning Approach

Abstract

In an increasingly competitive business environment, organizations must innovate continuously and adopt workable practices to succeed. Business model innovation has become an effective means of adding value, improving efficiency, and securing a favorable market position. This paper investigates the link between sustainability indicators and competitive advantage using machine learning methods. A dataset of publicly listed companies containing environmental, social, and governance (ESG) variables was analyzed to predict overall corporate sustainability grades. Three classification models were implemented: logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN). Logistic regression and SVM achieved the highest prediction accuracy, 94.37%, outperforming KNN (88.73%). These results suggest that ESG indicators are reliable predictors of market positioning and sustainability performance. The study also indicates that sustainability-based, innovative business models can strengthen stakeholder trust and sustainable competitive advantage, and that machine learning can help investors and managers make informed strategic decisions.