Predicting Product Innovation of Firms in Vietnam: Data Mining Approach

Product innovation, defined as the development and introduction of new or significantly improved products, is a key strategy for firms to achieve sustained competitive advantage. This study aims to explore the factors predicting product innovation using a comprehensive data mining approach. It examines how various factors, such as research and development spending, formal training, competition, quality certifications, firm size, and firm age, impact the level of innovation incorporated into new products by firms in Vietnam. Data from Vietnamese firms were analyzed, extracted from the World Bank’s “Vietnam Enterprise Survey” database. The survey aimed to understand the experiences of firms in Vietnam’s private sector. The dataset included six input variables and one target variable, product innovation. Data mining methods employed in this study include decision tree (DT) and support vector machine (SVM). The research results indicate that all input variables significantly predict product innovation and SVM is the better method to predict product innovation of firms. This study makes several important contributions to academic research. First, it is among the pioneering studies to apply data mining methods for predicting product innovation in firms. Second, it offers valuable insights by identifying key internal factors that significantly influence product innovation, providing a clearer understanding of what drives innovation within organizations.