Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression

Glauco H.S. Mendes, Gilberto Miller Devós Ganga


Critical success factors in new product development (NPD) in the Brazilian small and medium enterprises (SMEs) are identified and analyzed. Critical success factors are best practices that can be used to improve NPD management and performance in a company. However, the traditional method for identifying these factors is survey methods. Subsequently, the collected data are reduced through traditional multivariate analysis. The objective of this work is to develop a logistic regression model for predicting the success or failure of the new product development. This model allows for an evaluation and prioritization of resource commitments. The results will be helpful for guiding management actions, as one way to improve NPD performance in those industries.


new product development, applied statistics, logistic regression

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