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



Título del documento: Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression
Revista: Journal of technology management & innovation
Base de datos: CLASE
Número de sistema: 000404232
ISSN: 0718-2724
Autors: 1
1
Institucions: 1Universidade Federal de Sao Carlos, Departamento de Engenharia de Producao, Sao Carlos, Sao Paulo. Brasil
Any:
Volum: 8
Número: 3
Paginació: 89-97
País: Chile
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico, descriptivo
Resumen en inglés 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
Disciplines Administración y contaduría,
Economía
Paraules clau: Administración de instituciones,
Empresas,
Brasil,
Logística,
Desarrollo de productos,
Regresión logística,
Econometría
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