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



Document title: Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression
Journal: Journal of technology management & innovation
Database: CLASE
System number: 000404232
ISSN: 0718-2724
Authors: 1
1
Institutions: 1Universidade Federal de Sao Carlos, Departamento de Engenharia de Producao, Sao Carlos, Sao Paulo. Brasil
Year:
Volumen: 8
Number: 3
Pages: 89-97
Country: Chile
Language: Inglés
Document type: Artículo
Approach: Analítico, descriptivo
English abstract 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
Keyword: Administración de instituciones,
Empresas,
Brasil,
Logística,
Desarrollo de productos,
Regresión logística,
Econometría
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