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

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 identif...

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Autores principales:	de Sousa Mendes,Glauco Henrique, Miller Devós Ganga,Gilberto
Lenguaje:	English
Publicado:	Universidad Alberto Hurtado. Facultad de Economía y Negocios 2013
Materias:	new product development applied statistics logistic regression
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-27242013000400008
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spelling	oai:scielo:S0718-272420130004000082014-01-29Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regressionde Sousa Mendes,Glauco HenriqueMiller Devós Ganga,Gilberto new product development applied statistics logistic regression 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.info:eu-repo/semantics/openAccessUniversidad Alberto Hurtado. Facultad de Economía y NegociosJournal of technology management & innovation v.8 n.3 20132013-11-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-27242013000400008en10.4067/S0718-27242013000400008
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	new product development applied statistics logistic regression
spellingShingle	new product development applied statistics logistic regression de Sousa Mendes,Glauco Henrique Miller Devós Ganga,Gilberto Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression
description	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.
author	de Sousa Mendes,Glauco Henrique Miller Devós Ganga,Gilberto
author_facet	de Sousa Mendes,Glauco Henrique Miller Devós Ganga,Gilberto
author_sort	de Sousa Mendes,Glauco Henrique
title	Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression
title_short	Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression
title_full	Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression
title_fullStr	Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression
title_full_unstemmed	Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression
title_sort	predicting success in product development: the application of principal component analysis to categorical data and binomial logistic regression
publisher	Universidad Alberto Hurtado. Facultad de Economía y Negocios
publishDate	2013
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-27242013000400008
work_keys_str_mv	AT desousamendesglaucohenrique predictingsuccessinproductdevelopmenttheapplicationofprincipalcomponentanalysistocategoricaldataandbinomiallogisticregression AT millerdevosgangagilberto predictingsuccessinproductdevelopmenttheapplicationofprincipalcomponentanalysistocategoricaldataandbinomiallogisticregression
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Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression

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