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Statistical Predictors of Project Management Maturity

dc.contributor.authorCelani de Souza, Helder Jose [UNESP]
dc.contributor.authorSalomon, Valerio Antonio Pamplona [UNESP]
dc.contributor.authorSanches da Silva, Carlos Eduardo
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionFederal University of Itajuba
dc.date.accessioned2025-04-29T20:17:48Z
dc.date.issued2023-09-01
dc.description.abstractGlobal scenarios of organizations show investments wasted in projects with poor performances in more than 11 percent of cases, according to the Project Management Institute. This research aims to guide organizations in assertively investing in the right pertinent factors to improve project success rates and speed up project management maturity at a higher accuracy level using statistical predictions. Challenging existing drivers for project management maturity models and expanding their current practical view will be the result of a quantitative methodology based on a survey supported by data collection targeting the project management community in Brazil. The originality and value of this research are in contributing to the development of new project maturity models statistically supported by the increasing rate of maturity accuracy, which can be continually improved by confident data input into the model. The results show a high correlation between the performance measurement system and the project success rate associated with project management maturity. In addition, this research contemplates the relationship between organizational culture, business type, and project management office and project management maturity.en
dc.description.affiliationDepartment of Production Universidade Estadual Paulista (UNESP–Sao Paulo State University)
dc.description.affiliationInstitute of Industrial Engineering and Management Federal University of Itajuba
dc.description.affiliationUnespDepartment of Production Universidade Estadual Paulista (UNESP–Sao Paulo State University)
dc.format.extent868-888
dc.identifierhttp://dx.doi.org/10.3390/stats6030054
dc.identifier.citationStats, v. 6, n. 3, p. 868-888, 2023.
dc.identifier.doi10.3390/stats6030054
dc.identifier.issn2571-905X
dc.identifier.scopus2-s2.0-85172773340
dc.identifier.urihttps://hdl.handle.net/11449/310025
dc.language.isoeng
dc.relation.ispartofStats
dc.sourceScopus
dc.subjectpartial least squares regression
dc.subjectperformance measurement
dc.subjectproject management
dc.titleStatistical Predictors of Project Management Maturityen
dc.typeArtigopt
dspace.entity.typePublication
unesp.author.orcid0000-0003-1345-1006[1]
unesp.author.orcid0000-0002-5619-5076[2]
unesp.author.orcid0000-0002-7329-6565[3]

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