Fatores críticos de sucesso em projetos internacionais de construção civil

Kikuti, Karina

doi:10.11606/d.12.2016.tde-06122016-160123

Cited by 1 publication

(1 citation statement)

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“…When analyzing CSF in international civil construction projects with the application of a questionnaire to 27 management professionals, Kikuti (2016) determined that the ten CSF in projects proposed by Pinto & Slevin (1988) contemplate the processes to be considered in the execution of projects. Leite (2018) evaluated CSF in civil construction projects using the method of systematic literature review and its subsequent validation with the application of a semistructured questionnaire to Portuguese project managers.…”

Section: Training Of Artificial Neural Networkmentioning

confidence: 99%

Proposed model of analysis of the perception of the relative importance of Critical Success Factors (CSF) in the civil construction industry (CCI) using Artificial Neural Networks (ANNs): application in the academic universe

et al. 2021

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Critical Success Factors (CSF) identify key areas for a company to succeed. This study creates a model to analyze CSF in civil construction project management, using Artificial Neural Networks (ANNs). For that, a literature review was performed to identify CSF emphasizing project management. Once the CSF were identified, a questionnaire was sent to educational institutions to evaluate the effect of each factor. Response analysis was made by the Relative Importance Index, using ANN coupled with the resilient propagation algorithm to evaluate the CSF. A total of 37,822 articles were found in 2,328 journals. Of 874 e-mails sent, 191 were answered. The respondents were distributed in 26 Brazilian states, with 70% of them being professors/researchers, 26% coordinators, 2% Rector, and 1% Director/Manager. Weights were determined using the Garson algorithm. The most critical factor in project management was ‘Unrealistic inspection and test methods in the contract’. Artificial Neural Networks produce subsidies to know the relevance of the input variables adopted and constitute an effective means for modeling nonlinear variables.

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Section: Training Of Artificial Neural Networkmentioning

confidence: 99%