Modeling the leadership - project performance relation: radial basis function, Gaussian and Kriging methods as alternatives to linear regression

Authors:
Marco AuréLio De Oliveira;Osmar Possamai;Luiz V. O. Dalla Valentina;Carlos Alberto Flesch
Affiliations:
Universidade Federal de Santa Catarina (UFSC), Departamento de Engenharia de Produção e Sistemas, ZIP Code 88040-970 Florianópolis, SC, Brazil;Universidade Federal de Santa Catarina (UFSC), Departamento de Engenharia de Produção e Sistemas, ZIP Code 88040-970 Florianópolis, SC, Brazil;Universidade do Estado de Santa Catarina (UDESC), Departamento de Engenharia Mecínica, ZIP Code 89219-710 Joinville, SC, Brazil and Sociedade Educacional de Santa Catarina (SOCIESC), Departam ...;Universidade Federal de Santa Catarina (UFSC), Departamento de Engenharia de Mecínica, ZIP Code 88040-970 Florianópolis, SC, Brazil
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

The purpose of this paper is to analyze alternative forecasting methods that produce results at least similar to or better than linear regression (MLR) that can be used in the modeling of social systems. While organizations may be considered as typically non-linear systems, the common feature of most models found in literature continues to be the use of linear regression techniques. From a case study, advanced statistical methods of Gaussian and Kriging are evaluated, as well as an artificial intelligence (AI) tool, the radial basis function (RBF). The results show the best performance of the suggested methods compared to MLR, especially RBF, because of its uniform prediction behavior throughout all ranges of evaluation. These techniques, although somewhat unconventional in social systems modeling, present a potential contribution in increasing the accuracy and precision of the predictions allowing a more accurate assessment of the impact of certain strategies on the project performance to be made before the allocation of material, human and financial resources.