Abstract
Team effectiveness has been the focus of numerous studies since teams play an increasingly decisive role in modern organizations. In the present paper, our attention is centered on team viability, which is one dimension of team effectiveness. Given the challenges that actual teams face today, exploring the conditions and processes that enhance the capacity of teams to adapt and continue to work together is a fundamental research path to pursue. In this study, team psychological capital and team learning were considered as antecedents of team viability. The relationships that team psychological capital and team learning establish with team viability were explored as accurately as possible. Typically, these relationships are assumed to be linear as multivariate linear models are often used. However, these linear models fail to explain possible nonlinear relations between variables, expected to exist in dynamic systems as teams. Adopting computational modeling strategies in the context of organizational psychology has become more common. In this paper, radial basis function models and neural networks were used to study the complex relationships between team psychological capital, team learning and team viability.
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This work was supported by the Fundação para a Ciência e a Tecnologia (FCT) under project grants UID/MULTI/00308/2013 and POCI-01-0145-FEDER-008540.
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Dimas, I.D., Rocha, H., Rebelo, T., Lourenço, P.R. (2017). A Neural Network Model for Team Viability. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_38
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DOI: https://doi.org/10.1007/978-3-319-62395-5_38
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