2026-06-14T03:38:28Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/202612024-11-29T05:00:36Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967

00925njm 22002777a 4500 dc Fuster-Parra, Pilar author Garcia-Mas, Alex author Cantallops, Jaume author Javier Ponseti, F author Luo, Yuhua author 2016-05 The aim of this study is to rank some features that characterize the psychological dynamics of cooperative team work in order to determine priorities for interventions and formation: leading positive feedback, cooperative manager and collaborative manager features. From a dataset of 20 cooperative sport teams (403 soccer players), the characteristics of the prototypical sports teams are studied using an average Bayesian network (BN) and two special types of BNs, the Bayesian classifiers: naive Bayes (NB) and tree augmented naive Bayes (TAN). BNs are selected as they are able to produce probability estimates rather than predictions. BN results show that the antecessors (the top features ranked) are the team members' expectations and their attraction to the social aspects of the task. The main node is formed by the cooperative behaviors, the consequences ranked at the BN bottom (ratified by the TAN trees and the instantiations made), the roles assigned to the members and their survival inside the same team. These results should help managers to determine contents and priorities when they have to face team-building actions. Fuster-Parra P, Garcia-Mas A, Cantallops J, Javier Ponseti F, Luo Y. Ranking Features on Psychological Dynamics of Cooperative Team Work through Bayesian Networks. Symmetry-Basel. 2016 May;8(5):34. 10.3390/sym8050034 2073-8994 Symmetry-Basel http://hdl.handle.net/20.500.13003/10388 2-s2.0-84969764583 http://hdl.handle.net/20.500.12105/20261 377795700006 Bayesian networks Cohesion team Collaborative work Cooperative teams Naive Bayes Symmetric independencies Team dynamics Tree augmented naive Bayes Ranking Features on Psychological Dynamics of Cooperative Team Work through Bayesian Networks