Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach

Detalhes bibliográficos
Autor(a) principal: Dimas, Isabel Dórdio
Data de Publicação: 2020
Outros Autores: Lourenço, Paulo Renato Martins Ribeiro da Silva, Rebelo, Teresa, Rocha, Humberto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/91186
https://doi.org/10.1177/1046496420944488
Resumo: This study explores the relationship between team cohesion and team learning by adopting a nonlinear approach. A quantitative study with a sample composed of 82 organizational teams was conducted. Radial Basis Function (RBF) interpolation models were used and results showed that the best predicting ability was obtained by the Thin Plate RBF model, which revealed that an increase in both dimensions of cohesion leads to an increase in team learning up to a certain threshold. Moreover, our results showed that the maximum value of team learning is obtained at higher values of task cohesion and moderate values of social cohesion.
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spelling Maximizing Learning Through Cohesion: Contributions From a Nonlinear ApproachTeam cohesionTeam learningNonlinear approachRadial basis functionsThis study explores the relationship between team cohesion and team learning by adopting a nonlinear approach. A quantitative study with a sample composed of 82 organizational teams was conducted. Radial Basis Function (RBF) interpolation models were used and results showed that the best predicting ability was obtained by the Thin Plate RBF model, which revealed that an increase in both dimensions of cohesion leads to an increase in team learning up to a certain threshold. Moreover, our results showed that the maximum value of team learning is obtained at higher values of task cohesion and moderate values of social cohesion.Sage2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/91186http://hdl.handle.net/10316/91186https://doi.org/10.1177/1046496420944488eng1046-49641552-8278https://journals.sagepub.com/doi/10.1177/1046496420944488Dimas, Isabel DórdioLourenço, Paulo Renato Martins Ribeiro da SilvaRebelo, TeresaRocha, Humbertoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-05-25T06:13:54Zoai:estudogeral.uc.pt:10316/91186Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:11:07.330768Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach
title Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach
spellingShingle Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach
Dimas, Isabel Dórdio
Team cohesion
Team learning
Nonlinear approach
Radial basis functions
title_short Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach
title_full Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach
title_fullStr Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach
title_full_unstemmed Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach
title_sort Maximizing Learning Through Cohesion: Contributions From a Nonlinear Approach
author Dimas, Isabel Dórdio
author_facet Dimas, Isabel Dórdio
Lourenço, Paulo Renato Martins Ribeiro da Silva
Rebelo, Teresa
Rocha, Humberto
author_role author
author2 Lourenço, Paulo Renato Martins Ribeiro da Silva
Rebelo, Teresa
Rocha, Humberto
author2_role author
author
author
dc.contributor.author.fl_str_mv Dimas, Isabel Dórdio
Lourenço, Paulo Renato Martins Ribeiro da Silva
Rebelo, Teresa
Rocha, Humberto
dc.subject.por.fl_str_mv Team cohesion
Team learning
Nonlinear approach
Radial basis functions
topic Team cohesion
Team learning
Nonlinear approach
Radial basis functions
description This study explores the relationship between team cohesion and team learning by adopting a nonlinear approach. A quantitative study with a sample composed of 82 organizational teams was conducted. Radial Basis Function (RBF) interpolation models were used and results showed that the best predicting ability was obtained by the Thin Plate RBF model, which revealed that an increase in both dimensions of cohesion leads to an increase in team learning up to a certain threshold. Moreover, our results showed that the maximum value of team learning is obtained at higher values of task cohesion and moderate values of social cohesion.
publishDate 2020
dc.date.none.fl_str_mv 2020
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dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/91186
http://hdl.handle.net/10316/91186
https://doi.org/10.1177/1046496420944488
url http://hdl.handle.net/10316/91186
https://doi.org/10.1177/1046496420944488
dc.language.iso.fl_str_mv eng
language eng
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1552-8278
https://journals.sagepub.com/doi/10.1177/1046496420944488
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