The Impact of Cognitive Style Diversity on Implicit Learning in Teams.

cognitive diversity collective intelligence implicit coordination team learning teams

Journal

Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902

Informations de publication

Date de publication:
2019
Historique:
received: 02 10 2018
accepted: 14 01 2019
entrez: 23 2 2019
pubmed: 23 2 2019
medline: 23 2 2019
Statut: epublish

Résumé

Organizations are increasingly looking for ways to reap the benefits of cognitive diversity for problem solving. A major unanswered question concerns the implications of cognitive diversity for longer-term outcomes such as team learning, with its broader effects on organizational learning and productivity. We study how cognitive style diversity in teams-or diversity in the way that team members encode, organize and process information-indirectly influences team learning through collective intelligence, or the general ability of a team to work together across a wide array of tasks. Synthesizing several perspectives, we predict and find that cognitive style diversity has a curvilinear-inverted U-shaped-relationship with collective intelligence. Collective intelligence is further positively related to the rate at which teams learn, and is a mechanism guiding the indirect relationship between cognitive style diversity and team learning. We test the predictions in 98 teams using ten rounds of the minimum-effort tacit coordination game. Overall, this research advances our understanding of the implications of cognitive diversity for organizations and why some teams demonstrate high levels of team learning in dynamic situations while others do not.

Identifiants

pubmed: 30792672
doi: 10.3389/fpsyg.2019.00112
pmc: PMC6374291
doi:

Types de publication

Journal Article

Langues

eng

Pagination

112

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Auteurs

Ishani Aggarwal (I)

Brazilian School of Public and Business Administration, FGV, Rio de Janeiro, Brazil.

Anita Williams Woolley (AW)

Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, United States.

Christopher F Chabris (CF)

Geisinger Health System, Lewisburg, PA, United States.

Thomas W Malone (TW)

Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, United States.

Classifications MeSH