AbstractPurpose – The purpose of this paper is to investigate how dimensions related to teamwork and teamclimate can influence decision making and learning of teams (performance). In order to understandwhich factors are more effective, several relevant group and team characteristics drawn from classicalliterature on groups and more recent empirical team simulation research have been considered.Design/methodology/approach – The paper presents the results of a longitudinal study carriedout during four months. A total of 183 Italian participants, divided into 50 teams of three (n ¼ 24), four(n ¼ 19) and five (n ¼ 7) members, have been involved in a business game developed by severalEuropean savings banks and simulating a real stock market environment. The aim of each team is notonly to earn virtual money, but also learning long-term strategies to develop profitable investmentswithout losing sight of economic factors.Findings – Based on literature review, the authors tested three group levels (intragroup relationslevel, self-member level and group-design level) by making three hypotheses concerning the teamsinvolved in the simulation and investigated the communication and innovation (CI) dimension fromthe Italian version of the team climate inventory (TCI) by Ragazzoni et al. A correlation between teamperformance and CI was found (r ¼ 0.301 p ¼ 0.048), which is in line with the hypothesis that suchfactors as communication and support for innovation can affect the decision-making performance.Originality/value – The results presented in the paper let practitioners understand which dynamicscharacterize teamwork activities and how such aspects as communication and support for innovationcan lead to group learning and decision-making performance. The simulation used in this research isan empirical way to study team performance and group learning without other noise variables.

Studying teamwork and team climate by using a business simulation. How communication and innovation can improve group learning and decision-making performance

CESCHI, Andrea;DOROFEEVA, Ksenia;SARTORI, Riccardo
2014

Abstract

AbstractPurpose – The purpose of this paper is to investigate how dimensions related to teamwork and teamclimate can influence decision making and learning of teams (performance). In order to understandwhich factors are more effective, several relevant group and team characteristics drawn from classicalliterature on groups and more recent empirical team simulation research have been considered.Design/methodology/approach – The paper presents the results of a longitudinal study carriedout during four months. A total of 183 Italian participants, divided into 50 teams of three (n ¼ 24), four(n ¼ 19) and five (n ¼ 7) members, have been involved in a business game developed by severalEuropean savings banks and simulating a real stock market environment. The aim of each team is notonly to earn virtual money, but also learning long-term strategies to develop profitable investmentswithout losing sight of economic factors.Findings – Based on literature review, the authors tested three group levels (intragroup relationslevel, self-member level and group-design level) by making three hypotheses concerning the teamsinvolved in the simulation and investigated the communication and innovation (CI) dimension fromthe Italian version of the team climate inventory (TCI) by Ragazzoni et al. A correlation between teamperformance and CI was found (r ¼ 0.301 p ¼ 0.048), which is in line with the hypothesis that suchfactors as communication and support for innovation can affect the decision-making performance.Originality/value – The results presented in the paper let practitioners understand which dynamicscharacterize teamwork activities and how such aspects as communication and support for innovationcan lead to group learning and decision-making performance. The simulation used in this research isan empirical way to study team performance and group learning without other noise variables.
Team; Business simulation; Communication and innovation; Decision Making; Group learning
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/719364
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