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

Andrea Ceschi, Ksenia Dorofeeva, Riccardo Sartori


Purpose ‐ The purpose of this paper is to investigate how dimensions related to teamwork and team climate can influence decision making and learning of teams (performance). In order to understand which factors are more effective, several relevant group and team characteristics drawn from classical literature on groups and more recent empirical team simulation research have been considered. Design/methodology/approach ‐ The paper presents the results of a longitudinal study carried out during four months. A total of 183 Italian participants, divided into 50 teams of three (n=24), four (n=19) and five (n=7) members, have been involved in a business game developed by several European savings banks and simulating a real stock market environment. The aim of each team is not only to earn virtual money, but also learning long-term strategies to develop profitable investments without losing sight of economic factors. Findings ‐ Based on literature review, the authors tested three group levels (intragroup relations level, self-member level and group-design level) by making three hypotheses concerning the teams involved in the simulation and investigated the communication and innovation (CI) dimension from the Italian version of the team climate inventory (TCI) by Ragazzoni et al. A correlation between team performance and CI was found (r=0.301 p=0.048), which is in line with the hypothesis that such factors as communication and support for innovation can affect the decision-making performance. Originality/value ‐ The results presented in the paper let practitioners understand which dynamics characterize teamwork activities and how such aspects as communication and support for innovation can lead to group learning and decision-making performance. The simulation used in this research is an empirical way to study team performance and group learning without other noise variables.

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