Condition Monitoring/Predictive Maintenance

Global teams: a network analysis : Table of Contents

Team Performance Management >> 
Abstract:
Purpose – In the last few years, several empirical studies about the determinants and success factors of global teams have been published. While these studies show many interesting results, they are often focused on single variables such as cultural homogeneity, cooperation length, or task complexity, but rarely analyze the complex relationships between these concepts. The aim of this paper is to explore how members of global teams consider the relevance of different determinants of their cooperation, how these determinants are interrelated, and how they influence team performance. Design/methodology/approach – In this paper, a network study of global teams in the automotive and airline industries is presented. Based on interviews with the members of nine teams in three companies the software programs NVivo and UCINET were applied for a construct causal network analysis of the relationships between various team characteristics and their impact on team performance. Findings – The study shows that the interaction of team members from different cultures does not directly impact the productivity and creativity of teams. This relationship is rather influenced by various determinants such as task complexity, language skills, communication media and intercultural training. Research limitations/implications – A restriction of this study is its regional concentration on teams with members from European cultures. Future research should broaden this perspective and focus on global teams with a more diverse composition in terms of culture. For example, it would be interesting to know whether for global teams in Asia, South America or Asia similar or different determinants are relevant. Originality/value – The study enhances the knowledge of the complex interrelationships between various determinants of global teams and their impact on team performance. A major methodological contribution is the analysis of real teams, enabling a far more realistic picture than previous experimental studies conducted in this area that
deal with simulated teams, whose members do not have a shared past nor a shared future....(Read whole article/video)

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