This article explores 1 mechanism by which leader personality affects organizational performance. The authors hypothesized and tested the effects of leader personality on the group dynamics of the top management team (TMT) and of TMT dynamics on organizational performance. To test their hypotheses, the authors used the group dynamics q-sort method, which is designed to permit rigorous, quantitative comparisons of data derived from qualitative sources. Results from independent observations of chief executive officer (CEO) personality and TMT dynamics for 17 CEOs supported the authors' hypothesized relationships both between CEO personality and TMT group dynamics and between TMT dynamics and organizational performance.

The approach/inhibition theory of power proposes that elevated social power increases the experience and expression of positive emotions and that reduced social power increases the experience and expression of negative emotions (Keltner, Gruenfeld, & Anderson, 2003). The evidence to date for these proposed relationships is correlational. Studies that have attempted to find a causal link between power and emotions have failed to do so. The current study manipulated social power in 61 three-person groups that engaged in a meaningful discussion (explanations for poverty in the US) that produced disagreements and strong emotions. High power individuals experienced and expressed more positive emotions and less anger than low power individuals did. High power individuals were also more likely than low power individuals to openly express their opinions during the group discussion. Implications for theory and future research are discussed.

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This article unveils a new research methodology for the study of group decision making in organizations-the group dynamics q-sort (GDQ). The GDQ is a 100-item instrument designed to study group process across a wide variety of situations and using a wide variety of data sources. It combines the descriptive richness of the qualitative approach with the rigor of a quantitative approach by creating a common data language to describe process across groups, observers, and time. This article introduces the GDQ in the following five sections: (a) the development of the group dynamics q-sort, (b) comparisons of the GDQ with other traditional research methods (i.e., questionnaires, case studies, experiments, and behavioral process coding), (c) the mechanics of employing the GDQ, (d) a demonstration of the method for studying dynamic group processes over time, and (e) summary and other potential applications of the method.