Virtual teams that span multiple geographic and cultural boundaries have become commonplace in numerous organizations due to the competitive advantages they provide in human resources, products, financial means, knowledge sharing and many others. However, the promises of multinational and multicultural (MNMC) distributed teams are accompanied by a number of challenges. Many research studies have suggested that one of the most challenging barriers to the effective implementation of MNMC distributed teams is culture. In this study, data collected from the experiment conducted by the NATO RTO Human Factors and Medicine Panel Research Task Group (HFM-138/RTG) on “Adapatability in Multinational Coalitions” has been analyzed to study the effects of cultural diversity on team planning and communication. Fuzzy decision trees have been derived to model the effects, and information visualization techniques are used to facilitate understanding of the derived classification patterns. Results of the research suggest that there are no single and straightforward conclusions on how cultural diversity affects team planning and communication. Different dimensions of culture values interact in influencing team behaviors. However, diversities in power distance and masculinity seem to play more influential roles than others.

Given a set of known numbers, there are many measures of the degree of inhomogeneity within the set such as the standard deviation, the relative mean difference, and the Gini coefficient. This paper discusses conceptual issues (such as qualitative versus quantitative diversity, and the group as a population versus as a sample), desired properties (such as symmetry and invariance properties), and technical considerations (such as working with differences versus deviations, or absolute versus squared values) in choosing an index suitable for describing the degree of inhomogeneity or diversity in a group of people or computer agents. In particular, it is argued that the relative mean difference and the Gini coefficient are not well-suited as indexes of cultural diversity. This paper then addresses two apparently neglected inverse problems: Given a pre-specified degree of inhomogeneity, what set of unknown numbers has the desired degree of inhomogeneity? And, in particular, what set has the maximal possible degree of inhomogeneity? The solution requires that the set of permissible numbers be bounded with minimum and maximum values. A key benefit of such inverse procedures is that agent-based groups with pre-selected degrees of cultural diversity can be formed to test hypotheses using the full range of possible diversities and thereby avoid statistical problems due to restriction of range effects.