Interaction patterns and themes of male, female, and mixed groups.
ABSTRACT All-male, all-female, and mixed groups were observed for possible differences in interactional style. The groups met for 5 11/2-hr unstructured meetings. Portions of the tape-recorded sessions were analyzed by the General Inquirer computer-aided content analysis system. Leadership, defined as rank order of Ss initiating interaction, showed greater variation along sessions in the female than in the male group, whereas in the mixed group the males initiated and received more interaction than the females. Exercise of power, defined as amount of talking to the group as a whole rather than to individuals, occurred more often in the male groups than in the female. In the mixed groups, the female pattern did not change, but the males addressed the group as a whole less often in mixed groups. A 3rd difference was found on the variable of intimacy and openness. Female group members revealed more information about themselves and their feelings than the male group members. In the mixed group, males shared more about themselves than in the all-male group. Sex role pressures are considered to be a contributing factor to the results. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Conference Paper: Gender and Tenure Diversity in GitHub Teams[Show abstract] [Hide abstract]
ABSTRACT: Software development is usually a collaborative venture. Open Source Software (OSS) projects are no exception; in-deed, by design, the OSS approach can accommodate teams that are more open, geographically distributed, and dynamic than commercial teams. This, we find, leads to OSS teams that are quite diverse. Team diversity, predominantly in of-fline groups, is known to correlate with team output, mostly with positive effects. How about in OSS? Using GITHUB, the largest publicly available collection of OSS projects, we studied how gender and tenure diversity relate to team productivity and turnover. Using regression modeling of GITHUB data and the results of a survey, we show that both gender and tenure diversity are positive and significant predictors of productivity, together explaining a sizable fraction of the data variability. These results can inform decision making on all levels, leading to better out-comes in recruiting and performance.CHI Conference on Human Factors in Computing Systems, Seoul, Korea; 04/2015