![Sample our Communication Studies journals, sign in here to start your access, 2013 & 2014 volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5927/TOC-Subject-Sample-2021-Information-Science2.jpg"})
Abstract
Organizational trends toward team-based structures, globalization, and reliance on communication technology have spurred research addressing the communication processes of virtual teams. However, much of the extant research focuses on the ways virtual teams differ from conventional, face-to-face teams and fails to examine variations in virtual team characteristics that may impact team communication behaviors and experiences. The study reported here identifies two categories of virtual team characteristics (communicative and structural) and uses these to explore their relationships to team communication technology use and team outcomes. Analysis of data from 98 virtual teams reveals that structural features primarily relate to media use, whereas communication considerations are associated with team outcomes. Additional analyses indicate that various communicative predictors moderate the relationships between technology use and outcomes. These results point to theoretical and practical implications for researchers, team members, managers, and organizations related to virtual team design and communication processes.
The authors acknowledge Scott D'Urso and Adam Reed for their help with data collection/coding.
Notes
A previous version of this work was presented as a Top 3 Paper in the Organizational Communication Division at the International Communication Association Annual Conference, New Orleans, LA (May 2004).
1. Since selection of the WWW format could indicate a predisposition for electronic media or differences in type of participant, we conducted analyses to determine whether media use and sample demographics differed across survey types. Of the 14 media assessed, only one differed across survey versions. WWW respondents reported greater use of instant messaging technologies (M=129.20, SD=200.59) than individuals who completed the paper version (M=48.24, SD=156.88), F = 4.25, p=.04. Comparison of demographic variables revealed no differences across survey format for sex, supervisory status, size of team, percentage of time spent working with the team, number of locations for team members, number of time zones in which team members were located, and number of virtual teams to which participants belonged. However, there were significant differences in the two tenure measures (team and organization) indicating that WWW-based representatives had shorter organizational and team tenure than paper-based responses. Overall, there were very few differences across the participants who chose to respond via WWW or pencil-and-paper; thus, the data were combined for analyses.
2. To assess whether individual characteristics may lead to individually biased reports of team behaviors, we examined the relationships between individual characteristics (team tenure, supervisory status, and sex) and the dependent variables from the study (usage frequency for the media categories and key outcomes). Tenure with the virtual team was not significantly correlated with any of the team outcomes and there were no differences in media use as a function of supervisory status. For sex, there was only one slight, but still nonsignificant, difference in reports of team trust with females (M=3.96, SD=0.47) reporting higher team trust than males (M=3.78, SD=0.41), F(1, 95) = 3.02, p=.09. Because individual differences such as tenure, supervisory status, and sex did not influence reports of the dependent variables, this is further justification for the use of informed team respondents in this study.
3. To limit the number of variables, only communicative predictors that explained significant variance in analyses of Hypothesis 2 were included in the moderator analyses. Tests for moderator variables were computed using regression analyses that included product terms consisting of each technology use variable multiplied by the moderator variables. To prevent multicollinearity among predictors and interaction terms, all variables were centered prior to analysis. The technology use variable was entered in a first step, followed by the moderators (communication predictors), then the product terms. A significant change in the R 2 value at the third step indicates that incorporation of the moderator variables increased explained variance. Positive beta values for a moderator indicate that the impact of technology use upon outcomes is greater when the moderator variable is at higher levels. Negative beta values for moderators suggest that the impact of the technology use variable upon the outcome is greater when the communicative predictor is at lower levels.