<<Back to RESEARCH

Virtual Teams

Collaboration across time, space and structural barriers is an enduring organizational goal. Yet, problems such as coordination, effective communication or cultural differences still persist, and can hamper the distant team's performance.

In this thread of my research I look deeper into the behavioral and social characteristics of distributed group members (interests, motivations, personality), and use them as input for algorithms that bring together more harmonious virtual teams. Which profile elements can accurately model distant worker behavior? Which are the most effective strategies of building an ad-hoc remote team?

​This line of research has implications for computer-supported collaborative work.

Related Publications

Personality Matters: Balancing for Personality Types Leads to Better Outcomes for Crowd Teams

Lykourentzou I., Antoniou A., Naudet Y., Dow S. (2016), 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2016),
Honorable mention (top 5%)

When personalities clash, teams operate less effectively. In this work we examine how personality compatibility affects performance and individual perceptions in crowd teams, where the workforce is more transient and diverse. Using the DISC personality test, we composed 14 five-person teams (N=70) with either a harmonious coverage of personalities (balanced) or a surplus of leader-type personalities (imbalanced). Results show that balancing for personality leads to significantly better performance on a collaborative task. Balanced teams exhibited less conflict and their members reported higher levels of satisfaction and acceptance. This work demonstrates a simple personality matching strategy for forming more effective teams in crowdsourcing contexts. 

Team Dating: A Self-Organized Team Formation Strategy for Collaborative Crowdsourcing

Lykourentzou I., Wang S, Kraut R. E., Dow S. P. (2016), CHI'16 Extended Abstracts: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Online crowds have the potential to do more complex work in teams, rather than as individuals. However, at such a large scale, team formation can be difficult to coordinate. (How) can we rely on the crowd itself to organize into effective teams? This work explores a strategy for "team dating", a self-organized crowd team formation approach where workers try out and rate different candidate partners. In two online experiments, we find that team dating affects the way that people select partners and how they evaluate them. We use these results to draw useful conclusions for the future of team dating and its implications for collaborative crowdsourcing.​

Related Projects

aCCoRdO: Computational methods for human use optimization in complex crowdsourcing 
​Funding: National Research Fund of Luxembourg (FNR)