Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

1. At the Boundaries of the iField: Virtual Organizations and the Mag Lab Michelle M. Kazmer

2. Virtual Scientific Teams: Life-cycle Formation and Long-Term Scientific Collaboration • Kathy Burnett, Gary Burnett, Michelle Kazmer, Paul Marty, Besiki Stvilia, with Chris Hinnant and external evaluator from University of Maryland, Ken Fleischmann • Funded by the National Science Foundation (award number OCI-0942855) Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

3. Virtual? • Why the word “virtual” appears in both titles Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

4. Mag Lab • National High Magnetic Field Laboratory in Tallahassee, FL (sites at Los Alamos National Laboratory in New Mexico and University of Florida in Gainesville) • Annually more than 900 visiting scientists and engineers from around the world conduct experiments using the Mag Lab's equipment Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

5. Teamwork • Scientists and engineers work in teams • Most aspects of their collaboration are done from their home sites -- which often aren’t co-located with each other • Coordination, planning, writing, etc. are done “virtually” … in the NSF sense: dispersed geographically, but functioning coherently via cyberinfrastructure Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

6. At the boundaries of the iField? • Information science, psychology, management science, computer science, sociology, research policy, social studies of science, philosophy, and in disciplines in which scientific collaboration occurs • Collaborating scientists exchange, create, and disseminate information Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

7. Today’s highlights • Focus on two aspects of this project today: • Its use of theory: information worlds theory • Its study of remote collaboration lifecycles with equal attention paid to the "ending" stages Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

8. Theory of Information Worlds • Gary Burnett & Paul Jaeger • Combines concepts drawn from Elfreda Chatman & Jurgen Habermas • Examines role of information, information behaviors, & perceived value of information across social contexts large & small Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

9. Theory of Information Worlds • Chatman: “Small Worlds” -- Information within local, largely homogeneous social settings • Habermas: “Lifeworld” – Sum total of all information resources & norms culture-wide • Burnett & Jaeger: Information within individual world & interactions across multiple worlds Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

10. Theory of Information Worlds • Key concepts: • Social Norms: Shared understanding of “Rightness” & “Wrongness” in observable social behaviors • Social Types: Shared perceptions of individuals’ roles in context • Information Value: Shared understanding of what’s worth attention & what information is meaningful • Information Behavior: Full range of normative behaviors related to Information • Boundaries: Interfaces between worlds, points at which worlds come into contact with each other Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

11. Theory of Information Worlds • Arguments: • Worlds are not isolated, but interact with one another • Differences in perceptions of norms, value, etc. can lead to conflict & breakdown in communication across worlds • Understanding of Information Worlds can help ameliorate such conflict Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

12. Theory of Information Worlds • Possible applications: • Virtual & Other Communities • Information Policy & Political Analysis • Information & Gender, Ethnicity, & Cultural Difference • Media Studies • Library Services & Community Service • Popular Culture Studies • Current Study: • Life Cycles of Virtual Scientific Work Teams at FSU Mag Lab Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

13. Group process models • Many famous models including Tuckman (& Jensen), McGrath, Gersick • Newer models such as Arrow, McGrath & Berdahl • Sonnenwald’s framework in today’s reading (foundation, formulation, sustainment and conclusion) Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

14. Outcomes and products • Outcomes and products are important • But what happens during the ending stage affects • Willingness to work together • Willingness to work with distant others • Willingness to use communication technologies • The beginning stages of the next lifecycle Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

15. The nuts and bolts • Modeling lifecycles of scientific collaborations by • Incorporating true cyclicality and feedback • Using multiple short-term projects to model long-term collaborative research agendas • Taking a multiple-case approach • Using multiple types of data Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

16. Research questions • Does a virtual team’s lifecycle influence team members’ willingness to work together again? • Does a virtual team’s lifecycle influence team members’ willingness to work in virtual teams again? • Do virtual teams generate output comparable to the output of co-located teams? • Does the degree of multi- or interdisciplinarity within a team influence its lifecycle or outcomes? Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

17. Research questions • Do groups share norms and expectations of CMC-based interactions? • Are such norms ad-hoc or are they established (formally or informally) externally? • Is there evidence of conflicting norms, or of multiple "information worlds" coming into contact or conflict during collaborations? • Are team and project types linked to team outcomes or to team members’ willingness to work in virtual teams again? Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

18. Data sources • Documentary artifacts of virtual communication • Observations of teams at the Mag Lab doing experiments • Semi-structured intensive interviews with scientists (critical incidents) Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

19. Data analysis methods • Content analysis (codes from theory, from continuing analysis, in vivo) • Social network analysis (based on communication artifacts, publications, interview data, patent information) Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration

20. In conclusion … • Study of scientific collaboration • Scientists using the Mag Lab • Using information worlds theory • Multiple types of data and analysis methods • Model the lifecycles of distributed scientific teams as part of long-term scientific collaborations Virtual Scientific Teams: Life-cycle Formation and Long-Term Collaboration