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

Explore the formation and collaboration lifecycle of virtual scientific teams, examining the role of information and information behaviors. Study outcomes and products, as well as the effects of the ending stage on future collaborations.

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

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  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

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