Controlling and Coordinating
Large, Complex, and Distributed
Scientific Research Collaborations
GENI Engineering Conference 7
Laurie J. Kirsch
Sandra A. Slaughter
Professor
Information Systems
Katz Graduate School of Business
The University of Pittsburgh
lkirsch@katz.pitt.edu
Professor
Information Technology Management
College of Management
Georgia Institute of Technology
sandra.slaughter@mgt.gatech.edu
March 17, 2010
Presentation @ GEC7
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Who are we and why are we here?
• Management scholars
• Conduct research on the management of
information technology and systems
projects
• Prior and current NSF-funded studies to
examine the management of large scientific
research projects with a “cyber” component
(“Cyber-infrastructure projects”)
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Examples of Large Collaborative,
Cyber-infrastructure Projects
• NEES – George E. Brown, Jr. Network for
Earthquake Engineering Simulation
• Teragrid / XD – High-performance network of
super computers proving cyber-infrastructure for
open scientific research
• iPLANT – cyber-infrastructure collaborative for
plant sciences
• GENI – Global Environment for Network
Innovations
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Research Objectives for our
Study of GENI
• Identify mechanisms that are needed to
effectively structure, govern, and manage
projects like GENI
• Understand communication and interaction patterns (who
is communicating and collaborating with whom)
• Suggest control / coordination techniques
• Get feedback from the GENI community
March 17, 2010
• Note: we are NOT reviewing projects as part of the GPO's
performance assessment. We keep all identities
anonymous.
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Our progress to date
• We have conducted interviews with several GENI
stakeholders across different roles
• We have collected publicly available archival data
capturing interactions between GENI stakeholders
and have conducted a social network analysis of the
data
• Would like to share our initial results from the
social network analysis of GENI…
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A Social Network Analysis of GENI
• Social network analysis identifies the communication
and interaction patterns of individuals in a community
• Identifies the most “central” (e.g. influential)
individuals and those who are the information brokers
(e.g., boundary spanners).
• Patterns of communication and interaction reflect how
information, knowledge and ideas are exchanged in a
community
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A Social Network Analysis of GENI
(Cont’d)
• Social network analysis can identify sub-groups and
cliques and signal the potential for conflict
• Patterns of interaction and communication relate to
performance, innovation and other project outcomes
A social network analysis of GENI was conducted to
determine the structure of the interactions between GENI
stakeholders in Spiral 1
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Possible Social Structures
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Fully Connected
Classic Organizational Hierarchy
Nearest Neighbor
Autonomous
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Possible Social Structures
(Cont’d)
Core-Periphery or “onion” (Open Source communities)
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Why is the type of social
structure important?
• Different parts of the network can coallesce into “cliques” or
sub-groups that differentiate themselves from other sub-groups
and this can cause conflict
Left cluster:
Affiliation: Industry
Expertise: Management
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Right cluster:
Affiliation: Academia
Expertise: Biology
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So what is the social structure
of GENI?
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The Social Structure of GENI
• Based on:
– publicly available data collected from 2007 to 2009 (from
GENI website)
– people who participated in GENI events during that time
period (PIs, GPO, NSF, Working groups, others)
– projects (Spiral 1) and clusters during that time period.
– "events" occurring during that time period that connect those
participants (e.g., attendance at GEC 1-6, meetings,
milestone completion, email exchanges)
– 667 individuals who participated in 1,195 events during that
time period
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The Social Structure of GENI: 2007
PIs
GPO
NSF
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The Social Structure of GENI: 2008
PIs
GPO
NSF
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The Social Structure of GENI: 2009
PIs
GPO
NSF
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Initial Observations
• The GENI community has the social structure most
similar to an open source (e.g., core-periphery)
community
• There are some isolated individuals and groups but
over time, the core of GENI is expanding to include
many participants
• Some participants are influential throughout, others
emerge as leaders, and others lose influence –
reflects the changing pattern of stakeholder
participation in GENI
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Next Steps
• We analyzed the social structure of GENI based on
archival data – but, we are missing interactions between
PIs
• We are sending Spiral 1 PIs a link to a communication
survey – please complete the survey so we can finish
our analysis and present the results at an upcoming GEC
• We are interested to talk with GEC attendees about their
experiences in GENI – please contact us if you would
like to talk with us or if you would like to know where
you are in the GENI social structure!
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Thank you!
• Our contact information:
– Professor Laurie Kirsch
• The University of Pittsburgh
• lkirsch@katz.pitt.edu
– Professor Sandra Slaughter
• Georgia Institute of Technology
• sandra.slaughter@mgt.gatech.edu
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Additional Slides
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Motivation
• Challenges we observe in managing cyberinfrastructure projects:
• Large and complex
• High uncertainty and risk
• Volatile and emergent requirements
• Constrained by budget and schedule
• Distributed knowledge and collaborators across
institutions
• Diverse collaborators with different motives and
incentives
• Difficulty of communication and coordination
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Research Approach
• Qualitative analyses
– Develop timeline of GENI, including key events and
decisions
– Conduct targeted interviews of GENI stakeholders to
understand their experiences in the project
– Synthesize recommendations for communication,
coordination and commitment
• Quantitative analyses
– Social network analysis of patterns of communication
and interactions between GENI stakeholders
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Why is the type of social
structure important?
• Different structures have different advantages
and disadvantages:
• Traditional hierarchy – efficient but inflexible
• Fully connected team – effective but time consuming
• Autonomous – no information transfer
• Nearest neighbor – convenient, but slow and
information distorted as passed along
• Core-periphery – brings in many perspectives, but
depends on boundary spanners
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The Social Structure of GENI: 2007
(including “independents”)
PIs
GPO
NSF
Other
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The Social Structure of GENI: 2008
(including “independents”)
PIs
GPO
NSF
Other
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The Social Structure of GENI: 2009
(including “independents”)
PIs
GPO
NSF
Other
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Critical Dimensions for
Managing CyberInfrastructure Projects
Communication
Control/
Coordination
Commitment
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Communication Challenges
• Distributed communities of stakeholders
• Must have effective mechanisms for distributed
communication (not face-to-face) between team members
• Common, shared infrastructure for project management
and technical development is helpful
• Diverse groups of stakeholders
• Requirements determination processes are critical
• Need for communication and requirements discovery
mechanisms to foster collaboration across stakeholders
• Stakeholders who span across multiple groups are needed
to facilitate communication
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Control / Coordination Challenges
• Constraints on schedule, budget, quality
• Extensive project planning, monitoring and reporting is
needed
• Formal oversight is required
• Iterative development (innovation) process
• Must manage “incubation” or “experimental” process
• Need to facilitate technical integration which can be very
complex
• How to reconcile need for flexibility in development process
with need for formality in project management
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Control / Coordination Challenges
• Funding
• Funded by external agencies and must report to them
• Complex project funding arrangements must be managed
• Different stakeholder communities
• Control is indirect, complex and difficult to exert (i.e.,
different cultures, organizations)
• Project Director plays an especially critical role in bridging
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Commitment Challenges
• Community-based instead of formal
organizational roles
• Stakeholders need to have clearly defined roles,
standards and codes of conduct
• Self-regulation mechanisms (reputation, trust, etc.)
are required
• Shared vision and consensus-based decision making
processes are vital for commitment
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