Enabling science and technology research teams: a breadmaking metaphor

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Educause Quarterly, 2010, issue 1 (in press)
Enabling Science and Technology Research Teams: A
Breadmaking Metaphor
By Deana Pennington
Key Takeaways
• The transformative potential of technology-enabled science argues for creating
cross-disciplinary teams of scientists and IT specialists.
• Achieving the vision of technology revolutionizing academia in general, and the
sciences in particular, requires overcoming the barriers to collaboration.
• A breadmaking metaphor provides a framework for considering the many factors
that influence the outcome of collaborative interactions between scientists and IT
experts in e-research.
Anyone who has been involved with a cross-disciplinary team that combines scientists
and information technology specialists knows just how tough it can be to move these
efforts forward. Decades of experience point to the transformative potential of
technology-enabled science efforts, and the success stories offer hope for future efforts.
But for every success story there are many failures, particularly in the natural and social
sciences, which have not traditionally used advanced technologies. Despite good
intentions and many hours in arduous meetings, these efforts often go quietly into the
night — or occasionally screaming into oblivion.
This situation does not seem to be unique to research teams. Campus IT professionals in
all capacities are thrust into settings where they must work with nontechnical colleagues.
Often these efforts are problematic: miscommunication leads to good technical solutions
that unfortunately do not meet user needs; tempers flare, and the result is high stress for
everyone. The technology revolution on campus sometimes resembles something from a
horror novel — unexpected monsters cropping up in unexpected places. Yet there
remains the vision of technology revolutionizing academia in general, and the sciences in
particular. Achieving this vision, though, requires overcoming the barriers to
collaboration. First, though, the barriers must be understood.
After years of working as a scientist on a number of these teams and observing that our
collaboration issues were not unique, I set out to better understand the process of
collaboration and to develop models of collaboration that could inform these efforts. The
issues are complex. There are many different ways of thinking about them, and many
different disciplinary perspectives provide relevant research findings. Fully articulating
the issues would require a large volume, or more likely several volumes. In dealing with
the copious relevant literature, I have found it useful to structure the issues through use of
a breadmaking metaphor. Each element of the metaphor relates to various lines of
research that contribute to an understanding of the e-research process. Here, I present the
metaphor along with a few insights from ongoing research in the hope that others
engaged in similar efforts can take advantage of these concepts, which I have found
useful.

The Breadmaking Metaphor
Cross-disciplinary collaboration is like making bread. Of the ingredients used in making
bread, some are more alike than others. The dry ingredients (flour, sugar, salt) are very
similar in appearance and could be confused for one another by a casual observer, but in
fact they have quite different characteristics and functions. Breadmaking also requires
some ingredients that are decidedly different from the dry ingredients. All breads require
some kind of liquid (milk, water, oil), for example. The combination of dry and liquid
ingredients transforms the collective into a material with completely new properties —
neither dry nor liquid but dough. Think of the ingredients as different disciplinary
perspectives.
One can make bread with just dry and liquid ingredients. However, the addition of yeast
drastically changes the results. Yeast has some very interesting characteristics. A small
amount can have transformational impact, but the impact is nonlinear. The rate of change
in impact is greatest with small increases in amount — larger increases have no added
effect and can become detrimental by overpowering the qualities of the other ingredients.
Many different components of collaboration could be considered yeast. In this
formulation, IT is the yeast.
In order to make bread, the ingredients must be placed in a bowl. The bowl is a place
where the ingredients can interact. Note that the ingredients cannot get in the bowl by
their own efforts — it requires an outside force sufficient to displace their mass and
overcome their inertia. The outside force is societal change and normative institutional
arrangements that motivate e-research efforts.
Once in the bowl, the ingredients must be mixed and kneaded. The kneading process is
necessary but difficult and requires substantial time and energy. Kneading does not just
distribute the ingredients; it builds and strengthens strands of gluten that contain the gases
that cause bread to rise. On the other hand, too much kneading can remove the elasticity
from those strands and ruin the dough. Mixing and kneading is the process of cross-
disciplinary learning and interaction. The strands are connections and linkages created
between participants.
Just as the ingredients cannot place themselves in the bowl, they cannot mix and knead
themselves. The mixer is another outside force, different from the force that put the
ingredients in the bowl. The mixer has been designed specifically to perform this
function. The mixer is a person(s) who orchestrates cross-disciplinary interactions.
Once mixed, the new concoction must be allowed to rise. Acted on immediately, the
yeast will not be able to fully invade the entire mixture, and the gases that cause the bread
to be light and fluffy will not form. If too much time is allocated for rising, however, the
bread collapses. Rising is the time needed to allow e-research ideas to incubate.
When the bread has risen, it needs to be baked. This also requires time and energy, and
the baking time cannot be rushed by turning up the temperature. During this time all of
the ingredients interact with each other, forming a solidified structure. Baking is the
collaborative work done that produces e-research outcomes.
Key Components Highlighted by the Metaphor
A number of key components of e-research collaboration are highlighted by the metaphor
(Table 1). These can be categorized as issues of external forces, place, time, and process
— issues common to all phenomenological models. A number of theories and conceptual

frameworks are relevant to the different elements. In this section I briefly describe some
of these elements.
Table 1. Metaphorical Elements, Corresponding Model Elements, and Relevant
Theory
Metaphorical
Element Cross-Disciplinary
Collaboration Model Element Relevant Theory and
Concepts
Ingredients Individuals
Disciplinary perspectives Individual creativity
Diversity and heterogeneity
Yeast Enabling technologies
Moving force Societal change
Normative action by institutions Theories of action
Organizational change
Bowl Interacting place, face-to-face or
virtual Physical collaboration places
Virtual collaboratories
Mixing and
kneading Collaboration methods, tools, and
processes Psychology of learning
Organizational learning
Team processes
Boundary objects
Mixing force Collaboration facilitator, mediator,
catalyst, and synergist Transformational leadership
Boundary spanning
Rising E-research design Incubation
Participatory design methods
Ill-structured problems
Baking Cross-disciplinary application
development Project management
Ingredients
The ingredients are individuals with different disciplinary specialties. Some are more
alike in their perspective than others. Conceptual frameworks of a physicist and a
computer scientist are more alike, for instance, than a sociologist and a computer
scientist. Conversely, two physicists may have distinctly different research interests. To
someone from outside that domain, they appear similar, but from within the domain there
are distinctive differences. All are not the same. Illustrating this, Figure 1 shows a partial
map of science constructed by analysis of journal use. Journals located close to each
other in this space are accessed by the same people; those that are far apart are rarely
accessed by the same people.

Figure 1. Partial Map of Journal Use Analysis
Image modified from Bollen et al.1; used with permission
Any collaborative e-research team can be described by a conceptual arrangement
comprised of pair-wise conceptual connections between any two participants. There is a
tendency for those who are most alike to converge quickly on creative ideas that span
their interests, leaving those who are most different isolated. (See “Team Structure.”)
{insert link to sidebar 1} Disciplines exist because of this tendency and the creative
outcomes that ensue from interacting with those who have similar but not identical
perspectives. Yet conceptual heterogeneity brought about by major differences in
perspectives creates an enhanced opportunity for radically new outcomes to emerge.
Cross-disciplinary e-research collaboration depends on integrating these heterogeneous
perspectives.
Motivating Force
Major e-research funding incentives have emerged over the past decade. Despite these
incentives, few domain researchers engage in e-research. We have a propensity to
continue to act and interact with the same people, and in the same ways as in the past,
unless a compelling reason to change arises. Institutional arrangements such as
universities, foundations, and funding agencies are the outside forces that motivate
involvement in an e-research project. They must create incentives that bring ingredients
to the bowl. Note, however, that this is not accomplished by getting rid of the containers.
Most individuals will remain in their departments and disciplines while engaging in e-
research projects. (For more on this point, see “Challenges to Interdisciplinary
Research.”) {link to sidebar 2}
Career success in academia is tightly coupled to productivity as measured by
publications. It is much easier to work and publish within one’s discipline than to engage
in lengthy, arduous collaborations with IT researchers that can require years to produce
publishable outcomes. This can be a career inhibitor for any researcher, but for new

faculty facing tenure decisions, it is a career killer. In e-research, considerable time and
effort are expended searching for appropriate and innovative matches between emerging
technology research and the needs of scientists, then iterating over many potential
solutions. The outcomes from these exploratory efforts are rarely publishable.
New, compelling incentives are needed, but it is unclear what those incentives might be,
or who is responsible for generating them. As Denise Caruso and Diana Rhoten noted:
[I]nterdisciplinary success depends on exploration and curiosity in the service of
solving a problem or answering a question, which may or may not yield the kind
of tangible result we expect from traditional research…it requires tenacity and a
tolerance for ambiguity that many traditional researchers find difficult to
maintain.2
Bowl
In the past, the bowl was a physical place where people met face-to-face, and there will
continue to be a need for such face-to-face meetings (see “Co-located Teams”). {insert
link to sidebar 3} Rumors of the death of distance are highly exaggerated3 (with credit to
Mark Twain). Still, in the digitally connected society, people may interact from
anywhere. The emerging bowls of the future are virtual places — scientific
collaboratories or virtual organizations. To be effective, virtual places must provide many
of the same capabilities as face-to-face meetings. This raises a number of technical issues
as well as social interaction and collaboration issues that have not been present in the past
and that require cultural and organizational change. However, the vision of virtual
collaboration is not simply to enable the same interactions as usual from a distance;
rather, virtual collaboration is an opportunity to engage with others in ways never before
envisioned. Computing becomes a collaborator, contributing input and resources that
humans cannot. Computing provides an opportunity to marshal the vast knowledge, data,
and resources relevant to a problem in ways that individual faculty and researchers could
never hope to accomplish. Indeed, opportunities abound for creative combinations that
emerge from interactions between collaborators with heterogeneous perspectives — and
the web is the ultimate source of heterogeneous perspectives. Emergent cognition arises
from humans and computers collaborating to explore, discover, and interact in the virtual
world. Nevertheless, empirical findings suggest that collaboratories that seek to engage
participants in tightly coupled interactions are less likely to be successful than looser
collaborations (see “Collaboratories”). {insert link to sidebar 4} Mechanisms must be
developed for harnessing the potential of collaboration across heterogeneous perspectives
before these technical approaches will reach their full potential.
Mixing and Kneading
Mixing and kneading are the processes by which disciplinary knowledge is exchanged,
cross-disciplinary learning occurs, and linkages between disciplines are discovered (see
“Success Factors”). {insert link to sidebar 5} These linkages form a coherent conceptual
framework from which creative content and connections can emerge. Effective
orchestration mechanisms that enable this creative collective thinking are poorly
understood. In cross-disciplinary teams depth and diversity of knowledge impede finding
creative linkages, yet those same characteristics are likely to yield radically creative ideas
when linkages are found.

Diana Rhoten4 studied six U.S. National Science Foundation (NSF) funded
interdisciplinary centers using social network and ethnographic analysis. She found that
although these institutions were organized to provide substantive support for
interdisciplinary research, and individual researchers within these organizations were
highly motivated to conduct interdisciplinary research, few truly interdisciplinary
outcomes were achieved. In particular, she highlighted the lack of unifying problem
definitions around which researchers could coalesce, leading to the “tendency to become
a nexus of loosely connected individuals searching for intersections, as opposed to
cohesive groups tackling well-defined problems.” This phenomenon points to the
difficulty of crossing disciplines, identifying the connections between perspectives, and
even clarifying a problem that can be addressed from different directions. These issues
are not unique to research centers; rather they are fundamental issues that any cross-
disciplinary team (including e-research teams) must confront. (For related research, see
“Diverse Perspectives.”) {insert link to sidebar 6}
Collaborative teams are groups of individuals who aggregate for the purpose of working
together on a shared problem. Yet as long as the problem definition is vague, such efforts
have difficulty gaining traction. The key challenge confronting e-research collaborators is
managing and enabling group creative thinking when collaborators have deep and highly
heterogeneous perspectives. The goal of such projects is not just IT research, but the
transformation of the domain through application of innovative computing
methodologies. Historically, finding the right connections that allow simultaneous
advances in both areas has been problematic. Domain researchers require small additions
of the right technologies; determining which technologies are relevant and appropriate is
critical. Yet domain researchers often have difficulty envisioning how emerging
technologies might impact their work, and IT researchers have difficulty communicating
potential outcomes in concrete, nontechnical terms. Consequently, there is a tendency for
e-research to result in an IT project with little application rather than a problem-centered
project that advances both areas (see “Science and Technology Collaborations”). {insert
link to sidebar 7} Researchers must create and develop conceptual connections that are
interesting and challenging for both. Concerted effort is needed on both sides to align
interests such that work meets both the technical goals and the goals of the end user.
Mixing Force
The mixer is not necessarily in charge of the group or necessarily the primary decision
maker. The mixer controls the mixing, consistent with organizational science studies of
critical leadership behaviors in situations that call for cooperation and transformative
outcomes. In collaborative teams, it is crucial that the process through which the final
problem is defined results in collective buy-in and that each individual understands his or
her role. This requires a leader who can successfully orchestrate generative elements and
interactions such that a shared vision emerges. The leader directs the process, not the
outcomes. (See “Critical Leadership Behaviors” necessary for success.) {insert link to
sidebar 8}
Studies of stakeholder organizations working together on complex social issues5 have
identified critical individuals referred to as “boundary spanners.” {insert link to sidebar
9} Boundary spanners facilitate and manage interactions, but they are more than just
facilitators or managers. They must act as translators but also as synergists. They must be

entrepreneurs and innovators, cultural brokers, trust builders, and catalytic leaders.
Although some leaders with these characteristics have arisen, many more are needed.
A better understanding of the overall collaboration process, and mixing processes in
particular, will enable training of a cohort of students in every discipline who can more
effectively engage in collaboration. Some of these people will combine native leadership
ability with understanding of collaboration processes to become the synergistic, catalytic
leaders of tomorrow.
Rising
It takes time for creative, interdisciplinary approaches to emerge in a complex project.
Disciplinary training and cross-disciplinary mixing are all preparation for the incubation
of ideas. {insert link to sidebar 10} The connections constructed during mixing form a
springboard for incubation, but the more heterogeneous the perspectives, the longer the
incubation period needed. Emergent ideas will be fuzzy and poorly defined initially.
Group activities can be structured to lead toward increasing problem definition.6
Nevertheless, incubation of creative ideas in individuals often requires long periods of
time; it is reasonable to expect that incubation in complex teams will require even longer.
Do not expect good things to emerge overnight, or from a single meeting.
The need for incubation suggests the need for time set aside specifically for cross-
disciplinary interactions, but also for time to reflect on those interactions. Similar to the
recognition that faculty can be energized by sabbaticals that allow them to focus their
effort, cross-disciplinary teams need time set aside for collaborators to be “synergized”
by crossing their efforts. Unlike sabbaticals, this time should not be a foregone
conclusion — not everyone has the goal of crossing disciplines. For those who do,
however, blocks of time set aside without the distractions of everyday work are
invaluable.
Baking
Many factors influence the ability of a collaborative group to take action together. Yet the
ability to generate creative approaches that effectively integrate across disciplinary
perspectives, and provide opportunities for all participants, is central. The team that
learns to think creatively together in the beginning is positioned to more effectively take
collaborative action together. Successful action is not guaranteed, but the absence of a
well-conceived cross-disciplinary agenda pretty much guarantees disappointing
collaborative action. Yet an agenda, in this context, must be a potential agenda, not a firm
agenda. A project takes a specific form that depends on input from all of the disciplines.
By the very nature of those interactions, the collaborative path may have high
uncertainty. Many iterations of design, testing, and revision might be required before
effective and satisfying solutions are reached. Any collaborative effort that includes
advanced IT must be prepared to confront major technical challenges that require time
and effort to resolve. (See “From E-Science Project to Cyberinfrastructure.”) {insert link
to sidebar 11} All participants must remain engaged while these issues are being
resolved. The rest of the ingredients can’t remove themselves from the bowl during this
process — the yeast must interact with the other ingredients.

Conclusion
Given the number of issues that confront cross-disciplinary science and technology
teams, and the wide range of knowledge and experience that must be brought to bear on
the process, it is no surprise that these efforts have a low success rate. On the contrary, it
is almost a miracle that any of them succeed! I believe that those that have succeeded
historically discover an interesting problem to work on by serendipity, and the desire of
the participants to tackle that problem overcomes all other issues. While this can lead to
great outcomes, we must do better than serendipity.
An analogous situation existed early in the 20th century with the rise of large
corporations. Large corporations did not replace small businesses; rather, they created a
new business environment. Along with that new environment, new professional niches
arose for people who managed the system. Clearly, there is a comparable need within
complex cross-disciplinary technology projects for professionals able to manage the
complexity of the project and address the different issues highlighted by the breadmaking
metaphor.
There are precedents for this role within the IT arena. Software companies employ
market analysts and product developers to ensure a match between the technologies they
develop and the users they hope to entice to purchase those products. Businesses employ
business analysts to ensure that the investments they make in technologies and systems fit
their business needs, and they employ training professionals to ensure that investments in
technology are matched with efforts that focus on developing the human capacity to use
those technologies. In both cases, the needs and tasks are fairly well defined. In academic
and research settings, the needs are not well defined because academia focuses on the
forefronts of knowledge rather than those areas that are well known, and research is, by
definition, an exploration into uncharted territory. Hence, the matching task is much more
difficult. It is time for academia to begin to define comparable professional positions for
those who will tackle the new complexities that are emerging — who are our
intermediaries and negotiators, the people who can facilitate the matching of needs and
solutions in our settings? Until we facilitate the work of our cross-disciplinary research
teams, we cannot improve their chances for success.
Acknowledgments
This work was supported by National Science Foundation grant numbers 0636317 and
0753336 for the CI-Team Demonstration and Implementation Projects: Advancing
Cyber-infrastructure Based Science Through Education, Training, and Mentoring of
Science Communities. The author gratefully acknowledges the many collaborators
involved in these projects, whose comments and insights have been useful and influential.
Endnotes
1. Johan Bollen, Herbert Van de Sompel, Aric Hagberg, Luis Bettencourt, Ryan
Chute, Marko A. Rodriguez, and Lyudmila Balakireva, (2009). “Clickstream Data
Yields High-Resolution Maps of Science,” PLoS One, Public Library of Science,
vol. 4, no. 3 (2009).
2. Denise Caruso and Diana Rhoten, “Lead, Follow, Get Out of the Way:
Sidestepping the Barriers to Effective Practice of Interdisciplinarity,” The Hybrid
Vigor Institute, San Francisco, CA, April 2001.

3. Gary M. Olson and Judith S. Olson, (2000). “Distance Matters,” Human-
Computer Interaction, vol. 15, no. 2 (Septmber 2000), pp. 139–178.
4. Diana Rhoten “Final Report: A Multi-Method Analysis of the Social and
Technical Conditions for Interdisciplinary Collaboration,” The Hybrid Vigor
Institute, San Francisco, CA, September 2003.
5. Paul Williams, “The Competent Boundary Spanner,” Public Administration, vol.
80, no. 1 (2002), pp. 103–124.
6. Deana D. Pennington, “Cross-Disciplinary Collaboration and Learning,” Ecology
& Society, vol. 13, no. 2 (December 2008), p. 8.
Deana Pennington
© 2010 Deana Pennington. The text of this article is licensed under the Creative
Commons Attribution-Noncommercial-Share Alike 3.0 license.
{sidebar 1}
Team Structure
Roger Guimera and his coauthors analyzed empirical data from citation analysis in four
scientific disciplines and combined that with simulation studies to investigate the
relationship between team structure and creativity of products. They used journal impact
factor as a surrogate for creativity of published outcomes. They found that teams that
included one or more new collaborators were more likely to publish in high-impact
journals than teams comprised entirely of individuals with prior collaborative
relationships. They interpreted this as an enhancement to creativity due to the
incorporation of a novel perspective.
Source
Roger Guimera, Brian Uzzi, Jarret Spiro, and Luis A. Nunes Amaral, “Team Assembly
Mechanisms Determine Collaboration Network Structure and Team Performance,”
Science, vol. 308, no. 5722 (April 29, 2005), pp. 697–702.
{sidebar 2}
Challenges to Interdisciplinary Research
Chris Golde and Hanna Gallagher found four challenges confronting PhD students
attempting to conduct interdisciplinary research:
1. Advisors who do not themselves cross disciplines.
2. Mastering potentially conflicting language, knowledge, and methods from two
fields.
3. Developing a network of peers and colleagues.
4. Fear about career impact due to difficulty publishing in established journals.
Source
Chris M. Golde and Hanna Alix and Gallagher, “The Challenges of Conducting
Interdisciplinary Research in Traditional Doctoral Programs,” Ecosystems, vol. 2 (1999),
pp. 281–285.
{sidebar 3}

Co-located Teams
Jonathon Cummings and Sara Kiesler compared collaborating cross-disciplinary teams
that were co-located at the same institution with comparable teams distributed across
multiple institutions. They found that co-located teams had a higher likelihood of success
as measured by participant assessment of outcomes. Distributed teams that incorporated
explicit management techniques performed better than those that did not, but still were
less successful than co-located teams.
Source
Jonathon N. Cummings and Sara Kiesler, “Collaborative Research Across Disciplinary
and Organizational Boundaries,” Social Studies of Science, vol. 35, no. 5 (2005), pp.
703–722.
{sidebar 4}
Collaboratories
Nathan Bos and his colleagues analyzed outcomes from 75 projects building large-scale
computer-supported scientific collaboratories. They generated a taxonomy of seven
collaboratory types:
1. Shared instrument
2. Community data systems
3. Open community contribution systems
4. Virtual communities of practice
5. Virtual learning communities
6. Distributed research centers
7. Community infrastructure projects
Their analysis suggested that collaboratories supporting data or tool-based loose
collaboration between scientists were generally more likely to succeed than those
supporting more tightly coupled sharing and co-creation of information and knowledge.
Source
Nathan Bos, Ann Zimmerman, Judith Olson, Jude Yew, Jason Yerkie, Erik Dahl, and
Gary Olson, “From Shared Databases to Communities of Practice: A Taxonomy of
Collaboratories,” Journal of Computer-Mediated Communication, vol. 12, no. 2 (2005),
pp. 652–672.
{sidebar 5}
Success Factors
Gary Olson and Judith Olson synthesized e-science case studies from over a decade of
investigation and found four factors influencing the success of these projects:
1. High common ground among participants
2. Loose coupling of work
3. Collaboration readiness (a community culture conducive to sharing)
4. Collaboration technology readiness
Common ground refers to knowledge participants have in common and are aware they
have in common.

Source
Gary M. Olson and Judith S. Olson, “Distance Matters,” Human-Computer Interaction,
vol. 15, no. 2 (Septmber 2000), pp. 139–178.
{sidebar 6}
Diverse Perspectives
John Levine and Richard Moreland synthesized social psychology studies of
collaboration and found that the combination of diverse perspectives on a team increases
the creativity of outcomes only if group processes mediate the impact of detrimental
effects of diversity such as interpersonal conflicts and communication difficulties.
Source
John M. Levine and Richard L. Moreland, “Collaboration: The Social Context of Theory
Development,” Personality and Social Psychology Review, vol. 8, no. 2 (2004), pp. 164–
172.
{sidebar 7}
Science and Technology Collaboration
David Ribes and Geoffrey Bowker investigated interactions between participants on the
GEON project and found that the co-creation of both science and technology took
considerable time and effort and was an ongoing issue throughout the project. Similarly,
Katherine Lawrence studied participant interactions on the LEAD project and found that
key issues involved managing multiple needs and engaging all participants. Ann
Zimmerman and Thomas Finholt interviewed scientists involved in the TeraGrid project
and found that few could envision a use of this system in their research.
Sources
David Ribes and Geoffrey C. Bowker, “Organizing for Multidisciplinary Collaboration:
The Case of the Geosciences Network,” in Scientific Collaboration on the Internet, Gary
M. Olson, Ann Zimmerman, and Nathan Bos, eds. (Cambridge, MA: MIT Press,
November 2008), pp. 311–330.
Katherine A. Lawrence, “Walking the Tightrope: The Balancing Acts of a Large E-
Research Project,” Computer Supported Cooperative Work, vol. 15, no. 4 (August 2006),
pp. 385–411.
Ann Zimmerman and Thomas A. Finholt, “Growing an Infrastructure: The Role of
Gateway Organizations in Cultivating New Communities of Users,” in Proceedings of
the 2007 International ACM Conference on Supporting Group Work (New York: ACM,
2007), pp. 239–248.
{sidebar 8}
Critical Leadership Behaviors
Gary Yukl, Angela Gordon, and Tom Taber synthesized 50 years of research on
leadership behavior and found that the three most critical leadership behaviors in
collaborative, transformative situations are:
1. Participatory decision making
2. Enabling innovative thinking

3. The ability to envision and articulate desirable outcomes that can be achieved by
the group
Source
Gary Yukl, Angela Gordon, and Tom Taber, “A Hierarchical Taxonomy of Leadership
Behavior: Integrating a Half Century of Behavior Research,” Journal of Leadership and
Organizational Studies, vol. 9, no. 1 (2002), pp. 15–32.
{sidebar 9}
Boundary Spanners
Natalia Levina and Emmanuelle Vaast studied multiple boundary spanners in two
different organizational settings and found that these critical participants may be
appointed or may emerge from within the group. To be effective, they must be viewed as
a legitimate (though possibly peripheral) participant in the fields being spanned,
recognized as a negotiator between fields, and motivated to act as a negotiator.
Source
Natalia Levina and Emmanuelle Vaast, “The Emergence of Boundary Spanning
Competence in Practice: Implications for Implementation and Use of Information
Systems,” MIS Quarterly, vol. 29, no. 2 (2005), pp. 335–363.
{sidebar 10}
Incubation
B. F. Spencer and colleagues reflected on their experiences managing the NEESgrid
project, noting that their initial planning period after receiving funding required a full
year and that plans continued to evolve throughout the project. While they were initially
funded for four years, they noted that projects of similar size and scope now commonly
take from six to ten years.
Source
B. F. Spencer Jr., Randal Butler, Kathleen Ricker, Doru Marcusiu, Thomas Finholt, Ian
Foster, and Carl Kesselman, “Cyberenvironment Project Management: Lessons
Learned,” National Science Foundation, September 2006.
{sidebar 11}
From E-Science Project to Cyberinfrastructure
David Ribes and Thomas Finholt studied e-science projects attempting to transition from
short-term development projects into long-term cyberinfrastructure. Among other
findings, they noted the unpredictability of development paths due to:
1. Unstable funding situations
2. Rapidly changing and emerging technologies
3. Uncertainties about constituencies and user requirements
They stated that “the novelty of CI often defies rationalized planning.”

Source
David Ribes and Thomas A. Finholt, “Tensions Across the Scales: Planning
Infrastructure for the Long-Term,” in Proceedings of the 2007 International ACM
Conference on Supporting Group Work (New York: ACM, 2007), pp. 229–238.