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The role of bridging organizations in environmental management: Examining social networks in working groups

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The linkage of diverse sets of actors and knowledge systems across management levels and institutional boundaries often poses one of the greatest challenges in adaptive management of natural resources. Bridging organizations can facilitate interactions among actors in management settings by lowering the transaction costs of collaboration. The Center for Ocean Solutions (COS) is an example of a bridging organization that is focused on linking actors within the ocean sciences and governance arena through the use of working groups. This research examines how network connections between group members affect working group functionality and, more specifically, whether cohesive network structures allow groups to more effectively achieve their goals and objectives. A mixed-methods approach, incorporating both qualitative and quantitative data collection and analysis methods, is employed to understand the structural characteristics of COS working groups. The study finds that cohesive network structures are not associated with increased working group functionality. Strong, centralized leadership is a better predictor of working group success in achieving goals and objectives.
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Kowalski, A. A., and L. D. Jenkins. 2015. The role of bridging organizations in environmental management: examining social
networks in working groups. Ecology and Society 20(2): 16. http://dx.doi.org/10.5751/ES-07541-200216
Research
The role of bridging organizations in environmental management: examining
social networks in working groups
Adam A. Kowalski 1 and Lekelia D. Jenkins 2
ABSTRACT. The linkage of diverse sets of actors and knowledge systems across management levels and institutional boundaries often
poses one of the greatest challenges in adaptive management of natural resources. Bridging organizations can facilitate interactions
among actors in management settings by lowering the transaction costs of collaboration. The Center for Ocean Solutions (COS) is an
example of a bridging organization that is focused on linking actors within the ocean sciences and governance arena through the use
of working groups. This research examines how network connections between group members affect working group functionality and,
more specifically, whether cohesive network structures allow groups to more effectively achieve their goals and objectives. A mixed-
methods approach, incorporating both qualitative and quantitative data collection and analysis methods, is employed to understand
the structural characteristics of COS working groups. The study finds that cohesive network structures are not associated with increased
working group functionality. Strong, centralized leadership is a better predictor of working group success in achieving goals and
objectives.
Key Words: bridging organizations; environmental management; social network analysis
INTRODUCTION
The linkage of diverse sets of actors and knowledge systems
across management levels and institutional boundaries often
poses one of the greatest challenges in adaptive management of
natural resources (Berkes 2002, Folke et al. 2005, Ostrom 2005,
Armitage et al. 2007). Bridging organizations offer a means to
improve environmental management outcomes by spanning the
science-policy interface to allow for the effective sharing of data,
information, and knowledge. Bridging organizations are
institutions that use specific mechanisms such as working groups
to link and facilitate interactions among individual actors in a
management setting. By lowering the transaction costs of
cooperation and collaboration through the coordination of tasks,
trust building, and social learning, bridging organizations help
establish communities of practice (Folke et al. 2005, Olsson et al.
2007, Berkes 2009, Crona and Parker 2012). Bridging
organizations can thus play a central role in solving social-
ecological problems through the provision of expert information
and opinion to decision makers (Haas 1992).
This study focuses on working groups at the Center for Ocean
Solutions (COS). COS, a nonprofit organization led by Stanford
University, is an example of a bridging organization that is
focused on linking scientists and mangers within the marine/
coastal management arena at the local, regional, and global scales,
in part through the use of working groups. Working groups
convened by COS aim to engage individuals across academic
disciplines and government agencies to solve specific marine-
related environmental problems. Given that bridging
organizations and participants invest a substantial amount of
resources into designing and implementing working groups,
research concerning the functionality and effectiveness of
working groups and similar bridging mechanisms is required to
see if such initiatives warrant the organizational and individual
investment.
We found that working group functionality is positively associated
with group leadership, not network cohesion as originally
hypothesized. This conclusion is supported both quantitatively
though a social network analysis (SNA) and qualitatively through
web-based surveys administered to working group members.
Although a cohesive network can allow group members to more
easily share information and directly communicate with one
another, it does not necessarily maximize group-level outputs. A
highly centralized network structure, in contrast, allows for a
more effective delegation of group tasks and activities as well as
a better understanding of group goals and objectives.
BACKGROUND
Theoretical context
Bridging organizations can improve environmental management
outcomes by connecting actors at different management levels
and across sectors to promote group decision making. Bridging
organizations are formal organizations that use specific
collaborative mechanisms, e.g., hosting working groups, to bring
together diverse actors (Crona and Parker 2012). Bridging
organizations are organizations that function as an arena for
knowledge coproduction, trust building, sense making, learning,
vertical and horizontal collaboration, and conflict resolution
(Berkes 2009).
Although such organizations vary in size, scope, and
formalization, environmental bridging organizations primarily
facilitate interactions between actors, both individuals and
groups, that might otherwise not communicate (Crona and Parker
2012, Rathwell and Peterson 2012). Working groups are
commonly used as a boundary object to facilitate collaboration
between scientists and decision makers (Guston 2001). Working
groups contribute to the overall management and governance
process through knowledge production and issue framing,
thereby spanning the gap between science and policy.
1Department of Geography, University of Washington, 2School of Marine and Environmental Affairs, University of Washington
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Nongovernmental organizations (NGOs) often act as bridging
organizations within the natural resource governance arena.
Considerable technical, financial, and personnel resources enable
NGOs to effectively link actors across institutional and spatial
boundaries (Mitchell 2009). Working groups within a bridging
organization can facilitate such efforts by providing a formal
mechanism and structure by which actors can interact. The
working group structure used by COS is based on the National
Center of Ecological Analysis and Synthesis (NCEAS) working
group model. Since 1995, NCEAS has aimed to engage a range
of scientific collaborators in the discussion and analysis of theory,
methods, and data primarily through the use of interdisciplinary
working groups (Hampton and Parker 2011). Hampton and
Parker (2011) conducted extensive research on the productivity
of NCEAS working groups and found that the number of group
meetings most influences working group productivity and
scientific impact. Such a collaborative environment is believed to
yield high degrees of trust, limit conflict, and facilitate creativity
(Hampton and Parker 2011). These findings inspired this study
to more closely examine the effects of social relations on working
group productivity through the use of SNA. This study does not
intend to compare results to that of Hampton and Parker since
different methodologies were used.
Center for Ocean Solutions
COS is a nonprofit environmental research organization that has
an overall mission to solve the major problems facing the oceans
and prepare leaders to confront those problems by focusing on
ocean/coastal ecosystems in the natural, physical, and social
sciences. To help achieve these goals, COS has established five
working groups that “synthesize the latest science and policy on
pressing issues to identify critical knowledge gaps and to create a
path for turning that information into policy actions” (COS,
http://web.archive.org/web/20130328043731/http://www.
centerforoceansolutions.org/initiatives/working-groups). As of
2012, working groups included the rapid detection of marine
pathogens, climate change and coral reefs, climate change and
pelagic predators, coastal hypoxia, and social-ecological resilience
in small-scale fisheries.
Working groups are convened to address complex and urgent
issues that no one individual alone has the knowledge to solve.
Individuals are chosen to participate to increase the scope of issue-
specific knowledge and practical solutions to the problem. The
selection criteria for each working group are very similar, thereby
allowing for intergroup comparison despite different group
compositions. Participants are from academia, federal and local
government, and NGOs. Group membership ranges from 12 to
more than 30 participants. We examined four of the five COS
working groups. One working group was not included in this study
because the group has formally concluded its activities; therefore,
comparable measures of social relations among group members
could not be obtained.
METHODS
Although social relations undoubtedly influence collaborative
processes in environmental management, researchers have only
limited empirical studies of how social relations affect the
functionality of bridging organizations (Bodin et al. 2011, Crona
and Park 2012). The use of SNA has the potential to offer valuable
and unique insights into this emerging research area. SNA seeks
to understand the structural variables of social relations, infer
relationships between structural characteristics and various
outcomes, examine connections (i.e., structure), among the
elements (i.e., actors), and locate areas of networks that can be
improved to enhance organizational outputs and outcomes
(Freeman 2004, Prell et al. 2009, Bodin et al. 2011). By exploring
the relational linkages between actors, SNA can quantitatively
and qualitatively analyze the social connections between
individuals, subgroups, and larger social systems (Scott 2000).
We used a mixed-methods approach, incorporating both
qualitative and quantitative social network analysis methods, to
test the primary hypothesis: Working group functionality is
positively associated with cohesive network structures.
This hypothesis is based on the proposition that cohesive
networks should be more effective at generating group-level
outputs, e.g., the achievement of specific goals and objectives. In
relation to this study, if the majority of working group members
are in direct contact with each other, then it is likely that they will
begin to share common ideas and practices (i.e., group
homogeneity) about how best to accomplish their goals and
objectives (Friedkin 1984, Fujimoto and Valente 2012).
Measures
Functionality (dependent variable)
Measuring the functionality or performance of networks is
inherently difficult because networks are composed of many
actors, all whom may have different views of what constitutes a
functional network (Koliba et al. 2011). In our study, functionality
is defined according to the achievement of specific goals and
objectives of each COS working group as stated in working group
proposals. This is a network-level outcome otherwise
unachievable by individual members and, thus, an appropriate
measure of working group functionality (Arganoff 2007, Provan
and Kenis 2007, Sandström and Carlsson 2008). The achievement
of specific goals and objectives of each COS working group was
qualitatively measured from open-ended survey responses and
quantitatively measured from closed-ended survey responses
from working group members. Working groups were regarded as
having low, moderate, or high functionality levels in relation to
each other based on a comparative measurement method
(Sandström 2011). This is an overall interpretation that took into
account both qualitative and quantitative data.
Network structure (independent variables)
Network structure is defined as the relational patterns, such as
structural cohesion, that emerge from social interaction between
two or more actors (Scott 2000, Freeman 2004). The primary
network concepts used in this study to measure structural
cohesion were density and centralization. Given the variety of
definitions of network cohesion, we used Moody and Whites
(2003:107) definition: “A group is structurally cohesive to the
extent that multiple independent relational paths among all pairs
of members hold it together.” These network concepts were
examined across three time domains in the survey instrument:
prior, i.e., before the working group began; current, i.e., when the
survey was administered; and potential future, i.e., desired
relationships after the working group ends.
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Working groups were regarded as having low, moderate, or high
levels of density, centralization, and cohesion in relation to each
other. This comparative measurement method, adopted from
Sandströms (2011) study of local fishery management, is an
overall interpretation based on the structural characteristics of
working group networks.
Density measures describe the degree to which a network is
connected. Specifically, density is the proportion of ties or
connections that are present in given network (Wasserman and
Faust 1994). A higher density value indicates that a network is
more connected. Centralization is another measure used to
examine network-level structure. Centralization measures reflect
the overall integration of a network. Larger centralization values
indicate that a single actor is more central in the network than
the other actors; thus, prominent structural positions are
unequally distributed (Wassermann and Faust 1994, Scott 2000).
Centralization measures are derived from centrality. Centrality
is an actor-level measure, focusing on the structural
characteristics of ties between nodes, which in this case are
individual actors (Bodin and Crona 2009). Centrality can
describe the most important, powerful, and/or prominent actors
located within a network (Scott 2000, Borgatti et al. 2009). This
study only analyzed degree centrality and degree centralization.
Degree centrality, the most basic of all the centrality measures,
is the number and strength of ties an actor has in a network (Prell
2011).
When using density, centralization, and centrality together to
examine structural cohesion, it is argued that a highly dense
network with low centralization is more cohesive than a highly
dense network that also has high centralization. Actors in a more
cohesive network have many ties to other group members and
are not as reliant on central or prominent actors to connect them;
i.e., there are more direct ties between actors (Prell 2011). This
follows Moody and Whites (2003:107) discussion of network
cohesion, in which they state that the “the strongest cohesive
groups are those in which every person is directly connected to
every other person.”
Data collection
Quantitative and qualitative data were collected via web-based
surveys. Surveys were conducted from December 2012 to
February 2013.
Sample design
We analyzed four separate working group networks at COS. From
this point forward, working groups will only be referred to by an
alphabetical letter (A, B, C, and D) for confidentiality purposes.
All working groups formally met between two to four times at
the time the survey was administered.
For the purpose of this study, individuals were considered to be
a member of a working group if they attended at least one of the
working group meetings. Given this criterion, the number of
members for each working group were as follows: A, n = 18; B,
n = 11; C, n = 22; and D, n = 17. Again for confidentiality reasons,
the specific institutional affiliations of individuals within each
network are not presented in this study.
A request to participate in the study, i.e., to have one’s name listed
in the survey instrument, was sent via email to all working group
members. Of these working group members, not all responded
to the requests to be included in the study. Those individuals that
did not respond could not have their name listed in the survey
instrument, in accordance with human subjects protections, so
no data were collected from or about them. Thus, the percentage
of working group members included in the survey for each
working group waa as follows: A, 72%; B, 73%; C, 64%; and D,
94%. The response rate for those individuals that completed the
survey was expectedly lower: A, 56%; B, 64%; C, 59%; and D,
67%. As is discussed in the analysis, specific analysis methods
were used to reliably interpret data sets with low response rates.
Survey design
A web-based survey was administered that consisted of 27 open-
ended and closed-ended questions. The open-ended questions
were intended to elicit qualitative information about the perceived
functionality of the specific working group that each respondent
belonged to. For example, respondents were asked to discuss
aspects of their working group that had most facilitated
communication and collaboration between members. Closed-
ended questions were designed to collect quantitative relational
or sociometric data. Respondents were asked to rate the quality,
intensity, and frequency of certain types of relationships with
every other participant in the survey based on a Likert scale
system. For example, respondents were asked how closely they
worked with group members prior to joining the working group,
how closely they now work with group members, and how closely
they believe they will work with group members in the future once
the working group concludes: 0 = do not know; 1 = not closely
(e.g., casual interactions at professional meetings); 2 = somewhat
closely (e.g., blue ribbon panel); and 3 = very closely (e.g.,
coauthorship). Thus, both directional and valued data were
collected for each working group network.
Attribute data about each respondent were also collected. These
included demographic data such as age, sex, affiliation, and length
of time as group member, the respondents perception of working
group functionality, and the respondents rating of certain
individual qualities of other group members, such as leadership
ability and quality of contribution to the group. Each survey
participant was asked to rate every other participant in the various
categories, as well as their own contribution to and involvement
in the group, on a scale of scale of 1 to 4 with 1 being the lowest
rating and 4 being the highest rating.
Analysis
Qualitative analysis
Qualitative data collected from the questionnaires were analyzed
using a constant comparison method. Collected responses were
compared against each other throughout the research process to
identify key themes relating to group functionality and network
structure (Corbin and Strauss 2008). All responses to the open-
ended survey questions were read multiple times. Notes were
taken during the first reading and then subsequently refined
through subsequent readings until clear themes were defined.
Quantitative analysis
Quantitative network data collected from the survey were
analyzed using the social network software package UCINET
(Borgatti et al. 2002). Density, centralization, and degree
centrality measures were calculated using UCINET functions.
Given a total response rate of 62% of all those who were sent the
survey, data collected on nonrespondents were used in the final
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analysis to increase the group member sample. When
nonrespondents were included in the data set, the percentage of
members included in the survey increased from 56% to 72%, 64%
to 73%, 59% to 64%, and 67% to 94% of Working Group A, B,
C, and D members, respectively. Because of the use of valued and
directional data in this study, density was equal to the average
strength of ties over all possible ties (Hanneman and Riddle
2005).
To increase the validity of the network analysis given the
incorporation of nonrespondent data, only in-degree measures
for centralization and centrality were calculated. In-degree ties
specifically describe the prestige or prominence of an actor within
a network by measuring the degree to which an actor receives ties
from other actors in the network (Hanneman and Riddle 2005).
According to one study (Costenbader and Valente 2003), in-
degree centrality, from which in-degree centralization is then
calculated, is a more stable centrality measure than most others
when respondents do not respond. The study concludes, “Even
at low sampling rates, in-degree had higher correlations between
the actual and the sample network measures than all of the other
centrality measures with the exception of simple eigenvector
centrality” (Constenbader and Valente 2003:291).
RESULTS
Functionality
The survey contained two primary open-ended questions that
pertained to working group functionality: (1) What aspects of the
working group help efficiency and overall functionality? (2) What
aspects of the working group are not helping efficiency and overall
functionality? Respondents were also able to provide additional
comments at the end of the survey. These questions examined the
degree to which group members agreed about the functionality
of their respective groups. In addition, key variables that affect
functionality were also identified through the open-ended
questions. Box 1 lists the most common themes in order of
frequency.
Qualitatively, working Group A was the most functional and
Working Group B was the least functional (Fig. 1). Working
Group A members were most positive in comparison to other
groups about their groups progress on achieving their goals and
objectives and the general functionality of the group.
Respondents overwhelmingly attributed the perceived success of
the working group to strong leadership. Working Group D
members exhibited a high degree of consensus on the goals and
objectives as well as the primary problems of the group, notably
a geographically dispersed membership and, thus, difficulty in
organizing frequent face-to-face meetings. Although Working
Group D had only been recently established at the time of survey,
group members were positive about their progress toward
achieving the groups goals and objectives. Working Group C
members were less positive about the groups productivity and
functionality because of a noted lack of communication between
members, but still felt that the group was achieving its goals and
objectives. Finally, Working Group B members exhibited the least
amount of consensus about the functionality of their group,
specifically what the goals and objectives of the group were.
Respondents in Working Group B often referred to a lack of
coordination and unclear member roles as a central problem.
Box 1
1. Functionality regarding leadership
a. Strong leadership is essential for group productivity.
b. Leadership keeps the group focused on specific goals and
objectives.
c. Lack of leadership or communication with leader(s) hinders
the groups ability to move forward on projects.
2. Functionality regarding face-to-face interactions
a. Face-to-face interactions are critical for building and
maintaining working relationships.
b. A geographically dispersed group membership makes regular
face-to-face meetings difficult.
3. Functionality regarding communication/interaction
a. Productivity is hindered by long time spans between
communications.
b. Group loses momentum and focus because members are often
too busy and overextended with other responsibilities.
c. Low meeting attendance occurs as a result of a lack of
communication and momentum.
4. Functionality regarding institutional/organizational support
a. Support from the hosting institution/organization reduces
administrative burden on group members.
b. Members no longer need to focus effort on meeting logistics
and can devote attention toward accomplishing the groups goals
and objectives.
Fig. 1. Comparison of quantitative (y-axis: degree that the
group is meeting its goals on a scale of 1 = not at all to 4 =
very) and qualitative (x-axis) measures of working group
functionality.
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The achievement of goals and objectives was also measured
quantitatively. Survey respondents were asked to rate the degree
to which they felt their respective working group was meeting its
goals and objectives (Fig. 1). Working Group A had the highest
percentage of respondents (80%) who believed the their working
group is very much achieving its goals and objectives, whereas the
B and C working groups had the lowest percentage of
respondents, 29% and 23%, respectively, who felt their working
group is very much achieving its goals and objectives.
Based on the qualitative and quantitative analysis, Working
Group A was regarded as the most functional working group,
having the greatest degree of positive consensus about the group’s
progress toward achieving its goals and objectives. Working
Group B was interpreted to be the least functional, with the lowest
proportion of members that believed the group is very to
moderately successful in achieving its goals and objectives and
with many members unclear about what the goals and objectives
were. The functionality of working groups C and D was not as
clearly identified. Although Working Group D respondents were
much more positive about group progress than Working Group
C members, there were many noted problems related to the
functionality of these groups, including a lack of communication
between members. Thus, working groups C and D are regarded
as moderately functional.
Network structure
Figure 2 shows how that the density of the workings groups,
except for Working Group B, increased across all three time
domains. Working Group B had the highest current density value
(2.74), whereas Working Group A had the lowest current density
value (1.58). The high-density values for Working Group B may
be a result of small network size (n = 11; Scott 2000). The positive
changes in the majority of density values indicate that working
group networks are becoming more connected through time and
stronger working relations are forming.
Fig. 2. Working group density over time.
Figure 3 shows that working groups A, C, and D became less
centralized from when the working groups were established to the
time of the survey, i.e., from the prior to the current time domain.
This indicates that the networks are becoming more integrated by
not relying as much on prominent members in the group to
connect group members. Working Group B had the lowest current
centralization value (7.94%), and the Working Group A had the
highest current centralization value (27.53%). Interestingly, aside
from Working Group A, centralization values actually increased
from the current to the future time domain. This suggests that
there are certain individuals known to be prominent within the
working group because of higher in-degree centrality values to
whom members would rather be connected to and work with in
the future. Thus, desired future connections became unequally
distributed within the networks. Attribute data indicate that most
of these desired future connections were to older males who are
more prominent in their field or are directly associated with COS
and its partners. Prestige or prominence outside of the working
group setting seemed to be associated with high future in-degree
centrality values.
Fig. 3. Working group centralization over time. Centralization
measures reflect the overall integration of a network. Larger
centralization values indicate that a single actor is more central
in the network than the other actors and, thus, prominent
structural positions are unequally distributed.
Table 1 shows the cohesiveness of all four working groups.
Working Group A was regarded as the most cohesive network,
whereas Working Group B was regarded as the least cohesive
network. Working groups C and D were interpreted to be
moderately cohesive because they have both density and
centralizations values that fell between those values for working
groups A and B.
Table 1. Assessment of current working group density,
centralization, and cohesion.
Working
Group
Density Centralization Cohesion
A Low High Low
B High Low High
C Moderate Low Moderate
D Moderate Moderate Moderate
Figure 4 shows the mean individual in-degree centrality of the
four working group. The mean individual in-degree centrality for
the working groups increased across all three times domains, with
the exception of Working Group B. This indicates that on average
individual group members formed stronger working relations
with one another and, based on the working group experience,
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would like to more closely work with each other in the future.
Working Group B only showed a slight increase from the current
to the future time domain and no change from the prior to the
future time domain. This relative lack of structural network
change is most likely because group members knew each other
relatively well prior to joining the group. The working group
activities, therefore, have not had a significant role in fostering
new relationships, unlike those of working groups A, C, and D.
Fig. 4. Working group centrality over time. Centrality can
describe the most important, powerful, and/or prominent
actors located within a network.
DISCUSSION
The primary hypothesis, i.e., working group functionality is
positively associated with cohesive network structures, was not
supported (Table 2). Working Group A, although the most
functional group, was the least cohesive network. In contrast,
Working Group B was the most cohesive network but the least
functional. Even though previous work has shown that the
measures used in this study provide an accurate portrayal of the
whole network based on a partial network sample (Constenbader
and Valente 2003), these results should be cautiously interpreted
given that each working group network was only partially
sampled.
Table 2. Comparison of working group functionality and
cohesion.
Working Group Functionality Cohesion
A High Low
B Low High
C Moderate Moderate
D Moderate Moderate
Figures 5 and 6 show the current network structures of working
groups A and B, respectively. It is important to recognize that in
a highly functional network, as exhibited by Working Group A
(Fig. 5), there is a clearly defined leadership structure. Although
a few individuals are on the periphery of the network, most
notably Nodes 11 and 23, one member in particular, Node 42,
occupies a very prominent position in the network. This is
evidence of a centralized network structure, indicating strong
leadership. Survey respondents in Working Group A repeatedly
identified Node 42 as someone who is able to effectively
communicate and delegate group tasks.
Fig. 5. Working Group A (most functional). Nodes represent
individual working group members. Node size represents the
member’s in-degree centrality. A larger node indicates a higher
in-degree centrality value for that member and, thus, more
prominence within the network.
Fig. 6. Working Group B (least functional). Nodes represent
individual working group members. Node size represents the
member’s in-degree centrality. A larger node indicates a higher
in-degree centrality value for that member and, thus, more
prominence within the network.
In contrast, Working Group B (Fig. 6), although well connected,
does not have a clear leadership structure. Most of the working
groups members occupy equally prominent positions within the
network. There is a lack of centralization. Survey respondents in
Working Group B indicated that the absence of a clear leader has
led to poorly delegated member tasks and overall uncertainty
surrounding working group goals and objectives.
We concluded that centralization or group leadership is a better
predictor of working group functionality than cohesion. These
findings are supported by other network studies that show that
leadership helps coordinate collective action and maximize group
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benefits in an environmental management setting (Crona and
Bodin 2006, Bodin and Crona 2008). Leaders are also
instrumental in the self-organizing process because they often
possess special skills that allow the group to accomplish initiatives
(Olsson et al. 2004). Furthermore, effective leadership in a
network setting is marked by the ability to softly guide the group
rather than force or coerce the group into certain actions. In a
nonhierarchical process, such as is found in COS working groups,
there is no top-down command and control structure. This type
of leadership is often essential for maintaining a positive group
dynamic (Arganoff 2007).
Within environmental bridging organizations, key individuals can
serve as liaisons between disciplines to facilitate social learning
and help resolve group issues (Crona and Parker 2012). As was
also found in our study, these leaders are well-respected and
established individuals within the scientific and policy
community. Group members were more likely to work with these
key individuals in the future, thereby confirming the impactful
role that leaders have in a working group setting. Overall, leaders
with the ability to communicate across disciplinary boundaries
are essential for bridging organizations operating at the science-
policy divide.
It is also important to recognize that all four working groups
underwent some form of structural network changes regardless
of functionality. Actor-level in-degree centrality values increased
from the current to the future time domain in all four networks,
signifying that group members are forming closer working
relationships. Ideally, these strengthened working relationships
will continue once the working groups conclude. In addition,
working groups A, C, and D are becoming more cohesive through
time. Understanding the value of centrality for working group
functionality is important because for specialized environmental
topics, working groups often consist of people who are actively
working together (Crona and Parker 2012). Organizers need to
understand whether this model for creating working groups is
most effective.
From a bridging organization theory perspective, our results
indicate that the working groups are successful in facilitating
interactions between individual actors. Whether or not all
working groups are achieving their goals and objectives, the
working group process is acting as an effective bridging
mechanism by positively changing the relational patterns between
actors. The technical, financial, and personnel resources that COS
is providing to these working groups are creating network
structures that allow for more interdisciplinary dialogue and a
desire for future collaboration. We found that, for the most part,
these structural changes lead to increased interactions and
stronger collaborative relationships among working group
members. When forming working groups, bridging organizations
should consider the degree of familiarity between group members.
Prior working group familiarity should be high enough to ensure
some level of compatibility but low enough that the working
group has the potential to be transformative in forming new
relationships.
Future research should aim to measure bridging organization and
working group functionality by policy-relevant outcomes, not
only structural network qualities and group outputs. The ultimate
purpose of these institutions is to produce information and
knowledge that can be used to influence policy decisions. This
includes reducing knowledge gaps as well as framing complex and
dynamic problems.
At this stage, however, COS working groups are presumably too
young for group outputs to influence and inform environmental
policy in a measurable way. At the time of this study, the oldest
of them had only been running for two years. Nonetheless, the
findings presented in this study should still be able to improve the
functionality of COS working groups and similar task-oriented,
interdisciplinary teams in the environmental science fields. Box 2
lists three primary recommendations to improve network
functions of such groups. The overall impact of these
recommendations, however, may vary from one organization to
another given that we did not specifically examine group diversity
and institutional factors.
Box 2
1. Have a defined leadership structure in which group leaders are
also accessible and in regular communication with all working
group members. Both qualitative and quantitative analysis reveals
that more centralized network structures facilitate group
interactions by having clearly established short-term and long-
term objectives.
2. In the absence of face-to-face group meetings, use other means
of communication so group members can discuss the progress of
the group in achieving its goals and objectives, thereby
maintaining focus and momentum. This will help build and
maintain working relationships between members, which survey
respondents believed is an important component of working
group functionality. Use of other means of communication will
require logistical support from the bridging organization to
reduce the burden on working group members, most of whom are
already busy with other professional commitments.
3. Maintain group focus through defined individual roles. Each
member should know what he or she is working toward as well
as what other members are contributing to the group. Defined
member roles, with specific duties and tasks to accomplish, will
help the group maintain focus and productivity even if long time
lapses remain between face-to-face meetings. This will require a
defined leadership structure to effectively delegate and monitor
individual tasks.
COS and other environmental bridging organizations can apply
these three recommendations to significantly improve working
group functionality. First, each working group’s scoping
document should clearly identify a group leader and define the
group leaders responsibilities to ensure that these key individuals
have the necessary authority to resolve any group issues and
maintain forward progress. Second, group members should
decide, preferably soon after establishment of the group, the
frequency of communication that they think will be required to
achieve the stated goals and objectives. Accordingly, group leaders
should possess the ability to change the frequency of
communications to improve functionality. Last, group leaders
should assign specific tasks to individual members or have a
process in which members volunteer to undertake certain tasks.
Ecology and Society 20(2): 16
http://www.ecologyandsociety.org/vol20/iss2/art16/
This can help resolve any ambiguity surrounding group member
contributions and institute a certain level of individual
accountability.
This study contributes to understanding the role that
environmental bridging organizations like COS play in spanning
the science-policy interface by mapping network structures
through time, analyzing select network variables, and examining
their association to group functionality. Although this project has
scholarly value in examining social networks within a natural
resource management context, the studys results can also be used
by COS, and potentially by similar organizations, in an applied
manner to improve the working group process and, ultimately,
the impact bridging organizations have on environmental policy.
Responses to this article can be read online at:
http://www.ecologyandsociety.org/issues/responses.
php/7541
Acknowledgments:
Thank you to the Center for Ocean Solutions and working group
members for their participation in this study.
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