Collaborative Network Topology Adaptation: Creating new Synergies
Working Paper
January 8, 2009
Dr. Alex Bordetsky
Professor
Naval Post Graduate School
Richard Bergin
Visiting Associate Professor
Naval Post Graduate School
Yaara Bergin
Research Associate
Naval Post Graduate School
1
Background
How collaborative networks morph and create synergies during a response effort is
shaped by a multitude of factors. Some of these factors are shaped by the trajectory of a
collaborative network topology and the [creation or presence] of Strong links (Jack 2005), while
others are influenced by Weak Ties (Baribasi 2002). However shaped or influenced, morphism
of collaborative network topologies often takes place during emergent events where a team
attempting to collaborate in real-time. While each response effort is unique, they all are all
emergent, posses various levels of uncertainty, and each experiences changes in its collaborative
network topology that creates varying degrees of synergy.
Note the changes in the collaborative network topology that occur during the following
scenario. Originating from Canada and destined for a port in the State of New Jersey, a routine
shipment of radioactive material (Cobalt-60) goes missing in New York State. New York State
Police assume incident command and issue a be-on-the-lookout for (BOLO) alert through normal
channels. This alert provided real-time, secure information dissemination to numerous local and
state organizations. The missing truck was subsequently found abandoned and empty in rural
New York. As incident commander, the New York State Police then convened and managed an
online collaborative session to coordinate the region-wide search for the missing shipment. This
session connected various local, state, and federal organizations using the video-teleconferencing
capability. Shortly after the discovery of the missing truck, a radiological detection sensor was
triggered at a Lincoln Tunnel toll plaza in New Jersey. An officer directed an unmarked
commercial van to a secondary screening point, while a sensor alert was automatically forwarded
to Lawrence Livermore National Labs for validation by subject matter experts.
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Secondary screening by Police confirmed the presence of Cobalt-60 and a shipping
container like the one depicted during the online collaborative session conducted by the New
York State Police. The response team was then alerted to the discovery of the container and a
second online collaboration session was called, this time by Port Authority Police as the incident
commander. As team members joined the online collaboration session to be briefed on this new
incident, they witnessed the driver of the vehicle being taken into custody and emergency
services units deploying, to include a K-9 explosive detection team, a commercial vehicle
inspection team with an advanced mobile radiological isotope identifier, and the police
helicopter. Real-time video feeds from Lincoln Tunnel cameras and the Police helicopter
hovering above provided those online with the ability to view streaming video of the incident
while it was underway.
Secondary inspection results, to include confirmation of the isotope, explosive trace
detection findings from an ion scan identifier, and direct visual observation of the K-9 alerting on
the van, provided real-time situational awareness simultaneously to multiple jurisdictions,
agencies, and levels of government. What began as a simple high-jacking in New York and a
routine commercial vehicle HazMat stop in New Jersey, quickly evolved into an interdiction of a
potential terrorist incident involving a possible radiological dispersal device (RDD). Additional
agencies were added to the online collaborative session and its focus quickly shifted to
containment of the incident, isolation of the scene, and the immediate sharing of intelligence
gained from the stop. The incident commander briefed and provided pictures of the driver in
custody, the vehicle, and its contents. He then began coordination of a perimeter security and
traffic diversion plan, and requested additional resources to include HazMat response and bomb
squad teams. (Adapted - Anthony 2006)
3
Understanding and measuring how changes in collaborative network topologies create
synergies during a response to a critical event is extremely important, but under-researched.
There are no existing frameworks or models that fully describe or measure variation in the
number and effect of Weak and Strong Ties generated by collaborative network morphism.
Literature Review
The review of literature defines synergy, discusses synergy as a systems thinking concept,
provides a context for study; Maritime Operations Interdiction (MIO) a large-scale
networking initiative, and describes prior work on the creation of Strong links and synergy.
Synergy
Webster’s dictionary defines synergy as simply “a mutually advantageous conjunction or
compatibility of distinct business participants or elements as resources or elements” (MerriamWebster 2008). Corning provides a typology for synergy that includes: Synergies of Scale (“A
large number of participants may produce combined effects that could not be achieved by any
individual, or even a smaller group”), Synergies of Division of Labor (“Better defined as the
combination of labor”), Synergies of Functional Complementarities (“Closely related to a
division/combination of labor is the concept of a functional complementarily. For instance, some
[team members] form symbiotic relationships with. They do not divide up a single task but
provide complementary functions”), Synergies of Information Sharing and Collective
Intelligence, and Synergies of Tool and Technology ‘symbioses’ (“In essence, tools and
[technology] represent a major form of synergy – a cooperative effect (or effects) that are not
otherwise attainable”) (Corning 2007).
For this study, I considered all five types of synergies mentioned in Corning’s typology;
Synergies of Scale, Synergies of Division of Labor, and Synergies of Functional
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Complementarities and adopted Klaus Krippendorff’s definition of synergy that is contained in
his unpublished report, “A Dictionary of Cybernetics.” “It is derived from the holist conviction
that the whole is more than the sum of its parts and, because the energy in a whole cannot exceed
the sum of the energies invested in each of its parts (see first law of thermodynamics), that there
must therefore be some quantity with respect to which the whole differs from the mere
aggregate. This quantity is called synergy. In practice, synergy is mostly a negative quantity
owing to the fact that all complex organisms consume energy merely for maintaining its own
structure. More loosely, synergy refers to the benefits of collaborative as opposed to individual
efforts.” (Krippendorff 1986)
Synergy as a Systems Thinking Approach
For the purposes of this study, which focuses on Synergies of: Scale, Division of Labor,
Functional Complementarities, Synergies of Information Sharing and Collective Intelligence,
and Synergies of Tool and Technology (Corning 2007), I will define the “benefits of
collaborative” (Krippendorff 1986) as the synergy created through the progressive creation of
Weak Ties and use of new Strong links within a changing collaborative network topology.
This definition of “benefits of collaborative” is supported by Capra’s description of
Systems Thinking, Gravnovetter’s work on Strong and Weak Ties (Gravnovetter 1973), and
Baribasi’s discussion on the role of Weak Ties (Baribasi 2002). “According to the systems view,
the essential properties of an organism, or living system, are the properties of the whole, which
none of the parts have. They arise from the interactions and relationships among the parts.
These properties are destroyed when the systems is dissected, either physically or theoretically,
into isolated elements. Although we can discern individual parts in any system, these parts are
5
not isolated, and the nature of the whole is always different for the mere sum of its parts” (Capra
1996).
Baribasi suggests that “Weak Ties play a critical role in our ability to communicate with
the outside world.” Additionally, “The Weak Ties, or acquaintances, are our bridge to the
outside world, since by frequenting different places they obtain their information from different
sources than out immediate friends” (Baribasi 2002).
Capra, Gravnovetter, and Baribasi provide us with a theoretical foundation for exploring
how synergy as a systems thinking approach may be applied to understanding the collaborative
benefits obtained by adaptive collaborative network topologies. In seeking to better understand
and measure the collaborative benefits, a large-scale networking initiative test bed was selected
to conduct a large-scale experiment.
Maritime Operations Interdiction (MIO): Large-scale networking initiative
Maritime Operations Interdiction is defined as Visiting, Boarding, Searching, and Seizing
(VBSS) operations. “These operations are conducted as a part of the maritime law enforcement
policy of each country inside their respective territorial waters or as a part of the homeland
security requirements as they are mandated today by the global war against terrorism. They are
often conducted by allied maritime forces in international waters seeking to prevent the transfer
of radiological or bio-chemical materials, as well as possible terrorists among the crew
members.” (Stavroulakis 2006)
MIO is a large-scale networking initiative focused on studying high-value target tracking
and emergency response coordination between geographically distributed teams and subject
mater experts seeking to enhance situational awareness (SA) and coordinate decision making.
6
The MIO experiments used the Tactical Network Topology (TNT), which is a large-scale
experiment test bed developed at NPS by Dr. Alex Bordetsky that is used to design and test
“wireless tactical network topologies that provides real-time access to unmanned aerial vehicles
(UAVs), unmanned ground vehicles (UGVs), unmanned surface vehicles (USV) and mobile
Special Operations Command (SOCOM)/Marine Corps units and for exploring collaboration for
high-value target tracking operations” (Bordetsky and Friman 2006). The test bed is also used
for “remote integration of UAVs/USVs in MIO or other scenarios, such as non-combatant
evacuation experiments, where flying UAVs or operating UGVs is not feasible” (Bordetsky and
Friman 2006).
Sitting on top of the TNT network is a set of collaborative technologies that include:
Groove (an application suite that contains a discussion board and chat capabilities), a file transfer
application, a Task Manger (“used to provide participants with a mechanism to monitor the
progress of events”), a Situation Awareness (SA) agent (“that provides geographic positions of
the assets and status of the network links for the mobile nodes”), an EWall system (“developed at
MIT and adapted to MIO operational picture by NPS, used to monitor information alerts.”
(Bordetsky and Friman 2006)), and a Voice Over IP (VOIP) application. This suite of tools
enabled participants to remotely monitor assets and the progress of events.
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Prior work on Weak links, Strong links, and synergy in information, organizational, and
network science research
This portion of the literature review focuses on providing support for a framework that
measures the variation and effect of Weak and Strong Ties on synergies resulting from the
morphism of collaborative network topologies during a critical event.
Sarah Jack’s (2005) research demonstrated that “Strong Ties provide the mechanism to
invoke 'Weak' Ties, represented by nodes operating in a wider social context”. Chituc &
Azevedo (2005) suggest that a dynamic transformation in the environment increases the
collaboration among different and geographically distributed entities that effectively combine the
most suitable set of skills and resources temporarily in order to achieve a common goal,
generating the so-called collaborative networks. Saiz, Rodríguez & Bas (2005) suggest that
cooperative interaction maximizes the combined capacities to reach the strategic objectives
through integrated solutions to provide efficiency and effectiveness in the operations meant to
take care of customers’ needs. Granovetter (1983) suggests that “Weak Ties provide people with
access to information and resources beyond those available in their own social circle; but Strong
Ties have greater motivation to be of assistance and are typically more easily available.” “Pool
(1980) argues that whether one uses Weak or Strong Ties for various purposes depends not only
on the number of Ties one has at various levels of tie strength but also on the utility of Ties of
different strength. Thus people for whom Weak Ties are much more useful than Strong Ties may
still be constrained to use the latter if Weak Ties make up an extremely small portion of their
contacts; conversely, one for whom Strong Ties are more useful may be socially isolated and
8
forced to fall back on Weak ones. Thus the analytic task is to identify factors affecting these
variations.” (Granovetter 1983).
Table 1 - (Weak and Strong Ties: Variation and Effect) on the next page summaries the
current literature on the variation and effects for each of five types of synergy: Scale, Division of
Labor, Functional Complementarities, Synergies of Information Sharing and Collective
Intelligence, and Tools and Technology
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Synergies of Scale
Synergies of Division of Labor
- Specialized activiTies that can be
mastered by a machine or human
that collectively create higher
levels of output as a whole that
would the production of all
specialized activiTies by one
entity.
Synergies of Functional
ComplementariTies
- “people form symbiotic
relationships, they do not
divide up a single task but
provide complementary
functions” (Corning 2007)
Synergies of Information
Sharing and Collective
Intelligence
“A diverse collection of
independently-deciding
individuals is likely to make
certain types of decisions
and predictions better than
individuals or even experts
(Surowiecki, James (2004).
Synergies of Tool and
Technology
Low – High
Low – High
Low – High
Low – High
Low – High
[Formation] of higher
numbers of Weak Ties
facilitate gaining access
to wider range and
number of resources.
“Weak Ties play a
crucial role in our ability
to communicate with the
outside world. (Baribasi
2002)
[Forming]of higher numbers of
“Weak Ties facilitate combining
the most suitable set of skills and
resources temporarily in order to
achieve a common goal” (Chituc &
Azevedo, 2005).
Formation of higher
numbers of Weak Ties
increases the diversity of
thought and opinion
resulting in synergies of
Information Sharing and
Collective Intelligence
(Surowiecki, James (2004).
Formation of higher
numbers of Weak Ties may
be enabled by open
collaborative network
topologies technology
infrastructure
Low – High
Low – High
Low – High
Low – High
Low – High
More frequent use of
“Strong Ties facilitate
cooperative interaction that
maximizes the [synergy] of
combined capacities to
reach the strategic
objective” Saiz, Rodríguez
& Bas (2005)
More frequent use of
Strong Ties may result in
group think and reduce
synergies of Information
Sharing and Collective
Intelligence
(Surowiecki, James (2004).
More frequent use of Strong
Ties may be re-enforced by
a closed collaborative
network topology
technology infrastructure
- “A large number of
participants may produce
combined effects that
could not be achieved by
any individual, or even a
smaller group” (Corning
2007)
Weak
Ties
Strong
Ties
More frequent use of
“Strong Ties provide a
mechanism to invoke
'Weak' Ties” (Jack 2005)
thus increasing the
potential for Synergies
of Scale.
TBD
“Perhaps the most important
source of weak ties is the division
of labor, since increasing
specialization and interdependence
result in a wide variety of
specialized role relationships in
which one knows only a small
segment of the other's personality.”
Granovetter, M (1983),
TBD
Table 1 – Weak and Strong Ties: Variation and Effect
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“tools and [technology]
represent a major form of
synergy – a cooperative
effect (or effects) that are
not otherwise attainable”)
(Corning 2007).”
Proposed application of synergy to Large Scale Collaborative Network Topologies:
Maritime Operations Interdiction (MIO)
Considering prior studies on changing collaborative network topologies, Strong and
Weak Ties, Corning’s synergy typology, and MIO, a framework is developed to explore the
variation and effect of synergy that is created through the formation of Weak Ties and the
creation of Strong Ties during a response effort. The TNT MIO test bed collaborative
environment provides an emergent environment where collaborative network topologies
experience morphism and exhibit various level of synergy associated with scale, division of
labor, and functional complementarities.
In this study, I will develop and test a new model that describes and measures the
variation in synergy that occurs when a collaborative network topology experiences morphism
due to emerging patterns in team collaboration when responding to an event. Specifically, I will
be seeking to measure synergies created from the formation of Weak Ties and the use of new
Strong Ties that occur during an MIO.
When measuring synergies, Busi & Bititci (2006) suggest considering; How multiple
individual measures can be aggregated to give an overall picture of the collaborative enterprise
performance; How can a [team] that belongs in more than one collaborative enterprise have one
single measurement system.
This study will focus on the formation of Weak Ties and the use of Strong Ties that
occurs during an “emergency response where coordination between geographically distributed
teams and subject mater experts [spanning various first responder disciplines] seeking to enhance
Situational Awareness (SA) phase as the primary objective” (Bordetsky and Friman 2006). For
the purposes of this study, the formation of Weak links and the use of Strong links will be
considered an end-state once a collaborative network has completed the process of morphism
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that results from seeking to enhance situational awareness. This approach is limited in that it will
not capture MIO as a whole, or the variation in synergy that occurs while a collaborative network
topology is changing from one form to another. The end-state analysis, however, will provide a
holistic view of the synergies created while seeking to enhance situational awareness.
Design
The design of a study on a large-scale networking initiative such as TNT MIO include a
description of the design components, desired relationships between those components, a review
of prior research on possible prototypes or relevant parameter-criteria space models, a proposed
parameter-criteria space framework for a desirable system model, a proposed multi-criteria
model for an adaptive collaborative network topology, and a Pareto set for the expected holistic
model.
Components:
The selected networking environment may be described as an adaptive collaborative
tactical network topology created to facilitate information sharing, knowledge sharing, and
decision making between nodes via a set of links. The building blocks consist of nodes that are
comprised of various individuals, teams, and organizations. The links are defined as
communication channels that take the form of either Weak or Strong Ties, are connected for a
particular duration of time, utilize a particular technology platform, and may be counted in terms
of the number present in an adaptive network topology over a particular period of time. For this
paper, the Tactical Network Topology (TNT) Maritime Interdiction Operations (MIO) 08 – 04
field experiment “Networking and Interagency Collaboration on Maritime-Sources Nuclear
Radiation Threat” (TNT MIO 08-04 2008) configuration will be used to provide a less abstract
and detailed description of the actual components (nodes and links) considered in this study.
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Figure 1 - “Radiological Interdiction and Command Center Communications” (TNT – MIO 0803 2008) below provides a visual representation of the expected nodes and links at the beginning
of the field exercise.
Radiological Interdiction and
Command Center Communications
Agency
Vessel
Waterside
Client
Wireless
Camera
Dat
Wir
el
a Fe
ess
Target
Vessel
Detector
Vessel
or
ns t
Se tpu
Ou
NPS
TCP-IP
Demo
Network
ta
Da
e
Fe
Agency
Ops
Center
Wireless
JSAS
TCP-IP
Network
Wir
el
s
les
ire
W
RadSensor
Boarding
Party
less
Wire
ed
Mobile
Client
ess
d
ss
le
ire
W
Wir
eles
s
Camera
Analytics
Lab
Figure 1 “Radiological Interdiction and Command Center Communications”
Relationships:
For this study, the formation and dissolution of Weak Ties and the use or dissolution of Strong
Ties are considered to be the desirable dynamic relationships between nodes that allow for the
modeling of synergy that occurs with an adaptive collaborative network topology. “The
"strength" of an interpersonal tie [within an adaptive collaborative technology network is defined
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as] a linear combination of the amount of time, the emotional intensity, the intimacy (or mutual
confiding), and the reciprocal services which characterize each tie” Granovetter (1973).
Montgomery suggests defining Strong Ties a social relationship between two agents that is
repeated over time (e.g. members of the same family or very close friends) and Weak Ties as a
transitory social encounter between two persons. (Montgomery, J.D., 1994). Calvo, Verdier, and
Zenou state that “by definition Weak Ties are transitory and only last for one period” (CalvoArmengol, Verdier, and Zenou 2007) (Montgomery, J.D. 1994). For the purposed of this study,
Weak Ties will be defined as those that did not exist prior to a particular scenario that is part of a
field study—specifically, where pre-existing reciprocal services between two nodes did not exist
prior to the TNT MIO field studies. Strong Ties will be defined as those that existed prior to the
field experiment—in particular, the TNT MIO field experiments have been repeated over time,
and have demonstrated reciprocal services which characterize each Strong Tie.
Prior Research on Collaborative Network Adaptation
Prior research on how adaptive collaborative networks morph and create synergies may
be grouped into three categories: Adaptive Structuration Theory (AST), Collaborative Capacity,
and Virtual Team Performance.
Adaptive Structuration Theory
The literature on Adaptive Structuration Theory (AST) provides “a viable approach for
studying the role of advanced information technologies in organization change. AST examines
the change process from two vantage points: (1) the types of structures that are provided by
advanced technologies [such as collaborative network topologies], and (2) the structures that
actually emerge in human action as people interact with these technologies” (Desanctis and
Poole 1994). Majchrzak further explores the relationship between social structure, technology in
14
use, and adaptation. “The structuring of technologies in use refers to the processes through which
users manipulate and reshape their technologies to accomplish work and the ways in which such
actors draw on the particular social contexts within which they work” (Majchrzak, Rice,
Malhotra, King, Ba, S.2000). The process of reshaping technology within the context of
adaptive collaborative network topologies may be understood as the creation and use of Weak
Ties and the use and dissolution of Strong Ties. “This dynamic adaptation processes may take
the form of a series of cycles of misalignments, followed by alignments, followed by more but
smaller misalignments” (Leonard-Barton’s (1988) or a set of “discontinuities that occurs during
brief windows of opportunity which open the constraint set” (Tyre and Orlikowski 1994). Taken
within the context of adaptive collaborative network topologies, both models (cycles of
misalignment and windows of opportunity) describe how nodes might adapt to changes in the
environment that impact the efficacy of existing Ties.
Virtual Team Performance and Collaborative Capacity
The literature on Virtual Teams provides a comprehensive set of constructs that predict
team performance in a virtual environment. Many of theses constructs were developed to
measure an individual’s or in this study, a nodes’ collaborative capacity. Collaborative capacity
for this study is defined as those individual characterizes that enable the creation and synergistic
use of Weak Ties and the synergistic use of existing Strong Ties. Those characteristics include
individual trust-based Social Capital, Expertise Location, Goal Similarity (congruence),
Anticipation of Value, and Access to Parties.
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Trust-based Social Capital
Coleman (1998) proposes three forms of social capital: obligations and expectations,
information channels, and social norms. Trust-based Social capital is formed through the
combined effect of interpersonal trust and social norms. When considering individual nodes in
an adaptive collaborative network topology, higher levels of interpersonal trust increase the use
and effectiveness of Strong Ties in creating various types of synergies.
Expertise Location
Farja and Sproull (2000) demonstrated in their research that “expertise coordination
shows a Strong relationship with team performance that remains significant over and above team
input characteristics, presence of expertise, and administrative coordination” (Faraj and Sproull
(2000). Their research suggests that individual node awareness of expertise location through
Weak and Strong Links provides synergy that exceeds the sum of the parts or “team input
characteristics.”
Goal Congruence
Goal congruence, or goal similarity, is the agreement of actions with team, group, or
organizational goals. Jehn’s (1995) research showed that goal similarity is positively associated
with individuals' satisfaction and intent to stay in the group. In terms of adaptive collaborative
networks, intent to stay together translates to an increased use of existing ties. As the level of
goal congruence increases, the use of Strong Ties increases. As the number of Weak Ties
increases, the level of goal congruence decreases, possibly resulting in a loss of synergy, and
ultimately, a realignment resulting in a reduction in the creation of newly formed Weak Ties and
a movement back toward higher use of Strong Ties.
Anticipation of value
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Anticipation of value “refers to a team member’s expectations about value creation as a
result of combining and exchanging recourses with each other” (Nahapiet and Ghoshal 1998).
“The causal relationship between group success-failure and subsequent attitudinal variables
(satisfaction and organizational commitment) is positively mediated by efficacy and outcome
expectancy variables” (Riggs, M., & Knight, P. (1994). Higher levels of anticipation of value
suggest suggests that individual team member or nodes in an adaptive collaborative network
topology expect to create various synergies through the creation of newly formed Weak Ties and
the use of Strong Ties that facilitate sharing resources.
Access to Parties
Access to parties refers to the opportunities to make knowledge combination and
exchange among team members (Nahapiet and Ghoshal 1998). “Beccara-Fernandez and
Sabherwal’s research showed that the combination and externalization process [creation and use
of newly formed Weak Ties], but not socialization and internalization process [use of Strong
Ties] affect perceived knowledge satisfaction “(Beccara-Fernandez and Sabherwal (2001).
Absorptive Capacity
“Absorptive capacity refers to the ability to recognize the value of new knowledge and
information, and to assimilate and use it” (Cohen and Levinthal (1990). Szulanski’ study
showed the major barriers to internal knowledge transfer to be knowledge-related factors such as
the recipient’s lack of absorptive capacity (0.54), causal ambiguity (0.34), and an arduous
relationship between the source and the recipient (0.33) (Szulanski, G. (1996). Szulanski’s
finding on factors constraining absorptive capacity suggest that individual nodes within an
adaptive collaborative network topology are constrained in terms of level of synergy that may be
17
obtained through concurrent Weak and Strong Ties. Miller’s magic rule of seven ±1 also
suggests that node capacity is constrained by various human cognitive factors.
Team performance is measured in terms the levels of synergies of Scale, Division of
Labor, Complementarities, Information Sharing and Collective Intelligence, and of Tool and
Technology achieved during a particular scenario, as affected by the adaptation (creations of
newly formed Weak links and the use of existing Strong links) of a collaborative network
topology. For this study, SA Capacity and Collaborative Capacity, Trust-based Social Capital,
Swift Trust, and Goal Congruency will be measured to evaluate how they moderate the creation,
dissolution, and use of Weak and Strong Ties, and how this generates various forms of synergy
in an adaptive collaborative network topology.
Proposed parameter-criteria space framework for a desirable system model
Considering the existing literature on AST, Collaborative Capacity, and Virtual Teams,
and the literature on interpersonal Ties (Weak and Strong links), a parameter-criteria space
framework was developed to propose a set of synergistic relationships between node capacity
and Weak and Strong Ties. Node capacity is measured in terms of human cognitive channel
capacity, or SA Capacity and Collaborative Capacity, which for this study is defined as the
individual node levels of Trust-based Social Capital, Swift Trust, and Goal Congruency. Table
2, “Node Capacity, Weak and Strong Ties,” illustrates a set of proposed relationships related to
node capacity and link type.
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Node Capacity
Links
Weak
Links
Strong
Links
Situational
Awareness
capacity
Collaborative
Capacity
Collaborative
Capacity
Collaborative
Capacity
Collaborative
Capacity
- Swift Trust
-Interpersonal
Trust
As the level of
Interpersonal
Trust increases,
the number of
Weak Ties
decreases.
- Goal
Congruence
As the number
of Weak Links
increases, the
level of goal
congruence
decreases.
– Adaptive model
As the level of
Interpersonal
Trust increases,
the use of
Strong Links
increases.
As the level of
goal congruence
increases, the
use of Strong
Ties increases.
Cycles of
misalignments may reenforce the use of
Strong Links..
The number of
concurrent
Weak links is
may be
extended
through the use
of collaborative
technologies.
As the level of
Swift Trust
increases, the
number of Weak
Links increases.
The number of
concurrent
Strong Links is
bounded by SA
capacity (7 ±1).
The level of
Swift Trust has
no effect on the
use of Strong
Ties.
The newly created
Weak Links may be
constrained by the
adaptive model.
Window of Opportunity
may increase the
number of newly
created Weak Links.
Table 2 -”Node Capacity and Weak and Strong Ties”
Proposed Multi-Criteria Model for Adaptive Collaborative Network Topology
Performance Criteria Definitions
Performance in an adaptive collaborative network topology multi-criteria model is
measured in terms of a high number of Ties (Weak and Strong) that enable high levels of:
Synergies of Scale, Synergies of Division of Labor, Synergies of Functional Complementarities,
Synergies of Information Sharing and Collective Intelligence, and Synergies of Tool and
Technology. Table 1, “Weak and Strong Ties: Variation and Effect,” provides a description of
each type of synergy. Table 2,”Node Capacity and Weak and Strong Ties,” contains a set of
proposed relationships between factors influencing the creation and dissolution of Weak Ties and
the use of Strong Ties.
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Design Variables:
Design variables are those that are under the immediate control of the systems architect.
For this study, design variables will be considered those that define nodes and links. Node
design variables include: SA Capacity, Interpersonal Trust, SWIFT Trust, and Goal
Congruency);. Link design variables include: Number Created, Number Used, Type (Weak and
Strong), and Technology Platform. Some of the design variables interact creating a suboptimization effect, and are referred to as opposing design variables. The following is a
description of node design variables, the link design variables and opposing design variables that
will be considered in a discussion of how Pareto analysis fits into the design of this experiment.
Nodes:
Nodes in a collaborative network topology may range from a sensor, a data source, an
individual, a team, or an organization. For this study, the unit of analysis is the individual.human
node. The design variables used in this study are SA Capacity, Collaborative Capacity and Goal
Congruence The following Multi-Criteria design variables were selected to measure node
capacity:
Situational Awareness Capacity:
Miller (1956) showed a number of remarkable coincidences between the channel capacity
of a number of human cognitive and perceptual tasks. In each case, the effective channel
capacity is equivalent to between five and nine equally-weighted error-less choices.
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Collaborative Capacity
Collaborative capacity is moderated by the level of Interpersonal Trust and/ or Swift
Trust between two nodes and the level of Goal Congruence among the linked nodes.
Interpersonal Trust
Interpersonal trust in an adaptive collaborative network relates to the use of Strong Links,
and is defined as individual trust-based social capital. This is supported by the relational view of
social capital that is embedded in trustworthiness, reliability, and an institutionalized collective
endeavor (Solow 2000). It has been suggested that this is precisely what gives social networks
their value in monitoring others’ actions (Arrow 2000).
Swift Trust
Swift trust in an adaptive collaborative network relates to the creation of Weak Links
and is defined as a temporary systems “where people are brought together to complete a given
task, have common factors which may include: Many different skills, Limited history of working
together and unlikely to work together ever again, Complex and non-standard tasks which are
only partly understood, Interdependent tasks that require a high degree of collaboration, Tight
timescales and high cost of failure” (Changing Minds 2008). Swift Trust in temporary systems is
not an interpersonal form of trust, but rather a cognitive and action form of trust (Meyerson,
Weick, and Krmer 1996). The cognitive and action form includes: Aligned activity that
reinforces a certain level of goal congruence forcing members to develop a system of trust;
Interdependence; Constrained environment; Time compression; Just-enough resources;
Professional role focus; Task/process focus; and a Trust broker who hires, fires, and leads the
charge. (Changing Minds 2008).
21
Goal Congruence
Goal Congruence “may be evaluated in terms of varied organizational goals, research
goals, or individual goals depending on unit of analysis used for each node. Goal Congruence
among Weak Ties may be weak and therefore increase agency costs reducing the benefit derived
from accessing better quality and more diversified information and resources. “During stability,
security, transition and reconstruction operations it is highly likely that, [various responding
teams with Weak Ties] will have some congruent goals, such as saving lives or property. It is
also likely that they will have some incongruent goals, such as protecting national interests
versus aligning with stakeholder groups. These incongruent may require resolution if conflict [or
loss of synergy] is to be avoided” (Zolin 2006).
Links:
Links in an adaptive collaborative network topology range from those established at all
layers of the OSI model to the social networking links. From this study, links are defined as
connections between individual human nodes and are measured in terms of their frequency of
use, their duration in hours and the technology platform used to provide an interface between
individual nodes. From this study, platforms include: Port Authority of NY and NJ Joint
Situational Awareness (JSAS) Tools, Naval Post Graduate School Situational Agent (NPS SA),
Groove (Microsoft collaboration platform), Telephony, Email, Domestic Nuclear Detection
Office, Joint Analysis Center (JAC), and the Space and Naval Warfare Command (SNWC) Blue
Force Tracker.
22
Opposing design objectives
Considering the proposed parameter-criteria space framework for a desirable system
model, two sets of opposing design objectives were identified: Number of Links vs. SA Channel
Capacity and the number of Weak Links vs. Level of Goal Congruency. Based on Miller’s
work, “The Magical Number Seven, Plus or Minus Two” (Miller 1956), channel capacity, or the
number of links that can be simultaneously maintained by one node in an adaptive collaborative
network topology, is limited to seven, plus or minus one. Increasing the number of channels or
links to a particular node beyond seven may result in no increase or a derogation of SA. As the
number of Weak Ties increase the level of goal congruency tends to decrease. A reduction in
goal congruency reduces Synergies of Scale, Synergies of Division of Labor, Synergies of
Functional Complementarities, and Synergies of Information Sharing and Collective
Intelligence.
Opposing design objects are sometimes accompanied with complementary design
objectives or the inclusion of synergies within the design itself. For this study, the number of
Weak Links vs. the level of Swift Trust and the use of Strong Links vs. the level of Interpersonal
Trust are both complementary design objectives, where both design objectives may be modeled
as maximizing criteria in a Pareto solution. As the level of Swift Trust increases, the number of
Weak links also increases. As the level of Interpersonal Trust increases, the use of Strong Links
increases.
Functional Constraints
A functional constraint is a variable that is assigned by the user of the system or
environmental factors. When considering the use of an adaptive collaborative network topology
during an emergent crisis or disaster, the types and number of functional constraints would vary
23
significantly. For this field study, a scenario is used, thus allowing for the control of the selected
scenario and the duration that scenario is allowed to play out over time.
Time
Using time as a functional constraint provides an opportunity to better understand the
relationship between the duration of a particular scenario, the amount of information sharing that
occurs via the number of newly created Weak Link and the use of Strong Link during that
scenario, and how synergies of information sharing and collective intelligence affect decision
making. Ahituv, Igbaria, and Sella, (1998) studied the “Effects of Time Pressure and
Completeness of Information on Performance.” Their study provided empirical support for the
following proposition: Decision makers under time pressure, as compared with decision maker
not under time pressure, use fewer but more important attributes, less complex decision rules,
weigh negative aspects more heavily, take fewer risks, and are less satisfied with their decisions
(Ahituv, Igbaria, and Sella 1998).
Scenario
The creation of newly formed Weak Links and the use of Strong Links in an adaptive
network topology will be moderated by the scenario played against the network topology. This
creates some limitations in terms of the generalizability of any scenario to a real world event.
However, a campaign of experiments during the discovery stage would provide for an
opportunity to evaluate the creation of newly formed Ties and the use of Strong Tiesacross a
series of experiments, prior to publishing a set of substantive theories on how collaborative
networks morph and create synergies during a response effort.
For this study, the following scenario will be used: “Experiment intelligence indicates
that a sophisticated terrorist group intends to smuggle key components of an improvised nuclear
24
device (IND) and/or radiological dispersion device (RDD) into the US where the group will
assemble it and detonate it for maximum effects. MIO begins with information feeds to JSAS
from partners in Europe that may have detected suspects and materials as they were leaving
transportation centers in Europe. The response actions unfolded indicate the threat of nuclear
material and/or IND/RDD being transported by a large and/or small vessel into the Port of NY &
NJ. The goal is to explore new sensor, networking, and situational awareness solutions for
tagging, monitoring, and interdicting large and small vessels threatening the Port” (TNT MIO
08-04 2008).
The table below provides a summary of the performance criteria definitions, design
variables, opposing and complementary design objectives, and the functional constraints.
Performance Criteria Definitions
Synergy of Scale
Synergy of Division of Labor Synergies
Synergy of Functional Complementarities
Synergy of Information Sharing and Collective Intelligence
Synergy of Tools and Technology
Design Variable
Nodes
Trust-based Social Capital
Swift Trust
Goal Congruency
Situational Awareness
Links
Type (Weak / Strong)
Number newly Created
Frequency of Use
Technology Platform
Opposing design objectives
# Weak Links vs. Level SA
# Weak Links vs. Level GC
Complementary Design Objective
# Weak Links vs. Level of Swift Trust
Use of Strong Links vs. Level of Trust-based Social Capital
Functional Constraints
Time
25
Range
Low - High
Low - High
Low - High
Low - High
Low - High
Low - High
Low - High
Low - High
Number of Channels
Weak / Strong
Low - High
Low - High
Type
Pareto
Pareto
Pareto
Pareto
Varied
Scenario
Varied
Table 3 “Criteria definitions, Design variables, Opposing and complementary design
objectives, and the Functional constraints”.
26
Pareto set for the expected adaptive collaborative network topology model
The ultimate result of multi-criteria analysis is referred to as a Pareto Set; this is the model
for the best possible optimization and maximization over N-dimensions of criteria and may be
graphically expressed as similar to x = y- -1 curve for every pair of criteria. Along this curve is the
set of best solutions, this method does not specify a single optimization of maximization unless
weights are applied (adapted – Clements 2006). For this study, four Pareto Sets will be
evaluated: Two opposing design options and two complementary design objectives.
1. (Opposing - Optimization) Number of Weak Links vs. the level of SA
2. (Opposing -Optimization) Number of Weak links vs. the level of Goal Congruence
27
3. (Complementary - Maximization) Number of Weak links vs. the level of Swift Trust
4. (complementary) The use of Strong Links vs. the level of Interpersonal Trust
28
Experiment:
Type of Experiment
This experiment may be considered part of an experimentation campaign that contains
components of both Discover of Hypothesis and Refined Hypothesis. Alberts refers to an
experimentation campaign as “a series of related activities that explore and mature knowledge
about a concept of interest. As illustrated in Figure 3-1, experimentation campaigns use the
different types of experiments in a logical way to move from an idea or concept to some
demonstrated military capability. Hence, experimentation campaigns are organized ways of
testing innovations that allow refinement and support increased understanding over time”
(Alberts 2002).
(Alberts 2002) - Modified
For this experiment,the “Preliminary Hypothesis” are those related to a proposed set of
relationships between the creation, use, and dissolution of Weak Ties and Strong Ties and the
29
creation of various types of synergy as depicted in Table 1, “Weak and Strong Ties: Variation
and Effect”.
The Refined Hypothesis is focused on evaluating the strength of the relationships
between situational awareness capacity and collaborative capacity and the creation, use, and
dissolution of Weak and Strong Ties. Alberts states that “Hypothesis testing experiments are the
classic type used by scholars to advance knowledge by seeking to falsify specific hypotheses
(specifically if…then statements) or discover their limiting conditions. They are also used to test
whole theories (systems of consistent, related hypotheses that attempt to explain some domain of
knowledge) or observable hypotheses derived from such theories. In a scientific sense,
hypothesis testing experiments build knowledge or refine our understanding of a knowledge
domain. In order to conduct hypothesis testing experiments, the experimenter(s) create a
situation in which one or more factors of interest (dependent variables) can be observed
systematically under conditions that vary the values of factors thought to cause change
(independent variables) in the factors of interest, while other potentially relevant factors (control
variables) are held constant, either empirically or through statistical manipulation.” (Alberts
2002). For this experiment, the scenario (directly) and time (indirectly) are held constant while
Synergy and Weak and Strong Ties (the dependent variables) are observed under conditions that
vary the value of various factors (independent variables).
Objective:
The objective of this experiment is to better understand how and which synergies are obtained
when the morphism of a collaborative network topology takes place during an emergent event
where a team is attempting to collaborate in real-time. Specifically how factors influencing the
creation, use and dissolution of Weak and Strong Ties impacts and/ or predicts the level of
30
various forms of synergy achieved during a particular scenario.
A qualitative analysis of
captured collaborative interactions will be used to better understand how and which synergies are
obtained and how they are influenced by the creation, use and dissolution of Weak and Strong
Ties.. A quantitative analysis of discussion treads, chats sessions, and a survey instrument will be
used to measure factors influencing the creation, use and dissolution of Weak and Strong Ties
Design Criteria:
The design criteria are a set of independent variables influencing the dependent variables
Weak and Strong Ties that in turn create various forms of synergy within an adaptive network
topology. These independent variables are grouped around nodes and links that are considered
to be the two major components of an adaptive collaborative network topology. Nodes are
defined as individuals who participate in a given scenario. Each node exhibits a certain level
(low – high) of Trust-based Social Capital, Swift Trust, and Goal Congruence and will maintain
a certain level of Situational Awareness of number of channels. Links are defined as the
relationships between nodes. These relationships take the form of either Weak or Strong Ties and
exhibit a range of use and communication through various types of technology platforms. For
this experiment (TNT MIO 08-04), four collaborative technology platforms were used. Google
Earth was used to provide situational awareness of the detection and target vessels during the
experiment in the form of an aerial view and dynamic representation of vessel movement.
GROOVE was the planned for primary collaborative platform where all exercise participants
collaborate through the use of discussion threads, chat sessions, and real-time file sharing
capabilities. NPS-CENETIX Video Conferencing One (VC1) was used to provide access to
various video streams, audio conferencing, back-up chat, and file sharing capabilities. NPSCENETIX Observer Notebook was used by NPS – only participants and observers were to post
31
messages related to the experiment itself, independent of activities related directly to the
scenario. In addition to the NPS-CENETIX collaboration technology platforms, the Port
Authority of NY and NJ provided Joint Situational Awareness System (JSAS). JSAS provided
integrated transmission of text, voice, and video, and the geospatial visualization of sensor data
and other collateral information, in a way that facilitated SSA and on-line collaboration.
Design Variable
Nodes
Trust-based Social Capital
Swift Trust
Goal Congruence
Situational Awareness
Links
Type (Weak / Strong)
Number newly Created
Frequency of Use
Technology Platforms
Low - High
Low - High
Low – High
Number of Channels
Weak / Strong
Low - High
Low - High
Type
Functional Constraints:
The functional constrains are time and the selected scenario. For this experiment, time is
bounded by the activities defined in two phases of the experiment that occur in a two day period.
“Scenario overview: Experiment intelligence indicates that a sophisticated terrorist group
intends to smuggle key components of an improvised nuclear device (IND) and/or radiological
dispersion device (RDD) into the US where the group will assemble it and detonate it for
maximum effects. MIO begins with information feeds to JSAS from partners in Europe that may
have detected suspects and materials as they were leaving transportation centers in Europe.
The response actions unfolded indicate the threat of nuclear material and or IND/RDD
being transported by a large and/or small vessel into the Port of NY & NJ. The goal is to explore
new sensor, networking, and situational awareness solutions for tagging, monitoring, and
32
interdicting large and small vessels threatening the Port. The Port of NY-NJ Experiment will be
conducted in two Phases (I & II). Figure 1 shows the location of target vessels in Phase I and II.
Figure 1 - Location of Command Post and Target Ship at Berth 17 (Phase I, Day 1) and two
Small Target Vessels (Phase II, Day 2).
Phase I
A Container Liner is docked (See Fig. 2) and a radioactive source is on board. The USCG and
CBP as the lead agencies for Port Security have mobilized resources. PAPD, FDNY, NYPD,
33
Figure 2 – Shipping Container
NJSP, Newark Fire, Elizabeth Fire, and Jersey City Fire establish unified command to detect
and interdict the threat by conducting boarding and search operations with hand held and
backpack radiological detection systems on the vessel (top deck, below deck, and container
area).
As each boarding team from the dockside independently detects the radioactive source
onboard, they will maintain network connectivity with their Command and Control (C2) Centers
(ship- to- ship and/or ship to shore) and collaborate using the NPS and JSAS situational
awareness tools for rapid decision making at each command and coordination center. Upon
detecting the radioactive sources, the boarding teams will transmit the spectral information via
JSAS to the DNDO Reach-back to receive real-time identification of the items. (DNDO will be
the primary reach-back agency for this experiment)
Additionally, the boarding teams, comprising the boarding party (USCG, CBP, NYPD,
FDNY, NJSP, NJFD) will conduct a further investigation of the vessel’s crewmembers, manifest,
route of travel, and biometrics (latent prints/facials) using network connectivity and collaborating
using the NPS and JSAS tools to seamlessly send real time data and situational awareness to all
the operational Command and Coordination Centers. The communications networks (voice and
data) from below deck will be validated during the demonstration. (Note: this is the only day the
Atlantic Container Liner is available)
Phase II
Two small vessels in the harbor contain a radioactive source on board. The USCG as the lead
agency for Port Security has mobilized resources (NYPD, FDNY, NJSP, NJ FD) to detect and
34
interdict the threat by conducting waterborne patrolling with radiological detection systems
around the Port. As each agency independently detects the radioactive source onboard, they will
maintain network connectivity with their Command and Control (C2) Centers (ship to ship
and/or ship to shore) and collaborate using the NPS and JSAS situational awareness tools for
rapid decision making at each Command and Coordination Center. Upon detecting the
radioactive sources, the search vessels/boarding teams will transmit the spectral information via
JSAS to the DNDO Reach-back to receive real-time identification of the items. (DNDO will be
the primary reach-back agency for this experiment) After the small vessels are identified, they
will be tagged and tracked en route to the Hampton Roads area for the next experiment”
(Bordetsky A. 2008).
Hypothesis Testing:
Factor Influencing the Creation, Use, and Dissolution of Weak and Strong Ties
Considering the design variables identified in Table 2, ”Node Capacity and Weak and
Strong Ties,” a set of hypotheses were developed to test the relationship between Situational
Awareness Capacity and Collaborative Capacity (Social-based Capital, Swift Trust, and Goal
Congruence) and the development, use, and dissolution of Weak and Strong ties. Below is the
set of hypotheses that will be examined in this study.
Weak Ties / Situational Awareness Capacity
H1n – The number of current weak links maintained by an individual node does not change
when a collaborative technology platform is used.
H1 - If collaborative technology platforms are used then the number of concurrent weak links
maintained by one node will increase beyond 7 ±1.
Weak Ties / Collaborative Capacity
H2n – The number of Weak Ties does no change depending on the level of Swift Trust, Socialbased Capital, or Goal Congruence.
35
H2a – If the level of Swift Trust increases, then the number of Weak Links will increase.
H2b – If the level of Social-based Capital increases, then the number of Weak Ties will decrease.
H2b – If the number of Weak Ties increases, then the level of Goal Congruence will decrease.
Strong Ties / Collaborative Capacity
H3n – The use and dissolution of Strong Ties does not change depending on the level of Swift
Trust, Social-based Capital, or Goal Congruence.
H3a - If the level of Social-based Capital increases, then the use of existing Strong Ties
increases.
H3b – If the level of Goal Congruence increases, then the use of existing Strong Ties increases.
Weak and Strong Ties and Synergy
Considering the relationships between Weak and Strong Ties identified in the Literature
(Table 1 -”Node Capacity and Weak and Strong Ties”) a second set of hypotheses were
developed to examine the relationship between Weak and Strong Ties and Synergies of Scale,
Synergies of Division of Labor, Synergies of Functional Complementarities, Synergies of
Information Sharing and Collective Intelligence, and Synergies of Tools and Technology. Below
is the set of hypotheses that will be examined in this study.
Weak Links / Synergy
H4n – The creation of new Weak Ties does not increase Synergies of Scale, Synergies of
Division of Labor, Synergies of Functional Complementarities, Synergies of Information Sharing
and Collective Intelligence, and Synergies of Tools and Technology.
H4a – If the formation of Weak Ties increases, then so does Synergies of Scale.
H4b – If the formation of Weak Ties increases, then so does the Synergies of Division of Labor.
H4c – If the formation of Weak Ties increases, then so does the Synergies of Information
Sharing and Collective Intelligence.
36
H4d – If the formation of Weak Ties increases, then so does the Synergies of Tools and
Technology.
Strong Links / Synergy
H5n – More frequent use of Strong Ties does not increase Synergies of Scale, Synergies of
Division of Labor, Synergies of Functional Complementarities, Synergies of Information Sharing
and Collective Intelligence, and Synergies of Tools and Technology.
H5a – If the use of Strong Ties increases, then so does Synergies of Scale.
H5b – If the use of Strong Ties increases, then so does the Synergies of Division of Labor.
H5c – If the use of Strong Ties increases, then so does the Synergies of Information Sharing and
Collective Intelligence.
H5d - If the use of Strong Ties increases, then so does the Synergies of Tools and Technology.
Sample Population
For this study the sample population will include all participants (non-Observers) of the
TNT MIO 08-4 Experiment Phase I. The use of Purposeful Sampling was to ensure that all nodes
on a given collaborative topology network were considered, a range of pre-exiting Weak and
Strong Ties were represented between and across agencies, and to facilitate the generalizing of
the results back to the population from which the sample were chosen. Please see Appendix A,
“Roles and Responsibilities for a listing of the sample population”.
Data Collection
In seeking to either support or negate H1 – H5, conversations and postings captured in
both GROOVE and the JSAS platforms will be collected. This includes all discussion thread
posts, chat sessions, and recoded voice conversations during the experiment. In seeking to either
support or negate the influence of Situational Awareness Capacity and Collaborative Capacity
(Trust-based Social Capital, Swift Trust, Goal Congruence) on the creation, use, and dissolution
of Weak and Strong Ties (H4 and H5), a survey instrument will be used to measure the
37
individual node’s perceptions of level of each construct. A validated survey instrument will be
used to collect data on Trust-based Capital and Goal Congruence (Majchrzak 2004). Items will
need to be developed to collect data on Situational Awareness Capacity and Swift Trust.
Considering possible discoveries that may occur during this experiment, items measuring the
following constructs will be included in the survey: Expertise Location, Access to Parties, and
Anticipation of Value (Majchrzak 2005). See Appendix B (Adapted) “Collaborative Capacity
Survey Instrument.” All question items on the survey will be measured using a seven-point
Likert scale. The possible responses ranged from “strongly agree” to “strongly disagree.”
Multiple questions were used to measure a single construct to compensate for how subjects
respond to questions with certain word structures. To reduce the incidence of monotonous
responses on the survey, the sequence of questions was randomized and half of the questions
were negated.
Proposed Data Analysis:
Both Qualitative and Quantitative analysis will be used to better understand the
relationships between the creation, use, and dissolution of Weak and Strong Ties and various
types of synergy. Quantitative analysis will be used to examine the influence of Situational
Awareness Capacity and Collaborative Capacity on the creation, use, and dissolution of Weak
and Strong Ties.
Qualitative Analysis
Qualitative analysis of discussion threads, posts, chat, and audio recordings will include
the use of open coding and axial coding to better understand synergies created by Weak and
Strong Ties. Open coding will be used to examine the data. “Data will be broken down into
discrete parts, closely examined, compared for similarities and differences, and questions are
38
asked about the phenomena as reflected in the data” (Strauss and Corbin 1990 p. 62). In this
experiment, I will be looking at how the creation, use, and dissolution of Weak and Strong Ties
create various forms of synergy in an adaptive collaborative network. After reading all content,
coding will be conducted line-by-line, by sentence, and in some instances, by groups of
sentences. Line-by-line coding is identified by Strauss and Corbin as highly generative and
useful during the early stages of a study (Strauss and Corbin 1990). Once phenomena are
identified in the data, concepts will be grounded around them (Strauss and Corbin 1990 p. 65).
Considering these concepts, axial coding will be used to explore possible relationships (H1 H5).
Quantitative Analysis
Survey data will be used to evaluate H1 – H3. Quantitative analysis will include
instrument validation, testing for convergent and discriminate validity, and to provide support for
hypothesis testing.
Instrument validation will focus on checking for internal consistency reliability.
Reliability of the instrument will be judged by estimating how well the items that reflect the
same construct yield similar results. For this study, Cronbach's Alpha (a) will be used to
measure internal consistency. Cronbach’s Alpha ranges between 0.0 and 1.0, and a value of
greater than 0.7 is considered sufficient for social research.
Support for construct validity will be derived from both the literature and qualitative
analysis of the postings, chat sessions, and recorded voice communications. Testing for
Convergent and Discriminate Validity, both considered sub-categories of Construct Validity, will
be supported through quantitative analysis by estimating the degree to which two measures are
39
related to each other using a correlation coefficient. Correlations between theoretically similar
items should be higher than correlations between theoretically dissimilar items.
Support for hypothesis testing will be performed using regression analysis to test the
strength of a set of relationships between the independent variables (Trust-based Social Capital,
Swift Trust, Situational Awareness Capacity, and Goal Congruence) and the dependent variables
(Weak and Strong Ties). Survey data will also be used to evaluate the Prato Analysis: Using
regression analysis as a form of multivariate analysis, opposing design options and two
complementary design objectives will be evaluated.
(Opposing - Optimization) Number of Weak Links vs. the level of SA
(Opposing -Optimization) Number of Weak Links vs. the level of Goal Congruence
(Complementary - Maximization) Number of Weak Links vs. the level of Swift Trust
(Complementary) The use of Strong Ties vs. the level of Interpersonal Trust
40
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33. Saiz JJA, Rodríguez RR, Bas AO. “A Performance Measurement System for Virtual and
Extended Enterprises”. In Collaborative Networks and their Breeding Environments. New
York: Springer, 2005.
34. synergy. (2008). In Merriam-Webster Online Dictionary.
Retrieved August 5, 2008, from http://www.merriam-webster.com/dictionary/synergy
35. Surowiecki, James (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the
Few and How Collective Wisdom Shapes Business, Economies, SocieTies and Nations
Little, Brown ISBN 0-316-86173-1
36. Szulanski, G. (1996) “Exploring Internal Stickiness: Impediments to the Transfer of Best
Practice Within the Firm”, Strategic Management Journal, 17(Winter Special Issue), 1996,
pp. 27-43.
37. TNT MIO 08-4, "Networking and Interagency Collaboration on Maritime-Sources Nuclear
Radiation Threat", Port of NY-NJ/Ft. Eustis/Europe, September 8-12, 2008
38. Tyre, M. J., and Orlikowski, W. J.(1994) “Windows of Opportunity: Temporal Patterns of
Technological Adaptation In Organizations,” Organization Science (5:1), February, pp.98118.
39. Zolin, R. “Swift Trust in Hastily Formed Networks” Cebrowski Institute, Naval Postgraduate
School.
43
Appendix A – Resources, Roles and Responsibilities (Bordetsky, A. (2008)
Resources:
“Naval Postgraduate School (Networks: ship-to-ship, ship-to-shore, Collaborative Technology,
Operations & Command Centers, VPN Reach-back, Unmanned vehicles, and Biometrics)
Lawrence Livermore National Laboratory (Radiation Sources, Radiation Detection, Radiation
Reach-back, Ultra-wide band Communication, Explosives Detection, HOPS, Export Control, and
Plume Modeling)
Port Authority NY-NJ (United States Coast Guard, Customs Border Protection, Port NewarkPort Authority of NY & NJ, PAPD EXPERIMENT Police, PAPD Emergency Services Unit,
Port Authority OEM, Jersey City/Newark/ Elizabeth Fire Departments, New York Police
Department, Fire Department NY, NYC OEM, New Jersey State Police Maritime Division, and
the Stevens Institute)
DNDO-JAC (JAC Operations section, JAC Information and Analysis section, Regional Reach
back Spectroscopists, National Reach back Spectroscopists (if necessary), and JACCIS
Components- TBD (State has not signed use agreement, MOA being drafted, will not be signed
in time for the exercise).
`
DoD and U.S. Government (USSOCOM J9, USCG, DNDO, U.S. Army Biometric Fusion
Center, OSD/HLD, HLS S&T, DOE Radiological Assistance Program)
Foreign Partners (Swedish Naval Warfare Center/Kokums, University of Bundeswehr at
Munich, Systematic/Danish Navy Training Center)
Corporate Partners:
 IST (Innovative Survivability Technologies) - Radiation detection software and ARAM
detector for fixed sensors
 CDI (Computer Deductions, Inc.) Biometrics identification hand-held devices
 Swe-Dish Satellite Systems (Swe-Dish antenna units and services for the broadband
reach back)
 Blackbird, Inc. (satellite e-tags and satellite pagers for linking with target small craft),
Persistent Systems Inc. (Wave Relay mesh ship-to-ship network)
 Software Defined Radios. (Software radios and p2p Boarding Party network, ship-tofast boat and on-the-deck communications)
 Rastech, Inc (Projectile launched sensors, projectile-based networking)
44
Roles and Responsibilities (Bordetsky 2008)
Experiment Design, Coordination, and Control
Leads
Dr. Alex Bordetsky-MIO Experiment Principal
Investigator (Experiment Design, Coordination,
and Control), NPS
Advisors
Dr. David Netzer- NPS Director, USSOCOMNPS Field Experimentation Cooperative, NPS
Experiment Advisor
Dr. Arden Dougan-LLNL Experiment Coordinator
(Experiment Design and Coordination)
Mr. David Dlugolenski-PANYNJ (NY-NJ
(Experiment Design, Coordination, and Control)
Dr. Bill Dunlop- LLNL Experiment Advisor
Mr. Keith Arthur-Ft. Eustis (Experiment Design
and Coordination)
Coordination Support
LCDR Dan Vogel-DNDO
Nuc/Rad Reach back ( scenario, planning)
LCDR John Looney
Riverine Area (Hampton Roads), scenario,
planning
JSAS environment
Dr. Erika Poulson, PANYNJ
Port of NY-NJ MIO Response Team
Boarding Team and Surface Assets
BOARDING TEAM
Lt C.K Moore
USCG Members w/
Detection equipment (Day 1)
Ft Wadsworth Operations Ctr
2 Boats (Drive by w/ ARAM) Day2
1 Boat (relay for network) Day 2
PAPD ESU
Boarding team w/ detection equipment
NYPD Boarding team
Lt Jim Griglio
TARGET VESSEL
Container Ship at Berth 17, Port of Newark
Bob Harley
TARGET BOAT
Lt. Ed Ditzel
Dep Insp John Nicholson
45
NJSP
TARGET BOAT
Marine Co. 9 Vessel
NPS Facilitators Supporting the Boarding Party on
the deck
Operation Centers
USCG Operations Center
Staten Island/ Fort WadsWorth
BC Donald Murphy
LCDR Mike Ferebee-USCG support
LCDR Jeff Olk-CBP Support
Marianna Verett-NYPD Support
Lt C.K Moore
PORT OF NEWARK Operations Center
FD MOBILE Operations Center-MCCTV
FD Operations Center
NPS Network Operations Center (Monterey)
SOCOM: Bob Bean
NPS: Col Bryan Hudgens
Ira Forman
NPS: Richard Bergin,
Ira Forman
NPS: Yaara Bergin
Lt Steve Rotello
NPS: Alex Bordetsky, Mike Clement, Eugene
Bourakov
Bob Harley
BC Donald Murphy
BC Donald Murphy
MAJ Brian Conrad, LCDR Ioannis Tzanos
Boarding Team and Surface Assets
TARGET Boat 1
Target Boat 2
?
BOARDING Vessel 1
TARGET Vessel 2
?
?
NPS Boarding Party Members
Bob Bean (SOCOM)
Marianna Verett (Biometrics)
Tom Calabro (CDI-Biometrics)
PA OEM , PA EOC (situation room)
PA MOBILE EOC and Duty Officer
PAPD PN Unified Command Center
Operation Centers
RIVERINE TOC at the MARAD Fleet Facility
CDR John Looney (NPS), Marty Cardwell
(LMCO), David Murray (LMCO), and Mike
French (AADT) Bob Yeates (AADT); Martin
Walker (JRRF)
SOCOM: Bob Bean
NPS: Networking and Experiment Control team
LLNL: Watch Officer
46
NUC/RAD Sensors and Remote Experts
LLNL Watch Officer
Drive-by and onboard detection
D-GPS and Laser Range Finder
ARAM sensors
Dr. Arden Dougan
Dr. Dave Trombino (LLNL)
Randy Sundberg, Drew Casavant (LLNL)
Brian Adlawan, Brad Hinrichs (IST-TEXTRON)
Biometrics Identification Sensors and Remote Experts
CDI Biometrics Boarding Officer
Tom Calabro (CDI)
NPS Biometrics Boarding Officer
Marianna Verett (NPS)
BFT Reachback (West Virginia)
Jon Hayes (BFT)
Networking Infrastructure
MIO Network with Ship-to-Shore, Ship-to-Ship, and
reach back to geographically distributed sites
PANYNJ Networking Infrastructure
OFDM 802.16 Network and Pico Cellular segments (Ft.
Eustis, California)
VPN Reach-back (PANYNJ, DNDO, LLNL, Overseas
sites)
Wave Relay Mesh Network (Ship-to-Shore, Target
Vessel deck, Drive-by boats and Riverine Area boats)
Utra-Wide Band Network for under-the-deck
communications
MIO Network Satellite Ground Stations
Dr. Alex Bordetsky, Eugene Bourakov,
Mike Clement (NPS), Dr. Herb Rubens
(PS)
Dr. Erika Poulsen (PANYNJ) and Mike
Germano
Eugene Bourakov (NPS)
Mike Clement (NPS)
Dr. Herb Rubens, Persistent Systems
Cique Romero (LLNL)
NPS: Mike Clement, MAJ Matt Senn,
MAJ Brian Conrad, Capt James Turner,
LCDR Ioannis Tzanos, LCDR Mike
Ferebee
Swe-Dish: Donovan Dinger
Electronic tags for small boats and vehicles tracking
Eugene Bourakov (NPS)
NPS NOC: MIO network monitoring, performance data
collection, emergency response
NPS: Maj Brian Conrad, LCDR Ioannis
Tzanos, Capt Chris Fodora
ONR 13: LCDR Rich Thorne, LCDR Rich
Moerling
47
COLLABORATIVE TECHNOLOGY AND SITUATIONAL AWARENESS
ENVIRONMENT
MIO Collaborative technology/SA Platform, CoT
Dr. Alex Bordetsky, Eugene Bourakov, Michael
integration
Clement (NPS)
JSAS Collaboration and SA
Dr. Erika Poulsen (PANYNJ), Fred Reimers
(BAE)
JAC Architecture
Robert Brecia, LCDR Dan Vogel (DNDO)
NPS SA and Vstream integration with JSAS
Eugene Bourakov (NPS)
CoT Integration and Groove management
Mike Clement (NPS),
Situational Understanding and Decision Process
Sue Hutchins (NPS)
analysis
Human Factors
John Keenan (ARDEC)
Social Network Dynamics and C2 Maturity Model Col Bryan Hudgens
Virtual C2 Environment and C2 Maturity Model
MAJ Carlos Vega
Spatial Annotation features
Mark St. John (SPAWAR)
EWall data fusion functionality
Dr. Paul Keel and Matt Sither (MIT)
Overseas Early Warning Sites
Swedish Naval Warfare Center-USV feeds,
detection alerts
Systematic team - Providing real time data
on counter diver detection (sonar) and
suspects biometrics using Systematic
Maritime C2 System in Port of Aarhus.
University of Bundeswehr, Munich
(Command Post in Munich and Mobile
Check Point in Bavarian Alps)
CDR Leif Hansson (Sweden)
Dr. Jens-Olov Lindh (Sweden)
Ulrik Høy-Petersen (Denmark)
Mikkel Verner Nielsen (Denmark)
Bjarne Ridderberg (Denmark)
LCDR Kent Meyer (NPS)
Dr. Stefan Pickl and Goran Mihlic (University of
Bundeswehr Munich)
LCDR Kent Meyer (NPS)
DoD and HLS Response Sites
LLNL Watch Officer-Export ControlRadiological Analysis
DNDO-JAC
Regional and Federal feedback
U.S. Army BFC
USSOCOM -USCG C2 Link
METOC Response, NPS NOC
Arden Dougan (LLNL at NPS)
Tzu Fang Wang, Joel Swanson (LLNL at NPS)
John Crandley (LLNL)
LCDR Dan Vogel, Robert Brecia
Jonathan Hayes (BFC)
Bob Bean, USSOCOM
Dr. Peter Guest and Mary Jordan (NPS)
48
Appendix B - Collaborative Capacity Survey Instrument (Majchrzak 2004)
Mutual Understanding Measurement
Construct
Definition
Measurement
Expertise
Location
The analysis
reveals that
expertise
coordination
shows a
strong
relationship
with team
performance
that remains
significant over
and above
team input
characteristics,
presence of
expertise, and
administrative
coordination.
1-5scale, range from "strongly disagree" to "strongly agree"
Team members in my distributed team…
[EXPLOCTN1] ……have a good “map” of each team member’s talents and skills.
[EXPLOCTN2]…work on tasks appropriate with their task-relevant knowledge and skills.
[EXPLOCTN3]…know their own skills and how they relate to the team’s work.
[EXPLOCTN4]…know who on the team has specialized skills and knowledge that is relevant to
the team’s work.
Original scale--- 1-5scale, range from "to a small extent" to "a great extent"
The team has a good "map" of each others' talents and skills
0.86
Team members are assigned to tasks commensurate with their task-relevant knowledge and
skill
0.85
Team members know what task-related skills and knowledge they each possess
0.81
Team members know who on the team has specialized skills and knowledge that is relevant to
their work 0.74
Rationale: changed scales for the convenience purpose so all the measures are
consistent throughout the study.
Results
showed that
goal similarity
is positively
associated
with
individuals'
satisfaction,
liking of other
and intent to
stay in the
group. Goal
similarity was
served as a
control
variable with a
coefficient
alpha of 0.83.
1-5scale, ranging from "strongly disagree" to "strongly agree"
[GOALSIM1] As a team, we have similar goals
[GOALSIM2] The main goals of our team are the same for all members in the team
[GOALSIM3] we on the team all agree on what is important to the team.
Original scale:
Goal Similarity (alpha -- .83)
As a work unit, we have similar goals.
The main goals of my work unit are the same for all members in my work unit.
We (my work unit) all agree on what is important to our group.
Goal
similarity
Rationale and modification: virtual team replaced “work unit”.
49
Relation Measurement
Construct Findings
Measurement
Trust-based
social capital
Relational view
of social capital
is embedded in
trustworthiness,
reliability and
institutionalised
collective
endeavour
(Solow 2000). It
has been
suggested that
this is precisely
what gives
social networks
their value in
monitoring
others’ actions
(Arrow 2000).
(Our Relation
Dimension)
No empirical
findings
(book)
Please indicate whether in general you agree or disagree with the following statements:
(1-5 Scale, ranging from “Strongly Agree” to “ Strongly Disagree”)
[TRUST1] Most people on this team are basically honest and can be trusted.
[TRUST2] On this team, team members are always interested only in their own welfare.
[TRUST3] Members in this team are always trustworthy.
[TRUST4] In this team, one has to be alert or someone is likely to take advantage of you.
[TRUST5] If I have a problem there is always someone to help me.
[TRUST6] I do not pay attention to the opinions of others in the team.
[TRUST7] Most people in this team are willing to help if you need it.
[TRUST8] I feel accepted as a member of this team.
[TRUST9] If you are not able to complete an activity at a given time, someone on this team will
pick it up and do it.
Original Scale:
a. Most people in this team are basically honest and can be trusted
b. People are always interested only in their own welfare
c. Members in this team are always more trustworthy than others
d. In this team one has to be alert or someone is likely to take advantage of you
e. If I have a problem there is always someone to help you
f. I do not pay attention to the opinions of others in the team.
g. Most people in this team are willing to help if you need it
i. I feel accepted as a member of this team.
j If you are not able to complete a task at a given time, someone in this team will pick it up and
do it.
Rationale and modification:
1. Throughout - village/community replaced with team.
2. One item removed because it did not fit - This team has prospered in the last (five
years)
3. Item j changed from “If you drop your purse or wallet in the neighborhood, someone
will see it and return it to you” to suit virtual team context.
Construct
Findings
Measurement
Access to
parties refers
to the
opportunities
to make
knowledge
combination
and
exchange
among team
members.
(Nahapiet
and Ghoshal
1998)
The result
showed that
combination and
externalization
process, but not
socialization and
internalization
process, affect
perceived
knowledge
satisfaction.
Thus, both of the
knowledge
management
processes that
provide explicit
knowledge – that
is, combination
processes,
which help
integrate several
codified areas of
knowledge, and
externalization
processes,
which help
explicate tacit
knowledge –
contribute to
knowledge
satisfaction.
1-5scale, ranging from "strongly disagree" to "strongly agree"
In terms of knowledge sharing practices of your distributed team, you are satisfied with
the…
[ACCESS1]…knowledge that is available to me from other members to help me perform my
activities.
[ACCESS2]…way knowledge is managed within the team.
[ACCESS3]…knowledge sharing among team members.
[ACCESS4]…knowledge that team members are able to provide from their external contacts
when knowledge is not available within the team.
[ACCESS5]…knowledge I am able to provide the team from my external contacts when
needed.
Access to Parties Measurement
Original scale:
Rationale: slightly reworded. 1&5 were
added to separate Outside access from
Inside access to intellectual capital
50
Anticipation of Value Measurement
Construct
Findings
Anticipation
of value
refers to
team
member’s
expectations
about value
creation as a
result of
combining
and
exchanging
recourses
with each
other.
(Nahapiet
and Ghoshal
1998)
The causal
relationship
between group
success-failure
and
subsequent
attitudinal
variables
(satisfaction
and
organizational
commitment) is
positively
mediated by
efficacy and
outcome
expectancy
variables.
Measurement
1-5scale, ranging from "strongly disagree" to "strongly agree"
I am excited to be a member of this distributed team because I feel that…
[ANTVALUE1]I am well recognized for actively providing and exchanging ideas/resources with
other team members.
[ANTVALUE2] Contributing ideas/resources to this team is not worth the effort.
[ANTVALUE3]Working with this team will lead to my intellectual growth.
[ANTVALUE4]Contributing high quality ideas/resources is a sure way to get respect in this
team.
[ANTVALUE5]My work evaluations will accurately reflect my work on this team.
[ANTVALUE6]Most of my good contribution to this team goes unnoticed by people outside the
team.
[ANTVALUE7]Top management feels that the task of the team is very important.
[ANTVALUE8]My rewards are determined by how well the team does its job
Original scale:
Personal Outcome Expectancy scale (alpha = 0.87)
Think about the results of doing your job well or doing your job poorly. Do important outcomes
depend upon how well you perform, or do most job-related outcomes occur whether or not you
do a good job? When answering the following questions, answer in reference to your current
job. Respond with 6-point scale: 1= strongly disagree, 2= disagree, 3= disagree somewhat, 4=
agree somewhat, 5=agree, 6= strongly agree.
1.
2.
3.
4.
5.
I am well rewarded for my good work.
Doing good work here is not worth the effort.
Performing your job well is a sure way to ahead here.
Most of my good work goes unnoticed.
Around here, such things as salary and promotions are determined by how well a
person does his or her job.
6. My work evaluations are accurate
7. Good work gets the same results as poor work in this job.
8. I must do a good job in order to get what I want.
Rationale:
Reworded
the
original
questions
to
reflect
three
notions:
interaction/combination/exchange; b) resources/ideas; c) value
51
a)
Combination Capability Measurement
Constr
uct
Finding
s
Recipient
lacks
absorptiv
e
capacity
Szulanski’
study
showed
the major
barriers to
internal
knowledg
e transfer
to be
knowledg
e-related
factors
such as
the
recipient’s
lack of
absorptiv
e capacity
(0.54),
causal
ambiguity
(0.34),
and an
arduous
relationsh
ip
between
the
source
and the
recipient
(0.33).
Definition
:
It refers
to the
ability to
recogniz
e the
value of
new
knowledg
e and
informati
on and to
assimilat
e and
use it.
Measurement
1-5scale, ranging from "strongly disagree" to "strongly agree"
Team members in your distributed team….
[ABSCAP1]…understand clearly the processes used to accomplish the team’s activities.
[ABSCAP2]…have the technical competence to understand and use the knowledge shared and created
by the team on the whole
[ABSCAP3]…have the organizational competence to understand and use the knowledge shared and
created by the team on the whole.
[ABSCAP4]…are generally able to exploit new knowledge created within the team.
Original Scales:
Recipient lacks absorptive capacity (alpha = 0.83, Items= 9)
Members of <<recipient>> have a common language to deal with the <<practice>>; <<recipient>> had a
version of what it was trying to achieve through the transfer; <<recipient>> had information on the stateof-the-art of the <<practice>>; <<recipient>> had a clear division of roles and responsibilities to
implement the <<practice>>; <<recipient>> had the necessary skills to implement the <<practice>>;
<<recipient>> had the technical competence to absorb the <<practice>>; <<recipient>> had the
managerial competence to absorb the <<practice>>; It is well known who can best exploit new
information about the <<practice>> within <<recipient>>; it is well known who can help solve problems
associated within the <<practice>>;
Rationale: “ To what extent do members of our virtual team”… replaced “ members of our virtual
team”…Dropped items on our own:

Members of our virtual team have a common language to deal with the each other;

Members of our virtual team has a vision of what we were trying to accomplish through sharing
of knowledge

Members of our virtual team are clear on division of roles and responsibilities to accomplish our
tasks

Members of our virtual team have the necessary skills to share knowledge and accomplish the
tasks

It is well known who can help solve any problems that arise in the virtual team related to task at
hand
52