Collaborative Network Topology Adaptation: Creating Synergies - PowerPoint PPT Presentation

Loading...

PPT – Collaborative Network Topology Adaptation: Creating Synergies PowerPoint presentation | free to download - id: 1ff986-ZDc1Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Collaborative Network Topology Adaptation: Creating Synergies

Description:

... K (1986)', 'A Dictionary of Cybernetics', an 80 ... In Merriam-Webster Online Dictionary. Retrieved August 5, 2008, from http://www.merriam-webster.com ... – PowerPoint PPT presentation

Number of Views:33
Avg rating:3.0/5.0
Slides: 31
Provided by: richardb159
Learn more at: http://www.nps.edu
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Collaborative Network Topology Adaptation: Creating Synergies


1
Collaborative Network Topology Adaptation
Creating Synergies
  • Dr. Alex Bordetsky,
  • Richard Bergin, and
  • Yaara Bergin

2
  • Synergy
  • Synergy / Weak and Strong Ties
  • Design
  • Experiment
  • Objective
  • Resources, Roles and Responsibilities (Appendix
    A)
  • Experiment Type
  • Preliminary Hypothesis Testing
  • Refined Hypothesis Testing
  • Controls (Functional Constraints)
  • Scenario - TNT MIO 08 04
  • Hypothesis Testing
  • Factor Influencing the Creation, Use, and
    Dissolution of Weak and Strong Ties
  • Weak and Strong Ties and Synergy
  • Sample Population
  • Data Collection
  • Proposed Analysis
  • Qualitative
  • Preliminary Hypothesis Testing

3
Literature ReviewSynergy
  • Corning provides a typology for synergy that
    includes Synergies of Scale. Division of Labor,
    Functional Complementarities, Information Sharing
    and Collective Intelligence, and Tools and
    Technology. (Corning 2007)
  • For this study, I considered all five types of
    synergy and adopted Klaus Krippendorffs
    definition of synergy
  • 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. More loosely,
    synergy refers to the benefits of collaborative
    as opposed to individual efforts. (Krippendorff
    1986)
  • benefits of collaborative (Krippendorff 1986)
    is defined as the synergy created through the
    creation, use of Weak Ties and the use and
    dissolution of Strong Ties within an adaptive
    collaborative network topology.

4
Synergy / Weak and Strong Ties
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 (Corning 2007) 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 tools and technology represent a major form of synergy a cooperative effect (or effects) that are not otherwise attainable) (Corning 2007).
Weak Ties 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) Low HighFormingof 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). 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), Low High TBD Low High 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). Low High Formation of higher numbers of Weak Ties may be enabled by open collaborative network topologies technology infrastructure
Strong Ties Low HighMore frequent use of Strong Ties provide a mechanism to invoke 'Weak' Ties (Jack 2005) thus increasing the potential for Synergies of Scale. Low High TBD 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) Low High More frequent use of Strong Ties may result in group think and reduce synergies of Information Sharing and Collective Intelligence (Surowiecki, James (2004). Low High More frequent use of Strong Ties may be re-enforced by a closed collaborative network topology technology infrastructure
5
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.
6
Prior Research on Collaborative Network Adaptation
  • Adaptive Structuration Theory
  • The process of reshaping technology within the
    context of adaptive collaborative network
    topologies may be understood as the creation and
    of Weak Ties and the use and dissolution of
    Strong Ties. This dynamic adaptation processes
    may take form of a series of cycles of
    misalignments, followed by alignments, followed
    by more but smaller misalignments
    (Leonard-Bartons (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 impacts the
    efficacy of existing Ties

7
Prior Research on Collaborative Network Adaptation
  • Collaborative Capacity / Virtual Team Performance
  • Collaborative capacity for this study is defined
    as those individual characterizes that enable the
    creation and use of Weak Ties and the use and
    dissolution of existing Strong Ties. Those
    characteristics include individual Trust-based
    Social Capital, Swift Trust, Expertise Location,
    Goal Similarity (congruence), Anticipation of
    Value, Access to Parties, and Absorptive Capacity

8
Proposed parameter-criteria spaceframework for a
desirable system model
Considering the existing literature on AST,
Collaborative Capacity, Virtual Teams and the
literature on Weak and Strong Ties 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 nodes level of
Trust-based Social Capital, Swift Trust, and Goal
Congruency.
Node Capacity
Links Situational Awareness capacity Miller (1956) Cohen and Levinthal (1990)Szulanski (1996) Collaborative Capacity - Swift Trust (Zolin 2006) Collaborative Capacity-Trust-based Social Capital Coleman (1998) Collaborative Capacity - Goal Congruence(Jenh 1995) Collaborative Capacity Adaptive model Leonard-Barton (1998) Tyre and Orlikowski (1994)
Weak 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. As the level of Interpersonal Trust increases the number of Weak Ties decreases. As the number of Weak links increases the level of goal congruence decreases The newly created Weak links may be constrained by the adaptive model. Window of Opportunity may increase the number of newly created Weak links.
Strong Links The number of concurrent Strong links is bounded by S/A capacity (7) - 1 The level of Swift Trust has not effect on the use of Strong Ties. 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 re-enforce the use of Strong links were
Table 2 -Node Capacity and Weak and Strong Ties
9
Proposed Multi-Criteria Model for Adaptive
Collaborative Network Topology
  • Functional Constraints (Controls)
  • 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
    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 or time

10
Experiment
  • Objective
  • Resources, Roles and Responsibilities (Appendix
    A)
  • Experiment Type
  • Preliminary Hypothesis Testing
  • Refined Hypothesis Testing
  • Controls (Functional Constraints)
  • Scenario - TNT MIO 08 04
  • Hypothesis Testing
  • Factor Influencing the Creation, Use, and
    Dissolution of Weak and Strong Ties
  • Weak and Strong Ties and Synergy
  • Sample Population
  • Data Collection
  • Proposed Analysis
  • Qualitative
  • Preliminary Hypothesis Testing
  • Quantitative
  • Instrument Validation
  • Construct Validity
  • Refined Hypothesis Testing

11
Objective
  • To better understand how and which synergies are
    obtained as the morphism of a collaborative
    network topology takes place during emergent
    events 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 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

12
Type of Experiment
13
Preliminary Hypothesis Testing
Independent Variables
Weak Links Low - High
Strong Links Low - High
Dependent Variables
Synergy Various Types Present / Not Present
14
Refined Hypothesis Testing
Independent Variables
Trust-based Social Capital Low - High
Swift Trust Low - High
Goal Congruence Low High
Situational Awareness Number of Channels
Dependent Variables
Weak and Strong Ties Number Newly Created, Frequency of Use, and Number Dissolved
15
Controls (Functional Constraints)
  • Scenario (TNT MIO 08 04 Phase I II)
  • Terrorist group intends to smuggle key components
    of an improvised nuclear device (IND) and/or
    radiological dispersion device (RDD) into the US
  • 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.

16
Controls (Functional Constraints)
  • Scenario (TNT MIO 08 04 Phase I II)
  • Phase I - A Container Liner is docked and a
    radioactive source is on board. The USCG and CBP
    as the lead agencies for Port Security have
    mobilized resources. PAPD, FDNY, NYPD, 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).

17
Controls (Functional Constraints)
  • Scenario (TNT MIO 08 04 Phase I 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
    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 (Bordetsky 2008)

18
Hypothesis Testing (Factor Influencing the
Creation, Use, and Dissolution of Weak and Strong
Ties)
  • 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 level of Swift Trust, Social-based
    Capital, or Goal Congruence.
  • H2a If the level of Swift Trust increases then
    the number of weak links will also 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.

19
Hypothesis Testing Weak and Strong Ties and
Synergy
  • 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
  • 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

20
Sample Population
  • Sample population will include all participants
    (non-Observers) of the TNT MIO 08-4 Experiment
    Phase I and II.
  • 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 generalizing
    the results back to the population from which the
    in sample were chosen

21
Data Collection
  • Recorded conversations and postings captured in
    both the GROOVE and the JSAS platforms will be
    collected.
  • This includes all discussion thread posts, chat
    sessions and recoded voice conversations during
    the experiments
  • A survey instrument will be used to measure the
    individual perceptions of the level of
    situational awareness capacity and collaborative
    capacity that includes Trust-based Social
    Capital, Swift Trust, and Goal Congruence.
  • A validated survey instrument will be used
    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 response ranged from strongly
    agree to strongly disagree.
  • Multiple questions were used to measure 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.

22
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.

23
Proposed Data Analysis
  • Qualitative AnalysisQualitative 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 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.

24
Proposed Data Analysis
  • Quantitative Analysis
  • Survey data will be used to evaluate H1 H3.
    Quantitative analysis will include instrument
    validation, testing for convergent and
    discriminate validity, and hypothesis testing.
  • Instrument validation will focus on checking for
    internal consistency reliability. For this study
    Cronbach's Alpha (a) will be used to measure
    internal consistency. Cronbachs Alpha ranges
    between 0.0 and 1.0, and value of greater than
    0.7 is considered sufficient for social research.
  • 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 that are both considered
    sub-categories of construct validity will be
    supported thought quantitative analysis by
    estimating the degree to which two measures are
    related to each other using a correlation
    coefficient.

25
Proposed Data Analysis
  • Quantitative Analysis
  • Survey data will be used to evaluate H1 H3.
    Quantitative analysis will include instrument
    validation, testing for convergent and
    discriminate validity, and hypothesis testing.
  • Hypothesis testing will be performed using
    regression analysis the 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).
  • Pareto Analysis - Survey data will also be used
    to evaluate the Pareto Analysis Regression
    analysis is a form of multivariate analysis.
  • Opposing design options and two complementary
    design objectives will be evaluated

26
Pareto set for the expected adaptive
collaborative network topology model
  • Four Pareto sets will be evaluated Two opposing
    design options and two complementary design
    objectives.

(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 links vs. the
    level of Interpersonal Trust

27
References
  1. Ahituv, Igbaria, and Sella, (1998) The Effects
    of Time Pressure and Completeness of Information
    on Decision Making, Journal of Management
    Information Systems Fall, 1998 Vol. 15 2 p9
    153-172
  2. Alberts, D. et. al., Code of Best Practices
    Experimentation, Information Age Transformation
    Series, CCRP, 2002
  3. Anthony, Michael (2005) Regional Information
    Joint Awareness NetworkUS Army Communications-
    Electronic Research
  4. Barabasi, A.(2002), Linked The New Science of
    Networks. Plume Publishing, New York.
  5. Beccara-Fernandez, I. And R. Sabherwal (2001)
    Organizational Knowledge Management A
    Contingency Perspective, JMIS, 181, Summer
    2001, pp. 23-56
  6. Bordetsky A. and Friman, H. (2006) Case Studies
    of Decision Support Models for Collaboration in
    Tactical Mobile Environments, 12th ICCRTS
    Adapting C2 to the 21st Century
  7. Bordetsky A. (2008) TNT MIO 08-4 Networking and
    Interagency Collaboration on Maritime-Sourced
    Nuclear Radiation Threat Port of NY-NJ/Ft.
    Eustis/Europe, September 8-12, 2008
  8. Busi M, Bititci US. Collaborative Performance
    Management Present Gaps and Future Research. In
    International Journal of Productivity and
    Performance Management 2006 Vol. 55 No. 1 7-25.
  9. Capra, F., (1996). The Web of Life. Anchor Books,
    New York
  10. Calvo-Armengol, A, Verdier, T., Zenou,T., (2007)
    Strong and Weak Ties in employment and crime,
    Journal of Public EconomicsVolume 91, Issues
    1-2, , February 2007, Pages 203-233.

28
References (cont.)
  1. Clements M. 2006, Design Paper Adaptive Network
    Resource Management IS4710, Winter 2006 Dr.
    Alex Bordetsky.
  2. Corning A. Peter, (2007) Synergy Goes to War A
    Bioeconomic Theory of Collective Violence.
    Journal of Bioeconomics. Boston 2007. Vol. 9,
    Iss. 2 p. 109 (36 pages)
  3. Cohen, Wesley M. Levinthal, Daniel A.
    Absorptive capacity A new perspective on
    learning and innovation. Administrative Science
    Quarterly, Mar90, Vol. 35 Issue 1, p128, 25p
  4. Coleman J. (1988) "Social Capital in the Creation
    of Human CapitalSocial Capital in the Creation of
    Human Capital", The American Journal of
    Sociology, Vol. 94, Supplement Organizations and
    Institutions Sociological and Economic
    Approaches to the Analysis of Social Structure
    (1988), pp. S95-S120
  5. Desanctis, G. and Poole, M. S. (1994). Capturing
    the complexity in advanced technology use
    adaptive structuration theory. Organization
    Science, 5(2)121-147.
  6. Faraj, S., Sproull, L. (2000). Coordinating
    expertise in software development teams.
    Management Science, 46(12), pp. 1554-1568.
  7. Francisco R, Azevedo, A (200?), Dynamic
    Performance Management in Business Networks
    Environment, (complete citation pending)
  8. Fuller, B. and Applewhite E, J., (1975, 1979)
    Synergetics Explorations in the Geometry of
    Thinking, First Published by Macmillan Publishing
    Co. Inc. 1975, 1979.
  9. Gravnovetter, M. (1973), The Strength of Weak
    Ties, American Journal of Sociology, May 1973.
  10. Granovetter, M (1983), The Strength of Weak Ties
    A Network Theory Revisited Sociological Theory,
    Vol. 1, (1983), pp. 201-233 published by
    American Sociological Association

29
References (cont.)
  1. Jack, Sarah(2005), The Role, Use and Activation
    of Strong and Weak Network Ties A Qualitative
    Analysis, Journal of Management Studies, Volume
    42 Issue 6, Pages 1233
  2. Jehn, K. A. (1995). A Multimethod Examination of
    the Benefits and Detriments of Intragroup
    Conflict, Administrative Science Quarterly, Vol.
    40, p.256282.
  3. Krippendorff, K (1986)', A Dictionary of
    Cybernetics", an 80 p. unpublished report dated
    Feb. 2, 1986 - http//pespmc1.vub.ac.be/ASC/Synerg
    y.html
  4. Leonard-Barton, D (1988) Implementation as
    Mutual Adaptation of Technology and
    Organization, Research Policy (17), pp. 251-267.
  5. Majchrzak A., Jarvenpaa, S., Hollingshead, A.,
    (2007) Coordinating Expertise Among Emergent
    Groups Responding to Disasters Organization
    Science. Linthicum Jan/Feb 2007. Vol. 18, Iss.
    1 p. 147 (17 pages)
  6. Majchrzak, A., Rice R.E., Malhotra, A., King, N,
    Ba, S.(2000) "Technology Adaptation The Case of
    a Computer-Supported Inter-Organizational Virtual
    Team " MIS Quarterly, Vol. 24, No. 4 (Dec.,
    2000), pp. 569-600
  7. Miller, G. (1956) The Magical Number Seven, Plus
    or Minus Two. The Psychological Review, 1956,
    vol. 63, Issue 2, pp. 81-97
  8. Montgomery, J.D., (1994).Weak Ties, employment,
    and inequality an equilibrium analysis. American
    Journal of Sociology 99, 12121236.
  9. Meyerson, D., Weick, K.E., and Krmer, R.M. Swift
    trust and Temporary Groups. In R.M. Kramer
    T.R.Tyler (eds.), Twr in Organizations (pp. 166-
    195).Thousand Oaks, CA Sage Publications, 1996.
  10. Nahapiet, J.,Ghoshal, S. (1998), Social Capital,
    Intellectual Capital, and the Organizational
    Advantage, The Academy of Management Review, Vol.
    23, No. 2 (Apr., 1998), pp. 242-266

30
References (cont.)
  • Riggs, M., Knight, P. (1994). The impact of
    perceived group success-failure on motivational
    beliefs and attitudes A causal model. Journal of
    Applied Psychology, 79, 755-766
  • Stavroulakis, G. (2006), Rapidly Deployable, Self
    Forming, Wireless Networks for Maritime
    Interdiction Operations, Master's thesis Naval
    Postgraduate School
  • 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.
  • Anselm L. Strauss and Juliet Corbin (1990)
    Basics of Qualitative Research Grounded theory
    procedures and techniques London Sage ISBN
    0803932510.
  • synergy. (2008). In Merriam-Webster Online
    Dictionary. Retrieved August 5, 2008, from
    http//www.merriam-webster.com/dictionary/synergy
  • 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
  • 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.
  • 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
  • Tyre, M. J., and Orlikowski, W. J.(1994)
    Windows of Opportunity Temporal Patterns of
    Technological Adaptation In Organizations,
    Organization Science (51), February, pp.98-118.
  • Zolin, R. Swift Trust in Hastily Formed
    Networks Cebrowski Institute, Naval Postgraduate
    School.
About PowerShow.com