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Collaborative Network Topology Adaptation: Creating Synergies

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  • 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
        • Quantitative
          • Instrument Validation
          • Construct Validity
          • Refined Hypothesis Testing
          • Pareto Analysis
  • 3. Literature Review: Synergy
    • 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 Krippendorff;s 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 Low – High More frequent use of Strong Ties may be re-enforced by a closed collaborative network topology technology infrastructure 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 facilitate cooperative interaction that maximizes the [synergy] of combined capacities to reach the strategic objective” Saiz, Rodríguez & Bas (2005) Low – High TBD Low – High More frequent use of “Strong Ties provide a mechanism to invoke 'Weak' Ties” (Jack 2005) thus increasing the potential for Synergies of Scale. Strong Ties Low – High Formation of higher numbers of Weak Ties may be enabled by open collaborative network topologies technology infrastructure 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 TBD Low – High [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). “ 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 [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) Weak Ties 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).” 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 Functional Complementarities - “people form symbiotic relationships, they do not divide up a single task but provide complementary functions” (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 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)
  • 5. Components 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. 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.
  • 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-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 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 space framework for a desirable system model Node Capacity Table 2 -”Node Capacity and Weak and Strong Ties” 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. Cycles of misalignments may re-enforce the use of Strong links were As the level of goal congruence increases the use of Strong Ties increases As the level of Interpersonal Trust increases the use of Strong Links increases The level of Swift Trust has not effect on the use of Strong Ties. The number of concurrent Strong links is bounded by S/A capacity (7) + - 1 Strong Links The newly created Weak links may be constrained by the adaptive model. Window of Opportunity may increase the number of newly created Weak links. As the number of Weak links increases the level of goal congruence decreases As the level of Interpersonal Trust increases the number of Weak Ties decreases. As the level of Swift Trust increases, the number of Weak Links increases. The number of concurrent Weak links is may be extended through the use of collaborative technologies Weak Links Collaborative Capacity – Adaptive model Leonard-Barton (1998) Tyre and Orlikowski (1994) Collaborative Capacity - Goal Congruence (Jenh 1995) Collaborative Capacity -Trust-based Social Capital Coleman (1998) Collaborative Capacity - Swift Trust (Zolin 2006) Situational Awareness capacity Miller (1956) Cohen and Levinthal (1990) Szulanski (1996) Links
  • 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
        • Pareto Analysis
  • 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 [Weak and Strong Ties] and Synergy [S/A and Collaborative Capacity] and [Weak and Strong Ties]
  • 13. Preliminary Hypothesis Testing Present / Not Present Synergy Various Types Dependent Variables Low - High Strong Links Low - High Weak Links Independent Variables
  • 14. Refined Hypothesis Testing
    • Number Newly Created, Frequency of Use, and Number Dissolved
    Weak and Strong Ties Dependent Variables
    • Number of Channels
    Situational Awareness
    • Low – High
    Goal Congruence
    • Low - High
    Swift Trust
    • Low - High
    Trust-based Social Capital Independent Variables
  • 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 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 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. Cronbach’s 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
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  • 28. References (cont.)
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