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Knowledge Sharing over social networking systems

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Presented on the MOSI internal seminar on the 06th of Oktober 2006

Presented on the MOSI internal seminar on the 06th of Oktober 2006

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Knowledge Sharing over social networking systems Knowledge Sharing over social networking systems Presentation Transcript

  • Knowledge sharing over social networking systems
  • Content
    • Context :
      • Open innovation
      • Knowledge & learning
      • Knowledge sharing
    • Case study of SNS
    • Ecademy data :
      • Structure
      • Evolution
      • Survey
    • Knosos
    • ADIIKS
  • Context : open innovation
    • The knowledge boundaries of the organisation become fuzzy
    • Closed Innovation :
      • R&D done internally
      • E.g. : Bell labs, Xerox, …
    • Open Innovation (Chesborough 2003) :
      • organisations are open to innovations which originated outside their boundaries
      • organisations are prepared to let the world outside their boundaries use their innovations
  • Context : open innovation
    • Examples of open innovation :
      • Proctor&Gamble : innocentive.com
      • IBM : collaboration with academics & open source projects
      • Bekaert : cognistreamer.com
      • Crowdsourcing : cambrianhouse.com
      • Flandersdrive.be
  • Context : open innovation Crowdsourcing @ Cambrain house
  • Context : open innovation
    • Role of R&D in open innovation environment = to integrate external and internal knowledge to produce innovation :
    • "You need not invent the most new knowledge or the best new knowledge to win. Instead, you win by making the best use of internal and external knowledge in a timely way, creatively combining that knowledge in new and different ways to create new products or services" (Chesborough 2003,p52)
  • Context : open innovation
    • Required to obtain open innovation :
      • Awareness of external knowledge
      • Communication
      • Collaboration
      • => Knowledge sharing over social networking systems
  • Context : Knowledge
    • Knowledge
      • true justified belief
      • exists in the heads of people (cognitive systems)
    • Information
      • signal which can reduce uncertainty and ambiguity
      • the constituent of knowledge
      • exists outside the heads of people
    • Managing information ≠ managing knowledge
  • Context : Learning Cognitive system Environment Non-cognitive system Cognitive systems Action Information transfer Cognitive processing
  • Context : Knowledge sharing
    • People who share knowledge with individuals from different domains are more creative (Burt 2000, Kasperson 1978)
    • Complex and messy problems can only be solved by interacting directly with other individuals instead of internalizing information from a DB (Vennix 1996, Contant et al 1996)
  • Context : Knowledge sharing
    • Elements that influence knowledge contribution :
      • Probability that you will get something in return
      • Value of what you expect to get in return
      • Probability that the receiver will be able to use the knowledge in the same way as the source
      • The expected loss of value of the source’s knowledge as a result of sharing it
      • How hard it is to externalise the knowledge
      • How much it costs to transfer the knowledge
  • Social networking systems
    • SNS : aim to expand the social network of users
    • Examples :
      • Dating sites
      • Friendship sites (e.g. Orkut, mySpace)
      • Business sites (e.g. OpenBC, Ecademy)
  • Social networking systems
    • Case-based analysis of a number of SNS (openBC, Orkut, Ecademy, linkedIn, mySpace, Connection) revealed a common structure :
  • Social networking systems : autopoiesis
    • Autopoiesis : “The autopoietic organisation is defined as a unity by a network of productions of components which (i) participate recursively in the same network of productions of components which produced these components, and (ii) realize the network of productions as a unity in the space in which the component exists.” (Varela et al 1974, p188)
    • Autopoiesis = self-reproduction (Greek)
    • Autopoiesis is what distinguishes living from non living systems
  • Social networking systems : autopoiesis
    • Six Key points (Varela et al 1974) :
      • when these apply to a system, it can be said to be autopoietic
      • e.g. :
        • Cell
        • Cognition
        • Society
    • Allopoiesis (not alive) :
      • components are produced by external entity
      • e.g. :
        • Car
        • Computer
  • Individual communication identity tools cognitive system 1 2 3 4 5 6 communication identity tools cognitive system 1 2 3 4 5 Group communication identity tools cognitive system 1 2 3 4 5 6 Dyadic System-wide tools A B C D E F
  • Social networking systems : autopoiesis
    • Essential finding : boundaries are essential to the “aliveness” of the system
    • Supported by Ecademy data :
      • Significantly difference in knowledge sharing volume between open and closed groups
      • 3x more knowledge sharing in closed groups
  • Ecademy data
    • Ecademy : publishes FOAF files :
    • <foaf:knows> <foaf:Person rdf:nodeID=&quot;_72689&quot;> <foaf:name> Tony Bobberson </foaf:name> <foaf:mbox_sha1sum>c85e8a77a76b5ce1f79af7fd2ddc3e6a0696bb7b</foaf:mbox_sha1sum> <rdfs:seeAlso dc:title=&quot;Ecademy FOAF file&quot; rdf:resource=&quot;http://www.ecademy.com/module.php?mod=network&op=foafrdf&uid=72666&quot;/> </foaf:Person> <ecademy:numberOfMessages> 20 </ecademy:numberOfMessages> </foaf:knows> <foaf:knows> <foaf:Person rdf:nodeID=&quot;_30853&quot;> <foaf:name> Frank Frankers </foaf:name> <foaf:mbox_sha1sum>52ba79e35b0e06faf8882831ba9e38fbdc2b8737</foaf:mbox_sha1sum> <rdfs:seeAlso dc:title=&quot;Ecademy FOAF file&quot; rdf:resource=&quot;http://www.ecademy.com/module.php?mod=network&op=foafrdf&uid=30853&quot;/> </foaf:Person> <ecademy:numberOfMessages> 30 </ecademy:numberOfMessages> </foaf:knows>
  • Ecademy data
    • SocNetAnalyzer :
      • Download FOAF periodically
      • Parse FOAF
      • Convert FOAF files to a Graph, which could be analysed and manipulated using the JUNG java Graph API
  • Ecademy data
    • Used Ecademy data to :
      • Study structure
      • Study evolution of the network
      • Draw a sample for a survey
  • Ecademy data - stucture
  • Ecademy data - Structure
    • Power law :
      • Characterized by very small number of nodes with a high number of connections
      • Distribution exists in :
        • Pareto 20-80 law on distribution of wealth
        • Structure of the www
        • Spread of diseases
        • Society => Celebrities
  • Ecademy data - structure fits 99% of the data =>power law distribution
  • Ecademy data - structure
    • Small-world networks :
      • Small average separation (6 degrees in society, according to Milgram (1967))
      • High degree of clustering
    • Conditions for small-world state (Watts & Stogatz (1998) :
      • d ≈d random
      • c>>c random
    • d= average distance between nodes
    • c= average clustering coefficient
  • Ecademy data - structure Random network Ecademy data
    • Ecademy network can
    • certainly has small-world
    • properties
  • Ecademy data - evolution
    • Why does the network evolve as it does ?
    • Very hard to answer, because of different possible effects
    • Stochastic actor-driven modeling (Snijder 2005) : permits the testing of network effects, while controlling for other variables
    • Works longitudinally, e.g. :
      • [email_address]
      • [email_address]
  • Ecademy data - evolution
    • Algorithm :
      • Select a node who is permitted to create a new edge
      • A utility function, composed of a number of selected effects is maximized to select the edge to create
      • After a complete run, estimated statistics are compared to statistics in the measured network and parameters are adapted
  • Ecademy data - evolution
    • Selected data :
      • Small subset of the network which had many strong connections (exchanged more than 50 messages)
      • 3 measurements
  • Ecademy data - evolution
    • Selected effects :
      • Network effects :
        • Transitivity
        • Number of nodes at distance 2
        • Degree of alter
      • Attribute effects :
        • Similarity
        • Activity
        • Popularity
  • Ecademy data - evolution
    • Findings :
      • Structure is mainly caused by degree of alter effect : people tend to create connections to other people who already have many connections
      • People with the same account types connect to each other
      • Women increase their number of connections at a faster rate than men
      • People with the same sex connect to each other
  • Ecademy data - evolution
    • Common offline social mechanisms, like transitivity, do not influence the structure of the system in a social networking system.
    • Recommendation : Make social structure more transparent than what is currently done in existing social networking systems
  • Ecademy data - survey
    • Random selection of 2000 relationships with strength > 10
    • Sent online questionnaire which investigated the influence of certain variables on knowledge sharing and the way people share knowledge
    • Response rate : 33,8%
  • Ecademy data - survey
    • Findings : face-to-face relationships still have a positive influence on
        • Relationship strength
        • Communication intensity
        • Transactive memory directory development
        • Knowledge sharing
    • Ecademy has led to new relationships, but mainly weak ones
  • Ecademy data - survey
  • Ecademy data - survey
    • Recommendation : when starting up new social networking systems in or between organisations, combine this with regular face-to-face events
    • Integrating VoIP services in existing and new SNS can be beneficial
    • Providing clues about transactive memory directories is important
  • Knosos
    • Builds on PhD findings
    • www.knosos.be
    • Tagviz : www.knosos.be/tagviz/
  • ADIIKS
    • Advanced Distributed Interactive Knowledge Sharing
    • Brussels region Impulse program
    • 7-8 FTE researchers
    • Themes :
      • Living ontology (VUB-StarLab)
      • Adaptive navigation (VUB-WISE)
      • Matching (ULB-CAD/CAM)
      • Multilingual support (EHB-CVC)
      • GDS (VUB-MOSI/ULB SMG)
      • Knowledge contribution study (VUB-MOSI)
      • Pattern development (VUB-MOSI)