Knowledge Sharing over social networking systems


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

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Knowledge Sharing over social networking systems

  1. 1. Knowledge sharing over social networking systems
  2. 2. Content <ul><li>Context : </li></ul><ul><ul><li>Open innovation </li></ul></ul><ul><ul><li>Knowledge & learning </li></ul></ul><ul><ul><li>Knowledge sharing </li></ul></ul><ul><li>Case study of SNS </li></ul><ul><li>Ecademy data : </li></ul><ul><ul><li>Structure </li></ul></ul><ul><ul><li>Evolution </li></ul></ul><ul><ul><li>Survey </li></ul></ul><ul><li>Knosos </li></ul><ul><li>ADIIKS </li></ul>
  3. 3. Context : open innovation <ul><li>The knowledge boundaries of the organisation become fuzzy </li></ul><ul><li>Closed Innovation : </li></ul><ul><ul><li>R&D done internally </li></ul></ul><ul><ul><li>E.g. : Bell labs, Xerox, … </li></ul></ul><ul><li>Open Innovation (Chesborough 2003) : </li></ul><ul><ul><li>organisations are open to innovations which originated outside their boundaries </li></ul></ul><ul><ul><li>organisations are prepared to let the world outside their boundaries use their innovations </li></ul></ul>
  4. 4. Context : open innovation <ul><li>Examples of open innovation : </li></ul><ul><ul><li>Proctor&Gamble : </li></ul></ul><ul><ul><li>IBM : collaboration with academics & open source projects </li></ul></ul><ul><ul><li>Bekaert : </li></ul></ul><ul><ul><li>Crowdsourcing : </li></ul></ul><ul><ul><li> </li></ul></ul>
  5. 5. Context : open innovation Crowdsourcing @ Cambrain house
  6. 6. Context : open innovation <ul><li>Role of R&D in open innovation environment = to integrate external and internal knowledge to produce innovation : </li></ul><ul><li>&quot;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&quot; (Chesborough 2003,p52) </li></ul>
  7. 7. Context : open innovation <ul><li>Required to obtain open innovation : </li></ul><ul><ul><li>Awareness of external knowledge </li></ul></ul><ul><ul><li>Communication </li></ul></ul><ul><ul><li>Collaboration </li></ul></ul><ul><ul><li>=> Knowledge sharing over social networking systems </li></ul></ul>
  8. 8. Context : Knowledge <ul><li>Knowledge </li></ul><ul><ul><li>true justified belief </li></ul></ul><ul><ul><li>exists in the heads of people (cognitive systems) </li></ul></ul><ul><li>Information </li></ul><ul><ul><li>signal which can reduce uncertainty and ambiguity </li></ul></ul><ul><ul><li>the constituent of knowledge </li></ul></ul><ul><ul><li>exists outside the heads of people </li></ul></ul><ul><li>Managing information ≠ managing knowledge </li></ul>
  9. 9. Context : Learning Cognitive system Environment Non-cognitive system Cognitive systems Action Information transfer Cognitive processing
  10. 10. Context : Knowledge sharing <ul><li>People who share knowledge with individuals from different domains are more creative (Burt 2000, Kasperson 1978) </li></ul><ul><li>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) </li></ul>
  11. 11. Context : Knowledge sharing <ul><li>Elements that influence knowledge contribution : </li></ul><ul><ul><li>Probability that you will get something in return </li></ul></ul><ul><ul><li>Value of what you expect to get in return </li></ul></ul><ul><ul><li>Probability that the receiver will be able to use the knowledge in the same way as the source </li></ul></ul><ul><ul><li>The expected loss of value of the source’s knowledge as a result of sharing it </li></ul></ul><ul><ul><li>How hard it is to externalise the knowledge </li></ul></ul><ul><ul><li>How much it costs to transfer the knowledge </li></ul></ul>
  12. 12. Social networking systems <ul><li>SNS : aim to expand the social network of users </li></ul><ul><li>Examples : </li></ul><ul><ul><li>Dating sites </li></ul></ul><ul><ul><li>Friendship sites (e.g. Orkut, mySpace) </li></ul></ul><ul><ul><li>Business sites (e.g. OpenBC, Ecademy) </li></ul></ul>
  13. 13. Social networking systems <ul><li>Case-based analysis of a number of SNS (openBC, Orkut, Ecademy, linkedIn, mySpace, Connection) revealed a common structure : </li></ul>
  14. 14. Social networking systems : autopoiesis <ul><li>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) </li></ul><ul><li>Autopoiesis = self-reproduction (Greek) </li></ul><ul><li>Autopoiesis is what distinguishes living from non living systems </li></ul>
  15. 15. Social networking systems : autopoiesis <ul><li>Six Key points (Varela et al 1974) : </li></ul><ul><ul><li>when these apply to a system, it can be said to be autopoietic </li></ul></ul><ul><ul><li>e.g. : </li></ul></ul><ul><ul><ul><li>Cell </li></ul></ul></ul><ul><ul><ul><li>Cognition </li></ul></ul></ul><ul><ul><ul><li>Society </li></ul></ul></ul><ul><li>Allopoiesis (not alive) : </li></ul><ul><ul><li>components are produced by external entity </li></ul></ul><ul><ul><li>e.g. : </li></ul></ul><ul><ul><ul><li>Car </li></ul></ul></ul><ul><ul><ul><li>Computer </li></ul></ul></ul>
  16. 16. 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
  17. 17. Social networking systems : autopoiesis <ul><li>Essential finding : boundaries are essential to the “aliveness” of the system </li></ul><ul><li>Supported by Ecademy data : </li></ul><ul><ul><li>Significantly difference in knowledge sharing volume between open and closed groups </li></ul></ul><ul><ul><li>3x more knowledge sharing in closed groups </li></ul></ul>
  18. 18. Ecademy data <ul><li>Ecademy : publishes FOAF files : </li></ul><ul><li><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;;/> </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;;/> </foaf:Person> <ecademy:numberOfMessages> 30 </ecademy:numberOfMessages> </foaf:knows> </li></ul>
  19. 19. Ecademy data <ul><li>SocNetAnalyzer : </li></ul><ul><ul><li>Download FOAF periodically </li></ul></ul><ul><ul><li>Parse FOAF </li></ul></ul><ul><ul><li>Convert FOAF files to a Graph, which could be analysed and manipulated using the JUNG java Graph API </li></ul></ul>
  20. 20. Ecademy data <ul><li>Used Ecademy data to : </li></ul><ul><ul><li>Study structure </li></ul></ul><ul><ul><li>Study evolution of the network </li></ul></ul><ul><ul><li>Draw a sample for a survey </li></ul></ul>
  21. 21. Ecademy data - stucture
  22. 22. Ecademy data - Structure <ul><li>Power law : </li></ul><ul><ul><li>Characterized by very small number of nodes with a high number of connections </li></ul></ul><ul><ul><li>Distribution exists in : </li></ul></ul><ul><ul><ul><li>Pareto 20-80 law on distribution of wealth </li></ul></ul></ul><ul><ul><ul><li>Structure of the www </li></ul></ul></ul><ul><ul><ul><li>Spread of diseases </li></ul></ul></ul><ul><ul><ul><li>Society => Celebrities </li></ul></ul></ul>
  23. 23. Ecademy data - structure fits 99% of the data =>power law distribution
  24. 24. Ecademy data - structure <ul><li>Small-world networks : </li></ul><ul><ul><li>Small average separation (6 degrees in society, according to Milgram (1967)) </li></ul></ul><ul><ul><li>High degree of clustering </li></ul></ul><ul><li>Conditions for small-world state (Watts & Stogatz (1998) : </li></ul><ul><ul><li>d ≈d random </li></ul></ul><ul><ul><li>c>>c random </li></ul></ul><ul><li>d= average distance between nodes </li></ul><ul><li>c= average clustering coefficient </li></ul>
  25. 25. Ecademy data - structure Random network Ecademy data <ul><li>Ecademy network can </li></ul><ul><li>certainly has small-world </li></ul><ul><li>properties </li></ul>
  26. 26. Ecademy data - evolution <ul><li>Why does the network evolve as it does ? </li></ul><ul><li>Very hard to answer, because of different possible effects </li></ul><ul><li>Stochastic actor-driven modeling (Snijder 2005) : permits the testing of network effects, while controlling for other variables </li></ul><ul><li>Works longitudinally, e.g. : </li></ul><ul><ul><li>[email_address] </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul>
  27. 27. Ecademy data - evolution <ul><li>Algorithm : </li></ul><ul><ul><li>Select a node who is permitted to create a new edge </li></ul></ul><ul><ul><li>A utility function, composed of a number of selected effects is maximized to select the edge to create </li></ul></ul><ul><ul><li>After a complete run, estimated statistics are compared to statistics in the measured network and parameters are adapted </li></ul></ul>
  28. 28. Ecademy data - evolution <ul><li>Selected data : </li></ul><ul><ul><li>Small subset of the network which had many strong connections (exchanged more than 50 messages) </li></ul></ul><ul><ul><li>3 measurements </li></ul></ul>
  29. 29. Ecademy data - evolution <ul><li>Selected effects : </li></ul><ul><ul><li>Network effects : </li></ul></ul><ul><ul><ul><li>Transitivity </li></ul></ul></ul><ul><ul><ul><li>Number of nodes at distance 2 </li></ul></ul></ul><ul><ul><ul><li>Degree of alter </li></ul></ul></ul><ul><ul><li>Attribute effects : </li></ul></ul><ul><ul><ul><li>Similarity </li></ul></ul></ul><ul><ul><ul><li>Activity </li></ul></ul></ul><ul><ul><ul><li>Popularity </li></ul></ul></ul>
  30. 30. Ecademy data - evolution <ul><li>Findings : </li></ul><ul><ul><li>Structure is mainly caused by degree of alter effect : people tend to create connections to other people who already have many connections </li></ul></ul><ul><ul><li>People with the same account types connect to each other </li></ul></ul><ul><ul><li>Women increase their number of connections at a faster rate than men </li></ul></ul><ul><ul><li>People with the same sex connect to each other </li></ul></ul>
  31. 31. Ecademy data - evolution <ul><li>Common offline social mechanisms, like transitivity, do not influence the structure of the system in a social networking system. </li></ul><ul><li>Recommendation : Make social structure more transparent than what is currently done in existing social networking systems </li></ul>
  32. 32. Ecademy data - survey <ul><li>Random selection of 2000 relationships with strength > 10 </li></ul><ul><li>Sent online questionnaire which investigated the influence of certain variables on knowledge sharing and the way people share knowledge </li></ul><ul><li>Response rate : 33,8% </li></ul>
  33. 33. Ecademy data - survey <ul><li>Findings : face-to-face relationships still have a positive influence on </li></ul><ul><ul><ul><li>Relationship strength </li></ul></ul></ul><ul><ul><ul><li>Communication intensity </li></ul></ul></ul><ul><ul><ul><li>Transactive memory directory development </li></ul></ul></ul><ul><ul><ul><li>Knowledge sharing </li></ul></ul></ul><ul><li>Ecademy has led to new relationships, but mainly weak ones </li></ul>
  34. 34. Ecademy data - survey
  35. 35. Ecademy data - survey <ul><li>Recommendation : when starting up new social networking systems in or between organisations, combine this with regular face-to-face events </li></ul><ul><li>Integrating VoIP services in existing and new SNS can be beneficial </li></ul><ul><li>Providing clues about transactive memory directories is important </li></ul>
  36. 36. Knosos <ul><li>Builds on PhD findings </li></ul><ul><li> </li></ul><ul><li>Tagviz : </li></ul>
  37. 37. ADIIKS <ul><li>Advanced Distributed Interactive Knowledge Sharing </li></ul><ul><li>Brussels region Impulse program </li></ul><ul><li>7-8 FTE researchers </li></ul><ul><li>Themes : </li></ul><ul><ul><li>Living ontology (VUB-StarLab) </li></ul></ul><ul><ul><li>Adaptive navigation (VUB-WISE) </li></ul></ul><ul><ul><li>Matching (ULB-CAD/CAM) </li></ul></ul><ul><ul><li>Multilingual support (EHB-CVC) </li></ul></ul><ul><ul><li>GDS (VUB-MOSI/ULB SMG) </li></ul></ul><ul><ul><li>Knowledge contribution study (VUB-MOSI) </li></ul></ul><ul><ul><li>Pattern development (VUB-MOSI) </li></ul></ul>