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

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

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

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)

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

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

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)

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

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)

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 :

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

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

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

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 : 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

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

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

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

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 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]

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

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

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

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

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

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%

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

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

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/

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)

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)