synergies between social network analysis, communities...
TRANSCRIPT
Enriching Knowledge Networks
Synergies between Social Network Analysis, Communities of Practice and Knowledge Maps
by Ronèl DAVEL
2017 South African Knowledge Management Summit Page 2
Presentation Outline
Research Problem Key ConceptsResearch
MethodologyLimitations
Main Discoveries Lessons Learned Contribution to KM Questions
2017 South African Knowledge Management Summit Page 3
What inspired this study?
►Research Problem
► Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
“The most useful information is rarely that which flows down the formal chain of command in an organisation, or that which can be inferred from price signals. Rather, it is that which is obtained from someone you have dealt with in the past and found to be reliable.” - Walter W. Powell
2017 South African Knowledge Management Summit Page 4
Research Question
MAIN RESEARCH PROBLEM:
How can synergies between SNA, CoPs and knowledge maps reinforce knowledge networks?
►Research Problem
► Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
2017 South African Knowledge Management Summit Page 5
Establish the level of interaction with the actual experts in knowledge networks by linking key network positions with the experts pinpointed in knowledge maps.
Determine whether any correlation exists between the levels of CoP participation and network positions held by individuals.
Investigate how the establishment of CoPs and the distribution of knowledge maps could influence knowledge network structures, specifically in terms of cliques, cut-points and hubs.
Examine in what way CoPs can influence network connectivityconsidering whole network assessments.
Research Objectives
►Research Problem
►Key Concepts
►Research
Methodology
►Limitations
►Main Discoveries
►Lessons Learned
►Contribution to KM
►Questions
2017 South African Knowledge Management Summit Page 6
Key Concepts
Linking Social Capital & KM
Social Networks vs Knowledge Networks
Social Capital
KM
Network Structures
Advancing KM through
Social Capital
SNA Metrics
KnowledgeNetworks
SNA
Social Networks
KNA
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
2017 South African Knowledge Management Summit Page 7
Social Capital
“… everyone you now know, everyone you knew and everyone who knows you even though you do not know them.”
- Burt 1992
The need for individuals to connectwith others in order to look forresources that they do not have attheir own disposal.
- Lesser & Prusak 1999
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
2017 South African Knowledge Management Summit Page 8
Knowledge Management
“…the concept under which information is turned into actionableknowledge and made available effortlessly in a usable form to thepeople who can apply it.”
- Patel & Harty 1998
“…tools, techniques, and strategies to retain, analyse, organise,improve, and share business expertise.”
- Groff & Jones 2003
“A capability to create, enhance and share intellectual capitalacross the organisation…”
- Lank 1997
Intellectual (knowledge
asset)
Library & Information
ScienceBusiness
Cognitive Science
Process-Technology
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
2017 South African Knowledge Management Summit Page 9
Advancing KM through Social Capital
The influence of social capital on KM processes
Social capital encourages participation
Discovering knowledge via network relationships
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
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Network Structures
Knowledge assets are frequently based on the experience and expertise of an organisation’s employees and in order to remain
competitive it is vital for organisations to be able to make use thereof.
Social capital resides in the connections between people within a social network and is primarily concerned with the value that
is created owing to these relations. Social networks can thus be regarded as vital sources of social capital.
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
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Network Structures
According to studies by Barabási (2002) centralised, distributed and decentralised social network structures are ideal environments for explicit, tacit and potential knowledge respectively.
EXPLICIT• The focal node in the network manages the knowledge
flow.
• Knowledge flows hierarchically from the top down and from the bottom to the top.
TACIT• No specific actor manages the flow of knowledge.
• Knowledge flows horizontally from one actor to another.
• Each actor has knowledge links to a few other actors.
POTENTIAL• Hubs in the knowledge network control the flow of
knowledge and intermediate between different groups.
• Some actors are more connected than others
Ce
ntr
alis
ed
De
cen
tral
ise
d
Dis
trib
ute
d
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
2017 South African Knowledge Management Summit Page 12
Social Networks & SNA
• an informal body consisting of a set of actors and the relationships between them. These relationships can be weak or strong; similar or diverse; and has an effect on the creation and distribution of knowledge among its members.
Social Networks
• visual and mathematical tools and techniques that are utilised to identify and analyse relationship patterns among actors within a network.
• an “organisational x-ray” that detects relationships that are not normally visible.
Social Network Analysis
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
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Social Networks vs Organisational Structures
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
Kagiso
Williams
Cohen
Isacs
Radebe
Dunn
Cross
Smith
Bell
Steyn
Roux
Taylor
Tau
Moore
Miller
Shapiro
Smit
Pieters
Pieters
Williams
Smit
Shapiro
Tau
RouxKagiso
Cohen
Taylor
Bell
Smith
Cross
Moore
Dunn
Isacs
Miller
Steyn
Radebe
Kagiso
Roux
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Knowledge Networks & KNA
• If social networks disclose who knows whom, knowledge networks disclose who knows what.
• actors and resources, where the relationships between them bring about knowledge capturing, transfer and knowledge creation.
• emphasise the exchange of information and knowledge as transactional content.
Knowledge Networks
• an extension of SNA
• helps to disclose what facilitates or hampers knowledge flows, who knows whom and who shares what information and knowledge with whom.
Knowledge Network Analysis
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
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SNA Metrics
Whole Network Assessment
• Network size
• Network reachability
• Network centralisation
• Network density
Network Structure
• Cliques
• Cut-points/Bottlenecks
• Hubs
Prominence
• Betweenness centrality
• Closeness centrality
• Degree centrality
• Eigenvector centrality
Distance
• Maximum Flow
• Geodesic distances
• Diameter
• Average path length
• Isolates
Connectivity
• Point connectivity
• Reciprocity
• Tie strength
► Research Problem
►Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
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Process Design
Research Phases
Preparation
Executing the Research
Deciphering the Results
Refine Research Objectives
Develop Instruments
SNA Survey Questions
Skills Audit (K-Map) Questionnaire
Interview Questions
Participants
Identify Participants
Communicate Objective
Obtain buy-in
Interpret Results
Analyse & Compare Data
Map Knowledge Networks Map Knowledge Role-players
► Research Problem
► Key Concepts
►Research
Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
Conduct SNA
‘Before’ snapshot
Distribute Skills/
Expertise Map
Identify & Establish
CoPs
Conduct SNA
‘After’ snapshot
Map Skills/ Expertise
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Sample Population
Contextualising the sample population:
Subdivision 115 members (+1)
(production support & system testing)
Subdivision 28 members
(building big-end solutions)
Subdivision 313 members (+1)(systems analysis)
Subdivision 411 members
(integrating environments)
Coding
• 49 employees in the division• 1 was on maternity leave• 1 was seconded to another division
• = TOTAL of 47 members
Participants per Subdivision
SD1 SD2 SD3 SD4 Total
Skills Audit 15 8 13 11 47
SNA 1 15 8 13 11 47
Joined CoPs 9 6 8 7 30
SNA 2 9 6 8 7 30
► Research Problem
► Key Concepts
►Research
Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
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Sample Population
► Research Problem
► Key Concepts
►Research
Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
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Data Collection Methods & Instruments
Group Interviews1. Confirm sample population &
identify subject matters2. Confirm Skills Audit Results3. Put SNA1 outcomes in
perspective4. Confirm CoPs to be established5. Assess SNA2 & CoP participation
Online Questionnaires
1. Skills Audit2. SNA13. SNA2
Indirect Unobtrusive
Measures1. Computer logs
► Research Problem
► Key Concepts
►Research
Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
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Mixed Methods Approach
► Research Problem
► Key Concepts
►Research
Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
Follo
w u
p w
ith
…
Inte
rpre
tati
on
QUANTITATIVEDATA COLLECTION & ANALYSIS
QUALITATIVEDATA COLLECTION & ANALYSIS
Skills Audit Interview 1:Identify required skills
SNA1Constructed
Network Maps
Interview 3:Assess SNA1 Results
Skills Maps2 3 4 5 6 7 8 91
11 12 13 14 15 16 17 1810
(18 Subject Matters)
Correspond with CoP Topics
Knowledge
Frequency
Responsiveness
Engagement
Level of Trust
1 2 3 4
Interview 5:Assess SNA2 Networks
& CoP Participation
Selecting CoP Topics Created Cops Interview 4:
Confirm CoP Topics
Measure CoP Participation
Map CoP Positions
Interview 5:Assess SNA2 Networks
& CoP Participation
Interview 2:Confirm Skills Maps
SNA 2
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Networks Constructed
► Research Problem
► Key Concepts
►Research
Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
Knowledge
Frequency
Responsiveness
Engagement
Trust
DIMENSION OBJECTIVE IMPACT ON CREATION AND SHARING OF KNOWLEDGE
▪ Raise awareness of ‘who knows what’ and ‘who is being contacted for what’
▪ Before approaching someone, one must have at least some perception of their expertise.
▪ Measure how often network members interact & strength of relationships
▪ Mature relations are more intense and are based on the intensity and frequency of interactions.
▪ Understand who is able to reach whom within a sufficient time frame.
▪ Improve speed of access/responsiveness to knowledge sharing.
▪ Knowing who is knowledgeable is only useful if one can gain access to their knowledge in time.
▪ Access is profoundly influenced by the closeness of one’s relationship as well as physical proximity, organisational design and collaborative technologies available.
▪ Problem solving through cognitive engagement.
▪ Improve the effectiveness with which people learn from one another.
▪ Attempt to understand someone’s need for information, before answering, are more helpful in terms of knowledge creation.
▪ Instead of dumping information, people first attempt to understand the problem as experienced by the seeker and fashion their knowledge to the problem at hand.
▪ Learning from a safe relationship.
▪ Encouraging people to voice riskier ideas will result in more creative solutions.
▪ When a person asks for information, they could become vulnerable.
▪ The ability to admit a knowledge deficiency often results in creativity and learning.
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Relationships Considered
► Research Problem
► Key Concepts
►Research
Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
Knowledge
Frequency
Responsiveness
Engagement
Trust
DIMENSION RELATIONSHIPS DELIBERATED DISCARDED RELATIONSHIPS
▪ 4 Subjects on which CoPs were constructed
▪ 14 Subjects
▪ At least once a week▪ At least every month
▪ At least every quarter▪ Ad hoc - (less than 4 times per year)▪ No contact
▪ Always responds within time▪ Usually responds within time
▪ Responds, but usually late▪ Often fails to respond▪ No contact
▪ Learns from this person regarding work-related problems
▪ Actively assists to reflect on problems and provides guidance to reach effective solutions
▪ Only points to information ▪ Input hardly ever assists to resolve problems▪ No contact
▪ Comfortable to share ideas▪ Very comfortable to share ideas
▪ Not so comfortable to share ideas▪ Very uncomfortable to share ideas▪ No contact
Cu
ttin
g-p
oin
t
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Constructing and comparing networks
Skills Maps vs Knowledge Networks
Linking CoP participation
with Key Network Positions
Comparing Knowledge
Network Structures
The influence of CoPs and
Knowledge Maps on Network Connectivity
Frequency
Access
Engagement
Trust
Network Structures • Cliques• Cutpoints• Hubs
Network Connectivity • Network Size• Density • Reachability• Centralisation
1 2 3 4
Knowledge► Research Problem
► Key Concepts
►Research
Methodology
► Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
SNA1 & Knowledge Maps SNA1 & CoP Participation SNA1 & SNA2 SNA1 & SNA2
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Limitations
• Less members participated in CoPs & SNA2
• Subdued CoP participation
• Shorter intervals between SNA1 & SNA2
Restructuring
• Less members participated in SNA2
Comparing the same data
• Frequency, Responsiveness, Engagement and Trust were handled as a whole and not per knowledge dimension
Whole network perspective
► Research Problem
► Key Concepts
► Research Methodology
►Limitations
► Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
2017 South African Knowledge Management Summit Page 25
linking key network positions with the experts pinpointed in knowledge maps
Main Discoveries
► Research Problem
► Key Concepts
► Research Methodology
► Limitations
►Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
Knowledge networks can ascertain if actual experts are approached for information
By combining knowledge networks and skills maps one can pinpoint non-expert authorities
Fusing knowledge networks and skills maps expose the nature of specialist relationships
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Main Discoveries
► Research Problem
► Key Concepts
► Research Methodology
► Limitations
►Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
Correlations between the levels of CoP participation and network positions held by individuals
CoP participation levels can be linked to knowledge network positions
Knowledge network positions influenced members’ disposition to join CoPs
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Main Discoveries
► Research Problem
► Key Concepts
► Research Methodology
► Limitations
►Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
Influence on knowledge network structures in terms of cliques, cut-points and hubs
Knowledge and information is transferred more effectively within knowledge networks as a result of CoPs
K-NETWORK 1
MEMBERS BEFORE AFTER
7 - -
6 1 0
5 4 9
4 9 11
3 18 6
TOTAL 32 26
K-NETWORK 2
BEFORE AFTER
- -
0 4
0 7
0 19
10 19
10 49
K-NETWORK 3
BEFORE AFTER
0 2
1 3
1 28
4 12
19 5
25 50
K-NETWORK 4
BEFORE AFTER
- -
1 7
3 11
22 15
10 11
36 44
Structural changes regarding # of cliquesK-NETWORK 1
BEFORE AFTER
BLOCKS 4 5
CUT-PTS 2 2
ISOLATES 1 2
K-NETWORK 2
BEFORE AFTER
4 1
3 0
9 1
K-NETWORK 3
BEFORE AFTER
2 2
1 1
4 1
K-NETWORK 4
BEFORE AFTER
3 4
2 2
5 1
Structural changes regarding cut-points
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Main Discoveries
► Research Problem
► Key Concepts
► Research Methodology
► Limitations
►Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
CoPs can influence network connectivity considering whole network assessments
Net
wo
rk S
ize
• The count of the number of members/nodes within a network.
• Indicates how big or small the network is.
3 of the 4 knowledge networks became much more populated.
Applied SNA Metrics
CentralisationSize Density Reachability
Net
wo
rk D
ensi
ty
• The proportion of direct ties in a network relative to the total number of possible ties.
• Measures the health and effectiveness of a network.
Density declined overall , impacting on the speed at which interaction took place among network members. Average degree increased in 3 knowledge networks signifying more interaction between network members . N
etw
ork
Rea
chab
ility • The accessibility of points of the network based on a notion of ‘path’.
• The degree to which any member of a network can reach other members within the network.
• Isolates: actors who does not form part of a specific network.
• Average geodesic distance: shortest av. path length between actors
3 of the 4 knowledge networks increased significantly regarding reachability as they each had only 1 isolate left.
Net
wo
rk C
entr
alis
atio
n
• The degree to which relationships in a network revolve around one or a few central network members.
• High network centrality implies that knowledge flows within a network depend on a few single nodes and the removal of these nodes may distort the knowledge flows.
• In-degree centralisation | out-degree centralisation | betweenness
In-degree: remained low to moderate overall = only a few members were approached by the rest of the members in the network.Out-degree: increased overall = more network members began to interact with their co-workers.Betweenness : remained moderate to low, indicating limited structural constraints concerning the flow of information.
The implementation of CoPs can lead to improved dissemination of knowledge
Network density can indicate if CoPs produced more trusted relationships and faster knowledge transfer
CoP activity can impact on the size of knowledge networks
CoPs can influence the level of interaction within knowledge networks
The formation of CoPs can result in improved connectivity within knowledge networks
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► Research Problem
► Key Concepts
► Research Methodology
► Limitations
►Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
The influence of CoPs and Knowledge Maps
on Network Connectivity
Whole Network Analysis: Knowledge Network 1
Size Density Reachability Centralisation
RespondentsNetwork
participantsPossible
connectionsActual
connectionsDensity
Average degree
No outgoing ties
No incoming ties
IsolatesAverage geodesic distance
Out-degree In-degree Betweenness
BEFORE 30 29 812 110 12,6% 3.7 3 2 1 2.2 58,3% 58,3% 57,4%
AFTER 30 28 756 101 11,6% 3.4 8 1 2 2 70% 48,6% 46,3%
Before After
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► Research Problem
► Key Concepts
► Research Methodology
► Limitations
►Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
The influence of CoPs and Knowledge Maps
on Network Connectivity
Whole Network Analysis: Knowledge Network 2
Size Density Reachability Centralisation
RespondentsNetwork
participantsPossible
connectionsActual
connectionsDensity
Average degree
No outgoing ties
No incoming ties
IsolatesAverage geodesic distance
Out-degree In-degree Betweenness
BEFORE 30 21 420 42 4,8% 1.4 7 3 9 1.8 23,5% 23,5% 3,9%
AFTER 30 29 812 139 16% 4.6 3 2 1 2.1 33,4% 4.,5% 13,3%
Before After
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► Research Problem
► Key Concepts
► Research Methodology
► Limitations
►Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
The influence of CoPs and Knowledge Maps
on Network Connectivity
Whole Network Analysis: Knowledge Network 3
Size Density Reachability Centralisation
RespondentsNetwork
participantsPossible
connectionsActual
connectionsDensity
Average degree
No outgoing ties
No incoming ties
IsolatesAverage geodesic distance
Out-degree In-degree Betweenness
BEFORE 30 25 600 72 8,3% 2.4 8 3 4 2.8 37,8% 30,7% 12,2%
AFTER 30 29 812 150 17,2% 5 7 2 1 2.4 57,1% 28,5% 24,1%
Before After
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► Research Problem
► Key Concepts
► Research Methodology
► Limitations
►Main Discoveries
► Lessons Learned
► Contribution to KM
► Questions
The influence of CoPs and Knowledge Maps
on Network Connectivity
Whole Network Analysis: Knowledge Network 4
Size Density Reachability Centralisation
RespondentsNetwork
participantsPossible
connectionsActual
connectionsDensity
Average degree
No outgoing ties
No incoming ties
IsolatesAverage geodesic distance
Out-degree In-degree Betweenness
BEFORE 30 26 650 96 11% 3.2 2 4 5 2.8 27,8% 35% 15,5%
AFTER 30 29 812 125 14,4% 4.2 3 3 1 2.7 49,3% 35,1% 16,8%
Before After
2017 South African Knowledge Management Summit Page 33
The influence of CoPs and Knowledge Maps
on Network Connectivity
Whole Network Analysis: Frequency of Interaction
Density Reachability Centralisation
Possible connections
Actual connections
DensityNo outgoing
tiesNo incoming
ties
Average geodesic distance
Out-degree In-degree Betweenness
BEFORE 870 217 49% 1 1 1.5 53,2% 31,7% 21,5%
AFTER 870 165 44% 2 0 1.6 54,7% 33,3% 25,5%
weekly
Bef
ore
monthly quarterly Ad hoc
Aft
er
2017 South African Knowledge Management Summit Page 34
The influence of CoPs and Knowledge Maps
on Network Connectivity
Whole Network Analysis: Responsiveness
always within time
Bef
ore
usually within time usually late often fails to respond
Aft
er
Density Reachability Centralisation
Possible connections
Actual connections
DensityNo outgoing
tiesNo incoming
ties
Average geodesic distance
Out-degree In-degree Betweenness
BEFORE 870 365 42% 0 1 1.6 31,2% 52,6% 7%
AFTER 870 338 40% 0 1 1.7 37,3% 58,7% 15,6%
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The influence of CoPs and Knowledge Maps
on Network Connectivity
Whole Network Analysis: Level of Engagement
actively assists
Bef
ore
learns from only points to information input seldom assist
Aft
er
Density Reachability Centralisation
Possible connections
Actual connections
DensityNo outgoing
tiesNo incoming
ties
Average geodesic distance
Out-degree In-degree Betweenness
BEFORE 870 324 40% 2 0 1.7 62,4% 33,9% 9,1%
AFTER 870 296 39% 2 0 1.7 59,3% 34,4% 16,6%
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The influence of CoPs and Knowledge Maps
on Network Connectivity
Whole Network Analysis: Trust
very comfortable
Bef
ore
comfortable not so comfortable very uncomfortable
Aft
er
Density Reachability Centralisation
Possible connections
Actual connections
DensityNo outgoing
tiesNo incoming
ties
Average geodesic distance
Out-degree In-degree Betweenness
BEFORE 870 388 66% 2 0 1.3 35,4% 35,4% 6,2%
AFTER 870 314 59% 0 0 1.4 42,1% 42,1% 15,4%
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Lessons Learned
► Research Problem
► Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
►Lessons Learned
► Contribution to KM
► Questions
“Mistakes are lessons by another name.” - Rosi S. Phillips
2017 South African Knowledge Management Summit Page 38
Contribution to Knowledge Management
► Research Problem
► Key Concepts
► Research Methodology
► Limitations
► Main Discoveries
► Lessons Learned
►Contribution to KM
► Questions
MAIN RESEARCH PROBLEM:
How can synergies between SNA, CoPs and knowledge maps reinforce knowledge networks?
Conduct a skills audit and
construct a knowledge map
Identify the sample
population
Distribute knowledge
maps
Establish CoPs regarding
predefined subject matters
Compare the results from
SNA1 & SNA2
Determine how synergies between SNA, CoPs and knowledge maps reinforced
knowledge networks
Execute an SNA and construct
knowledge networks
(SNA1)
Re-execute the SNA and
reconstruct knowledge networks
(SNA2)
2017 South African Knowledge Management Summit Page 39
Thank You
“In today's highly connected
world, we are learning to trust
in the ‘wisdom of crowds’, to
learn together with others and
to look to our peers for
shared experiences.”
– Geoff Parcell
2017 South African Knowledge Management Summit Page 40
Questions
► Research Problem
► Key Concepts
► Research Methodology
► Presenting the Results
► Conclusion
►Questions