Synergies between Social Network Analysis, Communities...

Enriching Knowledge Networks

Synergies between Social Network Analysis, Communities of Practice and Knowledge Maps

by Ronèl DAVEL

http://www.google.co.za/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjQ5smdg8zVAhXIPxQKHTd4DkoQjRwIBw&url=http://excelsiar.co.za/sars/sars-logo/&psig=AFQjCNEI5hWIu64UY1qUG3-k4rLcm1bk8g&ust=1502432465209931

2017 South African Knowledge Management Summit

Presentation Outline

Research Problem Key ConceptsResearch

MethodologyLimitations

Main Discoveries Lessons Learned Contribution to KM Questions

https://www.google.co.za/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwi9xNrel6_PAhUFcBoKHd6XDAIQjRwIBw&url=https://www.saiie.co.za/cms/category/42-&psig=AFQjCNHV9CxF7QTTEBSocfkL7tyqMqqn5Q&ust=1475053249480305



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




Research Question

MAIN RESEARCH PROBLEM:

How can synergies between SNA, CoPs and knowledge maps reinforce knowledge networks?

►Research Problem

► Key Concepts


► Limitations


► Lessons Learned


► Questions




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




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


► Limitations


► Lessons Learned


► Questions




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


►Key Concepts


► Limitations


► Lessons Learned


► Questions




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


►Key Concepts


► Limitations


► Lessons Learned


► Questions




Advancing KM through Social Capital

The influence of social capital on KM processes

Social capital encourages participation

Discovering knowledge via network relationships


►Key Concepts


► Limitations


► Lessons Learned


► Questions




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.


►Key Concepts


► Limitations


► Lessons Learned


► Questions




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


►Key Concepts


► Limitations


► Lessons Learned


► Questions




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


►Key Concepts


► Limitations


► Lessons Learned


► Questions




Social Networks vs Organisational Structures


►Key Concepts


► Limitations


► Lessons Learned


► 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




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


►Key Concepts


► Limitations


► Lessons Learned


► Questions




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


►Key Concepts


► Limitations


► Lessons Learned


► Questions




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


► Key Concepts

►Research

Methodology

► Limitations


► Lessons Learned


► Questions

Conduct SNA

‘Before’ snapshot

Distribute Skills/

Expertise Map

Identify & Establish

CoPs

Conduct SNA

‘After’ snapshot

Map Skills/ Expertise




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


► Key Concepts

►Research

Methodology

► Limitations


► Lessons Learned


► Questions




Sample Population


► Key Concepts

►Research

Methodology

► Limitations


► Lessons Learned


► Questions




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


► Key Concepts

►Research

Methodology

► Limitations


► Lessons Learned


► Questions




Mixed Methods Approach


► Key Concepts

►Research

Methodology

► Limitations


► Lessons Learned


► 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




Networks Constructed


► Key Concepts

►Research

Methodology

► Limitations


► Lessons Learned


► 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.




Relationships Considered


► Key Concepts

►Research

Methodology

► Limitations


► Lessons Learned


► 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




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


► Lessons Learned


► Questions

SNA1 & Knowledge Maps SNA1 & CoP Participation SNA1 & SNA2 SNA1 & SNA2




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


► Key Concepts


►Limitations


► Lessons Learned


► Questions




linking key network positions with the experts pinpointed in knowledge maps

Main Discoveries


► Key Concepts


► Limitations

►Main Discoveries

► Lessons Learned


► 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




Main Discoveries


► Key Concepts


► Limitations

►Main Discoveries

► Lessons Learned


► 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




Main Discoveries


► Key Concepts


► Limitations

►Main Discoveries

► Lessons Learned


► 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




Main Discoveries


► Key Concepts


► Limitations

►Main Discoveries

► Lessons Learned


► 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





► Key Concepts


► Limitations

►Main Discoveries

► Lessons Learned


► 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





► Key Concepts


► Limitations

►Main Discoveries

► Lessons Learned


► Questions





RespondentsNetwork


connectionsActual

connectionsDensity

Average degree

No outgoing ties

No incoming ties



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





► Key Concepts


► Limitations

►Main Discoveries

► Lessons Learned


► Questions





RespondentsNetwork


connectionsActual

connectionsDensity

Average degree

No outgoing ties

No incoming ties



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





► Key Concepts


► Limitations

►Main Discoveries

► Lessons Learned


► Questions





RespondentsNetwork


connectionsActual

connectionsDensity

Average degree

No outgoing ties

No incoming ties



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






Whole Network Analysis: Frequency of Interaction

Density Reachability Centralisation

Possible connections

Actual connections

DensityNo outgoing

tiesNo incoming

ties

Average geodesic distance


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






Whole Network Analysis: Responsiveness

always within time

Bef

ore

usually within time usually late often fails to respond

Aft

er



Actual connections

DensityNo outgoing

tiesNo incoming

ties



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%






Whole Network Analysis: Level of Engagement

actively assists

Bef

ore

learns from only points to information input seldom assist

Aft

er



Actual connections

DensityNo outgoing

tiesNo incoming

ties



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%






Whole Network Analysis: Trust

very comfortable

Bef

ore

comfortable not so comfortable very uncomfortable

Aft

er



Actual connections

DensityNo outgoing

tiesNo incoming

ties



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%




Lessons Learned


► Key Concepts


► Limitations


►Lessons Learned


► Questions

“Mistakes are lessons by another name.” - Rosi S. Phillips



https://www.google.co.za/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjJ0ZGNz9PRAhWDvRoKHbeFD4YQjRwIBw&url=https://www.thefix.com/content/10-lessons-i-learned-my-relapses&psig=AFQjCNFcaGPMkVng6D396XH2gc6c0NUyQg&ust=1485100608734898

http://www.google.co.za/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwiio-yhzdPRAhXC2hoKHWjnCIIQjRwIBw&url=http://www.pretzellogic.org/2016/10/28/60-days-in-6-things-i-learned-about-marketing-automation/lessons-learned/&psig=AFQjCNFcaGPMkVng6D396XH2gc6c0NUyQg&ust=1485100608734898


Contribution to Knowledge Management


► Key Concepts


► Limitations


► 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)




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




Questions


► Key Concepts


► Presenting the Results

► Conclusion

►Questions



Synergies between Social Network Analysis, Communities...

Documents

Transcript of Synergies between Social Network Analysis, Communities...