SOCIAL NETWORK ANALYSIS OF

INTANGIBLES

Ali Anani

INTRODUCTION- VISUALIZING INTANGIBLES

Social Network Analysis (SNA) and Organizational Network Analysis (ONA) are continuously gathering momentum in studying relationships among agents or actors of these networks

I noticed the scarcity of applying SNA and ONA in networks in which actors are not humans, but intangible factors such as values and emotional intelligence.

TWO EXAMPLES OF POTENTIAL APPLICATIONS

I wish to introduce two ideas for analysis by

using the tools employed in SNA

One example is related to the study of

organizational performance

A second example is related to the study of

human emotions and their interactions

I employ a different technique than those I

used in three previous presentations(1, 2, 3)

ANALYSIS OF ORGANIZATIONAL PERFORMANCE

Donald Clark Donald published an impressive

chart showing the linkages of factors affecting

organizational performance.

I reproduced the chart in a SNA format to allow

for the study of the interacting parameters

using NodeXL Excel Template developed by

Microsoft.

THE RATIONALE

Soft skills are receiving widening attention for

their role in enhancing organizational

performance. As we talk about centrality of

human agents; by the same token we may

discuss the motivators that cause these

actions.

THE PERFORMANCE NETWORK

In this presentation a directed relationship has

been assumed between all factors that interact

to yield the observed performance.

EI stands for Emotional Intelligence

The resulting network shows the type of

interactions and their consequences

The size of vertices is proportional to the

Closeness Centrality

THE PERFORMANCE NETWORK- 2

THE PERFORMANCE NETWORK- 3

We have four

clusters with

components

of each

cluster

sharing the

same color in

this “Circle”

arrangement.

THE PERFORMANCE NETWORK- 2

The overall metrics of the network are:

Maximum Vertices in a Connected Component 15

Maximum Edges in a Connected Component 19

Maximum Geodesic Distance (Diameter) 7

Average Geodesic Distance 3.09

Graph Density 0.09

REMARKS

The low graph density (0.09) suggests the

possibility of having more interactions among

performance agents

There exists differences in other parameters

such as Betweenness Centrality, Closeness

Centrality, Ejgenvector Centrality and Clustering

Coefficient. See next slide. Refer in particular to

the differences in the Closeness Centrality

REMARKS- 2 SHOWING IMPORTANCE OF THE

CLASSIFYING FACTOR CLOSENESS CENTRALITY

0.000

1.000

2.000

3.000

4.000

5.000

6.000

0 10 20 30

Betweenness Centrality

Closeness Centrality

Eigenvector Centrality

Clustering Coefficient

THE HAREL-KOREN FAST MULTISCALE- ANOTHER

FACET OF THE NETWORK

QUICK INSPECTION

Inspection of the performance network factors

may reveal areas that deserve rethinking. For

example, that there exists no direct linkage

between engagement and values may deserve

a second thinking.

EMOTIONS AND THEIR INTERACTIONS

Disagreements on emotions are common, including the agreement on basic emotions whether there are six or eight of basic emotions. The transformation, mixing and overlapping of emotions are also topics open for varying opinions. For excellent references refer to Personality and susceptibility to positive and negative emotional states and Anger and Disgust: Discrete or Overlapping Categories?

SUMMARY OF FINDINGS

The first eight rows

comprise the eight basic

feelings with their edges

colored in orange in the

next slide

To verify the mixing of two

emotions such as Fear +

sadness to give surprise

a directional relationship

was drawn, and in this

case from fear to surprise

and sadness to surprise

THE CIRCLE ARRANGEMENT OF FEELINGS WITH

VERTICES SIZED TO CLOSENESS CENTRALITY

A SECOND VIEW: HAREL-KOREN FAST

MULTISCALE

ADDING A DYNAMIC FILTER

By increasing

closeness

centrality from 1.7

to 2.15 these are

the remaining

feelings

AN EXTRA GRAPH FFOR CLARITY OF THE

NETWORK

Emotional

clusters-

spheres with

same color

include the

components of

each cluster

THE GREED – FEAR QUADRANT

The next slides elaborate on the greed – fear quadrant and the possibility of producing different paths (bifurcation) depending on which quadrant we are in. These slides show the complexity of emotions as well.

Such graphs with the aid of emotional network analysis may help in uncovering the way emotions interact.

SNA are for intangible factors as well

Fear-Greed Quadrants

Low

fear, low

greed

High

fear, low

greed

High fear, high

greed

Low

fear, high

greed

Greed

Fear

THE GREED – FEAR QUADRANT

Low

fear, low

greed

High fear +

acceptance (loss of

greed) yields to

contempt

High fear, high greed

Low fear, high greed

gives

Surprise +

Fear yields

awe

Fast bifurcation between self-control

and lack of it. Rate of bifurcation may

lead to complex behavior

High anticipation

with joy yielding

optimism

Surprise mixed

with sadness to

yield

disappointment

Fear

Greed

Fast bifurcation between self-control and

lack of it. Rate of bifurcation may lead to

complex behavior