using system dynamics to analyze innovation diffusion processes within intra-organizational...

Using System Dynamics to Analyze Innovation Diffusion

Processes within Intra-organizational NetworksProcesses within Intra-organizational Networks

Philipp Wunderlich, Andreas Philipp Wunderlich, Andreas

Purpose: to demonstrate the usefulness

analyze intra-organizational innovation diffusion

Method: a purely algebraic model is replicated

environment before it is extended by relaxing

group diffusion and inter-group diffusion takegroup diffusion and inter-group diffusion take

Findings: the parallel occurrence of intra-

the outcome of diffusion processes significantly

reinforcing dynamics of intra-group diffusionreinforcing dynamics of intra-group diffusion

diffusion is heavily influenced by the structure

Implications: adopter-dominated groups should

adopter-dominated groups should be isolated

probability and speed of successful innovationprobability and speed of successful innovation

Limitations: only one network structure between

are considered to be homogeneous.

Krackhardt’s (1997) diffusion model

Positive word-of-mouth Adopters

Group 1

Negative word-of mouth

convert toNon-adopter Adopter

Non-adopters

Group 1

conversion1

Negative word-of mouth Group 1

Principle of “Optimal Viscosity”

1

3

3 3 32 2 2 2

1

0.5

5 5 5 5 5 54 4 4 4 4 43

3

3

2

2

1

1

0.5

2

1

0

5 5 5 5 5 541

1 1 1 1 10 45 90 135 180 225 270

Time (Day)Average Adopter Fraction: MGr1_Migration17.5

Average Adopter Fraction: MGr1_Migration15.0


1 12 23 3

1

02

0

Adopters

Adopters

Adopters

* Radboud University Nijmegen, the Netherlands,




3 34 45 5

Adopters

Adopters

Adopters

* Radboud University Nijmegen, the Netherlands,

Using System Dynamics to Analyze Innovation Diffusion

organizational Networksorganizational Networks

Philipp Wunderlich, Andreas Größler, Jac Vennix*Philipp Wunderlich, Andreas Größler, Jac Vennix*

usefulness of system dynamics as a methodology to

diffusion processes.

replicated and analyzed in a system dynamics

relaxing the restrictive assumption that intra-

take place consecutively.take place consecutively.

-group and inter-group diffusion can change

significantly. The interplay between the self-

diffusion and the balancing dynamics of inter-groupdiffusion and the balancing dynamics of inter-group

structure of the network.

should be connected to each other while non-

isolated from each other in order to increase the

innovation diffusions.innovation diffusions.

between groups was examined and all groups

Stock/flow structure of Krackhardt’s (1997) model

migration a21 migration a54migration a43migration a32

Adopters

Group 1

Adopters

Group 2

Adopters

Group 3

Adopters

Group 4

Adopters

Group 5

migration a12 migration a23 migration a34 migration a45

migration a21 migration a54migration a43migration a32

adopters

Group 1

Non-adopters

Group 2

Non-adopters

Group 3

Non-adopters

Group 4

Non-adopters

Group 5

conversion1 conversion2 conversion3 conversion4 conversion5

Group 1 Group 2 Group 3 Group 4 Group 5

migration n12 migration n23 migration n34 migration n45

migration n54migration n43migration n32migration n21

Comparison Krackhardt / System Dynamics

543 3 32 2 2 21 1 1 1 1 543 32 2 21 1 1 1 1

5

5

4

44

3

3

3 3 3

2

2

2 2 2 21 1 1 1 11

0.5

5

5

4

4

4

3

3

3 3

2 2

2

2 2 21 1 1 1 1 1

5 5 5 5

5

4 4 43 32

45 90 135 180 225 270

Time (Day)

[gr1] : MGr1_Migration09.8



1 12 23 3

0 5 5 5 5 54 4 4 43 332

0 45 90 135 180 225 270

Time (Day)

Adopters [gr1] : MGr1_Migration10.4



1 12 23 3

Netherlands, {p.wunderlich, a.groessler, j.vennix}@fm.ru.nl




3 34 45 5




3 34 45 5

Netherlands, {p.wunderlich, a.groessler, j.vennix}@fm.ru.nl

using system dynamics to analyze innovation diffusion processes within intra-organizational...

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