Poster presentation at the 2012 International System Dynamics Conference, St. Gallen, Switzerland

1. Using System Dynamics to Analyze Innovation Diffusion
Processes within Intra-organizational Networks
organizational
Philipp Wunderlich, Andreas Größler, Jac Vennix*
Purpose: to demonstrate the usefulness of system dynamics as a methodology to
analyze intra-organizational innovation diffusion processes.
Method: a purely algebraic model is replicated and analyzed in a system dynamics
environment before it is extended by relaxing the restrictive assumption that intra-
group diffusion and inter-group diffusion take place consecutively.
Findings: the parallel occurrence of intra- -group and inter-group diffusion can change
the outcome of diffusion processes significantly The interplay between the self-
significantly.
reinforcing dynamics of intra-group diffusion and the balancing dynamics of inter-group
diffusion is heavily influenced by the structure of the network.
Implications: adopter-dominated groups should be connected to each other while non-
adopter-dominated groups should be isolated from each other in order to increase the
probability and speed of successful innovation diffusions.
Limitations: only one network structure between groups was examined and all groups
are considered to be homogeneous.
Krackhardt’s (1997) diffusion model Stock/flow structure of Krackhardt’s (1997) model
migration a21 migration a32 migration a43 migration a54
migration a12 migration a23 migration a34 migration a45
Positive word-of-mouth Adopters Adopters Adopters Adopters Adopters
Group 1 Group 2 Group 3 Group 4 Group 5
Non-adopter convert to Adopter conversion1 conversion2 conversion3 conversion4 conversion5
Non-adopters
adopters Non-adopters Non-adopters Non-adopters Non-adopters
Negative word-of mouth Group 1 Group 2 Group 3 Group 4 Group 5
migration n12 migration n23 migration n34 migration n45
migration n21 migration n32 migration n43 migration n54
Principle of “Optimal Viscosity” Comparison Krackhardt / System Dynamics
1 2 23 23 23
3 1 12 123 1 23 12345 12 1 23 12345
1 1 4 1 1 1 1
0.5 23 2 2
0.5 0.5
3 3 4
4 2
1 2 34 5 45 45 45 45 45 2 5 2 3
0 34 5 3 45 45 5 0 345 3 45 45 45 5
0
1 1 1 1 1 0 45 90 135 180 225 270 0 45 90 135 180 225 270
0 45 90 135 180 225 270
Time (Day) Time (Day)
Time (Day)
Adopters [gr1] : MGr1_Migration09.8 1 1 Adopters [gr1] : MGr1_Migration10.4 1 1
Average Adopter Fraction: MGr1_Migration17.5 1 1 1
Average Adopter Fraction: MGr1_Migration15.0 2 2 Adopters [gr2] : MGr1_Migration09.8 2 2 Adopters [gr2] : MGr1_Migration10.4 2 2
Average Adopter Fraction: MGr1_Migration12.5 3 3 Adopters [gr3] : MGr1_Migration09.8 3 3 Adopters [gr3] : MGr1_Migration10.4 3 3
Average Adopter Fraction: MGr1_Migration10.0 4 4 Adopters [gr4] : MGr1_Migration09.8 4 4 Adopters [gr4] : MGr1_Migration10.4 4 4
Average Adopter Fraction: MGr1_Migration07.5 5 5 Adopters [gr5] : MGr1_Migration09.8 5 5 Adopters [gr5] : MGr1_Migration10.4 5 5
* Radboud University Nijmegen, the Netherlands, {p.wunderlich, a.groessler, j.vennix}@fm.ru.nl