A theoretical framework and classification of capability areas

5 Classification (RQ3)
5.1 Exploratory classification by cluster analysis
We applied trial-and-error to choose the algorithmic method and the number of clusters that best fit
our data, by using SPSS (version 18). The independent variables were the 17 capability areas. Since
they are binary (i.e. present or not), no standardisation was required.
• First, all methods available in SPSS (version 18) were tried on the full dataset (N=69),
resulting in four methods with clusters containing at least three BPMMs. Other methods with
clusters containing one or two BPMMs are considered as less reliable, and thus omitted.
• Next, to obtain stable results, these four methods were used on a split dataset, comprising
only BPMMs for generic business processes (N=37). It makes abstraction of the specific
process types included, i.e. supply chain or collaboration processes, to be applicable to any
process type. All four methods, resulting from the previous step, showed similarity on the
split dataset for two clusters. Since two clusters merely confirm the previous distinction
between BPM maturity and BPO maturity, we opted for a refinement into more clusters to
provide more information. This option resulted in two methods, each with three clusters, that
stayed fairly similar on the split dataset: Ward’s method and k-means.
The previous steps reduced our choice to two algorithmic methods (i.e. Ward and k-means) with three
clusters. Both clustering solutions were further examined to evaluate which one best fits our data.
• A Cohen’s Kappa value was computed as a measure of agreement on group memberships. A
good agreement was found between both methods on the full dataset (kappa=0.4