Proceedings of the 12th European Conference on Knowledge ...

Claudia Jooß et al
Regarding <strong>the</strong> actually implemented cross sectional measures in Fig. 1 <strong>the</strong> focus is set on <strong>the</strong>
segment <strong>of</strong> Scientific Co-operation underlining a major challenge <strong>of</strong> cluster coordination (cf. chapter
2). In order to examine <strong>the</strong>se measures, a set <strong>of</strong> quantitative and qualitative methodologies is crucial
to manage <strong>the</strong> highly cross linked scientific cooperations. Therefore, three different methodologies
(used to generate three different data sets) are explained in <strong>the</strong> following lines.
3.1 Performance Measurement
The implementation <strong>of</strong> a research-cluster-specific Balanced-Scorecard-Approach – as a first
methodology used – refers to <strong>the</strong> model <strong>of</strong> Kaplan and Norton (1992) and constitutes a “performance
measurement system” originally designed as a communication, information and learning system for
enterprises (Kaplan & Norton 1992). The approach is re-designed to <strong>the</strong> needs <strong>of</strong> scientific research
clusters and apart from an implementation in <strong>the</strong> production engineering cluster it is used in <strong>the</strong>
Cluster <strong>of</strong> Excellence “Tailor-Made Fuels from Biomass” at RWTH Aachen University (cf. Welter et al.
2010). The approach is characterized by <strong>the</strong> measurement and comparison <strong>of</strong> performance indicators
on different hierarchical layers (e. g. <strong>the</strong> layer <strong>of</strong> sub-project leaders or <strong>the</strong> layer <strong>of</strong> <strong>the</strong> management
board) and bases on a survey among all cluster actors to evaluate for instance <strong>the</strong> quality <strong>of</strong> scientific
cooperation or <strong>the</strong> scientific output. With reference to <strong>the</strong> initial Balanced Scorecard, <strong>the</strong> clusterspecific
tool contains four adapted perspectives which strictly refer to <strong>the</strong> overall vision <strong>of</strong> <strong>the</strong> cluster –
<strong>the</strong> solution <strong>of</strong> <strong>the</strong> Polylemma <strong>of</strong> Production Technology:
1 st The Internal Perspective/Research Cooperation,
2 nd The Learning and Development Perspective,
3 rd The Output/Customer Perspective,
4 th The Financial Perspective.
This enables <strong>the</strong> measurement <strong>of</strong> scientific oriented performance indicators, e. g. <strong>the</strong> generation <strong>of</strong>
excellent publications or <strong>the</strong> education <strong>of</strong> excellent researchers in production engineering. Concerning
this, <strong>the</strong> development <strong>of</strong> <strong>the</strong> different sub-targets can be measured yearly by means <strong>of</strong> cluster-internal
evaluations. Through yearly iterations, data is collected and analysed in order to work out
recommendations toge<strong>the</strong>r with <strong>the</strong> management board <strong>of</strong> <strong>the</strong> cluster. Thus, <strong>the</strong> performance <strong>of</strong> <strong>the</strong>
cluster can be reflected to enable a steering <strong>of</strong> <strong>the</strong> entire network. Fur<strong>the</strong>rmore, this supports <strong>the</strong>
adaption <strong>of</strong> cluster strategies through <strong>the</strong> consideration <strong>of</strong> relevant external influences (single-looplearning)
and <strong>the</strong> adaption <strong>of</strong> fundamental management ideologies and values (double-loop-learning)
(cf. Brall 2010; Kaplan & Norton 1997).
Until today, three Balanced-Scorecard-based evaluations were implemented in <strong>the</strong> Cluster <strong>of</strong>
Excellence with 117 respondents in average. In contrast to Kaplan and Norton, who do not suggest a
certain method to generate and to collect key performance indicators, a standardized survey has
been developed by <strong>the</strong> Cross Sectional Processes toge<strong>the</strong>r with <strong>the</strong> management board <strong>of</strong> <strong>the</strong>
cluster. By this means performance indicators <strong>of</strong> a scientific network become measurable (e. g. <strong>the</strong>
quality <strong>of</strong> cooperation). Analyzed by use <strong>of</strong> descriptive statistics, <strong>the</strong> performance indicators become
measurable and comparable through arithmetic averages or median.
3.2 Direct Evaluations
In addition to <strong>the</strong> yearly Balanced-Scorecard-based evaluation (which constitutes an indirect form <strong>of</strong>
evaluation), cluster-specific meetings and events (like colloquia for employees) are evaluated with
additional surveys by <strong>the</strong> participants subsequently to each event. This second methodology is
implemented to gain fur<strong>the</strong>r feedback <strong>of</strong> <strong>the</strong> use <strong>of</strong> single cross sectional measures at a respective
phase <strong>of</strong> network development.
3.3 Structured Interviews
In contrast to <strong>the</strong> performance measurement approach and direct evaluations, a qualitative method <strong>of</strong>
generating data is given by <strong>the</strong> method <strong>of</strong> structured interviews with participants <strong>of</strong> <strong>the</strong> cluster. Hence,
24 interviews (representing all sub-projects <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence) were carried out on different
hierarchical levels by representatives <strong>of</strong> <strong>the</strong> Cross Sectional Processes – including pr<strong>of</strong>essors,
leading researchers, members <strong>of</strong> <strong>the</strong> management board and sub-project leaders – to enable a broad
review <strong>of</strong> already introduced cross sectional measures.
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