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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. 484
3.4 Overall Analysis and Triangulation Claudia Jooß et al With <strong>the</strong> approach <strong>of</strong> <strong>the</strong> Grounded Theory (Glaser & Strauss 2008, Strauss & Corbin 1996, Strübing 2008), which depicts a widespread method for <strong>the</strong> development <strong>of</strong> organizational management <strong>the</strong>ories and models, a data based <strong>the</strong>ory can be developed to illustrate <strong>the</strong> complex social reality by data. Because <strong>of</strong> this, a data triangulation <strong>of</strong> <strong>the</strong> three introduced methodologies is necessary. During <strong>the</strong> process <strong>of</strong> triangulation, hypo<strong>the</strong>ses are formulated defining cross sectional measures that aim at ensuring <strong>the</strong> high quality <strong>of</strong> scientific output at a certain phase <strong>of</strong> network development (initial phase, stable phase and steady phase) (Ahrens et al. 2004). 4. Discussion <strong>of</strong> preliminary results Cross Sectional Processes in <strong>the</strong> Cluster <strong>of</strong> Excellence “Integrative Production Technology for High- Wage Countries” at RWTH Aachen University provide cluster-specific means <strong>of</strong> learning and knowledge management to support <strong>the</strong> high quality <strong>of</strong> scientific output in <strong>the</strong> cluster. The following preliminary results <strong>of</strong> <strong>the</strong> yearly performance measurement, <strong>the</strong> direct evaluations and <strong>the</strong> structured interviews outlined several aspects that have to be considered: Through <strong>the</strong> triangulated data analysis <strong>the</strong> importance <strong>of</strong> more intensive cross sectional activities on <strong>the</strong> level <strong>of</strong> <strong>the</strong> four core Integrated Cluster Domains (ICDs) was underlined for a successful cluster development. Following <strong>the</strong> results <strong>of</strong> <strong>the</strong> data, especially in <strong>the</strong> initial phase <strong>of</strong> <strong>the</strong> cluster, <strong>the</strong> work on <strong>the</strong> level <strong>of</strong> <strong>the</strong> single ICDs has to be initialized and supported e. g. through regular ICD-meetings in order to foster <strong>the</strong> exchange <strong>of</strong> sub-project leaders and researchers. As a reaction to <strong>the</strong> results <strong>of</strong> <strong>the</strong> Balanced-Scorecard-based evaluations from 2008 to 2010, an optimization <strong>of</strong> interlinking <strong>the</strong> different ICDs was advised. This was realized through so called ‘demonstrators’ by <strong>the</strong> cluster management board. Demonstrators can be described as highly cross linked sub-projects integrating researchers from different ICDs and/or single partners from industry to foster cross sectional scientific exchange on <strong>the</strong> one hand and <strong>the</strong> integration <strong>of</strong> applied research on <strong>the</strong> o<strong>the</strong>r hand. Fur<strong>the</strong>r figures from <strong>the</strong> analysed data made obvious that <strong>the</strong> personal benefit for researchers participating in meetings decreases with an augmenting number <strong>of</strong> participants in meetings. Considering that, a stronger implementation <strong>of</strong> regular meetings with smaller groups <strong>of</strong> participants (e. g. sub-projects and ICDs) has to be favoured to foster <strong>the</strong> interdisciplinary cooperation instead <strong>of</strong> too many meetings on <strong>the</strong> level <strong>of</strong> a general assembly. Especially following <strong>the</strong> interview statements <strong>of</strong> project leaders, a new definition <strong>of</strong> <strong>the</strong> role <strong>of</strong> project leaders as knowledge agents has to be aspired, because it is mainly this group that integrates and transfers project-specific knowledge into <strong>the</strong> ICD and o<strong>the</strong>r parts <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence. Moreover, with regard to o<strong>the</strong>r forms <strong>of</strong> cross linking actors within <strong>the</strong> cluster, <strong>the</strong> data analyses <strong>of</strong> <strong>the</strong> Cross Sectional Processes revealed that although <strong>the</strong>re is a high awareness among <strong>the</strong> researchers about <strong>the</strong> need <strong>of</strong> digital tools to network different actors, <strong>the</strong> current networking devices are not used regularly (e. g. <strong>the</strong> common data platform). Possible solutions thus comprise e. g. a stronger integration <strong>of</strong> <strong>the</strong> data platform into <strong>the</strong> <strong>of</strong>ficial cluster homepage, <strong>the</strong> integration <strong>of</strong> a semantic web into <strong>the</strong> data platform to connect contents with single scientists and projects or <strong>the</strong> integration <strong>of</strong> calendar functions for common appointments and seminars with regard to advanced training activities. In general a more interactive form <strong>of</strong> digital networking is desired following <strong>the</strong> data analyses and seems to be promising considering an increase <strong>of</strong> interdisciplinary cooperation as well as <strong>the</strong> scientific output. As an overall cross sectional measure, <strong>the</strong> regular implementation <strong>of</strong> <strong>the</strong> Balanced-Scorecard- Approach has been shown as important to control <strong>the</strong> research paths <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence in all development phases <strong>of</strong> <strong>the</strong> network by means <strong>of</strong> a comparable common data set. The performance measurement tool enables a reflection and redesign <strong>of</strong> already introduced operational activities on <strong>the</strong> one hand and <strong>the</strong> adjustment <strong>of</strong> long-term management strategies on <strong>the</strong> o<strong>the</strong>r hand. Referring to <strong>the</strong> process <strong>of</strong> critical examining long-term strategies, Kaplan and Norton speak <strong>of</strong> “Double-Loop-Learning” (Kaplan & Norton 1997). With regard to <strong>the</strong> production engineering focus <strong>of</strong> <strong>the</strong> cluster, <strong>the</strong> Balanced-Scorecard-Approach thus enables <strong>the</strong> regular measurement <strong>of</strong> <strong>the</strong> status quo to support <strong>the</strong> high quality <strong>of</strong> <strong>the</strong> scientific output or <strong>the</strong> training <strong>of</strong> excellent researchers. Although <strong>the</strong> process <strong>of</strong> double-loop- 485
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Proceedings <stron
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Contents Title Author(s) Picture <s
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Title Author(s) Picture of<
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Volume Two The Influence of
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The Symbolic Innovation of<
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Biographies of <st
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Jan Pawlowski works as Prof
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in academia. He holds the</
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Biomedical Engineering Research Cen
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Mathematics and Ph
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Thi Hai Hang Nguyen is a lecturer a
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Dan Savescu graduate TCM Faculty, p
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Teaches Business Computing and Stat
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Table 1: Inn-KM approach contents 2
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Knowledge Dynamics and Organisation
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Eckhard Ammann We distinguish three
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Eckhard Ammann quality dimension ar
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Eckhard Ammann The second cycle, Cy
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Figure 7: Supervised learning-by-do
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Identifying and Ranking the
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Manouchehr Ansari et al users <stro
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2.5 leadership and strategy Manouch
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Manouchehr Ansari et al conducted s
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Attitude of Senior
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Contextual Adaptive Visualization E
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2.4 Rights transfer Simona-Clara B
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A Framework for the</strong
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Leila Beig et al Additionally, Wei
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Leila Beig et al The proposed KM au
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Leila Beig et al source context Ele
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Leila Beig et al Figure 1: Learning
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Leila Beig et al Figure 3 visually
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Leila Beig et al based on collectiv
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Experiential Knowledge Creation Pro
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Pavel Bogolyubov Figure 1: number <
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4. Results and discussion Pavel Bog
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Pavel Bogolyubov Knowledge Manageme
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Pavel Bogolyubov McNamee, R. C., N.
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Table 2: Synthesis
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Ettore Bolisani, Marco Paiola and E
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Strategies for Increasing Knowledge
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Constantin Bratianu, Adriana Agapie
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Klaus Bredl, Amrei Groß and Jane F
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Camelia Burja and Vasile Burja Appr
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∀ , i ≥ 0 v u i where: ui repre
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Camelia Burja and Vasile Burja Jack
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Adriana Schiopoiu Burlea With respe
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Adriana Schiopoiu Burlea 3. ESF rol
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3.2 Discussions Adriana Schiopoiu B
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Simon Cadez and Vlado Dimovski with
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Simon Cadez and Vlado Dimovski scho
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Why Should I Share my new ideas? Cu
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National Knowledge Management Strat
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Implementing a work-life balance cu
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The Influence of R
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3.2.4 Knowledge-sharing Huei-Fang C
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Huei-Fang Chen and Yi-Wen Lin that
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Huei-Fang Chen and Yi-Wen Lin Hair,
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2.1.2 Social capital Marguerite Cro
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Marguerite Cronk 2.3 Social capital
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An Exploratory Study of</st
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4.1.2 The researchers Françoise de
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The Application of
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Nasser Easa and Robin Fincham knowl
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Network Management as a way to Mana
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Initiation Configuration Interessem
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Tiit Elenurm development visions. C
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References Tiit Elenurm Accorsi, F.
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A proposed Framework for Discoverin
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2.3 Knowledge mapping Mohamad Ali F
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Knowledge Workers: A Typology Frame
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Jiří Franek and Eva Grublova crea
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Jiří Franek and Eva Grublova Know
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Middle Managers’ Maturity <strong
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Charles Gagné et al attributable t
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Adoption of Knowle
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The Chain Value Process and Knowled
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Nebojsa Graca and Ana Lucija Gojako
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creating new, added, essential qual
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Intellectual Capital in Universitie
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Developing a Knowledge Strategy Usi
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3.1 Acquire Ciara Heavin and Freder
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