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

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Claudia Jooß et al at RWTH Aachen University in 2006 bringing toge<strong>the</strong>r interdisciplinary scientists – mainly from production and material sciences – into a common network to investigate integrative production <strong>the</strong>ories. It integrates a highly complex and heterogeneous network <strong>of</strong> 19 university institutes, 7 associated institutes and fur<strong>the</strong>r non-university research institutions (e. g. from <strong>the</strong> Fraunh<strong>of</strong>er Society) as well as different industrial advisors. Because <strong>of</strong> this, <strong>the</strong> organizational structure <strong>of</strong> <strong>the</strong> cluster contains a cross sectional research field to cope with <strong>the</strong> challenge <strong>of</strong> interdisciplinarity in <strong>the</strong> context <strong>of</strong> production engineering. With reference to <strong>the</strong> introduced cluster, contemporary tendencies such as <strong>the</strong> integration <strong>of</strong> cognitive elements or <strong>the</strong> contribution <strong>of</strong> information technologies to production engineering extend <strong>the</strong> scope <strong>of</strong> classical mechanical engineering. Fur<strong>the</strong>rmore, <strong>the</strong> challenge can be characterized by external requirements to interdisciplinary research following <strong>the</strong> strategies <strong>of</strong> <strong>the</strong> German Research Foundation, <strong>the</strong> German Science Council or <strong>the</strong> <strong>European</strong> Union. According to that, after an overview <strong>of</strong> actual challenges in interdisciplinary research and network management, <strong>the</strong> cross sectional approach will be outlined. Moreover, <strong>the</strong> development <strong>of</strong> a model for <strong>the</strong> management <strong>of</strong> Cross Sectional Processes and <strong>the</strong> respective methodological approach are discussed. The paper concludes with preliminary findings in cluster-specific interdisciplinary research and recommendations for successful cooperations in <strong>the</strong> context <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence and its learning and knowledge management activities. Fur<strong>the</strong>r possible research questions in <strong>the</strong> scope <strong>of</strong> Cross Sectional Processes will be introduced in a final outlook <strong>of</strong> <strong>the</strong> paper. 2. Interdisciplinary Network Management as a Contemporary Challenge in Research Environments Processes <strong>of</strong> cross linking and networking reveal chances as well as challenges for involved organizations and networks such as a production engineering oriented Cluster <strong>of</strong> Excellence. Thus interdisciplinary work depicts a research field exploring phenomena like an augmenting complexity through heterogeneous partners, a growing application <strong>of</strong> hybrid methodologies or an augmenting dynamic <strong>of</strong> innovation cycles (Bryson 2009). Because <strong>of</strong> this <strong>the</strong> question <strong>of</strong> managing interdisciplinary cooperation needs to be fur<strong>the</strong>r discussed (Öberg 2011). Primarily, two conditions have to be fulfilled to guarantee network coordination (Siebert 2006): On <strong>the</strong> one hand, <strong>the</strong> condition <strong>of</strong> effectiveness demands a higher performance <strong>of</strong> networks than in o<strong>the</strong>r forms <strong>of</strong> coordination like market or hierarchy. Concerning research networks, <strong>the</strong> output through cooperation is supposed to be higher than in conventional forms <strong>of</strong> non-integrative research. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> condition <strong>of</strong> efficiency aims at <strong>the</strong> satisfaction <strong>of</strong> all actors. In <strong>the</strong> context <strong>of</strong> research networks this means that <strong>the</strong> benefit <strong>of</strong> cooperation has to be greater than communication and cooperation costs to be executed by each actor (Siebert 2006). For <strong>the</strong> development <strong>of</strong> a structure and organization <strong>of</strong> Clusters <strong>of</strong> Excellence and adequate Cross Sectional Processes <strong>the</strong> following aspects have to be considered in order to ensure <strong>the</strong> high quality <strong>of</strong> <strong>the</strong> cluster output: “One <strong>of</strong> <strong>the</strong> main attractions <strong>of</strong> interdisciplinary research is that it allows researchers freedom from disciplinary constraints” (Repko et al. 2010). But <strong>the</strong> heterogeneity <strong>of</strong> actors can lead to structural, cognitive and cultural challenges in coordination and cooperation, because diverse disciplines (e. g. production and material engineering, informatics as well as business sciences) are integrated in a network applying different or partly shared methodologies, <strong>the</strong>ories and technical terms (cf. ibid.). This gives space to new scientific possibilities as well as to new scientific challenges regarding communication (like <strong>the</strong> lack <strong>of</strong> a common language) and cooperation among single disciplines (Röbbecke et al. 2004, Sauer 2005). Hence, <strong>the</strong> cultural differences within <strong>the</strong> cluster afford chances as well as risks concerning <strong>the</strong> performance <strong>of</strong> <strong>the</strong> entire cluster. Especially project oriented networks distinguished by a limited project period are characterized by a high staff turnover (Sydow 2010). Because <strong>of</strong> this, an on-going fluctuation <strong>of</strong> actors may have chances and risks regarding <strong>the</strong> coherence <strong>of</strong> <strong>the</strong> network which may lead to a delay in <strong>the</strong> process <strong>of</strong> network development. The highly-complex research processes in networks such as a Cluster <strong>of</strong> Excellence demand <strong>the</strong> identification and continuous adaption <strong>of</strong> knowledge interfaces in terms <strong>of</strong> human, organizational and technological aspects. Following Sydow (2010) this implicates that methods <strong>of</strong> evaluation and reflection have to be included into a network management approach. Thus, a model for <strong>the</strong> management <strong>of</strong> Cross Sectional Processes has to contain elements <strong>of</strong> evaluation and reflection. 482
Claudia Jooß et al With reference to <strong>the</strong> challenges <strong>of</strong> interdisciplinary management in scientific oriented networks, <strong>the</strong> Cross Sectional Processes comprise two approaches: The provision <strong>of</strong> special services cross-linking <strong>the</strong> cluster actors, so called “Network Services” (Sydow & Zeichhardt 2009, Buhl & Meier zu Köcker 2009, Jooß et al. 2010) (e. g. organizational measures like colloquia for employees or digital knowledge management tools) aims at promoting <strong>the</strong> cluster-internal cooperation and <strong>the</strong> scientific output. Cross Sectional Processes also comprise an own scientific layer in <strong>the</strong> Cluster <strong>of</strong> Excellence “Integrative Production Technology for High-Wage Countries” researching on <strong>the</strong> continuous optimization <strong>of</strong> cross linking interdisciplinary scientific actors. The results <strong>of</strong> a performance measurement system (cf. chapter 3.1) are used for a fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> entire cluster strategy and single “Network Services” (ibid.). A strong dependency exists between both approaches. On <strong>the</strong> one hand, results <strong>of</strong> <strong>the</strong> scientific layer continuously influence a modification <strong>of</strong> <strong>the</strong> “Network Services” (ibid.). On <strong>the</strong> o<strong>the</strong>r hand, new research questions arise with <strong>the</strong> implementation <strong>of</strong> different “Network Services” (ibid.). 3. Cross Sectional Processes and Methodology Used With reference to contemporary challenges in interdisciplinary network management, fur<strong>the</strong>r research on an overall model for <strong>the</strong> management <strong>of</strong> Cross Sectional Processes in <strong>the</strong> context <strong>of</strong> scientific research networks like <strong>the</strong> Cluster <strong>of</strong> Excellence “Integrative Production Technology for High-Wage Countries” is necessary. Although e. g. Defila et al. (2006) define in <strong>the</strong>ir work relevant steps to manage interdisciplinary projects, fur<strong>the</strong>r specific aspects in <strong>the</strong> context <strong>of</strong> highly-complex interdisciplinary research networks, like different hierarchical levels or staff-turnover, have to be considered in this research field. These aspects directly influence <strong>the</strong> model for <strong>the</strong> management <strong>of</strong> Cross Sectional Processes which simultaneously includes <strong>the</strong> levels: human, organization and technology. Hence, to face this challenge and to answer <strong>the</strong> research question (cf. chapter 1), Cross Sectional Processes are developing a model to point out specific measures for a promotion <strong>of</strong> interdisciplinary cooperation and cluster development at respective phases <strong>of</strong> network development. Ahrens et al. (2004) classify phases <strong>of</strong> network development in initial phase, stable phase and steady phase (Ahrens et al. 2004). According to this, a toolbox <strong>of</strong> cross sectional measures for different levels <strong>of</strong> cluster-internal scientific cooperation (such as Knowledge Organisation, Research Organisation, Communication, and Knowledge Output) is implemented. The field-tested measures originating from <strong>the</strong> Federal Ministry <strong>of</strong> Education and Research (BMBF)-project SENEKA were adapted to <strong>the</strong> needs <strong>of</strong> a production engineering cluster. On basis <strong>of</strong> <strong>the</strong> experience in cross linking heterogeneous actors, first measures <strong>of</strong> <strong>the</strong> toolbox were advised to be implemented in <strong>the</strong> cluster in accordance with <strong>the</strong> steering committee and <strong>the</strong> cluster management board (cf. Fig. 1). Figure 1: Toolbox <strong>of</strong> Cross Sectional Measures 483
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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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Abdallah Al-Shawabkeh, Alexander K<
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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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Serious Games in the</stron
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Albena Antonova and Anandasivakumar
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Albena Antonova and Aniko Csepregi
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Seyed Esmaeil Asgharpour and Gholam
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Attitude of Senior
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4.5 Comparative Advantage Seyed Esm
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Contextual Adaptive Visualization E
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Xiaoyan Bai, David White and David
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Simona-Clara Bârsan, Mihaela-Georg
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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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Didiosky Benítez et al Figure 1: E
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Didiosky Benítez et al meanings ac
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Didiosky Benítez et al In previous
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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 Table 3: Countries
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Pavel Bogolyubov McNamee, R. C., N.
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Ettore Bolisani, Francesca Gambarot
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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 Tabl
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Camelia Burja and Vasile Burja info
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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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Adriana Schiopoiu Burlea In <strong
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3.2 Discussions Adriana Schiopoiu B
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Adriana Schiopoiu Burlea Nonaka, I.
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Simon Cadez and Vlado Dimovski with
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Simon Cadez and Vlado Dimovski scho
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Simon Cadez and Vlado Dimovski It i
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Why Should I Share my new ideas? Cu
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Francesco Calza, Rossella Canestrin
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National Knowledge Management Strat
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Behiye Çavuşoğlu and Mustafa Sag
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Implementing a work-life balance cu
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Juan-Gabriel Cegarra-Navarro, Mª E
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The Influence of R
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Huei-Fang Chen and Yi-Wen Lin outco
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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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Marguerite Cronk Figure 4: The rela
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An Exploratory Study of</st
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Françoise de Viron, Thomas Lederer
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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 3. Th
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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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Eva Eckenhofer An
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Eva Eckenhofer par
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Initiation Configuration Interessem
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Eva Eckenhofer Eve
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The Essence of Kno
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Emmanuel Innocents Edoun and Valden
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4. Case study and analysis Emmanuel
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2. The PUS-project Anandasivakumar
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Jamal El-Den and Xin Zhou This rese
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Jamal El-Den and Xin Zhou Boyce, C.
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Tiit Elenurm development visions. C
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Tiit Elenurm companies the<
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References Tiit Elenurm Accorsi, F.
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Isaac Enakimio and Abdallah Al-Shaw
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A proposed Framework for Discoverin
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2.3 Knowledge mapping Mohamad Ali F
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Mohamad Ali Feyz, Babak Akhgar and
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Mohamad Ali Feyz, Babak Akhgar and
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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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Jiří Franek and Eva Grublova memb
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Middle Managers’ Maturity <strong
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Zoltán Gaál et al investigated m
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Zoltán Gaál et al characterised b
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Charles Gagné et al This paper pre
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Charles Gagné et al attributable t
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Charles Gagné et al intermediary d
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Adoption of Knowle
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1.1.2 SMEs and KMS Tendayi Gondo an
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Tendayi Gondo and Edmore Kori ambit
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The Chain Value Process and Knowled
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Manel González-Piñero et al Captu
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4. What a biofeedb
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References Manel González-Piñero
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Nebojsa Graca and Ana Lucija Gojako
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creating new, added, essential qual
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Instance Schema Figure 4: Cyclic tr
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Receive suggestions Compare suggest
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The Not-Invented-Here Syndrome in A
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Opinion of colleag
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Appendix David Grosse Kathoefer and
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Individual Level Influencers on Tac
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Oscar Guerra and Janeth Rojas Accor
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Why does knowledge transfer process
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Understanding Personal Knowledge De
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Intellectual Capital in Universitie
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Table 3: Opinions about Human Capit
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Developing a Knowledge Strategy Usi
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Harold Harlow knowledge to create c
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Understanding the
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3.1 Acquire Ciara Heavin and Freder
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Ciara Heavin and Frederic Adam Trav
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From Knowledge Acquisition to Knowl
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Peter Marshall and Damian Gordon Ma
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