S06 - Joos et al_2013

8/10/2019 S06 - Joos et al_2013

1/11

The Journal of American Business Review, Cambridge * Vol. 1 * Num. 2 * Summer * 2013 189

Evaluation, Steering, and Support of Knowledge Production in

Interdisciplinary Clusters of Excellence One Step Beyond

Claudia Joo, IMA/ZLW & IfU, RWTH Aachen University, Germany

Dr. Rene Vossen, IMA/ZLW & IfU, RWTH Aachen University, Germany

Prof. Dr. Sabina Jeschke, IMA/ZLW & IfU, RWTH Aachen University, Germany

ABSTRACT

Size, complexity, and dynamics of interdisciplinary research clusters result in an advantage with regard toresolving hybrid and specific problems. The variety of different scientific experiences, disciplines, and cultures in(mutual) scientific and operative research, however, can lead to tension and conflict in cooperation (Loibl 2005).This explains the high demand for structural and procedural framework conditions to support interdisciplinaryknowledge production within interdisciplinary research clusters; thus, constant evaluation, steering, and support ofknowledge production is required. This continuous steering and support of knowledge production is also necessaryfor the successful orientation of all cluster actors, teams, projects, and partial projects toward the superordinateobjective of the German Research Foundation (DFG)the knowledge objectives derived from these as well as the

defined vision of the individual research clusters.

This paper discusses the impact of already experienced knowledge engineering tools by taking the casestudy of scientific DFG clusters, the so-called clusters of excellence, of the RWTH Aachen University as anexample. This impact is mirrored by consequent learning processes and successes in learning within the researchsubject clusters of excellence. A thesis concerning further needs for research of the nationwide Excellence Initiative,which covers further research constructs, is elaborated in an outlook that goes one step beyond and opens up theresearch horizon for years to come.

INTRODUCTION

In 2005/2006, the German Excellence Initiative was established by the federal government, the GermanResearch Foundation (DFG), and the advisory council on scientific matters to sponsor and support science andresearch. With the help of so-called future concepts,(1) clusters of excellence, (2) and graduate schools, (3)the Excellence Initiative aims to equally and broadly support top-level research and increase Germanys

attractiveness as a center for university and scientific research so as to sustainably strengthen the location forscience, improve Germanys international competitiveness, and visualize peaks in the areas of university andscience. (4)

The Excellence Initiative was structured in two promotion phases (2006/2007-2012 and 2012-2017) inwhich 45 graduate schools, 43 clusters of excellence, and 11 future concepts are currently supported in 44universities. (5) This paper focuses on clusters of excellence. Clusters of excellence are so complex that they areautomatically also broad with regard to the subject under study. Therefore, interdisciplinarity as well as challengesattached to the clusters emerge. The term cluster is not used selectively nationally or internationally. Thus, adefinition as well as objectives and valuation provisions are provided to point out the management requirements.

Vossen (2012) approached a notion for clusters of excellence on the basis of explanations of clusters byThomi/Stemberg (2008) via economic (Marshall 1920, Becattini 1990, Brusco 1992, Lerch 2009, Porter1998/2000/2002), spatially scientific(Williamson 1981, Cooke et al. 1996, Cooke/Schienstock 2000, Cooke 2009),

sociological(GREMI/Aydalot 1988, Fromhold-Eisebith 1999, Haasink 1997/2001, Wenger/Snyder 2000, Bettoni etal. 2004, Granovetter 1985/1992, Moody/White 2003), and knowledge-drivenperspectives as he worked out coreaspects of central features and terms. Referring to DFG (2006), Hlzl (2006), Sondermann et al. (2008), andRatschke (2009), the following definition of research clusters of excellence can be derived from Vossens analysis:

A scientific cluster of excellence is an interdisciplinary, research-driven system that displays a high degreeof complexity in terms of structure and behaviour. It is initiated by public means to work on a subordinatemutual task and is to be converted to sustainable structures. In this respect a scientific cluster of excellenceunites first of all actors from the area of science, but maintains cooperation relations with the economydepending on the thematic orientation as well. Due to the spatial concentration of heterogeneous actors,

8/10/2019 S06 - Joos et al_2013

2/11


resources, competences and infrastructure a cluster of excellence is denoted as highly complex and displaysdynamic interactions. The rationale is that spatial proximity promotes the development of knowledge andinnovations as well as the economic development of the location of the cluster and that it increases theinternational visibility of its top level research (Vossen 2012).

Apart from these core features, the DFG articulated clear aims and criteria for evaluation of clusters ofexcellence for research, for participating scientists as well as for the structures and management. Figure 1 lists the

criteria for evaluation.

Research Scientists Structures

Quality of research; originality andcoherence of the scientific program.

Highly qualified research groups andinternational visibility.

Integration of local research capacities, e.g.,not university-related research facilities.

Added value of interdisciplinarycooperation.

Opportunity to further develop the scientificcareer and attractiveness of up-and-comingyoung scientists.

Organization and management.

Influence on the research area in thefuture.

Concepts for qualification. Influence on the further structuraldevelopment of the university.

Plans for transferring the researchresults into practice.

Equality of opportunity and measures forwomen and men.

Figure 1: Criteria for evaluation for the first and second funding priority (DFG 2006)

Due to the innovative institutionalized form of scientific clusters of excellence, it is a special challenge tosteer the highly specific expert knowledge and the central, extensive research questions that create the completecluster. Therefore, another challenge is to develop further the excellence research approach. This requires constantsteering and evaluation to orientate all cluster actors, teams, projects, and partial projects toward the subordinateobjectives of the DFG, the knowledge aims derived and the vision of the cluster. There is an enormous needconcerning the verifiability of expedient knowledge activities for receiving relevant research output. Themanagement and steering of research associations occur only in combination with a concomitant reflection of thescientific aspirations in the form of operating numbers and indicators to be realized; thus, management and steeringcontemporarily become the focus of attention as far as research funding is concerned (Hombostel/von Ins 2009). Apre-emption of the demands of the sponsors, therefore, can only be realized through proactive and efficientmanagement with continuous evaluation mechanisms for processes and measures in alignment with the objectivesand visions (Schrder 2011).

KNOWLEDGE AND ORGANIZATIONAL LEARNING PROCESSES

When focusing on an expedient research process, it quickly becomes obvious that the capability totransform information and data into knowledge is driven by individual knowledge carriers and a joint knowledgebase (Probst et al. 2003). Keywords of the organizational knowledge base, hence, sum up individual and collectivebodies of knowledge at a certain point in time. This is the collective problem and action competence of anorganization that consists of explicit and implicit knowledge (Auer 2007). The knowledge base of an organizationcan be described systems theoretically as the cognitive sub-system of the social system organization, i.e. as a kindof interlinking of all minds that are located within the organization (Sammer 2000). This assumption, thus, intendsto ascribe knowledge to an organization. (6)

To serve this purpose, aids are needed to display the organizational knowledge processes clearly and showwhich individuals, relationships, and structures are decisive for the generation and application of (new) knowledge(Alwert 2006). The knowledge engineering tools that have been generated at the Institute of InformationManagement in Mechanical Engineering (IMA), Center for Learning and Knowledge Management (ZLW) and

Assoc. Institute for Management Cybernetics e.V. (IfU) (in short: IMA/ZLW & IfU) of the RWTH AachenUniversity start at this interface and pursue the aim of evaluating the knowledge progress and harnessing newknowledge for the research process, thus promoting new (knowledge) innovation and enabling management topursue the possibility of purposeful knowledge steering with the main aim of gaining competitive advantage. Thisclear management view and the concomitant reflection of the proceeding process of knowledge generation from theknowledge base result in new insights, relationships, and structures that can be described with adaptation learning,change learning, or process learning of organizations; this kind of learning, though, is then the basis for (additional)new knowledge (Alwert 2006).

8/10/2019 S06 - Joos et al_2013

3/11


For the learning theorists Argyris and Schn (1978), organizational learning processes are initialized by thediscrepancy between desired and actual acting and, hence, come along with the learning progress of an organizationand its employees. If no accordance exists between the acting results of an organization and the expectations(nominal-actual comparison), the action theories will be critically reflected and corrected.

Learning Loop 1:If correction of the deviations occurs under the circumstance of the retention of the existing management

perception (theories in use), the authors place it in the first of the three learning loops, which is adaptation learning(single-loop learning) (Argyris/Schn 1978). The reflection of similar experiences, situations, or conditions fromthe past leads to a virtually automatic gradual adaptation of the organizational structures, the organizationalactions and routines (Winkler et al. 2007, Alwert 2012).

Learning Loop 2:In contrast to adaptation learning, if new interpretation schemes involve corrections to new or changedenvironmental conditions, change learning or reflexive learning (double-loop learning) takes place. This kindof organizational learning relies on the willingness to change existing management perceptions(Argyris/Schn 1978) and leads to an adaptation of the organization as well as to other, often better results(Alwert 2012). This learning loop is not inevitably practicable, but depends on the assertiveness of theinitiators.

Learning Loop 3:

The third learning loop involves learning through double reflection (deutero learning); it contains thenecessity of learning to learn. In this case, the necessary self-reflection that is known in individual learningprocesses also occurs at the organizational level. The organization itself has information and, hence, theinherent essential decision premises to be able to adjust its own system to past and upcoming environmentalinfluences. The reflection of the learning context and the detection of learning obstacles and learning reliefsconstitute a decisive task (Argyris/Schn 1978). Deutero learning is based on the permanent reflection ofown theories in use.

In the following section, a cluster-internal strategic knowledge management process is introduced thatpromotes the acquisition, development, transfer, storage, use, and evaluation of knowledge in organizations.Therefore, knowledge engineering tools that address the evaluation, steering, and support of knowledge productionare introduced and matched to learning processes.

CLUSTER-INTERNAL STRATEGIC KNOWLEDGE MANAGEMENT

A cluster of excellence can be defined as a complex system that follows its own regularities, which arecontrolled by aims and rules. A management approach to such a complex system can be located in the field ofsystems science, especially cybernetics (Malik 2008, Mnstermann 2011, Strina 2012). The systems theory enablesa construct of description that makes different and complex structures conceivable and explicable which, thus,makes structured management possible. From the systems theoretical point of view, the steering and adaptation ofknowledge of a cluster of excellence can, thus, be defined as a control loop. (7) This control loop is characterized byits closed effect process in which the variables themselves influence each other. The human being is also to belocated as a contributing item within the control loop (DIN 19226-1).

In cybernetics, a control loop symbolizes growth of stability (Wiener 1948, Vester 2011). Due to thefeedback of its own output toward the renewed input (nominal-actual comparison) and the self-observation attachedto it, self-initiated regulation of the system can occur. Hence, the system tears most of the stability from the negativefeedback because the adaptation leads to the optimal result for the whole system. Positive feedback mostly leads totendencies of self-strengthening and, thus, through exponential growth to instability (Mnstermann 2011, Strina2012). According to Henning (1993), the regulation together with the feedback forms one of the main aspects ofthe automatic steering of real systems and processes. This regulation ensures aim-oriented and self-stabilizingsystem behavior (Henning 1993).

Managements central task is the coordination of system planning and steering. Hence, strategic knowledgemanagement can investigate, promote, and institutionalize organizational learning processes that supportmanagement in decision-making (Winkler et al. 2007). Referring to Winkler et al., the central tasks and objectives ofstrategic knowledge management can be derived, evaluated, and supported with the help of correspondingknowledge engineering tools:

8/10/2019 S06 - Joos et al_2013

4/11


Analysis of the coherence of effects between single components of the organizational knowledge base and clusteroutput or successDerivation and definition of indicators and operating numbers to evaluate knowledgeSteady surveillance and control of the defined indicators and operating numbersCompression and preparation of existing data to support decision makingDerivation of system interventions or concrete measures for the goal-oriented steering of the clusters

Systematic management of the organizational knowledge base is a core element in the value-addingactivities of existing knowledge. Holistic, knowledge-driven management coordinates the factor knowledge andthe organizational knowledge base as efficiently as possible. Strategic knowledge management beneficially supportsthe aims and strategies of the reference input (Winkler et al. 2007). Figure 2 portrays the control loop of the systemcluster of excellence.

Figure 2: Transfer of organizational learning and strategic knowledge management to a control loop (own depiction according to

Winkler et al. 2007)

The controlled system considered is the organization cluster of excellence. In this cluster, knowledgeanalysis is bestowed on the task of deriving specific knowledge aims from the objectives and strategies of the clusterof excellence and from the objectives and criteria of the DFG. Based on the knowledge aims, indicators andoperating numbers are defined that enable validation and the achievement of objectives (Winkler et al. 2007). In thephase of knowledge planning, objectives on the operative level are transferred and, hence, concrete measures areinitiated. The implementation of these measures by means of systems intervention and unplanned influences of so-called disturbance variables from the systems environment influence the system. The resulting system change isanalyzed with evaluation tools within the controlling system through the use of indicators. A nominal-actualcomparison is derived by the alignment with strategic knowledge aims (Winkler et al. 2007). The variableoriginating from the evaluation is restored via return. The knowledge analysis then ascertains the previouslypresented learning processes within organizations and the result from the discrepancy in an iterative process can bedefined as follows: If the deviation analysis leads to the situation that new measures for achieving the original

knowledge aims are instigated, it is single-loop learning. If new knowledge aimed to support organizationalobjectives is defined, it is double-loop learning the control loop is closed. For the long term, achieving theobjectives of knowledge management, thus, leads to an increased value of the organization.

CASE STUDY: KNOWLEDGE ENGINEERING TOOLS AND LEARNING PROCESSES

To promote interdisciplinary knowledge production, different knowledge engineering tools have beenexplored within the framework of a case study. These tools, thus, have already been implemented in the clusters ofexcellenceIntegrative Production Technology for High-Wage Countriesand Tailor Made Fuels from Biomassat theRWTH Aachen University in the first and, currently, second funding phases. Knowledge engineering focuses onwhether and why knowledge production can be promoted. Therefore, knowledge engineering tools address the

8/10/2019 S06 - Joos et al_2013

5/11


interaction among data, information, and knowledge on all organizational levels of interdisciplinary research clusters(ACCSE 2009/2010, ACCSE 2011/2012). This application-oriented research leads to services and products wherebyall cluster actors are integrated iteratively and cooperatively into the solution process (Joo et al. 2012). Therefore,the basic requirement for strategic knowledge management is the strategic control of knowledge. A control unit aimsto support continuously the management, steering, and regulation of highly complex scientific cooperation toimprove cluster performance. Important aspects of promoting knowledge production include the enhancement ofemployees satisfaction, efficient networking of actors, and the initiation of learning processes within the cluster.The implemented knowledge engineering of the cluster-specific performance can be achieved by use of thefollowing tools:

1.A cluster-specific balanced scorecard (BSC) for cluster-internal performance measurement (Welter et al.2012), including a benchmarking approach for cluster-external performance measurement (Kozielski2010, Joo et al. 2012) (see Figure 3).

Figure 3: Procedure and perspectives of the cluster-specific balanced scorecard

2. The development of methods and concepts analyzing quantity and growth of intellectual capital andknowledge gaps within the cluster of excellence (Vossen et al. 2012, Vossen 2012) (see Figure 4).

Figure 4: Procedure for developing/creating intellectual capital (IC) (Vossen 2012)

8/10/2019 S06 - Joos et al_2013

6/11


3. Cross-linking measures to support stabilization of the cluster and efficiency of scientific workingprocesses (see Figure 5).

Figure 5: Cross-linking measures and design elements for planning systems (Joo et al. 2012, Defila et al. 2006)

From Figure 2, the first knowledge engineering tool, the cluster-specific balanced scorecard, and the secondknowledge engineering tool, the intellectual capital, are assigned to the control unit. The third knowledgeengineering tool, the cross-linking measures, is assigned to the planning systems. (8) Concerning the cluster-specificbalanced scorecard (Joo et al. 2012), the procedure of which is displayed in Figure 3, the visions of the two clustersof excellence have been transferred into primary aims in cooperation with management of the clusters of excellence.The visions of the clusters of excellence are captured in various aspects (e.g., transparency of the research content,scientific output). The development of the cluster performance is measured with regard to the different aspectsthrough an annual evaluation of the entire period of the project. Along these iterations, data are gathered andanalyzed. After that, measures are worked out in cooperation with management to increase the performance of theclusters of excellence. (9) According to Welter (2013), by means of this regular comparison of the actual value andthe intended value on the one hand, and the long-term strategy on the other, an effect occurs that is called double-loop learning by Kaplan and Norton (Kaplan/Norton 1997):

A double-loop effect occurs when managers question requirements and consider whether the assumptionsthat they have been working on so far can be held upright under the present circumstances, observationsand experiences (Kaplan/Norton 1997; quoted in Havighorst/Mller 2000). Welter (2013) stressed that thefact that the use of the BSC is a matter of a dynamic process that implies a continuous reflection andadaptation of organization-internal structures as well as the BSC itself (Welter 2013).

Through the iterative approach single-step data collection, data analysis, analysis and reassessment of themanagement of the whole cluster, identification of appropriate measures, implementation of measures carried out ina cycle) that is contained in all of the three knowledge engineering tools, the aim is to achieve double-loop learningin the sense of reflexive change learning versus reactive adaptation learning (Pawlowsky 2003, Trantow 2012). Theknowledge engineering tools, cluster-specific balanced scorecard, intellectual capital, and cross-linking measuresaim at the evaluation, steering, controlling, and support of the knowledge production of the clusters of excellence.Therefore, these are located in the debate of science and economic politics concerning performance measurementand evaluation as well as the steering of research and its effects (German Council of Science and Humanities

[Wissenschaftsrat] 2011) that has been intense for years.

Proponents partly think, for example, that measurable differences in performance exist between researchinstitutions and researchers and that this demands an increase in performance and efficiency through the targeted useof resources (German Council of Science and Humanities [Wissenschaftsrat] 2011). Critics, though, see timeautonomy and believe in intrinsic motivation as the key to efficient and creative research (German Council ofScience and Humanities [Wissenschaftsrat] 2011). Hence, some argue that competition for new research results andtheir appreciation in the scientific community take place within the science system; external and monetarycompetitive impulses might, for example, lead to low-risk research within the mainstream, the decreasing diversity

8/10/2019 S06 - Joos et al_2013

7/11


of the researchers and research topics as well as the increasing compartmentalization of the published research

results (German Council of Science and Humanities [Wissenschaftsrat] 2011); [s]cientific misbehavior andinsufficient performances should, first of all, be sanctioned by the scientific community itself (German Council ofScience and Humanities [Wissenschaftsrat] 2011).

Therefore, evaluations that usually assess performance and achievement of institutional objectives inretrospect, ex post, or with an eye toward the future, ex ante, (10) should be considered early in the sense of double-

loop learning, already introduced to accompany the process and reflected through the various groups. Using theexample of the two clusters of excellence in Aachen, this target group also adaptively implemented the iterativedevelopment of these instruments in the specially established, process-accompanying research projects incooperation with cluster management, and it is examined regularly by a scientific and industrial community ofexperts.

SUMMARY

After a short introduction of the research subject of the Excellence Initiative in general and the clusters ofexcellence in particular, key features have been identified through a cluster definition and evaluation criteria onbehalf of the funding institution and presented to demonstrate the special challenge of steering both the highlyspecific expert knowledge and the overarching research questions and further developing the excellent researchapproach. Since this paper deals with the evaluation, steering, and support of knowledge production, the various

forms of organizational learning processes are introduced in another step.The organizational-sociological literature contains diverse differentiations depending on the underlying

learning theory (Trantow 2012). According to Trantow (2012), the typologies especially trace back to the works ofBateson (1992) and Argyris and Schn (1978) and are primarily based on the distinction of learning as a conditionedreaction or a result of reflection, insight, and development (Pawlowsky 2003, Trantow 2012). The learning theoristsArgyris and Schn have been discussed to raise awareness of the distinction of learning as conditioned reactionversus the result of reflection, insight, and development (Pawlowsky 2003).

Since interdisciplinary clusters of excellence are exposed to high expectations as far as knowledgeproduction is concerned and s ince synergy effects (11) are expected, strategic knowledge management has beentransferred to a control loop and organizational learning processes have been transferred to a cluster for internalstrategic knowledge management. Afterwards, the knowledge engineering tools developed within the process-accompanying research have been introduced, followed by discussion of which forms of organizational learning are

derived with the help of these tools. The aim of the concluding discussion is to think ahead to the need for researchinto knowledge engineering tools on the level of deuteron learning and to put forth a thesis that will induce furtherdiscussion.

DISCUSSION

The cluster-specific knowledge engineering tools would have to be adjusted and standardized with respectto possible comparability for the purpose of a nationwide benchmark of the whole Excellence Initiative throughfurther operationalization of the operating numbers. The third learning loop, deuteron learning, corresponds to ameta-level of learning and aims to increase the organizational capability to learn (Schreygg 2003, Trantow 2012).A decisive premise for initiating those learning processes presupposes a flexibility in the structure of the fundingprogram of the complete Excellence Initiative. If the promoters become involved with a nationwide reflection, thethird learning loop could be reached.

Currently, program-accompanying monitoring is designed by the Institute for Research Information andQuality Assurance (iFQ) and is orientated toward long-term observation of new funding measures and enablingstatements on its successes. (12) Within this monitoring of the Excellence Initiative, the authors argue in favor ofcybernetic monitoring of the Excellence Initiative that takes account of learning processes on the level of theExcellence Initiative, the single funding priorities (graduate schools, clusters of excellence, future concepts), and theindividual objective of single funding priorities during the life span of a project.

A systems theoretical, cybernetic insight perspective would enable a holistic and interdisciplinary-integrative point of view (Henning 1985 in Trantow 2012) because cybernetic aspects deal with inter alia feedbackcontrol, steering, and regulating processes in complex systems (Ashby 1956, Lattwein 2002, Hartmann 2005, Strina

8/10/2019 S06 - Joos et al_2013

8/11


2006, Brosze 2011). (13) According to the debate regarding performance measurement, evaluation and steering carehas certainly to be taken that reflection rather than surveillance characterizes the focus (Trantow 2012) to avoidinterference with organizational learning processes. Hence, a cybernetic monitoring model combined withintegration of knowledge engineering tools has been presented against the backdrop of the iterative proceduredescribed for supporting optimization of value creation processes.

ACKNOWLEDGMENT

This work was performed as part of the clusters of excellenceIntegrative Production Technology for High-Wage Countries and Tailor-Made Fuels from Biomass at RWTH Aachen University, which is funded by theExcellence Initiative of the German Research Foundation.

ENDNOTES:1. Future concepts are supposed to strengthen universities as entire institutions, so they can assert themselves in the leading group in the

international scientific competition. In a future concept, a university develops a long-term strategy how it consistently wants to extend andimprove its top research as well as its support of young professionals. http://www.dfg.de/download/pdf/dfg_im_profil/geschaeftsstelle/publikationen/exin_ broschuere_1104_dt.pdf.

2. Clusters of excellence concentrate the research potential at university locations in Germany and, thus, strengthen their international visibilityand competitiveness. Their main thought consists in the scientific networking and cooperation on research areas with an especially promisingfuture. Beside various facilities of universities, also not university related research institutes as well as industrial associates accordinglyparticipate in the clusters. http://www.dfg.de/download/pdf/dfg_im_profil/geschaeftsstelle/publikationen/exin_broschuere_1104_dt.pdf.

3. Graduate schools are supposed to join and improve the support of the scientific young professionals and making the researchs mark. Highlyqualified doctoral candidates are trained in an excellent research environment of these graduate schools.http://www.dfg.de/download/pdf/dfg_im_profil/geschaeftsstelle/publikationen/exin_broschuere_1104_dt.pdf.

4. http://www.dfg.de/foerderung/programme/exzellenzinitiative/index.html.5. http://www.dfg.de/foerderung/programme/exzellenzinitiative/index.html.6. The main concept of this paper is that an organization is a structured social system (Weinert 2004) that generates, maintains and changes

structures with the help of coordinated actions and, thus, makes existing complexity manageable (Alwert 2006).7. Key feature for the controlling is the closed effecting procedure where the variable constantly influences itself in the effect circle of the

control loop. [] A human can also be one part of the control loop (DIN 19226-1, emphasis in original).8. The dissertation of Vossen (Vossen 2012) is applied to the second knowledge engineering tool. Moreover, the knowledge engineering tools

and the accompanying research are described in Joo et al. (2012).9. Initially, the adapted design of Kaplan/Norton (1992) was intended as a communication, information, and learning tool. With this in mind,

cluster-specific adaption means to define the four perspectives in alignment with contents of the clusters consortium agreement as well asthe integration of an annual online-based evaluation. This iterative process has already been conducted four times in the cluster of excellenceIntegrative Production Technology for High-Wage Countries. The average entry number comes to 117. In the cluster of excellence Tailor-Made Fuels from Biomass it has been conducted only three times because it was approved one year later. The average entry number hereamounts 100. Both clusters of excellence were extended by five years in June 2012. The cluster-specific balanced scorecard is furtherinvestigated, implemented, and expanded within these clusters (Welter et al. 2012, Vossen et al. 2011, Welter et al. 2010, Guthrie et al. 2007,Hornbostel/von Ins 2009, Horvth/Seiter 2009).

10.Examples for ex-post evaluations are these evaluations that have been carried out by the Science Council (Wissenschaftsrat) of not universityrelated research institutions, examples for ex-ante evaluations are valuation procedures that have been carried out by the DFG and the

Science Council in the framework of the Excellence Initiative German Council of Science and Humanities (Wissenschaftsrat) 2011.11. Synergy is the interaction of different components in a system. Through the interaction, a system shows capabilities and a behavior that

cannot be achieved through the sum of the capabilities of its components. Moreover, if this interaction is directed at a goal, it is possible thatemergence occurs. This means that a certain behavior is not predictable on the basis of the behavior of its components. In organizations, thiscan be achieved through shared values and visions (Strina 2006).

12. http://www.forschungsinfo.de/projekte/Exzellenz/projekte_exzellenz_lang.asp13. In the recent sociologically characterized systems theory steering and regulating activities are also understood primarily in the sense of the

self-regulation of systems on the basis of feedback processes (Luhmann 1968, Wilke 2001b). A short overview over the developments of thesystems theory is provided by Wilke (2006). A collection of definitions concerning the term of cybernetics is to be found in Henning (1985)(Trantow 2012).

REFERENCES

Alwert, K. (2006): Wissensbilanzen fr mittelstndische Organisationen. Stuttgart.Alwert, K. (2012). Auf dem Weg in die wissensbasierte Wirtschaft. In: Pawlowsky P., Edvinsson L. (Eds.), Intellektuelles Kapital und

Wettbewerbsfhigkeit. Eine Bestandsaufnahme zu Theorie und Praxis, 97-134. Berlin.Argyris, C., Schn, D. A. (1978). Organisational Learning. A Theory of Action Perspective. Boston.Auer, T. (2007). ABC der Wissensgesellschaft. Reutlingen.Aydalot, P., Keeble, D. (1988). High technology industry and innovative environments in Europe: An overview. In: Aydalot, P., Keeble, D.

(Eds.), High Technology Industry and Innovative Environments: The European Experience, 1-25. London.Bateson, G. (1992). kologie des Geistes: Anthropologische, psychologische, biologische und epistemologische Perspektiven. Frankfurt am

Main.Becattini, G. (1990). The Marshallian Industrial District as a Socio-Economic Notion. In: Pyke, F. et al. (Eds.), Industrial Districts and Inter-Firm

Cooperation in Italy, 37-50. Geneva.Bettoni, M., Clases, C., Wehner, T. (2004). Communities of Practice im Wissensmanagement: Charakteristika, Initiierung und Gestaltung. In:

Reinmann, G., Mandl, H. (Eds.), Psychologie des Wissensmanagements: Perspektiven, Theorien und Methoden, 319-328. Gttingen.
http://www.dfg.de/download/pdf/http://www.dfg.de/download/pdf/

8/10/2019 S06 - Joos et al_2013

9/11


Brusco, S. (1992). Small firms and the provision of real services. In: Pyke, F., Sengenberger, W. (Eds.), Industrial Districts and Local EconomicRegeneration, 177-196. Geneva.

Cooke, P. (2009). Regionale Innovationssysteme, Cluster und die Wissenskonomie. In: Blttel-Mink, B., Ebner, A. (Eds.), Innovationssysteme:Technologie, Institutionen und die Dynamik der Wettbewerbsfhigkeit, 87-116. Wiesbaden.

Cooke, P., Schienstock, G. (2000). Structural Competitiveness and Learning Regions. In: Enterprise and Innovation Management Studies, vol. 1,nr. 3, 265-280.

Cooke, P., Schienstock, G., Boekholt, P., Schall, N. (1996). Regional Innovation Systems: Concepts, Analysis and Typology. In: ConferenceProceedings: EU-RESTPOR Conference Global Comparison of Regional RTD and Innovation Strategies for Development and

Cohesion. Belgium, Brusells, 19-21 Sept 1996.Defila, R., Di Giulio, A., Scheuermann, M. (2006). Forschungsverbundmanagement Handbuch zur Gestaltung inter- und transdisziplinrerProjekte. Zrich.

Fromhold-Eisebith, M. (1999). Das kreative Milieu nur theoretisches Konzept oder Instrument der Regionalentwicklung? In: Raumforschungund Raumordnung. vol. 57, nr. 2/3, 168-175.

German Council of Science and Humanities (Wissenschaftsrat) (2011). Empfehlungen zur Bewertung und Steuerung von Forschungsleistung.Retrieved from http:// www.wissenschaftsrat.de/download/archiv/1656-11.pdf.

Granovetter, M. S. (1985a). The Strength of Weak Ties. In: American Journal of Sociology, vol. 78, nr.6, 1360-1380.Granovetter, M. S. (1985b). Economic Action and Social Structure: The Problem of Embeddedness. In: American Journal of Sociology, vol. 91,

nr. 3, 481-510.Granovetter, M. S. (1992). Economic Institutions as a Social Construction: A Framework for Analysis. In: Acta Sociologica, vol. 35, 3-11.Guthrie, J., Neumann, R. (2007). Economic and non-financial performance indicators in universities. In: Public Management Review, vol. 9, no.

2, 231-252.Hassink, R. (1997). Die Bedeutung der Lernenden Regionen fr die regionale Innovationsfrderung. In: Geographische Zeitschrift, vol. 85, nr.

2/3, 159-173.Hassink, R. (2001). The Learning Region: A Fuzzy Concept or a Sound Theoretical Basis for Modern Regional Innovation Policies. In:

Zeitschrift fr Wirtschaftsgeographie, vol. 45, nr. 3/4, 219-230.

Havighorst, F., Mller, S. (2000). Mehr als Kennzahlen, Arbeitshilfe Nr. 29 der Hans-Bckler-Stiftung, Dsseldorf.Henning, K. (1993). Kybernetische Verfahren der Ingenieurwissenschaften Vorlesungsmanuskript. Aachen.Hornbostel, S., von Ins, M. (2009). Leistungsmessung in der Forschung Erfordernis oder Vermessenheit?. In: Laborwelt, vol. 5, 34-35.Horvth, P., Seiter, M. (2009). Performance Measurement. Die Betriebswirtschaft, vol. 69, no. 3, 393-414.IFQ. Monitoring der Exzellenzinitiative des Bundes und der Lnder. Retrieved from http://www.forschungsinfo.de/projekte/

Exzellenz/projekte_exzellenz_lang.asp.Jansen, C. (2003). Scorecard fr die Wissensmanagement-Performance in heterogenen Unternehmensnetzwerken. Fortschritt-Bericht VDI Reihe:

8: Me- Steuerungs- und Regelungstechnik, Volume No. 1024.Jeschke, S., Isenhardt, I., Henning, K. (Eds.) (2011). Automation, Communication and Cybernetics in Science and Engineering 2009/2010.

Berlin.Jeschke, S., Isenhardt, I., Hees, F., Henning, K. (Eds.) (2013). Automation, Communication and Cybernetics in Science and Engineering

2011/2012. Berlin.Joo, C., Vossen, R., Leisten, I., Richert A., Jeschke, S. (2012). Knowledge Engineering in Interdisciplinary Research Clusters. In: Conference

Proceedings: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). Hong Kong, 10-13 Dec2012.

Kaplan, R. S., Norton, D. P. (1992). The Balanced Scorecard Measures That Drive Performance. In: Harvard Business Review 01/02 1992, 71-79.

Kaplan, R. S., Norton, D. P. (1997). Balanced Scorecard Strategien erfolgreich umsetzen. Stuttgart.Kozielski, S. (2010). Integratives Kennzahlensystem fr den Werkzeugbau. Aachen.Lerch, F. (2009). Netzwerkdynamiken im Cluster. Optische Technologien in der Region Berlin-Brandenburg. Berlin.Loibl, M. (2005). Spannungen in Forschungsteams: Hintergrnde und Methoden zum konstruktiven Abbau von Konflikten in inter- und

transdisziplinren Projekten. Heidelberg.Malik, F. (2008). Strategie des Managements komplexer Systeme ein Beitrag zur Management-Kybernetik evolutionrer Systeme. Bern.Marshall, A. (1920). Principles of Economics. 8th Edition. London.Moody, J., White, D. R. (2003). Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups. In: American Sociological

Review, vol. 68, nr.1, 103-127.Mnstermann, T. (2011). Kulturgerechte Gestaltung von betrieblichen Vernderungs-projekten mit einem unternehmenskybernetischen Ansatz.

Norderstedt.Pawlowsky, P. (2003). The Treatment of Organizational Learning in Management Science. In: Dierkes, M., Berthoin, A., Child, J. (Eds.),

Handbook of Organizational Learning and Knowledge, 61-88. Oxford.Porter, M. E. (1998). On Competition. Boston.Porter, M. E. (2000). Location, Clusters, and Company Strategy. In: Clark, G. et al. (Eds.), Oxford Handbook of Economic Geography, 253-274.

Oxford.

Porter, M. E. (2002). Regional foundations of competitiveness and implications for the government policy. Paper presented at the Department ofTrade and Industry Workshop on Regional Competitiveness. London.Probst, G., Raub, S., Romhardt, K. (1998). Wissen managen. Wie Unternehmen ihre wertvollste Ressource optimal nutzen. Wiesbaden.Probst, G., Raub, S., Romhardt, K. (2003). Wissen managen. Wie Unternehmen ihre wertvollste Ressource optimal nutzen. Wiesbaden.Sammer, M. (2000). Vernetzung von Wissen in Organisationen: Gestaltung von Rahmenbedingungen. Doctoral thesis. Wiesbaden.Schreygg, G. (2003). Organisation: Grundlagen moderner Organisationsgestaltung. Mit Fallstudien, Wiesbaden.Schrder, S. (2011). Analyse der deutschlandweit eingesetzten Manahmen zum Controlling und zur Performancemessung von

Forschungsverbnden vor dem Hintergrund der Strkung und Weiterentwicklung des Wissenschafts- und WirtschaftsstandortesDeutschland. Master thesis, Aachen.

Strina, G. (2006). Zur Messbarkeit nicht-quantitativer Gren im Rahmen unternehmens-kybernetischer Prozesse. Habilitation at the RWTHUniversity.

8/10/2019 S06 - Joos et al_2013

10/11


Strina, G. (2012). Zur Messbarkeit nicht-quantitativer Gren im Rahmen unternehmenskybernetischer Prozesse. Aachen. (Status Jan 2013: notpublished yet).

Trantow, S. (2012). Ein kybernetisches Modell fr das Internationale Monitoring von F&E-Programmen im Bereich der Arbeitsforschung.Doctoral thesis. Aachen.

Vester, F. (2011). Die Kunst vernetzt zu denken: Ideen und Werkzeuge fr einen neuen Umgang mit Komplexitt. Ein Bericht an den Club ofRome. Mnchen.

Vossen, R. (2012). Ein Verfahren zur Ermittlung und Bewertung des Intellektuellen Kapitals von wissenschaftlichen Exzellenzclustern. Doctoralthesis. Aachen.

Vossen, R., Richert, A., Henning, K. (2012). Ein Verfahren zur Ermittlung und Bewertung des Intellektuellen Kapitals von wissenschaftlichenForschungsclustern: am Beispiel des DFG-Exzellenzclusters Tailor-Made Fuels from Biomass (TMFB) der RWTH AachenUniversity. In: Vernetztes Denken, geteiltes Wissen: auf dem Weg in die Wissenskonomie, wissenschaftliche Jahrestagung derGesellschaft fr Wirtschafts- und Sozialkybernetik vom 7.-8. Juli 2011 in Stuttgart. Berlin. (Status Jan 2013: not published yet).

Vossen, R., Welter, F., Leisten, I., Richert, A., Isenhardt, I. (2011). Making Scientific Performance Measurable - Experiences from a GermanCluster of Excellence. In Conference Proceedings: ICERI2011, 3878-3886. Spain, Madrid, 14-16 Nov 2011.

Welter, F. (2013). Regelung wissenschaftlicher Exzellenzcluster mittels scorecardbasierter Performancemessung. Doctoral thesis. Aachen. (StatusJan 2013: not published yet).

Welter, F., Joo, C., Richert A., Jeschke, S. (2011). Der Exzellenzcluster Integrative Produktionstechnik fr Hochlohnlnder an der RWTHAachen: Management und Controlling von Interdisziplinaritt. In: Innovation und Information: wissenschaftliche Jahrestagung derGesellschaft fr Wirtschafts- und Sozialkybernetik vom 3t-5 Dec 2008 in Oestrich-Winkel, 175-189. Berlin.

Welter, F., Schrder, S., Leisten, I., Richert, A., Jeschke, S. (2012). Scientific Performance Indicators - Empirical Results from CollaborativeResearch Centers and Clusters of Excellence in Germany. In: XVI IRSPM Conference. Rome, Italy, 11-13 April 2012 (Status Jan2013: not published yet).

Welter, F., Vossen, R., Richert, A., Isenhardt, I. (2010). Network Management for Clusters of Excellence: A Balanced-Scorecard Approach as aPerformance Measurement Tool. In: The Business Review, vol. 15, no. 1, 171-178. Cambridge.

Wenger, E., Snyder, W. (2000). Communities of Practice: The Organizational Frontier. In: Harvard Business Review, January-February, 139-

145.Wiener, N. (1948). Kybernetik. In: Futurum Exactum Ausgewhlte Schriften zur Kybernetik und Kommunikationstheorie. Dotzler, B. (Ed.).

Wien, 13-29.Williamson, O. E. (1981). The Economics of Organization. The Transaction Cost Approach. In: American Journal of Sociology, vol. 87, nr. 3,

548-577.Winkler, R., Bornemann, M., Ninaus, M., Willfort, R. (2007). Das Praxishandbuch Wissensmanagement: Integratives Wissensmanagement.

Graz.

8/10/2019 S06 - Joos et al_2013

11/11

Reproduced with permission of the copyright owner. Further reproduction prohibited without

permission.

Documents

S06 - Joos et al_2013