Network Management for Clusters of Excellence - A Balanced-Scorecard Approach as a Performance Measurement Tool

Welter, Florian; Vossen, René; Richert, Anja; Isenhardt, Ingrid

doi:10.1007/978-3-642-16208-4_17

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…Therefore, a continuous performance management, which considers the cluster-specific features, is required to evoke organizational learning processes which represent the basis for interdisciplinary cooperation and knowledge production [14] [15] [16] [17]. Consequently, an iterative regulatory process, modelled after [18] [19] [20] [21], was implemented to improve the cluster performance and the management itself (see Fig. 1).…”

Section: Cluster Performance Managementmentioning

confidence: 99%

Managing interdisciplinary research clusters

Müller¹,

Thiele²,

Jooß³

et al. 2015

2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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The complexity and dynamicity of interdisciplinary research clusters requires an efficient management in order to ensure a good performance. Therefore, this paper presents an iterative regulatory process for managing interdisciplinary research clusters which has been implemented at the two clusters of excellence at RWTH Aachen University. Thereby, an annual evaluation of the cluster performance through a cluster-specific employee survey forms the basis for the derivation and implementation of different measures. By evaluating the performance again, the loop of organizational learning starts anew. As one example of these measures, the colloquia of employees are described in further details because the adaptation and optimization of concepts illustrate the effects of the continuous improvement. Furthermore, the general performance development of the cluster was analyzed to show the comprehensive effects of the regulatory process.

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Section: Cluster Performance Managementmentioning

confidence: 99%

Managing interdisciplinary research clusters

Müller¹,

Thiele²,

Jooß³

et al. 2015

2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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“…identification of tailor-made measure of sc engineering. 25 The seven design elements can be attrib The three-staged weighting (relevant, import based on the data triangulation of direct evaluation and structured interviews. fig.…”

Section: Prototype 1: Cluster-specific Balanced Scorecardmentioning

confidence: 99%

Knowledge engineering in interdisciplinary research clusters

Joos

Vossen

Leisten

et al. 2012

2012 IEEE International Conference on Industrial Engineering and Engineering Management

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This paper proposes an innovative knowledge engineering approach for interdisciplinary research clusters. In science, different competences, knowledge, scientific experience and cultures converge. To cope with this challenge it is necessary to combine several dimensions of human, organization and technology within a holistic knowledge engineering approach. Cooperation structures have to be adapted accordingly. The authors present prototypes of an application oriented research to promote interdisciplinary knowledge production.

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