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Knowledge management has been becoming a decisive activity in the current openly competitive globalized economy. It has been empirically proven that knowledge, in combination with traditional production factors, drives the growth of enterprises, nations, and regions. Therefore, most countries tend to be knowledge based. The creation, dissemination, and use of knowledge represent key processes that help companies with their innovation activities. Consequently, innovation allows companies to generate added value and get ahead of their competitors. However, a number of recent studies have shown that innovation does not occur in isolation and innovation factors affect each industry differently. This is why economic entities fail – because they are unable to exploit the potential of cooperation (for example, using the Triple Helix principle in networks) and by ineffectively spending public funds to support innovative activities. On the other hand, Germany has the largest economy in the European Union and one of the strongest and most competitive economies in the world (Germany was ranked fourth in the 2015–2016 rankings of the Global Competitiveness Index carried out by the World Economic Forum). This is due to the fact that stimulus for investment, a good climate for innovation, and private-sector investment has crucial importance for the German government. Therefore, the goal of this article is to analyze the drivers influencing businesses´ innovation activities in different industries in Germany. We show that different factors influence the innovation activities of different industries. Our results provide (1) practical implications for policy makers (not only in Germany) and (2) an initial analysis of German industries and a proposal for partners and factors that affect the innovative activities of different sectors. This proposal could help other economies support the creation of innovation (especially, countries from Central and Eastern Europe – the CEE Countries). For the analysis, we created original multiple linear regression models using data from the Community Innovation Survey conducted in Germany between the years 2010– 2012. [ABSTRACT FROM AUTHOR]

The initiation of so called Clusters of Excellence constitutes a milestone with regard to Germany's efforts to foster integrative and interdisciplinary research. Clusters of Excellence constitute spatially concentrated research networks including about 20 university institutes, non-university research institutes as well as further selected partners and advisors from industry and science. The heterogeneity of the included 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 using different methodologies and technical terms. Therefore, the question arises in how far cross linked, knowledge-intensive and highly complex scientific co-operations can be organized to ensure a high quality of the research cluster output. To cope with the challenge of scientific cooperation, the conceptual framework of the Cross Sectional Processes in the Cluster of Excellence "Integrative Production Technology for High-Wage Countries" at RWTH Aachen University supports effective networking processes and strategic cluster development by means of learning and knowledge management. In order to contribute to the aforementioned research question, a model for the management of Cross Sectional Processes is developed. The model will define specific measures for a promotion of interdisciplinary cooperation and cluster development at respective phases of network development - e. g. from the initiation phase to the steady phase. Following the research approach of the Grounded Theory, the generation of the model bases on a triangulation of quantitative as well as qualitative data, captured through a Balanced-Scorecard-based performance measurement tool, direct evaluations and structured interviews with cluster members. First results of the data triangulation make obvious that e. g. the role of project leaders as cluster-internal knowledge agents is crucial for a successful knowledge transfer in a hierarchical Cluster of Excellence. Moreover, a high rate of staff turnover in the research environment strongly influences the implementation of cross sectional activities. A further transfer of cross sectional measures to other forms of scientific oriented clusters and comparable networks is aspired by the Cross Sectional Processes to support scientific oriented interdisciplinary cooperation in future. [ABSTRACT FROM AUTHOR]