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This book brings together original research on the role of networks in regional economic development and innovation. It presents a comprehensive framework synthesizing extant theories, a palette of real-world cases in the aerospace, automotive, life science, biotechnology and health care industries, and fundamental agent-based computer models elucidating the relation between regional development and network dynamics. The book is primarily intended for researchers in the fields of innovation economics and evolutionary economic geography, and particularly those interested in using agent-based models and empirical case studies. However, it also targets (regional) innovation policy makers who are not only interested in policy recommendations, but also want to understand the state-of-the-art agent-based modeling methods used to experimentally arrive at said recommendations

"This book brings together original research on the role of networks in regional economic development and innovation. It presents a comprehensive framework synthesizing extant theories, a palette of real-world cases in the aerospace, automotive, life science, biotechnology and health care industries, and fundamental agent-based computer models elucidating the relation between regional development and network dynamics. The book is primarily intended for researchers in the fields of innovation economics and evolutionary economic geography, and particularly those interested in using agent-based models and empirical case studies. However, it also targets (regional) innovation policy makers who are not only interested in policy recommendations, but also want to understand the state-of-the-art agent-based modeling methods used to experimentally arrive at said recommendations."--Provided by publisher

The operational context for higher education institutions has become increasingly competitive: universities have to compete on national and international markets for students, staff, funding and prestige. In this context, universities have increasingly become to think of themselves as actors who are in direct competition with others, and adapt their strategies to increase their status and survive in the new environment. The possibly best-known approach to measuring the status of universities is through the various ranking lists. Though focusing on different indicators like scientific performance or reputation, ranking lists can be considered as a mechanism for highlighting and even creating status hierarchies and providing information about the "market value" of universities. They have also contributed to the proliferation of various national policy schemes fostering elite universities, which aim to redress the dominance of US universities in the ranking lists. An increasingly important funding source for universities is the EU Framework Programmes, where the European Commission funds basic and applied research with industrial and societal relevance. The aim of our chapter is to explore whether established university rankings in their current form are appropriate instruments for predicting the performance of universities in the EU Framework Programmes and whether university's status has an influence on its access to FP funding. To address this question, we analyse the relative influence of two different university rankings on the performance in the EU Framework Programmes, while controlling for other factors like previous experience, availability of national funding sources, university size, relational capital, and institutional factors (EU membership age and English language). ; peerReviewed

The focus of this paper is on cross-region R&D collaboration funded by the 5th EU Framework Programme (FP5). The objective is to measure distance, institutional, language and technological barrier effects that may hamper collaborative activities between European regions. Particular emphasis is laid on measuring discrepancies between two types of collaborative R&D activities, those generating output in terms of scientific publications and those that do not. The study area is composed of 255 NUTS-2 regions that cover the pre-2007 member states of the European Union (excluding Malta and Cyprus) as well as Norway and Switzerland. We employ a negative binomial spatial interaction model specification to address the research question, along with an eigenvector spatial filtering technique suggested by Fischer and Griffith (2008) to account for the presence of network autocorrelation in the origin-destination cooperation data. The study provides evidence that the role of geographic distance as collaborative deterrent is significantly lower if collaborations generate scientific output. Institutional barriers do not play a significant role for collaborations with scientific output. Language and technological barriers are smaller but the estimates indicate no significant discrepancies between the two types of collaborative R&D activities that are in focus of this study.

Mapping of the territorial R&I ecosystems Grand Est, Styria and Värmland in a comparative perspective, with respect to (i) R&I governance, strategies and actors, and (ii) regarding their R&I activities and the role of digitalisation therein. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement nº 873010. Project title: Responsible Research and Innovation approach for transitioning the traditional industry regions into digitalised industry territories (DigiTeRRI). Project coordinator: Marianne Hörlesberger, AIT Austrian Institute of Technology GmbH Project URL: www.digiterri.eu