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Prior analyses of face-to-face contacts in collaborations have focused on one substitute only. Instead, we analyse various potential substitutes for face-to-face contacts in collaborations. Based on 45 interviews with academics from five leading European universities of technology our findings show that face-to-face contacts are closely intertwined with other mechanisms of coordination and communication for collaboration, particularly modern communication tools, proximity, and brokerage. Generally, to add personal and social proximity to their collaborations academics in our sample rely on face-to-face contacts. In their relationships with industrial partners, face-to-face contacts remain crucial to overcome cognitive and organizational distance. Yet when working with their peers, a number of partial substitutes for face-to-face contacts exist, knowingly combinations of temporary geographical proximity and modern communication tools. Moreover, PhD students can play a crucial role as junior brokers, overcoming a lack of face-to-face contacts between partners jointly supervising them while working in different locations. [ABSTRACT FROM AUTHOR]

In the applied sciences and in engineering there is often a significant overlap between work at universities and in industry. For the individual scholar, this may lead to serious conflicts when working on joint university–industry projects. Differences in goals, such as the university's aim to disseminate knowledge while industry aims to appropriate knowledge, might lead to complicated situations and conflicts of interest. The detailed cases of two electrical engineers and two architects working at two different universities of technology illustrate the kinds of problems individual scholars face in university–business collaborations. These cases are based on qualitative interviews and additional data and demonstrate that, while value conflicts emerge on the organizational level, it is primarily the individual researcher who must deal with such conflicts. This analysis adds to existing studies in two ways: first, it explicitly addresses normative issues framed in terms of ethical and social values, thereby going beyond the common social-science perspective of university–business collaboration. Secondly, it provides qualitative insights, thereby identifying details and issues not apparent in quantitative studies. In particular, it is evident that university–industry collaborations are prone to value conflicts not only in research but also in education and job training. [ABSTRACT FROM AUTHOR]

We look into the question whether heterogeneity stemming from research orientation, gender, or disciplinary and cultural differences with their PhD supervisors helps or hampers academics' careers. Based on a sample of 248 academics at two leading European universities of technology, we combine multinomial logit models and sequential logit models to understand career advancement. Our results show that heterogeneity stemming from research orientation is helpful. Academics who bridge between the quest for fundamental understanding and socio-economic relevance attain career success. Yet heterogeneity stemming from gender hinders careers: female academics face problems securing tenured positions and full professorships. Mentor–mentee heterogeneity only helps in early career transitions, but hampers advancement later on. Our insights offer suggestions to policymakers, university managers, and academics, because they help to identify promising academics, the right support for sitting staff members, measures correcting for gender imbalances, and can inform strategic choices regarding research orientation and PhD supervisors. [ABSTRACT FROM AUTHOR]

Previous studies investigating proximity and collaboration have not clarified personal elements, such as working or communication style. Here, we show that personal proximity-close similarity in terms of personal traits and behavioral patterns-substantially affects the whole life cycle of research collaborations. We conduct a multi-case study of Dutch nanotechnology researchers. We select our interviewees through a bibliometric analysis and focus on the most central Dutch nanotechnology researchers in the global network. Our results reveal that social proximity and temporary geographical proximity have indirect effects enabling potential partners to assess their personal proximity. Sufficient levels of personal proximity often make or break the deal, provided that partners' cognitive and organizational proximity-which are major drivers of research collaborations-suffice. Introducing personal proximity to analyze research collaborations puts previous findings on proximity dimensions' effect on collaboration in a new perspective. [ABSTRACT FROM AUTHOR]

Collaborations are particularly important for the development and deployment of technology. We analyse the influence of organizational, technological and geographical proximity on European nanotechnology collaborations with the help of a publication dataset and additional geographical information. While organizational proximity influences the output of collaborations only indirectly, geographical and technological proximity do so directly. Geographical proximity is most significant in statistical terms and technological proximity has the highest magnitude of effect. Consequently, the latter lends itself most for management and policy interventions, for example, by providing information on technological specialization of potential partners. Resumen Las colaboraciones son especialmente importantes para el desarrollo y el despliegue tecnológico. Se analiza la influencia de la proximidad organizacional, tecnológica y geográfica en colaboraciones europeas en nanotecnología, con la ayuda de un conjunto de datos publicados e información geográfica adicional. Si bien la proximidad organizacional influye en el resultado de las colaboraciones solo de manera indirecta, las proximidades geográfica y tecnológica influyen directamente. La proximidad geográfica es la más significativa en términos estadísticos y la proximidad tecnológica tiene la magnitud del efecto más elevada. Consecuentemente, esta última se presta más a las intervenciones de gestión y de políticas, por ejemplo, facilitando información sobre la especialización tecnológica de socios potenciales. [ABSTRACT FROM AUTHOR]

Innovation fosters structural change and growth and drives socio-economic change. The generation and diffusion of innovation is a dynamic process, which cannot adequately be guided by static policy conceptions. In this paper we will explore the possibility of devising and implementing a ‘learning’ innovation policy. As organisational and technological change is not completely haphazard, we argue that innovation policy can make use of structural regularities in socio-economic change whilst, at the same time, be open to advances in scientific knowledge. We devise a method that achieves these two aims and apply it to a concrete example (knowledge systems). We conclude with practical implications originating from such a ‘learning’ innovation policy. [Copyright &y& Elsevier]

An introduction to the journal is presented in which the editor discusses various reports published within the issue including one about networks and economic agglomerations, another one about network analysis application in innovation studies, and one on innovation and entrepreneurship.