Joint search by social and spatial proximity

Ministry of Education, Singapore under its Academic Research Funding Tier

Community structure and patterns of scientific collaboration in Business and Management

This is the author's accepted version of this article deposited at arXiv (arXiv:1006.1788v2 [physics.soc-ph]) and subsequently published in Scientometrics October 2011, Volume 89, Issue 1, pp 381-396. The final publication is available at link.springer.com http://link.springer.com/article/10.1007%2Fs11192-011-0439-1Author's note: 17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full pape

Shaping the formation of university-industry research collaborations: what type of proximity does really matter?

Research collaborations between universities and industry (U-I) are considered to be one important channel of potential localized knowledge spillovers (LKS). These collaborations favour both intended and unintended flows of knowledge and facilitate learning processes between partners from different organizations. Despite the copious literature on LKS, still little is known about the factors driving the formation of U-I research collaborations and, in particular, about the role that geographical proximity plays in the establishment of such relationships. Using collaborative research grants between universities and business firms awarded by the UK Engineering and Physical Sciences Research Council (EPSRC), in this article we disentangle some of the conditions under which different kinds of proximity contribute to the formation of U-I research collaborations, focusing in particular on clustering and technological complementarity among the firms participating in such partnerships

Communities and patterns of scientific collaboration in Business and Management

This is the author's accepted version of this article deposited at arXiv (arXiv:1006.1788v2 [physics.soc-ph]) and subsequently published in Scientometrics October 2011, Volume 89, Issue 1, pp 381-396. The final publication is available at link.springer.com http://link.springer.com/article/10.1007%2Fs11192-011-0439-1Author's note: 17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full pape

Communities and patterns of scientific collaboration

This is the author's accepted version of this article deposited at arXiv (arXiv:1006.1788v2 [physics.soc-ph]) and subsequently published in Scientometrics October 2011, Volume 89, Issue 1, pp 381-396. The final publication is available at link.springer.com http://link.springer.com/article/10.1007%2Fs11192-011-0439-1Author's note: 17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper version)17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper version)17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper version)17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper version)This paper investigates the role of homophily and focus constraint in shaping collaborative scientific research. First, homophily structures collaboration when scientists adhere to a norm of exclusivity in selecting similar partners at a higher rate than dissimilar ones. Two dimensions on which similarity between scientists can be assessed are their research specialties and status positions. Second, focus constraint shapes collaboration when connections among scientists depend on opportunities for social contact. Constraint comes in two forms, depending on whether it originates in institutional or geographic space. Institutional constraint refers to the tendency of scientists to select collaborators within rather than across institutional boundaries. Geographic constraint is the principle that, when collaborations span different institutions, they are more likely to involve scientists that are geographically co-located than dispersed. To study homophily and focus constraint, the paper will argue in favour of an idea of collaboration that moves beyond formal co-authorship to include also other forms of informal intellectual exchange that do not translate into the publication of joint work. A community-detection algorithm is applied to the co-authorship network of the scientists that submitted in Business and Management in the 2001 UK RAE. While results only partially support research-based homophily, they indicate that scientists use status positions for discriminating between potential partners by selecting collaborators from institutions with a rating similar to their own. Strong support is provided in favour of institutional and geographic constraints. Scientists tend to forge intra-institutional collaborations; yet, when they seek collaborators outside their own institutions, they tend to select those who are in geographic proximity

The spatial dimensions of innovation

The paper discusseses the spatial dimensions of innovation in Polish manufacturing companies. The conceptual framework of the paper is an understanding of social networks as a potential resource of the company, whether they are internal or external. Whether the company benefits from the potential resources attached to the network depends on the capabilities characterising the firm in terms of qualifications, organisational characteristics and attitude towards employees and towards other firms. This again is not only determined by personal characteristics of the management and staff, but also by the common perceptions, and the institutional infrastructure prevailing in the (local) society. In Poland the latter is closely connected with the process of transition since 1990. The paper reports from a study among Polish manufacturing companies. It categorises the types of innovation prevailing in the companies and detects the role of networks in the innovation process of the companies. To what extend do the companies draw on external networks, on what points of the innovation process are the networks involved, what kind of networks are involved, and not least, what are the spatial characteristics of the networks (local, national international). Finally how can the network strategy of the companies be explained? What factors seem to determine an active involvement of networks and what other factors seem to explain a self-sufficient strategy of innovation? What is the spatial extension of the networks, and are there systematic differences in the spatial extension of the networks? Does the transitional situation of the Polish society seem to favour certain strategies of innovation?

Introducing Preference Heterogeneity into a Monocentric Urban Model: an Agent-Based Land Market Model

This paper presents an agent-based urban land market model. We first replace the centralized price determination mechanism of the monocentric urban market model with a series of bilateral trades distributed in space and time. We then run the model for agents with heterogeneous preferences for location. Model output is analyzed using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression. We demonstrate that heterogeneity in preference for proximity alone is sufficient to generate urban expansion and that information on agent heterogeneity is needed to fully explain land rent variation over space. Our agent-based land market model serves as computational laboratory that may improve our understanding of the processes generating patterns observed in real-world data