Web indicators for complex innovation systems

Katz, J. Sylvan; Cothey, Viv

doi:10.3152/147154406781775922

Cited by 37 publications

(22 citation statements)

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“…This allows us to state that the British network receive more links than other countries, perhaps, due to linguistic reasons (THELWALL, TANG & PRICE, 2003). Another possible explanation is the relative large size of the British network which therefore offers a large number of target pages for link (KATZ & COTHEY, 2006 Table 2 shows the universities with a high nOutDegree. Like the index before, this one measures the normalized degree of out-coming links.…”

Section: +Site:{university Domaina} +Linkdomain:{university Domainb}mentioning

confidence: 99%

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Maps of the academic web in the European Higher Education Area — an exploration of visual web indicators

et al. 2007

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This paper shows maps of the web presence of the European Higher Education Area (EHEA) on the level of universities using hyperlinks and analyses the topology of the European academic network. Its purpose is to combine methods from Social Network Analysis (SNA) and cybermetric techniques in order to ask for tendencies of integration of the European universities visible in their web presence and the role of different universities in the process of the emergence of an European Research Area.We find as a main result that the European network is set up by the aggregation of welldefined national networks, whereby the German and British networks are dominant. The national networks are connected to each other through outstanding national universities in each country.

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Section: +Site:{university Domaina} +Linkdomain:{university Domainb}mentioning

confidence: 99%

“…Meanwhile, the analysis of web graphs in terms of scale-free or small world networks has been incorporated into information science (BJÖRNEBORN, 2001(BJÖRNEBORN, , 2003KATZ & COTHEY, 2006;THELWALL & WILKINSON, 2003). Web graphs based on hyperlinks are only one example of such studies.…”

Section: Introductionmentioning

confidence: 99%

Maps of the academic web in the European Higher Education Area — an exploration of visual web indicators

et al. 2007

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“…A scaling correlation with an exponent α6 ¼1 is indicative of scale-invariant properties. When α>1.0 the correlation is superlinear indicative of a Matthew effect or cumulative advantage [32,78,91]. When α<1.0 the correlation is sublinear indicative of an inverse Matthew effect or cumulative disadvantage.…”

Section: Scaling Correlationsmentioning

confidence: 99%

What is a Complex Innovation System?

Katz

2015

SSRN Journal

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Innovation systems are sometimes referred to as complex systems, something that is intuitively understood but poorly defined. A complex system dynamically evolves in non-linear ways giving it unique properties that distinguish it from other systems. In particular, a common signature of complex systems is scale-invariant emergent properties. A scale-invariant property can be identified because it is solely described by a power law function, f(x) = kx α , where the exponent, α, is a measure of scale-invariance. The focus of this paper is to describe and illustrate that innovation systems have properties of a complex adaptive system. In particular scale-invariant emergent properties indicative of their complex nature that can be quantified and used to inform public policy. The global research system is an example of an innovation system. Peer-reviewed publications containing knowledge are a characteristic output. Citations or references to these articles are an indirect measure of the impact the knowledge has on the research community. Peer-reviewed papers indexed in Scopus and in the Web of Science were used as data sources to produce measures of sizes and impact. These measures are used to illustrate how scale-invariant properties can be identified and quantified. It is demonstrated that the distribution of impact has a reasonable likelihood of being scale-invariant with scaling exponents that tended toward a value of less than 3.0 with the passage of time and decreasing group sizes. Scale-invariant correlations are shown between the evolution of impact and size with time and between field impact and sizes at points in time. The recursive or self-similar nature of scale-invariance suggests that any smaller innovation system within the global research system is likely to be complex with scale-invariant properties too.

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“…The concentration of outlinks by a central group of websites that links a significant part of the network can be illustrated with the help of the Gini coefficient which measures the inequality of a distribution. It has previously been used to evaluate the web visibility of innovation systems (Katz & Cothey, 2006). The coefficient of 0 expresses total equality and a coefficient of 1 indicates maximal inequality.…”

Section: Data Analysis Structural Analysis: Comparing the In And Out Damentioning

confidence: 99%

Mapping the network structure of science parks

Minguillo

Thelwall

2012

Aslib Proceedings

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Purpose -This study introduces a method based on link analysis to investigate the structure of the R&D support infrastructure associated with science parks in order to determine whether this webometric approach gives plausible results. Design/Methodology/Approach -Three science parks from Yorkshire and the Humber in the UK were analysed with webometric and social network analysis techniques. Interlinking networks were generated through the combination of two different data sets extracted from three sources (Yahoo!, Bing, SocSciBot). Findings -These networks suggest that institutional sectors, representing business, universities and public bodies, are primarily tied together by a core formed by research institutions, support structure organisations and business developers. The comparison of the findings with traditional indicators suggests that the web-based networks reflect the offline conditions and policy measures adopted in the region, giving some evidence that the webometric approach is plausible to investigating science park networks. Originality/value -This is the first study that applies a web-based approach to investigate to what extent the science parks facilitate a closer interaction between the heterogeneous organisations that converge in R&D networks. This indicates that link analysis may help to get a first insight into the organisation of the R&D support infrastructure provided by science parks.

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Web indicators for complex innovation systems

Cited by 37 publications

References 0 publications

Maps of the academic web in the European Higher Education Area — an exploration of visual web indicators

Maps of the academic web in the European Higher Education Area — an exploration of visual web indicators

What is a Complex Innovation System?

Mapping the network structure of science parks

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