Our selected sample of universities is derived from the 2014 edition of the Leiden Ranking (Waltman et al. 2012), which consists of the world’s 750 largest research-intensive universities according to their total research publication output in the CWTS-licensed offline version of Thomson Reuters’ Web of Science database. Table 1 presents the selected metrics, and their corresponding data source indicated between parentheses. Some of these metrics were extracted from existing web-based open-access sources,Footnote 6 others were produced within the CWTS in-house information system especially for this study.

Table 1 Summary description of selected size-independent metrics Full size table

Clearly a university’s UIC intensity (‘ %UIC’), i.e. the share of such co-authored research publications within the organization’s total publication output, is the end result of a many inputs and processes, the determinants and contributing factors of which are based on dynamic mix of proximity-based relationships with industry and the business sector. %UIC is also one of five ‘flow’ metrics in the RIU.

The metric ‘%MA UIC’ refers to UICs where at least one of the authors has both a university affiliations and an industry affiliation, which enables us to capture parts of both the social proximity and cognitive proximity dimension of university–industry relationships. These ‘boundary spanning’ individuals are likely represent shared organizational interests or backgrounds between universities and industry of the kind that creates mutual trust and aid in effective flows of knowledge or personnel. Note than %MA UIC quantities are likely to be affected by researcher mobility patterns, institutional policies on academic appointments, as well as national laws and regulations that endorse or prohibit multiple appointments (Yegros–Yegros and Tijssen 2014).

%LOCAL UIC and %DOMESTIC UIC represent the ‘geographical proximity’ dimension, where ‘local’ is measured in terms of physical distance (research partners within a 50 km radius), while ‘domestic’ refers to partners located within the same country. For universities located at national borders, ‘local’ is not necessarily a subset of ‘domestic’. %LOCAL UIC closely relates to %MA UIC because people with simultaneous affiliations tend to have these at relatively close distance (for practical reasons of commuting). Broadening our analytical scope from ‘research’ to ‘technology’, the metric ‘%CO-PATENT’ captures ‘institutional proximity’, i.e. close relationships in terms of shared intellectual property right protection arrangements. Applying for joint patents highlights a large measure of connectedness in terms of the novel technology’s underlying R&D but also the alignment of strategic objectives to exploit the IP.

By focusing on how the scientific knowledge contributes to technological development, ‘%NPLR’ adds to ‘cognitive proximity’ dimension of our analysis. Here we assume that when the list of ‘non patent literature references’ in a patent contains a ‘citation’ to a university research publication, the technology represented by the ‘citing’ patent is now related (directly or indirectly) to the ‘cited’ research publication. Since the vast majority of patents are granted to business enterprises in manufacturing sectors, if university research publications are cited by many patents, the research is likely to have been of major relevance to technological development in the private sector. Similarly, if a university has many publications cited by patents, we may assume that it’s research portfolio was of relevance to industrial R&D and technological development. Patents are cited by subsequent patents, if the cited technology contributes to further technological development. %NPLR is the second of five ‘flow’ metrics in the TR Innovation Ranking.

Highly-cited patents are often international breakthrough technologies. %NPLR–HICI counts the number of times university research publications are cited by these ‘elite’ patents are generally seen as ‘industrially important’ technologies (Carpenter et al. 1981; OECD 2013). A high score of this metric indicates that universities are likely to have contributed knowledge of relevance to major technological developments. The NPLR data in this study were extracted from Worldwide Patent Statistical Database (PATSTAT) produced by the European Patent Office (EPO). PATSTAT contains patent applications that were filed at patent offices of major industrialized countries (notably USA, Japan, South Korea, Germany, China and Brazil), and major international offices such as WIPO (worldwide) and EPO itself (Europe). PATSTAT offers a broader coverage of the patent literature than the Derwent/WIPO database used by Thomson Reuters for their Ranking of Innovative Universities.

Patent applications that often are filed on two or three patent legislations are referred to as ‘triadic patents’ (i.e. those filed in Japan and at EPO, and also granted in the USA). Equivalent patent publications were grouped in ‘patent families’. The %CO-PATENT data were extracted by from the PATSTAT database at INCENTIM (Catholic University Leuven, Belgium), while %NPLR data were produced at CWTS. The NPLR-HICI relates to the 10 % most highly cited patent patents across all patent families in the PATSTAT database. Each NPLR refers either to single patents, or a single representative of patent families to remove double counting. Note that USPTO patent applications tend to contain relatively many patents from USA-based companies, each with relatively large numbers of NPLRs.

Despite the inherent limitations associated with our sample of only 750 universities, the inevitable biases of the data sources and the (still) small number of available linkage metrics, we assume that this information source provides a sufficiently robust dataset to analyze statistical relationships between UIC-based metrics and patent-based linkage metrics.

Table 2 provides summary statistics of each metric for this set of universities.Footnote 7 Note that these comprises exclusively of size-independent metrics, thus enabling a size-corrected comparison across a diversity of universities. Some of the UIC-based metrics are close related: %LOCAL UIC is a subset of %DOMESTIC UIC (with the exception of extremely small countries such as Singapore). %MA UIC is also closely related to %DOMESTIC UIC because people tend to have simultaneous institutional affiliations at both universities and companies if locations are within an easy travelling range—usually within the same country.

Table 2 Summary statistics of metrics (750 universities) Full size table

By applying similar weights for each metric, RIU offers the user the benefit of transparency. The end-result, a ‘league table’, is nonetheless highly arbitrary because there is neither a theoretical justification nor a statistical rational for those weights. Data reduction techniques can help reduce or remove redundancies between metrics, where lower weights are assigned to those metrics that add little additional information. Such redundancies can be detected by applying statistical analysis to pairwise correlation coefficients between the selected metrics. Table 3 presents those coefficients, where the Pearson correlation coefficients exhibit the same pattern as the (rank-ordered) Spearman coefficients. UIC-intensity (%UIC) is correlated very significantly with %MA_UIC and %NPLR. In other words, a university’s UIC performance appears to be closely linked to researchers with a university affiliation and a corporate address and related to the impact of its research on technological development. Most of the other correlation coefficients among the metrics are also positive, albeit less significant. Collectively, they all reflect a broader underlying phenomenon best described as a ‘university–industry R&D linkage’.

Table 3 Correlations between linkage metrics (750 universities): Pearson correlation coefficients in lower-diagonal section; Spearman rank correlation coefficients in upper-diagonal section Full size table

Principal component analysis (PCA), which draws its data from all these Pearson correlation coefficients, highlights underlying dimensions of these interrelationships.Footnote 8 The PCA results in Table 4 shows a first component explaining 36 % of all statistical variance. The second component, accounting for an additional 17 %, mainly highlights the weak positive correlation between both NPLR-based metrics. We therefore decided to select the first component only. This component comprises university–industry R&D linkages of various kinds, reflecting in decreasing order by weight: joint knowledge creation collaboration (%UIC), social connectedness and cognitive proximity (%MA UIC), knowledge diffusion and cognitive proximity (%NPLR), and geographical proximity between partners (%DOMESTIC UIC). Of lesser relevance are ‘local’ partners, as compared to ‘domestic’, a result partly explained by the overlap with %MA UIC that may also capture geographic proximity. Similarly, %NPLR HICI is largely incorporated by %NPLR. The low weight assigned to %CO-PATENT is less easily explained and probably an outcome of its overlap with several of the other metrics.

Table 4 Principal component analysis and component weights (750 universities) Full size table

Each university’s score on this component is identical to its loading on the first component as mentioned in Table 4 (these loadings were calculated according to the regression method). In our further analysis, we will refer to this component as the University–Industry R&D Linkage Index (truncated to: U-I R&D Index). “Appendix” contains the top 100 lists according to this ranking.