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The paper is the first attempt to integrate microdata on universities and firms across most European countries in order to disentangle the impact of knowledge spillovers from human capital (graduates) and intellectual capital (codified research output) on the performance of firms. Data cover all Higher Education Institutions (HEIs) registered in the official European Tertiary Education Register (ETER). Data on performance of firms are from ORBIS and refer to change in the 2011–2015 period in turnover, total assets, intangible assets, and employment. Firms are georeferred and the spillovers from all HEIs located at a given distance are summed and integrated. The findings suggest that, among knowledge spillovers, the creation of human capital via education of students has a larger impact than the circulation of research knowledge. Moreover, the two factors seem to be complements rather than substitutes. Spatial proximity is important for embodied knowledge spillovers (i.e. educated people), while for codified and disembodied spillovers (citations to publications) the spatial dimension is less relevant. The findings have important managerial and policy-making consequences.

The estimation of economies of scope between research and teaching has been the object of a large literature in economics of education and efficiency analysis, with parametric and non-parametric specifications. The paper contributes to the literature by building a pan-European dataset that integrates official statistics on higher education at country level with bibliometric indicators. The dataset allows a breakdown by scientific and educational field, accounting for the heterogeneity among disciplines. We applied a technique which has not been used for the efficiency estimation of economies of scope in higher education, namely seemingly unrelated regression (SUR) applied to separate input–output equations describing the production of education and research. We found confirmation for economies of scope in some fields and with some specifications, or no relation between the equations. In no case did we find diseconomies of scope between teaching and research.

The representation of the product use context is a well established design practice in Engineering Design. Recently, design theory is studying the product interaction involving several cognitive aspects such as the possible conditions in which a wrong interaction occurs. The aim of this paper is to find a quantitative evidence of the causes of these misuses. In particular, this study focuses on the detection of bad design and biases.In this paper, we propose a method that helps to the automatic detection of bad design and biases from patents. The method is based on an approach that defines syntactic rules to detect sentences containing these artifacts. These rules are defined based on an exploratory analysis of the explicit mention of “bad design” and “bias” and then, tested with multiple experiments on a sample of patents. The results give a first quantitative evidence of the presence of bad design and biases in patents and consequently of their importance in the design theory. In particular, it is provided a fine grain analysis of the linguistic structure of sentences containing these artifacts helping designers in detecting automatically them from patents.

Data, information and knowledge are strongly involved in Engineering Design (ED) process. Despite the crucial role played by data in the design process, there is a lack of studies about how different data are used and generated by the various phases of the ED process. This study is a first attempt to fill this gap by mapping which data types are involved in the different ED phases from a research perspective.In order to achieve this objective, we used a methodology based on Text Mining. Firstly, we retrieve a corpus of scientific papers related to ED; then, we build two lexicons to recognize ED phases and data types; finally, we collect these entities within ED papers and map the relations between them.The methodology application allows the building of a network graph for visualizing the relations among data lexicon and ED lexicon. Then, we investigate the specific relations among data types and ED phases by building a heatmap to investigate data types from 3 different perspective.The insight coming from our analysis shows that ED studies have a great potential in the usage of many data sources, but also that there exist some gaps to be solved in order to reach a more effective data usage in the context of ED.

In recent years, the growing interest of universities in valorization of research activities (tipically through technology transfer—patenting and licensing—and academic entrepreneurship) has generated a debate on its impact on scientific knowledge production. There have been controversies on the potentially negative effect of university research valorization on public research, in terms of quality, long term vs short term orientation, and accessibility. The debate has been traditionally framed in terms of substitution vs complementarity between public research and valorization. In this paper we argue that the key to understand the relation between public research and valorization lies in the role of research funding, particularly: (a) competitive public research funding; (b) third party commercial funding. If the funding system (both public and private) takes the value of academic research as signal of quality, then high quality researchers are able to attract extra-funds beyond ordinary endowment. In turn, these additional funds can be used as a source to organize an effective and long-term division of labor in laboratories in order to support inventions and licensing (patents) and entrepreneurial activities (spin-off companies). In this case academic quality and commercialization are strong complement and positive feedback loops are generated. This interpretation on the role of funding is strongly supported by original data from the Italian Evaluation of Research Quality VQR 2011–2014 dataset. The paper is based on a unique and novel dataset, that integrates data on valorization of research activities at individual and institutional level on all the Italian universities and microdata on research quality on 32,616 researchers in STEM disciplines.

With the rise of the impact assessment revolution, governments and public opinion have started to ask researchers to give evidence of their impact outside the traditional audiences, i.e. students and researchers. There is a mismatch between the request to demonstrate the impact and the current methodologies for impact assessment. This mismatch is particularly worrisome for the research in Social Sciences and Humanities. This paper gives a contribution by examining systematically a key element of impact, i.e. the social groups that are directly or indirectly affected by the results of research. We use a Text mining approach applied to the Research Excellence Framework (REF) collection of 6,637 impact case studies in order to identify social groups mentioned by researchers. Differently from previous studies, we employ a lexicon of user groups that includes 76,857 entries, which saturates the semantic field, permits the identification of all users and opens the way to normalization. We then develop three new metrics measuring Frequency, Diversity and Specificity of user expressions. We find that Social Sciences and Humanities exhibit a distinctive structure with respect to frequency and specificity of users.

The European Tertiary Education Register (ETER)has established itself as the reference point for any analysis of higher education based on microdata, that is, on data related to individual institutions. ETER covers both university (PhD granting) and non-university institutions (e.g. Fachochschule) on the basis of microdata validated byNational Statistical Authoritiesacross all European Union countries and several affiliated countries. In recent years theEuropean Commission, Directorate General Education, Youth and Culture, has launched an initiative to foster the exchange of students and academic staff in a structured and permanent way. The European University Initiative supports alliances of universities that build up joint curricula, or developnew educational programmes based on complementary resources. The Initiative is too young to evaluate its impact, but it is important to start a collective reflection on the startup period, in the light of the more general issue of the reform andrelaunch of the European Higher Education Area. The Policy Brief makes use of ETER data to compareuniversities that are members of alliances to non-members. The Brief finds that members of alliancesare larger, more internationalized and more research intensive. At the same time, they do not differ in terms of disciplinary profile. &nbsp