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Higher education institutions (HEIs), based on learning, innovation, and research, can support the progress of civil society. Many HEIs are implementing sustainability practices and projects to counteract climate change, often involving youth participation. The present study aimed at identifying how sustainable communities may be fostered in a university setting. To that end, a questionnaire was administered to engineering students at the start and end of a course on energy issues, assessing their perceptions of sustainability using multi-criteria decision analysis. The results showed that students placed greater value on sustainability at the end of the course. Additionally, the findings highlight that the implementation of projects aimed at tackling real problems may be useful for disseminating knowledge and sustainable practices. The main implications of this study indicate that sustainable communities in academia lay on six foundational pillars: sustainable education, energy (and resource) independence, subsidies in support of the green economy, initiatives aimed at reducing the carbon footprint, energy community development, and new green professional opportunities. [ABSTRACT FROM AUTHOR]

This work explores the relationship between multinational R&D and innovation productivity among top corporate knowledge and R&D producers by adopting a twofold concept of internationalisation: (1) the firm's degree of R&D internationalisation, and (2) the firm's geographic diversification. We model the patent production process with an appropriate and robust conditional Data Envelopment Analysis (DEA) estimator, using a unique database of firms that matches financial indicators and patent information. Our results reinforce the fundamental role of internationalisation in the knowledge production process when the internationalisation process is properly and strategically managed. We interpret our empirical evidence through the theoretical lens of the learning theory of internationalisation, and we postulate that a high R&D intensity is a key driver to overcoming the challenges of internationalisation. [ABSTRACT FROM AUTHOR]

In this article, we contribute to the scant literature covering quantitative studies on the determinants of the non-academic staff incidence in higher education institutions by analysing how the proportion of non-academic staff is related to key features such as size, prestige, year of foundation and financial structure of universities. We apply nonlinear regression analysis to compare HEIs across Europe and the USA, taking into account time and cross-country heterogeneity of the two balanced panel datasets concerning European and American universities over a period of 6 years (2011–2016 for Europe and 2012–2017 for the USA). Evidence suggests that in both Europe and the USA, public and larger (if sufficiently large) as well as more research-oriented units are characterised by a higher proportion of non-academic staff. In Europe, we observe an inverted U-shaped effect of the share of non-personnel expenditure and the foundation year on the proportion of non-academic staff, while the proportion of non-academic staff decreases with the share of core and third-party funding. For the USA, we obtain similar findings except that the share of core funding and third-party funding is characterised by a U-shaped effect, and the impact of the share of non-personnel expenditure has no empirical effect on the proportion of non-academic staff. Additionally, we discover that some factors that contribute to the proportion of non-academic staff may constitute indicators of performance, suggesting the need for further research to extend our knowledge on the complex issue of the role played by non-academic staff in university performance. [ABSTRACT FROM AUTHOR]

We propose an innovative use of the Leiden Rankings (LR) in institutional management. Although LR only consider research output of major universities reported in Web of Science (WOS) and share the limitations of other existing rankings, we show that they can be used as a base of a heuristic approach to identify "outlying" institutions that perform significantly below or above expectations. Our approach is a non-rigorous intuitive method ("heuristic") because is affected by all the biases due to the technical choices and incompleteness that affect the LR but offers the possibility to discover interesting findings to be systematically verified later. We propose to use LR as a departure base on which to apply statistical analysis and network mapping to identify "outlier" institutions to be analyzed in detail as case studies. Outliers can inform and guide science policies about alternative options. Analyzing the publications of the Politecnico di Bari in more detail, we observe that "small teams" led by young and promising scholars can push the performance of a university up to the top of the LR. As argued by Moed (Applied evaluative informetrics. Springer International Publishing, Berlin, 2017a), supporting "emerging teams", can provide an alternative to research support policies, adopted to encourage virtuous behaviours and best practices in research. The results obtained by this heuristic approach need further verification and systematic analysis but may stimulate further studies and insights on the topics of university rankings policy, institutional management, dynamics of teams, good research practice and alternative funding methods. [ABSTRACT FROM AUTHOR]

In this paper we provide a review of the main functionalities of a Visual Analytics Environment (VAE) developed for the assessment of data and information quality in the context of performance evaluation of research organizations. Performing data and information quality tests are necessary procedures to ensure the bibliometric and research performance evaluation analysis of organizations have the necessary robustness. The proposed environment is helpful to guide the user to an Information Quality-aware development of Performance models. This interactive visual analytics environment offers to the user the possibility to produce and compare information quality-aware indicators, exploring and defining correct behavior, identifying anomalous cases from both data quality and information quality perspectives, and supporting the user in forming hypotheses on possible causes for those anomalies. The proposed approach, exploiting visual interactive exploration results in a more efficient process, minimizing the number of cases for which a manual investigation is needed. The illustration on European higher education institutions data demonstrates the use of the presented functionalities and their benefits. [ABSTRACT FROM AUTHOR]

In this paper we answer the question of how evaluation should be by proposing a good evaluation of research practices. A good evaluation of research practices, intended as social practices à la MacIntyre, should take into account the stable motivations and the traits of the characters (i.e. the virtues) of researchers. We also show that a good evaluation is also just, beyond the sense of fairness, as working on good research practices implies keep into account a broader sense of justice. After that, we propose the development of a knowledge base for the assessment of "good" evaluations of research practices to implement a questionnaire for the assessment of researchers' virtues. Although the latter is a challenging task, the use of ontologies and taxonomic knowledge, and the reasoning algorithms that can draw inferences on the basis of such knowledge represents a way for testing the consistency of the information reported in the questionnaire and to analyse correctly and coherently how the data is gathered through it. Finally, we describe the potential application usefulness of our proposal for the reform of current research assessment systems. [ABSTRACT FROM AUTHOR]

This work discusses the issue on how to include data about property and violent crimes in the production technology for the assessment of police technical efficiency. It applies recent advances in Directional Distances and Nonparametric Estimators. We claim that crime is an external variable not under the control of the decision units in view of the fact that it is exogenously determined. The results from the Conditional Directional Distance Analysis can be relevant to cities with high property misdemeanors and homicide rates. Our analysis may be helpful to obtain a more robust and fair classification of police and justice units under similar circumstances, determine the empirical effect of crime on police productivity, their optimal input–output relationship, explore potential associations and compensation effects, and rewarding efficient policy makers in the prevention of crime based on measures of police efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Ensuring the quality of integrated data is undoubtedly one of the main problems of integrated data systems. When focusing on multi-national and historical data integration systems, where the "space" and "time" dimensions play a relevant role, it is very much important to build the integration layer in such a way that the final user accesses a layer that is "by design" as much complete as possible. In this paper, we propose a method for accessing data in multipurpose data infrastructures, like data integration systems, which has the properties of (i) relieving the final user from the need to access single data sources while, at the same time, (ii) ensuring to maximize the amount of the information available for the user at the integration layer. Our approach is based on a completeness-aware integration approach which allows the user to have ready available all the maximum information that can get out of the integrated data system without having to carry out the preliminary data quality analysis on each of the databases included in the system. Our proposal of providing data quality information at the integrated level extends then the functions of the individual data sources, opening the data infrastructure to additional uses. This may be a first step to move from data infrastructures towards knowledge infrastructures. A case study on the research infrastructure for the science and innovation studies shows the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Henk started his work at the Department of Science Studies of the "Leids Instituut voor Beleidsonderzoek Nederland" (LISBON) at Leiden University In 1981 and received his PhD degree at Leiden University In 1989. In 2009, Henk was appointed professor at the Leiden University as recognition for his tremendous contribution to the development of CWTS. Graph B Henk F. Moed b (1951-2021) Professor Henk F. Moed was one of the outstanding and world-leading contributors to the advancement of Scientometric methodologies with a special focus on research evaluation. [Extracted from the article]

The 17th International Conference of the International Society for Scientometrics and Informetrics (ISSI) took place at Sapienza University of Rome from September 2-5, 2019. Organized under the auspices of the International Society for Scientometrics and Informetrics (ISSI), the ISSI conference has long been an important event for the entire scientific community and practitioners interested in the development and application of informetric and scientometric tools. Our student travel award, in its second iteration, provides monetary support for students to attend the ISSI conference. [Extracted from the article]

The heterogeneity of the Higher Education (HE) Institutions is one of the main critical issues in the assessment of their performance. This paper adopts a multi-level and multi-dimensional perspective, combining national (macro) and institution (micro) level data, and measuring both research and teaching activity, using performance indicators derived from the European Tertiary Education Register, CWTS Leiden Ranking, and PATSTAT patent database. Clustering and efficiency analysis are combined to characterize the heterogeneity of national HE systems in European countries, and reveal the potential of using micro level data to characterize national level performance. Large differences are observed between the European countries, partially due to the fact that they are in different phases of their scientific (and economic) development and of the re-structuring of their HE systems. Evidence is found that universities specializing either in teaching or in research tend to have a higher efficiency than those institutions balancing research and teaching. Tradeoffs are observed between undergraduate and post-graduate activities, and a "Matthew cumulative effect" seems in place on the European institutions analysed: high quality research is able to attract external funds that stimulate innovative and patenting activities that in turn are self-reinforcing to the scientific activities. The results reveal once more the limits and dangers of one-dimensional approaches to the performance of HEIs. [ABSTRACT FROM AUTHOR]

The quantitative evaluation of research is currently carried out by means of indicators calculated on data extracted and integrated by analysts who elaborate them by creating illustrative tables and plots of results. In this approach, the robustness of the metrics used and the possibility for users of the metrics to intervene in the evaluation process are completely neglected. We propose a new approach which is able to move forward, from indicators' development to an interactive performance model's development. It combines the advantages of the ontology-based data access paradigm with the flexibility and robustness of a visual analytics environment putting the consumer/stakeholder at the centre of the evaluation. A detailed description of such an approach is presented in the paper. The approach is illustrated and evaluated trough a comprehensive user's study that proves the added capabilities and the benefits that a user of performance models can have by using this approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose the adoption of moral philosophy and in particular normative ethics, to clarify the concept of "good" evaluation of "research practices". Using MacIntyre (1985)'s notion of a practice we argue that research is a form of social practice. As a result of this characterization, we claim that research practice typically requires three typologies of researcher: the leader, the good researcher and the honest researcher. Reflecting on what is a "good" research practice and on what is the role of researchers in it provides insight into some aspects of both the self-assessment process and how this promotes individual improvement. Moreover, this kind of reflection helps us to describe the functions (missions) of the research practices. A "good" evaluation should take into account all the building constituents of a "good" research practice and should be able to discriminate between good and bad research practices, while enforcing the functions of good research practices. We believe that these reflections may be the starting point for a paradigm shift in the evaluation of research practices which replaces an evaluation centred on products with an evaluation focused on the functions of these practices. In the last sections of the paper, we introduce and discuss an important aspect for the implementation of the proposed framework. This relates to the assessment of the virtues of researchers involved in a good research practice. Some examples of questions and preliminary items to include in a questionnaire for the assessment of Virtues in Research Practices are also provided. [ABSTRACT FROM AUTHOR]

In this paper, we compare the distribution of Elsevier Scopus subject areas of authors' documents, their bibliographical references and their citing documents. We compute the complement of the Herfindahl–Hirschman index as a measure of multidisciplinarity. We analyse a sample of 120 researchers belonging to two groups, one from the Italian Institute of Technology (IIT, whose work is expected to be highly multidisciplinary) and one from the National Institute for Nuclear Physics (INFN, whose work is expected to be much less multidisciplinary). We show that the two groups are distinguishable through the measured index values. By using the subject areas of authors' bibliographical references we obtain a better identification of the two groups than relying on the subject areas of the author's documents. We then extend the analysis to 3317 researchers belonging to seven Italian Scientific-Disciplinary sectors (SSD) providing insights about the degree of multidisciplinarity within each SSD. The results seem interesting for assessing the multidisciplinarity of younger researchers with scarce scientific output and few citations. [ABSTRACT FROM AUTHOR]

Directional distances provide useful, flexible measures of technical efficiency of production units relative to the efficient frontier of the attainable set in input-output space. In addition, the additive nature of directional distances permits negative input or outputs quantities. The choice of the direction allows analysis of different strategies for the units attempting to reach the efficient frontier. Simar et al. (Eur J Oper Res 220:853–864, 2012) and Simar and Vanhems (J Econom 166:342–354, 2012) develop asymptotic properties of full-envelopment, FDH and DEA estimators of directional distances as well as robust order-m and order- α directional distance estimators. Extensions of these estimators to measures conditioned on environmental variables Z are also available (e.g., see Daraio and Simar in Eur J Oper Res 237:358–369, 2014). The resulting estimators have been shown to share the properties of their corresponding radial measures. However, to date the algorithms proposed for computing the directional distance estimates suffer from various numerical drawbacks (Daraio and Simar in Eur J Oper Res 237:358–369, 2014). In particular, for the order-m versions (conditional and unconditional) only approximations, based on Monte-Carlo methods, have been suggested, involving additional computational burden. In this paper we propose a new fast and efficient method to compute exact values of the directional distance estimates for all the cases (full and partial frontier cases, unconditional or conditional to external factors), that overcome all previous difficulties. This new method is illustrated on simulated and real data sets. Matlab code for computation is provided in an "Appendix". [ABSTRACT FROM AUTHOR]

The investigation of the dynamics of national disciplinary profiles is at the forefront in quantitative investigations of science. We propose a new approach to investigate the complex interactions among scientific disciplinary profiles. The approach is based on recent pseudo-likelihood techniques introduced in the framework of machine learning and complex systems. We infer, in a Bayesian framework, the network topology and the related interdependencies among national disciplinary profiles. We analyse data extracted from the Incites database which relate to the national scientific production of most productive world countries at disciplinary level over the period 1992-2016. [ABSTRACT FROM AUTHOR]

The quantitative evaluation of Social Science and Humanities (SSH) and the investigation of the existing similarities between SSH and Life and Hard Sciences (LHS) represent the forefront of scientometrics research. We analyse the scientific production of the universe of Italian academic scholars , over a 10-year period across 2002-2012, from a national database built by the Italian National Agency for the Evaluation of Universities and Research Institutes. We demonstrate that all Italian scholars of SSH and LHS are equals, as far as their publishing habits. They share the same general law, which is a lognormal. At the same time, however, they are different, because we measured their scientific production with different indicators required by the Italian law; we eliminated the 'silent' scholars and obtained different scaling values-proxy of their productivity rates. Our findings may be useful to further develop indirect quali-quantitative comparative analysis across heterogeneous disciplines and, more broadly, to investigate on the generative mechanisms behind the observed empirical regularities. [ABSTRACT FROM AUTHOR]

The article focuses on the grand challenges in data integration for research and innovation (R&I) activities including the fast growing availability of open and linked data, the rapid evolution of big data into a big data science, and the multidimensionality and growing complexity of research assessment. Topics include the importance of data quality in research, the significant role of subject classification system in research funding allocation, and how to handle research identification.

We illustrate the usefulness of an Ontology-Based Data Management (OBDM) approach to develop an open information system, allowing for a deep level of interoperability among different databases, and accounting for additional dimensions of data quality compared to the standard dimensions of the OECD (Quality framework and guidelines for OECD statistical activities, OECD Publishing, Paris, 2011) Quality Framework. Recent advances in engineering in computer science provide promising tools to solve some of the crucial issues in data integration for Research and Innovation. [ABSTRACT FROM AUTHOR]

This paper proposes an Ontology-Based Data Management (OBDM) approach to coordinate, integrate and maintain the data needed for Science, Technology and Innovation (STI) policy development. The OBDM approach is a form of integration of information in which the global schema of data is substituted by the conceptual model of the domain, formally specified through an ontology. Implemented in Sapientia, the ontology of multi-dimensional research assessment, it offers a transparent platform as the base for the assessment process; it enables one to define and specify in an unambiguous way the indicators on which the evaluation is based, and to track their evolution over time; also it allows to the analysis of the effects of the actual use of the indicators on the behavior of scholars, and spot opportunistic behaviors; and it provides a monitoring system to track over time the changes in the established evaluation criteria and their consequences for the research system. It is argued that easier access to and a more transparent view of scientific-scholarly outcomes help to improve the understanding of basic science and the communication of research outcomes to the wider public. An OBDM approach could successfully contribute to solve some of the key issues in the integration of heterogeneous data for STI policies. [ABSTRACT FROM AUTHOR]

The performance of economic producers is often affected by external or environmental factors that, unlike the inputs and the outputs, are not under the control of the Decision Making Units (DMUs). These factors can be included in the model as exogenous variables and can help to explain the efficiency differentials, as well as improve the managerial policy of the evaluated units. A fully nonparametric methodology, which includes external variables in the frontier model and defines conditional DEA and FDH efficiency scores, is now available for investigating the impact of external-environmental factors on the performance. In this paper, we offer a state-of-the-art review of the literature, which has been proposed to include environmental variables in nonparametric and robust (to outliers) frontier models and to analyse and interpret the conditional efficiency scores, capturing their impact on the attainable set and/or on the distribution of the inefficiency scores. This paper develops and complements the approach of Bădin et al. () by suggesting a procedure that allows us to make local inference and provide confidence intervals for the impact of the external factors on the process. We advocate for the nonparametric conditional methodology, which avoids the restrictive 'separability' assumption required by the two-stage approaches in order to provide meaningful results. An illustration with real data on mutual funds shows the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a ‘scaling’ approach to compare the scientific performance of Italian heterogeneous academic disciplines. This method is based on the idea that, after eliminating the percentages of ‘silent’ researchers, the distribution of bibliometric parameters of the different academic fields can be superimposed and collapse into a unique master curve by a single scaling parameter. By using data on the scientific production of around 2,500 scholars of the university of Rome ‘La Sapienza’ from the Web of Science from 2004 to 2008, we (i) demonstrate the existence of a master curve, (ii) determine the scaling factors that work like rates of substitution to compare the scientific production across different academic fields on a common ground, (iii) show that the master bibliometric distribution follows a log-normal law and (iv) illustrate the relevance of the proposed approach for research assessment and allocation of competitive funding at the university level. [ABSTRACT FROM AUTHOR]

In the last decades of the twentieth century universities in Europe and other OECD countries have undergone a profound transformation. They have evolved from mainly élite institutions for teaching and research to large (public and private) organisations responsible for mass higher education and the production and distribution of new knowledge. Increasingly, new knowledge is produced by universities not only for its own sake but also for potential economic gains. [ABSTRACT FROM AUTHOR]

The explanation of productivity differentials is very important to identify the economic conditions that create inefficiency and to improve managerial performance. In the literature two main approaches have been developed: one-stage approaches and two-stage approaches. Daraio and Simar (2005, J Prod Anal 24(1):93–121) propose a fully nonparametric methodology based on conditional FDH and conditional order- m frontiers without any convexity assumption on the technology. However, convexity has always been assumed in mainstream production theory and general equilibrium. In addition, in many empirical applications, the convexity assumption can be reasonable and sometimes natural. Lead by these considerations, in this paper we propose a unifying approach to introduce external-environmental variables in nonparametric frontier models for convex and nonconvex technologies. Extending earlier contributions by Daraio and Simar (2005, J Prod Anal 24(1):93–121) as well as Cazals et al. (2002, J Econometrics 106:1–25), we introduce a conditional DEA estimator, i.e., an estimator of production frontier of DEA type conditioned to some external-environmental variables which are neither inputs nor outputs under the control of the producer. A robust version of this conditional estimator is proposed too. These various measures of efficiency provide also indicators of convexity which we illustrate using simulated and real data. [ABSTRACT FROM AUTHOR]

This paper proposes a general formulation of a nonparametric frontier model introducing external environmental factors that might influence the production process but are neither inputs nor outputs under the control of the producer. A representation is proposed in terms of a probabilistic model which defines the data generating process. Our approach extends the basic ideas from Cazals et al. (2002) to the full multivariate case. We introduce the concepts of conditional efficiency measure and of conditional efficiency measure of order- m. Afterwards we suggest a practical way for computing the nonparametric estimators. Finally, a simple methodology to investigate the influence of these external factors on the production process is proposed. Numerical illustrations through some simulated examples and through a real data set on Mutual Funds show the usefulness of the approach. [ABSTRACT FROM AUTHOR]

The paper assesses the empirical foundation of two largely held assumptions in science policy making, namely scale and agglomeration effects. According to the former effect, scientific production may be subject to increasing returns to scale, defined at the level of administrative units, such as institutes or departments. A rationale for concentrating resources on larger units clearly follows from this argument. According to the latter, scientific production may be positively affected by external economies at the geographical level, so that concentrating institutes in the same area may improve scientific spillover, linkages and collaborations. Taken together, these arguments have implicitly or explicitly legitimated policies aimed at consolidating institutes in public sector research and at creating large physical facilities in a small number of cities. The paper is based on the analysis of two large databases, built by the authors from data on the activity of the Italian National Research Council in all scientific fields and of the French INSERM in biomedical research. Evidence from the two institutions is that the two effects do not receive empirical support. The implications for policy making and for the theory of scientific production are discussed. [ABSTRACT FROM AUTHOR]

Age effects in scientific production are a consolidated stylised fact in the literature. At the level of scientist productivity declines with age following a predictable pattern. The problem of the impact of age structure on scientific productivity at the level of institutes is much less explored. The paper examines evidence from the Italian National Research Council. The path of hiring of junior researchers along the history of the institution is reconstructed. We find that age structure has a depressing effect on productivity and derive policy implications. The dynamics of growth of research institutes is introduced as a promising research field. [ABSTRACT FROM AUTHOR]