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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.

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., Author(s): Alessandro Avenali [sup.1] , Cinzia Daraio [sup.1] , Joanna Wolszczak-Derlacz [sup.2] Author Affiliations: (1) grid.7841.a, Dipartimento Di Ingegneria Informatica Automatica E Gestionale Antonio Ruberti (DIAG), Sapienza University of Rome, [...]

The increasing availability of longitudinal data (repeated numerical observations of the same units at different times) requires the development of flexible techniques to automatically detect errors in such data. Besides standard types of errors, which can be treated with generic error correction techniques, large longitudinal datasets may present specific problems not easily traceable by the generic techniques. In particular, after applying those generic techniques, time series in the data may contain trends, natural fluctuations and possible surviving errors. To study the data evolution, one main issue is distinguishing those elusive errors from the rest, which should be kept as they are and not flattened or altered. This work responds to this need by identifying some types of elusive errors and by proposing a statistical-mathematical approach to capture their complexity that can be applied after the above generic techniques. The proposed approach is based on a system of indicators and works at the formal level by studying the differences between consecutive values of data series and the symmetries and asymmetries of these differences. It operates regardless of the specific meaning of the data and is thus applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing two key variables: "Total academic staff" and "Total number of enrolled students", which are two of the most important variables, often used in empirical analysis as a proxy for size, and are considered by policymakers at the European level. The results are very promising. [ABSTRACT FROM AUTHOR]

The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’ websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities’ websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for “profiling” the analyzed universities.

This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.

This paper proposes a holistic framework for the development of models for the assessment of research activities and their impacts. It distinguishes three dimensions, including in an original way, data as a main dimension, together with theory and methodology. Each dimension of the framework is further characterized by three main building blocks: education, research, and innovation (theory); efficiency, effectiveness, and impact (methodology); and availability, interoperability, and “unit-free” property (data). The different dimensions and their nine constituent building blocks are attributes of an overarching concept, denoted as “quality.” Three additional quality attributes are identified as implementation factors (tailorability, transparency, and openness) and three “enabling” conditions (convergence, mixed methods, and knowledge infrastructures) complete the framework. A framework is required to develop models of metrics. Models of metrics are necessary to assess the meaning, validity, and robustness of metrics. The proposed framework can be a useful reference for the development of the ethics of research evaluation. It can act as a common denominator for different analytical levels and relevant aspects and is able to embrace many different and heterogeneous streams of literature. Directions for future research are provided.

The development of information and communication technologies accompanied by the exponential growth of electronic scholarly publications and computer archives has led, on the one hand, to the development of techniques for the systematic analysis of published literature and, on the other hand, to a significant growth in the number of literature reviews conducted and published. A systematic literature review seeks to comprehensively identify and synthesize all studies conducted on a given topic. However, how does one ensure that the entire state of the art on a given topic has been covered? How does one collect and select a large number of publications on a given topic, and how does one analyze the knowledge extracted from these publications? In other words, how do we use systematic literature analysis to produce new knowledge from existing knowledge? To make the best use of the various tools available today for conducting systematic reviews, it is necessary to be aware of their limitations. In the paper, we provide illustrations, examples, and detailed information on the various alternatives for conducting systematic reviews, presenting a review of available sources and tools. We report an analysis of the articles published in the journal International Transactions in Operational Research to show the potential and limitations of systematic review versus domain expert‐based selection. Finally, we show that a good systematic review relies on the intelligent combination of the latest tools available for conducting systematic research with the knowledge of domain experts. [ABSTRACT FROM AUTHOR]

One of the most significant problems in the development of quantitative models to assess the performance of decision‐making units (DMUs) is the availability of information coded into variables and indicators that are proxies for the relevant dimensions of the models: resources or inputs, products or services or outputs, and heterogeneous contextual factors including environmental factors. Data envelopment analysis (DEA) and balanced scorecard (BSC) are two of the best‐known and applied tools to model and measure the performance of DMUs. Within the information set requested to model the performance, the most critical and sensitive variables and indicators related to the intangible capital of organizations, which includes with a primary role the intellectual capital (IC). Although they are very different, DEA (based on linear programming) and BSC (including a set of indicators along four dimensions) have recently been combined to try to address the problem indicated earlier, namely, having a set of variables and indicators available to better measure the performance of DMUs. We apply a three‐level methodology combining (i) a series of systematic reviews, (ii) a bibliometric analysis of all the published works found, and (iii) an analysis of the so‐called grey literature contained in the reports of knowledge‐based organizations. The main results obtained are (1) a comprehensive survey and mapping of all scientific works combining DEA, BSC, and IC including the gender dimension; (2) an integral and inclusive list integrating all indicators found in both published works and reports, reclassified according to the main dimensions of the IC. [ABSTRACT FROM AUTHOR]