Efficiency measurement of research groups using Data Envelopment Analysis and Bayesian networks

Abstract

Applications of non-parametric frontier production methods such as Data Envelopment Analysis (DEA) have gained popularity and recognition in scientometrics. DEA seems to be a useful method to assess the efficiency of research units in different fields and disciplines. However, DEA results give only a synthetic measurement that does not expose the multiple relationships between scientific production variables by discipline. Although some papers mention the need for studies by discipline, they do not show how to take those differences into account in the analysis. Some studies tend to homogenize the behaviour of different practice communities. In this paper we propose a framework to make inferences about DEA efficiencies, recognizing the underlying relationships between production variables and efficiency by discipline, using Bayesian Network (BN) analysis. Two different DEA extensions are applied to calculate the efficiency of research groups: one called CCRO and the other Cross Efficiency (CE). A BN model is proposed as a method to analyze the results obtained from DEA. BNs allow us to recognize peculiarities of each discipline in terms of scientific production and the efficiency frontier. Besides, BNs provide the possibility for a manager to propose what-if scenarios based on the relations found.

Exploring the Effects of North–South and South–South Research Collaboration in Emerging Economies, the Colombian Case

“…Due to research collaboration, scientific productivity, understood as efficient use of resources leading to high quality products, improves with variations across scientific fields (Abramo, D’Angelo, & Di Costa, 2009; Ruiz et al, 2010) and different patterns of internationalization as shown in cases from Europe (Abramo, D’Angelo, & Solazzi, 2011) and Latin America (Orozco & Villaveces, 2015). Besides the obvious condition of partners being from different nations, international research collaboration (IRC) produces impacts that are different to research collaboration between researchers from the same country.…”

Productive efficiency analysis of quantitative economics journals through Stochastic Frontier Analysis using panel data

“…Similar studies include Bonaccorsi, Daraio and Simar (2006), where Italian universities are analysed, and Bonaccorsi and Daraio (2003), who consider institutes of the French INSERM and biomedical research institutes of the Italian CNR. Ruiz et al (2010) examine the efficiency in the scientific production of a sample of Colombian research groups. Agasisti, Catalano, Landoni and Varganti (2012) analyse the production of 69 academic departments located in Italy.…”

To publish or not to publish? On the aggregation and drivers of research performance

“…Vinkler (1998, 2006), Ruiz et al (2010), and Bonaccorsi et al (2006), for instance, developed composite (i.e., multidimensional) indicators for evaluating the research performances of research institutes.…”

“… 1 Note that the use of multidimensional measures has not only been suggested for evaluating individual researcher performances, but also for evaluating research departments. Vinkler ( 1998 , 2006 ), Ruiz et al ( 2010 ), and Bonaccorsi et al ( 2006 ), for instance, developed composite (i.e., multidimensional) indicators for evaluating the research performances of research institutes. …”

“… 8 Although the DEA model has not been applied in the construction of RES-scores, the literature counts various studies focusing on the efficiency in research activities of universities or (university or research) departments (e.g., Johnes and Johnes 1993 ; Beasly 1995 ; Bonaccorsi et al 2006 ; Ruiz et al 2010 ; Cherchye and Vanden Abeele 2005 ). …”