Diego Chavarro

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

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.

Under-reporting research relevant to local needs in the global south. Database biases in the representation of knowledge on rice

This paper aims at improving our understanding of the attributes of academic researchers that influence the capacity to identify and exploit entrepreneurial opportunities. We investigate a number of factors highlighted in the literature as influencing the entrepreneurial activities undertaken by academics. Our results show that identification and exploitation of entrepreneurial opportunities are shaped by different factors. While identification of commercial opportunities is driven by prior entrepreneurial experience and the excellence of the academic work, exploitation of entrepreneurial opportunities is driven by the extent of previous collaboration with industry partners, cognitive integration and prior entrepreneurial experience.Trabajo presentado a la 15th International Conference on Scientometrics & Informetrics, celebrada en Estambul (Turquia) del 29 de junio al 4 de julio de 2015.