Authors:
Cristhian Ruiz Universidad de los Andes Center for Basic and Applied Interdisciplinary Studies on Complexity, CeiBA-Complejidad Calle 19 No. 1 – 11 Bogotá DC Colombia

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Ricardo Bonilla Universidad de los Andes Center for Basic and Applied Interdisciplinary Studies on Complexity, CeiBA-Complejidad Calle 19 No. 1 – 11 Bogotá DC Colombia

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Diego Chavarro Universidad de los Andes Vice-Rectory for Research Bogotá DC Colombia

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Luis Orozco Universidad de los Andes School of Management Bogotá DC Colombia

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Roberto Zarama Universidad de los Andes Center for Basic and Applied Interdisciplinary Studies on Complexity, CeiBA-Complejidad Calle 19 No. 1 – 11 Bogotá DC Colombia

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Xavier Polanco Universidad del Rosario Center for Basic and Applied Interdisciplinary Studies on Complexity, CeiBA-Complejidad Bogotá DC Colombia

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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.

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Scientometrics
Language English
Size B5
Year of
Foundation
1978
Volumes
per Year
1
Issues
per Year
12
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0138-9130 (Print)
ISSN 1588-2861 (Online)