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

Author

Listed:
  • Cristhian Fabián Ruiz

    (Universidad de los Andes)

  • Ricardo Bonilla

    (Universidad de los Andes)

  • Diego Chavarro

    (Universidad de los Andes)

  • Luis Antonio Orozco

    (Universidad de los Andes)

  • Roberto Zarama

    (Universidad de los Andes)

  • Xavier Polanco

    (Universidad del Rosario)

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.

Suggested Citation

  • Cristhian Fabián Ruiz & Ricardo Bonilla & Diego Chavarro & Luis Antonio Orozco & Roberto Zarama & Xavier Polanco, 2010. "Efficiency measurement of research groups using Data Envelopment Analysis and Bayesian networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 711-721, June.
    • Handle: RePEc:spr:scient:v:83:y:2010:i:3:d:10.1007_s11192-009-0122-y
    DOI: 10.1007/s11192-009-0122-y
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    References listed on IDEAS

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    2. Kristof Witte & Nicky Rogge, 2010. "To publish or not to publish? On the aggregation and drivers of research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 657-680, December.
    3. Ortega, Francisco J. & Gavilan, Jose M., 2013. "The measurement of production efficiency in scientific journals through stochastic frontier analysis models: Application to quantitative economics journals," Journal of Informetrics, Elsevier, vol. 7(4), pages 959-965.
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    5. Gonzalo Ordóñez‐Matamoros & Michelle Vernot‐López & Ornella Moreno‐Mattar & Luis Antonio Orozco, 2020. "Exploring the Effects of North–South and South–South Research Collaboration in Emerging Economies, the Colombian Case," Review of Policy Research, Policy Studies Organization, vol. 37(2), pages 174-200, March.
    6. Reddy, Manohar Bayyapu & Bielov, Constantine & Finley, Benjamin & Kilkki, Kalevi & Mitomo, Hitoshi, 2019. "Efficiency of Mobile Network Operators from a Data Service Perspective," 30th European Regional ITS Conference, Helsinki 2019 205208, International Telecommunications Society (ITS).
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    8. Gavilan, José M. & Ortega, Francisco J., 2020. "Productive efficiency analysis of quantitative economics journals through Stochastic Frontier Analysis using panel data || Análisis de eficiencia productiva de revistas de economía cuantitativa a trav," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 30(1), pages 297-311, December.

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