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DEA and ranking with the network-based approach: a case of R&D performance

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  • Liu, John S.
  • Lu, Wen-Min

Abstract

This study enhances the network-based approach, which is a novel method to increase discrimination in data envelopment analysis. The enhancements include removing the bias caused by a scale difference among organizations and highlighting the approach's ability to identify the strengths and weaknesses of each organization. The former makes the approach applicable to both the constant returns of scale (CRS) and the variable returns of scale (VRS) models. The network-based approach applies the centrality concept developed in social network analysis to discriminate efficient decision making organizations as determined by standard data envelopment analysis (DEA). More specifically, the results of data envelopment analysis are transformed into a directed and weighted network in which each node represents a decision making organization and the link between a pair of node represents the referencing relationship between the pair. The centrality value for each efficient organization provides the base for discrimination and ranking. This network-based approach suggests aggregating DEA results of different input/output combinations such that the merits of each organization under various situations can be considered. The final ranking of this approach favors organizations that have their strengths evenly spread and tends to screen out specialized efficient organizations. As a real world example, the approach is applied to evaluate and rank the R&D (research and development) performance of Taiwan's government-supported research institutes. The cross-organizations and within-organization strengths for each efficient research institute are identified after applying the approach. A two-stage R&D evaluation model separates the R&D process into the technology development and technology diffusion stage. The resulting performance map differentiates the research institutes into four categories--Achievers, Marketers, Innovators, and Underdogs.

Suggested Citation

  • Liu, John S. & Lu, Wen-Min, 2010. "DEA and ranking with the network-based approach: a case of R&D performance," Omega, Elsevier, vol. 38(6), pages 453-464, December.
  • Handle: RePEc:eee:jomega:v:38:y:2010:i:6:p:453-464
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. You, Yan Q. & Jie, Tao, 2016. "A study of the operation efficiency and cost performance indices of power-supply companies in China based on a dynamic network slacks-based measure model," Omega, Elsevier, vol. 60(C), pages 85-97.
    2. repec:eee:ejores:v:266:y:2018:i:3:p:990-999 is not listed on IDEAS
    3. repec:eee:soceps:v:61:y:2018:i:c:p:16-28 is not listed on IDEAS
    4. Roza Azizi & Reza Kazemi Matin, 2016. "Ranking Two-Stage Production Units in Data Envelopment Analysis," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(01), pages 1-19, February.
    5. Lu, Wen-Min & Liu, John S. & Kweh, Qian Long & Wang, Chung-Wei, 2016. "Exploring the benchmarks of the Taiwanese investment trust corporations: Management and investment efficiency perspectives," European Journal of Operational Research, Elsevier, vol. 248(2), pages 607-618.
    6. Lee, Boon L. & Worthington, Andrew C., 2016. "A network DEA quantity and quality-orientated production model: An application to Australian university research services," Omega, Elsevier, vol. 60(C), pages 26-33.
    7. Zhong, Wei & Yuan, Wei & Li, Susan X. & Huang, Zhimin, 2011. "The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data," Omega, Elsevier, vol. 39(4), pages 447-455, August.
    8. Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada, 2011. "Reducing differences between profiles of weights: A "peer-restricted" cross-efficiency evaluation," Omega, Elsevier, vol. 39(6), pages 634-641, December.
    9. Lee, Seonghee & Lee, Hakyeon, 2015. "Measuring and comparing the R&D performance of government research institutes: A bottom-up data envelopment analysis approach," Journal of Informetrics, Elsevier, vol. 9(4), pages 942-953.
    10. Lee, Hakyeon & Shin, Juneseuk, 2014. "Measuring journal performance for multidisciplinary research: An efficiency perspective," Journal of Informetrics, Elsevier, vol. 8(1), pages 77-88.
    11. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    12. Sagarra, Marti & Mar-Molinero, Cecilio & Agasisti, Tommaso, 2017. "Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling," Omega, Elsevier, vol. 67(C), pages 123-133.
    13. repec:spr:scient:v:96:y:2013:i:3:d:10.1007_s11192-013-0974-z is not listed on IDEAS
    14. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2013. "Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system," Omega, Elsevier, vol. 41(1), pages 131-142.
    15. Sahoo, Biresh K. & Tone, Kaoru, 2013. "Non-parametric measurement of economies of scale and scope in non-competitive environment with price uncertainty," Omega, Elsevier, vol. 41(1), pages 97-111.
    16. Aristovnik, Aleksander, 2014. "Efficiency of the R&D Sector in the EU-27 at the Regional Level: An Application of DEA," MPRA Paper 59081, University Library of Munich, Germany.
    17. repec:gam:jsusta:v:9:y:2017:i:12:p:2297-:d:122520 is not listed on IDEAS
    18. Ghahraman, Abaghan & Prior, Diego, 2016. "A learning ladder toward efficiency: Proposing network-based stepwise benchmark selection," Omega, Elsevier, vol. 63(C), pages 83-93.

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