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Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature

Author

Listed:
  • S. Lozano

    (University of Seville)

  • L. Calzada-Infante

    (Universidad de Oviedo)

  • B. Adenso-Díaz

    (Universidad de Oviedo)

  • S. García

    (Universidad de Oviedo)

Abstract

A network analysis (NA) of keyword co-occurrences for a broad collection of Data envelopment analysis (DEA) papers in the period 2008–2017 is carried out. The raw keywords have been cleaned up and standardized to consolidate and increase the consistency of the keywords. The resulting network has been characterized using network-level as well as node-level NA measures. Although the size of the network steadily increases with time, the average path length does not, showing its small world character. The disassortativity of the network indicates that the keywords used in a given paper generally include one or more common, frequently-used terms plus other less common terms that refer to the specific context of the research. The evolving nature of the keyword network is highlighted with some DEA keywords staying at the top of the ranking during the whole period and other emerging topics significantly increasing their strength during this period. The community structure of the network, which reflects the field’s knowledge structure, is also presented. The identified communities generally include specific DEA methodology terms, linked with corresponding application areas as well as with some geographical references. Also, the ego-network of some sample keywords is shown, and some research gaps in DEA are identified.

Suggested Citation

  • S. Lozano & L. Calzada-Infante & B. Adenso-Díaz & S. García, 2019. "Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 609-629, August.
    • Handle: RePEc:spr:scient:v:120:y:2019:i:2:d:10.1007_s11192-019-03132-w
    DOI: 10.1007/s11192-019-03132-w
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    References listed on IDEAS

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    Cited by:

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    3. Lu, Wei & Liu, Zhifeng & Huang, Yong & Bu, Yi & Li, Xin & Cheng, Qikai, 2020. "How do authors select keywords? A preliminary study of author keyword selection behavior," Journal of Informetrics, Elsevier, vol. 14(4).
    4. Kaushik, Vineet & Tewari, Shobha & Sahasranamam, Sreevas & Hota, Pradeep Kumar, 2023. "Towards a precise understanding of social entrepreneurship: An integrated bibliometric–machine learning based review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    5. Paola Bernardi & Alberto Bertello & Canio Forliano & Ludovico Bullini Orlandi, 2022. "Beyond the “ivory tower”. Comparing academic and non-academic knowledge on social entrepreneurship," International Entrepreneurship and Management Journal, Springer, vol. 18(3), pages 999-1032, September.
    6. Wei Jiang & Jiankai Zhou & Yangping Gao & Yintong Wang, 2021. "Exploring Methods of the Sustainable Development for Safety Engineering International Course," Sustainability, MDPI, vol. 13(9), pages 1-17, May.
    7. Nicanor García Álvarez & Belarmino Adenso-Díaz & Laura Calzada-Infante, 2021. "Maritime Traffic as a Complex Network: a Systematic Review," Networks and Spatial Economics, Springer, vol. 21(2), pages 387-417, June.
    8. Jinqing Yang & Zhifeng Liu & Yong Huang, 2024. "From informal to formal: scientific knowledge role transition prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4909-4935, August.
    9. Katchanov, Yurij L. & Markova, Yulia V., 2022. "Dynamics of senses of new physics discourse: Co-keywords analysis," Journal of Informetrics, Elsevier, vol. 16(1).
    10. Jinqing Yang & Xiufeng Cheng & Guanghui Ye & Yuchen Zhang, 2024. "Understanding scientific knowledge evolution patterns based on egocentric network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6719-6750, November.

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