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A meta-frontier network data envelopment analysis approach for the measurement of technological bias with network production structure

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  • Ming-Miin Yu

    (National Taiwan Ocean University)

  • Li-Hsueh Chen

    (Fo Guang University)

Abstract

Typically, the meta-frontier network data envelopment analysis model can be used to evaluate the technological gaps in individual stages simultaneously. However, the technological gap in each stage could also have a biased effect, i.e. one which improves the productivity of a subset of input or output factors. Since decision-making units may face different operational technologies, and have two-stage network structures, this study develops a new approach to investigate the favored direction of technology shift. The novelty of this approach is to identify that the technological gaps could affect the overall production function, both with the input technological bias in the first stage and with the output technological bias in the second stage. By comparing the difference between meta-technology and group technology, the favored direction of technology shift for individual decision-making units can be judged. The proposed approach is illustrated using data from 109 Taiwanese tourist hotels in 2015.

Suggested Citation

  • Ming-Miin Yu & Li-Hsueh Chen, 2020. "A meta-frontier network data envelopment analysis approach for the measurement of technological bias with network production structure," Annals of Operations Research, Springer, vol. 287(1), pages 495-514, April.
  • Handle: RePEc:spr:annopr:v:287:y:2020:i:1:d:10.1007_s10479-019-03436-3
    DOI: 10.1007/s10479-019-03436-3
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    Cited by:

    1. J. David Cummins & María Rubio-Misas, 2022. "Integration and convergence in efficiency and technology gap of European life insurance markets," Annals of Operations Research, Springer, vol. 315(1), pages 93-119, August.
    2. Pooja Bansal & Aparna Mehra & Sunil Kumar, 2022. "Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 297-324, January.
    3. Radovanović, Sandro & Savić, Gordana & Delibašić, Boris & Suknović, Milija, 2022. "FairDEA—Removing disparate impact from efficiency scores," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1088-1098.
    4. Chin‐wei Huang & Hsiao‐Yin Chen, 2023. "Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 1904-1919, June.
    5. Gowangwoo Park & Seok-Kee Lee & Kanghwa Choi, 2021. "Evaluating the Service Operating Efficiency and Its Determinants in Global Consulting Firms: A Metafrontier Analysis," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    6. Ana Pérez-González & Pablo Carlos & Elisa Alén, 2022. "An analysis of the efficiency of football clubs in the Spanish First Division through a two-stage relational network DEA model: a simulation study," Operational Research, Springer, vol. 22(3), pages 3089-3112, July.

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