IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v12y2024i8p196-d1445382.html
   My bibliography  Save this article

Cost Inefficiency of Japanese Railway Companies and Impacts of COVID-19 Pandemic and Digital Transformation

Author

Listed:
  • Hideaki Endo

    (Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, 3-3-6, Shibaura, Minato-ku, Tokyo 108-0023, Japan)

  • Mika Goto

    (Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, 3-3-6, Shibaura, Minato-ku, Tokyo 108-0023, Japan)

Abstract

The outbreak of the COVID-19 pandemic seriously affected railway businesses. The motivation of this study is to provide vital information to railway company management and policymakers by quantitatively assessing the cost efficiency of railway operations. We examine the efficiency of Japanese listed railway companies by applying stochastic frontier analysis to their operational and financial data from 2005 to 2020. Then, we classify the companies into four groups by cost efficiency levels and identify the characteristics of the best-practice companies. Furthermore, we analyze the factors influencing cost efficiency before and during the pandemic. Finally, we discuss the sustainable business practices and measures of digital transformation (DX) that can be applied to improve efficiency and survive severe events like the pandemic. From the results, we reveal that cost-efficient companies succeeded in securing profits through the creation of new services by proactive DX investments. The practical contributions of this study are threefold: quantifying the deterioration in efficiency due to the pandemic; identifying characteristics of best-practice companies; and examining the relationship between cost efficiency levels and concrete measures and investments for sustainable business practices. This study proposes a new analytical framework that combines conventional methods.

Suggested Citation

  • Hideaki Endo & Mika Goto, 2024. "Cost Inefficiency of Japanese Railway Companies and Impacts of COVID-19 Pandemic and Digital Transformation," Economies, MDPI, vol. 12(8), pages 1-37, July.
  • Handle: RePEc:gam:jecomi:v:12:y:2024:i:8:p:196-:d:1445382
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/12/8/196/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7099/12/8/196/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bourjade, Sylvain & Muller-Vibes, Catherine, 2023. "Optimal leasing and airlines' cost efficiency: A stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    2. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    3. Karanki, Fecri & Lim, Siew Hoon, 2021. "Airport use agreements and cost efficiency of U.S. airports," Transport Policy, Elsevier, vol. 114(C), pages 68-77.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    2. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    3. António Afonso & Ana Patricia Montes & José M. Domínguez, 2024. "Measuring Tax Burden Efficiency in OECD Countries: An International Comparison," CESifo Working Paper Series 11333, CESifo.
    4. Khanal, Aditya & Koirala, Krishna & Regmi, Madhav, 2016. "Do Financial Constraints Affect Production Efficiency in Drought Prone Areas? A Case from Indonesian Rice Growers," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230087, Southern Agricultural Economics Association.
    5. Wu, Yanrui, 1995. "The productive efficiency of Chinese iron and steel firms A stochastic frontier analysis," Resources Policy, Elsevier, vol. 21(3), pages 215-222, September.
    6. Firna Varina & Sri Hartoyo & Nunung Kusnadi & Amzul Rifin, 2020. "The Determinants of Technical Efficiency of Oil Palm Smallholders in Indonesia," International Journal of Economics and Financial Issues, Econjournals, vol. 10(6), pages 89-93.
    7. Rossi, Martín, 2000. "Análisis de eficiencia aplicado a la regulación ¿Es importante la Distribución Elegida para el Término de Ineficiencia?," UADE Textos de Discusión 22_2000, Instituto de Economía, Universidad Argentina de la Empresa.
    8. Dhehibi, Boubaker & Lachaal, Lassaad & Elloumi, Mohamed & Messaoud, Emna B., 2007. "Measurement and Sources of Technical Inefficiency in the Tunisian Citrus Growing Sector," 103rd Seminar, April 23-25, 2007, Barcelona, Spain 9391, European Association of Agricultural Economists.
    9. Anthony Rezitis & Kostas Tsiboukas & Stauros Tsoukalas, 2002. "Measuring technical efficiency in the Greek agricultural sector," Applied Economics, Taylor & Francis Journals, vol. 34(11), pages 1345-1357.
    10. Rosen Azad Chowdhury & Dilshad Jahan & Tapas Mishra & Mamata Parhi, 2023. "A Quality Dimension? A Re-appraisal of Financial Development and Economic Growth Nexus in a Quality-Quantity Setting," Working Papers 2023-02, Swansea University, School of Management.
    11. V. Vandenberghe, 2018. "The Contribution of Educated Workers to Firms’ Efficiency Gains: The Key Role of Proximity to the ‘Local’ Frontier," De Economist, Springer, vol. 166(3), pages 259-283, September.
    12. repec:use:tkiwps:1818 is not listed on IDEAS
    13. B. E. Bravo‐Ureta & L. Rieger, 1990. "Alternative Production Frontier Methodologies And Dairy Farm Efficiency," Journal of Agricultural Economics, Wiley Blackwell, vol. 41(2), pages 215-226, May.
    14. Gangopadhyay, Partha & Jain, Siddharth & Bakry, Walid, 2022. "In search of a rational foundation for the massive IT boom in the Australian banking industry: Can the IT boom really drive relationship banking?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    15. Liu, Lili, 1991. "Entry-exit, learning, and productivity change : evidence from Chile," Policy Research Working Paper Series 769, The World Bank.
    16. Sébastien Marchand, 2011. "Technical Efficiency, Farm Size and Tropical Deforestation in the Brazilian Amazonian Forest," Working Papers halshs-00552981, HAL.
    17. Managi, Shunsuke & Opaluch, James J. & Jin, Di & Grigalunas, Thomas A., 2006. "Stochastic frontier analysis of total factor productivity in the offshore oil and gas industry," Ecological Economics, Elsevier, vol. 60(1), pages 204-215, November.
    18. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.
    19. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.
    20. Mehdi Farsi & Aurelio Fetz & Massimo Filippini, 2007. "Benchmarking and Regulation in the Electricity Distribution Sector," CEPE Working paper series 07-54, CEPE Center for Energy Policy and Economics, ETH Zurich.
    21. Alfonso Flores-Lagunes & William C. Horrace & Kurt E. Schnier, 2007. "Identifying technically efficient fishing vessels: a non-empty, minimal subset approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 729-745.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecomi:v:12:y:2024:i:8:p:196-:d:1445382. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.