IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2024i1p207-d1557181.html
   My bibliography  Save this article

Operational Efficiency of Pharmaceutical Companies in China: Based on Three-Stage DEA with Undesirable Outputs

Author

Listed:
  • Jiaqiang Sun

    (Faculty of Humanities, Management & Science, Universiti Putra Malaysia Bintulu Campus, Jalan Nyabau, P.O. Box 396, Bintulu 97008, Sarawak, Malaysia)

  • Anita Binti Rosli

    (Faculty of Humanities, Management & Science, Universiti Putra Malaysia Bintulu Campus, Jalan Nyabau, P.O. Box 396, Bintulu 97008, Sarawak, Malaysia)

  • Adrian Daud

    (Faculty of Humanities, Management & Science, Universiti Putra Malaysia Bintulu Campus, Jalan Nyabau, P.O. Box 396, Bintulu 97008, Sarawak, Malaysia)

Abstract

After a period of rapid growth, China’s pharmaceutical industry is facing multiple challenges, including insufficient innovation and severe pollution. Current research on the efficiency of pharmaceutical companies in China primarily focuses on financial or innovation aspects. Therefore, a holistic approach to operational efficiency is needed. To measure the operational efficiency of pharmaceutical companies in China more accurately and holistically, while accounting for environmental pollution, this study employs a three-stage Data Envelopment Analysis (DEA) model with undesirable outputs to evaluate efficiency across five dimensions: market performance, profitability, financial risk control, innovation, and sustainability. This approach integrates financial, innovation, and sustainability indicators to provide a more industry-specific framework for efficiency measurement. Furthermore, integrating with Stochastic Frontier Analysis (SFA) allows for revealing the impact of environmental factors on efficiency. The results show that both technical efficiency (TE) and pure technical efficiency (PTE) are relatively low in the first and third stages, with significant regional disparities. After excluding environmental factors, some regions—typically economically developed areas—showed improved overall efficiency. This indicates that the local environment in these regions is not conducive to the development of pharmaceutical enterprises. The SFA results further demonstrate that investments in education and high-level talent significantly enhance efficiency, whereas pollutant emissions and per capita income reduce operational efficiency. The findings suggest that local governments should enhance the operational efficiency of pharmaceutical enterprises by investing in education, attracting skilled talent, and improving waste infrastructure. Additionally, less efficient firms are encouraged to optimize resource allocation to achieve higher efficiency.

Suggested Citation

  • Jiaqiang Sun & Anita Binti Rosli & Adrian Daud, 2024. "Operational Efficiency of Pharmaceutical Companies in China: Based on Three-Stage DEA with Undesirable Outputs," Sustainability, MDPI, vol. 17(1), pages 1-27, December.
  • Handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:207-:d:1557181
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/1/207/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/1/207/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mingli Song & Guangshe Jia & Puwei Zhang, 2020. "An Evaluation of Air Transport Sector Operational Efficiency in China based on a Three-Stage DEA Analysis," Sustainability, MDPI, vol. 12(10), pages 1-16, May.
    2. Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.
    3. Markose Chekol Zewdie & Michele Moretti & Daregot Berihun Tenessa & Zemen Ayalew Ayele & Jan Nyssen & Enyew Adgo Tsegaye & Amare Sewnet Minale & Steven Van Passel, 2021. "Agricultural Technical Efficiency of Smallholder Farmers in Ethiopia: A Stochastic Frontier Approach," Land, MDPI, vol. 10(3), pages 1-17, March.
    4. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, September.
    5. Harold Fried & Shelton Schmidt & Suthathip Yaisawarng, 1999. "Incorporating the Operating Environment Into a Nonparametric Measure of Technical Efficiency," Journal of Productivity Analysis, Springer, vol. 12(3), pages 249-267, November.
    6. Gangyi Wang & Chang’e Zhao & Yuzhuo Shen & Ni Yin, 2021. "Estimation of cost efficiency of fattening pigs, sows, and piglets using SFA approach analysis: Evidence from China," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-22, December.
    7. Atakelty Hailu & Terrence S. Veeman, 2001. "Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 605-616.
    8. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    9. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    10. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    11. Musa Hasen Ahmed & Kumilachew Alamerie Melesse, 2018. "Impact of off-farm activities on technical efficiency: evidence from maize producers of eastern Ethiopia," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 6(1), pages 1-15, December.
    12. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.
    13. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    14. Cristina Fróes de Borja Reis & José Paulo Guedes Pinto, 2022. "Center–periphery Relationships of Pharmaceutical Value Chains: A Critical Analysis based on Goods and Knowledge Trade Flows," Review of Political Economy, Taylor & Francis Journals, vol. 34(1), pages 124-145, January.
    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. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    2. Xiang Ji & Jie Wu & Qingyuan Zhu & Jiasen Sun, 2019. "Using a hybrid heterogeneous DEA method to benchmark China’s sustainable urbanization: an empirical study," Annals of Operations Research, Springer, vol. 278(1), pages 281-335, July.
    3. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    4. Afzalinejad, Mohammad, 2020. "Reverse efficiency measures for environmental assessment in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    5. Song, Malin & An, Qingxian & Zhang, Wei & Wang, Zeya & Wu, Jie, 2012. "Environmental efficiency evaluation based on data envelopment analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4465-4469.
    6. César Salazar & Roberto Cárdenas-Retamal & Marcela Jaime, 2023. "Environmental efficiency in the salmon industry—an exploratory analysis around the 2007 ISA virus outbreak and subsequent regulations in Chile," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8107-8135, August.
    7. Chu, Junfei & Shao, Caifeng & Emrouznejad, Ali & Wu, Jie & Yuan, Zhe, 2021. "Performance evaluation of organizations considering economic incentives for emission reduction: A carbon emission permit trading approach," Energy Economics, Elsevier, vol. 101(C).
    8. Chiu, Yung-Ho & Lee, Jen-Hui & Lu, Ching-Cheng & Shyu, Ming-Kuang & Luo, Zhengying, 2012. "The technology gap and efficiency measure in WEC countries: Application of the hybrid meta frontier model," Energy Policy, Elsevier, vol. 51(C), pages 349-357.
    9. Cordero Ferrera, Jose Manuel & Alonso Morán, Edurne & Nuño Solís, Roberto & Orueta, Juan F. & Souto Arce, Regina, 2013. "Efficiency assessment of primary care providers: A conditional nonparametric approach," MPRA Paper 51926, University Library of Munich, Germany.
    10. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    11. Xiang Ji & Jiasen Sun & Qunwei Wang & Qianqian Yuan, 2019. "Revealing Energy Over-Consumption and Pollutant Over-Emission Behind GDP: A New Multi-criteria Sustainable Measure," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1391-1421, December.
    12. Beltrán-Esteve, Mercedes & Picazo-Tadeo, Andrés J., 2017. "Assessing environmental performance in the European Union: Eco-innovation versus catching-up," Energy Policy, Elsevier, vol. 104(C), pages 240-252.
    13. Shih-Heng Yu, 2019. "Benchmarking and Performance Evaluation Towards the Sustainable Development of Regions in Taiwan: A Minimum Distance-Based Measure with Undesirable Outputs in Additive DEA," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1323-1348, August.
    14. Yu, Ming-Miin & Chen, Li-Hsueh, 2016. "Centralized resource allocation with emission resistance in a two-stage production system: Evidence from a Taiwan’s container shipping company," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 650-671.
    15. García-Alonso, Carlos R. & Salvador-Carulla, Luis & Fernández-Rodríguez, Vicente, 2015. "Evaluation of system efficiency using the Monte Carlo DEA: The case of small health areasAuthor-Name: Torres-Jiménez, Mercedes," European Journal of Operational Research, Elsevier, vol. 242(2), pages 525-535.
    16. Jie Wu & Qingyuan Zhu & Junfei Chu & Liang Liang, 2015. "Two-Stage Network Structures with Undesirable Intermediate Outputs Reused: A DEA Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 455-477, October.
    17. Yu Liu & Rui-tang Guo & Wei-guo Pan, 2024. "Evaluation of carbon emission efficiency and spatial relevance in the thermal power industry: evidence from China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 22715-22745, September.
    18. Victoria Wojcik & Harald Dyckhoff & Marcel Clermont, 2019. "Is data envelopment analysis a suitable tool for performance measurement and benchmarking in non-production contexts?," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 559-595, December.
    19. Lin, Ruiyue & Peng, Yudan, 2024. "A new cross-efficiency meta-frontier analysis method with good ability to identify technology gaps," European Journal of Operational Research, Elsevier, vol. 314(2), pages 735-746.
    20. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.

    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:jsusta:v:17:y:2024:i:1:p:207-:d:1557181. 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.