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中国上市制药公司效率分析:一种结合三阶段数据包络分析与非期望产出、主成分分析和托比特回归的方法。

Efficiency analysis of listed pharmaceutical companies in China: A method combining three-stage DEA with undesirable output, PCA, and tobit regression.

作者信息

Sun Jiaqiang, Rosli Anita Binti, Daud Adrian

机构信息

Department of Social Science & Management, Faculty of Humanities, Management & Science, Universiti Putra Malaysia Bintulu Campus, Bintulu, Sarawak, Malaysia.

Research and Development Center, Fujian Yongjing Technology Co., Ltd., Nanping, Fujian, China.

出版信息

PLoS One. 2025 Aug 22;20(8):e0329767. doi: 10.1371/journal.pone.0329767. eCollection 2025.

DOI:10.1371/journal.pone.0329767
PMID:40845007
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12373225/
Abstract

The pharmaceutical industry plays a vital role in safeguarding public health and enhancing industrial competitiveness. However, pharmaceutical industry in China faces persistent challenges, including high pollution, insufficient innovation, and limited profit margins, which constrain its global competitiveness. The current researches on the operational efficiency were either from financial or innovation aspects-lacking a comprehensive assessment framework. Moreover, the impact of environments on efficiency relied on limited indicators and lacked in multicollinearity research. Finally, there was an absence of research on internal resource allocation affects operation efficiency. To fill this, this study aims to evaluate the current operation efficiency of the listed pharmaceutical enterprises in China from finance, innovation, and sustainability, and to reveal the influence environments on efficiency and the impact of the internal resource allocation on efficiency. To achieve these objectives, the study adopts a Three-stage Data Envelopment Analysis with undesirable outputs, integrated with Principal Component Analysis and Tobit regression, to comprehensively evaluate the operational efficiency of Chinese listed pharmaceutical enterprises over the ten-year period from 2013 to 2022 across the aforementioned three dimensions. The findings reveal generally low efficiency with significant regional disparities. North and Northwest China benefit from favorable environmental conditions, while Northeast China suffers from negative impacts. Improvements in external factors, such as innovation, living standards, labor supply, and openness level reduce costs, whereas internal uncontrollable factors, such as state-owned enterprise attributes, increase costs and suppress research and development. In terms of resource allocation, higher management expenses, and personnel allocations decrease efficiency, while increased sales expenditures and improved staff quality enhance efficiency. This study constructs a comprehensive framework for evaluating the operational efficiency and reveals the impact of external environments and internal resource allocation on efficiency. It provides empirical support for policymakers and operation managers seeking to improve the efficiency of the pharmaceutical companies.

摘要

制药行业在保障公众健康和提升产业竞争力方面发挥着至关重要的作用。然而,中国制药行业面临着持续的挑战,包括高污染、创新不足和利润率有限等问题,这些都制约了其全球竞争力。目前关于运营效率的研究要么来自财务方面,要么来自创新方面,缺乏一个全面的评估框架。此外,环境对效率的影响所依赖的指标有限,且缺乏多重共线性研究。最后,缺乏关于内部资源配置对运营效率影响的研究。为了填补这一空白,本研究旨在从财务、创新和可持续性三个方面评估中国上市制药企业的当前运营效率,并揭示环境对效率的影响以及内部资源配置对效率的影响。为实现这些目标,本研究采用带有非期望产出的三阶段数据包络分析,并结合主成分分析和Tobit回归,对2013年至2022年这十年间中国上市制药企业在上述三个维度的运营效率进行全面评估。研究结果显示,整体效率普遍较低,且存在显著的区域差异。中国北方和西北地区受益于有利的环境条件,而东北地区则受到负面影响。创新、生活水平、劳动力供应和开放程度等外部因素的改善会降低成本,而国有企业属性等内部不可控因素则会增加成本并抑制研发。在资源配置方面,较高的管理费用和人员配置会降低效率,而增加销售支出和提高员工素质则会提高效率。本研究构建了一个评估运营效率的综合框架,并揭示了外部环境和内部资源配置对效率的影响。它为寻求提高制药公司效率的政策制定者和运营管理者提供了实证支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/789ece26897f/pone.0329767.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/b350361f6d51/pone.0329767.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/910431da9a16/pone.0329767.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/27880d1851df/pone.0329767.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/789ece26897f/pone.0329767.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/b350361f6d51/pone.0329767.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/910431da9a16/pone.0329767.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/27880d1851df/pone.0329767.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e33b/12373225/789ece26897f/pone.0329767.g004.jpg

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