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

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.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-08-22
DOI
https://doi.org/10.1371/journal.pone.0329767
Citations
1
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
2.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Anita Binti Rosli, Adrian Daud, Jiaqiang Sun
1 citations
PLoS ONE
Efficiency Analysis Using DEA
2.11
article

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

Anita Binti Rosli, Adrian Daud, Jiaqiang Sun
article en
1 citations

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.

PLoS ONEVol. 20(8)
Universiti Putra Malaysia (MY), Universiti Malaysia Sarawak (MY), Fujian University of Technology (CN)
Openalex Percentile: Top 11%
Efficiency Analysis Using DEA
2.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.