本文精选了管理科学学报（英文版）2021年12月发表的论文，提供管理学研究领域最新学术动态。

 

A review of DEA methods to identify and measure congestion

原刊和作者：

Journal of Management Science and Engineering 2021年 12月

Xiantong Ren （University of Chinese Academy of Sciences）

Chen Jiang （University of Chinese Academy of Sciences）

Mohammad Khoveyni （Islamic Azad University）

Zhongcheng Guan （University of Chinese Academy of Sciences）

Guoliang Yang （University of Chinese Academy of Sciences）

Abstract

Congestion is an economic phenomenon of overinvestment that occurs when excessive inputs decrease the maximally possible outputs. Although decision-makers are unlikely to decrease outputs by increasing inputs, congestion is widespread in reality. Identifying and measuring congestion can help decision-makers detect the problem of overinvestment. This paper reviews the development of the concept of congestion in the framework of data envelopment analysis (DEA), which is a widely accepted method for identifying and measuring congestion. In this paper, six main congestion identification and measurement methods are analysed through several numerical examples. We investigate the ideas of these methods, the contributions compared with the previous methods, and the existing shortcomings. Based on our analysis, we conclude that existing congestion identification and measurement methods are still inadequate. Three problems are anticipated for further study: maintaining the consistency between congestion and overinvestment, considering joint weak disposability assumption between desirable outputs and undesirable outputs, and quantifying the degree of congestion.

Link: https://doi.org/10.1016/j.jmse.2021.05.003

 

 

Comparison of dimension reduction methods for DEA under big data via Monte Carlo simulation

原刊和作者：

Journal of Management Science and Engineering 2021年 12月

Zikang Chen （Renmin University of China）

Song Han （Renmin University of China）

Abstract

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a big data problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.

Link: https://doi.org/10.1016/j.jmse.2021.09.008

 

 

Investigating the role of emissions trading policy to reduce emissions and improve the efficiency of industrial green innovation

原刊和作者：

Journal of Management Science and Engineering 2021年 12月

Jingxiao Zhang （Chang'an University）

Xinran Sun （Shanghai Jiao Tong University）

Hui Li （Chang'an University）

Simon Patrick Philbin （London South Bank University）

Pablo Ballesteros-Pérez （Universitat Politecnica de Valencia）

Martin Skitmore （Bond University）

Han Lin （Nanjing Audit University）

Abstract

Rapid economic development usually leads to serious environmental pollution problems. In order to solve the problem of pollutant emission in sustainable industrial development, it is urgent to examine the implementation effect of emissions trading policy (ETP) and its impact on green industrial development. This study adopts China's ETP as a case study and selects provincial panel data from 2004 to 2018. We first use a non-radial, non-directed, slack-based measure-directional distance function (SBM-DDF) to measure industrial green innovation efficiency. Then we use a difference in differences (DID) model to empirically test the emissions reduction effect of China's policy and whether it promotes industrial green innovation. Thereafter, results show that: (1) the ETP reduces sulfur dioxide (SO2) emissions indicating the effectiveness of the policy; (2) the policy significantly improves industrial green innovation efficiency, meaning it promotes the sustainable development of the economy; (3) heterogeneity analysis highlights that ETP produces greater benefits for the most polluted regions of China which have more strict environmental regulations. The study examines the effect of emissions trading policy implementation from a new perspective. The study also provides a reference point for China to further refine its policy mechanisms and for other countries to formulate suitable ETP.

Link: https://doi.org/10.1016/j.jmse.2021.09.006

 

 

Enhancing the green efficiency of fundamental sectors in China's industrial system: A spatial-temporal analysis

原刊和作者：

Journal of Management Science and Engineering 2021年 12月

Jiangxue Zhang （Beijing Normal University）

Xu Liu （Beijing Normal University）

Xue Zhang （Harbin University of Commerce）

Yuan Chang （Central University of Finance and Economics）

Changbo Wang （Nanjing University of Aeronautics and Astronautics）

Lixiao Zhang （Beijing Normal University）

Abstract

Due to sectoral interactions in the economy, the overall green efficiency (GE) of China's industrial system relies heavily on fundamental sectors that contribute substantial energy to the supply chain production of other sectors but shows low sectoral GE. For the three fundamental sectors in China's industrial systems, namely the smelting and pressing of nonferrous metals (SPNFM), the processing of petroleum, coking, and nuclear fuel (PPCNF); and the manufacturing of nonmetallic mineral products (MNMMP), we employed a three-stage data envelopment analysis (DEA) model to measure GE in the fundamental sectors in 30 provinces from 2010 to 2015. We then adopted a stochastic frontier analysis (SFA) model to evaluate the influence of technological innovation (TI), industrial agglomeration (IA), environmental regulation (ER), and intraindustry competition (IC). The results showed that GE in the three fundamental sectors varied spatially. Specifically, TI promoted GE in MNMMP, but the effect was not obvious in the SPNFM and PPCNF sectors. Moreover, ER had positive impacts on GE in the fundamental sectors. The effects of IA and IC on GE in the fundamental sectors varied in direction and strength. After eliminating the impacts of environmental effects and statistical noise, the real GE in the three fundamental sectors varied significantly compared to the comprehensive GE. Policy opportunities for enhancing GE in the fundamental sectors mainly lie in region-specific policy and regulations that avoid a one-size-fits-all governance approach.

Link: https://doi.org/10.1016/j.jmse.2021.03.002

 

 

Stochastic leader-follower DEA models for two-stage systems

Journal of Management Science and Engineering 2021年 12月

Zhongbao Zhou （Hunan University）

Wenting Sun （Hunan University）

Helu Xiao （Hunan Normal University）

Qianying Jin （Central University of Finance and Economics）

Wenbin Liu （University of Kent; Hunan University）

Abstract

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). In recent years, it has been widely used to evaluate two-stage systems under different organization mechanisms. This study modifies the conventional leaderefollower DEA models for two-stage systems by considering the uncertainty of data. The dual deterministic linear models are first constructed from the stochastic CCR models under the assumption that all components of inputs, outputs, and intermediate products are related only with some basic stochastic factors, which follow continuous and symmetric distributions with nonnegative compact supports. The stochastic leaderefollower DEA models are then developed for measuring the efficiencies of the two stages. The stochastic efficiency of the whole system can be uniquely decomposed into the product of the efficiencies of the two stages. Relationships between stochastic efficiencies from stochastic CCR and stochastic leaderefollower DEA models are also discussed. An example of the commercial banks in China is considered using the proposed models under different risk levels.

Link: https://doi.org/10.1016/j.jmse.2021.02.004

 

 

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Journal of Management Science and Engineering 2021年 12月

Nan Zhu （Southwestern University of Finance and Economics）

Chuanjin Zhu （Southwestern University of Finance and Economics）

Ali Emrouznejad （Aston University）

Abstract

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.

Link: https://doi.org/10.1016/j.jmse.2020.10.001

 

 

The evolution and determinants of Chinese property insurance companies' profitability: A DEA-based perspective

Journal of Management Science and Engineering 2021年 12月

Tengyu Zhao （University of Chinese Academy of Sciences）

Ruimin Pei （University of Chinese Academy of Sciences）

Jiaofeng Pan （University of Chinese Academy of Sciences）

Abstract

The property insurance industry grows fast in China and it is necessary to further investigate the profitability of the Chinese property insurance industry. This study investigates the evolution and determinants of the profitability of 53 Chinese property insurers during the year 2013-2017. Profitability is measured by profit ratio efficiency by data envelopment analysis (DEA) methodology and a profit ratio change index is applied to compare the performance of these insurers over different periods. Tobit regression models are used to investigate several influencing factors of profitability. The empirical results show the importance of proper arrangement of costs and revenues for an insurer and help to better understand the effect of firm size, age, and product specification on profitability. Some policy implications and suggestions are also proposed.

Link: https://doi.org/10.1016/j.jmse.2021.09.005

 

 

Research performance evaluation of Chinese university: A non-homogeneous network DEA approach

Journal of Management Science and Engineering 2021年 12月

Tao Ding （Hefei University of Technology）

Jie Yang （Hefei University of Technology）

Huaqing Wu （Hefei University of Technology）

Yao Wen （Central South University）

Changchun Tan （Hefei University of Technology）

Abstract

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments' performance of a university, and especially, none of them consider the non-homogeneity among the universities' departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.

Link: https://doi.org/10.1016/j.jmse.2020.10.003

 

 

Operating efficiency in Chinese universities: An extended two-stage network DEA approach

Journal of Management Science and Engineering 2021年 12月

Ya Chen （Hefei University of Technology）

Xuanxuan Ma （Huaibei Rural Commercial Bank）

Ping Yan （Zhongnan University of Economics and Law）

Mengyuan Wang （Hefei University of Technology）

Abstract

Operating efficiency of universities is widely concerned by the education community. As a non-parametric method for efficiently handling multiple inputs and outputs, data envelopment analysis (DEA) is often used for measuring the operating efficiency. However, shared input resources are often ignored in the existing DEA studies. In order to remedy the shortcoming with a focus on teaching and research processes of universities, this paper adopts an extended two-stage network DEA approach to measure the operating efficiency of 52 universities in China using a data set in 2014. The main findings show that: (1) Among the operating efficiency of 52 universities, about one third and two thirds of universities are efficient and inefficient, respectively. It may reflect some problems such as inefficient use of resources or unsatisfactory outcomes for these inefficient universities. By giving first priority to universities' teaching or research process, we provide alternative ways for teaching-oriented or research-oriented universities to benchmark and improve their performance. (2) For the heterogeneity efficiency analysis of different universities, the operating efficiency of non-985 universities are significantly higher than that of 985 universities, while there is only a small difference on the operating efficiency between comprehensive universities and science & engineering universities. Although the efficiency of the central and western universities is slightly better than that of the eastern universities in terms of the average efficiency, there is no significant efficiency difference among the eastern, central, and western regions statistically. Hence, to improve the operating efficiency of Chinese universities, the Chinese government should improve the financial allocation mechanism and introduce successful budget performance management. For the Chinese universities, they should formulate teaching and scientific research plans according to their own research needs and development goals.

Link: https://doi.org/10.1016/j.jmse.2021.08.005