Super-efficiency Data Envelopment Analysis Research Articles

Assessing the eco-efficiency of industrial investment in China: a DEA approach

Abstract Although the rapid growth of industrial investment has greatly contributed to Chinese socioeconomic development, the consequent resource depletion and environmental degradation have seriously restricted its potential for sustainability. Hence, the concept of eco-efficiency of industrial investment (EEII) is constructed under the comprehensive perspective of economic benefits, energy consumption and environmental impact. To assess the true EEII in China, a new super-efficient undesirable-outputs slacks-based measure DEA model is proposed by adding a new constraint condition to rebuild the reference set. The proposed model not only can consider the joint production of desirable and undesirable outputs, but also achieves full ranking of all decision-making units. Taking 30 provinces in China as an example, our empirical results indicate that the eco-efficiency of China’s industrial investment is generally low (0.643) in 2015–2020, and there exists significant regional disparity, in that the average value of EEII in the eastern regions (0.851) is the highest, followed by the central regions (0.594) and the western regions (0.469). Finally, this study provides some suggestions for policymakers to promote the EEII based on its influencing factors, analysed using panel-data Tobit regression.

Performance-based salary distribution ratio in clinical departments of public hospitals based on improved DEA method

Performance-based salary distribution is one of the important contents of modern hospital management system. In general, the distribution of performance salary in public hospitals of China can be divided into two stages: one is from hospitals to departments, the other is from departments to individuals. It is of great significance to improve the performance-based salary distribution system in clinical departments of public hospital, which is beneficial to ensure the public nature, motivate hospital staff to work hard, and raise public healthcare service quality. Therefore, this paper focuses on the issue of performance-based salary distribution in clinical departments of public hospital, adopts super-efficiency DEA model to evaluate the performance of each clinical department, introduces a new utility function for processing the original values of DEA efficient DMUs in order to encourage more clinical departments to pursue higher performance, and finally verifies the comprehensive model by empirical analysis. The result of empirical analysis shows that the performance of DMU7 is highest with an efficiency value of 1.53, followed by DMU3, DMU8, and DMU1. The efficiency value of DMU9 is lowest in all clinical departments.

How financial development scale and R&D influence regional innovation efficiency: empirical evidence from the financial industry.

Financial elements and R&D elements are significant drivers for enhancing regional innovation efficiency. This paper measures financial elements by financial development scale and the marketization level of the financial industry and R&D elements by the inputs and flow of R&D personnel and R&D capital and specifically considers R&D element flow to quantify the consequential spatial spillover effects. Based on provincial panel data from 2008 to 2018, the paper firstly estimates the regional innovation efficiency of China’s 30 provincial-level administrative regions using super-efficiency DEA and then conducts an empirical analysis of the influence of financial elements and R&D elements on regional innovation efficiency by the use of the Tobit model and three spatial econometric models. It is found that, by and large, the financial development scale, the marketization level of the financial industry, the inputs of R&D personnel and R&D capital, and R&D capital flow all have significant effects on regional innovation efficiency. Nonetheless, by region, R&D personnel flow in central China can significantly boost regional innovation efficiency while fails in eastern and western China. From the spatial perspective, both financial elements and R&D elements have significant positive spatial spillover effects. Therefore, in order to bolster regional innovation efficiency, it is crucial to improve the allocation of financial elements and R&D elements and build a tight regional collaborative innovation network.

Analysis on Operating Efficiency of Chinese Securities Companies Based on Super Efficiency DEA and DEA-Malmquist Index Method

With the expansion of China's securities market scale, securities companies as an important participant in the securities market have also developed rapidly. Under the influence of the global epidemic, the transaction volume of securities companies continued to decline, the profit prospects of securities firms increased, liquidity continued to be loose, and the overall policy budget was surplus. Through the efficiency analysis of securities companies, we can understand the resource utilization and industry conditions of securities companies, find out the gaps and deficiencies, and then improve, and improve the operating efficiency of the industry. In order to evaluate the operating efficiency of securities companies more scientific and objective, this paper adopts DEA-Malmquist index and super-efficiency DEA method, based on the financial statement data disclosed by 25 securities companies in 2016~2020, establishes an evaluation index system, analyzes the operating efficiency of securities companies, calculates and analyzes the operating efficiency of securities companies and reveals their internal reasons. The research results show that (1) steady improvement of operating productivity; (2) technical progress is the main factor; operating efficiency of (3) with different securities companies, scale efficiency weakly hinders technical efficiency; (4) Large securities companies should pay attention to control scale, develop innovative business, strengthen cost management, risk control and risk response ability.

Analysis on Operating Efficiency of Chinese Securities Companies Based on Super Efficiency DEA and DEA-Malmquist Index Method

Analysis on Operating Efficiency of Chinese Securities Companies Based on Super Efficiency DEA and DEA-Malmquist Index Method

Resolving the infeasibility of the super-efficiency DEA based on DDF

Resolving the infeasibility of the super-efficiency DEA based on DDF

Efficiency Analysis of Malaysian General Insurance Companies Using Data Envelopment and Super-efficiency Approach

Due to the phenomenal growth of the insurance sector, extensive research has been conducted to evaluate the insurers' efficiency at both domestic and international levels. Thus, this paper illustrates the applications of Data Envelopment Analysis (DEA) to measure the relative efficiency of 18 general insurance companies that were operating in Malaysia in the year 2018. The inputs included in this study are fees, commission, and management expenses while the outputs are net premium and generated investment income. The results reveal that the four general insurance companies can be identified as efficient units. The standard DEA method cannot discriminate efficient units. This study employs the super-efficiency DEA model to select the most efficient unit among them. This study makes a significant contribution to the financial industry especially for matters concerning insurance, as there is a lack of studies on exclusive efficiency analysis of general insurance companies in Malaysia. In addition, it has major implications for insurance decision makers, government agencies and emerging insurance markets. The information obtained from this study will benefit the insurance company’s top management in devising viable strategies to enhance the efficiency of the company.

Study on the Temporal and Spatial Differentiation of Provincial Tourism Efficiency in Eastern China and Influencing Factors

Tourism efficiency can be used to effectively measure the utilization of regional tourism resources and the state of tourism economic development. Based on the super efficiency DEA model, Malmquist index, and spatial econometric model, this article measures the static and dynamic tourism efficiency of 11 provinces and cities in eastern China for 2010 to 2019. In combining ArcGIS 10.0 and MATLAB 2016b software, this article studies the temporal and spatial differentiation of tourism efficiency in eastern provinces and cities and influencing factors. The results show that (1) the overall tourism efficiency of eastern provinces is at a high level and relatively stable, but the regional distribution is quite varied, and problems of spatial imbalance are prominent; (2) the overall tourism efficiency of eastern provinces is increasing, and the change index of technical efficiency contributes the most, followed by the change index of scale efficiency; and (3) industrial status, traffic conditions, tourism resource endowment, and the labour force are the main factors affecting the temporal and spatial differentiation of tourism efficiency in eastern provinces and cities, while the level of economic development and information technology have no significant impact.

Influence of marine industrial agglomeration and environmental regulation on marine innovation efficiency—From an innovation value chain perspective

Innovation is an important driving force for realizing high-quality green development of the marine economy. Based on the innovation value chain, this study incorporates three marine waste pollutants into the two-stage marine innovation efficiency research framework and uses the super-efficiency DEA model to measure the overall efficiency and sub-process efficiency of the marine innovation of China’s coastal regions from 2006 to 2016. We apply the dynamic panel model and the threshold effect model to empirically test the impact of marine industrial agglomeration and environmental regulation on the efficiency of two-stage marine innovation. We find that (1) during the inspection period, the overall efficiency average of China’s marine innovation was 0.99; the marine green R&D efficiency average was 0.67; and the commercialization efficiency average was 1.31. At this stage, the improvement of marine innovation efficiency depends on commercialization efficiency and is restricted to green R&D efficiency. (2) An obvious inverted U-shaped relationship exists between marine industrial agglomeration and marine innovation. Environmental regulations play a significant positive role in promoting marine innovation. (3) An environmental regulation threshold exists in the process of marine industrial agglomeration that affects both the overall and the commercialization processes.

Land-Use Change and Efficiency in Laos’ Special Economic Zones

Special economic zones (SEZs) are important in Laos due to their ability to attract foreign investment, realize industrialization, and promote economic globalization. Based on Laos’ SEZs in operation, this study explored land-use intensity, structural evolution and land-use efficiency in Laos’ SEZs via the land-use dynamic degree, information entropy, super-efficiency data envelopment analysis (DEA) and gray relational analysis (GRA). The study determined that the total land-use area in Laos’ SEZs continuously increased from 2014 to 2020. The land-use intensity changes in the SEZs can be divided into three types, i.e., high intensity, medium intensity and low intensity, and most SEZs belonged to the medium-intensity type. The proportion of land used in production systems in Laos’ SEZs increased the most, and the proportion of infrastructure land notably decreased. The overall information entropy of the land-use structure exhibited an initial downward and then an upward trend. In 2018, the land-use efficiency in the Savan-Seno SEZ, Vientiane Industrial and Trade Area, Dongphosy SEZ, and Golden Triangle SEZ was relatively optimal. The basic factors of the industrial space and the factors reflecting international cooperation attributes were highly related to the land-use efficiency in Laos’ SEZs.

Analysis on the efficiency of China's top ten joint-stock banks based on the super-efficiency DEA method

Banks are special types of economic and financial institutions that specialize in various currency and credit loan businesses. They have always been regarded as the most important part of a country's real economy. It has a major issue related to the national economy and people's livelihood. Production, operation and management activities and the healthy development of the financial industry are very inseparable from the coexistence of financial banks. Joint-stock banks are also a very important part of the bank. With the current rapid development of China's modern financial industry, the market for joint-stock banks The competition is becoming more and more fierce. Bank efficiency is an important indicator to evaluate the profitability and competitiveness of each bank. Efficiency evaluation is of great significance to the sustainable development of banks. Super-efficiency DEA is an important method to study the efficiency of companies. This paper uses the super-efficiency DEA method to study the efficiency of the top ten domestic joint-stock banks with scale efficiency indicators and technical efficiency indicators. This research shows that during the epidemic period, reducing operating costs is of great significance to improving bank efficiency, getting rid of the shackles of business outlets, financial innovation through the Internet and big data, and allowing customers to enjoy banking services anytime and anywhere has gradually become the goal of major listed banks.

The Evaluation and Its Key Influencing Factors Analysis of “Double First-Class” Universities’ Teaching Efficiency in China Based on SBM-DEA and Its Visual Decision Analysis System Development

Based on a systematical summary of relevant research on university teaching efficiency, an evaluation index system was built to evaluate teaching efficiency of “Double First-Class” universities in China. The evaluation model to evaluate the teaching efficiency of these universities was built based on Slack-Based Measure model using Superefficiency Data Envelopment Analysis (SBM-DEA). Teaching efficiency of 36 “Double First-Class” universities had been evaluated after their assessment indexes data had been collected. The influencing relations between the teaching efficiency of “Double First-Class” universities and dual prices of each evaluation index had been analysed. It had been proven that teaching efficiency could be greatly improved by increasing the index value with a higher double price. We proceeded to count the frequencies of evaluation indicators with the highest or the second highest dual prices. The indexes with higher total importance were defined as the key factors for the development of teaching efficiency in China’s “Double First-Class” universities. Several countermeasures had been proposed to improve the development of “Double First-Class” universities’ teaching efficiency. Finally, based on the above theoretical research, the evaluation and decision analysis software for teaching efficiency in “Double First-Class” universities in China had been developed by Visual Basic 2015 and MATLAB 2015 with Access database to store the data. It provides a convenient visualization tool to manage the data of the evaluation index system and make the data analysis of the SBM-DEA model.

Does innovative city construction improve the industry–university–research knowledge flow in urban China?

This study measured the knowledge innovation efficiency (KIE) and knowledge transformation efficiency (KTE) of industry–university–research knowledge flow using super-efficiency data envelopment analysis method. Furthermore, spatial difference-in-difference model was used to investigate the effect of innovative city pilot policy (ICPP) on KIE and KTE. The results indicated that ICPP had a significantly positive influence on KIE and KTE. The robustness of this result was verified. Moreover, the relationship scale of the urban innovation network had a significant inverted U-shaped effect on KIE and KTE, and the centrality of the urban innovation network had a significant positive influence on KIE and KTE. Meanwhile, innovation financial support partially mediated ICPP's effect on KIE, and innovation cooperation dependence partially mediated ICPP's effect on KTE. This study expands theoretical analyses of ICPP's effects on the KIE and KTE of industry–university–research knowledge flow, giving consideration to spatial spillover effects. We also extend the research by considering the effect of changes in the innovation potential of pilot cities on KIE and KTE.

Reverse Logistics Enterprise Performance Research Based on Super-Efficiency DEA and LMBP Neural Network

On the basis of considering the characteristics of reverse logistics enterprises, this paper uses stakeholder theory to establish an evaluation system containing 36 indicators in five dimensions, based on the balanced scorecard, and adopts PCA optimization index. In view of the performance evaluation problem, first, the performance data is obtained by using the super-efficiency DEA, then the enterprise performance of A company is fitted by LMBP neural network to obtain the quantitative model of performance evaluation, and finally, according to the resulting weight reference performance benchmark, the direction of reverse logistics enterprise performance improvement is given. The simulation results show that the index system can react to the reverse logistics characteristics very well, and the BP neural network based on LM algorithm can accurately and efficiently evaluate the performance of reverse logistics enterprises which has better generalization, and can find out the key performance indicators and give reasonable optimization direction for enterprises by reverse tracking the weights of the neural network.

Determinants of hospital inefficiency. The case of Polish county hospitals.

Local hospitals play a crucial role in the healthcare system. In this study, the efficiency of Polish county hospitals is assessed by considering characteristics of hospitals that may determine their performance, such as the form of ownership, size, and staff structure. The main goal was to analyze the effect of three possible determinants on efficiency: ownership, the presence of an Emergency Department, and the presence of an Intensive Care Unit. The study covered different subgroups of hospitals and different approaches of inputs and outputs. An input-oriented radial super-efficiency DEA model under variable returns to scale was used for the efficiency analysis, and then differences between distributions of efficient and inefficient units were evaluated using a Chi-square test. A Kruskal-Wallis test was also used to analyze differences in mean efficiency. Inefficiency scores were regressed with hospital characteristics to test for other determinants. These results did not confirm differences in efficiency concerning ownership. However, in some subgroups of hospitals, running an Emergency Department or an Intensive Care Unit had a significant effect. Tobit regression results provided additional insight into how an Emergency Department or Intensive Care Unit can affect efficiency. Both cases had an effect of increasing inefficiency, and the data suggested that the department/unit size plays an important role.

Industrial ecological efficiency of cities in the Yellow River Basin in the background of China’s economic transformation: spatial-temporal characteristics and influencing factors

At present, China's economic development has entered a "new normal." Exploring industrial ecological efficiency (IEE) in the background of economic transformation is of great significance to promote China's industrial transformation and upgrading and achieving high-quality economic development. Based on the super-efficiency DEA model, this study evaluated the IEE of cities in the Yellow River Basin from 2008 to 2017. Exploratory spatial data analysis methods were used to explore the spatial-temporal evolutionary characteristics, and a panel regression model was established to explore the influencing factors of IEE. The research results showed that the IEE in the Yellow River Basin exhibited an elongated S-shaped evolutionary trend from 2008 to 2017, and the mean IEE of cities presented a trend, whereby Yellow River Basin's regions could be ranked in the following order: lower reaches > middle reaches > upper reaches. There was significant spatial autocorrelation of the IEE in the Yellow River Basin, and the hot and cold spots showed an obvious "spatial clubs" phenomenon. The results of panel regression show that the influence factors of IEE in the Yellow River Basin showed spatial heterogeneity in their effect.

The Influence of E-Marketing on Performance of Real Estate Enterprises: Based on Super-Efficiency DEA and Grey Entropy Methods

E-marketing is an important tool for real estate enterprises. We evaluate 3 online marketing channels of 44 Chinese real estate companies. Super-efficiency DEA and grey entropy methods are applied to analyse the influence of E-marketing on the performance of real estate enterprises. We find that E-marketing will affect the business performance of real estate companies. Real estate company managers should adopt more strategies to improve corporate performance.

Performance Evaluation of China Internet Monetary Fund Based on Super-efficient DEA

Since 2013, China’s Internet money fund market has entered a new era. On June 17, 2013, Yu’e Bao, jointly launched by Alipay and Tianhong Fund Company, was the first to go public. In just a few short years, the Internet money fund market has developed in full swing, and Tencent, Baidu, and JD have also participated in the development of related change wealth management businesses. This article uses super-efficiency DEA to evaluate fund performance. Through the validity test of 16 sample fund products in 2019, 7 sample funds are valid according to the DEA; and 16 sample fund products in 2020 are tested for validity, 9 sample funds are valid according to the DEA. The research found that most of the Internet financial products have not yet reached their effectiveness, which is mainly reflected in the fund’s custody and management fees. There is still a lot of room for development in China’s Internet fund market.

Evaluating performance of super-efficiency models in ranking efficient decision-making units based on Monte Carlo simulations

In response to the limitation of classical Data Envelopment Analysis (DEA) models, the super efficiency DEA models, including Andersen and Petersen (Manag Sci 39(10): 1261–1264, 1993)’s model (hereafter called AP model) and Li et al. (Eur J Oper Res 255(3): 884–892, 2016)’s cooperative-game-based model (hereafter called L–L model), have been proposed to rank efficient decision-making units (DMUs). Although both models have been widely applied in practice, there is a paucity of research examining the performance of the two models in ranking efficient DMUs. Consequently, it is unclear how close the rankings obtained by the two models are to the “true” ones. Among the very few studies, Banker et al. (Ann Oper Res 250(1): 21–35, 2017) pointed out that the ranking performance of the AP model is unsatisfactory; Li et al. (Eur J Oper Res 255(3): 884–892, 2016) and Hinojosa et al. (Exp Syst Appl 80(9): 273–283, 2017) demonstrated the L–L model’s capability of ranking efficient DMUs without addressing the ranking performance. In this study, we, thus, examine the ranking performance of the two super-efficiency models. In evaluating their performance, we carry out Monte Carlo simulations based on the well-known Cobb–Douglas production function and adopt Kendall rank correlation coefficient. Unlike Banker et al. (Ann Oper Res 250(1): 21–35, 2017), we use the rankings obtained based on the two models and the “true” ones as the basis of performance evaluation in our simulations. Moreover, we consider several types of returns to scale (RS) and study the impact of changes of some parameters on the ranking performance. In view of the importance, we also carry out additional simulations to examine the influence of technical inefficiency on the two models’ ranking performance. Based on the simulation results, we conclude: (1) Under different RS, the ranking performance of the two models remains the same when changing parameters, e.g., the distribution of input variables; (2) Under different RS, when technical inefficiency (in comparison with random noise) is more important, the two models have satisfactory performance by providing rankings that are close to, or the same as, the “true” ones; (3) The L–L model has better performance than the AP model and is more robust. This is especially true when technical inefficiency is less important; (4) Under different RS, when technical inefficiency is less important, both models have unsatisfactory ranking performance; and (5) The relative importance of technical inefficiency plays an prominent role in ranking efficient DMUs.

Innovation ability of universities and the efficiency of university–industry knowledge flow: the moderating effect of provincial innovative agglomeration

PurposeThe purpose of this paper is to examine the influence of the innovation ability of universities (IAU) on the efficiency of University–Industry knowledge flow and investigate whether the level of provincial innovative agglomeration (PIA) moderates the relationship between IAU and the efficiency of the University–Industry knowledge flow.Design/methodology/approachThis study uses the super-efficiency data envelopment analysis model to measure knowledge research efficiency (KRE) and knowledge transformation efficiency (KTE) and then studies the influencing mechanism of the two kinds of efficiency using the spatial Tobit model with panel data from 2008 to 2017.FindingsThe results show that the overall KRE in Chinese universities is higher than the KTE. IAU has a significantly positive impact on KRE and KTE. PIA has a significantly inverted U-shaped influence on KRE and KTE and positively moderates the promoting effect of IAU on KRE and KTE.Research limitations/implicationsDue to the limitations of the data, this paper only selects several secondary indicators to measure KRE and KTE with reference to previous studies.Practical implicationsThis study enriches the future research of University–Industry cooperation and knowledge flow and it is conducive to promoting the efficiency of University–Industry knowledge research and transformation from the perspective of universities, enterprises and local governments.Originality/valueThis study proposes the concept of University–Industry knowledge flow and divides the knowledge flow into the knowledge research stage and the knowledge transformation stage based on the knowledge supply chain theory. Moreover, the paper expands the theoretical framework of the impact of IAU on the efficiency of University–Industry knowledge flow and provides findings on the moderating effect of PIA.