Evaluating air route performance with context-dependent data envelopment analysis: A case study in Taiwan

Efficiency evaluations with context-dependent and measure-specific data envelopment approaches: An application in a World Bank supported project

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.

Data envelopment analysis (DEA) identifies an empirical efficient frontier of a set of peer decision making units (DMUs) with multiple inputs and outputs. The efficient frontier is characterized by the DMUs with an unity efficiency score. The performance of inefficient DMUs is characterized with respect to the identified efficient frontier. If the performance of inefficient DMUs deteriorates or improves (up to the frontier), the efficient DMUs still have an unity efficiency score. However, the performance of DMUs may be influenced by the context — e.g. a product may appear attractive against a background of less attractive alternatives and unattractive when compared to more attractive alternatives. With an application to Tokyo public libraries, the current paper presents and demonstrates a context-dependent DEA which measures the relative attractiveness of libraries on a specific performance level against libraries exhibiting poorer performance. The set of libraries are grouped into different levels of efficient frontiers. Each efficient frontier (on a specific performance level) is then used as evaluation context for the relative attractiveness. The performance of the efficient libraries changes as the inefficient libraries change their performance. The context-dependent DEA can also be used to differentiate the performance of efficient DMUs. The context-dependent DEA provides finer DEA results with respect to the performance of all DMUs.

Purpose– The purpose of this paper is to measure the efficiencies of parallel subsystems with shared inputs/outputs. Each subsystem has not only a set of common inputs and outputs, but also some dedicated inputs and outputs as well as some shared inputs and outputs. A more general data envelopment analysis (DEA) approach is proposed to deal with this efficiency evaluation issue. Based on the proposed approach, mechanisms for shared inputs/outputs distribution and efficiency decomposition among sub-units are presented.Design/methodology/approach– To evaluate the efficiency of the parallel systems, this paper proposes a centralized DEA approach by assuming that the same input/output factor in a decision-making unit (DMU) has the same multiplier for all its sub-units. Furthermore, different proportions of shared inputs/outputs are imposed on sub-units within different DMUs in evaluating each DMU’s efficiency. The proposed approach is applied to evaluate the operational efficiencies of 18 railway firms in China.Findings– By using the proposed DEA approach, the efficiencies of the whole DMU and its sub-units can be measured at the same time, and the optimal allocation strategy of shared inputs/outputs can also be obtained. The proposed model is more reasonable and robust for measuring the operational performance of parallel systems with shared inputs and outputs. The efficiency of railway system in China is relatively low, and its inefficiency is largely caused by lower freight transportation performance. Great disparities among firms can be found in the passenger transportation efficiency and freight transportation efficiency.Research limitations/implications– This study develops the DEA model under the assumption of constant returns to scale, which can be directly extended to a situation with variable returns to scale.Practical implications– In this paper, the proposed approach is a more effective way to evaluate the efficiencies of parallel systems with shared inputs/outputs. With respect to the application, to improve the overall efficiency of China’s railway system, more efforts should be taken to improve its operational performance of freight transportation. Furthermore, firms’ disparities should also be considered when making these related policies.Originality/value– The proposed approach can evaluate the whole DMU and its sub-units at the same time. Considering simultaneously the common/dedicated/shared inputs/outputs, the proposed approach is more general than the existing approaches in the literature. In the described approach, the same type of input or output is assumed to have the same weight for all sub-units within one DMU. More importantly, the proposed model imposes different proportions of shared inputs/outputs on different DMUs’ sub-units when measuring the efficiency for each DMU.

Information technology (IT) is defined as the obtainment, procedure, storage and propagation of sounding, drawing, and textual information by combining microelectronics-based computing and telecommunications. Nowadays, IT is starting to spread further from the conventional personal computer and network technologies to integrations of other fields of technology such as the use of cell phones, televisions, automobiles, etc. In other words, IT has penetrated in daily life of human beings and become one part of the whole society. The importance of IT has become momentous. Therefore, to understand the performance of efficiency and productivity of the IT firms is critical for managers as well as for personal investors. Until now, there are very few researches tried to analyze final performance of the IT firms. As a result, this research intends to use traditional Data Envelopment Analysis (DEA) CCR or BCC models to evaluate the performance of IT firms. The Decision Making Units (DMUs) on this research are chosen from IT firms in S&P 500. However, the traditional DEA models are not fair models from the aspect of improper weight derivations. Thus, this paper intends to analyze the efficiency of IT firms in S&P 500 efficiencies by using multiple objective programming (MOP) based Data Envelopment Analysis (DEA). In a MOP based DEA approach, DMUs will be evaluated based on an equal standard and the results will be evaluated more fairly. The world’s leading IT firms in S&P 500 will be evaluated based on publicly available financial reports of the fiscal year 2010. In addition, the newly developed MOP can improve the traditional DEA’s unfair weights problems and benchmark the efficiency of IT firms in S&P 500 correctly. In the empirical study, the MOP based DEA demonstrated that F5 Networks should be the communications equipment companies of IT worthwhile to be invested. In the future, performance evaluation results can be served as foundations for investment strategies definition.KeywordsInformation Technology (IT)Standard and Poor’s 500 indexPerformance EvaluationData Envelopment Analysis (DEA)Multiple Objective Programming (MOP)

Efficiency evaluation based on data envelopment analysis in the big data context

In many applications of data envelopment analysis (DEA) approach, there is often a fixed cost or resource which is imposed on all decision making units (DMUs). One nature question then is: how can this cost or resource be assigned in a reasonable way to various DMUs? In this paper, we present a new DEA-based method for obtaining this cost or resource allocation. To illustrate our methods, numerical results for an example from the literature are presented.

Traditional data envelopment analysis (DEA) is used under the premise that inputs and outputs are exact values. If it is not true, the DEA approach is unavailable. However, it is common that some of the entries in the data are missing in practice. As a result, the current paper performs efficiency evaluation with missing data considering the missing-data properties (missing-data patterns and missing-data mechanisms). A multiple imputation (MI) approach is used to estimate the missing values. The MI approach is applied to a forest reorganisation problem for reliability. An example of public secondary schools is given to illustrate the proposed technique. When input or output values for decision making units (DMUs) continuously vary under an interval, the current paper characterises a DMU’s pessimistic and optimistic efficiency functions of an input or output of most interest. A Monte Carlo simulation technique is used to obtain a DMU’s efficiency distribution.

Abstract. The traditional data envelopment analysis (DEA) model ignores the cooperative relationship among decision-making units (DMUs), so it is difficult to evaluate the DMUs efficiency reasonably. In this study, we use a cross-efficiency and bootstrap truncated regression (BTR) model to analyze the determining factors of digital mobile e-learning. The empirical results of this research indicate the following results: (1) Importing digital mobile e-learning can really enhance the efficiency of school management. (2) The school size, tablet PC numbers, total equipment expenses associated with tablet PC and school location are important determinants for affecting the efficiency of school management. Owing to the government is full implementation of the new learns model, that is, to be where the students able to experience the authentic joy of new learning model and attract students join. The result of the study suggested that in order to increase the school’s cross-efficiency model efficiency. The first assist the school in upgrading the Wi-Fi technology and network equipment. In general, the school adds to the Wi-Fi technology and network equipment. That would enlarge the school network and as to attract more school will adopt the new learning. It is where the students able to experience the authentic joy of new learning model and attract students join. Thus, the schools will increase school size. However, it should be noted that total equipment expenses associated with tablet PC have the negative influence on school management efficiency due to the increasing costs for furnishing the related internet and network equipment or device to facilitate for teaching and learning among teachers and students by digital mobile e-learning. The results of this research can also be the reference for educational authorities when formulating policies and regulations for promoting digital mobile e-learning in high school in Taiwan. Keywords. Operating efficiency, Digital mobile e-Learning, Data envelopment analysis (DEA), Truncated bootstrapped regression (TBR), Cross efficiency model. JEL. I21, I25, I28.

Traditional data envelopment analysis (DEA) and diversification DEA are two common data-driven evaluation approaches, which have been widely used in the estimation of portfolio efficiency. The above two DEA approaches usually use the risk and expected return indicators to build the input-output process of portfolios. However, this input-output process derived from the risk and expected return is inconsistent with the actual investment process, since the real input should be the initial wealth, and the output should be the terminal wealth. To address this problem, we propose a novel input-output process based on the initial and terminal wealth of portfolios. We transform the terminal wealth into the rate of return and construct a stochastic attainable set by using portfolio returns. We provide three deterministic estimation approaches to deal with the stochastic attainable set, and then obtain three deterministic attainable sets. We further propose three stochastic DEA models to estimate the portfolio efficiency by using the above three deterministic attainable sets. Finally, we provide an empirical analysis to assess the portfolio efficiency of 50 open-ended funds in China. The results show that there are some differences in the portfolio efficiency and its ranking between the proposed DEA models and the existing DEA models, which further verify the rationality of the proposed DEA models.

The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with exact values of inputs and outputs, but it cannot handle imprecise data. Imprecise data, for example, can be expressed in the form of the interval data or mixtures of interval data and ordinal data. In this study, a cross-efficiency method is introduced into the DEA model to calculate the interval of cross-efficiency values, based on which a new TOPSIS method is proposed to rank the DMUs. Two examples are presented to illustrate and validate the proposed method.

The analysis of optimal output efficiency with traditional data envelopment analysis (DEA) cannot be directly applied to a production strategy of promoting the sustainable development of Chinese banks, because both inputs and outputs increase during economic development. In this paper, a new DEA is presented with two innovations. The First innovation is that, the research data are extended to time-series data instead of cross-section data as before. The second is, the simultaneous increase in input and output from the input/output decrease/increase production is developed. As an application of the BCC (Banker, Charnes, Cooper) Bi-objective Generalized DEA (Bi-GDEA) model, the simulation strategies were well implemented in Chinese banks in 2017 using data from 2004 to 2015. The three inputs and one output of the banking sector have been adjusted to make each year’s decision making unit (DMU) efficient in the framework of 2004 to 2017, and both inputs and outputs are monotone increased. It may provide an alternative strategy for the effectiveness and sustainability of Chinese banks development.

Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability

A traditional DEA (data envelopment analysis) model is useful for estimating the best performances for a successful and a more efficiency DMU (decision-making unit). This study uses a traditional DEA model to estimate the efficiency score of bank z in Taiwan and get a good efficiency score based on the 2006 data, however the bank z was taken over by the government in early 2007. In illustrating this paradox phenomenon, the study reconsiders the tradition DEA model by contributing a worst-practice frontier under a traditional DEA model. Findings: the bank z is located on the intersection of the worst-practice frontier and the traditional DEA model frontier. This study provides a critical determinant of a bank’s failure that a bank has a location such as the bank z.