Context-dependent Data Envelopment Analysis Research Articles

Closest target for the orientation-free context-dependent DEA under variable returns to scale

An important branch of data envelopment analysis (DEA) is context-dependent DEA, which evaluates efficiency by combining the attractiveness and progress for a particular decision-making unit (DMU). Traditionally, context-dependent DEA models are based on the assumption of constant returns to scale. Two limitations are found when directly extending original radial context-dependent DEA (ORCD-DEA) models into variable returns to scale versions. One is that it may not be possible to determine the attractiveness of a DMU that logically must be attractive in that context. The other problem is that the progress measure cannot ensure an inefficient DMU projects to a Pareto-efficient frontier. A small numerical example is used to illustrate these two issues. In order to overcome these deficiencies, the concept of closest target is introduced to determine the attractiveness and progress for each DMU. The closest target method can further improve DMUs’ performance with less wastes in inputs or underproduction in outputs. Finally, a practical application involving computer printers is presented.

Context-based competition strategy and performance analysis with fixed-sum outputs: an application to banking sector

In the prior literature on performance measurement of firms with fixed-sum outputs, an equilibrium-efficient frontier is constructed. This paper shows that a single equilibrium-efficient frontier needs a significant trade-off between efficient and inefficient firms, and this may be impossible in practical applications. We develop a data envelopment analysis (DEA) model to construct multiple equilibrium-efficient frontiers in the presence of fixed-sum outputs. The approach uses context-dependent DEA that refers to a DEA approach where a set of firms are assessed against a particular assessment context. Numerical examples are used to illustrate the applicability of the approach.

A Comprehensive Evaluation of Sustainable Development Ability and Pathway for Major Cities in China

Sustainability consists of economic, environmental, and societal aspects. Concomitant with China’s rapid growth, the evaluation of China’s ability for sustainable development (SD) has become a topic of interest. Unlike previous studies that are based on provincial regions and focus on economic and/or environmental evaluations, we have evaluated the comprehensive SD ability of 34 major cities in China using the context-dependent data envelopment analysis (CD-DEA) and proposed benchmark-learning pathways. The results indicate that the SD ability of China’s major cities may be classified and ordered from low tiers to high tiers: high energy consumption and polluting industries—intensive industries—fixed asset investments and service industries—innovation, quality of life and societal services industries. As a whole, cities along the coast have a higher ability for SD than inland cities, and southern cities also rate higher than northern cities. Cities that are prioritized by government policies and funding also have higher SD abilities than other cities. We recommend that cities with lower abilities (i.e., cities in the second, third, and fourth tiers) should adopt the following points in their developmental pathways (in this order): the control of energy consumption and environmental pollution, industry upgrading and redevelopment of the city, and the development of an environment that encourages innovation and provides ample employment. This study finds that GDP is no longer an issue that restricts the SD ability of China’s major cities, as compared to other factors. As China progresses towards the sustainable cities, focus should be placed on the development of a knowledge-intensive economy, balanced development, and pollution prevention.

The performance measurement of listed companies of the agribusiness sector on the stock exchange of Thailand

Listed companies in the agribusiness sector of the stock exchange play a key role in driving the economic system of Thailand. They have an important role in terms of being the producers of agricultural commodities by covering all products from the upstream to downstream. The performance measurements of listed companies contain important information for forecasting the trends of agricultural production and yield. The paper studied and considered much of the literature on the performance measurement of organizations to look for relative variables and the optimal methodology. The study used panel data from 2011–2014, which collected the listed annual companies’ statements. The aim of the study was to employ the Slacks Based Measure context-dependent Data Envelopment Analysis (SBM context-dependent DEA) for evaluating the efficiency in Decision Making Units (DMUs). The SBM context-dependent DEA was used to measure the performance of listed companies by employing attractiveness and progress scores. The results showed that when a listed company has a higher attractiveness and lower progress score, it has a better performance than its competitors and does not need to improve its efficiency. Therefore, the empirical results of the study can help farmers and governments to realize the trends of agricultural productivity and also help the listed companies to understand the characteristics of their competitors, leading to improvements in their organizations.

Context-dependent data envelopment analysis with common set of weights

ABSTRACTThe original context-dependent data envelopment analysis measures the attractiveness and progress of decision-making units (DMUs) through calculating the radial distance between the DMU under evaluation and the evaluation context. It only shows how distinct the DMU is from a single specific virtual DMU on the evaluation context, and ignores the entire evaluation context. In this paper, we incorporate the common set of weights into the original context-dependent data envelopment analysis. This improvement ensures evaluation consistency via providing an overall evaluation context.

A hybrid data envelopment analysis approach to analyse college graduation rate at higher education institutions

ABSTRACTCollege graduation rates have become a primary focus in measuring institutional performance and accountability in higher education. In 2009, President Obama set a goal for the United States to have the highest proportion of college graduates in the world by 2020. With the heightened focus on transparency and accountability in higher education today, university administrators are developing internal strategies to improve graduation rates. In fact, it is not only significantly important to institutions, but also to individuals and to the nation as a whole to increase college graduation rates. In this paper, a hybrid data envelopment analysis (DEA) approach is implemented for the very same purpose by combining with the cross industry standard process for data mining (CRISP-DM) methodology. The approach is illustrated by a case study at a U.S.-based four-year public university. We identify the most important predictors of graduation which help improve graduation rates by the CRISP-DM method. It shows that Fall term grade point average (GPA), Housing status, High school and Spring term GPA were the four highest determinative factors while monetary variables and the ethnic background of the student were revealed to be the least important ones. The results also indicated that students living on campus were more likely to complete within six years. For the detailed improvement strategies for increasing college graduation rate, we use the hybrid DEA methodology (an input-oriented bounded-and-discrete-data DEA model and context-dependent DEA) to evaluate the performance of college undergraduate students. These analyses provide potentially useful information and policy support for university administrators.

Best cooperative partner selection and input resource reallocation using DEA

Reallocation of input resources (RIR) is a process by which certain decision making units (DMUs) reallocate resources among themselves; a process that occurs frequently in many enterprises. In this paper, a new data envelopment analysis (DEA) approach is developed to select the best cooperative partner DMU. Context-dependent DEA is used to identify the different levels of best-practice frontiers. Two DEA-based models are established for two cooperative scenarios, namely, resources pooling only and best-practice sharing. A cooperative method is applied to determine how to reallocate the input resources, and Shapley value is used to estimate the revenue changes that the various DMUs should expect after RIR. Two different situations with different objectives are considered. One objective is to maximize total revenue for the partnership, while the other is to maximize the Shapley value. The proposed approaches are illustrated with two examples.

Resource allocation based on context-dependent data envelopment analysis and a multi-objective linear programming approach

This paper discusses a mechanism for the allocation of resources among a set of decision making units (DMUs) which are managed by a centralized control unit in an organization. Data envelopment analysis (DEA) and multi-objective linear programming (MOLP) are integrated to deal with this resource allocation problem. Also, context-dependent DEA is introduced to identify the changed production possibility set after resource allocation, which determines the production plans that are feasible with input increase or decrease in general. For the centralized unit, the MOLP approach is proposed to simultaneously maximize total output and effectiveness while minimizing the total allocated variable input consumption. Among these objectives, the effectiveness is determined by the output growth rate for all DMUs, which can reflect the effects obtained by allocating the input resources that are not used up, such as new equipment. In addition, we restrict the production of limited resources to the new most productive scale size (MPSS) region where the DMUs have the best economic characteristics. Finally, an example is employed to illustrate the approach.

Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection

Stepwise benchmark target selection in data envelopment analysis (DEA) is a realistic and effective method by which inefficient decision-making units (DMUs) can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets), an approach that integrates the cross-efficiency DEA, K-means clustering and context-dependent DEA methods to minimize resource improvement pattern inconsistency in the selection of the intermediate benchmark targets (IBTs) of an inefficient DMU. The specific advantages and overall effectiveness of the proposed method were demonstrated by application to a case study of 34 actual container terminal ports and the successful determination of the stepwise benchmarking path of an inefficient DMU.

Evaluating the comparative efficiency of the postal services in OECD countries using context-dependent and measure-specific data envelopment analysis

Purpose – In an effort to help policy makers develop competitive postal service strategies, the purpose of this paper is to evaluate the comparative operating efficiencies of postal services across the Organization for Economic Cooperation and Development (OECD) nations and then identify room for service improvement. Design/methodology/approach – As a better alternative to the conventional data envelopment analysis (DEA) which requires the proportional improvements of inputs and outputs simultaneously, the authors propose the combined use of both context-dependent and measure-specific DEAs to measure the relative attractiveness and progress of the national postal operators of OECD countries. Findings – Defying the conventional notion that public enterprises operate less efficiently than private enterprises, the author discovered that some state-owned public enterprises such as postal service operators could still be efficient if managed properly. Even inefficient postal services operators could significantly improve their service performances, once they identified the root causes of their service failures. Through a series of model experiments and testing, the authors found that proposed context-dependent and measure-specific DEA models were more useful for finding such causes than the conventional DEA model. Practical implications – For public officials and policy makers, the proposed DEAs can pinpoint what it takes to become more efficient and what steps need to be taken to improve postal service operations gradually. Originality/value – This paper is the first to combine the context-dependent DEA with measure-specific DEA to evaluate the comparative efficiency (or progress) and inefficiency (or regress) of the national postal operators of 25 OECD countries.

Forecasting Models Evaluation Using A Slacks-Based Context-Dependent DEA Framework

Xu and Ouenniche (2012a) proposed an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) based model to address a common methodological issue in the evaluation of competing forecasting models; namely, ranking models based on a single performance measure at a time, which typically leads to conflicting ranks. However, their approach suffers from a number of issues. In this paper, we overcome these issues by proposing a slacks-based context-dependent DEA framework and use it to rank forecasting models of oil prices volatility.

A clustering method for evaluating the environmental performance based on slacks-based measure

The conventional clustering algorithms are mostly distance-based, which can lead to distorted results in the evaluation of production unit’s performance. As a non-parametric method, data envelopment analysis (DEA) has become a popular approach to measuring the production process performance. However, few researchers paid attention to the relationship between clustering approach and DEA. In this paper, we use a non-radial DEA framework (slacks-based measure, SBM) to classify the environmental performance of Chinese industry, forming a benchmark-based clustering approach. Additionally, we employ the context-dependent DEA method to get the sub-clusters for detailed managerial meaning. An application in real world is given to explain the usage and effectiveness of the proposed SBM-based clustering method, and the result is compared with the conventional distance-defined k-means clustering approach.

CONTEXT-DEPENDENT ASSESSMENT OF THE EFFICIENCY OF LITHUANIAN FAMILY FARMS

The context-dependent data envelopment analysis ensures that the observations are stratified with respect to their levels of efficiency. The latter instance of stratification enables one to draw more reasonable recommendations regarding performance improvements. The research aims to identify and quantify the discrepancies in efficiency levels among Lithuanian family farms. The crop farms were grouped into the nine strata associated with different levels of efficiency, the livestock farms were grouped into the five strata, and the mixed farms were grouped into the six strata. Therefore, the crop farms appeared to be the most heterogeneous in terms of efficiency and productivity. The results showed that the mixed and livestock farms were specific with certain strata featuring inflated values of the progress scores. Accordingly, support measures for the mixed and crop farms should take into account the underlying differences in the farm performance. JEL Codes: C14, C44, D24, Q12.

RANKING AND BENCHMARKING OF THE ASIAN GAMES ACHIEVEMENTS BASED ON DEA: THE CASE OF GUANGZHOU 2010

Precedent research based on data envelopment analysis (DEA) has been conducted to rank and benchmark the achievements of participating nations/regions in the Olympics, while this paper contributes to the Asian Games, with further concerns on two main issues, namely the common comparison basis for ranking decision making units (DMUs) and the reference feasibility between the inefficient DMUs and their benchmark targets. This paper extends previous DEA research by introducing an improved context-dependent DEA model, in which empirical results establish a unique and fair ranking system for all participating nations/regions, implying two corresponding suggestions for rank improvement. A series of stepwise learning targets are further identified, alternatively providing a gradual performance improvement path for the inefficient participants. The above results will be helpful for strategic decision making in sport management of the Asian nations/regions.

Facilitating Benchmarking with Strategic Grouping and Data Envelopment Analysis: The Case of International Tourist Hotels in Taiwan

For a performance measure to be instructional in future operations, selecting the right strategic groups of firms from which appropriate benchmark targets are to determined is important in the development of business strategies. The immediate question is how to group firms and choose the best benchmark. In this study, a revised context-dependent data envelopment analysis model is applied to identify multilevel strategic groups. Empirical data are obtained for 56 international tourist hotels in Taiwan. Five strategic groups are identified in terms of efficiency frontiers, and the hotels within the groups are ranked by attractiveness scores that reveal their competitiveness. The hotels are also assessed by progress scores that reveal the effort needed for them to advance to a higher level. By integrating both these scores, a clear benchmarking path is designed for each hotel. This path can guide inefficient hotels to identify suitable role models for excellence.

An application of multi-component ranking in banks by context-dependent DEA for non-extreme efficient DMUs

In most applications of data envelopment analysis (DEA) presented in the literature, the models are designed to acquire a single measure of efficiency. However in many instances, the decision making units (DMUs) can be separated into different components. This paper deals with ranking in DEA analysis of multi-component decision making units (DMUs). Using DEA technology, we first show how efficiency of each unit and its components can be obtained then from a practical viewpoint the issue of ranking multicomponent decision making units will be discussed. The significant feature of this method is that it does not have much to do with theoretical concepts and it is has been mostly taught over for applicational issues.

Context-dependent data envelopment analysis with cross-efficiency evaluation

To address some problems with the original context-dependent data envelopment analysis (DEA), this paper proposes a new version of context-dependent DEA; this version is based on cross-efficiency evaluations. One of the problems with the original context-dependent DEA is that the attractiveness and progress measures only represent the radial distance between the decision-making unit (DMU) under evaluation and the evaluation context. This representation only shows how distinct the DMU is from a single specific DMU on the evaluation context, not from the entire evaluation context overall. Another problem is that the magnitude of attractiveness and progress scores in the original context-dependent DEA may not have significant meanings. It may not be proper to say that a DMU is more attractive simply because it has a higher attractiveness score for the same reason that the performance of inefficient DMUs cannot be compared with one another simply based on their efficiency scores. We incorporate cross-efficiency evaluations into the context-dependent DEA to overcome the preceding shortcomings of the original context-dependent DEA. We also demonstrate the proposed model's appropriateness and usefulness with an illustrative example.

Context-Dependent Data Envelopment Analysis with Interval Data

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and Output) are often not so deterministic, therefore they usually are imprecise data such as interval data, hence the DEA models becomes a nonlinear programming problem and is called imprecise DEA (IDEA). In this paper the context-dependent DEA models for DMUs with interval data is extended. First, we consider each DMU (which has interval data) as two DMUs (which have exact data) and then, by solving some DEA models, we can find intervals for attractiveness degree of those DMUs. Finally, some numerical experiment is used to illustrate the proposed approach at the end of paper.

Improved slack-based context-dependent DEA – A study of international tourist hotels in Taiwan

In this paper, we improve the slack-based measure (SBM) of efficiency in context-dependent data envelopment analysis (DEA) and apply in measuring the performance of 34 selected Taiwan‘s International Tourist Hotels. Empirical results indicate that (1) the market differentiates five performance levels forming the benchmark structure for 34 hotels; (2) the hotels with higher attractiveness can be served as learning targets for the hotels in the lagging levels so as to establish the best path for performance improvements; (3) the hotels in the leading levels can use lower progress to analyze potential competitors in the lagging levels. The results of this study can provide hotel’s managers with insights of competitive advantage and help them with strategic decision making.

Assurance region context-dependent DEA with an application to Taiwanese hotel industry

This study proposes assurance region (AR) context-dependent data envelopment analysis (DEA) and applies the restricted weights derived by analytic hierarchy process in measuring the performance of 34 selected Taiwanese international tourist hotels during the years 1997?2006. Empirical results indicate that: AR context-dependent DEA has more of an efficiency segment than context-dependent DEA; the market differentiates 10 performance levels that form the benchmark structure for 34 hotels; hotels with higher attractiveness can serve as learning targets for hotels at lagging levels so as to establish the best path for performance improvement; and hotels at leading levels can use lower progress to analyse potential competitors at lagging levels. The results of this study can provide hotel managers with insights into competitive advantage and can help them with strategic decision-making.