Showing papers on "Data envelopment analysis published in 2012"

Environmental efficiency evaluation based on data envelopment analysis: A review

Abstract: The paper aims to investigate the achievements of the theoretical and practical basis of environmental policy analysis in order to study their works and point out the future possible research direction. It sorts out researches about environmental efficiency assessment and reviews the works about the theory and application of efficiency analysis around the world. It is suggested that environmental efficiency evaluation theory under small samples and DEA method with undesirable outputs will further extend the research on environmental efficiency evaluation. Also, this review confirms that more studies in methods and their applications in this area are in urgent need.

Stochastic Non-Smooth Envelopment of Data: Semi-Parametric Frontier Estimation Subject to Shape Constraints

Abstract: The field of productive efficiency analysis is currently divided between two main paradigms: the deterministic, nonparametric Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). This paper examines an encompassing semiparametric frontier model that combines the DEA-type nonparametric frontier, which satisfies monotonicity and concavity, with the SFA-style stochastic homoskedastic composite error term. To estimate this model, a new two-stage method is proposed, referred to as Stochastic Non-smooth Envelopment of Data (StoNED). The first stage of the StoNED method applies convex nonparametric least squares (CNLS) to estimate the shape of the frontier without any assumptions about its functional form or smoothness. In the second stage, the conditional expectations of inefficiency are estimated based on the CNLS residuals, using the method of moments or pseudolikelihood techniques. Although in a cross-sectional setting distinguishing inefficiency from noise in general requires distributional assumptions, we also show how these can be relaxed in our approach if panel data are available. Performance of the StoNED method is examined using Monte Carlo simulations.

Measuring the efficiency of customer satisfaction and loyalty for mobile phone brands with DEA

Abstract: The concept of customer satisfaction and loyalty (CS&L) has attracted much attention in recent years. A key motivation for the fast growing emphasis on CS&L can be attributed to the fact that higher customer satisfaction and loyalty can lead to stronger competitive position resulting in larger market share and profitability. Using a data envelopment analysis (DEA) approach, in this study we analyzed and compared CS&L efficiency for mobile phone brands in an emerging telecommunication market, Turkey. The constructs of European Customer Satisfaction Index (ECSI) model are treated and used as input and output indicators of our DEA model. Drawing on the perceptual responses of 251 mobile phone users, the DEA models reveal that from the top six mobile phone brands in Turkey, Nokia features as the most efficient brand followed by LG and Sonny Ericsson in terms of CS&L efficiency, while Motorola, Samsung and Panasonic rank as the least efficient brands.

Supplier selection using analytic network process and data envelopment analysis

Abstract: The present study intends to present a supplier selection method, which also considers green indicators due to environmental protection issues, by using an analysis network process (ANP) as well as data envelopment analysis (DEA). ANP which is able to consider the interdependency between criteria releases the constraint of DEA that the users cannot set up criteria weight preferences. The model evaluation results for high-tech industry show that the proposed method can provide not only reasonable outcomes with a smaller number of decision making units, but can also provide more consistent results.

Measuring Eco-Inefficiency: A New Frontier Approach

Abstract: Growing social concerns over the environmental externalities associated with business activities are pushing firms to identify activities that create economic value with less environmental impact and to become more eco-efficient. Over the past two decades, researchers have increasingly used frontier efficiency models to evaluate productive efficiency in the presence of undesirable outputs, such as greenhouse gas emissions or toxic emissions. In this paper, we identify critical flaws in existing frontier models and show that these models can identify eco-inefficient firms as eco-efficient. We develop a new eco-inefficiency frontier model that rectifies these problems. Our model calculates an eco-inefficiency score for each firm and improvements in outputs necessary to attain eco-efficiency. We demonstrate through a Monte Carlo experiment that our eco-inefficiency model provides a more reliable measurement of corporate eco-inefficiency than the existing frontier models. We also extend the single-output Cobb-Douglas production function to multiple desirable and undesirable outputs. This extension allows for greater flexibility in the simulation analysis of frontier models.

Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment

Abstract: This article presents the development of a conceptual framework which aims to assess Decision Making Units (DMUs) from multiple perspectives. The proposed conceptual framework combines the Balanced Scorecard (BSC) method with the non-parametric technique known as Data Envelopment Analysis (DEA) by using various interconnected models which try to encapsulate four perspectives of performance (financial, customers, internal processes, learning and growth). The practical relevance of the conceptual model has been tested by using it to assess the performance of DMUs in a multinational company which operates in two business areas. Various models were developed with the collaboration of the directors of the company in order to conceive an appropriate and consensual framework, which may provide useful information for the company. The application of the conceptual framework provides structured information regarding the performance of each DMU (from multiple perspectives) and ways to improve it. By integrating the BSC and the DEA approaches this research helps to identify where there is room for improving organisational performance and points out opportunities for reciprocal learning between DMUs. In doing so, this article provides a set of recommendations relating to the successful application of DEA and its integration with the BSC, in order to promote a continuous learning process and to bring about improvements in performance.

The effects of corporate governance on airline performance: Production and marketing efficiency perspectives

Abstract: This study explores the relationship between operating performance and corporate governance in 30 airline companies operating in the US. First, this study applies a two-stage Data Envelopment Analysis (DEA) to evaluate the production efficiency and marketing efficiency of the airlines. Our findings indicate that, in general, there is not as much dispersion in the relative productive efficiencies of the airlines as there is in their marketing efficiencies. The low-cost airlines, on average, are more efficient carriers than the full-service ones, but less efficient marketers. Secondly, truncated regression is used to explore whether the characteristics of corporate governance affect airline performance. The results demonstrate that corporate governance influences firm performance significantly. Finally, we address the managerial decision-making matrix and make suggestions to help airline managers improve performance.

A meta-analysis of DEA and SFA studies of the technical efficiency of seaports: A comparison of fixed and random-effects regression models

Abstract: This paper presents a meta-analysis of variations in seaports’ Mean Technical Efficiency (MTE) scores based on 40 studies published in refereed academic journals. We link the variation in estimated MTE scores to differences in the following factors: the frontier methodology used, which essentially are the Data Envelopment Analysis (DEA) and the Stochastic Frontier Analysis (SFA); regions where seaports are situated; type of data used; number of observations; and the total number of variables used. Furthermore, we compare fixed-effects against a random-effects regression model where the latter assumes that the individual study specific characteristics matter while the former assumes that there is one general tendency across all studies. We present several findings based on the data: (1) the random-effects model outperforms the fixed effects model in explaining the variations in MTEs, (2) recently published studies have lower MTE scores as compared with earlier published studies, (3) studies that used nonparametric DEA models depict higher MTE scores as compared with those that used SFA models, (4) panel data studies have lower TE scores as compared with cross-sectional data, and (5) studies using European seaport data produce lower MTE scores when compared with the rest of the world. Finally, our results contradict some previous meta-analysis studies of TE scores. We encourage the use of random-effects models in meta-analysis studies because they account for individual study specific effects.