A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis

Cheng, Gang; Zervopoulos, Panagiotis D.; Qian, Zhenhua

doi:10.1016/j.ejor.2012.09.031

Cited by 87 publications

(41 citation statements)

References 23 publications

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“…There are three popular methods which have been used: the Range Directional Measure (RDM) proposed by Portela et al (2004), the Modified Slack-Based Measure (MSBM) proposed by Sharp et al (2006), and the Semi-Oriented Radial Measure (SORM) proposed by Emrouznejad et al (2010). Recently a fourth method, Variant of Radial Measure (VRM), has been introduced by Cheng et al (2011).…”

Section: Selecting Inputs and Outputsmentioning

confidence: 99%

Chinese companies distress prediction: an application of data envelopment analysis

Crook

Andreeva

2014

Journal of the Operational Research Society

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Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency.In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency -Technical Efficiency, we assume Variable Returns to Scale, and decompose Technical Efficiency into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the levels of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, whilst the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power is improved by this corporate efficiency information.

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Section: Selecting Inputs and Outputsmentioning

confidence: 99%

Chinese companies distress prediction: an application of data envelopment analysis

Crook

Andreeva

2014

Journal of the Operational Research Society

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“…The first possibility would be either to treat the negative or undesirable outputs as inputs (Dyckhoff andAllen, 2001, Korhonen andLuptacik, 2004), or to invert the 3 The presence of negative values in data can cause similar problems. See Cheng et al (2013) for details. 4 SeeHua and Bian (2007) or You and Yan (2011) for an extended review of these methods.…”

Section: Dealing With Undesirable Outputsmentioning

confidence: 99%

Efficiency assessment of primary care providers: A conditional nonparametric approach

Ferrera

Alonso-Morán

Nuño-Solinís

et al. 2015

European Journal of Operational Research

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This paper uses a fully nonparametric approach to estimate efficiency measures for primary care units incorporating the effect of environmental factors. This methodology allows us to account for different types of variables (continuous and discrete) regarding the main characteristics of patients served by those providers. In addition, we use an extension of this nonparametric approach to deal with the presence of undesirable outputs in data, represented by the number of readmissions and hospitalization rates of ambulatory care sensitive condition (ASCS). The empirical results of our application show that all the exogenous variables considered have a significant and negative effect on efficiency estimates.

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“…Wong (2012) points out that the existing techniques for big data analytic are, in general, likely to be mechanistic. Additionally, many researchers point out that big data analytic technique to aid the development of new products are relatively underemphasised (Ozer, 2011;Cheng et al, 2013;Manyika et al, 2013).…”

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confidence: 99%

Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph

Tan

Zhan

et al. 2015

International Journal of Production Economics

341

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Chingter (2015) Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. International Journal of Production Economics, Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/31811/4/IJPE_BIG%20DATA_New%20Version.pdf The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm's existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.

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A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis

Abstract: ABSTRACT

Cited by 87 publications

References 23 publications

Chinese companies distress prediction: an application of data envelopment analysis

Chinese companies distress prediction: an application of data envelopment analysis

Efficiency assessment of primary care providers: A conditional nonparametric approach

Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph

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