Relationship between DEA models without explicit inputs and DEA-R models

Mozaffari, Mohammad Reza; Gerami, Javad; Jablonský, Josef

doi:10.1007/s10100-012-0273-4

Cited by 30 publications

(19 citation statements)

References 13 publications

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“…In this model, the weights w ir are the linear program variables. The properties of Model ( 5) and its comparison with DEA models are presented in the following, as pointed out in the works of Mozaffari et al (2014a). The dual problem of Model ( 5) is as follows, considering the dual variables λ j ; j ¼ 1; 2; .…”

Section: Cra Modelmentioning

confidence: 99%

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Two-stage incentives system for commercial banks based on centralized resource allocation model in DEA-R

Kamyab¹,

Mozaffari

Gerami

et al. 2020

IJPPM

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PurposeIt is always of great importance for managers in organizations to evaluate their staff members and create incentive systems, using instruments such as Data Envelopment Analysis (DEA) and DEA-R (DEA models based on ratio analysis). The purpose of this paper is to propose a two-stage network incentives system for commercial banks.Design/methodology/approachCentralized Resource Allocation (CRA) models make it possible to project all decision-making units (DMUs) onto the efficient frontier by solving a single linear programming model. In this paper, we use our proposed DEA-R-based CRA models to evaluate commercial banks in a two-stage case when the only ratios available are the assets-to-costs and income-to-assets vectors.FindingsThirteen commercial banks modeled as two-stage networks were evaluated by the models proposed in two different cases of ratio data. Results suggest that the proposed methodology yields more accurate efficiency scores, thus allowing better discrimination among DMUs. Furthermore, evaluating the DMUs when they are structured as two-stage (or even three-stage) networks makes it possible to examine the incentives system in more detail. Therefore, the use of incentive systems by managers would allow a better focus on the priority activities of commercial banks and a faster movement toward the frontier of best practices.Originality/valueThe super-efficiency scores of a number of commercial banks are evaluated based on the CRA model, as a cornerstone criterion for the two-stage evaluation in DEA-R, thus allowing the rank of each commercial bank in terms of the incentives system rather on the performance of the productive process.

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Section: Cra Modelmentioning

confidence: 99%

“…Definition 2. Mozaffari et al (2014a). DMU p is CCR-R-I efficient (input-oriented CCR-R efficient) if and only if the optimal objective function value of Model (8) γ * R ¼ 1: The following properties are attributed to Models ( 6) and ( 8).…”

Section: Cra Modelmentioning

confidence: 99%

Two-stage incentives system for commercial banks based on centralized resource allocation model in DEA-R

Kamyab¹,

Mozaffari

Gerami

et al. 2020

IJPPM

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“…Data envelopment analysis (DEA) is a widely-accepted non-parametric method for efficiency evaluation of decision making units (DMUs) [2] . It begins with the CCR model introduced by Charnes as the first DEA model.…”

Section: Basic Theorymentioning

confidence: 99%

A Fuzzy Non-radial Data Envelopment Model: A Case Study of Commercial Banks

Yuan¹

2015

Advances in Computer Science Research

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“…A ratio-based DEA model can be formulated similar to a classical DEA model without explicit inputs [36] [37]. Since the data in a DEA model are in different units of measurements and some output-ratios are negative, the normalized counterparts of output-ratios are computed as follows [38]: …”

Section: A Ratio-based Dea Modelmentioning

confidence: 99%

Using DEA and AHP for Ratio Analysis

Pakkar¹

2014

AJOR

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This research proposes an integrated approach to the Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP) methodologies for ratio analysis. According to this, we compute two sets of weights of ratios in the DEA framework. All ratios are treated as outputs without explicit inputs. The first set of weights represents the most attainable efficiency level for each Decision Making Unit (DMU) in comparison to the other DMUs. The second set of weights represents the relative priority of output-ratios using AHP. We assess the performance of each DMU in terms of the relative closeness to the priority weights of output-ratios. For this purpose, we develop a parametric goal programming model to measure the deviations between the two sets of weights. Increasing the value of a parameter in a defined range of efficiency loss, we explore how much the deviations can be improved to achieve the desired goals of the decision maker. This may result in various ranking positions for each DMU in comparison to the other DMUs. An illustrated example of eight listed companies in the steel industry of China is used to highlight the usefulness of the proposed approach.

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Relationship between DEA models without explicit inputs and DEA-R models

Cited by 30 publications

References 13 publications

Two-stage incentives system for commercial banks based on centralized resource allocation model in DEA-R

Two-stage incentives system for commercial banks based on centralized resource allocation model in DEA-R

A Fuzzy Non-radial Data Envelopment Model: A Case Study of Commercial Banks

Using DEA and AHP for Ratio Analysis

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