Developing Common Set of Weights with Considering Nondiscretionary Inputs and Using Ideal Point Method

Mavi, Reza Kiani; Kazemi, Sajad; Jahangiri, Jay M.

doi:10.1155/2013/906743

Cited by 15 publications

(4 citation statements)

References 39 publications

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“…Researchers have used different techniques to generate a CSW, for example, compromise programming (Kao and Hung, 2005;Kiani Mavi et al, 2012), ideal point method for stochastic data (Tavana et al, 2015) and non-discretionary inputs (Kiani Mavi et al, 2013), utility function (Jahanshahloo et al, 2017), mean-variance criteria (Razavi Hajiagha et al, 2018) and regression analysis .…”

Section: Common Weight Analysis In Data Envelopment Analysismentioning

confidence: 99%

Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis

Mavi

Saen

et al. 2021

SCM

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PurposeDespite unanimity in the literature that eco-innovation (EI) leads to sustainable development, evidence remains limited on measuring EI efficiency with the Malmquist productivity index (MPI). In conventional data envelopment analysis (DEA) models, decision-making units (DMUs) are inclined to assign more favorable weights, even zero, to the inputs and outputs to maximize their own efficiency. This paper aims to overcome this shortcoming by developing a common set of weights (CSW). Design/methodology/approachUsing goal programming, this study develops a CSW model to evaluate the EI efficiency of the organization for economic co-operation and development (OECD) countries and track their changes with MPI during 2010–2018. FindingsAchieving a complete ranking of DMUs, findings show the higher discrimination power of the proposed CSW compared with the original DEA models. Furthermore, results reveal that Iceland, Latvia and Luxembourg are the only OECD countries that have incessantly improved their EI productivity (MPI > 1) from 2010 to 2018. On the other hand, Japan is the OECD country that has experienced the highest yearly EI efficiency during 2010–2018. This paper also found that Iceland has the highest MPI over 2010–2018. Practical implicationsMore investment in environmental research and development (R&D) projects instead of generic R&D enables OECD members to realize more opportunities for sustainable development through minimizing energy use and environmental pollution in any form of waste and greenhouse gas emissions. Originality/valueIn addition to developing a novel common weights model for DEA-MPI to measure and evaluate the EI of OECD countries, this paper develops a CSW model by including the undesirable outputs for EI analysis.

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Section: Common Weight Analysis In Data Envelopment Analysismentioning

confidence: 99%

Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis

Mavi

Saen

et al. 2021

SCM

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“…Makui et al (2008), using goal programming approach, developed common weights for DEA models that improve the discrimination power. Kiani Mavi et al (2013) and Tavana et al (2015) used the ideal point concept to develop common set of weights. Vaz et al (2010) implemented the network-DEA model to assess the efficiency of the retail stores that have many selling divisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Common weights in dynamic network DEA with goal programming approach for performance assessment of insurance companies in Iran

Gharakhani

Eshlaghy

Hafshejani

et al. 2018

MRR

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Purpose Conventional data envelopment analysis (DEA) models permit each decision-making unit (DMU) to assess its efficiency score with the most favorable weights. In other words, each DMU selects the best weighting schemes to obtain maximum efficiency for itself. Therefore, using different sets of weights leads to many different efficient DMUs, which makes comparing and ranking them on a similar basis impossible. Another issue is that often more than one DMU is evaluated as efficient because the selection of weights is flexible; therefore, all DMUs cannot be completely differentiated. The purpose of this paper is to development a common weight in dynamic network DEA with a goal programming approach. Design/methodology/approach In this paper, a goal programming approach has been proposed to generate common weights in dynamic network DEA. To validate the applicability of the proposed model, the data of 30 non-life insurance companies in Iran during 2013-2015 have been used for measuring their efficiency scores and ranking all of the companies. Findings Findings show that the proposed methodology is an effective and practical approach to measure the efficiency of DMUs with dynamic network structure. Originality/value The proposed model delivers more knowledge of the common weight approaches and improves the DEA theory and methodology. This model makes it possible to measure efficiency scores and compare all DMUs from multiple different standpoints. Further, this model allows one to not only calculate the overall efficiency of DMUs throughout the time period but also consider dynamic change of the time period efficiency and dynamic change of the divisional efficiency of DMUs.

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“…A citation-based systematic literature review on banking sector performance by Ahmad et al [6] demonstrates that studies in this realm widely have employed stochastic frontier analysis (SFA) as a parametric approach, whereas data envelopment analysis (DEA) is prevalent in nonparametric approach to measure the banking sector performance and efficiency. For example, recent studies such as Tavana et al [7], Barros and Wanke [5] and Kiani Mavi et al [8] have focused on the efficiency analysis using DEA models.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional DEA models accept that all DMUs are homogenous and operate in a similar operating environment [8]. However, in real-world situations, different environmental variables should be considered in the assessment process.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

Kazemi

Mavi

Emrouznejad

et al. 2021

IFS

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Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

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Developing Common Set of Weights with Considering Nondiscretionary Inputs and Using Ideal Point Method

Cited by 15 publications

References 39 publications

Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis

Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis

Common weights in dynamic network DEA with goal programming approach for performance assessment of insurance companies in Iran

Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

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