Decomposition weights and overall efficiency in two-stage additive network DEA

Guo, Chuanyin; Shureshjani, Roohollah Abbasi; Foroughi, Ali Asghar; Zhu, Joe

doi:10.1016/j.ejor.2016.08.002

Cited by 118 publications

(61 citation statements)

References 24 publications

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“…Their work indicates the interference of weights with the estimated stage efficiencies and OE. Guo et al (2017) innovations' inefficiency. Fang, Guan, Lu, Zhou, and Deng (2013) attempt to analyze changing trends in the input-output efficiency of urban agglomerations in China on the basis of DEA approach and detect that the cities achieving highest efficiency scores all belong to the parts of SEZs.…”

Section: Application Of Dea In Measuring Sez Performancesmentioning

confidence: 99%

“…The additive two-stage DEA is suitable for current research that avoids the evaluation of a single DEA model (Halkos, Tzeremes, & Kourtzidis, 2015). We establish the research model by adjusting the new overall efficiency (OE) equation developed by Guo, Shureshjani, Foroughi, and Zhu (2017) and by considering the weights for substage processes, which is comprehensively explained in Section 3. Accounting the weights will reveal the relative importance of each stage on the total governance performance.…”

Section: Introductionmentioning

confidence: 99%

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The effect of special economic zones on governance performance and their spillover effects in Chinese provinces

Kuo

Nguyen

et al. 2020

Manage Decis Econ

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Section: Application Of Dea In Measuring Sez Performancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of special economic zones on governance performance and their spillover effects in Chinese provinces

Kuo

Nguyen

et al. 2020

Manage Decis Econ

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“…In general, DEA researchers employ two different approaches to evaluating efficiency of two‐stage systems: defining ratio efficiency or using possible production set (PPS). Various models have been developed for multiplier and envelopment two‐stage networks (Chen, Cook, & Zhu, ; Despotis, Koronakos, & Sotiros, ; Guo, Abbasi Shureshjani, Foroughi, & Zhu, ; Kao & Hwang, ; Kao & Hwang, ; Rho & An, ; Tone & Tsutsui, ) based on these two approaches. However, these models still demonstrate deficiencies in evaluating overall efficiency, divisional efficiency, and projection (benchmark DMU; Chen, Cook, Kao, & Zhu, ).…”

Section: Introductionmentioning

confidence: 99%

Pareto–Koopmans efficiency in two‐stage network data envelopment analysis in the presence of undesirable intermediate products and nondiscretionary factors

2019

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Many studies have recently been conducted on the evaluation of system performance with a two‐stage network structure in data envelopment analysis (DEA) literature. One of the topics of interest to researchers has been the mitigation of undesirable products or nondiscretionary factors into their corresponding possible production set (PPS) and their impact on overall efficiency calculations. Determination of decision‐making units (DMUs) with Pareto–Koopmans efficiency status is decisive in identifying benchmark units. The calculated overall efficiency status is compromised when both undesirable products and nondiscretionary factors are present. This work utilizes an axiomatic approach. A novel PPS for a two‐stage network in presence of undesirable intermediate products and nondiscretionary exogenous inputs is introduced. Based on this PPS and by focusing on the principle of mathematical dominance, new models for evaluating overall and divisional efficiencies are presented. In addition, by proposing a two‐step network DEA approach, a necessary and sufficient condition for detection of DMUs with Pareto–Koopmans efficiency status is provided. And by introducing a two‐step algorithm, a novel technique for determining overall efficiency conditions is produced. Finally, the proposed technology is applied to a practical example, and outcomes are discussed.

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“…Much of the subsequent research on the two stage network DEA models has focussed on developments to allow for the decomposition of the efficiency of the respective stages and the determination of overall efficiency. In this respect, two key approaches have been identified, firstly, the multiplier approach, where overall efficiency is determined as the product of the efficiencies of the two stages (Kao and Hwang, 2008 Zhu, 2016). This reveals an emphasis on refinements in the theory, whereas the purpose of this paper is on the critical review of existing network DEA applications with the purpose of determining their usefulness in performance measurement systems for support units within businesses.…”

Section: Development Of Data Envelopment Analysismentioning

confidence: 99%

Data envelopment analysis in performance measurement: a critical analysis of the literature

Shewell

Migiro

2016

Problems and Perspectives in Management

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(2016). Data envelopment analysis in performance measurement: a critical analysis of the literature. Problems and Perspectives in Management,. doi:10.21511/ppm.14(3-3) This study examines the benefits of data envelopment analysis (DEA) in evaluating the performance of decision making units (DMUs). DEA is a mathematical programming tool applied in performance measurement. The problem identified is establishing business support units as value adding business units. A case is made for applying DEA when evaluating the performance of such business support units. To this end, a literature review of the results of applications of DEA to the evaluation of information technology and purchasing supply chain management functions was conducted. The findings indicate the benefits of DEA are that the method identifies efficient performers in a given population and, therefore, allows for benchmarking against the 'best in class' performer. This as opposed to more commonly used parametric methods, such as regression analysis, which result in a comparator that represents the average performance for a given population, therefore, allowing only for measurement against the average. In addition, the findings indicate that in respect of business support units, the DEA methodology allows for the incorporation of intermediate outcomes, which facilitates the measurement of the contribution of these units to overall company performance. Although the DEA methodology has been widely applied, it is still not as well known or generally applied as the more common approaches. The recommendations made in this paper will be beneficial in bringing DEA to the attention of decision-makers. The recommendations will also raise awareness of the potential benefits to be realised when applying the method in developing performance measurement frameworks for business support units.

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Decomposition weights and overall efficiency in two-stage additive network DEA

Cited by 118 publications

References 24 publications

The effect of special economic zones on governance performance and their spillover effects in Chinese provinces

The effect of special economic zones on governance performance and their spillover effects in Chinese provinces

Pareto–Koopmans efficiency in two‐stage network data envelopment analysis in the presence of undesirable intermediate products and nondiscretionary factors

Data envelopment analysis in performance measurement: a critical analysis of the literature

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