Data envelopment analysis efficiency in two-stage networks with feedback

Liang, Liang; Li, Zhaoqiong; Cook, Wade D.; Zhu, Joe

doi:10.1080/0740817x.2010.509307

Cited by 74 publications

(29 citation statements)

References 14 publications

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“…Additionally, a multi-stage model was proposed by some scholars. Liang and Li [16] classified the number of SCI papers and patents as the first-stage output, and the research funds obtained as the second-stage output. As for doctoral students, Cook and Green [17] treated them as the intermediate variable because they were the output of the education stage as well as the input of the scientific research stage.…”

Section: Key Indicators and Conceptual Framework For Social Science Rmentioning

confidence: 99%

Sustainable Evaluation of Social Science Research in Higher Education Institutions Based on Data Envelopment Analysis

Wang

Han-hui

2017

Sustainability

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This paper divides the typical social scientific research process into two stages: the research development stage and the research innovation stage, with personnel as a shared input between them. We construct an input-shared multi-period two-stage DEA (data envelopment analysis) model as measurement of sustainability capacities of social scientific research, and then measure both the overall efficiencies and the stage efficiencies of scientific research of 18 HEIs (higher education institutions) in Jiangsu province of China from 2010 to 2014. There are some revelatory traits among the overall efficiencies and stage efficiencies of HEIs. As for the HEIs, the efficiencies of the first stage of certain HEIs that are better in social science are higher than those which have advantage in natural science. The results are the opposite in the second stage. Additionally, in virtue of common-weight global MPI (Malmquist Productivity Index), we figure out the dynamic efficiencies within and between HEIs. Finally, we appeal to administration departments to establish academic sharing systems and construct interdisciplinary ecosystems among HEIs, which aims to pool the knowledge, experience, and capacities of the region's academic actors and improve the sustainable development and innovation abilities of social scientific research. In addition, collaborating with industrial organizations is also a strategic avenue to improve their research abilities, which can push the boundaries of sustainable development and also bring vigor in social scientific research.

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Section: Key Indicators and Conceptual Framework For Social Science Rmentioning

confidence: 99%

Sustainable Evaluation of Social Science Research in Higher Education Institutions Based on Data Envelopment Analysis

Wang

Han-hui

2017

Sustainability

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“…The efficiency review of the models that have feedback has always been of interest among the researchers due to its common uses. For example, in (Liang et al, 2011), the efficiency of the two-stage model was investigated. Wang et al (1997) and Seiford and Zhu (1999), were the first researchers who studied the two-stage models.…”

Section: Introductionmentioning

confidence: 99%

A two-stage method for assessing the efficiency of the three-stage series network data envelopment analysis model with two feedback

Ghomi¹,

Rahmani²,

Bafruei³

2019

10.5267/j.dsl

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Data envelopment analysis models play an important role in decision making. In this paper, onestage and two-stage nonlinear programming problems are investigated in order to evaluate the efficiency of two types of network data envelopment analysis model. The first type of network data envelopment analysis model has a series structure with three stages and a feedback between the last step and the middle step, the second model has a three-stage series structure with two feedback between the final step and the first step and the middle step. By examining the overall efficiency of the models based on the one-stage programming problem, a two-stage programming problem is also applied in order to evaluate the efficiency of each step. In order to solve onestage nonlinear programming problems and two-stage linear and nonlinear programming problems derived from modeling, a linearization method based on coordinate transformation, and constant assumption and gradual growth of some variables is presented. In the last section, the proposed methods have been discussed using some numerical examples.

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“…During the loop-computing process, where every HEI is free to choose its own input-output weights For the jth (j = 1, · · · , n) HEI, its inputs and outputs are denoted by X ij , i = 1, · · · , m and Y rj , r = 1, · · · , s, respectively. Similarly to previous studies which employed an input-oriented DEA model to analyze the performance of HEIs [22,23], we also regard that HEIs in Jiangsu Province have a larger degree of control over their inputs and adopt input orientation. Considering the time span only covering the Twelfth Five-Year period, we assume that the context is under constant returns to scale (CRS), and that the optimal efficiency for the kth HEI can be calculated through the basic linear programming formulation.…”

Section: Modelmentioning

confidence: 99%

“…With the help of specific systems in which the production process is featured as a multistage link, a lot of studies have been conducted; among these, some typical studies use a two-stage process to detect the realistic individual stage efficiencies and specific inefficient stages [22,23,25,26], which coincides with Section 2, and the basic process is shown in Figure 4. Similarly, for the jth (j = 1, · · · , n) HEI, its inputs, intermediate variables, and outputs are represented by X ij , i = 1, · · · , m, Z f j , f = 1, · · · , g, and Y rj , r = 1, · · · , s, respectively.…”

Section: Modelmentioning

confidence: 99%

“…Johnes and Li [21] applied DEA to analyze the relative research efficiency of 109 Chinese universities from 2003 to 2004 and the conclusions showed that the efficiencies were higher in comprehensive universities compared to specialist universities; the same results were also found for universities located in the coastal region compared with those in the western region. Liang, Li [22] developed a model which realistically considers the university production process, where outputs from the second stage can be fed back to the first stage, then applied this model to a set of Chinese universities. In this paper, we deconstruct the social science research process in HEIs into two-stage networks, taking the individual components and the data structure in the Humanities and Social Science Research Management System of the National Ordinary Institutions of Higher Learning (Jiangsu Province) into account, which can provide a mechanism where we can determine the inefficient stage exactly and prescribe specific improvement measures.…”

Section: The Social Science Research Context In Heismentioning

confidence: 99%

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Sustainability in Chinese Higher Educational Institutions’ Social Science Research: A Performance Interface toward Efficiency

Wang

Han-hui

2017

Sustainability

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Sustainability issues in higher educational institutions' (HEIs) research, especially in the social science field, have attracted increasing levels of attention in higher education administration in recent decades as HEIs are confronted with a growing pressure worldwide to increase the efficiency of their research activities under a limited volume and relatively equitable division of public funding resources. This paper introduces a theoretical analysis framework based on a data envelopment analysis, separating the social science research process into a foundation stage and a construction stage, and then projecting each HEI into certain quadrants to form several clusters according to their overall and stage efficiencies and corresponding Malmquist Productivity Indices. Furthermore, the interfaces are formed in each cluster as feasible potential improvement directions. The empirical results in detail are demonstrated from a data set of Chinese HEIs in Jiangsu Province over the Twelfth Five-Year period as offering a closer approximation to the "China social science research best practice".

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Data envelopment analysis efficiency in two-stage networks with feedback

Cited by 74 publications

References 14 publications

Sustainable Evaluation of Social Science Research in Higher Education Institutions Based on Data Envelopment Analysis

Sustainable Evaluation of Social Science Research in Higher Education Institutions Based on Data Envelopment Analysis

A two-stage method for assessing the efficiency of the three-stage series network data envelopment analysis model with two feedback

Sustainability in Chinese Higher Educational Institutions’ Social Science Research: A Performance Interface toward Efficiency

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