Measuring the R&amp;D efficiency of regions by a parallel DEA game model

Zuo, Kairui; Guan, Jiancheng

doi:10.1007/s11192-017-2380-4

Cited by 31 publications

(8 citation statements)

References 54 publications

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“…There are input redundancy and output deficiency in both the G20 developed and developing countries. From the perspective of input indicators redundancy, R&D personnel redundancy occurred frequently (5)(6)(7)(8)(9)(10) in Japan and the Russian Federation, while R&D expenditure redundancy occurred frequently (5)(6)(7)(8)(9)(10) in Canada, France, Italy, the Republic of Korea, South Africa, and the United Kingdom. The output deficiency mainly focuses on the scientific and technological output, while the economic performance performs well.…”

Section: Input Redundancy and Output Deficiency Of Astimentioning

confidence: 99%

“…Innovation efficiency, which is "the ability to translate inputs into innovation outputs" by definition, has become very important and attractive to scholars and governments [7,8]. Because of the unique advantages in the efficiency evaluation of multi-input and multioutput [9], the Data Envelopment Analysis (DEA) has been widely used to measure the relative efficiency of Decision-Making Units (DMUs) by estimating the ratio of outputs to inputs [10][11][12]. Many studies investigated innovation efficiency at the national [13][14][15], regional [10,16,17], and institutional levels [18][19][20] by means of DEA.…”

Section: Introductionmentioning

confidence: 99%

“…From the perspective of input indicators redundancy, R&D personnel redundancy occurred frequently(5)(6)(7)(8)(9)(10) in Japan and the Russian Federation,…”

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confidence: 99%

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International Comparison of the Efficiency of Agricultural Science, Technology, and Innovation: A Case Study of G20 Countries

et al. 2021

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An efficiency-oriented innovation analysis will enhance the understanding of the operational quality related to the transformation process of limited innovation investments for improving innovation outputs. The purpose of this study was to measure the static-dynamic efficiency of agricultural science, technology, and innovation (ASTI) and identify the efficiency determinants across the Group of Twenty (G20) countries. First, the static comprehensive efficiency of ASTI was measured employing the Data Envelopment Analysis (DEA)-BCC model, and some of the binding constraints to higher efficiency were investigated. Then, we applied the DEA-Malmquist index model to calculate the efficiency change of ASTI in certain periods and decomposed the sources of efficiency change. Finally, the G20 countries were classified into four-level clusters based on the rankings of efficiency measurement and capability evaluation of ASTI to locate the type of ASTI level and identify the type change in both the efficiency and capability. The empirical results indicate the following. (1) The efficiency range of the G20 developing countries was relatively larger than the G20 developed countries. The G20 developed countries showed a fluctuating downward trend, while the G20 developing countries showed an upward trend from the perspective of efficient proportion. The R&D expenditure redundancy and the agricultural journal papers deficiency were the main binding constraints to the higher efficiency of ASTI. (2) The total factor productivity change (TFPC) of ASTI showed an alternating trend of “decline–growth–continuous decline–growth recovery”, where the G20 developed countries experienced “growth–decline–growth” and the G20 developing countries underwent a fluctuating upward trend. The TFPC of ASTI in most G20 countries was primarily due to technological change. (3) The G20 developed countries usually had advantages in capacity, while the G20 developing countries performed better in efficiency.

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Section: Input Redundancy and Output Deficiency Of Astimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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International Comparison of the Efficiency of Agricultural Science, Technology, and Innovation: A Case Study of G20 Countries

et al. 2021

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“…Some interesting methods for quantitative measurement of innovation performance are presented in surveys of Fritsch and Slavtchev (2011), Hudec and Prochádzková (2013), Zuo and Guan (2017). In another interesting approach, Zemtsov and Kotsemir (2019) showed that DEA could be a widespread tool for surveying the region's innovative results.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative analysis of the regional efficiency in Serbia: DEA approach

Radonjić¹

2020

Industrija

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The main aim of the paper is to offer a statistical identification of differences in efficiency between Serbian regions. The regional resources covered by the analysis are presented in the form of infrastructure, investment in new fixed assets and employment levels. For the researching purpose, data envelopment method (DEA) is used as a major component in identifying regions that use their resources effectively. This approach of mathematical programming optimization tends to evaluate the efficiency of the analyzed regional spatial aspects. Moreover, studying regional differences and generally spatial efficiency creates a significant basis for mapping key problems of regional development in Serbia and provides important guidelines for the creation and implementation of regional policies.

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“…One of the weaknesses of the DEA is that it may give several efficient organizations with an efficiency value of 1 after performing the evaluation, so it is almost impossible to separate the efficiency of these organizations with this method (Hou et al, 2018). Moreover, because the interrelationship among DMUs is neglected, DEA has a major lack of ability to distinguish between those DMUs at all (Zuo & Guan, 2017). In another word, if the number of indicators is high compared to the number of DMUs, the evaluation operation will face a problem and the model will present an efficiency value of 1 for most organizations mistakenly (Geng et al, 2018), therefore, the DMUs number must be minimally twice higher than the total indicators number, i.e., sum of inputs and outputs (Nourani et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

DEVELOPING AN INTEGRATED MODEL FOR EVALUATING R&D ORGANIZATIONS’ PERFORMANCE: COMBINATION OF DEA-ANP

Hoseini

Fallahpour

Wong

et al. 2021

Technological and Economic Development of Economy

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Assessing the performance of the Research and development (R&D) organizations to achieve higher productivity, growth, and development is always a critical necessity. Therefore, developing a more accurate model to evaluate the performance is always required. For this purpose, this study is aimed at developing a decision-making model for evaluating R&D performance. The model comes up with determining the most proper evaluative criteria for assessing R&D organizations. Then, it integrates Data Envelopment Analysis (DEA) with Analytical Network Process (ANP) to assess R&D performance. This paper is aimed to develop an integrated model for evaluating R&D performance. The findings of the study show that the DEA-ANP model is an accurate and acceptable model for evaluating R&D organizations’ performance.

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Measuring the R&D efficiency of regions by a parallel DEA game model

Cited by 31 publications

References 54 publications

International Comparison of the Efficiency of Agricultural Science, Technology, and Innovation: A Case Study of G20 Countries

International Comparison of the Efficiency of Agricultural Science, Technology, and Innovation: A Case Study of G20 Countries

Comparative analysis of the regional efficiency in Serbia: DEA approach

DEVELOPING AN INTEGRATED MODEL FOR EVALUATING R&D ORGANIZATIONS’ PERFORMANCE: COMBINATION OF DEA-ANP

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