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Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences

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  • Xiong, Xi
  • Yang, Guo-liang
  • Guan, Zhong-cheng

Abstract

Various studies have been devoted to the evaluation of the research and development (R&D) performances of universities and research institutes. However, existing studies tend to focus on static systems, that is, systems with no intertemporal effect. To tackle this issue, this study attempts to assess relative R&D efficiency of institutes from a dynamic perspective. The unified two-stage model proposed by Kao (2017) made a contribution to combining division efficiencies in the multiplier form with frontier projections in the envelopment form in a unified framework. We develop his model in a dynamic framework into which the effects of carry-over activities are embedded across the period. If the dynamic effects in the efficiency measures are not considered, the results will be biased. This is one of the few studies to examine dynamic effects within the framework of the R&D process. Our analysis is based on samples of 17 research institutes in the Chinese Academy of Sciences over the period of 2012–2015. When compared with the proposed data envelope analysis (DEA) model, results show that the static DEA model may underestimate the R&D efficiency scores. The institutes experienced significant improvements in system efficiency, mainly due to the improvements in transfer efficiency. However, there is still much room for improvement in transferring scientific and technological (S&T) achievements. We also find that the resource scale played an important role in influencing basic research. Finally, the projections of inefficient institutes indicate that most institutes had insufficient carry-over inputs (newly approved projects and management cost) based on the average four-year values, and existing slack resources for managers to improve the future performance.

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  • Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2018. "Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences," Journal of Informetrics, Elsevier, vol. 12(3), pages 784-805.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:3:p:784-805
    DOI: 10.1016/j.joi.2018.07.003
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    2. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2020. "Estimating the multi-period efficiency of high-tech research institutes of the Chinese Academy of Sciences: A dynamic slacks-based measure," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    3. Wuzhao Xue & Hua Li & Rizwan Ali & Ramiz ur Rehman & Gonzalo Fernández-Sánchez, 2021. "Assessing the Static and Dynamic Efficiency of Scientific Research of HEIs China: Three Stage DEA–Malmquist Index Approach," Sustainability, MDPI, vol. 13(15), pages 1-21, July.
    4. Piaopeng Song & Yuxiao Gu & Bin Su & Arifa Tanveer & Qiao Peng & Weijun Gao & Shaomin Wu & Shihong Zeng, 2023. "The Impact of Green Technology Research and Development (R&D) Investment on Performance: A Case Study of Listed Energy Companies in Beijing, China," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    5. Kun Chen & Xian-tong Ren & Guo-liang Yang & Hai-bo Qin, 2022. "The other side of the coin: The declining of Chinese social science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 127-143, January.
    6. Wu, Yueh-Cheng & Lin, Sheng-Wei, 2022. "Efficiency evaluation of Asia's cultural tourism using a dynamic DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    7. Huang, Shwu-Huei & Yu, Ming-Miin & Huang, Ya-Ling, 2022. "Evaluation of the efficiency of the local tax administration in Taiwan: Application of a dynamic network data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
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    9. See, Kok Fong & Ma, Zhanxin & Tian, Yuzhen, 2023. "Examining the efficiency of regional university technology transfer in China: A mixed-integer generalized data envelopment analysis framework," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    10. Jiawei Yang & Lei Fang, 2022. "Average lexicographic efficiency decomposition in two-stage data envelopment analysis: an application to China’s regional high-tech innovation systems," Annals of Operations Research, Springer, vol. 312(2), pages 1051-1093, May.
    11. Shiping Mao & Marios Dominikos Kremantzis & Leonidas Sotirios Kyrgiakos & George Vlontzos, 2022. "R&D Performance Evaluation in the Chinese Food Manufacturing Industry Based on Dynamic DEA in the COVID-19 Era," Agriculture, MDPI, vol. 12(11), pages 1-19, November.
    12. Sangpil Yoon & Gyuhyung Kim & Yanghon Chung & Hosung Son, 2023. "Is customer involvement always beneficial for R&D efficiency? The difference between high‐tech and low‐tech industries," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(3), pages 1678-1688, April.
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    14. Yukun Shi & Duchun Wang & Zimeng Zhang, 2022. "Categorical Evaluation of Scientific Research Efficiency in Chinese Universities: Basic and Applied Research," Sustainability, MDPI, vol. 14(8), pages 1-16, April.

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