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Evaluating R&D and Transformation Functional Platforms’ Operational Performance Using a Data Envelopment Analysis Model: A Comparative Study

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  • Yuhong Cao

    (School of Management, Shanghai University, 99 Shangda Road, BaoShan District, Shanghai 200444, China)

  • Jianxin You

    (School of Economics & Management, Tongji University, 1239 Siping Road, Shanghai 200092, China)

  • Yongjiang Shi

    (Institute for Manufacturing, University of Cambridge, Cambridge CB30FS, UK)

  • Wei Hu

    (School of Economics and Trade, Shanghai Urban Construction Vocational College, 2080 Nanting Road, Fengxian District, Shanghai 201415, China)

Abstract

The purpose of this paper is to provide a contribution to the development of R&D and transformation functional platforms by identifying key performance influencing factors in the use of data envelopment analysis (DEA) to analyze platform operation performance status and reasons. The DEA method is undertaken to calculate the comprehensive efficiency, pure technical efficiency and scale efficiency of R&D and transformation functional platforms in China’s 30 provinces within the period 2016–2018. Based on the 2018 pure technical efficiency and scale efficiency calculations, the K-means clustering method was used to classify the R&D and transformation functional platforms of 30 provinces. Finally, according to the clustering results, the corresponding clustering improvement scheme is given. The operational level of R&D and transformation functional platforms in many provinces of China still needs to be improved: the R&D and transformation capabilities are weak, the market share of leading products is low, the ability of new technology value-added is insufficient, and the development of R&D and transformation functional platforms has regional imbalance. This study is based solely on statistical data, these data alone obviously cannot fully describe and evaluate the real state of R&D and transformation functional platform due to the complexity and diversity of platforms. Further research is needed to generalize beyond the performance indicators constructed in this paper. For the problems of low overall operation efficiency, unbalanced regional development, redundancy of input resources and lack of professional management personnel in the operation of R&D and transformation functional platforms, policy suggestions can be put forward according to clustering results and input and output adjustment values calculated based on relaxation variables. The study presenting a methodology for analyzing R&D and transformation functional platforms’ operation performance, and the conclusions will provide reference for the development of platforms and high-tech industries.

Suggested Citation

  • Yuhong Cao & Jianxin You & Yongjiang Shi & Wei Hu, 2019. "Evaluating R&D and Transformation Functional Platforms’ Operational Performance Using a Data Envelopment Analysis Model: A Comparative Study," Sustainability, MDPI, vol. 11(18), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:5023-:d:267117
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    References listed on IDEAS

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