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Using acknowledgement data to characterize funding organizations by the types of research sponsored: the case of robotics research


  • Cristian Mejia

    () (Tokyo Institute of Technology)

  • Yuya Kajikawa

    () (Tokyo Institute of Technology)


Abstract Funded research has been linked to academic production and performance. While the presence of funding acknowledgements may serve as an indicator of quality to some extent, we still lack tools to evaluate whether funding agencies allocate resources to novel and innovative research rather than mature fields. We address this issue in the present study by using bibliometrics. In particular, we exploit the citation network properties of academic articles to classify specific research fields into four categories: change maker, breakthrough, incremental, and matured. We then use funding acknowledgement information to identify the sponsors involved in each research type to characterize funding agencies. We focus our analysis on the robotics field in order to reveal international trends of financial acknowledgements. We find that the incremental and matured research areas show the highest counts of funding acknowledgements. Moreover, although research funded by some agencies is mostly recognized as incremental-type research, those in other categories may perform better in terms of the number of citations. Additionally, we analyze the interest of selected funding agencies in granular subject categories. The characterization of funding agencies in this study may help policymakers and funding organizations assess or adjust their strategies, benchmark with other key players, and obtain an overview of local and global acknowledgement trends.

Suggested Citation

  • Cristian Mejia & Yuya Kajikawa, 2018. "Using acknowledgement data to characterize funding organizations by the types of research sponsored: the case of robotics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 883-904, March.
  • Handle: RePEc:spr:scient:v:114:y:2018:i:3:d:10.1007_s11192-017-2617-2
    DOI: 10.1007/s11192-017-2617-2

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    References listed on IDEAS

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    More about this item


    Acknowledgement analysis; Funding analysis; Citation network; Emerging technology; Robotics;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D


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