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

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  • 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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    Citations

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    Cited by:

    1. Corsini, Alberto & Pezzoni, Michele, 2023. "Does grant funding foster research impact? Evidence from France," Journal of Informetrics, Elsevier, vol. 17(4).
    2. Weishu Liu & Li Tang & Guangyuan Hu, 2020. "Funding information in Web of Science: an updated overview," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1509-1524, March.
    3. Alberto Corsini & Michele Pezzoni, 2022. "Does grant funding foster research impact? Evidence from France," SciencePo Working papers Main hal-03912647, HAL.
    4. Pengfei Jia & Weixi Xie & Guangyao Zhang & Xianwen Wang, 2023. "Do reviewers get their deserved acknowledgments from the authors of manuscripts?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5687-5703, October.
    5. Qianqian Jin & Hongshu Chen & Ximeng Wang & Tingting Ma & Fei Xiong, 2022. "Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5415-5440, September.
    6. Manoj Kumar Verma & Daud Khan & Mayank Yuvaraj, 2023. "Scientometric assessment of funded scientometrics and bibliometrics research (2011–2021)," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4305-4320, August.
    7. Alberto Corsini & Michele Pezzoni, 2022. "Does grant funding foster research impact? Evidence from France," Working Papers hal-03912647, HAL.
    8. Lili Miao & Vincent Larivi`ere & Feifei Wang & Yong-Yeol Ahn & Cassidy R. Sugimoto, 2023. "Cooperation and interdependence in global science funding," Papers 2308.08630, arXiv.org, revised Feb 2024.
    9. Min Song & Keun Young Kang & Tatsawan Timakum & Xinyuan Zhang, 2020. "Examining influential factors for acknowledgements classification using supervised learning," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    10. Balázs Győrffy & Andrea Magda Nagy & Péter Herman & Ádám Török, 2018. "Factors influencing the scientific performance of Momentum grant holders: an evaluation of the first 117 research groups," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 409-426, October.
    11. Nina Smirnova & Philipp Mayr, 2023. "A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 709-734, January.

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    More about this item

    Keywords

    Acknowledgement analysis; Funding analysis; Citation network; Emerging technology; Robotics;
    All these keywords.

    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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