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

Cristian Mejia () and Yuya Kajikawa ()
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Cristian Mejia: Tokyo Institute of Technology
Yuya Kajikawa: Tokyo Institute of Technology

Scientometrics, 2018, vol. 114, issue 3, 883-904

Abstract: 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.

Keywords: Acknowledgement analysis; Funding analysis; Citation network; Emerging technology; Robotics; 62H25; 91B82 (search for similar items in EconPapers)
JEL-codes: C38 C81 D02 O32 (search for similar items in EconPapers)
Date: 2018
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