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General laws of funding for scientific citations: how citations change in funded and unfunded research between basic and applied sciences


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Purpose

The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences.

Design/methodology/approach

A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of Science database.

Findings

The original results reveal general characteristics of the diffusion of science in research fields: a) Funded articles receive higher citations compared to unfunded papers in journals; b) Funded articles exhibit a super-linear growth in citations, surpassing the increase seen in unfunded articles. This finding reveals a higher diffusion of scientific knowledge in funded articles. Moreover, c) funded articles in both basic and applied sciences demonstrate a similar expected change in citations, equivalent to about 1.23%, when the number of funded papers increases by 1% in journals. This result suggests, for the first time, that funding effect of scientific research is an invariant driver, irrespective of the nature of the basic or applied sciences.

Originality/value

This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and society. These findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied sciences.

Practical implications

This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.

eISSN:
2543-683X
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology, Project Management, Databases and Data Mining