期刊论文

Chi, MM

英文

Information Processing & Management

0306-4573

2024

61

3

103649

10.1016/j.ipm.2024.103649

Science funding Prior research on science funding predominantly emphasizes quantitative analyses, exploring fund distribution across disciplines, cognate areas, and institutions, and evaluating validity metrics such as research output. For instance, Kamenetzky (2013) dissected how funding influences for the U.S. National Science Foundation varied across disciplines. Wu (2015) highlighted the skewed distribution of NSFC funding, noting a concentration of funds towards elite institutions, such as Project 985 Science funding Prior research on science funding predominantly emphasizes quantitative analyses, exploring fund distribution across disciplines, cognate areas, and institutions, and evaluating validity metrics such as research output. For instance, Kamenetzky (2013) dissected how funding influences for the U.S. National Science Foundation varied across disciplines. Wu (2015) highlighted the skewed distribution of NSFC funding, noting a concentration of funds towards elite institutions, such as Project 985

本校为第一机构

While extensive research has delved into various facets of science funding outputs and the determinants of funding approval, prevailing methodologies predominantly rely on descriptive statistics or regression analyses. These approaches often miss a holistic view that integrates the interplay of multiple influential factors. In this study, we leverage the scientific research productivity model to introduce the Institution-Capability-Demographics framework, encompassing three pivotal dimensions: institutional characteristics, individual capabilities, and individual demographics. Adopting a confi...