Bibliometric analysis of author count, funding, and citations in AI research

Abstract

• This study employs three content analysis methods, including descriptive statistics, the chi-square test, and ANOVA test. • Descriptive statistics aim to provide a basic overview of the research data, facilitating an understanding of the general characteristics of the dataset. • The chi-square test is used to examine the association between variables, determining whether two categorical variables are significantly related. • The ANOVA test is used to compare the differences between three or more samples, determining whether there are significant differences between different groups from question hypotheses. • These hypotheses and methods together form the foundation of the analytical framework of this study, aiming to explore the relationship between journal paper ranking, the number of authors, research funding support, and citation count. The academic community places significant emphasis on publishing research in SCI and SSCI journals, which are known for their credibility, high quality, and strong reputations. Most research requires significant investment in human resources and equipment, making the acquisition of research funding crucial. Understanding the motivation behind authorship and its association with citation patterns in SCI and SSCI journals represents a significant research concern in bibliometric studies. This study identifies the relationship among author number, research funding, and citation count using content analysis techniques, including the chi-square and analysis of variance tests. Investment in AI research, development, and applications is increasing; thus, this study examines 4,488 articles published in the field of artificial intelligence (AI) from Springer in 2018. The empirical results indicate that (1) the average number of authors is highest in Q1 journals, with non-single-author papers being more common than single-author papers and concentrated in higher rankings; (2) papers with research funding are more common than those without; (3) papers with citations are more frequent than those without; (4) the ranking of papers with research funding and citations is higher than that of other papers without funding; and (5) the average citation count of papers with research funding leads in Q1 and is higher than in other rankings. This study is the first attempt at highlighting papers in the field of AI from Springer. The results and important findings provide useful references for researchers, reviewers, publishers, and interested parties with different purposes for academic and technical publications with sustained success. This study uniquely integrates four dimensions—author count, research funding, journal ranking, and citation count—to offer novel insights into academic publishing performance in the AI field.

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