Does Diversity of Expertise Drive Citation Impact? Evidence from Computer Science. Salatino, A., Osborne, F., Recupero, D. R., Angioni, S., & Motta, E. Scientometrics, February, 2026.
Does Diversity of Expertise Drive Citation Impact? Evidence from Computer Science [link]Paper  doi  abstract   bibtex   
Abstract High-quality scientific research plays a pivotal role in advancing society, stimulating economic growth, protecting the environment, and driving technological innovation. Understanding the key factors that lead to impactful research is thus crucial as it can steer the development of more effective policies to enhance the research enterprise. Extensive literature emphasises that the composition of a research team is vital for generating innovative and impactful scientific work. Many studies have focused on how team diversity, including aspects like ethnicity, gender, and international background, affects research outcomes. These types of diversities often correlate positively with the impact of research. In this paper, we investigate a less-explored dimension of diversity: the diversity of authors’ areas of expertise. This aspect has received limited attention, primarily due to the challenges involved in defining and measuring it. We present new AI-driven methods to quantify this diversity of expertise and conduct an extensive analysis of over 944,000 Computer Science papers. Specifically, this study investigates the relationship between the authors’ diversity of expertise and the number of citations their paper receives within the first five years. For each paper, we modelled the expertise of each individual author and then quantified the overall diversity within the author team. We then performed a statistical analysis that revealed a significant positive correlation between two diversity metrics and the number of citations received. This suggests that, in the field of Computer Science, diversity of expertise is a key driver of high-impact research.
@article{salatino_does_2026,
	title = {Does {Diversity} of {Expertise} {Drive} {Citation} {Impact}? {Evidence} from {Computer} {Science}},
	issn = {0138-9130, 1588-2861},
	shorttitle = {Does {Diversity} of {Expertise} {Drive} {Citation} {Impact}?},
	url = {https://link.springer.com/10.1007/s11192-026-05560-x},
	doi = {10.1007/s11192-026-05560-x},
	abstract = {Abstract
            High-quality scientific research plays a pivotal role in advancing society, stimulating economic growth, protecting the environment, and driving technological innovation. Understanding the key factors that lead to impactful research is thus crucial as it can steer the development of more effective policies to enhance the research enterprise. Extensive literature emphasises that the composition of a research team is vital for generating innovative and impactful scientific work. Many studies have focused on how team diversity, including aspects like ethnicity, gender, and international background, affects research outcomes. These types of diversities often correlate positively with the impact of research. In this paper, we investigate a less-explored dimension of diversity: the diversity of authors’ areas of expertise. This aspect has received limited attention, primarily due to the challenges involved in defining and measuring it. We present new AI-driven methods to quantify this diversity of expertise and conduct an extensive analysis of over 944,000 Computer Science papers. Specifically, this study investigates the relationship between the authors’ diversity of expertise and the number of citations their paper receives within the first five years. For each paper, we modelled the expertise of each individual author and then quantified the overall diversity within the author team. We then performed a statistical analysis that revealed a significant positive correlation between two diversity metrics and the number of citations received. This suggests that, in the field of Computer Science, diversity of expertise is a key driver of high-impact research.},
	language = {en},
	urldate = {2026-02-24},
	journal = {Scientometrics},
	author = {Salatino, Angelo and Osborne, Francesco and Recupero, Diego Reforgiato and Angioni, Simone and Motta, Enrico},
	month = feb,
	year = {2026},
}

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