The association between prior knowledge and the disruption of an article

Libo Sheng, Dongqing Lyu, Xuanmin Ruan, Hongquan Shen and Ying Cheng ()
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Libo Sheng: Nanjing University
Dongqing Lyu: Nanjing University of Finance & Economics
Xuanmin Ruan: Nanjing University
Hongquan Shen: Nanjing University
Ying Cheng: Nanjing University

Scientometrics, 2023, vol. 128, issue 8, No 23, 4751 pages

Abstract: Abstract Disruptive research that reveals an important innovation in science can reshape existing pathways. This paper studies the relationship between the prior knowledge the research builds upon and disruption in science. To measure the disruption of an article and operationalize prior knowledge, we use the disruption index ( $$D\ index$$ D i n d e x ) and examine six characteristics of references of an article: amount, recency, impact, disruption, novelty and homogeneity. Ordinary least squares regression is conducted on a set of 1,310,837 articles from 2001 to 2010 from the PubMed knowledge graph (PKG) dataset. Our primary finding shows that the recency and homogeneity of prior knowledge are negatively associated with disruption, while we found positive relationships between the amount, impact, disruption and novel combinations of prior knowledge and disruption. Our robustness checks further confirm these conclusions. This study deepens our understanding of the association between prior knowledge and disruption, and has significant implications for researchers to search for and synthesize different types of prior knowledge.

Keywords: Prior knowledge; Disruption; Biomedical; Regression analysis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-023-04751-0

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