The distinctive innovation patterns and network embeddedness of scientific prizewinners

Science prizes purportedly reward innovation and explorations of new phenomena. Yet in practice, prizes may inadvertently divert resources from similarly impactful but less celebrated scholars. Despite this paradox, and even as prizes proliferate, knowledge of how prizewinning relates to innovation is nascent. Analyzing 2,460 worldwide prizes, we compared the innovativeness of over 23,000 prizewinners and matched nonprizewinners whose performance records were statistically equivalent up to the prize year. First, we find that prizewinners are more innovative. Their research is more likely to combine existing ideas in new ways, integrate a topic's historical and contemporary thinking, and incorporate interdisciplinary perspectives. Second, although prizewinners and matched nonprizewinners have statistically equivalent impact and productivity records up to the prize year, at about five years before the prize, prizewinners' papers become more innovative than their matched peers. This difference widens each year, peaks during the prize year, and then persists for the remainder of their careers. Third, network embeddedness predicts unusual innovativeness. Compared to nonprizewinners, prizewinners' collaborations are shorter in duration, encompass wider exposure to unfamiliar topics, and involve coauthors whose networks minimally overlap with each other. The findings' implications for innovation in science and the efficacy of reward systems and innovation in science are discussed.

Authors

Institutions

Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-09-29
DOI
https://doi.org/10.1073/pnas.2424143122
Primary Topic
scientometrics and bibliometrics research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The distinctive innovation patterns and network embeddedness of scientific prizewinners

Ching Jin, Brian Uzzi, Chaolin Tian, Yurui Huang et al.
Proceedings of the National Academy of Sciences
scientometrics and bibliometrics research
article

The distinctive innovation patterns and network embeddedness of scientific prizewinners

Ching Jin, Brian Uzzi, Chaolin Tian, Yurui Huang, Yifang Ma
article en

Abstract

Science prizes purportedly reward innovation and explorations of new phenomena. Yet in practice, prizes may inadvertently divert resources from similarly impactful but less celebrated scholars. Despite this paradox, and even as prizes proliferate, knowledge of how prizewinning relates to innovation is nascent. Analyzing 2,460 worldwide prizes, we compared the innovativeness of over 23,000 prizewinners and matched nonprizewinners whose performance records were statistically equivalent up to the prize year. First, we find that prizewinners are more innovative. Their research is more likely to combine existing ideas in new ways, integrate a topic's historical and contemporary thinking, and incorporate interdisciplinary perspectives. Second, although prizewinners and matched nonprizewinners have statistically equivalent impact and productivity records up to the prize year, at about five years before the prize, prizewinners' papers become more innovative than their matched peers. This difference widens each year, peaks during the prize year, and then persists for the remainder of their careers. Third, network embeddedness predicts unusual innovativeness. Compared to nonprizewinners, prizewinners' collaborations are shorter in duration, encompass wider exposure to unfamiliar topics, and involve coauthors whose networks minimally overlap with each other. The findings' implications for innovation in science and the efficacy of reward systems and innovation in science are discussed.

Proceedings of the National Academy of SciencesVol. 122(40)
Kellogg's (Canada) (CA), Southern University of Science and Technology (CN), University of Warwick (GB), Northwestern University (PH)
Openalex Percentile: Top 74%
scientometrics and bibliometrics research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The distinctive innovation patterns and network embeddedness of scientific prizewinners — Ching Jin, Brian Uzzi, et al. · Proceedings of the National Academy of Sciences (2025) | TGRS Research Map | TGRS