The distinctive innovation patterns and network embeddedness of scientific prizewinners
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.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (5)
Chaolin Tian
Department of Statistics and Data Science, Southern University of Science and Technology
Yurui Huang
Department of Statistics and Data Science, Southern University of Science and Technology
Ching Jin
Centre for Interdisciplinary Methodologies, Faculty of Social Sciences, University of Warwick
Yifang Ma
Department of Statistics and Data Science, Southern University of Science and Technology
Brian Uzzi