Bias against novelty in science: A cautionary tale for users of bibliometric indicators

Jian Wang, Reinhilde Veugelers and Paula Stephan

No 520305, Working Papers of Department of Management, Strategy and Innovation, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven

Abstract: Research which explores unchartered waters has a high potential for major impact but also carries a high uncertainty of having minimal impact. Such explorative research is often described as taking a novel approach. This study examines the complex relationship between pursuing a novel approach and impact. We measure novelty by examining the extent to which a published paper makes first time ever combinations of referenced journals, taking into account the difficulty of making such combinations. We apply this newly developed measure of novelty to a set of one million research articles across all scientific disciplines. We find that highly novel papers, defined to be those that make more (distinct) new combinations, have more than a triple probability of being a top 1% highly cited paper when using a sufficiently long citation time window to assess impact. Moreover, follow-on papers that cite highly novel research are themselves more likely to be highly cited. However, novel research is also risky as it has a higher variance in the citation performance. These findings are consistent with the “high risk/high gain” characteristic of novel research. We also find that novel papers are typically published in journals with a lower than expected Impact Factor and are less cited when using a short time window. Our findings suggest that science policy, in particular funding decisions which are over reliant on traditional bibliometric indicators based on short-term direct citation counts and Journal Impact Factors, may be biased against novelty.

Date: 2015-12
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Published in FEB Research Report - MSI_1514

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https://lirias.kuleuven.be/retrieve/354584 MSI_1514 (application/pdf)

Related works:
Journal Article: Bias against novelty in science: A cautionary tale for users of bibliometric indicators (2017)
Working Paper: Bias against Novelty in Science: A Cautionary Tale for Users of Bibliometric Indicators (2016)
Working Paper: Bias against Novelty in Science: A Cautionary Tale for Users of Bibliometric Indicators (2016)
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