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Cited by 540

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

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

Open access
Wang, Jian, Veugelers, Reinhilde, Stephan, Paula·2017·Research Policy

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.

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Metadata

Authors
Wang, J., Veugelers, R., & Stephan, P.
Venue
Research Policy
Publisher
Elsevier BV
Published
2017-10
Volume
46
Issue
8
Pages
1416-1436
ISSN
0048-7333 (ISSN-L)
OpenAlex
W3123050710
Cited by
540
References
8
FWCI
33.46
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Bias against novelty in science: A cautionary tale for users of bibliometric indicators. https://app.cochat.ai/papers/10.1016/j.respol.2017.06.006
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Wang, J., Veugelers, R., & Stephan, P. (2017). Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy. https://doi.org/10.1016/j.respol.2017.06.006

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.