Revisiting the exploration-exploitation behavior of scholars' research topic selection: Evidence from a large-scale bibliographic database

Huang, Shengzhi; Lu, Wei; Bu, Yi; Huang, Yong

doi:10.1016/j.ipm.2022.103110

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…In conclusion, this study suggests that journals publishing early COVID-19 papers are more likely to share shrinking but lasting citation premiums, double-contributed by COVID-19 and non-COVID-19 papers. It highlights the benefits of timely research and publishing in emerging and frontier topics, which is also supported by other empirical evidence (32). Moreover, as shown by the results, JIF and other citation-based journal metrics can be boosted by the citation premium, though it may only restrict to a short term.…”

Section: Impact On Journal Metricssupporting

confidence: 78%

The significant yet short-term influence of research covidization on journal citation metrics

Zheng

2023

Preprint

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COVID-19 has emerged as a significant research hotspot in recent years, leading to a surge in production and citations received by COVID-19 papers. While concerns have been raised about the potential citation boost on journals associated with publishing COVID-19 papers, the extent and mechanisms of such gain remain unclear. This study uses a generalized difference-in-differences approach to examine the impact of publishing COVID-19 papers on journal citations and related metrics in four highly covidized fields. Our results demonstrate that journals starting publishing COVID-19 papers in health sciences fields in 2020 experienced a significant increase in citations compared with other journals. This trend continued in 2021, although to a lesser extent. However, such citation premiums became insignificant for journals starting to publish COVID-19 papers in 2021. In some fields, we also observed that COVID-19 papers increased the citations of non-COVID-19 papers in the same journals, but only for journals starting to publish COVID-19 papers in 2020. Our heterogeneity test indicates that COVID-19 papers published in prestigious journals brought more significant citation premiums to the journals and non-COVID-19 papers in most fields. We finally show that these citation premiums can affect various citation-based journal metrics. Our findings reveal a “gold rush” pattern in which early entrants are more likely to establish their citation advantage in research hotspots and caution against using such metrics to evaluate journal quality.

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Section: Impact On Journal Metricssupporting

confidence: 78%

The significant yet short-term influence of research covidization on journal citation metrics

Zheng

2023

Preprint

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“…Besides isolated interactions with either social or knowledge determinants, their synthesized impacts on the dynamics of ST&I are significant (Sun et al, 2013; Wang et al, 2014). In a large‐scale science of science study, Huang et al (2022) observed the preference of productive and impactful researchers in exploratory innovation. We thus summarized:Assumption Exploratory innovation is positively correlated with research productivity and impacts, and is synergistically influenced by social and knowledge interactions.…”

Section: Methodology: Diffusion‐based Network Analyticsmentioning

confidence: 99%

“…Social scientists recognize exploration and exploitation as alternative research strategies for scholars to shape/reshape knowledge trajectories and promote research productivity and impact (Foster et al, 2015; Huang et al, 2022). Besides science policies, the literature has identified several crucial determinants of exploratory innovation (Keshavarz & Shekari, 2020; Liu, Wang, et al, 2018a; Zeng et al, 2019), for example, career age and hot streaks, research achievement and reputation, and topic nature.…”

Section: Introductionmentioning

confidence: 99%

Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

Zhang

et al. 2023

Asso for Info Science & Tech

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Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter‐/cross‐/multi‐disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion‐based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co‐topic layer and a co‐authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments—one with a local dataset and the other with a global dataset—demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

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“…It is interesting to check if the leading author tends to be the first author, the last author, or the author with the longest academic age (Drenth, 1998;Hundley et al, 2013;Sekara et al, 2018), and how this pattern differs in different disciplines. The identification of a paper's leading author would help in the study of a scientist's research agenda (Huang et al, 2023;S. Huang et al, 2022;X.…”

Section: The Calculation Of Parameter αmentioning

confidence: 99%

An author credit allocation method with improved distinguishability and robustness

Jia

2023

Journal of Data and Information Science

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Purpose The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations. Design/methodology/approach We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts. We also remove the target paper in calculating the contribution of co-citations. Following previous studies, we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society (APS) and the Microsoft Academic Graph (MAG) dataset to test the accuracy of our proposed method (NCCAS). In addition, we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS. Finding Compared with the state-of-the-art methods, NCCAS gives the most accurate prediction of Nobel laureates. Furthermore, the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors. The results by NCCAS are also more robust to malicious manipulation. Finally, we perform ablation studies to show the contribution of different components in our methods. Research limitations Due to limited ground truth on the true leading author of a work, the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers. Practical implications NCCAS is successfully applied to a large number of publications, demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders. Originality/value Compared with existing methods, NCCAS not only identifies the leading author of a paper more accurately, but also makes the deification more distinguishable and more robust, providing a new tool for related studies.

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Revisiting the exploration-exploitation behavior of scholars' research topic selection: Evidence from a large-scale bibliographic database

Cited by 7 publications

References 42 publications

The significant yet short-term influence of research covidization on journal citation metrics

The significant yet short-term influence of research covidization on journal citation metrics

Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

An author credit allocation method with improved distinguishability and robustness

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