Journal article
Understanding and predicting future research impact at different career stages-A social network perspective
Journal of the Association for Information Science and Technology, Vol.72(4), pp.454-472
04/2021
DOI: 10.1002/asi.24415
Abstract
Performance assessment is ubiquitous and crucial in people analytics. Scientific impact, particularly, plays a significant role in the academia. This paper attempts to understand researchers' career trajectories by considering the research community as a social network, where individuals build ties with each other via coauthorship. The resulting linkage facilitates information flow and affects researchers' future impact. Consequently, we systematically investigate the career trajectories of researchers with respect to research impact using the social capital theory as our theoretical foundation. Specifically, for early-stage and mid-career academics, we find that connections with prominent researchers associate with greater impact. Brokerage positions, in addition, are beneficial to a researcher's research impact in the long run. For senior researchers, however, the only social network feature that significantly affects their future impact is the reputation of their recently built ties. Finally, we build predictive models on future research impact which can be leveraged by both organizations and individuals. This paper provides empirical evidence for how social networks provide signals on researchers' career dynamics guided by social capital theory. Our findings have implications for individual researchers to strategically plan and promote their careers and for research institutions to better evaluate current as well as prospective employees.
Details
- Title: Subtitle
- Understanding and predicting future research impact at different career stages-A social network perspective
- Creators
- Zhiya Zuo - City University of Hong KongKang Zhao - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of the Association for Information Science and Technology, Vol.72(4), pp.454-472
- Publisher
- Wiley
- DOI
- 10.1002/asi.24415
- ISSN
- 2330-1635
- eISSN
- 2330-1643
- Number of pages
- 19
- Grant note
- Digital Innovation Laboratory of Department of Information Systems, City University of Hong Kong
- Language
- English
- Date published
- 04/2021
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380402302771
Metrics
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