Paper Info
Title
Parallel or Intersecting Lines? Intelligent Bibliometrics for Investigating the Involvement of Data Science in Policy Analysis
Abstract
Efforts to involve data science in policy analysis can be traced back decades but transforming analytic findings into decisions is still far from straightforward task. Data-driven decision-making requires understanding approaches, practices, and research results from many disciplines, which makes it interesting to investigate whether data science and policy analysis are moving in parallel or whether their pathways have intersected. Our investigation, from a bibliometric perspective, is driven by a comprehensive set of research questions, and we have designed an intelligent bibliometric framework that includes a series of traditional bibliometric approaches and a novel method of charting the evolutionary pathways of scientific innovation, which is used to identify predecessor-descendant relationships in technological topics. Our investigation reveals that data science and policy analysis have intersecting lines, and it can foresee that a cross-disciplinary direction in which policy analysis interacting with data science has become an emergent area in both communities. However, equipped with advanced data analytic techniques, data scientists are moving faster and further than policy analysts. The empirical insights derived from our research should be beneficial to academic researchers and journal editors in related research communities, as well as policy-makers in research institutions and funding agencies.
Year
DOI
Venue
2021
10.1109/TEM.2020.2974761
IEEE Transactions on Engineering Management
Keywords
DocType
Volume
Bibliometrics,data science,policy analysis,science maps
Journal
68
Issue
ISSN
Citations 
5
0018-9391
6
PageRank 
References 
Authors
0.55
0
5
Name
Order
Citations
PageRank
Yi Zhang19510.69
Alan L. Porter239832.61
Scott W. Cunningham3275.06
Denise Chiavetta491.75
Nils Newman5777.07