Parallel or intersecting lines? Intelligent bibliometrics for investigating the involvement of data science in policy analysis

Yi Zhang, Alan L. Porter, Scott W. Cunningham, Denise Chiavetta, Nils Newman

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
8 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)1259-1271
Number of pages13
JournalIEEE Transactions on Engineering Management
Volume68
Issue number5
Early online date9 Mar 2020
DOIs
Publication statusPublished - 31 Oct 2021

Keywords

  • bibliometrics
  • science maps
  • policy analysis
  • data science

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