Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations

Yu Shi, Joe Zhu, Vincent Charles

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper provides a comprehensive review of the applications of data science techniques and methodologies in productivity. The paper is structured as a combination of a bibliometric analysis and an empirical review. In the bibliometric analysis, the sources, authorship, and documents are reviewed and discussed. Visualisation aids, including summative tables and figures, are incorporated. In the empirical review, the corpus of 533 articles identified are reviewed based on the application areas of data science approaches and the primary methodology of the papers, and the selected most impactful and relevant papers in each methodological category are discussed in detail. The objective of this paper is to provide an overview of the current predominant trends and patterns in data science and productivity, explore how the interplay has been manifested, and provide an outlook on future research orientations.

Original languageEnglish
Pages (from-to)975-988
Number of pages14
JournalJournal of the Operational Research Society
Volume72
Issue number5
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • Data science
  • productivity
  • review
  • survey

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