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

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  • Yu Shi
  • Joe Zhu
  • Vincent Charles

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.

Suggested Citation

  • Yu Shi & Joe Zhu & Vincent Charles, 2021. "Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(5), pages 975-988, May.
    • Handle: RePEc:taf:tjorxx:v:72:y:2021:i:5:p:975-988
    DOI: 10.1080/01605682.2020.1860661
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